add benchmark for ops (#176)

Co-authored-by: “Peiyuan Zhang” <a1286225768@gmail.com>

.github/ISSUE_TEMPLATE/1-bug-report.yml

@@ -0,0 +1,29 @@
+name: 🐞 Bug report
+description: Create a report to help us reproduce and fix the bug
+title: "[Bug] "
+labels: ['Bug']
+
+body:
+- type: textarea
+  attributes:
+    label: Environment
+    description: |
+      Please share your environment with us. You can run the command **python fastvideo/utils/env_utils.py** and copy-paste its output below.
+    placeholder: FastVideo version, platform, python version, cuda version...
+  validations:
+    required: true
+- type: textarea
+  attributes:
+    label: Describe the bug
+    description: A clear and concise description of what the bug is.
+  validations:
+    required: true
+- type: textarea
+  attributes:
+    label: Reproduction
+    description: |
+      What command or script did you run? Which **model** are you using?
+    placeholder: |
+      A placeholder for the command.
+  validations:
+    required: true

.github/ISSUE_TEMPLATE/2-feature-request.yml

@@ -0,0 +1,17 @@
+name: 🚀 Feature request
+description: Suggest an idea for this project
+title: "[Feature] "
+
+body:
+- type: textarea
+  attributes:
+    label: Motivation
+    description: |
+      A clear and concise description of the motivation of the feature.
+  validations:
+    required: true
+- type: textarea
+  attributes:
+    label: Related resources
+    description: |
+      If there is an official code release or third-party implementations, please also provide the information here, which would be very helpful.

.github/ISSUE_TEMPLATE/config.yml

@@ -0,0 +1 @@
+blank_issues_enabled: false

.github/workflows/codespell.yml

@@ -0,0 +1,45 @@
+name: codespell
+
+on:
+  # Trigger the workflow on push or pull request,
+  # but only for the main branch
+  push:
+    branches:
+      - main
+    paths:
+      - "**/*.py"
+      - "**/*.md"
+      - "**/*.rst"
+      - pyproject.toml
+      - requirements-lint.txt
+      - .github/workflows/codespell.yml
+  pull_request:
+    branches:
+      - main
+    paths:
+      - "**/*.py"
+      - "**/*.md"
+      - "**/*.rst"
+      - pyproject.toml
+      - requirements-lint.txt
+      - .github/workflows/codespell.yml
+
+jobs:
+  codespell:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Check out repository
+        uses: actions/checkout@v3
+
+      - name: Set up Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: '3.12'  # or any version you need
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install -r requirements-lint.txt
+      - name: Spelling check with codespell
+        run: |
+          # Refer to the above environment variable here
+          codespell --toml pyproject.toml $CODESPELL_EXCLUDES

.github/workflows/ruff.yml

@@ -0,0 +1,50 @@
+name: ruff
+
+on:
+  # Trigger the workflow on push or pull request,
+  # but only for the main branch
+  push:
+    branches:
+      - main
+    paths:
+      - "**/*.py"
+      - pyproject.toml
+      - requirements-lint.txt
+      - .github/workflows/matchers/ruff.json
+      - .github/workflows/ruff.yml
+  pull_request:
+    branches:
+      - main
+    # This workflow is only relevant when one of the following files changes.
+    # However, we have github configured to expect and require this workflow
+    # to run and pass before github with auto-merge a pull request. Until github
+    # allows more flexible auto-merge policy, we can just run this on every PR.
+    # It doesn't take that long to run, anyway.
+    #paths:
+    #  - "**/*.py"
+    #  - pyproject.toml
+    #  - requirements-lint.txt
+    #  - .github/workflows/matchers/ruff.json
+    #  - .github/workflows/ruff.yml
+
+jobs:
+  ruff:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Check out repository
+        uses: actions/checkout@v3
+
+      - name: Set up Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: '3.12'  # or any version you need
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install -r requirements-lint.txt
+      - name: Analysing the code with ruff
+        run: |
+          ruff check .
+      - name: Run isort
+        run: |
+          isort . --check-only

.github/workflows/test.yml

@@ -0,0 +1,30 @@
+name: Run Tests
+
+on:
+  push:
+    branches: [ main ]
+  pull_request:
+    branches: [ main ]
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Check out repository
+        uses: actions/checkout@v3
+
+      - name: Set up Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: '3.12'  # or any version you need
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip setuptools wheel
+          pip install torch
+          pip install packaging ninja
+          pip install -e .
+          pip install pytest
+      - name: Run Pytest
+        run: |
+          pytest

.github/workflows/yapf.yml

@@ -0,0 +1,38 @@
+name: yapf
+
+on:
+  # Trigger the workflow on push or pull request,
+  # but only for the main branch
+  push:
+    branches:
+      - main
+    paths:
+      - "**/*.py"
+      - .github/workflows/yapf.yml
+  pull_request:
+    branches:
+      - main
+    paths:
+      - "**/*.py"
+      - .github/workflows/yapf.yml
+
+jobs:
+  yapf:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Check out repository
+        uses: actions/checkout@v3
+
+      - name: Set up Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: '3.12'  # or any version you need
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install yapf==0.32.0
+          pip install toml==0.10.2
+      - name: Running yapf
+        run: |
+          yapf --diff --recursive .

.gitignore

@@ -20,7 +20,6 @@ wandb/
 *.pt
 cache_dir/
 wandb/
-test*
 sample_video*
 sample_image*
 512*

README.md

@@ -1,19 +1,187 @@
-prepare the src data
-```
-python scripts/download_hf.py --repo_id=FastVideo/Distill-30K-Data-Src --local_dir=data/Distill-30K-Data-Src --repo_type=dataset
-cd data/Distill-30K-Data-Src
-cat archive_name.tar.gz.* > Distill-30K-Data-Src.tar.gz
-rm archive_name.tar.gz.*
-tar --use-compress-program="pigz --processes 64" -xvf Distill-30K-Data-Src.tar.gz
-mv ephemeral/hao.zhang/codefolder/FastVideo-OSP/data/Distill-30K-Src/* .
-rm -r ephemeral
-rm Distill-30K-Data-Src.tar.gz
-cd ../..
-```
-Now the src data is stored in folder data/Distill-30K-Data-Src, adjust the path in `data/Distill-30K-Src/merge.txt` correspondingly
+<div align="center">
+<img src=assets/logo.jpg width="30%"/>
+</div>
 
-Adjust the `SHARD_NUM` (total shards num) and `SHARD_IDX` (idx of the shard to preprocess) correspoindingly in `./scripts/preprocess_hunyuan_data.sh` for each GPU node to preprocess different shards
+FastVideo is a lightweight framework for accelerating large video diffusion models.
+
+https://github.com/user-attachments/assets/064ac1d2-11ed-4a0c-955b-4d412a96ef30
+
+
+<p align="center">
+    🤗 <a href="https://huggingface.co/FastVideo/FastHunyuan"  target="_blank">FastHunyuan</a>  | 🤗 <a href="https://huggingface.co/FastVideo/FastMochi-diffusers" target="_blank">FastMochi</a> | 🎮 <a href="https://discord.gg/REBzDQTWWt" target="_blank"> Discord </a> | 🕹️ <a href="https://replicate.com/lucataco/fast-hunyuan-video" target="_blank"> Replicate </a> 
+</p> 
+
+
+FastVideo currently offers: (with more to come)
+
+- FastHunyuan and FastMochi: consistency distilled video diffusion models for 8x inference speedup.
+- First open distillation recipes for video DiT, based on [PCM](https://github.com/G-U-N/Phased-Consistency-Model).
+- Support distilling/finetuning/inferencing state-of-the-art open video DiTs: 1. Mochi 2. Hunyuan.
+- Scalable training with FSDP, sequence parallelism, and selective activation checkpointing, with near linear scaling to 64 GPUs.
+- Memory efficient finetuning with LoRA, precomputed latent, and precomputed text embeddings.
+
+Dev in progress and highly experimental.
+
+## 🎥 More Demos
+
+Fast-Mochi comparison with original Mochi, achieving an 8X diffusion speed boost with the FastVideo framework.
+
+https://github.com/user-attachments/assets/5fbc4596-56d6-43aa-98e0-da472cf8e26c
+
+Comparison between OpenAI Sora, original Hunyuan and FastHunyuan
+
+https://github.com/user-attachments/assets/d323b712-3f68-42b2-952b-94f6a49c4836
+
+Comparison between original FastHunyuan, LLM-INT8 quantized FastHunyuan and NF4 quantized FastHunyuan
+
+https://github.com/user-attachments/assets/cf89efb5-5f68-4949-a085-f41c1ef26c94
+
+## Change Log
+- ```2025/01/13```: Support Lora finetuning for HunyuanVideo.
+- ```2024/12/25```: Enable single 4090 inference for `FastHunyuan`, please rerun the installation steps to update the environment.
+- ```2024/12/17```: `FastVideo` v1.0 is released.
+
+
+## 🔧 Installation
+The code is tested on Python 3.10.0, CUDA 12.1 and H100.
 ```
-bash ./scripts/preprocess_hunyuan_data.sh
+./env_setup.sh fastvideo
 ```
-The preprocessed data will be stored in `data/HD-Hunyuan-30K-Distill-Data_Shard*`
+
+## 🚀 Inference
+
+### Inference FastHunyuan on single RTX4090
+We now support NF4 and LLM-INT8 quantized inference using BitsAndBytes for FastHunyuan. With NF4 quantization, inference can be performed on a single RTX 4090 GPU, requiring just 20GB of VRAM.
+```bash
+# Download the model weight
+python scripts/huggingface/download_hf.py --repo_id=FastVideo/FastHunyuan-diffusers --local_dir=data/FastHunyuan-diffusers --repo_type=model
+# CLI inference
+bash scripts/inference/inference_hunyuan_hf_quantization.sh
+```
+For more information about the VRAM requirements for BitsAndBytes quantization, please refer to the table below (timing measured on an H100 GPU):
+
+
+| Configuration                  | Memory to Init Transformer | Peak Memory After Init Pipeline (Denoise) | Diffusion Time | End-to-End Time |
+|--------------------------------|----------------------------|--------------------------------------------|----------------|-----------------|
+| BF16 + Pipeline CPU Offload    | 23.883G                   | 33.744G                                    | 81s            | 121.5s          |
+| INT8 + Pipeline CPU Offload    | 13.911G                   | 27.979G                                    | 88s            | 116.7s          |
+| NF4 + Pipeline CPU Offload     | 9.453G                    | 19.26G                                     | 78s            | 114.5s          |
+           
+
+
+For improved quality in generated videos, we recommend using a GPU with 80GB of memory to run the BF16 model with the original Hunyuan pipeline. To execute the inference, use the following section:
+
+### FastHunyuan
+```bash
+# Download the model weight
+python scripts/huggingface/download_hf.py --repo_id=FastVideo/FastHunyuan --local_dir=data/FastHunyuan --repo_type=model
+# CLI inference
+bash scripts/inference/inference_hunyuan.sh
+```
+You can also inference FastHunyuan in the [official Hunyuan github](https://github.com/Tencent/HunyuanVideo).
+
+### FastMochi
+
+```bash
+# Download the model weight
+python scripts/huggingface/download_hf.py --repo_id=FastVideo/FastMochi-diffusers --local_dir=data/FastMochi-diffusers --repo_type=model
+# CLI inference
+bash scripts/inference/inference_mochi_sp.sh
+```
+
+
+## 🎯 Distill
+Our distillation recipe is based on [Phased Consistency Model](https://github.com/G-U-N/Phased-Consistency-Model). We did not find significant improvement using multi-phase distillation, so we keep the one phase setup similar to the original latent consistency model's recipe.
+We use the [MixKit](https://huggingface.co/datasets/LanguageBind/Open-Sora-Plan-v1.1.0/tree/main/all_mixkit) dataset for distillation. To avoid running the text encoder and VAE during training, we preprocess all data to generate text embeddings and VAE latents.
+Preprocessing instructions can be found [data_preprocess.md](docs/data_preprocess.md). For convenience, we also provide preprocessed data that can be downloaded directly using the following command:
+```bash
+python scripts/huggingface/download_hf.py --repo_id=FastVideo/HD-Mixkit-Finetune-Hunyuan --local_dir=data/HD-Mixkit-Finetune-Hunyuan --repo_type=dataset
+```
+Next, download the original model weights with:
+```bash
+python scripts/huggingface/download_hf.py --repo_id=FastVideo/hunyuan --local_dir=data/hunyuan --repo_type=model # original hunyuan
+python scripts/huggingface/download_hf.py --repo_id=genmo/mochi-1-preview --local_dir=data/mochi --repo_type=model # original mochi
+```
+To launch the distillation process, use the following commands:
+```
+bash scripts/distill/distill_hunyuan.sh # for hunyuan
+bash scripts/distill/distill_mochi.sh # for mochi
+```
+We also provide an optional script for distillation with adversarial loss, located at `fastvideo/distill_adv.py`. Although we tried adversarial loss, we did not observe significant improvements.
+## Finetune
+### ⚡ Full Finetune
+Ensure your data is prepared and preprocessed in the format specified in [data_preprocess.md](docs/data_preprocess.md). For convenience, we also provide a mochi preprocessed Black Myth Wukong data that can be downloaded directly:
+```bash
+python scripts/huggingface/download_hf.py --repo_id=FastVideo/Mochi-Black-Myth --local_dir=data/Mochi-Black-Myth --repo_type=dataset
+```
+Download the original model weights as specified in [Distill Section](#-distill):
+
+Then you can run the finetune with:
+```
+bash scripts/finetune/finetune_mochi.sh # for mochi
+```
+**Note that for finetuning, we did not tune the hyperparameters in the provided script.**
+### ⚡ Lora Finetune 
+
+Hunyuan supports Lora fine-tuning of videos up to 720p. Demos and prompts of Black-Myth-Wukong can be found in [here](https://huggingface.co/FastVideo/Hunyuan-Black-Myth-Wukong-lora-weight). You can download the Lora weight through:
+```bash
+python scripts/huggingface/download_hf.py --repo_id=FastVideo/Hunyuan-Black-Myth-Wukong-lora-weight --local_dir=data/Hunyuan-Black-Myth-Wukong-lora-weight --repo_type=model
+```
+#### Minimum Hardware Requirement
+- 40 GB GPU memory each for 2 GPUs with lora.
+- 30 GB GPU memory each for 2 GPUs with CPU offload and lora.  
+
+
+Currently, both Mochi and Hunyuan models support Lora finetuning through diffusers. To generate personalized videos from your own dataset, you'll need to follow three main steps: dataset preparation, finetuning, and inference.
+
+#### Dataset Preparation
+We provide scripts to better help you get started to train on your own characters!  
+You can run this to organize your dataset to get the videos2caption.json before preprocess. Specify your video folder and corresponding caption folder (caption files should be .txt files and have the same name with its video):
+```
+python scripts/dataset_preparation/prepare_json_file.py --video_dir data/input_videos/ --prompt_dir data/captions/ --output_path data/output_folder/videos2caption.json --verbose
+```
+Also, we provide script to resize your videos:
+```
+python scripts/data_preprocess/resize_videos.py 
+```
+#### Finetuning
+After basic dataset preparation and preprocess, you can start to finetune your model using Lora:
+```
+bash scripts/finetune/finetune_hunyuan_hf_lora.sh
+```
+#### Inference
+For inference with Lora checkpoint, you can run the following scripts with additional parameter `--lora_checkpoint_dir`:
+```
+bash scripts/inference/inference_hunyuan_hf.sh 
+```
+**We also provide scripts for Mochi in the same directory.**
+
+#### Finetune with Both Image and Video
+Our codebase support finetuning with both image and video. 
+```bash
+bash scripts/finetune/finetune_hunyuan.sh
+bash scripts/finetune/finetune_mochi_lora_mix.sh
+```
+For Image-Video Mixture Fine-tuning, make sure to enable the `--group_frame` option in your script.
+
+## 📑 Development Plan
+
+- More distillation methods
+  - [ ] Add Distribution Matching Distillation
+- More models support
+  - [ ] Add CogvideoX model
+- Code update
+  - [ ] fp8 support
+  - [ ] faster load model and save model support
+
+## 🤝 Contributing
+
+We welcome all contributions. Please run `bash format.sh` before submitting a pull request.
+
+## 🔧 Testing
+Run `pytest` to verify the data preprocessing, checkpoint saving, and sequence parallel pipelines. We recommend adding corresponding test cases in the `test` folder to support your contribution.
+
+## Acknowledgement
+We learned and reused code from the following projects: [PCM](https://github.com/G-U-N/Phased-Consistency-Model), [diffusers](https://github.com/huggingface/diffusers), [OpenSoraPlan](https://github.com/PKU-YuanGroup/Open-Sora-Plan), and [xDiT](https://github.com/xdit-project/xDiT).
+
+We thank MBZUAI and Anyscale for their support throughout this project.

assets/prompt.txt

@@ -1,9 +1,8 @@
-A hand enters the frame, pulling a sheet of plastic wrap over three balls of dough placed on a wooden surface. The plastic wrap is stretched to cover the dough more securely. The hand adjusts the wrap, ensuring that it is tight and smooth over the dough. The scene focuses on the hand's movements as it secures the edges of the plastic wrap. No new objects appear, and the camera remains stationary, focusing on the action of covering the dough.
-A vintage train snakes through the mountains, its plume of white steam rising dramatically against the jagged peaks. The cars glint in the late afternoon sun, their deep crimson and gold accents lending a touch of elegance. The tracks carve a precarious path along the cliffside, revealing glimpses of a roaring river far below. Inside, passengers peer out the large windows, their faces lit with awe as the landscape unfolds.
-A crowded rooftop bar buzzes with energy, the city skyline twinkling like a field of stars in the background. Strings of fairy lights hang above, casting a warm, golden glow over the scene. Groups of people gather around high tables, their laughter blending with the soft rhythm of live jazz. The aroma of freshly mixed cocktails and charred appetizers wafts through the air, mingling with the cool night breeze.
-En "The Matrix", Neo, interpretado por Keanu Reeves, personifica la lucha contra un sistema opresor a través de su icónica imagen, que incluye unos anteojos oscuros. Estos lentes no son solo un accesorio de moda; representan una barrera entre la realidad y la percepción. Al usar estos anteojos, Neo se sumerge en un mundo donde la verdad se oculta detrás de ilusiones y engaños. La oscuridad de los lentes simboliza la ignorancia y el control que las máquinas tienen sobre la humanidad, mientras que su propia búsqueda de la verdad lo lleva a descubrir sus auténticos poderes. La escena en que se los pone se convierte en un momento crucial, marcando su transformación de un simple programador a "El Elegido". Esta imagen se ha convertido en un ícono cultural, encapsulando el mensaje de que, al enfrentar la oscuridad, podemos encontrar la luz que nos guía hacia la libertad. Así, los anteojos de Neo se convierten en un símbolo de resistencia y autoconocimiento en un mundo manipulado.
-Medium close up. Low-angle shot. A woman in a 1950s retro dress sits in a diner bathed in neon light, surrounded by classic decor and lively chatter. The camera starts with a medium shot of her sitting at the counter, then slowly zooms in as she blows a shiny pink bubblegum bubble. The bubble swells dramatically before popping with a soft, playful burst. The scene is vibrant and nostalgic, evoking the fun and carefree spirit of the 1950s.
-Will Smith eats noodles.
-A short clip of the blonde woman taking a sip from her whiskey glass, her eyes locking with the camera as she smirks playfully. The background shows a group of people laughing and enjoying the party, with vibrant neon signs illuminating the space. The shot is taken in a way that conveys the feeling of a tipsy, carefree night out. The camera then zooms in on her face as she winks, creating a cheeky, flirtatious vibe.
-A superintelligent humanoid robot waking up. The robot has a sleek metallic body with futuristic design features. Its glowing red eyes are the focal point, emanating a sharp, intense light as it powers on. The scene is set in a dimly lit, high-tech laboratory filled with glowing control panels, robotic arms, and holographic screens. The setting emphasizes advanced technology and an atmosphere of mystery. The ambiance is eerie and dramatic, highlighting the moment of awakening and the robot's immense intelligence. Photorealistic style with a cinematic, dark sci-fi aesthetic. Aspect ratio: 16:9 --v 6.1
-A chimpanzee lead vocalist singing into a microphone on stage. The camera zooms in to show him singing. There is a spotlight on him.
+Will Smith casually eats noodles, his relaxed demeanor contrasting with the energetic background of a bustling street food market. The scene captures a mix of humor and authenticity. Mid-shot framing, vibrant lighting.
+A lone hiker stands atop a towering cliff, silhouetted against the vast horizon. The rugged landscape stretches endlessly beneath, its earthy tones blending into the soft blues of the sky. The scene captures the spirit of exploration and human resilience. High angle, dynamic framing, with soft natural lighting emphasizing the grandeur of nature.
+A hand with delicate fingers picks up a bright yellow lemon from a wooden bowl filled with lemons and sprigs of mint against a peach-colored background. The hand gently tosses the lemon up and catches it, showcasing its smooth texture. A beige string bag sits beside the bowl, adding a rustic touch to the scene. Additional lemons, one halved, are scattered around the base of the bowl. The even lighting enhances the vibrant colors and creates a fresh, inviting atmosphere.
+A curious raccoon peers through a vibrant field of yellow sunflowers, its eyes wide with interest. The playful yet serene atmosphere is complemented by soft natural light filtering through the petals. Mid-shot, warm and cheerful tones.
+A superintelligent humanoid robot waking up. The robot has a sleek metallic body with futuristic design features. Its glowing red eyes are the focal point, emanating a sharp, intense light as it powers on. The scene is set in a dimly lit, high-tech laboratory filled with glowing control panels, robotic arms, and holographic screens. The setting emphasizes advanced technology and an atmosphere of mystery. The ambiance is eerie and dramatic, highlighting the moment of awakening and the robots immense intelligence. Photorealistic style with a cinematic, dark sci-fi aesthetic. Aspect ratio: 16:9 --v 6.1
+fox in the forest close-up quickly turned its head to the left
+Man walking his dog in the woods on a hot sunny day
+A majestic lion strides across the golden savanna, its powerful frame glistening under the warm afternoon sun. The tall grass ripples gently in the breeze, enhancing the lion's commanding presence. The tone is vibrant, embodying the raw energy of the wild. Low angle, steady tracking shot, cinematic.

cog.yaml

@@ -0,0 +1,24 @@
+# Configuration for Cog ⚙️
+# Reference: https://cog.run/yaml
+
+build:
+  gpu: true
+  cuda: "12.1"
+  python_version: "3.10"
+  python_packages:
+    - "torch==2.4.0"
+    - "torchvision"
+    - "ninja==1.11.1.3"
+    - "transformers==4.46.1"
+    - "git+https://github.com/huggingface/diffusers.git@bf64b32652a63a1865a0528a73a13652b201698b"
+    - "accelerate==1.0.1"
+    - "safetensors==0.4.5"
+    - "peft==0.13.2"
+    - "packaging==24.2"
+    - "git+https://github.com/hao-ai-lab/FastVideo"
+
+  run:
+    - FLASH_ATTENTION_SKIP_CUDA_BUILD=TRUE pip install flash-attn --no-build-isolation
+    - curl -o /usr/local/bin/pget -L "https://github.com/replicate/pget/releases/latest/download/pget_$(uname -s)_$(uname -m)" && chmod +x /usr/local/bin/pget
+
+predict: "predict.py:Predictor"

demo/gradio_web_demo.py

@@ -1,64 +1,68 @@
+import argparse
+import os
+import tempfile
+
 import gradio as gr
 import torch
-from fastvideo.model.pipeline_mochi import MochiPipeline
-from fastvideo.model.modeling_mochi import MochiTransformer3DModel
 from diffusers import FlowMatchEulerDiscreteScheduler
 from diffusers.utils import export_to_video
+
 from fastvideo.distill.solver import PCMFMScheduler
-import tempfile
-import os
-import argparse
+from fastvideo.models.mochi_hf.modeling_mochi import MochiTransformer3DModel
+from fastvideo.models.mochi_hf.pipeline_mochi import MochiPipeline
 
 
 def init_args():
     parser = argparse.ArgumentParser()
     parser.add_argument("--prompts", nargs="+", default=[])
-    parser.add_argument("--num_frames", type=int, default=163)
+    parser.add_argument("--num_frames", type=int, default=25)
     parser.add_argument("--height", type=int, default=480)
     parser.add_argument("--width", type=int, default=848)
-    parser.add_argument("--num_inference_steps", type=int, default=64)
+    parser.add_argument("--num_inference_steps", type=int, default=8)
     parser.add_argument("--guidance_scale", type=float, default=4.5)
     parser.add_argument("--model_path", type=str, default="data/mochi")
-    parser.add_argument("--seed", type=int, default=42)
+    parser.add_argument("--seed", type=int, default=12345)
     parser.add_argument("--transformer_path", type=str, default=None)
-    parser.add_argument("--scheduler_type", type=str, default="euler")
+    parser.add_argument("--scheduler_type",
+                        type=str,
+                        default="pcm_linear_quadratic")
     parser.add_argument("--lora_checkpoint_dir", type=str, default=None)
     parser.add_argument("--shift", type=float, default=8.0)
-    parser.add_argument("--num_euler_timesteps", type=int, default=100)
-    parser.add_argument("--linear_threshold", type=float, default=0.025)
-    parser.add_argument("--linear_range", type=float, default=0.5)
+    parser.add_argument("--num_euler_timesteps", type=int, default=50)
+    parser.add_argument("--linear_threshold", type=float, default=0.1)
+    parser.add_argument("--linear_range", type=float, default=0.75)
     parser.add_argument("--cpu_offload", action="store_true")
     return parser.parse_args()
 
 
 def load_model(args):
-    device = "cuda" if torch.cuda.is_available() else "cpu"
     if args.scheduler_type == "euler":
         scheduler = FlowMatchEulerDiscreteScheduler()
     else:
+        linear_quadratic = True if "linear_quadratic" in args.scheduler_type else False
         scheduler = PCMFMScheduler(
             1000,
             args.shift,
             args.num_euler_timesteps,
-            False,
+            linear_quadratic,
             args.linear_threshold,
             args.linear_range,
         )
 
     if args.transformer_path:
-        transformer = MochiTransformer3DModel.from_pretrained(args.transformer_path)
+        transformer = MochiTransformer3DModel.from_pretrained(
+            args.transformer_path)
     else:
         transformer = MochiTransformer3DModel.from_pretrained(
-            args.model_path, subfolder="transformer/"
-        )
+            args.model_path, subfolder="transformer/")
 
-    pipe = MochiPipeline.from_pretrained(
-        args.model_path, transformer=transformer, scheduler=scheduler
-    )
+    pipe = MochiPipeline.from_pretrained(args.model_path,
+                                         transformer=transformer,
+                                         scheduler=scheduler)
     pipe.enable_vae_tiling()
-    pipe.to(device)
-    if args.cpu_offload:
-        pipe.enable_model_cpu_offload()
+    # pipe.to(device)
+    # if args.cpu_offload:
+    pipe.enable_sequential_cpu_offload()
     return pipe
 
 
@@ -75,10 +79,8 @@ def generate_video(
     randomize_seed=False,
 ):
     if randomize_seed:
-        seed = torch.randint(0, 1000000, (1,)).item()
+        seed = torch.randint(0, 1000000, (1, )).item()
 
-    pipe = load_model(args)
-    print("load model successfully")
     generator = torch.Generator(device="cuda").manual_seed(seed)
 
     if not use_negative_prompt:
@@ -108,9 +110,10 @@ examples = [
 ]
 
 args = init_args()
-
+pipe = load_model(args)
+print("load model successfully")
 with gr.Blocks() as demo:
-    gr.Markdown("# Mochi Video Generation Demo")
+    gr.Markdown("# Fastvideo Mochi Video Generation Demo")
 
     with gr.Group():
         with gr.Row():
@@ -134,34 +137,35 @@ with gr.Blocks() as demo:
                     step=32,
                     value=args.height,
                 )
-                width = gr.Slider(
-                    label="Width", minimum=256, maximum=1024, step=32, value=args.width
-                )
+                width = gr.Slider(label="Width",
+                                  minimum=256,
+                                  maximum=1024,
+                                  step=32,
+                                  value=args.width)
 
             with gr.Row():
                 num_frames = gr.Slider(
                     label="Number of Frames",
-                    minimum=8,
-                    maximum=256,
+                    minimum=21,
+                    maximum=163,
                     value=args.num_frames,
                 )
                 guidance_scale = gr.Slider(
                     label="Guidance Scale",
                     minimum=1,
-                    maximum=20,
+                    maximum=12,
                     value=args.guidance_scale,
                 )
                 num_inference_steps = gr.Slider(
                     label="Inference Steps",
-                    minimum=10,
+                    minimum=4,
                     maximum=100,
                     value=args.num_inference_steps,
                 )
 
             with gr.Row():
-                use_negative_prompt = gr.Checkbox(
-                    label="Use negative prompt", value=False
-                )
+                use_negative_prompt = gr.Checkbox(label="Use negative prompt",
+                                                  value=False)
             negative_prompt = gr.Text(
                 label="Negative prompt",
                 max_lines=1,
@@ -169,9 +173,11 @@ with gr.Blocks() as demo:
                 visible=False,
             )
 
-            seed = gr.Slider(
-                label="Seed", minimum=0, maximum=1000000, step=1, value=args.seed
-            )
+            seed = gr.Slider(label="Seed",
+                             minimum=0,
+                             maximum=1000000,
+                             step=1,
+                             value=args.seed)
             randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
             seed_output = gr.Number(label="Used Seed")
 

docs/data_preprocess.md

@@ -0,0 +1,68 @@
+
+
+
+## 🧱 Data Preprocess
+
+To save GPU memory, we precompute text embeddings and VAE latents to eliminate the need to load the text encoder and VAE during training.
+
+
+We provide a sample dataset to help you get started. Download the source media using the following command:
+```bash
+python scripts/huggingface/download_hf.py --repo_id=FastVideo/Image-Vid-Finetune-Src --local_dir=data/Image-Vid-Finetune-Src --repo_type=dataset
+```
+To preprocess the dataset for fine-tuning or distillation, run:
+```
+bash scripts/preprocess/preprocess_mochi_data.sh # for mochi
+bash scripts/preprocess/preprocess_hunyuan_data.sh # for hunyuan
+```
+
+The preprocessed dataset will be stored in `Image-Vid-Finetune-Mochi` or `Image-Vid-Finetune-HunYuan` correspondingly.
+
+### Process your own dataset
+
+If you wish to create your own dataset for finetuning or distillation, please structure you video dataset in the following format:
+
+path_to_dataset_folder/
+├── media/
+│   ├── 0.jpg
+│   ├── 1.mp4
+│   ├── 2.jpg
+├── video2caption.json
+└── merge.txt
+
+Format the JSON file as a list, where each item represents a media source:
+
+For image media,
+```
+{
+    "path": "0.jpg",
+    "cap": ["captions"]
+}
+```
+For video media, 
+```
+{
+    "path": "1.mp4",
+    "resolution": {
+      "width": 848,
+      "height": 480
+    },
+    "fps": 30.0,
+    "duration": 6.033333333333333,
+    "cap": [
+      "caption"
+    ]
+  }
+```
+
+Use a txt file (merge.txt) to contain the source folder for media and the JSON file for meta information:
+
+```
+path_to_media_source_foder,path_to_json_file
+```
+
+Adjust the `DATA_MERGE_PATH` and `OUTPUT_DIR` in `scripts/preprocess/preprocess_****_data.sh` accordingly and run:
+```
+bash scripts/preprocess/preprocess_****_data.sh
+```
+The preprocessed data will be put into the `OUTPUT_DIR` and the `videos2caption.json` can be used in finetune and distill scripts.

env_setup.sh

@@ -0,0 +1,12 @@
+#!/bin/bash
+
+# install torch
+pip install torch==2.5.0 torchvision --index-url https://download.pytorch.org/whl/cu121
+
+# install FA2 and diffusers
+pip install packaging ninja && pip install flash-attn==2.7.0.post2 --no-build-isolation 
+
+pip install -r requirements-lint.txt
+
+# install fastvideo
+pip install -e .

fastvideo/data_preprocess/preprocess_text_embeddings.py

@@ -1,22 +1,23 @@
 import argparse
-import torch
-from accelerate.logging import get_logger
-from fastvideo.models.mochi_hf.pipeline_mochi import MochiPipeline
-from diffusers.utils import export_to_video
 import json
 import os
+
+import torch
 import torch.distributed as dist
+from accelerate.logging import get_logger
+from diffusers.utils import export_to_video
+from diffusers.video_processor import VideoProcessor
+from torch.utils.data import DataLoader, Dataset
+from torch.utils.data.distributed import DistributedSampler
+from tqdm import tqdm
+
+from fastvideo.utils.load import load_text_encoder, load_vae
 
 logger = get_logger(__name__)
-from torch.utils.data import Dataset
-from torch.utils.data.distributed import DistributedSampler
-from torch.utils.data import DataLoader
-from fastvideo.utils.load import load_text_encoder, load_vae
-from diffusers.video_processor import VideoProcessor
-from tqdm import tqdm
 
 
 class T5dataset(Dataset):
+
     def __init__(
         self,
         json_path,
@@ -26,7 +27,8 @@ class T5dataset(Dataset):
         self.vae_debug = vae_debug
         with open(self.json_path, "r") as f:
             train_dataset = json.load(f)
-            self.train_dataset = sorted(train_dataset, key=lambda x: x["latent_path"])
+            self.train_dataset = sorted(train_dataset,
+                                        key=lambda x: x["latent_path"])
 
     def __getitem__(self, idx):
         caption = self.train_dataset[idx]["caption"]
@@ -34,15 +36,17 @@ class T5dataset(Dataset):
         length = self.train_dataset[idx]["length"]
         if self.vae_debug:
             latents = torch.load(
-                os.path.join(
-                    args.output_dir, "latent", self.train_dataset[idx]["latent_path"]
-                ),
+                os.path.join(args.output_dir, "latent",
+                             self.train_dataset[idx]["latent_path"]),
                 map_location="cpu",
             )
         else:
             latents = []
 
-        return dict(caption=caption, latents=latents, filename=filename, length=length)
+        return dict(caption=caption,
+                    latents=latents,
+                    filename=filename,
+                    length=length)
 
     def __len__(self):
         return len(self.train_dataset)
@@ -56,27 +60,31 @@ def main(args):
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
     torch.cuda.set_device(local_rank)
     if not dist.is_initialized():
-        dist.init_process_group(
-            backend="nccl", init_method="env://", world_size=world_size, rank=local_rank
-        )
+        dist.init_process_group(backend="nccl",
+                                init_method="env://",
+                                world_size=world_size,
+                                rank=local_rank)
 
     videoprocessor = VideoProcessor(vae_scale_factor=8)
     os.makedirs(args.output_dir, exist_ok=True)
     os.makedirs(os.path.join(args.output_dir, "video"), exist_ok=True)
     os.makedirs(os.path.join(args.output_dir, "latent"), exist_ok=True)
     os.makedirs(os.path.join(args.output_dir, "prompt_embed"), exist_ok=True)
-    os.makedirs(os.path.join(args.output_dir, "prompt_attention_mask"), exist_ok=True)
+    os.makedirs(os.path.join(args.output_dir, "prompt_attention_mask"),
+                exist_ok=True)
 
-    latents_json_path = os.path.join(
-        args.output_dir, "videos2caption_temp.json"
-    )
+    latents_json_path = os.path.join(args.output_dir,
+                                     "videos2caption_temp.json")
     train_dataset = T5dataset(latents_json_path, args.vae_debug)
-    text_encoder = load_text_encoder(args.model_type, args.model_path, device=device)
+    text_encoder = load_text_encoder(args.model_type,
+                                     args.model_path,
+                                     device=device)
     vae, autocast_type, fps = load_vae(args.model_type, args.model_path)
     vae.enable_tiling()
-    sampler = DistributedSampler(
-        train_dataset, rank=local_rank, num_replicas=world_size, shuffle=True
-    )
+    sampler = DistributedSampler(train_dataset,
+                                 rank=local_rank,
+                                 num_replicas=world_size,
+                                 shuffle=True)
     train_dataloader = DataLoader(
         train_dataset,
         sampler=sampler,
@@ -89,25 +97,25 @@ def main(args):
         with torch.inference_mode():
             with torch.autocast("cuda", dtype=autocast_type):
                 prompt_embeds, prompt_attention_mask = text_encoder.encode_prompt(
-                    prompt=data["caption"],
-                )
+                    prompt=data["caption"], )
                 if args.vae_debug:
                     latents = data["latents"]
-                    video = vae.decode(latents.to(device), return_dict=False)[0]
+                    video = vae.decode(latents.to(device),
+                                       return_dict=False)[0]
                     video = videoprocessor.postprocess_video(video)
                 for idx, video_name in enumerate(data["filename"]):
-                    prompt_embed_path = os.path.join(
-                        args.output_dir, "prompt_embed", video_name + ".pt"
-                    )
-                    video_path = os.path.join(
-                        args.output_dir, "video", video_name + ".mp4"
-                    )
+                    prompt_embed_path = os.path.join(args.output_dir,
+                                                     "prompt_embed",
+                                                     video_name + ".pt")
+                    video_path = os.path.join(args.output_dir, "video",
+                                              video_name + ".mp4")
                     prompt_attention_mask_path = os.path.join(
-                        args.output_dir, "prompt_attention_mask", video_name + ".pt"
-                    )
+                        args.output_dir, "prompt_attention_mask",
+                        video_name + ".pt")
                     # save latent
                     torch.save(prompt_embeds[idx], prompt_embed_path)
-                    torch.save(prompt_attention_mask[idx], prompt_attention_mask_path)
+                    torch.save(prompt_attention_mask[idx],
+                               prompt_attention_mask_path)
                     print(f"sample {video_name} saved")
                     if args.vae_debug:
                         export_to_video(video[idx], video_path, fps=fps)
@@ -125,7 +133,8 @@ def main(args):
     if local_rank == 0:
         # os.remove(latents_json_path)
         all_json_data = [item for sublist in gathered_data for item in sublist]
-        with open(os.path.join(args.output_dir, "videos2caption.json"), "w") as f:
+        with open(os.path.join(args.output_dir, "videos2caption.json"),
+                  "w") as f:
             json.dump(all_json_data, f, indent=4)
 
 
@@ -139,7 +148,8 @@ if __name__ == "__main__":
         "--dataloader_num_workers",
         type=int,
         default=1,
-        help="Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.",
+        help=
+        "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.",
     )
     parser.add_argument(
         "--train_batch_size",
@@ -147,13 +157,16 @@ if __name__ == "__main__":
         default=1,
         help="Batch size (per device) for the training dataloader.",
     )
-    parser.add_argument("--text_encoder_name", type=str, default="google/t5-v1_1-xxl")
+    parser.add_argument("--text_encoder_name",
+                        type=str,
+                        default="google/t5-v1_1-xxl")
     parser.add_argument("--cache_dir", type=str, default="./cache_dir")
     parser.add_argument(
         "--output_dir",
         type=str,
         default=None,
-        help="The output directory where the model predictions and checkpoints will be written.",
+        help=
+        "The output directory where the model predictions and checkpoints will be written.",
     )
     parser.add_argument("--vae_debug", action="store_true")
     args = parser.parse_args()

fastvideo/data_preprocess/preprocess_vae_latents.py

@@ -1,19 +1,17 @@
-from fastvideo.dataset import getdataset
-from torch.utils.data import DataLoader
-from fastvideo.utils.dataset_utils import Collate
 import argparse
-import torch
-from accelerate import Accelerator
-from accelerate.logging import get_logger
-from accelerate.utils import ProjectConfiguration
 import json
 import os
-from diffusers import AutoencoderKLMochi
+
+import torch
 import torch.distributed as dist
+from accelerate.logging import get_logger
+from torch.utils.data import DataLoader
 from torch.utils.data.distributed import DistributedSampler
-from fastvideo.utils.load import load_vae
 from tqdm import tqdm
 
+from fastvideo.dataset import getdataset
+from fastvideo.utils.load import load_vae
+
 logger = get_logger(__name__)
 
 
@@ -22,9 +20,10 @@ def main(args):
     world_size = int(os.getenv("WORLD_SIZE", 1))
     print("world_size", world_size, "local rank", local_rank)
     train_dataset = getdataset(args)
-    sampler = DistributedSampler(
-        train_dataset, rank=local_rank, num_replicas=world_size, shuffle=True
-    )
+    sampler = DistributedSampler(train_dataset,
+                                 rank=local_rank,
+                                 num_replicas=world_size,
+                                 shuffle=True)
     train_dataloader = DataLoader(
         train_dataset,
         sampler=sampler,
@@ -32,12 +31,14 @@ def main(args):
         num_workers=args.dataloader_num_workers,
     )
 
-    encoder_device = torch.device(f"cuda" if torch.cuda.is_available() else "cpu")
+    encoder_device = torch.device(
+        "cuda" if torch.cuda.is_available() else "cpu")
     torch.cuda.set_device(local_rank)
     if not dist.is_initialized():
-        dist.init_process_group(
-            backend="nccl", init_method="env://", world_size=world_size, rank=local_rank
-        )
+        dist.init_process_group(backend="nccl",
+                                init_method="env://",
+                                world_size=world_size,
+                                rank=local_rank)
     vae, autocast_type, fps = load_vae(args.model_type, args.model_path)
     vae.enable_tiling()
     os.makedirs(args.output_dir, exist_ok=True)
@@ -47,14 +48,12 @@ def main(args):
     for _, data in tqdm(enumerate(train_dataloader), disable=local_rank != 0):
         with torch.inference_mode():
             with torch.autocast("cuda", dtype=autocast_type):
-                latents = vae.encode(data["pixel_values"].to(encoder_device))[
-                    "latent_dist"
-                ].sample()
+                latents = vae.encode(data["pixel_values"].to(
+                    encoder_device))["latent_dist"].sample()
             for idx, video_path in enumerate(data["path"]):
                 video_name = os.path.basename(video_path).split(".")[0]
-                latent_path = os.path.join(
-                    args.output_dir, "latent", video_name + ".pt"
-                )
+                latent_path = os.path.join(args.output_dir, "latent",
+                                           video_name + ".pt")
                 torch.save(latents[idx].to(torch.bfloat16), latent_path)
                 item = {}
                 item["length"] = latents[idx].shape[1]
@@ -68,7 +67,8 @@ def main(args):
     dist.all_gather_object(gathered_data, local_data)
     if local_rank == 0:
         all_json_data = [item for sublist in gathered_data for item in sublist]
-        with open(os.path.join(args.output_dir, "videos2caption_temp.json"), "w") as f:
+        with open(os.path.join(args.output_dir, "videos2caption_temp.json"),
+                  "w") as f:
             json.dump(all_json_data, f, indent=4)
 
 
@@ -79,13 +79,12 @@ if __name__ == "__main__":
     parser.add_argument("--model_type", type=str, default="mochi")
     parser.add_argument("--data_merge_path", type=str, required=True)
     parser.add_argument("--num_frames", type=int, default=163)
-    parser.add_argument("--shard_num", type=int)
-    parser.add_argument("--shard_idx", type=int)
     parser.add_argument(
         "--dataloader_num_workers",
         type=int,
         default=1,
-        help="Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.",
+        help=
+        "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.",
     )
     parser.add_argument(
         "--train_batch_size",
@@ -93,12 +92,15 @@ if __name__ == "__main__":
         default=16,
         help="Batch size (per device) for the training dataloader.",
     )
-    parser.add_argument(
-        "--num_latent_t", type=int, default=28, help="Number of latent timesteps."
-    )
+    parser.add_argument("--num_latent_t",
+                        type=int,
+                        default=28,
+                        help="Number of latent timesteps.")
     parser.add_argument("--max_height", type=int, default=480)
     parser.add_argument("--max_width", type=int, default=848)
-    parser.add_argument("--video_length_tolerance_range", type=int, default=2.0)
+    parser.add_argument("--video_length_tolerance_range",
+                        type=int,
+                        default=2.0)
     parser.add_argument("--group_frame", action="store_true")  # TODO
     parser.add_argument("--group_resolution", action="store_true")  # TODO
     parser.add_argument("--dataset", default="t2v")
@@ -108,23 +110,25 @@ if __name__ == "__main__":
     parser.add_argument("--speed_factor", type=float, default=1.0)
     parser.add_argument("--drop_short_ratio", type=float, default=1.0)
     # text encoder & vae & diffusion model
-    parser.add_argument("--text_encoder_name", type=str, default="google/t5-v1_1-xxl")
+    parser.add_argument("--text_encoder_name",
+                        type=str,
+                        default="google/t5-v1_1-xxl")
     parser.add_argument("--cache_dir", type=str, default="./cache_dir")
     parser.add_argument("--cfg", type=float, default=0.0)
     parser.add_argument(
         "--output_dir",
         type=str,
         default=None,
-        help="The output directory where the model predictions and checkpoints will be written.",
+        help=
+        "The output directory where the model predictions and checkpoints will be written.",
     )
     parser.add_argument(
         "--logging_dir",
         type=str,
         default="logs",
-        help=(
-            "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
-            " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
-        ),
+        help=
+        ("[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
+         " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."),
     )
 
     args = parser.parse_args()

fastvideo/data_preprocess/preprocess_validation_text_embeddings.py

@@ -1,19 +1,13 @@
 import argparse
-import torch
-from accelerate.logging import get_logger
-from fastvideo.models.mochi_hf.pipeline_mochi import MochiPipeline
-from diffusers.utils import export_to_video
-import json
 import os
+
+import torch
 import torch.distributed as dist
+from accelerate.logging import get_logger
+
+from fastvideo.utils.load import load_text_encoder
 
 logger = get_logger(__name__)
-from torch.utils.data import Dataset
-from torch.utils.data.distributed import DistributedSampler
-from torch.utils.data import DataLoader
-from fastvideo.utils.load import load_text_encoder, load_vae
-from diffusers.video_processor import VideoProcessor
-from tqdm import tqdm
 
 
 def main(args):
@@ -24,11 +18,14 @@ def main(args):
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
     torch.cuda.set_device(local_rank)
     if not dist.is_initialized():
-        dist.init_process_group(
-            backend="nccl", init_method="env://", world_size=world_size, rank=local_rank
-        )
+        dist.init_process_group(backend="nccl",
+                                init_method="env://",
+                                world_size=world_size,
+                                rank=local_rank)
 
-    text_encoder = load_text_encoder(args.model_type, args.model_path, device=device)
+    text_encoder = load_text_encoder(args.model_type,
+                                     args.model_path,
+                                     device=device)
     autocast_type = torch.float16 if args.model_type == "hunyuan" else torch.bfloat16
     # output_dir/validation/prompt_attention_mask
     # output_dir/validation/prompt_embed
@@ -37,10 +34,9 @@ def main(args):
         os.path.join(args.output_dir, "validation", "prompt_attention_mask"),
         exist_ok=True,
     )
-    os.makedirs(
-        os.path.join(args.output_dir, "validation", "prompt_embed"), exist_ok=True
-    )
-    json_data = []
+    os.makedirs(os.path.join(args.output_dir, "validation", "prompt_embed"),
+                exist_ok=True)
+
     with open(args.validation_prompt_txt, "r", encoding="utf-8") as file:
         lines = file.readlines()
     prompts = [line.strip() for line in lines]
@@ -48,12 +44,11 @@ def main(args):
         with torch.inference_mode():
             with torch.autocast("cuda", dtype=autocast_type):
                 prompt_embeds, prompt_attention_mask = text_encoder.encode_prompt(
-                    prompt
-                )
+                    prompt)
                 file_name = prompt.split(".")[0]
-                prompt_embed_path = os.path.join(
-                    args.output_dir, "validation", "prompt_embed", f"{file_name}.pt"
-                )
+                prompt_embed_path = os.path.join(args.output_dir, "validation",
+                                                 "prompt_embed",
+                                                 f"{file_name}.pt")
                 prompt_attention_mask_path = os.path.join(
                     args.output_dir,
                     "validation",
@@ -61,7 +56,8 @@ def main(args):
                     f"{file_name}.pt",
                 )
                 torch.save(prompt_embeds[0], prompt_embed_path)
-                torch.save(prompt_attention_mask[0], prompt_attention_mask_path)
+                torch.save(prompt_attention_mask[0],
+                           prompt_attention_mask_path)
                 print(f"sample {file_name} saved")
 
 
@@ -75,7 +71,8 @@ if __name__ == "__main__":
         "--output_dir",
         type=str,
         default=None,
-        help="The output directory where the model predictions and checkpoints will be written.",
+        help=
+        "The output directory where the model predictions and checkpoints will be written.",
     )
     args = parser.parse_args()
     main(args)

fastvideo/dataset/__init__.py

@@ -1,45 +1,34 @@
-from transformers import AutoTokenizer
-
 from torchvision import transforms
 from torchvision.transforms import Lambda
+from transformers import AutoTokenizer
+
 from fastvideo.dataset.t2v_datasets import T2V_dataset
-from fastvideo.dataset.latent_datasets import LatentDataset
-from fastvideo.dataset.transform import (
-    Normalize255,
-    TemporalRandomCrop,
-    CenterCropResizeVideo,
-)
+from fastvideo.dataset.transform import (CenterCropResizeVideo, Normalize255,
+                                         TemporalRandomCrop)
 
 
 def getdataset(args):
     temporal_sample = TemporalRandomCrop(args.num_frames)  # 16 x
     norm_fun = Lambda(lambda x: 2.0 * x - 1.0)
     resize_topcrop = [
-        CenterCropResizeVideo((args.max_height, args.max_width), top_crop=True),
+        CenterCropResizeVideo((args.max_height, args.max_width),
+                              top_crop=True),
     ]
     resize = [
         CenterCropResizeVideo((args.max_height, args.max_width)),
     ]
-    transform = transforms.Compose(
-        [
-            # Normalize255(),
-            *resize,
-            # RandomHorizontalFlipVideo(p=0.5),  # in case their caption have position decription
-            # norm_fun
-        ]
-    )
-    transform_topcrop = transforms.Compose(
-        [
-            Normalize255(),
-            *resize_topcrop,
-            # RandomHorizontalFlipVideo(p=0.5),  # in case their caption have position decription
-            norm_fun,
-        ]
-    )
+    transform = transforms.Compose([
+        # Normalize255(),
+        *resize,
+    ])
+    transform_topcrop = transforms.Compose([
+        Normalize255(),
+        *resize_topcrop,
+        norm_fun,
+    ])
     # tokenizer = AutoTokenizer.from_pretrained("/storage/ongoing/new/Open-Sora-Plan/cache_dir/mt5-xxl", cache_dir=args.cache_dir)
-    tokenizer = AutoTokenizer.from_pretrained(
-        args.text_encoder_name, cache_dir=args.cache_dir
-    )
+    tokenizer = AutoTokenizer.from_pretrained(args.text_encoder_name,
+                                              cache_dir=args.cache_dir)
     if args.dataset == "t2v":
         return T2V_dataset(
             args,
@@ -53,11 +42,13 @@ def getdataset(args):
 
 
 if __name__ == "__main__":
-    from accelerate import Accelerator
-    from fastvideo.dataset.t2v_datasets import dataset_prog
     import random
+
+    from accelerate import Accelerator
     from tqdm import tqdm
 
+    from fastvideo.dataset.t2v_datasets import dataset_prog
+
     args = type(
         "args",
         (),
@@ -75,7 +66,8 @@ if __name__ == "__main__":
             "interpolation_scale_h": 1,
             "interpolation_scale_w": 1,
             "cache_dir": "../cache_dir",
-            "image_data": "/storage/ongoing/new/Open-Sora-Plan-bak/7.14bak/scripts/train_data/image_data.txt",
+            "image_data":
+            "/storage/ongoing/new/Open-Sora-Plan-bak/7.14bak/scripts/train_data/image_data.txt",
             "video_data": "1",
             "train_fps": 24,
             "drop_short_ratio": 1.0,
@@ -93,7 +85,8 @@ if __name__ == "__main__":
     for idx in tqdm(range(num)):
         image_data = dataset_prog.img_cap_list[idx]
         caps = [
-            i["cap"] if isinstance(i["cap"], list) else [i["cap"]] for i in image_data
+            i["cap"] if isinstance(i["cap"], list) else [i["cap"]]
+            for i in image_data
         ]
         try:
             caps = [[random.choice(i)] for i in caps]

fastvideo/dataset/latent_datasets.py

@@ -1,11 +1,13 @@
-import torch
-from torch.utils.data import Dataset
 import json
 import os
 import random
 
+import torch
+from torch.utils.data import Dataset
+
 
 class LatentDataset(Dataset):
+
     def __init__(
         self,
         json_path,
@@ -18,10 +20,10 @@ class LatentDataset(Dataset):
         self.datase_dir_path = os.path.dirname(json_path)
         self.video_dir = os.path.join(self.datase_dir_path, "video")
         self.latent_dir = os.path.join(self.datase_dir_path, "latent")
-        self.prompt_embed_dir = os.path.join(self.datase_dir_path, "prompt_embed")
-        self.prompt_attention_mask_dir = os.path.join(
-            self.datase_dir_path, "prompt_attention_mask"
-        )
+        self.prompt_embed_dir = os.path.join(self.datase_dir_path,
+                                             "prompt_embed")
+        self.prompt_attention_mask_dir = os.path.join(self.datase_dir_path,
+                                                      "prompt_attention_mask")
         with open(self.json_path, "r") as f:
             self.data_anno = json.load(f)
         # json.load(f) already keeps the order
@@ -39,14 +41,15 @@ class LatentDataset(Dataset):
     def __getitem__(self, idx):
         latent_file = self.data_anno[idx]["latent_path"]
         prompt_embed_file = self.data_anno[idx]["prompt_embed_path"]
-        prompt_attention_mask_file = self.data_anno[idx]["prompt_attention_mask"]
+        prompt_attention_mask_file = self.data_anno[idx][
+            "prompt_attention_mask"]
         # load
         latent = torch.load(
             os.path.join(self.latent_dir, latent_file),
             map_location="cpu",
             weights_only=True,
         )
-        latent = latent.squeeze(0)[:,  :self.num_latent_t]
+        latent = latent.squeeze(0)[:, -self.num_latent_t:]
         if random.random() < self.cfg_rate:
             prompt_embed = self.uncond_prompt_embed
             prompt_attention_mask = self.uncond_prompt_mask
@@ -57,9 +60,8 @@ class LatentDataset(Dataset):
                 weights_only=True,
             )
             prompt_attention_mask = torch.load(
-                os.path.join(
-                    self.prompt_attention_mask_dir, prompt_attention_mask_file
-                ),
+                os.path.join(self.prompt_attention_mask_dir,
+                             prompt_attention_mask_file),
                 map_location="cpu",
                 weights_only=True,
             )
@@ -92,16 +94,15 @@ def latent_collate_function(batch):
                 0,
                 max_w - latent.shape[3],
             ),
-        )
-        for latent in latents
+        ) for latent in latents
     ]
     # attn mask
     latent_attn_mask = torch.ones(len(latents), max_t, max_h, max_w)
     # set to 0 if padding
     for i, latent in enumerate(latents):
-        latent_attn_mask[i, latent.shape[1] :, :, :] = 0
-        latent_attn_mask[i, :, latent.shape[2] :, :] = 0
-        latent_attn_mask[i, :, :, latent.shape[3] :] = 0
+        latent_attn_mask[i, latent.shape[1]:, :, :] = 0
+        latent_attn_mask[i, :, latent.shape[2]:, :] = 0
+        latent_attn_mask[i, :, :, latent.shape[3]:] = 0
 
     prompt_embeds = torch.stack(prompt_embeds, dim=0)
     prompt_attention_masks = torch.stack(prompt_attention_masks, dim=0)
@@ -110,10 +111,13 @@ def latent_collate_function(batch):
 
 
 if __name__ == "__main__":
-    dataset = LatentDataset("data/Mochi-Synthetic-Data/merge.txt", num_latent_t=28)
+    dataset = LatentDataset("data/Mochi-Synthetic-Data/merge.txt",
+                            num_latent_t=28)
     dataloader = torch.utils.data.DataLoader(
-        dataset, batch_size=2, shuffle=False, collate_fn=latent_collate_function
-    )
+        dataset,
+        batch_size=2,
+        shuffle=False,
+        collate_fn=latent_collate_function)
     for latent, prompt_embed, latent_attn_mask, prompt_attention_mask in dataloader:
         print(
             latent.shape,

fastvideo/dataset/t2v_datasets.py

@@ -1,18 +1,18 @@
 import json
-import os, io, csv, math, random
-import numpy as np
-from einops import rearrange
-from decord import VideoReader
-from os.path import join as opj
+import math
+import os
+import random
 from collections import Counter
+from os.path import join as opj
 
+import numpy as np
 import torch
-from torch.utils.data.dataset import Dataset
-from torch.utils.data import DataLoader, Dataset, get_worker_info
-from tqdm import tqdm
-from PIL import Image
-from fastvideo.utils.dataset_utils import DecordInit
 import torchvision
+from einops import rearrange
+from PIL import Image
+from torch.utils.data import Dataset
+
+from fastvideo.utils.dataset_utils import DecordInit
 from fastvideo.utils.logging_ import main_print
 
 
@@ -27,6 +27,7 @@ class SingletonMeta(type):
 
 
 class DataSetProg(metaclass=SingletonMeta):
+
     def __init__(self):
         self.cap_list = []
         self.elements = []
@@ -45,7 +46,8 @@ class DataSetProg(metaclass=SingletonMeta):
 
         for i in range(self.num_workers):
             self.n_used_elements[i] = 0
-            per_worker = int(math.ceil(len(self.elements) / float(self.num_workers)))
+            per_worker = int(
+                math.ceil(len(self.elements) / float(self.num_workers)))
             start = i * per_worker
             end = min(start + per_worker, len(self.elements))
             self.worker_elements[i] = self.elements[start:end]
@@ -57,8 +59,8 @@ class DataSetProg(metaclass=SingletonMeta):
             worker_id = work_info.id
 
         idx = self.worker_elements[worker_id][
-            self.n_used_elements[worker_id] % len(self.worker_elements[worker_id])
-        ]
+            self.n_used_elements[worker_id] %
+            len(self.worker_elements[worker_id])]
         self.n_used_elements[worker_id] += 1
         return idx
 
@@ -66,14 +68,19 @@ class DataSetProg(metaclass=SingletonMeta):
 dataset_prog = DataSetProg()
 
 
-def filter_resolution(h, w, max_h_div_w_ratio=17 / 16, min_h_div_w_ratio=8 / 16):
+def filter_resolution(h,
+                      w,
+                      max_h_div_w_ratio=17 / 16,
+                      min_h_div_w_ratio=8 / 16):
     if h / w <= max_h_div_w_ratio and h / w >= min_h_div_w_ratio:
         return True
     return False
 
 
 class T2V_dataset(Dataset):
-    def __init__(self, args, transform, temporal_sample, tokenizer, transform_topcrop):
+
+    def __init__(self, args, transform, temporal_sample, tokenizer,
+                 transform_topcrop):
         self.data = args.data_merge_path
         self.num_frames = args.num_frames
         self.train_fps = args.train_fps
@@ -92,9 +99,7 @@ class T2V_dataset(Dataset):
         self.v_decoder = DecordInit()
         self.video_length_tolerance_range = args.video_length_tolerance_range
         self.support_Chinese = True
-        self.shard_num = args.shard_num
-        self.shard_idx = args.shard_idx
-        if not ("mt5" in args.text_encoder_name):
+        if "mt5" not in args.text_encoder_name:
             self.support_Chinese = False
 
         cap_list = self.get_cap_list()
@@ -104,7 +109,8 @@ class T2V_dataset(Dataset):
         self.lengths = self.sample_num_frames
 
         n_elements = len(cap_list)
-        dataset_prog.set_cap_list(args.dataloader_num_workers, cap_list, n_elements)
+        dataset_prog.set_cap_list(args.dataloader_num_workers, cap_list,
+                                  n_elements)
 
         print(f"video length: {len(dataset_prog.cap_list)}", flush=True)
 
@@ -132,8 +138,7 @@ class T2V_dataset(Dataset):
         assert os.path.exists(video_path), f"file {video_path} do not exist!"
         frame_indices = dataset_prog.cap_list[idx]["sample_frame_index"]
         torchvision_video, _, metadata = torchvision.io.read_video(
-            video_path, output_format="TCHW"
-        )
+            video_path, output_format="TCHW")
         video = torchvision_video[frame_indices]
         video = self.transform(video)
         video = rearrange(video, "t c h w -> c t h w")
@@ -173,7 +178,8 @@ class T2V_dataset(Dataset):
         )
 
     def get_image(self, idx):
-        image_data = dataset_prog.cap_list[idx]  # [{'path': path, 'cap': cap}, ...]
+        image_data = dataset_prog.cap_list[
+            idx]  # [{'path': path, 'cap': cap}, ...]
 
         image = Image.open(image_data["path"]).convert("RGB")  # [h, w, c]
         image = torch.from_numpy(np.array(image))  # [h, w, c]
@@ -182,20 +188,15 @@ class T2V_dataset(Dataset):
         #     h, w = i.shape[-2:]
         #     assert h / w <= 17 / 16 and h / w >= 8 / 16, f'Only image with a ratio (h/w) less than 17/16 and more than 8/16 are supported. But found ratio is {round(h / w, 2)} with the shape of {i.shape}'
 
-        image = (
-            self.transform_topcrop(image)
-            if "human_images" in image_data["path"]
-            else self.transform(image)
-        )  #  [1 C H W] -> num_img [1 C H W]
+        image = (self.transform_topcrop(image) if "human_images"
+                 in image_data["path"] else self.transform(image)
+                 )  #  [1 C H W] -> num_img [1 C H W]
         image = image.transpose(0, 1)  # [1 C H W] -> [C 1 H W]
 
         image = image.float() / 127.5 - 1.0
 
-        caps = (
-            image_data["cap"]
-            if isinstance(image_data["cap"], list)
-            else [image_data["cap"]]
-        )
+        caps = (image_data["cap"] if isinstance(image_data["cap"], list) else
+                [image_data["cap"]])
         caps = [random.choice(caps)]
         text = caps
         input_ids, cond_mask = [], []
@@ -249,13 +250,12 @@ class T2V_dataset(Dataset):
                     cnt_no_resolution += 1
                     continue
                 else:
-                    if (
-                        resolution.get("height", None) is None
-                        or resolution.get("width", None) is None
-                    ):
+                    if (resolution.get("height", None) is None
+                            or resolution.get("width", None) is None):
                         cnt_no_resolution += 1
                         continue
-                    height, width = i["resolution"]["height"], i["resolution"]["width"]
+                    height, width = i["resolution"]["height"], i["resolution"][
+                        "width"]
                     aspect = self.max_height / self.max_width
                     hw_aspect_thr = 1.5
                     is_pick = filter_resolution(
@@ -273,7 +273,7 @@ class T2V_dataset(Dataset):
                 i["num_frames"] = math.ceil(fps * duration)
                 # max 5.0 and min 1.0 are just thresholds to filter some videos which have suitable duration.
                 if i["num_frames"] / fps > self.video_length_tolerance_range * (
-                    self.num_frames / self.train_fps * self.speed_factor
+                        self.num_frames / self.train_fps * self.speed_factor
                 ):  # too long video is not suitable for this training stage (self.num_frames)
                     cnt_too_long += 1
                     continue
@@ -281,21 +281,19 @@ class T2V_dataset(Dataset):
                 # resample in case high fps, such as 50/60/90/144 -> train_fps(e.g, 24)
                 frame_interval = fps / self.train_fps
                 start_frame_idx = 0
-                frame_indices = np.arange(
-                    start_frame_idx, i["num_frames"], frame_interval
-                ).astype(int)
+                frame_indices = np.arange(start_frame_idx, i["num_frames"],
+                                          frame_interval).astype(int)
 
                 # comment out it to enable dynamic frames training
-                if (
-                    len(frame_indices) < self.num_frames
-                    and random.random() < self.drop_short_ratio
-                ):
+                if (len(frame_indices) < self.num_frames
+                        and random.random() < self.drop_short_ratio):
                     cnt_too_short += 1
                     continue
 
                 #  too long video will be temporal-crop randomly
                 if len(frame_indices) > self.num_frames:
-                    begin_index, end_index = self.temporal_sample(len(frame_indices))
+                    begin_index, end_index = self.temporal_sample(
+                        len(frame_indices))
                     frame_indices = frame_indices[begin_index:end_index]
                     # frame_indices = frame_indices[:self.num_frames]  # head crop
                 i["sample_frame_index"] = frame_indices.tolist()
@@ -311,7 +309,7 @@ class T2V_dataset(Dataset):
                 sample_num_frames.append(i["sample_num_frames"])
             else:
                 raise NameError(
-                    f"Unknown file extention {path.split('.')[-1]}, only support .mp4 for video and .jpg for image"
+                    f"Unknown file extension {path.split('.')[-1]}, only support .mp4 for video and .jpg for image"
                 )
         # import ipdb;ipdb.set_trace()
         main_print(
@@ -326,14 +324,16 @@ class T2V_dataset(Dataset):
         decord_vr = self.v_decoder(path)
         video_data = decord_vr.get_batch(frame_indices).asnumpy()
         video_data = torch.from_numpy(video_data)
-        video_data = video_data.permute(0, 3, 1, 2)  # (T, H, W, C) -> (T C H W)
+        video_data = video_data.permute(0, 3, 1,
+                                        2)  # (T, H, W, C) -> (T C H W)
         return video_data
 
     def read_jsons(self, data):
         cap_lists = []
         with open(data, "r") as f:
             folder_anno = [
-                i.strip().split(",") for i in f.readlines() if len(i.strip()) > 0
+                i.strip().split(",") for i in f.readlines()
+                if len(i.strip()) > 0
             ]
         print(folder_anno)
         for folder, anno in folder_anno:
@@ -342,11 +342,7 @@ class T2V_dataset(Dataset):
             for i in range(len(sub_list)):
                 sub_list[i]["path"] = opj(folder, sub_list[i]["path"])
             cap_lists += sub_list
-        total = len(cap_lists)
-        shard_size = (total + self.shard_num - 1) // self.shard_num  # Ceiling division
-        start_idx = self.shard_idx * shard_size
-        end_idx = min(start_idx + shard_size, total)
-        return cap_lists[start_idx:end_idx]
+        return cap_lists
 
     def get_cap_list(self):
         cap_lists = self.read_jsons(self.data)

fastvideo/dataset/transform.py

@@ -1,7 +1,8 @@
-import torch
-import random
 import numbers
-from torchvision.transforms import RandomCrop, RandomResizedCrop
+import random
+
+import torch
+from PIL import Image
 
 
 def _is_tensor_video_clip(clip):
@@ -20,21 +21,19 @@ def center_crop_arr(pil_image, image_size):
     https://github.com/openai/guided-diffusion/blob/8fb3ad9197f16bbc40620447b2742e13458d2831/guided_diffusion/image_datasets.py#L126
     """
     while min(*pil_image.size) >= 2 * image_size:
-        pil_image = pil_image.resize(
-            tuple(x // 2 for x in pil_image.size), resample=Image.BOX
-        )
+        pil_image = pil_image.resize(tuple(x // 2 for x in pil_image.size),
+                                     resample=Image.BOX)
 
     scale = image_size / min(*pil_image.size)
-    pil_image = pil_image.resize(
-        tuple(round(x * scale) for x in pil_image.size), resample=Image.BICUBIC
-    )
+    pil_image = pil_image.resize(tuple(
+        round(x * scale) for x in pil_image.size),
+                                 resample=Image.BICUBIC)
 
     arr = np.array(pil_image)
     crop_y = (arr.shape[0] - image_size) // 2
     crop_x = (arr.shape[1] - image_size) // 2
-    return Image.fromarray(
-        arr[crop_y : crop_y + image_size, crop_x : crop_x + image_size]
-    )
+    return Image.fromarray(arr[crop_y:crop_y + image_size,
+                               crop_x:crop_x + image_size])
 
 
 def crop(clip, i, j, h, w):
@@ -44,7 +43,7 @@ def crop(clip, i, j, h, w):
     """
     if len(clip.size()) != 4:
         raise ValueError("clip should be a 4D tensor")
-    return clip[..., i : i + h, j : j + w]
+    return clip[..., i:i + h, j:j + w]
 
 
 def resize(clip, target_size, interpolation_mode):
@@ -153,16 +152,14 @@ def random_shift_crop(clip):
     h, w = clip.size(-2), clip.size(-1)
 
     if h <= w:
-        long_edge = w
         short_edge = h
     else:
-        long_edge = h
         short_edge = w
 
     th, tw = short_edge, short_edge
 
-    i = torch.randint(0, h - th + 1, size=(1,)).item()
-    j = torch.randint(0, w - tw + 1, size=(1,)).item()
+    i = torch.randint(0, h - th + 1, size=(1, )).item()
+    j = torch.randint(0, w - tw + 1, size=(1, )).item()
     return crop(clip, i, j, th, tw)
 
 
@@ -177,9 +174,8 @@ def normalize_video(clip):
     """
     _is_tensor_video_clip(clip)
     if not clip.dtype == torch.uint8:
-        raise TypeError(
-            "clip tensor should have data type uint8. Got %s" % str(clip.dtype)
-        )
+        raise TypeError("clip tensor should have data type uint8. Got %s" %
+                        str(clip.dtype))
     # return clip.float().permute(3, 0, 1, 2) / 255.0
     return clip.float() / 255.0
 
@@ -217,6 +213,7 @@ def hflip(clip):
 
 
 class RandomCropVideo:
+
     def __init__(self, size):
         if isinstance(size, numbers.Number):
             self.size = (int(size), int(size))
@@ -246,8 +243,8 @@ class RandomCropVideo:
         if w == tw and h == th:
             return 0, 0, h, w
 
-        i = torch.randint(0, h - th + 1, size=(1,)).item()
-        j = torch.randint(0, w - tw + 1, size=(1,)).item()
+        i = torch.randint(0, h - th + 1, size=(1, )).item()
+        j = torch.randint(0, w - tw + 1, size=(1, )).item()
 
         return i, j, th, tw
 
@@ -256,6 +253,7 @@ class RandomCropVideo:
 
 
 class SpatialStrideCropVideo:
+
     def __init__(self, stride):
         self.stride = stride
 
@@ -314,9 +312,9 @@ class LongSideResizeVideo:
         else:
             h = int(h * self.size / w)
             w = self.size
-        resize_clip = resize(
-            clip, target_size=(h, w), interpolation_mode=self.interpolation_mode
-        )
+        resize_clip = resize(clip,
+                             target_size=(h, w),
+                             interpolation_mode=self.interpolation_mode)
         return resize_clip
 
     def __repr__(self) -> str:
@@ -337,8 +335,7 @@ class CenterCropResizeVideo:
     ):
         if len(size) != 2:
             raise ValueError(
-                f"size should be tuple (height, width), instead got {size}"
-            )
+                f"size should be tuple (height, width), instead got {size}")
         self.size = size
         self.top_crop = top_crop
         self.interpolation_mode = interpolation_mode
@@ -352,9 +349,10 @@ class CenterCropResizeVideo:
                 size is (T, C, crop_size, crop_size)
         """
         # clip_center_crop = center_crop_using_short_edge(clip)
-        clip_center_crop = center_crop_th_tw(
-            clip, self.size[0], self.size[1], top_crop=self.top_crop
-        )
+        clip_center_crop = center_crop_th_tw(clip,
+                                             self.size[0],
+                                             self.size[1],
+                                             top_crop=self.top_crop)
         # import ipdb;ipdb.set_trace()
         clip_center_crop_resize = resize(
             clip_center_crop,
@@ -397,9 +395,9 @@ class UCFCenterCropVideo:
             torch.tensor: scale resized / center cropped video clip.
                 size is (T, C, crop_size, crop_size)
         """
-        clip_resize = resize_scale(
-            clip=clip, target_size=self.size, interpolation_mode=self.interpolation_mode
-        )
+        clip_resize = resize_scale(clip=clip,
+                                   target_size=self.size,
+                                   interpolation_mode=self.interpolation_mode)
         clip_center_crop = center_crop(clip_resize, self.size)
         return clip_center_crop
 
@@ -430,11 +428,13 @@ class KineticsRandomCropResizeVideo:
 
     def __call__(self, clip):
         clip_random_crop = random_shift_crop(clip)
-        clip_resize = resize(clip_random_crop, self.size, self.interpolation_mode)
+        clip_resize = resize(clip_random_crop, self.size,
+                             self.interpolation_mode)
         return clip_resize
 
 
 class CenterCropVideo:
+
     def __init__(
         self,
         size,
@@ -571,9 +571,8 @@ class DynamicSampleDuration(object):
     def __call__(self, t, h, w):
         if self.extra_1:
             t = t - 1
-        truncate_t_list = list(range(t + 1))[t // 2 :][
-            :: self.t_stride
-        ]  # need half at least
+        truncate_t_list = list(
+            range(t + 1))[t // 2:][::self.t_stride]  # need half at least
         truncate_t = random.choice(truncate_t_list)
         if self.extra_1:
             truncate_t = truncate_t + 1
@@ -581,27 +580,26 @@ class DynamicSampleDuration(object):
 
 
 if __name__ == "__main__":
-    from torchvision import transforms
-    import torchvision.io as io
-    import numpy as np
-    from torchvision.utils import save_image
     import os
 
-    vframes, aframes, info = io.read_video(
-        filename="./v_Archery_g01_c03.avi", pts_unit="sec", output_format="TCHW"
-    )
+    import numpy as np
+    import torchvision.io as io
+    from torchvision import transforms
+    from torchvision.utils import save_image
 
-    trans = transforms.Compose(
-        [
-            Normalize255(),
-            RandomHorizontalFlipVideo(),
-            UCFCenterCropVideo(512),
-            # NormalizeVideo(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True),
-            transforms.Normalize(
-                mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True
-            ),
-        ]
-    )
+    vframes, aframes, info = io.read_video(filename="./v_Archery_g01_c03.avi",
+                                           pts_unit="sec",
+                                           output_format="TCHW")
+
+    trans = transforms.Compose([
+        Normalize255(),
+        RandomHorizontalFlipVideo(),
+        UCFCenterCropVideo(512),
+        # NormalizeVideo(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True),
+        transforms.Normalize(mean=[0.5, 0.5, 0.5],
+                             std=[0.5, 0.5, 0.5],
+                             inplace=True),
+    ])
 
     target_video_len = 32
     frame_interval = 1
@@ -615,9 +613,10 @@ if __name__ == "__main__":
     # print(start_frame_ind)
     # print(end_frame_ind)
     assert end_frame_ind - start_frame_ind >= target_video_len
-    frame_indice = np.linspace(
-        start_frame_ind, end_frame_ind - 1, target_video_len, dtype=int
-    )
+    frame_indice = np.linspace(start_frame_ind,
+                               end_frame_ind - 1,
+                               target_video_len,
+                               dtype=int)
     print(frame_indice)
 
     select_vframes = vframes[frame_indice]
@@ -628,13 +627,14 @@ if __name__ == "__main__":
     print(select_vframes_trans.shape)
     print(select_vframes_trans.dtype)
 
-    select_vframes_trans_int = ((select_vframes_trans * 0.5 + 0.5) * 255).to(
-        dtype=torch.uint8
-    )
+    select_vframes_trans_int = ((select_vframes_trans * 0.5 + 0.5) *
+                                255).to(dtype=torch.uint8)
     print(select_vframes_trans_int.dtype)
     print(select_vframes_trans_int.permute(0, 2, 3, 1).shape)
 
-    io.write_video("./test.avi", select_vframes_trans_int.permute(0, 2, 3, 1), fps=8)
+    io.write_video("./test.avi",
+                   select_vframes_trans_int.permute(0, 2, 3, 1),
+                   fps=8)
 
     for i in range(target_video_len):
         save_image(

fastvideo/distill.py

@@ -1,57 +1,47 @@
+# !/bin/python3
+# isort: skip_file
 import argparse
 import math
 import os
-from fastvideo.utils.parallel_states import (
-    initialize_sequence_parallel_state,
-    destroy_sequence_parallel_group,
-    get_sequence_parallel_state,
-    nccl_info,
-)
-from fastvideo.utils.communications import sp_parallel_dataloader_wrapper, broadcast
-from fastvideo.models.mochi_hf.mochi_latents_utils import normalize_dit_input
-from fastvideo.utils.validation import log_validation
 import time
-from torch.utils.data import DataLoader
+from collections import deque
+from copy import deepcopy
+
 import torch
-from torch.distributed.fsdp import ShardingStrategy
-from torch.distributed.fsdp import (
-    FullyShardedDataParallel as FSDP,
-    StateDictType,
-    FullStateDictConfig,
-)
-from fastvideo.models.mochi_hf.pipeline_mochi import linear_quadratic_schedule
-import json
-from torch.utils.data.distributed import DistributedSampler
-from fastvideo.utils.dataset_utils import LengthGroupedSampler
+import torch.distributed as dist
 import wandb
 from accelerate.utils import set_seed
-from tqdm.auto import tqdm
-from fastvideo.utils.fsdp_util import get_dit_fsdp_kwargs, apply_fsdp_checkpointing
-from diffusers import (
-    FlowMatchEulerDiscreteScheduler,
-)
-from fastvideo.utils.load import  load_transformer
-from fastvideo.distill.solver import EulerSolver, extract_into_tensor
-from copy import deepcopy
+from diffusers import FlowMatchEulerDiscreteScheduler
 from diffusers.optimization import get_scheduler
 from diffusers.utils import check_min_version
-from fastvideo.dataset.latent_datasets import LatentDataset, latent_collate_function
-import torch.distributed as dist
-from safetensors.torch import save_file
 from peft import LoraConfig
-from torch.distributed.fsdp import (
-    FullyShardedDataParallel as FSDP,
-)
-from fastvideo.utils.checkpoint import (
-    save_checkpoint,
-    save_lora_checkpoint,
-    resume_lora_optimizer,
-)
+from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
+from torch.distributed.fsdp import ShardingStrategy
+from torch.utils.data import DataLoader
+from torch.utils.data.distributed import DistributedSampler
+from tqdm.auto import tqdm
+
+from fastvideo.dataset.latent_datasets import (LatentDataset,
+                                               latent_collate_function)
+from fastvideo.distill.solver import EulerSolver, extract_into_tensor
+from fastvideo.models.mochi_hf.mochi_latents_utils import normalize_dit_input
+from fastvideo.models.mochi_hf.pipeline_mochi import linear_quadratic_schedule
+from fastvideo.utils.checkpoint import (resume_lora_optimizer, save_checkpoint,
+                                        save_lora_checkpoint)
+from fastvideo.utils.communications import (broadcast,
+                                            sp_parallel_dataloader_wrapper)
+from fastvideo.utils.dataset_utils import LengthGroupedSampler
+from fastvideo.utils.fsdp_util import (apply_fsdp_checkpointing,
+                                       get_dit_fsdp_kwargs)
+from fastvideo.utils.load import load_transformer
+from fastvideo.utils.parallel_states import (destroy_sequence_parallel_group,
+                                             get_sequence_parallel_state,
+                                             initialize_sequence_parallel_state
+                                             )
+from fastvideo.utils.validation import log_validation
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
 check_min_version("0.31.0")
-import time
-from collections import deque
 
 
 def main_print(content):
@@ -59,31 +49,6 @@ def main_print(content):
         print(content)
 
 
-def save_checkpoint(transformer, rank, output_dir, step):
-    main_print(f"--> saving checkpoint at step {step}")
-    with FSDP.state_dict_type(
-        transformer,
-        StateDictType.FULL_STATE_DICT,
-        FullStateDictConfig(offload_to_cpu=True, rank0_only=True),
-    ):
-        cpu_state = transformer.state_dict()
-    # todo move to get_state_dict
-    if rank <= 0:
-        save_dir = os.path.join(output_dir, f"checkpoint-{step}")
-        os.makedirs(save_dir, exist_ok=True)
-        # save using safetensors
-        weight_path = os.path.join(save_dir, "diffusion_pytorch_model.safetensors")
-        save_file(cpu_state, weight_path)
-        config_dict = dict(transformer.config)
-        if "dtype" in config_dict:
-            del config_dict["dtype"]  # TODO
-        config_path = os.path.join(save_dir, "config.json")
-        # save dict as json
-        with open(config_path, "w") as f:
-            json.dump(config_dict, f, indent=4)
-    main_print(f"--> checkpoint saved at step {step}")
-
-
 def reshard_fsdp(model):
     for m in FSDP.fsdp_modules(model):
         if m._has_params and m.sharding_strategy is not ShardingStrategy.NO_SHARD:
@@ -92,18 +57,20 @@ def reshard_fsdp(model):
 
 def get_norm(model_pred, norms, gradient_accumulation_steps):
     fro_norm = (
-        torch.linalg.matrix_norm(model_pred, ord="fro") / gradient_accumulation_steps
-    )
-    largest_singular_value = (
-        torch.linalg.matrix_norm(model_pred, ord=2) / gradient_accumulation_steps
-    )
-    absolute_mean = torch.mean(torch.abs(model_pred)) / gradient_accumulation_steps
-    absolute_max = torch.max(torch.abs(model_pred)) / gradient_accumulation_steps
+        torch.linalg.matrix_norm(model_pred, ord="fro") /  # codespell:ignore
+        gradient_accumulation_steps)
+    largest_singular_value = (torch.linalg.matrix_norm(model_pred, ord=2) /
+                              gradient_accumulation_steps)
+    absolute_mean = torch.mean(
+        torch.abs(model_pred)) / gradient_accumulation_steps
+    absolute_max = torch.max(
+        torch.abs(model_pred)) / gradient_accumulation_steps
     dist.all_reduce(fro_norm, op=dist.ReduceOp.AVG)
     dist.all_reduce(largest_singular_value, op=dist.ReduceOp.AVG)
     dist.all_reduce(absolute_mean, op=dist.ReduceOp.AVG)
-    norms["fro"] += torch.mean(fro_norm).item()
-    norms["largest singular value"] += torch.mean(largest_singular_value).item()
+    norms["fro"] += torch.mean(fro_norm).item()  # codespell:ignore
+    norms["largest singular value"] += torch.mean(
+        largest_singular_value).item()
     norms["absolute mean"] += absolute_mean.item()
     norms["absolute max"] += absolute_max.item()
 
@@ -131,22 +98,16 @@ def distill_one_step(
     ema_decay,
     pred_decay_weight,
     pred_decay_type,
+    hunyuan_teacher_disable_cfg,
 ):
     total_loss = 0.0
     optimizer.zero_grad()
     model_pred_norm = {
-        "fro": 0.0,
+        "fro": 0.0,  # codespell:ignore
         "largest singular value": 0.0,
         "absolute mean": 0.0,
         "absolute max": 0.0,
     }
-    distill_cfg = distill_cfg.split(",")
-    distill_cfg = [float(cfg) for cfg in distill_cfg]
-    # randomly pick 
-    distill_cfg = distill_cfg[torch.randint(0, len(distill_cfg), (1,)).item()]
-    distill_cfg  = torch.tensor([distill_cfg], device=transformer.device, dtype=torch.bfloat16) * 1000
-    if sp_size > 1:
-        broadcast(distill_cfg)
     for _ in range(gradient_accumulation_steps):
         (
             latents,
@@ -157,55 +118,54 @@ def distill_one_step(
         model_input = normalize_dit_input(model_type, latents)
         noise = torch.randn_like(model_input)
         bsz = model_input.shape[0]
-        index = torch.randint(
-            0, num_euler_timesteps, (bsz,), device=model_input.device
-        ).long()
+        index = torch.randint(0,
+                              num_euler_timesteps, (bsz, ),
+                              device=model_input.device).long()
         if sp_size > 1:
             broadcast(index)
         # Add noise according to flow matching.
         # sigmas = get_sigmas(start_timesteps, n_dim=model_input.ndim, dtype=model_input.dtype)
         sigmas = extract_into_tensor(solver.sigmas, index, model_input.shape)
-        sigmas_prev = extract_into_tensor(solver.sigmas_prev, index, model_input.shape)
+        sigmas_prev = extract_into_tensor(solver.sigmas_prev, index,
+                                          model_input.shape)
 
-        timesteps = (sigmas * noise_scheduler.config.num_train_timesteps).view(-1)
+        timesteps = (sigmas *
+                     noise_scheduler.config.num_train_timesteps).view(-1)
         # if squeeze to [], unsqueeze to [1]
 
-        timesteps_prev = (
-            sigmas_prev * noise_scheduler.config.num_train_timesteps
-        ).view(-1)
+        timesteps_prev = (sigmas_prev *
+                          noise_scheduler.config.num_train_timesteps).view(-1)
         noisy_model_input = sigmas * noise + (1.0 - sigmas) * model_input
         # Predict the noise residual
         with torch.autocast("cuda", dtype=torch.bfloat16):
-            kwargs = {
+            teacher_kwargs = {
                 "hidden_states": noisy_model_input,
                 "encoder_hidden_states": encoder_hidden_states,
                 "timestep": timesteps,
                 "encoder_attention_mask": encoder_attention_mask,  # B, L
                 "return_dict": False,
             }
-            if model_type == "hunyuan":
-                kwargs["guidance"] = distill_cfg
-            model_pred = transformer(**kwargs)[0]
+            if hunyuan_teacher_disable_cfg:
+                teacher_kwargs["guidance"] = torch.tensor(
+                    [1000.0],
+                    device=noisy_model_input.device,
+                    dtype=torch.bfloat16)
+            model_pred = transformer(**teacher_kwargs)[0]
 
         # if accelerator.is_main_process:
         model_pred, end_index = solver.euler_style_multiphase_pred(
-            noisy_model_input, model_pred, index, multiphase
-        )
+            noisy_model_input, model_pred, index, multiphase)
         with torch.no_grad():
             w = distill_cfg
             with torch.autocast("cuda", dtype=torch.bfloat16):
-
-                kwargs = {
-                    "hidden_states": noisy_model_input,
-                    "encoder_hidden_states": encoder_hidden_states,
-                    "timestep": timesteps,
-                    "encoder_attention_mask": encoder_attention_mask,  # B, L
-                    "return_dict": False,
-                }
-                if model_type == "hunyuan":
-                    kwargs["guidance"] = distill_cfg
-                cond_teacher_output = teacher_transformer(**kwargs)[0].float()
-            if not_apply_cfg_solver or model_type == "hunyuan":
+                cond_teacher_output = teacher_transformer(
+                    noisy_model_input,
+                    encoder_hidden_states,
+                    timesteps,
+                    encoder_attention_mask,  # B, L
+                    return_dict=False,
+                )[0].float()
+            if not_apply_cfg_solver:
                 uncond_teacher_output = cond_teacher_output
             else:
                 # Get teacher model prediction on noisy_latents and unconditional embedding
@@ -217,64 +177,58 @@ def distill_one_step(
                         uncond_prompt_mask.unsqueeze(0).expand(bsz, -1),
                         return_dict=False,
                     )[0].float()
-            teacher_output = cond_teacher_output + w * (
-                cond_teacher_output - uncond_teacher_output
-            )
-            x_prev = solver.euler_step(noisy_model_input, teacher_output, index)
+            teacher_output = cond_teacher_output + w * (cond_teacher_output -
+                                                        uncond_teacher_output)
+            x_prev = solver.euler_step(noisy_model_input, teacher_output,
+                                       index)
 
         # 20.4.12. Get target LCM prediction on x_prev, w, c, t_n
         with torch.no_grad():
             with torch.autocast("cuda", dtype=torch.bfloat16):
-                kwargs = {
-                    "hidden_states": x_prev,
-                    "encoder_hidden_states": encoder_hidden_states,
-                    "timestep": timesteps_prev,
-                    "encoder_attention_mask": encoder_attention_mask,  # B, L
-                    "return_dict": False,
-                }
-                if model_type == "hunyuan":
-                    kwargs["guidance"] = distill_cfg
                 if ema_transformer is not None:
-                    target_pred = ema_transformer(**kwargs)[0]
+                    target_pred = ema_transformer(
+                        x_prev.float(),
+                        encoder_hidden_states,
+                        timesteps_prev,
+                        encoder_attention_mask,  # B, L
+                        return_dict=False,
+                    )[0]
                 else:
-                    target_pred = transformer(**kwargs)[0]
+                    target_pred = transformer(
+                        x_prev.float(),
+                        encoder_hidden_states,
+                        timesteps_prev,
+                        encoder_attention_mask,  # B, L
+                        return_dict=False,
+                    )[0]
 
             target, end_index = solver.euler_style_multiphase_pred(
-                x_prev, target_pred, index, multiphase, True
-            )
+                x_prev, target_pred, index, multiphase, True)
 
         huber_c = 0.001
         # loss = loss.mean()
-        loss = (
-            torch.mean(
-                torch.sqrt((model_pred.float() - target.float()) ** 2 + huber_c**2)
-                - huber_c
-            )
-            / gradient_accumulation_steps
-        )
+        loss = (torch.mean(
+            torch.sqrt((model_pred.float() - target.float())**2 + huber_c**2) -
+            huber_c) / gradient_accumulation_steps)
         if pred_decay_weight > 0:
             if pred_decay_type == "l1":
                 pred_decay_loss = (
-                    torch.mean(torch.sqrt(model_pred.float() ** 2))
-                    * pred_decay_weight
-                    / gradient_accumulation_steps
-                )
+                    torch.mean(torch.sqrt(model_pred.float()**2)) *
+                    pred_decay_weight / gradient_accumulation_steps)
                 loss += pred_decay_loss
             elif pred_decay_type == "l2":
                 # essnetially k2?
-                pred_decay_loss = (
-                    torch.mean(model_pred.float() ** 2)
-                    * pred_decay_weight
-                    / gradient_accumulation_steps
-                )
+                pred_decay_loss = (torch.mean(model_pred.float()**2) *
+                                   pred_decay_weight /
+                                   gradient_accumulation_steps)
                 loss += pred_decay_loss
             else:
-                assert NotImplementedError("pred_decay_type is not implemented")
+                assert NotImplementedError(
+                    "pred_decay_type is not implemented")
 
         # calculate model_pred norm and mean
-        get_norm(
-            model_pred.detach().float(), model_pred_norm, gradient_accumulation_steps
-        )
+        get_norm(model_pred.detach().float(), model_pred_norm,
+                 gradient_accumulation_steps)
         loss.backward()
 
         avg_loss = loss.detach().clone()
@@ -284,13 +238,12 @@ def distill_one_step(
     # update ema
     if ema_transformer is not None:
         reshard_fsdp(ema_transformer)
-        for p_averaged, p_model in zip(
-            ema_transformer.parameters(), transformer.parameters()
-        ):
+        for p_averaged, p_model in zip(ema_transformer.parameters(),
+                                       transformer.parameters()):
             with torch.no_grad():
                 p_averaged.copy_(
-                    torch.lerp(p_averaged.detach(), p_model.detach(), 1 - ema_decay)
-                )
+                    torch.lerp(p_averaged.detach(), p_model.detach(),
+                               1 - ema_decay))
 
     grad_norm = transformer.clip_grad_norm_(max_grad_norm)
     optimizer.step()
@@ -321,7 +274,7 @@ def main(args):
     if rank <= 0 and args.output_dir is not None:
         os.makedirs(args.output_dir, exist_ok=True)
 
-    # For mixed precision training we cast all non-trainable weigths to half-precision
+    # For mixed precision training we cast all non-trainable weights to half-precision
     # as these weights are only used for inference, keeping weights in full precision is not required.
 
     # Create model:
@@ -369,9 +322,12 @@ def main(args):
     if args.use_lora:
         transformer.config.lora_rank = args.lora_rank
         transformer.config.lora_alpha = args.lora_alpha
-        transformer.config.lora_target_modules = ["to_k", "to_q", "to_v", "to_out.0"]
+        transformer.config.lora_target_modules = [
+            "to_k", "to_q", "to_v", "to_out.0"
+        ]
         transformer._no_split_modules = no_split_modules
-        fsdp_kwargs["auto_wrap_policy"] = fsdp_kwargs["auto_wrap_policy"](transformer)
+        fsdp_kwargs["auto_wrap_policy"] = fsdp_kwargs["auto_wrap_policy"](
+            transformer)
 
     transformer = FSDP(
         transformer,
@@ -386,19 +342,16 @@ def main(args):
             ema_transformer,
             **fsdp_kwargs,
         )
-    main_print(f"--> model loaded")
+    main_print("--> model loaded")
 
     if args.gradient_checkpointing:
-        apply_fsdp_checkpointing(
-            transformer, no_split_modules, args.selective_checkpointing
-        )
-        apply_fsdp_checkpointing(
-            teacher_transformer, no_split_modules, args.selective_checkpointing
-        )
+        apply_fsdp_checkpointing(transformer, no_split_modules,
+                                 args.selective_checkpointing)
+        apply_fsdp_checkpointing(teacher_transformer, no_split_modules,
+                                 args.selective_checkpointing)
         if args.use_ema:
-            apply_fsdp_checkpointing(
-                ema_transformer, no_split_modules, args.selective_checkpointing
-            )
+            apply_fsdp_checkpointing(ema_transformer, no_split_modules,
+                                     args.selective_checkpointing)
     # Set model as trainable.
     transformer.train()
     teacher_transformer.requires_grad_(False)
@@ -406,9 +359,8 @@ def main(args):
         ema_transformer.requires_grad_(False)
     noise_scheduler = FlowMatchEulerDiscreteScheduler(shift=args.shift)
     if args.scheduler_type == "pcm_linear_quadratic":
-        linear_steps = int(
-            noise_scheduler.config.num_train_timesteps * args.linear_range
-        )
+        linear_steps = int(noise_scheduler.config.num_train_timesteps *
+                           args.linear_range)
         sigmas = linear_quadratic_schedule(
             noise_scheduler.config.num_train_timesteps,
             args.linear_quadratic_threshold,
@@ -424,7 +376,8 @@ def main(args):
     )
     solver.to(device)
     params_to_optimize = transformer.parameters()
-    params_to_optimize = list(filter(lambda p: p.requires_grad, params_to_optimize))
+    params_to_optimize = list(
+        filter(lambda p: p.requires_grad, params_to_optimize))
 
     optimizer = torch.optim.AdamW(
         params_to_optimize,
@@ -437,8 +390,7 @@ def main(args):
     init_steps = 0
     if args.resume_from_lora_checkpoint:
         transformer, optimizer, init_steps = resume_lora_optimizer(
-            transformer, args.resume_from_lora_checkpoint, optimizer
-        )
+            transformer, args.resume_from_lora_checkpoint, optimizer)
     main_print(f"optimizer: {optimizer}")
 
     # todo add lr scheduler
@@ -452,23 +404,19 @@ def main(args):
         last_epoch=init_steps - 1,
     )
 
-    train_dataset = LatentDataset(args.data_json_path, args.num_latent_t, args.cfg)
+    train_dataset = LatentDataset(args.data_json_path, args.num_latent_t,
+                                  args.cfg)
     uncond_prompt_embed = train_dataset.uncond_prompt_embed
     uncond_prompt_mask = train_dataset.uncond_prompt_mask
-    sampler = (
-        LengthGroupedSampler(
-            args.train_batch_size,
-            rank=rank,
-            world_size=world_size,
-            lengths=train_dataset.lengths,
-            group_frame=args.group_frame,
-            group_resolution=args.group_resolution,
-        )
-        if (args.group_frame or args.group_resolution)
-        else DistributedSampler(
-            train_dataset, rank=rank, num_replicas=world_size, shuffle=False
-        )
-    )
+    sampler = (LengthGroupedSampler(
+        args.train_batch_size,
+        rank=rank,
+        world_size=world_size,
+        lengths=train_dataset.lengths,
+        group_frame=args.group_frame,
+        group_resolution=args.group_resolution,
+    ) if (args.group_frame or args.group_resolution) else DistributedSampler(
+        train_dataset, rank=rank, num_replicas=world_size, shuffle=False))
 
     train_dataloader = DataLoader(
         train_dataset,
@@ -481,45 +429,42 @@ def main(args):
     )
 
     num_update_steps_per_epoch = math.ceil(
-        len(train_dataloader)
-        / args.gradient_accumulation_steps
-        * args.sp_size
-        / args.train_sp_batch_size
-    )
-    args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
+        len(train_dataloader) / args.gradient_accumulation_steps *
+        args.sp_size / args.train_sp_batch_size)
+    args.num_train_epochs = math.ceil(args.max_train_steps /
+                                      num_update_steps_per_epoch)
 
     if rank <= 0:
         project = args.tracker_project_name or "fastvideo"
         wandb.init(project=project, config=args)
 
     # Train!
-    total_batch_size = (
-        args.train_batch_size
-        * world_size
-        * args.gradient_accumulation_steps
-        / args.sp_size
-        * args.train_sp_batch_size
-    )
+    total_batch_size = (world_size * args.gradient_accumulation_steps /
+                        args.sp_size * args.train_sp_batch_size)
     main_print("***** Running training *****")
     main_print(f"  Num examples = {len(train_dataset)}")
     main_print(f"  Dataloader size = {len(train_dataloader)}")
     main_print(f"  Num Epochs = {args.num_train_epochs}")
     main_print(f"  Resume training from step {init_steps}")
-    main_print(f"  Instantaneous batch size per device = {args.train_batch_size}")
+    main_print(
+        f"  Instantaneous batch size per device = {args.train_batch_size}")
     main_print(
         f"  Total train batch size (w. data & sequence parallel, accumulation) = {total_batch_size}"
     )
-    main_print(f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
+    main_print(
+        f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
     main_print(f"  Total optimization steps = {args.max_train_steps}")
     main_print(
         f"  Total training parameters per FSDP shard = {sum(p.numel() for p in transformer.parameters() if p.requires_grad) / 1e9} B"
     )
     # print dtype
-    main_print(f"  Master weight dtype: {transformer.parameters().__next__().dtype}")
+    main_print(
+        f"  Master weight dtype: {transformer.parameters().__next__().dtype}")
 
     # Potentially load in the weights and states from a previous save
     if args.resume_from_checkpoint:
-        assert NotImplementedError("resume_from_checkpoint is not supported now.")
+        assert NotImplementedError(
+            "resume_from_checkpoint is not supported now.")
         # TODO
 
     progress_bar = tqdm(
@@ -584,46 +529,54 @@ def main(args):
             args.ema_decay,
             args.pred_decay_weight,
             args.pred_decay_type,
+            args.hunyuan_teacher_disable_cfg,
         )
 
         step_time = time.time() - start_time
         step_times.append(step_time)
         avg_step_time = sum(step_times) / len(step_times)
 
-        progress_bar.set_postfix(
-            {
-                "loss": f"{loss:.4f}",
-                "step_time": f"{step_time:.2f}s",
-                "grad_norm": grad_norm,
-                "phases": num_phases,
-            }
-        )
+        progress_bar.set_postfix({
+            "loss": f"{loss:.4f}",
+            "step_time": f"{step_time:.2f}s",
+            "grad_norm": grad_norm,
+            "phases": num_phases,
+        })
         progress_bar.update(1)
         if rank <= 0:
             wandb.log(
                 {
-                    "train_loss": loss,
-                    "learning_rate": lr_scheduler.get_last_lr()[0],
-                    "step_time": step_time,
-                    "avg_step_time": avg_step_time,
-                    "grad_norm": grad_norm,
-                    "pred_fro_norm": pred_norm["fro"],
-                    "pred_largest_singular_value": pred_norm["largest singular value"],
-                    "pred_absolute_mean": pred_norm["absolute mean"],
-                    "pred_absolute_max": pred_norm["absolute max"],
+                    "train_loss":
+                    loss,
+                    "learning_rate":
+                    lr_scheduler.get_last_lr()[0],
+                    "step_time":
+                    step_time,
+                    "avg_step_time":
+                    avg_step_time,
+                    "grad_norm":
+                    grad_norm,
+                    "pred_fro_norm":
+                    pred_norm["fro"],  # codespell:ignore
+                    "pred_largest_singular_value":
+                    pred_norm["largest singular value"],
+                    "pred_absolute_mean":
+                    pred_norm["absolute mean"],
+                    "pred_absolute_max":
+                    pred_norm["absolute max"],
                 },
                 step=step,
             )
         if step % args.checkpointing_steps == 0:
             if args.use_lora:
                 # Save LoRA weights
-                save_lora_checkpoint(
-                    transformer, optimizer, rank, args.output_dir, step
-                )
+                save_lora_checkpoint(transformer, optimizer, rank,
+                                     args.output_dir, step)
             else:
                 # Your existing checkpoint saving code
                 if args.use_ema:
-                    save_checkpoint(ema_transformer, rank, args.output_dir, step)
+                    save_checkpoint(ema_transformer, rank, args.output_dir,
+                                    step)
                 else:
                     save_checkpoint(transformer, rank, args.output_dir, step)
             dist.barrier()
@@ -657,11 +610,11 @@ def main(args):
                 )
 
     if args.use_lora:
-        save_lora_checkpoint(
-            transformer, optimizer, rank, args.output_dir, args.max_train_steps
-        )
+        save_lora_checkpoint(transformer, optimizer, rank, args.output_dir,
+                             args.max_train_steps)
     else:
-        save_checkpoint(transformer, rank, args.output_dir, args.max_train_steps)
+        save_checkpoint(transformer, rank, args.output_dir,
+                        args.max_train_steps)
 
     if get_sequence_parallel_state():
         destroy_sequence_parallel_group()
@@ -670,9 +623,10 @@ def main(args):
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
 
-    parser.add_argument(
-        "--model_type", type=str, default="mochi", help="The type of model to train."
-    )
+    parser.add_argument("--model_type",
+                        type=str,
+                        default="mochi",
+                        help="The type of model to train.")
 
     # dataset & dataloader
     parser.add_argument("--data_json_path", type=str, required=True)
@@ -683,7 +637,8 @@ if __name__ == "__main__":
         "--dataloader_num_workers",
         type=int,
         default=10,
-        help="Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.",
+        help=
+        "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.",
     )
     parser.add_argument(
         "--train_batch_size",
@@ -691,9 +646,10 @@ if __name__ == "__main__":
         default=16,
         help="Batch size (per device) for the training dataloader.",
     )
-    parser.add_argument(
-        "--num_latent_t", type=int, default=28, help="Number of latent timesteps."
-    )
+    parser.add_argument("--num_latent_t",
+                        type=int,
+                        default=28,
+                        help="Number of latent timesteps.")
     parser.add_argument("--group_frame", action="store_true")  # TODO
     parser.add_argument("--group_resolution", action="store_true")  # TODO
 
@@ -715,14 +671,16 @@ if __name__ == "__main__":
     parser.add_argument("--validation_steps", type=float, default=64)
     parser.add_argument("--log_validation", action="store_true")
     parser.add_argument("--tracker_project_name", type=str, default=None)
-    parser.add_argument(
-        "--seed", type=int, default=None, help="A seed for reproducible training."
-    )
+    parser.add_argument("--seed",
+                        type=int,
+                        default=None,
+                        help="A seed for reproducible training.")
     parser.add_argument(
         "--output_dir",
         type=str,
         default=None,
-        help="The output directory where the model predictions and checkpoints will be written.",
+        help=
+        "The output directory where the model predictions and checkpoints will be written.",
     )
     parser.add_argument(
         "--checkpoints_total_limit",
@@ -734,39 +692,37 @@ if __name__ == "__main__":
         "--checkpointing_steps",
         type=int,
         default=500,
-        help=(
-            "Save a checkpoint of the training state every X updates. These checkpoints can be used both as final"
-            " checkpoints in case they are better than the last checkpoint, and are also suitable for resuming"
-            " training using `--resume_from_checkpoint`."
-        ),
+        help=
+        ("Save a checkpoint of the training state every X updates. These checkpoints can be used both as final"
+         " checkpoints in case they are better than the last checkpoint, and are also suitable for resuming"
+         " training using `--resume_from_checkpoint`."),
     )
     parser.add_argument("--shift", type=float, default=1.0)
     parser.add_argument(
         "--resume_from_checkpoint",
         type=str,
         default=None,
-        help=(
-            "Whether training should be resumed from a previous checkpoint. Use a path saved by"
-            ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
-        ),
+        help=
+        ("Whether training should be resumed from a previous checkpoint. Use a path saved by"
+         ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
+         ),
     )
     parser.add_argument(
         "--resume_from_lora_checkpoint",
         type=str,
         default=None,
-        help=(
-            "Whether training should be resumed from a previous lora checkpoint. Use a path saved by"
-            ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
-        ),
+        help=
+        ("Whether training should be resumed from a previous lora checkpoint. Use a path saved by"
+         ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
+         ),
     )
     parser.add_argument(
         "--logging_dir",
         type=str,
         default="logs",
-        help=(
-            "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
-            " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
-        ),
+        help=
+        ("[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
+         " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."),
     )
 
     # optimizer & scheduler & Training
@@ -775,25 +731,29 @@ if __name__ == "__main__":
         "--max_train_steps",
         type=int,
         default=None,
-        help="Total number of training steps to perform.  If provided, overrides num_train_epochs.",
+        help=
+        "Total number of training steps to perform.  If provided, overrides num_train_epochs.",
     )
     parser.add_argument(
         "--gradient_accumulation_steps",
         type=int,
         default=1,
-        help="Number of updates steps to accumulate before performing a backward/update pass.",
+        help=
+        "Number of updates steps to accumulate before performing a backward/update pass.",
     )
     parser.add_argument(
         "--learning_rate",
         type=float,
         default=1e-4,
-        help="Initial learning rate (after the potential warmup period) to use.",
+        help=
+        "Initial learning rate (after the potential warmup period) to use.",
     )
     parser.add_argument(
         "--scale_lr",
         action="store_true",
         default=False,
-        help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
+        help=
+        "Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
     )
     parser.add_argument(
         "--lr_warmup_steps",
@@ -801,41 +761,47 @@ if __name__ == "__main__":
         default=10,
         help="Number of steps for the warmup in the lr scheduler.",
     )
-    parser.add_argument(
-        "--max_grad_norm", default=1.0, type=float, help="Max gradient norm."
-    )
+    parser.add_argument("--max_grad_norm",
+                        default=1.0,
+                        type=float,
+                        help="Max gradient norm.")
     parser.add_argument(
         "--gradient_checkpointing",
         action="store_true",
-        help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
+        help=
+        "Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
     )
     parser.add_argument("--selective_checkpointing", type=float, default=1.0)
     parser.add_argument(
         "--allow_tf32",
         action="store_true",
-        help=(
-            "Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
-            " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
-        ),
+        help=
+        ("Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
+         " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
+         ),
     )
     parser.add_argument(
         "--mixed_precision",
         type=str,
         default=None,
         choices=["no", "fp16", "bf16"],
-        help=(
-            "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
-            " 1.10.and an Nvidia Ampere GPU.  Default to the value of accelerate config of the current system or the"
-            " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
-        ),
+        help=
+        ("Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
+         " 1.10.and an Nvidia Ampere GPU.  Default to the value of accelerate config of the current system or the"
+         " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
+         ),
     )
     parser.add_argument(
         "--use_cpu_offload",
         action="store_true",
-        help="Whether to use CPU offload for param & gradient & optimizer states.",
+        help=
+        "Whether to use CPU offload for param & gradient & optimizer states.",
     )
 
-    parser.add_argument("--sp_size", type=int, default=1, help="For sequence parallel")
+    parser.add_argument("--sp_size",
+                        type=int,
+                        default=1,
+                        help="For sequence parallel")
     parser.add_argument(
         "--train_sp_batch_size",
         type=int,
@@ -849,12 +815,14 @@ if __name__ == "__main__":
         default=False,
         help="Whether to use LoRA for finetuning.",
     )
-    parser.add_argument(
-        "--lora_alpha", type=int, default=256, help="Alpha parameter for LoRA."
-    )
-    parser.add_argument(
-        "--lora_rank", type=int, default=128, help="LoRA rank parameter. "
-    )
+    parser.add_argument("--lora_alpha",
+                        type=int,
+                        default=256,
+                        help="Alpha parameter for LoRA.")
+    parser.add_argument("--lora_rank",
+                        type=int,
+                        default=128,
+                        help="LoRA rank parameter. ")
     parser.add_argument("--fsdp_sharding_startegy", default="full")
 
     # lr_scheduler
@@ -862,10 +830,9 @@ if __name__ == "__main__":
         "--lr_scheduler",
         type=str,
         default="constant",
-        help=(
-            'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
-            ' "constant", "constant_with_warmup"]'
-        ),
+        help=
+        ('The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
+         ' "constant", "constant_with_warmup"]'),
     )
     parser.add_argument("--num_euler_timesteps", type=int, default=100)
     parser.add_argument(
@@ -885,13 +852,15 @@ if __name__ == "__main__":
         action="store_true",
         help="Whether to apply the cfg_solver.",
     )
-    parser.add_argument(
-        "--distill_cfg", type=str
-    )
+    parser.add_argument("--distill_cfg",
+                        type=float,
+                        default=3.0,
+                        help="Distillation coefficient.")
     # ["euler_linear_quadratic", "pcm", "pcm_linear_qudratic"]
-    parser.add_argument(
-        "--scheduler_type", type=str, default="pcm", help="The scheduler type to use."
-    )
+    parser.add_argument("--scheduler_type",
+                        type=str,
+                        default="pcm",
+                        help="The scheduler type to use.")
     parser.add_argument(
         "--linear_quadratic_threshold",
         type=float,
@@ -904,13 +873,19 @@ if __name__ == "__main__":
         default=0.5,
         help="Range for linear quadratic scheduler.",
     )
-    parser.add_argument(
-        "--weight_decay", type=float, default=0.001, help="Weight decay to apply."
-    )
-    parser.add_argument("--use_ema", action="store_true", help="Whether to use EMA.")
-    parser.add_argument("--multi_phased_distill_schedule", type=str, default=None)
+    parser.add_argument("--weight_decay",
+                        type=float,
+                        default=0.001,
+                        help="Weight decay to apply.")
+    parser.add_argument("--use_ema",
+                        action="store_true",
+                        help="Whether to use EMA.")
+    parser.add_argument("--multi_phased_distill_schedule",
+                        type=str,
+                        default=None)
     parser.add_argument("--pred_decay_weight", type=float, default=0.0)
     parser.add_argument("--pred_decay_type", default="l1")
+    parser.add_argument("--hunyuan_teacher_disable_cfg", action="store_true")
     parser.add_argument(
         "--master_weight_type",
         type=str,

fastvideo/distill/discriminator.py

@@ -1,29 +1,11 @@
-from typing import Any, Dict, Optional, Union
-
-import torch
 import torch.nn as nn
-
-from diffusers.configuration_utils import ConfigMixin, register_to_config
-from diffusers.loaders import FromOriginalModelMixin, PeftAdapterMixin
-from diffusers.models.attention import JointTransformerBlock
-from diffusers.models.attention_processor import Attention, AttentionProcessor
-from diffusers.models.modeling_utils import ModelMixin
-from diffusers.models.normalization import AdaLayerNormContinuous
-from diffusers.utils import (
-    USE_PEFT_BACKEND,
-    is_torch_version,
-    logging,
-    scale_lora_layers,
-    unscale_lora_layers,
-)
-from diffusers.models.embeddings import CombinedTimestepTextProjEmbeddings, PatchEmbed
-from diffusers.models.transformers.transformer_2d import Transformer2DModelOutput
-from diffusers.models.transformers.transformer_sd3 import SD3Transformer2DModel
+from diffusers.utils import logging
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
 class DiscriminatorHead(nn.Module):
+
     def __init__(self, input_channel, output_channel=1):
         super().__init__()
         inner_channel = 1024
@@ -46,14 +28,10 @@ class DiscriminatorHead(nn.Module):
 
     def forward(self, x):
         b, twh, c = x.shape
-        # from fastvideo.utils.logging_ import ForkedPdb
-        # ForkedPdb().set_trace()
-        after_patching_height = 49
-        after_patching_wight = 80
-        t = twh // (after_patching_height * after_patching_wight)
-        x = x.view(-1, after_patching_height * after_patching_wight, c)
+        t = twh // (30 * 53)
+        x = x.view(-1, 30 * 53, c)
         x = x.permute(0, 2, 1)
-        x = x.view(b * t, c, after_patching_height, after_patching_wight)
+        x = x.view(b * t, c, 30, 53)
         x = self.conv1(x)
         x = self.conv2(x) + x
         x = self.conv_out(x)
@@ -61,34 +39,31 @@ class DiscriminatorHead(nn.Module):
 
 
 class Discriminator(nn.Module):
+
     def __init__(
         self,
         stride=8,
         num_h_per_head=1,
         adapter_channel_dims=[3072],
-        total_layers = 48,
+        total_layers=48,
     ):
         super().__init__()
         adapter_channel_dims = adapter_channel_dims * (total_layers // stride)
         self.stride = stride
         self.num_h_per_head = num_h_per_head
         self.head_num = len(adapter_channel_dims)
-        self.heads = nn.ModuleList(
-            [
-                nn.ModuleList(
-                    [
-                        DiscriminatorHead(adapter_channel)
-                        for _ in range(self.num_h_per_head)
-                    ]
-                )
-                for adapter_channel in adapter_channel_dims
-            ]
-        )
+        self.heads = nn.ModuleList([
+            nn.ModuleList([
+                DiscriminatorHead(adapter_channel)
+                for _ in range(self.num_h_per_head)
+            ]) for adapter_channel in adapter_channel_dims
+        ])
 
     def forward(self, features):
         outputs = []
 
         def create_custom_forward(module):
+
             def custom_forward(*inputs):
                 return module(*inputs)
 

fastvideo/distill/solver.py

@@ -3,11 +3,10 @@ from typing import Optional, Tuple, Union
 
 import numpy as np
 import torch
-
 from diffusers.configuration_utils import ConfigMixin, register_to_config
-from diffusers.utils import BaseOutput, logging
-from diffusers.utils.torch_utils import randn_tensor
 from diffusers.schedulers.scheduling_utils import SchedulerMixin
+from diffusers.utils import BaseOutput, logging
+
 from fastvideo.models.mochi_hf.pipeline_mochi import linear_quadratic_schedule
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
@@ -21,7 +20,7 @@ class PCMFMSchedulerOutput(BaseOutput):
 def extract_into_tensor(a, t, x_shape):
     b, *_ = t.shape
     out = a.gather(-1, t)
-    return out.reshape(b, *((1,) * (len(x_shape) - 1)))
+    return out.reshape(b, *((1, ) * (len(x_shape) - 1)))
 
 
 class PCMFMScheduler(SchedulerMixin, ConfigMixin):
@@ -40,26 +39,28 @@ class PCMFMScheduler(SchedulerMixin, ConfigMixin):
     ):
         if linear_quadratic:
             linear_steps = int(num_train_timesteps * linear_range)
-            sigmas = linear_quadratic_schedule(
-                num_train_timesteps, linear_quadratic_threshold, linear_steps
-            )
+            sigmas = linear_quadratic_schedule(num_train_timesteps,
+                                               linear_quadratic_threshold,
+                                               linear_steps)
             sigmas = torch.tensor(sigmas).to(dtype=torch.float32)
         else:
-            timesteps = np.linspace(
-                1, num_train_timesteps, num_train_timesteps, dtype=np.float32
-            )[::-1].copy()
+            timesteps = np.linspace(1,
+                                    num_train_timesteps,
+                                    num_train_timesteps,
+                                    dtype=np.float32)[::-1].copy()
             timesteps = torch.from_numpy(timesteps).to(dtype=torch.float32)
             sigmas = timesteps / num_train_timesteps
             sigmas = shift * sigmas / (1 + (shift - 1) * sigmas)
-        self.euler_timesteps = (
-            np.arange(1, pcm_timesteps + 1) * (num_train_timesteps // pcm_timesteps)
-        ).round().astype(np.int64) - 1
+        self.euler_timesteps = (np.arange(1, pcm_timesteps + 1) *
+                                (num_train_timesteps //
+                                 pcm_timesteps)).round().astype(np.int64) - 1
         self.sigmas = sigmas.numpy()[::-1][self.euler_timesteps]
         self.sigmas = torch.from_numpy((self.sigmas[::-1].copy()))
         self.timesteps = self.sigmas * num_train_timesteps
         self._step_index = None
         self._begin_index = None
-        self.sigmas = self.sigmas.to("cpu")  # to avoid too much CPU/GPU communication
+        self.sigmas = self.sigmas.to(
+            "cpu")  # to avoid too much CPU/GPU communication
         self.sigma_min = self.sigmas[-1].item()
         self.sigma_max = self.sigmas[0].item()
 
@@ -118,9 +119,9 @@ class PCMFMScheduler(SchedulerMixin, ConfigMixin):
     def _sigma_to_t(self, sigma):
         return sigma * self.config.num_train_timesteps
 
-    def set_timesteps(
-        self, num_inference_steps: int, device: Union[str, torch.device] = None
-    ):
+    def set_timesteps(self,
+                      num_inference_steps: int,
+                      device: Union[str, torch.device] = None):
         """
         Sets the discrete timesteps used for the diffusion chain (to be run before inference).
 
@@ -131,9 +132,10 @@ class PCMFMScheduler(SchedulerMixin, ConfigMixin):
                 The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
         """
         self.num_inference_steps = num_inference_steps
-        inference_indices = np.linspace(
-            0, self.config.pcm_timesteps, num=num_inference_steps, endpoint=False
-        )
+        inference_indices = np.linspace(0,
+                                        self.config.pcm_timesteps,
+                                        num=num_inference_steps,
+                                        endpoint=False)
         inference_indices = np.floor(inference_indices).astype(np.int64)
         inference_indices = torch.from_numpy(inference_indices).long()
 
@@ -141,8 +143,8 @@ class PCMFMScheduler(SchedulerMixin, ConfigMixin):
         timesteps = self.sigmas_ * self.config.num_train_timesteps
         self.timesteps = timesteps.to(device=device)
         self.sigmas_ = torch.cat(
-            [self.sigmas_, torch.zeros(1, device=self.sigmas_.device)]
-        )
+            [self.sigmas_,
+             torch.zeros(1, device=self.sigmas_.device)])
         self._step_index = None
         self._begin_index = None
 
@@ -204,18 +206,12 @@ class PCMFMScheduler(SchedulerMixin, ConfigMixin):
                 returned, otherwise a tuple is returned where the first element is the sample tensor.
         """
 
-        if (
-            isinstance(timestep, int)
-            or isinstance(timestep, torch.IntTensor)
-            or isinstance(timestep, torch.LongTensor)
-        ):
-            raise ValueError(
-                (
-                    "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
-                    " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
-                    " one of the `scheduler.timesteps` as a timestep."
-                ),
-            )
+        if (isinstance(timestep, int) or isinstance(timestep, torch.IntTensor)
+                or isinstance(timestep, torch.LongTensor)):
+            raise ValueError((
+                "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
+                " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
+                " one of the `scheduler.timesteps` as a timestep."), )
 
         if self.step_index is None:
             self._init_step_index(timestep)
@@ -233,7 +229,7 @@ class PCMFMScheduler(SchedulerMixin, ConfigMixin):
         self._step_index += 1
 
         if not return_dict:
-            return (prev_sample,)
+            return (prev_sample, )
 
         return PCMFMSchedulerOutput(prev_sample=prev_sample)
 
@@ -242,19 +238,21 @@ class PCMFMScheduler(SchedulerMixin, ConfigMixin):
 
 
 class EulerSolver:
+
     def __init__(self, sigmas, timesteps=1000, euler_timesteps=50):
         self.step_ratio = timesteps // euler_timesteps
-        self.euler_timesteps = (
-            np.arange(1, euler_timesteps + 1) * self.step_ratio
-        ).round().astype(np.int64) - 1
-        self.euler_timesteps_prev = np.asarray([0] + self.euler_timesteps[:-1].tolist())
+        self.euler_timesteps = (np.arange(1, euler_timesteps + 1) *
+                                self.step_ratio).round().astype(np.int64) - 1
+        self.euler_timesteps_prev = np.asarray(
+            [0] + self.euler_timesteps[:-1].tolist())
         self.sigmas = sigmas[self.euler_timesteps]
         self.sigmas_prev = np.asarray(
             [sigmas[0]] + sigmas[self.euler_timesteps[:-1]].tolist()
         )  # either use sigma0 or 0
 
         self.euler_timesteps = torch.from_numpy(self.euler_timesteps).long()
-        self.euler_timesteps_prev = torch.from_numpy(self.euler_timesteps_prev).long()
+        self.euler_timesteps_prev = torch.from_numpy(
+            self.euler_timesteps_prev).long()
         self.sigmas = torch.from_numpy(self.sigmas)
         self.sigmas_prev = torch.from_numpy(self.sigmas_prev)
 
@@ -267,10 +265,10 @@ class EulerSolver:
         return self
 
     def euler_step(self, sample, model_pred, timestep_index):
-        sigma = extract_into_tensor(self.sigmas, timestep_index, model_pred.shape)
-        sigma_prev = extract_into_tensor(
-            self.sigmas_prev, timestep_index, model_pred.shape
-        )
+        sigma = extract_into_tensor(self.sigmas, timestep_index,
+                                    model_pred.shape)
+        sigma_prev = extract_into_tensor(self.sigmas_prev, timestep_index,
+                                         model_pred.shape)
         x_prev = sample + (sigma_prev - sigma) * model_pred
         return x_prev
 
@@ -282,28 +280,29 @@ class EulerSolver:
         multiphase,
         is_target=False,
     ):
-        inference_indices = np.linspace(
-            0, len(self.euler_timesteps), num=multiphase, endpoint=False
-        )
+        inference_indices = np.linspace(0,
+                                        len(self.euler_timesteps),
+                                        num=multiphase,
+                                        endpoint=False)
         inference_indices = np.floor(inference_indices).astype(np.int64)
-        inference_indices = (
-            torch.from_numpy(inference_indices).long().to(self.euler_timesteps.device)
-        )
+        inference_indices = (torch.from_numpy(inference_indices).long().to(
+            self.euler_timesteps.device))
         expanded_timestep_index = timestep_index.unsqueeze(1).expand(
-            -1, inference_indices.size(0)
-        )
+            -1, inference_indices.size(0))
         valid_indices_mask = expanded_timestep_index >= inference_indices
-        last_valid_index = valid_indices_mask.flip(dims=[1]).long().argmax(dim=1)
+        last_valid_index = valid_indices_mask.flip(dims=[1]).long().argmax(
+            dim=1)
         last_valid_index = inference_indices.size(0) - 1 - last_valid_index
         timestep_index_end = inference_indices[last_valid_index]
 
         if is_target:
-            sigma = extract_into_tensor(self.sigmas_prev, timestep_index, sample.shape)
+            sigma = extract_into_tensor(self.sigmas_prev, timestep_index,
+                                        sample.shape)
         else:
-            sigma = extract_into_tensor(self.sigmas, timestep_index, sample.shape)
-        sigma_prev = extract_into_tensor(
-            self.sigmas_prev, timestep_index_end, sample.shape
-        )
+            sigma = extract_into_tensor(self.sigmas, timestep_index,
+                                        sample.shape)
+        sigma_prev = extract_into_tensor(self.sigmas_prev, timestep_index_end,
+                                         sample.shape)
         x_prev = sample + (sigma_prev - sigma) * model_pred
 
         return x_prev, timestep_index_end

fastvideo/distill_adv.py

@@ -1,98 +1,50 @@
+# !/bin/python3
+# isort: skip_file
 import argparse
-from email.policy import strict
-import logging
 import math
 import os
-import shutil
-from pathlib import Path
-from fastvideo.utils.parallel_states import (
-    initialize_sequence_parallel_state,
-    destroy_sequence_parallel_group,
-    get_sequence_parallel_state,
-    nccl_info,
-)
-from fastvideo.utils.communications import sp_parallel_dataloader_wrapper, broadcast
-from fastvideo.models.mochi_hf.mochi_latents_utils import normalize_dit_input
-from fastvideo.utils.validation import log_validation
 import time
-from torch.utils.data import DataLoader
-import torch
-from torch.distributed.fsdp import (
-    FullyShardedDataParallel as FSDP,
-    StateDictType,
-    FullStateDictConfig,
-)
-from fastvideo.utils.load import  load_transformer
+from collections import deque
+from copy import deepcopy
 
-from fastvideo.models.mochi_hf.pipeline_mochi import linear_quadratic_schedule
-import json
-from torch.utils.data.distributed import DistributedSampler
-from fastvideo.utils.dataset_utils import LengthGroupedSampler
+import torch
+import torch.distributed as dist
 import wandb
 from accelerate.utils import set_seed
+from diffusers import FlowMatchEulerDiscreteScheduler
+from diffusers.optimization import get_scheduler
+from diffusers.utils import check_min_version
+from peft import LoraConfig
+from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
+from torch.utils.data import DataLoader
+from torch.utils.data.distributed import DistributedSampler
 from tqdm.auto import tqdm
-from fastvideo.utils.fsdp_util import (
-    get_dit_fsdp_kwargs,
-    apply_fsdp_checkpointing,
-    get_discriminator_fsdp_kwargs,
-)
-import diffusers
-from diffusers import (
-    FlowMatchEulerDiscreteScheduler,
-)
+
+from fastvideo.dataset.latent_datasets import (LatentDataset,
+                                               latent_collate_function)
 from fastvideo.distill.discriminator import Discriminator
 from fastvideo.distill.solver import EulerSolver, extract_into_tensor
-from copy import deepcopy
-from diffusers.optimization import get_scheduler
-from fastvideo.models.mochi_hf.modeling_mochi import MochiTransformer3DModel
-from diffusers.utils import check_min_version
-from fastvideo.dataset.latent_datasets import LatentDataset, latent_collate_function
-import torch.distributed as dist
-from peft import LoraConfig
-from torch.distributed.fsdp import (
-    FullyShardedDataParallel as FSDP,
-)
+from fastvideo.models.mochi_hf.mochi_latents_utils import normalize_dit_input
+from fastvideo.models.mochi_hf.pipeline_mochi import linear_quadratic_schedule
 from fastvideo.utils.checkpoint import (
-    save_lora_checkpoint,
-    resume_lora_optimizer,
-    resume_training,
-    save_checkpoint_generator_discriminator,
-    resume_training_generator_discriminator,
-)
-# from fastvideo.utils.checkpoint import save_checkpoint
+    resume_lora_optimizer, resume_training_generator_discriminator,
+    save_checkpoint, save_lora_checkpoint)
+from fastvideo.utils.communications import (broadcast,
+                                            sp_parallel_dataloader_wrapper)
+from fastvideo.utils.dataset_utils import LengthGroupedSampler
+from fastvideo.utils.fsdp_util import (apply_fsdp_checkpointing,
+                                       get_discriminator_fsdp_kwargs,
+                                       get_dit_fsdp_kwargs)
+from fastvideo.utils.load import load_transformer
 from fastvideo.utils.logging_ import main_print
-from torch.distributed.fsdp import FullOptimStateDictConfig
-from safetensors.torch import save_file
-
-def save_checkpoint(model, rank, output_dir, step, discriminator=False):
-    with FSDP.state_dict_type(
-        model,
-        StateDictType.FULL_STATE_DICT,
-        FullStateDictConfig(offload_to_cpu=True, rank0_only=True),
-        FullOptimStateDictConfig(offload_to_cpu=True, rank0_only=True),
-    ):
-        cpu_state = model.state_dict()
-
-    # todo move to get_state_dict
-    save_dir = os.path.join(output_dir, f"checkpoint-{step}")
-    os.makedirs(save_dir, exist_ok=True)
-    # save using safetensors
-    if rank <= 0 and not discriminator:
-        weight_path = os.path.join(save_dir, "diffusion_pytorch_model.safetensors")
-        save_file(cpu_state, weight_path)
-        config_dict = dict(model.config)
-        config_path = os.path.join(save_dir, "config.json")
-        # save dict as json
-        with open(config_path, "w") as f:
-            json.dump(config_dict, f, indent=4)
-    else:
-        weight_path = os.path.join(save_dir, "discriminator_pytorch_model.safetensors")
-        save_file(cpu_state, weight_path)
+from fastvideo.utils.parallel_states import (destroy_sequence_parallel_group,
+                                             get_sequence_parallel_state,
+                                             initialize_sequence_parallel_state
+                                             )
+from fastvideo.utils.validation import log_validation
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
 check_min_version("0.31.0")
-import time
-from collections import deque
 
 
 def gan_d_loss(
@@ -104,7 +56,7 @@ def gan_d_loss(
     encoder_hidden_states,
     encoder_attention_mask,
     weight,
-    discriminator_head_stride
+    discriminator_head_stride,
 ):
     loss = 0.0
     # collate sample_fake and sample_real
@@ -131,10 +83,9 @@ def gan_d_loss(
     fake_outputs = discriminator(fake_features)
     real_outputs = discriminator(real_features)
     for fake_output, real_output in zip(fake_outputs, real_outputs):
-        loss += (
-            torch.mean(weight * torch.relu(fake_output.float() + 1))
-            + torch.mean(weight * torch.relu(1 - real_output.float()))
-        ) / (discriminator.head_num * discriminator.num_h_per_head)
+        loss += (torch.mean(weight * torch.relu(fake_output.float() + 1)) +
+                 torch.mean(weight * torch.relu(1 - real_output.float()))) / (
+                     discriminator.head_num * discriminator.num_h_per_head)
     return loss
 
 
@@ -146,7 +97,7 @@ def gan_g_loss(
     encoder_hidden_states,
     encoder_attention_mask,
     weight,
-    discriminator_head_stride
+    discriminator_head_stride,
 ):
     loss = 0.0
     features = teacher_transformer(
@@ -158,13 +109,10 @@ def gan_g_loss(
         output_features_stride=discriminator_head_stride,
         return_dict=False,
     )[1]
-    fake_outputs = discriminator(
-        features,
-    )
+    fake_outputs = discriminator(features, )
     for fake_output in fake_outputs:
         loss += torch.mean(weight * torch.relu(1 - fake_output.float())) / (
-            discriminator.head_num * discriminator.num_h_per_head
-        )
+            discriminator.head_num * discriminator.num_h_per_head)
     return loss
 
 
@@ -189,7 +137,7 @@ def distill_one_step_adv(
     not_apply_cfg_solver,
     distill_cfg,
     adv_weight,
-    discriminator_head_stride
+    discriminator_head_stride,
 ):
     optimizer.zero_grad()
     discriminator_optimizer.zero_grad()
@@ -203,20 +151,22 @@ def distill_one_step_adv(
     model_input = normalize_dit_input(model_type, latents)
     noise = torch.randn_like(model_input)
     bsz = model_input.shape[0]
-    index = torch.randint(
-        0, num_euler_timesteps, (bsz,), device=model_input.device
-    ).long()
+    index = torch.randint(0,
+                          num_euler_timesteps, (bsz, ),
+                          device=model_input.device).long()
     if sp_size > 1:
         broadcast(index)
     # Add noise according to flow matching.
     # sigmas = get_sigmas(start_timesteps, n_dim=model_input.ndim, dtype=model_input.dtype)
     sigmas = extract_into_tensor(solver.sigmas, index, model_input.shape)
-    sigmas_prev = extract_into_tensor(solver.sigmas_prev, index, model_input.shape)
+    sigmas_prev = extract_into_tensor(solver.sigmas_prev, index,
+                                      model_input.shape)
 
     timesteps = (sigmas * noise_scheduler.config.num_train_timesteps).view(-1)
     # if squeeze to [], unsqueeze to [1]
 
-    timesteps_prev = (sigmas_prev * noise_scheduler.config.num_train_timesteps).view(-1)
+    timesteps_prev = (sigmas_prev *
+                      noise_scheduler.config.num_train_timesteps).view(-1)
     noisy_model_input = sigmas * noise + (1.0 - sigmas) * model_input
 
     # Predict the noise residual
@@ -231,10 +181,8 @@ def distill_one_step_adv(
 
     # if accelerator.is_main_process:
     model_pred, end_index = solver.euler_style_multiphase_pred(
-        noisy_model_input, model_pred, index, multiphase
-    )
+        noisy_model_input, model_pred, index, multiphase)
 
-    weighting = 1.0
     # # simplified flow matching aka 0-rectified flow matching loss
     # # target = model_input - noise
     # target = model_input
@@ -243,15 +191,17 @@ def distill_one_step_adv(
         adv_index[i] = torch.randint(
             end_index[i].item(),
             end_index[i].item() + num_euler_timesteps // multiphase,
-            (1,),
+            (1, ),
             dtype=end_index.dtype,
             device=end_index.device,
         )
 
-    sigmas_end = extract_into_tensor(solver.sigmas_prev, end_index, model_input.shape)
-    sigmas_adv = extract_into_tensor(solver.sigmas_prev, adv_index, model_input.shape)
-    timesteps_end = (sigmas_end * noise_scheduler.config.num_train_timesteps).view(-1)
-    timesteps_adv = (sigmas_adv * noise_scheduler.config.num_train_timesteps).view(-1)
+    sigmas_end = extract_into_tensor(solver.sigmas_prev, end_index,
+                                     model_input.shape)
+    sigmas_adv = extract_into_tensor(solver.sigmas_prev, adv_index,
+                                     model_input.shape)
+    timesteps_adv = (sigmas_adv *
+                     noise_scheduler.config.num_train_timesteps).view(-1)
 
     with torch.no_grad():
         w = distill_cfg
@@ -275,9 +225,8 @@ def distill_one_step_adv(
                     uncond_prompt_mask.unsqueeze(0).expand(bsz, -1),
                     return_dict=False,
                 )[0].float()
-        teacher_output = cond_teacher_output + w * (
-            cond_teacher_output - uncond_teacher_output
-        )
+        teacher_output = cond_teacher_output + w * (cond_teacher_output -
+                                                    uncond_teacher_output)
         x_prev = solver.euler_step(noisy_model_input, teacher_output, index)
 
     # 20.4.12. Get target LCM prediction on x_prev, w, c, t_n
@@ -292,21 +241,19 @@ def distill_one_step_adv(
             )[0]
 
         target, end_index = solver.euler_style_multiphase_pred(
-            x_prev, target_pred, index, multiphase, True
-        )
+            x_prev, target_pred, index, multiphase, True)
 
-    real_adv = (
-        (1 - sigmas_adv) * target + (sigmas_adv - sigmas_end) * torch.randn_like(target)
-    ) / (1 - sigmas_end)
-    fake_adv = (
-        (1 - sigmas_adv) * model_pred
-        + (sigmas_adv - sigmas_end) * torch.randn_like(model_pred)
-    ) / (1 - sigmas_end)
+    real_adv = ((1 - sigmas_adv) * target +
+                (sigmas_adv - sigmas_end) * torch.randn_like(target)) / (
+                    1 - sigmas_end)
+    fake_adv = ((1 - sigmas_adv) * model_pred +
+                (sigmas_adv - sigmas_end) * torch.randn_like(model_pred)) / (
+                    1 - sigmas_end)
 
     huber_c = 0.001
     g_loss = torch.mean(
-        torch.sqrt((model_pred.float() - target.float()) ** 2 + huber_c**2) - huber_c
-    )
+        torch.sqrt((model_pred.float() - target.float())**2 + huber_c**2) -
+        huber_c)
     discriminator.requires_grad_(False)
     with torch.autocast("cuda", dtype=torch.bfloat16):
         g_gan_loss = adv_weight * gan_g_loss(
@@ -317,7 +264,7 @@ def distill_one_step_adv(
             encoder_hidden_states.float(),
             encoder_attention_mask,
             1.0,
-            discriminator_head_stride
+            discriminator_head_stride,
         )
     g_loss += g_gan_loss
     g_loss.backward()
@@ -375,7 +322,7 @@ def main(args):
     if rank <= 0 and args.output_dir is not None:
         os.makedirs(args.output_dir, exist_ok=True)
 
-    # For mixed precision training we cast all non-trainable weigths to half-precision
+    # For mixed precision training we cast all non-trainable weights to half-precision
     # as these weights are only used for inference, keeping weights in full precision is not required.
 
     # Create model:
@@ -389,7 +336,10 @@ def main(args):
         torch.float32 if args.master_weight_type == "fp32" else torch.bfloat16,
     )
     teacher_transformer = deepcopy(transformer)
-    discriminator = Discriminator(args.discriminator_head_stride, total_layers = 48 if args.model_type =="mochi" else 40)
+    discriminator = Discriminator(
+        args.discriminator_head_stride,
+        total_layers=48 if args.model_type == "mochi" else 40,
+    )
 
     if args.use_lora:
         transformer.requires_grad_(False)
@@ -418,14 +368,18 @@ def main(args):
         args.use_cpu_offload,
         args.master_weight_type,
     )
-    discriminator_fsdp_kwargs = get_discriminator_fsdp_kwargs(args.master_weight_type)
+    discriminator_fsdp_kwargs = get_discriminator_fsdp_kwargs(
+        args.master_weight_type)
     if args.use_lora:
         assert args.model_type == "mochi", "LoRA is only supported for Mochi model."
         transformer.config.lora_rank = args.lora_rank
         transformer.config.lora_alpha = args.lora_alpha
-        transformer.config.lora_target_modules = ["to_k", "to_q", "to_v", "to_out.0"]
+        transformer.config.lora_target_modules = [
+            "to_k", "to_q", "to_v", "to_out.0"
+        ]
         transformer._no_split_modules = no_split_modules
-        fsdp_kwargs["auto_wrap_policy"] = fsdp_kwargs["auto_wrap_policy"](transformer)
+        fsdp_kwargs["auto_wrap_policy"] = fsdp_kwargs["auto_wrap_policy"](
+            transformer)
 
     transformer = FSDP(
         transformer,
@@ -439,23 +393,21 @@ def main(args):
         discriminator,
         **discriminator_fsdp_kwargs,
     )
-    main_print(f"--> model loaded")
+    main_print("--> model loaded")
 
     if args.gradient_checkpointing:
-        apply_fsdp_checkpointing(
-            transformer, no_split_modules, args.selective_checkpointing
-        )
-        apply_fsdp_checkpointing(
-            teacher_transformer, no_split_modules, args.selective_checkpointing
-        )
+        apply_fsdp_checkpointing(transformer, no_split_modules,
+                                 args.selective_checkpointing)
+        apply_fsdp_checkpointing(teacher_transformer, no_split_modules,
+                                 args.selective_checkpointing)
     # Set model as trainable.
     transformer.train()
     teacher_transformer.requires_grad_(False)
     noise_scheduler = FlowMatchEulerDiscreteScheduler(shift=args.shift)
     if args.scheduler_type == "pcm_linear_quadratic":
         sigmas = linear_quadratic_schedule(
-            noise_scheduler.config.num_train_timesteps, args.linear_quadratic_threshold
-        )
+            noise_scheduler.config.num_train_timesteps,
+            args.linear_quadratic_threshold)
         sigmas = torch.tensor(sigmas).to(dtype=torch.float32)
     else:
         sigmas = noise_scheduler.sigmas
@@ -466,13 +418,14 @@ def main(args):
     )
     solver.to(device)
     params_to_optimize = transformer.parameters()
-    params_to_optimize = list(filter(lambda p: p.requires_grad, params_to_optimize))
+    params_to_optimize = list(
+        filter(lambda p: p.requires_grad, params_to_optimize))
 
     optimizer = torch.optim.AdamW(
         params_to_optimize,
         lr=args.learning_rate,
         betas=(0.9, 0.999),
-        weight_decay=1e-3,
+        weight_decay=args.weight_decay,
         eps=1e-8,
     )
 
@@ -480,15 +433,14 @@ def main(args):
         discriminator.parameters(),
         lr=args.discriminator_learning_rate,
         betas=(0, 0.999),
-        weight_decay=1e-3,
+        weight_decay=args.weight_decay,
         eps=1e-8,
     )
 
     init_steps = 0
     if args.resume_from_lora_checkpoint:
         transformer, optimizer, init_steps = resume_lora_optimizer(
-            transformer, args.resume_from_lora_checkpoint, optimizer
-        )
+            transformer, args.resume_from_lora_checkpoint, optimizer)
     elif args.resume_from_checkpoint:
         (
             transformer,
@@ -517,23 +469,19 @@ def main(args):
         last_epoch=init_steps - 1,
     )
 
-    train_dataset = LatentDataset(args.data_json_path, args.num_latent_t, args.cfg)
+    train_dataset = LatentDataset(args.data_json_path, args.num_latent_t,
+                                  args.cfg)
     uncond_prompt_embed = train_dataset.uncond_prompt_embed
     uncond_prompt_mask = train_dataset.uncond_prompt_mask
-    sampler = (
-        LengthGroupedSampler(
-            args.train_batch_size,
-            rank=rank,
-            world_size=world_size,
-            lengths=train_dataset.lengths,
-            group_frame=args.group_frame,
-            group_resolution=args.group_resolution,
-        )
-        if (args.group_frame or args.group_resolution)
-        else DistributedSampler(
-            train_dataset, rank=rank, num_replicas=world_size, shuffle=False
-        )
-    )
+    sampler = (LengthGroupedSampler(
+        args.train_batch_size,
+        rank=rank,
+        world_size=world_size,
+        lengths=train_dataset.lengths,
+        group_frame=args.group_frame,
+        group_resolution=args.group_resolution,
+    ) if (args.group_frame or args.group_resolution) else DistributedSampler(
+        train_dataset, rank=rank, num_replicas=world_size, shuffle=False))
 
     train_dataloader = DataLoader(
         train_dataset,
@@ -546,41 +494,37 @@ def main(args):
     )
     assert args.gradient_accumulation_steps == 1
     num_update_steps_per_epoch = math.ceil(
-        len(train_dataloader)
-        / args.gradient_accumulation_steps
-        * args.sp_size
-        / args.train_sp_batch_size
-    )
-    args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
+        len(train_dataloader) / args.gradient_accumulation_steps *
+        args.sp_size / args.train_sp_batch_size)
+    args.num_train_epochs = math.ceil(args.max_train_steps /
+                                      num_update_steps_per_epoch)
 
     if rank <= 0:
         project = args.tracker_project_name or "fastvideo"
         wandb.init(project=project, config=args)
 
     # Train!
-    total_batch_size = (
-        args.train_batch_size
-        * world_size
-        * args.gradient_accumulation_steps
-        / args.sp_size
-        * args.train_sp_batch_size
-    )
+    total_batch_size = (world_size * args.gradient_accumulation_steps /
+                        args.sp_size * args.train_sp_batch_size)
     main_print("***** Running training *****")
     main_print(f"  Num examples = {len(train_dataset)}")
     main_print(f"  Dataloader size = {len(train_dataloader)}")
     main_print(f"  Num Epochs = {args.num_train_epochs}")
     main_print(f"  Resume training from step {init_steps}")
-    main_print(f"  Instantaneous batch size per device = {args.train_batch_size}")
+    main_print(
+        f"  Instantaneous batch size per device = {args.train_batch_size}")
     main_print(
         f"  Total train batch size (w. data & sequence parallel, accumulation) = {total_batch_size}"
     )
-    main_print(f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
+    main_print(
+        f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
     main_print(f"  Total optimization steps = {args.max_train_steps}")
     main_print(
         f"  Total training parameters per FSDP shard = {sum(p.numel() for p in transformer.parameters() if p.requires_grad) / 1e9} B"
     )
     # print dtype
-    main_print(f"  Master weight dtype: {transformer.parameters().__next__().dtype}")
+    main_print(
+        f"  Master weight dtype: {transformer.parameters().__next__().dtype}")
 
     progress_bar = tqdm(
         range(0, args.max_train_steps),
@@ -598,6 +542,7 @@ def main(args):
     )
 
     step_times = deque(maxlen=100)
+
     # log_validation(args, transformer, device,
     #             torch.bfloat16, 0, scheduler_type=args.scheduler_type, shift=args.shift, num_euler_timesteps=args.num_euler_timesteps, linear_quadratic_threshold=args.linear_quadratic_threshold,ema=False)
     def get_num_phases(multi_phased_distill_schedule, step):
@@ -609,6 +554,7 @@ def main(args):
             if step <= int(phase_step):
                 return int(phase)
         return phase
+
     for i in range(init_steps):
         _ = next(loader)
     for step in range(init_steps + 1, args.max_train_steps + 1):
@@ -641,22 +587,20 @@ def main(args):
             args.not_apply_cfg_solver,
             args.distill_cfg,
             args.adv_weight,
-            args.discriminator_head_stride
+            args.discriminator_head_stride,
         )
 
         step_time = time.time() - start_time
         step_times.append(step_time)
         avg_step_time = sum(step_times) / len(step_times)
 
-        progress_bar.set_postfix(
-            {
-                "g_loss": f"{generator_loss:.4f}",
-                "d_loss": f"{discriminator_loss:.4f}",
-                "g_grad_norm": generator_grad_norm,
-                "d_grad_norm": discriminator_grad_norm,
-                "step_time": f"{step_time:.2f}s",
-            }
-        )
+        progress_bar.set_postfix({
+            "g_loss": f"{generator_loss:.4f}",
+            "d_loss": f"{discriminator_loss:.4f}",
+            "g_grad_norm": generator_grad_norm,
+            "d_grad_norm": discriminator_grad_norm,
+            "step_time": f"{step_time:.2f}s",
+        })
         progress_bar.update(1)
         if rank <= 0:
             wandb.log(
@@ -675,12 +619,11 @@ def main(args):
             main_print(f"--> saving checkpoint at step {step}")
             if args.use_lora:
                 # Save LoRA weights
-                save_lora_checkpoint(
-                    transformer, optimizer, rank, args.output_dir, step
-                )
+                save_lora_checkpoint(transformer, optimizer, rank,
+                                     args.output_dir, step)
             else:
                 # Your existing checkpoint saving code
-                # TODO 
+                # TODO
                 # save_checkpoint_generator_discriminator(
                 #     transformer,
                 #     optimizer,
@@ -690,7 +633,7 @@ def main(args):
                 #     args.output_dir,
                 #     step,
                 # )
-                save_checkpoint(transformer, rank, args.output_dir, step, discriminator)
+                save_checkpoint(transformer, rank, args.output_dir, step)
             main_print(f"--> checkpoint saved at step {step}")
             dist.barrier()
         if args.log_validation and step % args.validation_steps == 0:
@@ -707,14 +650,13 @@ def main(args):
                 linear_range=args.linear_range,
                 ema=False,
             )
-            
 
     if args.use_lora:
-        save_lora_checkpoint(
-            transformer, optimizer, rank, args.output_dir, args.max_train_steps
-        )
+        save_lora_checkpoint(transformer, optimizer, rank, args.output_dir,
+                             args.max_train_steps)
     else:
-        save_checkpoint(transformer, rank, args.output_dir, args.max_train_steps)
+        save_checkpoint(transformer, rank, args.output_dir,
+                        args.max_train_steps)
 
     if get_sequence_parallel_state():
         destroy_sequence_parallel_group()
@@ -723,9 +665,10 @@ def main(args):
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
 
-    parser.add_argument(
-        "--model_type", type=str, default="mochi", help="The type of model to train."
-    )
+    parser.add_argument("--model_type",
+                        type=str,
+                        default="mochi",
+                        help="The type of model to train.")
     # dataset & dataloader
     parser.add_argument("--data_json_path", type=str, required=True)
     parser.add_argument("--num_height", type=int, default=480)
@@ -735,7 +678,8 @@ if __name__ == "__main__":
         "--dataloader_num_workers",
         type=int,
         default=10,
-        help="Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.",
+        help=
+        "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.",
     )
     parser.add_argument(
         "--train_batch_size",
@@ -743,9 +687,10 @@ if __name__ == "__main__":
         default=16,
         help="Batch size (per device) for the training dataloader.",
     )
-    parser.add_argument(
-        "--num_latent_t", type=int, default=28, help="Number of latent timesteps."
-    )
+    parser.add_argument("--num_latent_t",
+                        type=int,
+                        default=28,
+                        help="Number of latent timesteps.")
     parser.add_argument("--group_frame", action="store_true")  # TODO
     parser.add_argument("--group_resolution", action="store_true")  # TODO
 
@@ -764,14 +709,16 @@ if __name__ == "__main__":
     parser.add_argument("--validation_steps", type=float, default=64)
     parser.add_argument("--log_validation", action="store_true")
     parser.add_argument("--tracker_project_name", type=str, default=None)
-    parser.add_argument(
-        "--seed", type=int, default=None, help="A seed for reproducible training."
-    )
+    parser.add_argument("--seed",
+                        type=int,
+                        default=None,
+                        help="A seed for reproducible training.")
     parser.add_argument(
         "--output_dir",
         type=str,
         default=None,
-        help="The output directory where the model predictions and checkpoints will be written.",
+        help=
+        "The output directory where the model predictions and checkpoints will be written.",
     )
     parser.add_argument(
         "--checkpoints_total_limit",
@@ -783,11 +730,10 @@ if __name__ == "__main__":
         "--checkpointing_steps",
         type=int,
         default=500,
-        help=(
-            "Save a checkpoint of the training state every X updates. These checkpoints can be used both as final"
-            " checkpoints in case they are better than the last checkpoint, and are also suitable for resuming"
-            " training using `--resume_from_checkpoint`."
-        ),
+        help=
+        ("Save a checkpoint of the training state every X updates. These checkpoints can be used both as final"
+         " checkpoints in case they are better than the last checkpoint, and are also suitable for resuming"
+         " training using `--resume_from_checkpoint`."),
     )
     parser.add_argument("--validation_prompt_dir", type=str)
     parser.add_argument("--shift", type=float, default=1.0)
@@ -795,28 +741,27 @@ if __name__ == "__main__":
         "--resume_from_checkpoint",
         type=str,
         default=None,
-        help=(
-            "Whether training should be resumed from a previous checkpoint. Use a path saved by"
-            ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
-        ),
+        help=
+        ("Whether training should be resumed from a previous checkpoint. Use a path saved by"
+         ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
+         ),
     )
     parser.add_argument(
         "--resume_from_lora_checkpoint",
         type=str,
         default=None,
-        help=(
-            "Whether training should be resumed from a previous lora checkpoint. Use a path saved by"
-            ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
-        ),
+        help=
+        ("Whether training should be resumed from a previous lora checkpoint. Use a path saved by"
+         ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
+         ),
     )
     parser.add_argument(
         "--logging_dir",
         type=str,
         default="logs",
-        help=(
-            "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
-            " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
-        ),
+        help=
+        ("[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
+         " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."),
     )
 
     # optimizer & scheduler & Training
@@ -825,25 +770,29 @@ if __name__ == "__main__":
         "--max_train_steps",
         type=int,
         default=None,
-        help="Total number of training steps to perform.  If provided, overrides num_train_epochs.",
+        help=
+        "Total number of training steps to perform.  If provided, overrides num_train_epochs.",
     )
     parser.add_argument(
         "--learning_rate",
         type=float,
         default=1e-4,
-        help="Initial learning rate (after the potential warmup period) to use.",
+        help=
+        "Initial learning rate (after the potential warmup period) to use.",
     )
     parser.add_argument(
         "--discriminator_learning_rate",
         type=float,
         default=1e-5,
-        help="Initial learning rate (after the potential warmup period) to use.",
+        help=
+        "Initial learning rate (after the potential warmup period) to use.",
     )
     parser.add_argument(
         "--scale_lr",
         action="store_true",
         default=False,
-        help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
+        help=
+        "Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
     )
     parser.add_argument(
         "--lr_warmup_steps",
@@ -851,41 +800,47 @@ if __name__ == "__main__":
         default=10,
         help="Number of steps for the warmup in the lr scheduler.",
     )
-    parser.add_argument(
-        "--max_grad_norm", default=1.0, type=float, help="Max gradient norm."
-    )
+    parser.add_argument("--max_grad_norm",
+                        default=1.0,
+                        type=float,
+                        help="Max gradient norm.")
     parser.add_argument(
         "--gradient_checkpointing",
         action="store_true",
-        help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
+        help=
+        "Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
     )
     parser.add_argument("--selective_checkpointing", type=float, default=1.0)
     parser.add_argument(
         "--allow_tf32",
         action="store_true",
-        help=(
-            "Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
-            " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
-        ),
+        help=
+        ("Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
+         " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
+         ),
     )
     parser.add_argument(
         "--mixed_precision",
         type=str,
         default=None,
         choices=["no", "fp16", "bf16"],
-        help=(
-            "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
-            " 1.10.and an Nvidia Ampere GPU.  Default to the value of accelerate config of the current system or the"
-            " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
-        ),
+        help=
+        ("Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
+         " 1.10.and an Nvidia Ampere GPU.  Default to the value of accelerate config of the current system or the"
+         " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
+         ),
     )
     parser.add_argument(
         "--use_cpu_offload",
         action="store_true",
-        help="Whether to use CPU offload for param & gradient & optimizer states.",
+        help=
+        "Whether to use CPU offload for param & gradient & optimizer states.",
     )
 
-    parser.add_argument("--sp_size", type=int, default=1, help="For sequence parallel")
+    parser.add_argument("--sp_size",
+                        type=int,
+                        default=1,
+                        help="For sequence parallel")
     parser.add_argument(
         "--train_sp_batch_size",
         type=int,
@@ -899,19 +854,24 @@ if __name__ == "__main__":
         default=False,
         help="Whether to use LoRA for finetuning.",
     )
-    parser.add_argument(
-        "--lora_alpha", type=int, default=256, help="Alpha parameter for LoRA."
-    )
-    parser.add_argument(
-        "--lora_rank", type=int, default=128, help="LoRA rank parameter. "
-    )
+    parser.add_argument("--lora_alpha",
+                        type=int,
+                        default=256,
+                        help="Alpha parameter for LoRA.")
+    parser.add_argument("--lora_rank",
+                        type=int,
+                        default=128,
+                        help="LoRA rank parameter. ")
     parser.add_argument("--fsdp_sharding_startegy", default="full")
-    parser.add_argument("--multi_phased_distill_schedule", type=str, default=None)
+    parser.add_argument("--multi_phased_distill_schedule",
+                        type=str,
+                        default=None)
     parser.add_argument(
         "--gradient_accumulation_steps",
         type=int,
         default=1,
-        help="Number of updates steps to accumulate before performing a backward/update pass.",
+        help=
+        "Number of updates steps to accumulate before performing a backward/update pass.",
     )
 
     # lr_scheduler
@@ -919,10 +879,9 @@ if __name__ == "__main__":
         "--lr_scheduler",
         type=str,
         default="constant",
-        help=(
-            'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
-            ' "constant", "constant_with_warmup"]'
-        ),
+        help=
+        ('The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
+         ' "constant", "constant_with_warmup"]'),
     )
     parser.add_argument("--num_euler_timesteps", type=int, default=100)
     parser.add_argument(
@@ -942,13 +901,15 @@ if __name__ == "__main__":
         action="store_true",
         help="Whether to apply the cfg_solver.",
     )
-    parser.add_argument(
-        "--distill_cfg", type=float, default=3.0, help="Distillation coefficient."
-    )
+    parser.add_argument("--distill_cfg",
+                        type=float,
+                        default=3.0,
+                        help="Distillation coefficient.")
     # ["euler_linear_quadratic", "pcm", "pcm_linear_qudratic"]
-    parser.add_argument(
-        "--scheduler_type", type=str, default="pcm", help="The scheduler type to use."
-    )
+    parser.add_argument("--scheduler_type",
+                        type=str,
+                        default="pcm",
+                        help="The scheduler type to use.")
     parser.add_argument(
         "--adv_weight",
         type=float,
@@ -961,20 +922,21 @@ if __name__ == "__main__":
         default=2,
         help="The stride of the discriminator head.",
     )
-    parser.add_argument(
-        "--linear_quadratic_threshold",
-        type=float,
-        default=0.025,
-        help="The threshold of the linear quadratic scheduler.",
-    )
     parser.add_argument(
         "--linear_range",
         type=float,
         default=0.5,
         help="Range for linear quadratic scheduler.",
     )
+    parser.add_argument("--weight_decay",
+                        type=float,
+                        default=0.001,
+                        help="Weight decay to apply.")
     parser.add_argument(
-        "--weight_decay", type=float, default=0.001, help="Weight decay to apply."
+        "--linear_quadratic_threshold",
+        type=float,
+        default=0.025,
+        help="The threshold of the linear quadratic scheduler.",
     )
     parser.add_argument(
         "--master_weight_type",

fastvideo/models/flash_attn_no_pad.py

@@ -1,21 +1,25 @@
+from einops import rearrange
 from flash_attn import flash_attn_varlen_qkvpacked_func
 from flash_attn.bert_padding import pad_input, unpad_input
-from einops import rearrange
 
 
-def flash_attn_no_pad(
-    qkv, key_padding_mask, causal=False, dropout_p=0.0, softmax_scale=None
-):
+def flash_attn_no_pad(qkv,
+                      key_padding_mask,
+                      causal=False,
+                      dropout_p=0.0,
+                      softmax_scale=None):
     # adapted from https://github.com/Dao-AILab/flash-attention/blob/13403e81157ba37ca525890f2f0f2137edf75311/flash_attn/flash_attention.py#L27
     batch_size = qkv.shape[0]
     seqlen = qkv.shape[1]
     nheads = qkv.shape[-2]
     x = rearrange(qkv, "b s three h d -> b s (three h d)")
     x_unpad, indices, cu_seqlens, max_s, used_seqlens_in_batch = unpad_input(
-        x, key_padding_mask
-    )
+        x, key_padding_mask)
 
-    x_unpad = rearrange(x_unpad, "nnz (three h d) -> nnz three h d", three=3, h=nheads)
+    x_unpad = rearrange(x_unpad,
+                        "nnz (three h d) -> nnz three h d",
+                        three=3,
+                        h=nheads)
     output_unpad = flash_attn_varlen_qkvpacked_func(
         x_unpad,
         cu_seqlens,
@@ -25,9 +29,8 @@ def flash_attn_no_pad(
         causal=causal,
     )
     output = rearrange(
-        pad_input(
-            rearrange(output_unpad, "nnz h d -> nnz (h d)"), indices, batch_size, seqlen
-        ),
+        pad_input(rearrange(output_unpad, "nnz h d -> nnz (h d)"), indices,
+                  batch_size, seqlen),
         "b s (h d) -> b s h d",
         h=nheads,
     )

fastvideo/models/hunyuan/constants.py

@@ -1,4 +1,5 @@
 import os
+
 import torch
 
 __all__ = [
@@ -33,8 +34,7 @@ C_SCALE = 1_000_000_000_000_000
 PROMPT_TEMPLATE_ENCODE = (
     "<|start_header_id|>system<|end_header_id|>\n\nDescribe the image by detailing the color, shape, size, texture, "
     "quantity, text, spatial relationships of the objects and background:<|eot_id|>"
-    "<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>"
-)
+    "<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>")
 PROMPT_TEMPLATE_ENCODE_VIDEO = (
     "<|start_header_id|>system<|end_header_id|>\n\nDescribe the video by detailing the following aspects: "
     "1. The main content and theme of the video."
@@ -42,8 +42,7 @@ PROMPT_TEMPLATE_ENCODE_VIDEO = (
     "3. Actions, events, behaviors temporal relationships, physical movement changes of the objects."
     "4. background environment, light, style and atmosphere."
     "5. camera angles, movements, and transitions used in the video:<|eot_id|>"
-    "<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>"
-)
+    "<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>")
 
 NEGATIVE_PROMPT = "Aerial view, aerial view, overexposed, low quality, deformation, a poor composition, bad hands, bad teeth, bad eyes, bad limbs, distortion"
 

fastvideo/models/hunyuan/diffusion/__init__.py

@@ -1,2 +1,3 @@
+# ruff: noqa: F401
 from .pipelines import HunyuanVideoPipeline
 from .schedulers import FlowMatchDiscreteScheduler

fastvideo/models/hunyuan/diffusion/pipelines/__init__.py

@@ -1 +1,2 @@
+# ruff: noqa: F401
 from .pipeline_hunyuan_video import HunyuanVideoPipeline

fastvideo/models/hunyuan/diffusion/pipelines/pipeline_hunyuan_video.py

@@ -17,41 +17,34 @@
 #
 # ==============================================================================
 import inspect
-from typing import Any, Callable, Dict, List, Optional, Union, Tuple
+from dataclasses import dataclass
+from typing import Any, Callable, Dict, List, Optional, Union
+
+import numpy as np
 import torch
 import torch.distributed as dist
-import numpy as np
-from dataclasses import dataclass
-from packaging import version
-
+import torch.nn.functional as F
 from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback
 from diffusers.configuration_utils import FrozenDict
 from diffusers.image_processor import VaeImageProcessor
 from diffusers.loaders import LoraLoaderMixin, TextualInversionLoaderMixin
 from diffusers.models import AutoencoderKL
 from diffusers.models.lora import adjust_lora_scale_text_encoder
-from diffusers.schedulers import KarrasDiffusionSchedulers
-from diffusers.utils import (
-    USE_PEFT_BACKEND,
-    deprecate,
-    logging,
-    replace_example_docstring,
-    scale_lora_layers,
-    unscale_lora_layers,
-)
-from diffusers.utils.torch_utils import randn_tensor
 from diffusers.pipelines.pipeline_utils import DiffusionPipeline
-from diffusers.utils import BaseOutput
+from diffusers.schedulers import KarrasDiffusionSchedulers
+from diffusers.utils import (USE_PEFT_BACKEND, BaseOutput, deprecate, logging,
+                             replace_example_docstring, scale_lora_layers)
+from diffusers.utils.torch_utils import randn_tensor
+from einops import rearrange
+
+from fastvideo.utils.communications import all_gather
+from fastvideo.utils.parallel_states import (get_sequence_parallel_state,
+                                             nccl_info)
 
 from ...constants import PRECISION_TO_TYPE
-from ...vae.autoencoder_kl_causal_3d import AutoencoderKLCausal3D
-from ...text_encoder import TextEncoder
 from ...modules import HYVideoDiffusionTransformer
-
-from einops import rearrange
-from fastvideo.utils.parallel_states import get_sequence_parallel_state, nccl_info
-from fastvideo.utils.communications import all_gather, all_to_all_4D
-import torch.nn.functional as F
+from ...text_encoder import TextEncoder
+from ...vae.autoencoder_kl_causal_3d import AutoencoderKLCausal3D
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
@@ -63,16 +56,14 @@ def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
     Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and
     Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4
     """
-    std_text = noise_pred_text.std(
-        dim=list(range(1, noise_pred_text.ndim)), keepdim=True
-    )
+    std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)),
+                                   keepdim=True)
     std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
     # rescale the results from guidance (fixes overexposure)
     noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
     # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
-    noise_cfg = (
-        guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg
-    )
+    noise_cfg = (guidance_rescale * noise_pred_rescaled +
+                 (1 - guidance_rescale) * noise_cfg)
     return noise_cfg
 
 
@@ -113,8 +104,7 @@ def retrieve_timesteps(
         )
     if timesteps is not None:
         accepts_timesteps = "timesteps" in set(
-            inspect.signature(scheduler.set_timesteps).parameters.keys()
-        )
+            inspect.signature(scheduler.set_timesteps).parameters.keys())
         if not accepts_timesteps:
             raise ValueError(
                 f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
@@ -125,8 +115,7 @@ def retrieve_timesteps(
         num_inference_steps = len(timesteps)
     elif sigmas is not None:
         accept_sigmas = "sigmas" in set(
-            inspect.signature(scheduler.set_timesteps).parameters.keys()
-        )
+            inspect.signature(scheduler.set_timesteps).parameters.keys())
         if not accept_sigmas:
             raise ValueError(
                 f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
@@ -169,7 +158,9 @@ class HunyuanVideoPipeline(DiffusionPipeline):
     model_cpu_offload_seq = "text_encoder->text_encoder_2->transformer->vae"
     _optional_components = ["text_encoder_2"]
     _exclude_from_cpu_offload = ["transformer"]
-    _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
+    _callback_tensor_inputs = [
+        "latents", "prompt_embeds", "negative_prompt_embeds"
+    ]
 
     def __init__(
         self,
@@ -193,29 +184,25 @@ class HunyuanVideoPipeline(DiffusionPipeline):
         self.args = args
         # ==========================================================================================
 
-        if (
-            hasattr(scheduler.config, "steps_offset")
-            and scheduler.config.steps_offset != 1
-        ):
+        if (hasattr(scheduler.config, "steps_offset")
+                and scheduler.config.steps_offset != 1):
             deprecation_message = (
                 f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
                 f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
                 "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
                 " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
                 " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
-                " file"
-            )
-            deprecate(
-                "steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False
-            )
+                " file")
+            deprecate("steps_offset!=1",
+                      "1.0.0",
+                      deprecation_message,
+                      standard_warn=False)
             new_config = dict(scheduler.config)
             new_config["steps_offset"] = 1
             scheduler._internal_dict = FrozenDict(new_config)
 
-        if (
-            hasattr(scheduler.config, "clip_sample")
-            and scheduler.config.clip_sample is True
-        ):
+        if (hasattr(scheduler.config, "clip_sample")
+                and scheduler.config.clip_sample is True):
             deprecation_message = (
                 f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
                 " `clip_sample` should be set to False in the configuration file. Please make sure to update the"
@@ -223,9 +210,10 @@ class HunyuanVideoPipeline(DiffusionPipeline):
                 " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
                 " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
             )
-            deprecate(
-                "clip_sample not set", "1.0.0", deprecation_message, standard_warn=False
-            )
+            deprecate("clip_sample not set",
+                      "1.0.0",
+                      deprecation_message,
+                      standard_warn=False)
             new_config = dict(scheduler.config)
             new_config["clip_sample"] = False
             scheduler._internal_dict = FrozenDict(new_config)
@@ -237,8 +225,10 @@ class HunyuanVideoPipeline(DiffusionPipeline):
             scheduler=scheduler,
             text_encoder_2=text_encoder_2,
         )
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
-        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
+        self.vae_scale_factor = 2**(len(self.vae.config.block_out_channels) -
+                                    1)
+        self.image_processor = VaeImageProcessor(
+            vae_scale_factor=self.vae_scale_factor)
 
     def encode_prompt(
         self,
@@ -303,23 +293,17 @@ class HunyuanVideoPipeline(DiffusionPipeline):
             else:
                 scale_lora_layers(text_encoder.model, lora_scale)
 
-        if prompt is not None and isinstance(prompt, str):
-            batch_size = 1
-        elif prompt is not None and isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            batch_size = prompt_embeds.shape[0]
-
         if prompt_embeds is None:
             # textual inversion: process multi-vector tokens if necessary
             if isinstance(self, TextualInversionLoaderMixin):
-                prompt = self.maybe_convert_prompt(prompt, text_encoder.tokenizer)
+                prompt = self.maybe_convert_prompt(prompt,
+                                                   text_encoder.tokenizer)
 
             text_inputs = text_encoder.text2tokens(prompt, data_type=data_type)
             if clip_skip is None:
-                prompt_outputs = text_encoder.encode(
-                    text_inputs, data_type=data_type, device=device
-                )
+                prompt_outputs = text_encoder.encode(text_inputs,
+                                                     data_type=data_type,
+                                                     device=device)
                 prompt_embeds = prompt_outputs.hidden_state
             else:
                 prompt_outputs = text_encoder.encode(
@@ -331,23 +315,23 @@ class HunyuanVideoPipeline(DiffusionPipeline):
                 # Access the `hidden_states` first, that contains a tuple of
                 # all the hidden states from the encoder layers. Then index into
                 # the tuple to access the hidden states from the desired layer.
-                prompt_embeds = prompt_outputs.hidden_states_list[-(clip_skip + 1)]
+                prompt_embeds = prompt_outputs.hidden_states_list[-(clip_skip +
+                                                                    1)]
                 # We also need to apply the final LayerNorm here to not mess with the
                 # representations. The `last_hidden_states` that we typically use for
                 # obtaining the final prompt representations passes through the LayerNorm
                 # layer.
                 prompt_embeds = text_encoder.model.text_model.final_layer_norm(
-                    prompt_embeds
-                )
+                    prompt_embeds)
 
             attention_mask = prompt_outputs.attention_mask
             if attention_mask is not None:
                 attention_mask = attention_mask.to(device)
                 bs_embed, seq_len = attention_mask.shape
-                attention_mask = attention_mask.repeat(1, num_videos_per_prompt)
+                attention_mask = attention_mask.repeat(1,
+                                                       num_videos_per_prompt)
                 attention_mask = attention_mask.view(
-                    bs_embed * num_videos_per_prompt, seq_len
-                )
+                    bs_embed * num_videos_per_prompt, seq_len)
 
         if text_encoder is not None:
             prompt_embeds_dtype = text_encoder.dtype
@@ -356,20 +340,21 @@ class HunyuanVideoPipeline(DiffusionPipeline):
         else:
             prompt_embeds_dtype = prompt_embeds.dtype
 
-        prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
+        prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype,
+                                         device=device)
 
         if prompt_embeds.ndim == 2:
             bs_embed, _ = prompt_embeds.shape
             # duplicate text embeddings for each generation per prompt, using mps friendly method
             prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt)
-            prompt_embeds = prompt_embeds.view(bs_embed * num_videos_per_prompt, -1)
+            prompt_embeds = prompt_embeds.view(
+                bs_embed * num_videos_per_prompt, -1)
         else:
             bs_embed, seq_len, _ = prompt_embeds.shape
             # duplicate text embeddings for each generation per prompt, using mps friendly method
             prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1)
             prompt_embeds = prompt_embeds.view(
-                bs_embed * num_videos_per_prompt, seq_len, -1
-            )
+                bs_embed * num_videos_per_prompt, seq_len, -1)
 
         return (
             prompt_embeds,
@@ -380,14 +365,15 @@ class HunyuanVideoPipeline(DiffusionPipeline):
 
     def decode_latents(self, latents, enable_tiling=True):
         deprecation_message = "The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead"
-        deprecate("decode_latents", "1.0.0", deprecation_message, standard_warn=False)
+        deprecate("decode_latents",
+                  "1.0.0",
+                  deprecation_message,
+                  standard_warn=False)
 
         latents = 1 / self.vae.config.scaling_factor * latents
         if enable_tiling:
             self.vae.enable_tiling()
-            image = self.vae.decode(latents, return_dict=False)[0]
-        else:
-            image = self.vae.decode(latents, return_dict=False)[0]
+        image = self.vae.decode(latents, return_dict=False)[0]
         image = (image / 2 + 0.5).clamp(0, 1)
         # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
         if image.ndim == 4:
@@ -439,17 +425,14 @@ class HunyuanVideoPipeline(DiffusionPipeline):
                         f"`video_length` has to be 1 or a multiple of 8 but is {video_length}."
                     )
 
-        if callback_steps is not None and (
-            not isinstance(callback_steps, int) or callback_steps <= 0
-        ):
+        if callback_steps is not None and (not isinstance(callback_steps, int)
+                                           or callback_steps <= 0):
             raise ValueError(
                 f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
-                f" {type(callback_steps)}."
-            )
+                f" {type(callback_steps)}.")
         if callback_on_step_end_tensor_inputs is not None and not all(
-            k in self._callback_tensor_inputs
-            for k in callback_on_step_end_tensor_inputs
-        ):
+                k in self._callback_tensor_inputs
+                for k in callback_on_step_end_tensor_inputs):
             raise ValueError(
                 f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
             )
@@ -457,15 +440,13 @@ class HunyuanVideoPipeline(DiffusionPipeline):
         if prompt is not None and prompt_embeds is not None:
             raise ValueError(
                 f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
-                " only forward one of the two."
-            )
+                " only forward one of the two.")
         elif prompt is None and prompt_embeds is None:
             raise ValueError(
                 "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
             )
-        elif prompt is not None and (
-            not isinstance(prompt, str) and not isinstance(prompt, list)
-        ):
+        elif prompt is not None and (not isinstance(prompt, str)
+                                     and not isinstance(prompt, list)):
             raise ValueError(
                 f"`prompt` has to be of type `str` or `list` but is {type(prompt)}"
             )
@@ -481,8 +462,7 @@ class HunyuanVideoPipeline(DiffusionPipeline):
                 raise ValueError(
                     "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
                     f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
-                    f" {negative_prompt_embeds.shape}."
-                )
+                    f" {negative_prompt_embeds.shape}.")
 
     def prepare_latents(
         self,
@@ -510,9 +490,10 @@ class HunyuanVideoPipeline(DiffusionPipeline):
             )
 
         if latents is None:
-            latents = randn_tensor(
-                shape, generator=generator, device=device, dtype=dtype
-            )
+            latents = randn_tensor(shape,
+                                   generator=generator,
+                                   device=device,
+                                   dtype=dtype)
         else:
             latents = latents.to(device)
 
@@ -604,7 +585,8 @@ class HunyuanVideoPipeline(DiffusionPipeline):
         negative_prompt: Optional[Union[str, List[str]]] = None,
         num_videos_per_prompt: Optional[int] = 1,
         eta: float = 0.0,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        generator: Optional[Union[torch.Generator,
+                                  List[torch.Generator]]] = None,
         latents: Optional[torch.Tensor] = None,
         prompt_embeds: Optional[torch.Tensor] = None,
         attention_mask: Optional[torch.Tensor] = None,
@@ -615,16 +597,13 @@ class HunyuanVideoPipeline(DiffusionPipeline):
         cross_attention_kwargs: Optional[Dict[str, Any]] = None,
         guidance_rescale: float = 0.0,
         clip_skip: Optional[int] = None,
-        callback_on_step_end: Optional[
-            Union[
-                Callable[[int, int, Dict], None],
-                PipelineCallback,
-                MultiPipelineCallbacks,
-            ]
-        ] = None,
+        callback_on_step_end: Optional[Union[Callable[[int, int, Dict],
+                                                      None], PipelineCallback,
+                                             MultiPipelineCallbacks, ]] = None,
         callback_on_step_end_tensor_inputs: List[str] = ["latents"],
         vae_ver: str = "88-4c-sd",
         enable_tiling: bool = False,
+        enable_vae_sp: bool = False,
         n_tokens: Optional[int] = None,
         embedded_guidance_scale: Optional[float] = None,
         **kwargs,
@@ -727,7 +706,8 @@ class HunyuanVideoPipeline(DiffusionPipeline):
                 "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`",
             )
 
-        if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
+        if isinstance(callback_on_step_end,
+                      (PipelineCallback, MultiPipelineCallbacks)):
             callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
 
         # 0. Default height and width to unet
@@ -763,18 +743,12 @@ class HunyuanVideoPipeline(DiffusionPipeline):
         else:
             batch_size = prompt_embeds.shape[0]
 
-        device = (
-            torch.device(f"cuda:{dist.get_rank()}")
-            if dist.is_initialized()
-            else self._execution_device
-        )
+        device = (torch.device(f"cuda:{dist.get_rank()}")
+                  if dist.is_initialized() else self._execution_device)
 
         # 3. Encode input prompt
-        lora_scale = (
-            self.cross_attention_kwargs.get("scale", None)
-            if self.cross_attention_kwargs is not None
-            else None
-        )
+        lora_scale = (self.cross_attention_kwargs.get("scale", None)
+                      if self.cross_attention_kwargs is not None else None)
 
         (
             prompt_embeds,
@@ -830,14 +804,15 @@ class HunyuanVideoPipeline(DiffusionPipeline):
             if prompt_mask is not None:
                 prompt_mask = torch.cat([negative_prompt_mask, prompt_mask])
             if prompt_embeds_2 is not None:
-                prompt_embeds_2 = torch.cat([negative_prompt_embeds_2, prompt_embeds_2])
+                prompt_embeds_2 = torch.cat(
+                    [negative_prompt_embeds_2, prompt_embeds_2])
             if prompt_mask_2 is not None:
-                prompt_mask_2 = torch.cat([negative_prompt_mask_2, prompt_mask_2])
+                prompt_mask_2 = torch.cat(
+                    [negative_prompt_mask_2, prompt_mask_2])
 
         # 4. Prepare timesteps
         extra_set_timesteps_kwargs = self.prepare_extra_func_kwargs(
-            self.scheduler.set_timesteps, {"n_tokens": n_tokens}
-        )
+            self.scheduler.set_timesteps, {"n_tokens": n_tokens})
         timesteps, num_inference_steps = retrieve_timesteps(
             self.scheduler,
             num_inference_steps,
@@ -869,28 +844,30 @@ class HunyuanVideoPipeline(DiffusionPipeline):
 
         world_size, rank = nccl_info.sp_size, nccl_info.rank_within_group
         if get_sequence_parallel_state():
-            latents = rearrange(
-                latents, "b t (n s) h w -> b t n s h w", n=world_size
-            ).contiguous()
+            latents = rearrange(latents,
+                                "b t (n s) h w -> b t n s h w",
+                                n=world_size).contiguous()
             latents = latents[:, :, rank, :, :, :]
 
         # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
         extra_step_kwargs = self.prepare_extra_func_kwargs(
             self.scheduler.step,
-            {"generator": generator, "eta": eta},
+            {
+                "generator": generator,
+                "eta": eta
+            },
         )
 
         target_dtype = PRECISION_TO_TYPE[self.args.precision]
-        autocast_enabled = (
-            target_dtype != torch.float32
-        ) and not self.args.disable_autocast
+        autocast_enabled = (target_dtype !=
+                            torch.float32) and not self.args.disable_autocast
         vae_dtype = PRECISION_TO_TYPE[self.args.vae_precision]
         vae_autocast_enabled = (
-            vae_dtype != torch.float32
-        ) and not self.args.disable_autocast
+            vae_dtype != torch.float32) and not self.args.disable_autocast
 
         # 7. Denoising loop
-        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
+        num_warmup_steps = len(
+            timesteps) - num_inference_steps * self.scheduler.order
         self._num_timesteps = len(timesteps)
 
         # if is_progress_bar:
@@ -900,40 +877,33 @@ class HunyuanVideoPipeline(DiffusionPipeline):
                     continue
 
                 # expand the latents if we are doing classifier free guidance
-                latent_model_input = (
-                    torch.cat([latents] * 2)
-                    if self.do_classifier_free_guidance
-                    else latents
-                )
+                latent_model_input = (torch.cat(
+                    [latents] *
+                    2) if self.do_classifier_free_guidance else latents)
                 latent_model_input = self.scheduler.scale_model_input(
-                    latent_model_input, t
-                )
+                    latent_model_input, t)
 
                 t_expand = t.repeat(latent_model_input.shape[0])
-                guidance_expand = (
-                    torch.tensor(
-                        [embedded_guidance_scale] * latent_model_input.shape[0],
-                        dtype=torch.float32,
-                        device=device,
-                    ).to(target_dtype)
-                    * 1000.0
-                    if embedded_guidance_scale is not None
-                    else None
-                )
+                guidance_expand = (torch.tensor(
+                    [embedded_guidance_scale] * latent_model_input.shape[0],
+                    dtype=torch.float32,
+                    device=device,
+                ).to(target_dtype) * 1000.0 if embedded_guidance_scale
+                                   is not None else None)
                 # predict the noise residual
-                with torch.autocast(
-                    device_type="cuda", dtype=target_dtype, enabled=autocast_enabled
-                ):
-                    # concat prompt_embeds_2 and prompt_embeds. Mismach fill with zeros
+                with torch.autocast(device_type="cuda",
+                                    dtype=target_dtype,
+                                    enabled=autocast_enabled):
+                    # concat prompt_embeds_2 and prompt_embeds. Mismatch fill with zeros
                     if prompt_embeds_2.shape[-1] != prompt_embeds.shape[-1]:
                         prompt_embeds_2 = F.pad(
                             prompt_embeds_2,
-                            (0, prompt_embeds.shape[2] - prompt_embeds_2.shape[1]),
+                            (0, prompt_embeds.shape[2] -
+                             prompt_embeds_2.shape[1]),
                             value=0,
                         ).unsqueeze(1)
                     encoder_hidden_states = torch.cat(
-                        [prompt_embeds_2, prompt_embeds], dim=1
-                    )
+                        [prompt_embeds_2, prompt_embeds], dim=1)
                     noise_pred = self.transformer(  # For an input image (129, 192, 336) (1, 256, 256)
                         latent_model_input,  # [2, 16, 33, 24, 42]
                         encoder_hidden_states,
@@ -941,16 +911,13 @@ class HunyuanVideoPipeline(DiffusionPipeline):
                         prompt_mask,  # [2, 256]fpdb
                         guidance=guidance_expand,
                         return_dict=False,
-                    )[
-                        0
-                    ]
+                    )[0]
 
                 # perform guidance
                 if self.do_classifier_free_guidance:
                     noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
                     noise_pred = noise_pred_uncond + self.guidance_scale * (
-                        noise_pred_text - noise_pred_uncond
-                    )
+                        noise_pred_text - noise_pred_uncond)
 
                 if self.do_classifier_free_guidance and self.guidance_rescale > 0.0:
                     # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
@@ -961,26 +928,29 @@ class HunyuanVideoPipeline(DiffusionPipeline):
                     )
 
                 # compute the previous noisy sample x_t -> x_t-1
-                latents = self.scheduler.step(
-                    noise_pred, t, latents, **extra_step_kwargs, return_dict=False
-                )[0]
+                latents = self.scheduler.step(noise_pred,
+                                              t,
+                                              latents,
+                                              **extra_step_kwargs,
+                                              return_dict=False)[0]
 
                 if callback_on_step_end is not None:
                     callback_kwargs = {}
                     for k in callback_on_step_end_tensor_inputs:
                         callback_kwargs[k] = locals()[k]
-                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+                    callback_outputs = callback_on_step_end(
+                        self, i, t, callback_kwargs)
 
                     latents = callback_outputs.pop("latents", latents)
-                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+                    prompt_embeds = callback_outputs.pop(
+                        "prompt_embeds", prompt_embeds)
                     negative_prompt_embeds = callback_outputs.pop(
-                        "negative_prompt_embeds", negative_prompt_embeds
-                    )
+                        "negative_prompt_embeds", negative_prompt_embeds)
 
                 # call the callback, if provided
                 if i == len(timesteps) - 1 or (
-                    (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0
-                ):
+                    (i + 1) > num_warmup_steps and
+                    (i + 1) % self.scheduler.order == 0):
                     if progress_bar is not None:
                         progress_bar.update()
                     if callback is not None and i % callback_steps == 0:
@@ -1003,29 +973,23 @@ class HunyuanVideoPipeline(DiffusionPipeline):
                     f"Only support latents with shape (b, c, h, w) or (b, c, f, h, w), but got {latents.shape}."
                 )
 
-            if (
-                hasattr(self.vae.config, "shift_factor")
-                and self.vae.config.shift_factor
-            ):
-                latents = (
-                    latents / self.vae.config.scaling_factor
-                    + self.vae.config.shift_factor
-                )
+            if (hasattr(self.vae.config, "shift_factor")
+                    and self.vae.config.shift_factor):
+                latents = (latents / self.vae.config.scaling_factor +
+                           self.vae.config.shift_factor)
             else:
                 latents = latents / self.vae.config.scaling_factor
 
-            with torch.autocast(
-                device_type="cuda", dtype=vae_dtype, enabled=vae_autocast_enabled
-            ):
+            with torch.autocast(device_type="cuda",
+                                dtype=vae_dtype,
+                                enabled=vae_autocast_enabled):
                 if enable_tiling:
                     self.vae.enable_tiling()
-                    image = self.vae.decode(
-                        latents, return_dict=False, generator=generator
-                    )[0]
-                else:
-                    image = self.vae.decode(
-                        latents, return_dict=False, generator=generator
-                    )[0]
+                if enable_vae_sp:
+                    self.vae.enable_parallel()
+                image = self.vae.decode(latents,
+                                        return_dict=False,
+                                        generator=generator)[0]
 
             if expand_temporal_dim or image.shape[2] == 1:
                 image = image.squeeze(2)

fastvideo/models/hunyuan/diffusion/schedulers/__init__.py

@@ -1 +1,2 @@
+# ruff: noqa: F401
 from .scheduling_flow_match_discrete import FlowMatchDiscreteScheduler

fastvideo/models/hunyuan/diffusion/schedulers/scheduling_flow_match_discrete.py

@@ -20,13 +20,10 @@
 from dataclasses import dataclass
 from typing import Optional, Tuple, Union
 
-import numpy as np
 import torch
-
 from diffusers.configuration_utils import ConfigMixin, register_to_config
-from diffusers.utils import BaseOutput, logging
 from diffusers.schedulers.scheduling_utils import SchedulerMixin
-
+from diffusers.utils import BaseOutput, logging
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
@@ -83,7 +80,8 @@ class FlowMatchDiscreteScheduler(SchedulerMixin, ConfigMixin):
 
         self.sigmas = sigmas
         # the value fed to model
-        self.timesteps = (sigmas[:-1] * num_train_timesteps).to(dtype=torch.float32)
+        self.timesteps = (sigmas[:-1] *
+                          num_train_timesteps).to(dtype=torch.float32)
 
         self._step_index = None
         self._begin_index = None
@@ -149,8 +147,7 @@ class FlowMatchDiscreteScheduler(SchedulerMixin, ConfigMixin):
 
         self.sigmas = sigmas
         self.timesteps = (sigmas[:-1] * self.config.num_train_timesteps).to(
-            dtype=torch.float32, device=device
-        )
+            dtype=torch.float32, device=device)
 
         # Reset step index
         self._step_index = None
@@ -177,9 +174,9 @@ class FlowMatchDiscreteScheduler(SchedulerMixin, ConfigMixin):
         else:
             self._step_index = self._begin_index
 
-    def scale_model_input(
-        self, sample: torch.Tensor, timestep: Optional[int] = None
-    ) -> torch.Tensor:
+    def scale_model_input(self,
+                          sample: torch.Tensor,
+                          timestep: Optional[int] = None) -> torch.Tensor:
         return sample
 
     def sd3_time_shift(self, t: torch.Tensor):
@@ -217,18 +214,12 @@ class FlowMatchDiscreteScheduler(SchedulerMixin, ConfigMixin):
                 returned, otherwise a tuple is returned where the first element is the sample tensor.
         """
 
-        if (
-            isinstance(timestep, int)
-            or isinstance(timestep, torch.IntTensor)
-            or isinstance(timestep, torch.LongTensor)
-        ):
-            raise ValueError(
-                (
-                    "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
-                    " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
-                    " one of the `scheduler.timesteps` as a timestep."
-                ),
-            )
+        if (isinstance(timestep, int) or isinstance(timestep, torch.IntTensor)
+                or isinstance(timestep, torch.LongTensor)):
+            raise ValueError((
+                "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
+                " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
+                " one of the `scheduler.timesteps` as a timestep."), )
 
         if self.step_index is None:
             self._init_step_index(timestep)
@@ -249,7 +240,7 @@ class FlowMatchDiscreteScheduler(SchedulerMixin, ConfigMixin):
         self._step_index += 1
 
         if not return_dict:
-            return (prev_sample,)
+            return (prev_sample, )
 
         return FlowMatchDiscreteSchedulerOutput(prev_sample=prev_sample)
 

fastvideo/models/hunyuan/idle_config.py

@@ -1,11 +1,14 @@
+# ruff: noqa: F405, F403
 import argparse
-from .constants import *
 import re
+
+from .constants import *
 from .modules.models import HUNYUAN_VIDEO_CONFIG
 
 
 def parse_args(namespace=None):
-    parser = argparse.ArgumentParser(description="HunyuanVideo inference script")
+    parser = argparse.ArgumentParser(
+        description="HunyuanVideo inference script")
 
     parser = add_network_args(parser)
     parser = add_extra_models_args(parser)
@@ -33,7 +36,8 @@ def add_network_args(parser: argparse.ArgumentParser):
         "--latent-channels",
         type=str,
         default=16,
-        help="Number of latent channels of DiT. If None, it will be determined by `vae`. If provided, "
+        help=
+        "Number of latent channels of DiT. If None, it will be determined by `vae`. If provided, "
         "it still needs to match the latent channels of the VAE model.",
     )
     group.add_argument(
@@ -41,13 +45,15 @@ def add_network_args(parser: argparse.ArgumentParser):
         type=str,
         default="bf16",
         choices=PRECISIONS,
-        help="Precision mode. Options: fp32, fp16, bf16. Applied to the backbone model and optimizer.",
+        help=
+        "Precision mode. Options: fp32, fp16, bf16. Applied to the backbone model and optimizer.",
     )
 
     # RoPE
-    group.add_argument(
-        "--rope-theta", type=int, default=256, help="Theta used in RoPE."
-    )
+    group.add_argument("--rope-theta",
+                       type=int,
+                       default=256,
+                       help="Theta used in RoPE.")
     return parser
 
 
@@ -98,9 +104,10 @@ def add_extra_models_args(parser: argparse.ArgumentParser):
         default=4096,
         help="Dimension of the text encoder hidden states.",
     )
-    group.add_argument(
-        "--text-len", type=int, default=256, help="Maximum length of the text input."
-    )
+    group.add_argument("--text-len",
+                       type=int,
+                       default=256,
+                       help="Maximum length of the text input.")
     group.add_argument(
         "--tokenizer",
         type=str,
@@ -131,7 +138,8 @@ def add_extra_models_args(parser: argparse.ArgumentParser):
     group.add_argument(
         "--apply-final-norm",
         action="store_true",
-        help="Apply final normalization to the used text encoder hidden states.",
+        help=
+        "Apply final normalization to the used text encoder hidden states.",
     )
 
     # - CLIP
@@ -224,13 +232,16 @@ def add_inference_args(parser: argparse.ArgumentParser):
         "--model-base",
         type=str,
         default="ckpts",
-        help="Root path of all the models, including t2v models and extra models.",
+        help=
+        "Root path of all the models, including t2v models and extra models.",
     )
     group.add_argument(
         "--dit-weight",
         type=str,
-        default="ckpts/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt",
-        help="Path to the HunyuanVideo model. If None, search the model in the args.model_root."
+        default=
+        "ckpts/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt",
+        help=
+        "Path to the HunyuanVideo model. If None, search the model in the args.model_root."
         "1. If it is a file, load the model directly."
         "2. If it is a directory, search the model in the directory. Support two types of models: "
         "1) named `pytorch_model_*.pt`"
@@ -241,13 +252,15 @@ def add_inference_args(parser: argparse.ArgumentParser):
         type=str,
         default="540p",
         choices=["540p", "720p"],
-        help="Root path of all the models, including t2v models and extra models.",
+        help=
+        "Root path of all the models, including t2v models and extra models.",
     )
     group.add_argument(
         "--load-key",
         type=str,
         default="module",
-        help="Key to load the model states. 'module' for the main model, 'ema' for the EMA model.",
+        help=
+        "Key to load the model states. 'module' for the main model, 'ema' for the EMA model.",
     )
     group.add_argument(
         "--use-cpu-offload",
@@ -271,7 +284,8 @@ def add_inference_args(parser: argparse.ArgumentParser):
     group.add_argument(
         "--disable-autocast",
         action="store_true",
-        help="Disable autocast for denoising loop and vae decoding in pipeline sampling.",
+        help=
+        "Disable autocast for denoising loop and vae decoding in pipeline sampling.",
     )
     group.add_argument(
         "--save-path",
@@ -303,7 +317,8 @@ def add_inference_args(parser: argparse.ArgumentParser):
         type=int,
         nargs="+",
         default=(720, 1280),
-        help="Video size for training. If a single value is provided, it will be used for both height "
+        help=
+        "Video size for training. If a single value is provided, it will be used for both height "
         "and width. If two values are provided, they will be used for height and width "
         "respectively.",
     )
@@ -311,7 +326,8 @@ def add_inference_args(parser: argparse.ArgumentParser):
         "--video-length",
         type=int,
         default=129,
-        help="How many frames to sample from a video. if using 3d vae, the number should be 4n+1",
+        help=
+        "How many frames to sample from a video. if using 3d vae, the number should be 4n+1",
     )
     # --- prompt ---
     group.add_argument(
@@ -325,31 +341,38 @@ def add_inference_args(parser: argparse.ArgumentParser):
         type=str,
         default="auto",
         choices=["file", "random", "fixed", "auto"],
-        help="Seed type for evaluation. If file, use the seed from the CSV file. If random, generate a "
+        help=
+        "Seed type for evaluation. If file, use the seed from the CSV file. If random, generate a "
         "random seed. If fixed, use the fixed seed given by `--seed`. If auto, `csv` will use the "
         "seed column if available, otherwise use the fixed `seed` value. `prompt` will use the "
         "fixed `seed` value.",
     )
-    group.add_argument("--seed", type=int, default=None, help="Seed for evaluation.")
+    group.add_argument("--seed",
+                       type=int,
+                       default=None,
+                       help="Seed for evaluation.")
 
     # Classifier-Free Guidance
-    group.add_argument(
-        "--neg-prompt", type=str, default=None, help="Negative prompt for sampling."
-    )
-    group.add_argument(
-        "--cfg-scale", type=float, default=1.0, help="Classifier free guidance scale."
-    )
+    group.add_argument("--neg-prompt",
+                       type=str,
+                       default=None,
+                       help="Negative prompt for sampling.")
+    group.add_argument("--cfg-scale",
+                       type=float,
+                       default=1.0,
+                       help="Classifier free guidance scale.")
     group.add_argument(
         "--embedded-cfg-scale",
         type=float,
         default=6.0,
-        help="Embeded classifier free guidance scale.",
+        help="Embedded classifier free guidance scale.",
     )
 
     group.add_argument(
         "--reproduce",
         action="store_true",
-        help="Enable reproducibility by setting random seeds and deterministic algorithms.",
+        help=
+        "Enable reproducibility by setting random seeds and deterministic algorithms.",
     )
 
     return parser

fastvideo/models/hunyuan/inference.py

@@ -1,33 +1,27 @@
 import os
-import time
 import random
-import functools
-from typing import List, Optional, Tuple, Union
-
+import time
 from pathlib import Path
-from loguru import logger
 
 import torch
-import torch.distributed as dist
-from fastvideo.models.hunyuan.constants import (
-    PROMPT_TEMPLATE,
-    NEGATIVE_PROMPT,
-    PRECISION_TO_TYPE,
-)
-from fastvideo.models.hunyuan.vae import load_vae
+from loguru import logger
+from safetensors.torch import load_file as safetensors_load_file
+
+from fastvideo.models.hunyuan.constants import (NEGATIVE_PROMPT,
+                                                PRECISION_TO_TYPE,
+                                                PROMPT_TEMPLATE)
+from fastvideo.models.hunyuan.diffusion.pipelines import HunyuanVideoPipeline
+from fastvideo.models.hunyuan.diffusion.schedulers import \
+    FlowMatchDiscreteScheduler
 from fastvideo.models.hunyuan.modules import load_model
 from fastvideo.models.hunyuan.text_encoder import TextEncoder
 from fastvideo.models.hunyuan.utils.data_utils import align_to
-from fastvideo.models.hunyuan.diffusion.schedulers import FlowMatchDiscreteScheduler
-from fastvideo.models.hunyuan.diffusion.pipelines import HunyuanVideoPipeline
-
-from fastvideo.utils.parallel_states import (
-    initialize_sequence_parallel_state,
-    nccl_info,
-)
+from fastvideo.models.hunyuan.vae import load_vae
+from fastvideo.utils.parallel_states import nccl_info
 
 
 class Inference(object):
+
     def __init__(
         self,
         args,
@@ -53,16 +47,17 @@ class Inference(object):
         self.use_cpu_offload = use_cpu_offload
 
         self.args = args
-        self.device = (
-            device
-            if device is not None
-            else "cuda" if torch.cuda.is_available() else "cpu"
-        )
+        self.device = (device if device is not None else
+                       "cuda" if torch.cuda.is_available() else "cpu")
         self.logger = logger
         self.parallel_args = parallel_args
 
     @classmethod
-    def from_pretrained(cls, pretrained_model_path, args, device=None, **kwargs):
+    def from_pretrained(cls,
+                        pretrained_model_path,
+                        args,
+                        device=None,
+                        **kwargs):
         """
         Initialize the Inference pipeline.
 
@@ -72,7 +67,8 @@ class Inference(object):
             device (int): The device for inference. Default is 0.
         """
         # ========================================================================
-        logger.info(f"Got text-to-video model root path: {pretrained_model_path}")
+        logger.info(
+            f"Got text-to-video model root path: {pretrained_model_path}")
 
         # ==================== Initialize Distributed Environment ================
         if nccl_info.sp_size > 1:
@@ -89,7 +85,10 @@ class Inference(object):
 
         # =========================== Build main model ===========================
         logger.info("Building model...")
-        factor_kwargs = {"device": device, "dtype": PRECISION_TO_TYPE[args.precision]}
+        factor_kwargs = {
+            "device": device,
+            "dtype": PRECISION_TO_TYPE[args.precision]
+        }
         in_channels = args.latent_channels
         out_channels = args.latent_channels
 
@@ -116,27 +115,22 @@ class Inference(object):
         # Text encoder
         if args.prompt_template_video is not None:
             crop_start = PROMPT_TEMPLATE[args.prompt_template_video].get(
-                "crop_start", 0
-            )
+                "crop_start", 0)
         elif args.prompt_template is not None:
-            crop_start = PROMPT_TEMPLATE[args.prompt_template].get("crop_start", 0)
+            crop_start = PROMPT_TEMPLATE[args.prompt_template].get(
+                "crop_start", 0)
         else:
             crop_start = 0
         max_length = args.text_len + crop_start
 
         # prompt_template
-        prompt_template = (
-            PROMPT_TEMPLATE[args.prompt_template]
-            if args.prompt_template is not None
-            else None
-        )
+        prompt_template = (PROMPT_TEMPLATE[args.prompt_template]
+                           if args.prompt_template is not None else None)
 
         # prompt_template_video
-        prompt_template_video = (
-            PROMPT_TEMPLATE[args.prompt_template_video]
-            if args.prompt_template_video is not None
-            else None
-        )
+        prompt_template_video = (PROMPT_TEMPLATE[args.prompt_template_video]
+                                 if args.prompt_template_video is not None else
+                                 None)
 
         text_encoder = TextEncoder(
             text_encoder_type=args.text_encoder,
@@ -190,7 +184,9 @@ class Inference(object):
                 model_path = dit_weight / f"pytorch_model_{load_key}.pt"
                 bare_model = True
             elif any(str(f).endswith("_model_states.pt") for f in files):
-                files = [f for f in files if str(f).endswith("_model_states.pt")]
+                files = [
+                    f for f in files if str(f).endswith("_model_states.pt")
+                ]
                 model_path = files[0]
                 if len(files) > 1:
                     logger.warning(
@@ -214,7 +210,9 @@ class Inference(object):
                     model_path = dit_weight / f"pytorch_model_{load_key}.pt"
                     bare_model = True
                 elif any(str(f).endswith("_model_states.pt") for f in files):
-                    files = [f for f in files if str(f).endswith("_model_states.pt")]
+                    files = [
+                        f for f in files if str(f).endswith("_model_states.pt")
+                    ]
                     model_path = files[0]
                     if len(files) > 1:
                         logger.warning(
@@ -238,9 +236,18 @@ class Inference(object):
         if not model_path.exists():
             raise ValueError(f"model_path not exists: {model_path}")
         logger.info(f"Loading torch model {model_path}...")
-        state_dict = torch.load(model_path, map_location=lambda storage, loc: storage)
+        if model_path.suffix == ".safetensors":
+            # Use safetensors library for .safetensors files
+            state_dict = safetensors_load_file(model_path)
+        elif model_path.suffix == ".pt":
+            # Use torch for .pt files
+            state_dict = torch.load(model_path,
+                                    map_location=lambda storage, loc: storage)
+        else:
+            raise ValueError(f"Unsupported file format: {model_path}")
 
-        if bare_model == "unknown" and ("ema" in state_dict or "module" in state_dict):
+        if bare_model == "unknown" and ("ema" in state_dict
+                                        or "module" in state_dict):
             bare_model = False
         if bare_model is False:
             if load_key in state_dict:
@@ -248,8 +255,7 @@ class Inference(object):
             else:
                 raise KeyError(
                     f"Missing key: `{load_key}` in the checkpoint: {model_path}. The keys in the checkpoint "
-                    f"are: {list(state_dict.keys())}."
-                )
+                    f"are: {list(state_dict.keys())}.")
         model.load_state_dict(state_dict, strict=True)
         return model
 
@@ -258,15 +264,18 @@ class Inference(object):
         if isinstance(size, int):
             size = [size]
         if not isinstance(size, (list, tuple)):
-            raise ValueError(f"Size must be an integer or (height, width), got {size}.")
+            raise ValueError(
+                f"Size must be an integer or (height, width), got {size}.")
         if len(size) == 1:
             size = [size[0], size[0]]
         if len(size) != 2:
-            raise ValueError(f"Size must be an integer or (height, width), got {size}.")
+            raise ValueError(
+                f"Size must be an integer or (height, width), got {size}.")
         return size
 
 
 class HunyuanVideoSampler(Inference):
+
     def __init__(
         self,
         args,
@@ -392,15 +401,13 @@ class HunyuanVideoSampler(Inference):
             ]
         elif isinstance(seed, int):
             seeds = [
-                seed + i
-                for _ in range(batch_size)
+                seed + i for _ in range(batch_size)
                 for i in range(num_videos_per_prompt)
             ]
         elif isinstance(seed, (list, tuple)):
             if len(seed) == batch_size:
                 seeds = [
-                    int(seed[i]) + j
-                    for i in range(batch_size)
+                    int(seed[i]) + j for i in range(batch_size)
                     for j in range(num_videos_per_prompt)
                 ]
             elif len(seed) == batch_size * num_videos_per_prompt:
@@ -414,7 +421,10 @@ class HunyuanVideoSampler(Inference):
             raise ValueError(
                 f"Seed must be an integer, a list of integers, or None, got {seed}."
             )
-        generator = [torch.Generator(self.device).manual_seed(seed) for seed in seeds]
+        # Peiyuan: using GPU seed will cause A100 and H100 to generate different results...
+        generator = [
+            torch.Generator("cpu").manual_seed(seed) for seed in seeds
+        ]
         out_dict["seeds"] = seeds
 
         # ========================================================================
@@ -443,7 +453,8 @@ class HunyuanVideoSampler(Inference):
         # Arguments: prompt, new_prompt, negative_prompt
         # ========================================================================
         if not isinstance(prompt, str):
-            raise TypeError(f"`prompt` must be a string, but got {type(prompt)}")
+            raise TypeError(
+                f"`prompt` must be a string, but got {type(prompt)}")
         prompt = [prompt.strip()]
 
         # negative prompt
@@ -466,9 +477,11 @@ class HunyuanVideoSampler(Inference):
         self.pipeline.scheduler = scheduler
 
         if "884" in self.args.vae:
-            latents_size = [(video_length - 1) // 4 + 1, height // 8, width // 8]
+            latents_size = [(video_length - 1) // 4 + 1, height // 8,
+                            width // 8]
         elif "888" in self.args.vae:
-            latents_size = [(video_length - 1) // 8 + 1, height // 8, width // 8]
+            latents_size = [(video_length - 1) // 8 + 1, height // 8,
+                            width // 8]
         n_tokens = latents_size[0] * latents_size[1] * latents_size[2]
 
         # ========================================================================
@@ -510,6 +523,7 @@ class HunyuanVideoSampler(Inference):
             is_progress_bar=True,
             vae_ver=self.args.vae,
             enable_tiling=self.args.vae_tiling,
+            enable_vae_sp=self.args.vae_sp,
         )[0]
         out_dict["samples"] = samples
         out_dict["prompts"] = prompt

fastvideo/models/hunyuan/modules/__init__.py

@@ -1,4 +1,4 @@
-from .models import HYVideoDiffusionTransformer, HUNYUAN_VIDEO_CONFIG
+from .models import HUNYUAN_VIDEO_CONFIG, HYVideoDiffusionTransformer
 
 
 def load_model(args, in_channels, out_channels, factor_kwargs):

fastvideo/models/hunyuan/modules/attenion.py

@@ -1,14 +1,10 @@
-import importlib.metadata
-import math
-
 import torch
-import torch.nn as nn
 import torch.nn.functional as F
 
-
-from fastvideo.utils.parallel_states import get_sequence_parallel_state, nccl_info
-from fastvideo.utils.communications import all_gather, all_to_all_4D
 from fastvideo.models.flash_attn_no_pad import flash_attn_no_pad
+from fastvideo.utils.communications import all_gather, all_to_all_4D
+from fastvideo.utils.parallel_states import (get_sequence_parallel_state,
+                                             nccl_info)
 
 
 def attention(
@@ -25,9 +21,11 @@ def attention(
     if attn_mask is not None and attn_mask.dtype != torch.bool:
         attn_mask = attn_mask.bool()
 
-    x = flash_attn_no_pad(
-        qkv, attn_mask, causal=causal, dropout_p=drop_rate, softmax_scale=None
-    )
+    x = flash_attn_no_pad(qkv,
+                          attn_mask,
+                          causal=causal,
+                          dropout_p=drop_rate,
+                          softmax_scale=None)
 
     b, s, a, d = x.shape
     out = x.reshape(b, s, -1)
@@ -49,8 +47,7 @@ def parallel_attention(q, k, v, img_q_len, img_kv_len, text_mask):
         def shrink_head(encoder_state, dim):
             local_heads = encoder_state.shape[dim] // nccl_info.sp_size
             return encoder_state.narrow(
-                dim, nccl_info.rank_within_group * local_heads, local_heads
-            )
+                dim, nccl_info.rank_within_group * local_heads, local_heads)
 
         encoder_query = shrink_head(encoder_query, dim=2)
         encoder_key = shrink_head(encoder_key, dim=2)
@@ -68,16 +65,20 @@ def parallel_attention(q, k, v, img_q_len, img_kv_len, text_mask):
     qkv = torch.stack([query, key, value], dim=2)
 
     attn_mask = F.pad(text_mask, (sequence_length, 0), value=True)
-    hidden_states = flash_attn_no_pad(
-        qkv, attn_mask, causal=False, dropout_p=0.0, softmax_scale=None
-    )
+    hidden_states = flash_attn_no_pad(qkv,
+                                      attn_mask,
+                                      causal=False,
+                                      dropout_p=0.0,
+                                      softmax_scale=None)
 
     hidden_states, encoder_hidden_states = hidden_states.split_with_sizes(
-        (sequence_length, encoder_sequence_length), dim=1
-    )
+        (sequence_length, encoder_sequence_length), dim=1)
     if get_sequence_parallel_state():
-        hidden_states = all_to_all_4D(hidden_states, scatter_dim=1, gather_dim=2)
-        encoder_hidden_states = all_gather(encoder_hidden_states, dim=2).contiguous()
+        hidden_states = all_to_all_4D(hidden_states,
+                                      scatter_dim=1,
+                                      gather_dim=2)
+        encoder_hidden_states = all_gather(encoder_hidden_states,
+                                           dim=2).contiguous()
     hidden_states = hidden_states.to(query.dtype)
     encoder_hidden_states = encoder_hidden_states.to(query.dtype)
 

fastvideo/models/hunyuan/modules/embed_layers.py

@@ -1,7 +1,7 @@
 import math
+
 import torch
 import torch.nn as nn
-from einops import rearrange, repeat
 
 from ..utils.helpers import to_2tuple
 
@@ -45,7 +45,8 @@ class PatchEmbed(nn.Module):
             bias=bias,
             **factory_kwargs,
         )
-        nn.init.xavier_uniform_(self.proj.weight.view(self.proj.weight.size(0), -1))
+        nn.init.xavier_uniform_(
+            self.proj.weight.view(self.proj.weight.size(0), -1))
         if bias:
             nn.init.zeros_(self.proj.bias)
 
@@ -66,7 +67,12 @@ class TextProjection(nn.Module):
     Adapted from https://github.com/PixArt-alpha/PixArt-alpha/blob/master/diffusion/model/nets/PixArt_blocks.py
     """
 
-    def __init__(self, in_channels, hidden_size, act_layer, dtype=None, device=None):
+    def __init__(self,
+                 in_channels,
+                 hidden_size,
+                 act_layer,
+                 dtype=None,
+                 device=None):
         factory_kwargs = {"dtype": dtype, "device": device}
         super().__init__()
         self.linear_1 = nn.Linear(
@@ -105,15 +111,14 @@ def timestep_embedding(t, dim, max_period=10000):
     .. ref_link: https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py
     """
     half = dim // 2
-    freqs = torch.exp(
-        -math.log(max_period)
-        * torch.arange(start=0, end=half, dtype=torch.float32)
-        / half
-    ).to(device=t.device)
+    freqs = torch.exp(-math.log(max_period) *
+                      torch.arange(start=0, end=half, dtype=torch.float32) /
+                      half).to(device=t.device)
     args = t[:, None].float() * freqs[None]
     embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
     if dim % 2:
-        embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
+        embedding = torch.cat(
+            [embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
     return embedding
 
 
@@ -140,9 +145,10 @@ class TimestepEmbedder(nn.Module):
             out_size = hidden_size
 
         self.mlp = nn.Sequential(
-            nn.Linear(
-                frequency_embedding_size, hidden_size, bias=True, **factory_kwargs
-            ),
+            nn.Linear(frequency_embedding_size,
+                      hidden_size,
+                      bias=True,
+                      **factory_kwargs),
             act_layer(),
             nn.Linear(hidden_size, out_size, bias=True, **factory_kwargs),
         )
@@ -150,8 +156,8 @@ class TimestepEmbedder(nn.Module):
         nn.init.normal_(self.mlp[2].weight, std=0.02)
 
     def forward(self, t):
-        t_freq = timestep_embedding(
-            t, self.frequency_embedding_size, self.max_period
-        ).type(self.mlp[0].weight.dtype)
+        t_freq = timestep_embedding(t, self.frequency_embedding_size,
+                                    self.max_period).type(
+                                        self.mlp[0].weight.dtype)
         t_emb = self.mlp(t_freq)
         return t_emb

fastvideo/models/hunyuan/modules/mlp_layers.py

@@ -6,8 +6,8 @@ from functools import partial
 import torch
 import torch.nn as nn
 
-from .modulate_layers import modulate
 from ..utils.helpers import to_2tuple
+from .modulate_layers import modulate
 
 
 class MLP(nn.Module):
@@ -32,21 +32,21 @@ class MLP(nn.Module):
         hidden_channels = hidden_channels or in_channels
         bias = to_2tuple(bias)
         drop_probs = to_2tuple(drop)
-        linear_layer = partial(nn.Conv2d, kernel_size=1) if use_conv else nn.Linear
+        linear_layer = partial(nn.Conv2d,
+                               kernel_size=1) if use_conv else nn.Linear
 
-        self.fc1 = linear_layer(
-            in_channels, hidden_channels, bias=bias[0], **factory_kwargs
-        )
+        self.fc1 = linear_layer(in_channels,
+                                hidden_channels,
+                                bias=bias[0],
+                                **factory_kwargs)
         self.act = act_layer()
         self.drop1 = nn.Dropout(drop_probs[0])
-        self.norm = (
-            norm_layer(hidden_channels, **factory_kwargs)
-            if norm_layer is not None
-            else nn.Identity()
-        )
-        self.fc2 = linear_layer(
-            hidden_channels, out_features, bias=bias[1], **factory_kwargs
-        )
+        self.norm = (norm_layer(hidden_channels, **factory_kwargs)
+                     if norm_layer is not None else nn.Identity())
+        self.fc2 = linear_layer(hidden_channels,
+                                out_features,
+                                bias=bias[1],
+                                **factory_kwargs)
         self.drop2 = nn.Dropout(drop_probs[1])
 
     def forward(self, x):
@@ -66,9 +66,15 @@ class MLPEmbedder(nn.Module):
     def __init__(self, in_dim: int, hidden_dim: int, device=None, dtype=None):
         factory_kwargs = {"device": device, "dtype": dtype}
         super().__init__()
-        self.in_layer = nn.Linear(in_dim, hidden_dim, bias=True, **factory_kwargs)
+        self.in_layer = nn.Linear(in_dim,
+                                  hidden_dim,
+                                  bias=True,
+                                  **factory_kwargs)
         self.silu = nn.SiLU()
-        self.out_layer = nn.Linear(hidden_dim, hidden_dim, bias=True, **factory_kwargs)
+        self.out_layer = nn.Linear(hidden_dim,
+                                   hidden_dim,
+                                   bias=True,
+                                   **factory_kwargs)
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         return self.out_layer(self.silu(self.in_layer(x)))
@@ -77,16 +83,21 @@ class MLPEmbedder(nn.Module):
 class FinalLayer(nn.Module):
     """The final layer of DiT."""
 
-    def __init__(
-        self, hidden_size, patch_size, out_channels, act_layer, device=None, dtype=None
-    ):
+    def __init__(self,
+                 hidden_size,
+                 patch_size,
+                 out_channels,
+                 act_layer,
+                 device=None,
+                 dtype=None):
         factory_kwargs = {"device": device, "dtype": dtype}
         super().__init__()
 
         # Just use LayerNorm for the final layer
-        self.norm_final = nn.LayerNorm(
-            hidden_size, elementwise_affine=False, eps=1e-6, **factory_kwargs
-        )
+        self.norm_final = nn.LayerNorm(hidden_size,
+                                       elementwise_affine=False,
+                                       eps=1e-6,
+                                       **factory_kwargs)
         if isinstance(patch_size, int):
             self.linear = nn.Linear(
                 hidden_size,
@@ -106,7 +117,10 @@ class FinalLayer(nn.Module):
         # Here we don't distinguish between the modulate types. Just use the simple one.
         self.adaLN_modulation = nn.Sequential(
             act_layer(),
-            nn.Linear(hidden_size, 2 * hidden_size, bias=True, **factory_kwargs),
+            nn.Linear(hidden_size,
+                      2 * hidden_size,
+                      bias=True,
+                      **factory_kwargs),
         )
         # Zero-initialize the modulation
         nn.init.zeros_(self.adaLN_modulation[1].weight)

fastvideo/models/hunyuan/modules/models.py

@@ -1,31 +1,28 @@
-from typing import Any, List, Tuple, Optional, Union, Dict
-from einops import rearrange
+from typing import Any, Dict, List, Optional, Tuple, Union
 
 import torch
 import torch.nn as nn
-import torch.nn.functional as F
-
-from diffusers.models import ModelMixin
 from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.models import ModelMixin
+from einops import rearrange
+
+from fastvideo.models.hunyuan.modules.posemb_layers import \
+    get_nd_rotary_pos_embed
+from fastvideo.utils.parallel_states import nccl_info
 
 from .activation_layers import get_activation_layer
-from .norm_layers import get_norm_layer
-from .embed_layers import TimestepEmbedder, PatchEmbed, TextProjection
 from .attenion import parallel_attention
+from .embed_layers import PatchEmbed, TextProjection, TimestepEmbedder
+from .mlp_layers import MLP, FinalLayer, MLPEmbedder
+from .modulate_layers import ModulateDiT, apply_gate, modulate
+from .norm_layers import get_norm_layer
 from .posemb_layers import apply_rotary_emb
-from .mlp_layers import MLP, MLPEmbedder, FinalLayer
-from .modulate_layers import ModulateDiT, modulate, apply_gate
 from .token_refiner import SingleTokenRefiner
-from fastvideo.models.hunyuan.modules.posemb_layers import get_nd_rotary_pos_embed
-
-from fastvideo.utils.parallel_states import (
-    nccl_info,
-)
 
 
 class MMDoubleStreamBlock(nn.Module):
     """
-    A multimodal dit block with seperate modulation for
+    A multimodal dit block with separate modulation for
     text and image/video, see more details (SD3): https://arxiv.org/abs/2403.03206
                                      (Flux.1): https://github.com/black-forest-labs/flux
     """
@@ -56,31 +53,31 @@ class MMDoubleStreamBlock(nn.Module):
             act_layer=get_activation_layer("silu"),
             **factory_kwargs,
         )
-        self.img_norm1 = nn.LayerNorm(
-            hidden_size, elementwise_affine=False, eps=1e-6, **factory_kwargs
-        )
+        self.img_norm1 = nn.LayerNorm(hidden_size,
+                                      elementwise_affine=False,
+                                      eps=1e-6,
+                                      **factory_kwargs)
 
-        self.img_attn_qkv = nn.Linear(
-            hidden_size, hidden_size * 3, bias=qkv_bias, **factory_kwargs
-        )
+        self.img_attn_qkv = nn.Linear(hidden_size,
+                                      hidden_size * 3,
+                                      bias=qkv_bias,
+                                      **factory_kwargs)
         qk_norm_layer = get_norm_layer(qk_norm_type)
-        self.img_attn_q_norm = (
-            qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs)
-            if qk_norm
-            else nn.Identity()
-        )
-        self.img_attn_k_norm = (
-            qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs)
-            if qk_norm
-            else nn.Identity()
-        )
-        self.img_attn_proj = nn.Linear(
-            hidden_size, hidden_size, bias=qkv_bias, **factory_kwargs
-        )
+        self.img_attn_q_norm = (qk_norm_layer(
+            head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs)
+                                if qk_norm else nn.Identity())
+        self.img_attn_k_norm = (qk_norm_layer(
+            head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs)
+                                if qk_norm else nn.Identity())
+        self.img_attn_proj = nn.Linear(hidden_size,
+                                       hidden_size,
+                                       bias=qkv_bias,
+                                       **factory_kwargs)
 
-        self.img_norm2 = nn.LayerNorm(
-            hidden_size, elementwise_affine=False, eps=1e-6, **factory_kwargs
-        )
+        self.img_norm2 = nn.LayerNorm(hidden_size,
+                                      elementwise_affine=False,
+                                      eps=1e-6,
+                                      **factory_kwargs)
         self.img_mlp = MLP(
             hidden_size,
             mlp_hidden_dim,
@@ -95,30 +92,30 @@ class MMDoubleStreamBlock(nn.Module):
             act_layer=get_activation_layer("silu"),
             **factory_kwargs,
         )
-        self.txt_norm1 = nn.LayerNorm(
-            hidden_size, elementwise_affine=False, eps=1e-6, **factory_kwargs
-        )
+        self.txt_norm1 = nn.LayerNorm(hidden_size,
+                                      elementwise_affine=False,
+                                      eps=1e-6,
+                                      **factory_kwargs)
 
-        self.txt_attn_qkv = nn.Linear(
-            hidden_size, hidden_size * 3, bias=qkv_bias, **factory_kwargs
-        )
-        self.txt_attn_q_norm = (
-            qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs)
-            if qk_norm
-            else nn.Identity()
-        )
-        self.txt_attn_k_norm = (
-            qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs)
-            if qk_norm
-            else nn.Identity()
-        )
-        self.txt_attn_proj = nn.Linear(
-            hidden_size, hidden_size, bias=qkv_bias, **factory_kwargs
-        )
+        self.txt_attn_qkv = nn.Linear(hidden_size,
+                                      hidden_size * 3,
+                                      bias=qkv_bias,
+                                      **factory_kwargs)
+        self.txt_attn_q_norm = (qk_norm_layer(
+            head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs)
+                                if qk_norm else nn.Identity())
+        self.txt_attn_k_norm = (qk_norm_layer(
+            head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs)
+                                if qk_norm else nn.Identity())
+        self.txt_attn_proj = nn.Linear(hidden_size,
+                                       hidden_size,
+                                       bias=qkv_bias,
+                                       **factory_kwargs)
 
-        self.txt_norm2 = nn.LayerNorm(
-            hidden_size, elementwise_affine=False, eps=1e-6, **factory_kwargs
-        )
+        self.txt_norm2 = nn.LayerNorm(hidden_size,
+                                      elementwise_affine=False,
+                                      eps=1e-6,
+                                      **factory_kwargs)
         self.txt_mlp = MLP(
             hidden_size,
             mlp_hidden_dim,
@@ -161,13 +158,14 @@ class MMDoubleStreamBlock(nn.Module):
 
         # Prepare image for attention.
         img_modulated = self.img_norm1(img)
-        img_modulated = modulate(
-            img_modulated, shift=img_mod1_shift, scale=img_mod1_scale
-        )
+        img_modulated = modulate(img_modulated,
+                                 shift=img_mod1_shift,
+                                 scale=img_mod1_scale)
         img_qkv = self.img_attn_qkv(img_modulated)
-        img_q, img_k, img_v = rearrange(
-            img_qkv, "B L (K H D) -> K B L H D", K=3, H=self.heads_num
-        )
+        img_q, img_k, img_v = rearrange(img_qkv,
+                                        "B L (K H D) -> K B L H D",
+                                        K=3,
+                                        H=self.heads_num)
         # Apply QK-Norm if needed
         img_q = self.img_attn_q_norm(img_q).to(img_v)
         img_k = self.img_attn_k_norm(img_k).to(img_v)
@@ -178,15 +176,18 @@ class MMDoubleStreamBlock(nn.Module):
             def shrink_head(encoder_state, dim):
                 local_heads = encoder_state.shape[dim] // nccl_info.sp_size
                 return encoder_state.narrow(
-                    dim, nccl_info.rank_within_group * local_heads, local_heads
-                )
+                    dim, nccl_info.rank_within_group * local_heads,
+                    local_heads)
 
             freqs_cis = (
                 shrink_head(freqs_cis[0], dim=0),
                 shrink_head(freqs_cis[1], dim=0),
             )
 
-            img_qq, img_kk = apply_rotary_emb(img_q, img_k, freqs_cis, head_first=False)
+            img_qq, img_kk = apply_rotary_emb(img_q,
+                                              img_k,
+                                              freqs_cis,
+                                              head_first=False)
             assert (
                 img_qq.shape == img_q.shape and img_kk.shape == img_k.shape
             ), f"img_kk: {img_qq.shape}, img_q: {img_q.shape}, img_kk: {img_kk.shape}, img_k: {img_k.shape}"
@@ -194,13 +195,14 @@ class MMDoubleStreamBlock(nn.Module):
 
         # Prepare txt for attention.
         txt_modulated = self.txt_norm1(txt)
-        txt_modulated = modulate(
-            txt_modulated, shift=txt_mod1_shift, scale=txt_mod1_scale
-        )
+        txt_modulated = modulate(txt_modulated,
+                                 shift=txt_mod1_shift,
+                                 scale=txt_mod1_scale)
         txt_qkv = self.txt_attn_qkv(txt_modulated)
-        txt_q, txt_k, txt_v = rearrange(
-            txt_qkv, "B L (K H D) -> K B L H D", K=3, H=self.heads_num
-        )
+        txt_q, txt_k, txt_v = rearrange(txt_qkv,
+                                        "B L (K H D) -> K B L H D",
+                                        K=3,
+                                        H=self.heads_num)
         # Apply QK-Norm if needed.
         txt_q = self.txt_attn_q_norm(txt_q).to(txt_v)
         txt_k = self.txt_attn_k_norm(txt_k).to(txt_v)
@@ -216,27 +218,27 @@ class MMDoubleStreamBlock(nn.Module):
 
         # attention computation end
 
-        img_attn, txt_attn = attn[:, : img.shape[1]], attn[:, img.shape[1] :]
+        img_attn, txt_attn = attn[:, :img.shape[1]], attn[:, img.shape[1]:]
 
-        # Calculate the img bloks.
-        img = img + apply_gate(self.img_attn_proj(img_attn), gate=img_mod1_gate)
+        # Calculate the img blocks.
+        img = img + apply_gate(self.img_attn_proj(img_attn),
+                               gate=img_mod1_gate)
         img = img + apply_gate(
             self.img_mlp(
-                modulate(
-                    self.img_norm2(img), shift=img_mod2_shift, scale=img_mod2_scale
-                )
-            ),
+                modulate(self.img_norm2(img),
+                         shift=img_mod2_shift,
+                         scale=img_mod2_scale)),
             gate=img_mod2_gate,
         )
 
-        # Calculate the txt bloks.
-        txt = txt + apply_gate(self.txt_attn_proj(txt_attn), gate=txt_mod1_gate)
+        # Calculate the txt blocks.
+        txt = txt + apply_gate(self.txt_attn_proj(txt_attn),
+                               gate=txt_mod1_gate)
         txt = txt + apply_gate(
             self.txt_mlp(
-                modulate(
-                    self.txt_norm2(txt), shift=txt_mod2_shift, scale=txt_mod2_scale
-                )
-            ),
+                modulate(self.txt_norm2(txt),
+                         shift=txt_mod2_shift,
+                         scale=txt_mod2_scale)),
             gate=txt_mod2_gate,
         )
 
@@ -275,29 +277,24 @@ class MMSingleStreamBlock(nn.Module):
         self.scale = qk_scale or head_dim**-0.5
 
         # qkv and mlp_in
-        self.linear1 = nn.Linear(
-            hidden_size, hidden_size * 3 + mlp_hidden_dim, **factory_kwargs
-        )
+        self.linear1 = nn.Linear(hidden_size, hidden_size * 3 + mlp_hidden_dim,
+                                 **factory_kwargs)
         # proj and mlp_out
-        self.linear2 = nn.Linear(
-            hidden_size + mlp_hidden_dim, hidden_size, **factory_kwargs
-        )
+        self.linear2 = nn.Linear(hidden_size + mlp_hidden_dim, hidden_size,
+                                 **factory_kwargs)
 
         qk_norm_layer = get_norm_layer(qk_norm_type)
-        self.q_norm = (
-            qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs)
-            if qk_norm
-            else nn.Identity()
-        )
-        self.k_norm = (
-            qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs)
-            if qk_norm
-            else nn.Identity()
-        )
+        self.q_norm = (qk_norm_layer(
+            head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs)
+                       if qk_norm else nn.Identity())
+        self.k_norm = (qk_norm_layer(
+            head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs)
+                       if qk_norm else nn.Identity())
 
-        self.pre_norm = nn.LayerNorm(
-            hidden_size, elementwise_affine=False, eps=1e-6, **factory_kwargs
-        )
+        self.pre_norm = nn.LayerNorm(hidden_size,
+                                     elementwise_affine=False,
+                                     eps=1e-6,
+                                     **factory_kwargs)
 
         self.mlp_act = get_activation_layer(mlp_act_type)()
         self.modulation = ModulateDiT(
@@ -324,11 +321,14 @@ class MMSingleStreamBlock(nn.Module):
     ) -> torch.Tensor:
         mod_shift, mod_scale, mod_gate = self.modulation(vec).chunk(3, dim=-1)
         x_mod = modulate(self.pre_norm(x), shift=mod_shift, scale=mod_scale)
-        qkv, mlp = torch.split(
-            self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1
-        )
+        qkv, mlp = torch.split(self.linear1(x_mod),
+                               [3 * self.hidden_size, self.mlp_hidden_dim],
+                               dim=-1)
 
-        q, k, v = rearrange(qkv, "B L (K H D) -> K B L H D", K=3, H=self.heads_num)
+        q, k, v = rearrange(qkv,
+                            "B L (K H D) -> K B L H D",
+                            K=3,
+                            H=self.heads_num)
 
         # Apply QK-Norm if needed.
         q = self.q_norm(q).to(v)
@@ -337,15 +337,18 @@ class MMSingleStreamBlock(nn.Module):
         def shrink_head(encoder_state, dim):
             local_heads = encoder_state.shape[dim] // nccl_info.sp_size
             return encoder_state.narrow(
-                dim, nccl_info.rank_within_group * local_heads, local_heads
-            )
+                dim, nccl_info.rank_within_group * local_heads, local_heads)
 
-        freqs_cis = (shrink_head(freqs_cis[0], dim=0), shrink_head(freqs_cis[1], dim=0))
+        freqs_cis = (shrink_head(freqs_cis[0],
+                                 dim=0), shrink_head(freqs_cis[1], dim=0))
 
         img_q, txt_q = q[:, :-txt_len, :, :], q[:, -txt_len:, :, :]
         img_k, txt_k = k[:, :-txt_len, :, :], k[:, -txt_len:, :, :]
         img_v, txt_v = v[:, :-txt_len, :, :], v[:, -txt_len:, :, :]
-        img_qq, img_kk = apply_rotary_emb(img_q, img_k, freqs_cis, head_first=False)
+        img_qq, img_kk = apply_rotary_emb(img_q,
+                                          img_k,
+                                          freqs_cis,
+                                          head_first=False)
         assert (
             img_qq.shape == img_q.shape and img_kk.shape == img_k.shape
         ), f"img_kk: {img_qq.shape}, img_q: {img_q.shape}, img_kk: {img_kk.shape}, img_k: {img_k.shape}"
@@ -466,15 +469,13 @@ class HYVideoDiffusionTransformer(ModelMixin, ConfigMixin):
         pe_dim = hidden_size // heads_num
         if sum(rope_dim_list) != pe_dim:
             raise ValueError(
-                f"Got {rope_dim_list} but expected positional dim {pe_dim}"
-            )
+                f"Got {rope_dim_list} but expected positional dim {pe_dim}")
         self.hidden_size = hidden_size
         self.heads_num = heads_num
 
         # image projection
-        self.img_in = PatchEmbed(
-            self.patch_size, self.in_channels, self.hidden_size, **factory_kwargs
-        )
+        self.img_in = PatchEmbed(self.patch_size, self.in_channels,
+                                 self.hidden_size, **factory_kwargs)
 
         # text projection
         if self.text_projection == "linear":
@@ -494,60 +495,48 @@ class HYVideoDiffusionTransformer(ModelMixin, ConfigMixin):
             )
         else:
             raise NotImplementedError(
-                f"Unsupported text_projection: {self.text_projection}"
-            )
+                f"Unsupported text_projection: {self.text_projection}")
 
         # time modulation
-        self.time_in = TimestepEmbedder(
-            self.hidden_size, get_activation_layer("silu"), **factory_kwargs
-        )
+        self.time_in = TimestepEmbedder(self.hidden_size,
+                                        get_activation_layer("silu"),
+                                        **factory_kwargs)
 
         # text modulation
-        self.vector_in = MLPEmbedder(
-            self.config.text_states_dim_2, self.hidden_size, **factory_kwargs
-        )
+        self.vector_in = MLPEmbedder(self.config.text_states_dim_2,
+                                     self.hidden_size, **factory_kwargs)
 
         # guidance modulation
-        self.guidance_in = (
-            TimestepEmbedder(
-                self.hidden_size, get_activation_layer("silu"), **factory_kwargs
-            )
-            if guidance_embed
-            else None
-        )
+        self.guidance_in = (TimestepEmbedder(
+            self.hidden_size, get_activation_layer("silu"), **factory_kwargs)
+                            if guidance_embed else None)
 
         # double blocks
-        self.double_blocks = nn.ModuleList(
-            [
-                MMDoubleStreamBlock(
-                    self.hidden_size,
-                    self.heads_num,
-                    mlp_width_ratio=mlp_width_ratio,
-                    mlp_act_type=mlp_act_type,
-                    qk_norm=qk_norm,
-                    qk_norm_type=qk_norm_type,
-                    qkv_bias=qkv_bias,
-                    **factory_kwargs,
-                )
-                for _ in range(mm_double_blocks_depth)
-            ]
-        )
+        self.double_blocks = nn.ModuleList([
+            MMDoubleStreamBlock(
+                self.hidden_size,
+                self.heads_num,
+                mlp_width_ratio=mlp_width_ratio,
+                mlp_act_type=mlp_act_type,
+                qk_norm=qk_norm,
+                qk_norm_type=qk_norm_type,
+                qkv_bias=qkv_bias,
+                **factory_kwargs,
+            ) for _ in range(mm_double_blocks_depth)
+        ])
 
         # single blocks
-        self.single_blocks = nn.ModuleList(
-            [
-                MMSingleStreamBlock(
-                    self.hidden_size,
-                    self.heads_num,
-                    mlp_width_ratio=mlp_width_ratio,
-                    mlp_act_type=mlp_act_type,
-                    qk_norm=qk_norm,
-                    qk_norm_type=qk_norm_type,
-                    **factory_kwargs,
-                )
-                for _ in range(mm_single_blocks_depth)
-            ]
-        )
+        self.single_blocks = nn.ModuleList([
+            MMSingleStreamBlock(
+                self.hidden_size,
+                self.heads_num,
+                mlp_width_ratio=mlp_width_ratio,
+                mlp_act_type=mlp_act_type,
+                qk_norm=qk_norm,
+                qk_norm_type=qk_norm_type,
+                **factory_kwargs,
+            ) for _ in range(mm_single_blocks_depth)
+        ])
 
         self.final_layer = FinalLayer(
             self.hidden_size,
@@ -571,11 +560,13 @@ class HYVideoDiffusionTransformer(ModelMixin, ConfigMixin):
 
     def get_rotary_pos_embed(self, rope_sizes):
         target_ndim = 3
-        ndim = 5 - 2
+
         head_dim = self.hidden_size // self.heads_num
         rope_dim_list = self.rope_dim_list
         if rope_dim_list is None:
-            rope_dim_list = [head_dim // target_ndim for _ in range(target_ndim)]
+            rope_dim_list = [
+                head_dim // target_ndim for _ in range(target_ndim)
+            ]
         assert (
             sum(rope_dim_list) == head_dim
         ), "sum(rope_dim_list) should equal to head_dim of attention layer"
@@ -602,26 +593,26 @@ class HYVideoDiffusionTransformer(ModelMixin, ConfigMixin):
         timestep: torch.LongTensor,
         encoder_attention_mask: torch.Tensor,
         output_features=False,
-        output_features_stride = 8,
+        output_features_stride=8,
         attention_kwargs: Optional[Dict[str, Any]] = None,
         return_dict: bool = False,
         guidance=None,
     ) -> Union[torch.Tensor, Dict[str, torch.Tensor]]:
-        if guidance == None:
-            guidance = torch.tensor(
-                [6016.0], device=hidden_states.device, dtype=torch.bfloat16
-            )
-        out = {}
+        if guidance is None:
+            guidance = torch.tensor([6016.0],
+                                    device=hidden_states.device,
+                                    dtype=torch.bfloat16)
         img = x = hidden_states
         text_mask = encoder_attention_mask
         t = timestep
         txt = encoder_hidden_states[:, 1:]
-        text_states_2 = encoder_hidden_states[:, 0, : self.config.text_states_dim_2]
-        _, _, ot, oh, ow = x.shape
+        text_states_2 = encoder_hidden_states[:, 0, :self.config.
+                                              text_states_dim_2]
+        _, _, ot, oh, ow = x.shape  # codespell:ignore
         tt, th, tw = (
-            ot // self.patch_size[0],
-            oh // self.patch_size[1],
-            ow // self.patch_size[2],
+            ot // self.patch_size[0],  # codespell:ignore
+            oh // self.patch_size[1],  # codespell:ignore
+            ow // self.patch_size[2],  # codespell:ignore
         )
         original_tt = nccl_info.sp_size * tt
         freqs_cos, freqs_sin = self.get_rotary_pos_embed((original_tt, th, tw))
@@ -646,11 +637,11 @@ class HYVideoDiffusionTransformer(ModelMixin, ConfigMixin):
         if self.text_projection == "linear":
             txt = self.txt_in(txt)
         elif self.text_projection == "single_refiner":
-            txt = self.txt_in(txt, t, text_mask if self.use_attention_mask else None)
+            txt = self.txt_in(txt, t,
+                              text_mask if self.use_attention_mask else None)
         else:
             raise NotImplementedError(
-                f"Unsupported text_projection: {self.text_projection}"
-            )
+                f"Unsupported text_projection: {self.text_projection}")
 
         txt_seq_len = txt.shape[1]
         img_seq_len = img.shape[1]
@@ -683,10 +674,11 @@ class HYVideoDiffusionTransformer(ModelMixin, ConfigMixin):
         img = x[:, :img_seq_len, ...]
 
         # ---------------------------- Final layer ------------------------------
-        img = self.final_layer(img, vec)  # (N, T, patch_size ** 2 * out_channels)
+        img = self.final_layer(img,
+                               vec)  # (N, T, patch_size ** 2 * out_channels)
 
         img = self.unpatchify(img, tt, th, tw)
-        assert return_dict == False, "return_dict is not supported."
+        assert not return_dict, "return_dict is not supported."
         if output_features:
             features_list = torch.stack(features_list, dim=0)
         else:
@@ -710,25 +702,24 @@ class HYVideoDiffusionTransformer(ModelMixin, ConfigMixin):
 
     def params_count(self):
         counts = {
-            "double": sum(
-                [
-                    sum(p.numel() for p in block.img_attn_qkv.parameters())
-                    + sum(p.numel() for p in block.img_attn_proj.parameters())
-                    + sum(p.numel() for p in block.img_mlp.parameters())
-                    + sum(p.numel() for p in block.txt_attn_qkv.parameters())
-                    + sum(p.numel() for p in block.txt_attn_proj.parameters())
-                    + sum(p.numel() for p in block.txt_mlp.parameters())
-                    for block in self.double_blocks
-                ]
-            ),
-            "single": sum(
-                [
-                    sum(p.numel() for p in block.linear1.parameters())
-                    + sum(p.numel() for p in block.linear2.parameters())
-                    for block in self.single_blocks
-                ]
-            ),
-            "total": sum(p.numel() for p in self.parameters()),
+            "double":
+            sum([
+                sum(p.numel() for p in block.img_attn_qkv.parameters()) +
+                sum(p.numel() for p in block.img_attn_proj.parameters()) +
+                sum(p.numel() for p in block.img_mlp.parameters()) +
+                sum(p.numel() for p in block.txt_attn_qkv.parameters()) +
+                sum(p.numel() for p in block.txt_attn_proj.parameters()) +
+                sum(p.numel() for p in block.txt_mlp.parameters())
+                for block in self.double_blocks
+            ]),
+            "single":
+            sum([
+                sum(p.numel() for p in block.linear1.parameters()) +
+                sum(p.numel() for p in block.linear2.parameters())
+                for block in self.single_blocks
+            ]),
+            "total":
+            sum(p.numel() for p in self.parameters()),
         }
         counts["attn+mlp"] = counts["double"] + counts["single"]
         return counts

fastvideo/models/hunyuan/modules/modulate_layers.py

@@ -18,9 +18,10 @@ class ModulateDiT(nn.Module):
         factory_kwargs = {"dtype": dtype, "device": device}
         super().__init__()
         self.act = act_layer()
-        self.linear = nn.Linear(
-            hidden_size, factor * hidden_size, bias=True, **factory_kwargs
-        )
+        self.linear = nn.Linear(hidden_size,
+                                factor * hidden_size,
+                                bias=True,
+                                **factory_kwargs)
         # Zero-initialize the modulation
         nn.init.zeros_(self.linear.weight)
         nn.init.zeros_(self.linear.bias)
@@ -70,6 +71,7 @@ def apply_gate(x, gate=None, tanh=False):
 
 
 def ckpt_wrapper(module):
+
     def ckpt_forward(*inputs):
         outputs = module(*inputs)
         return outputs
@@ -77,11 +79,8 @@ def ckpt_wrapper(module):
     return ckpt_forward
 
 
-import torch
-import torch.nn as nn
-
-
 class RMSNorm(nn.Module):
+
     def __init__(
         self,
         dim: int,
@@ -153,4 +152,5 @@ def get_norm_layer(norm_layer):
     elif norm_layer == "rms":
         return RMSNorm
     else:
-        raise NotImplementedError(f"Norm layer {norm_layer} is not implemented")
+        raise NotImplementedError(
+            f"Norm layer {norm_layer} is not implemented")

fastvideo/models/hunyuan/modules/norm_layers.py

@@ -3,6 +3,7 @@ import torch.nn as nn
 
 
 class RMSNorm(nn.Module):
+
     def __init__(
         self,
         dim: int,
@@ -74,4 +75,5 @@ def get_norm_layer(norm_layer):
     elif norm_layer == "rms":
         return RMSNorm
     else:
-        raise NotImplementedError(f"Norm layer {norm_layer} is not implemented")
+        raise NotImplementedError(
+            f"Norm layer {norm_layer} is not implemented")

fastvideo/models/hunyuan/modules/posemb_layers.py

@@ -1,10 +1,11 @@
+from typing import List, Tuple, Union
+
 import torch
-from typing import Union, Tuple, List
 
 
 def _to_tuple(x, dim=2):
     if isinstance(x, int):
-        return (x,) * dim
+        return (x, ) * dim
     elif len(x) == dim:
         return x
     else:
@@ -29,7 +30,7 @@ def get_meshgrid_nd(start, *args, dim=2):
     if len(args) == 0:
         # start is grid_size
         num = _to_tuple(start, dim=dim)
-        start = (0,) * dim
+        start = (0, ) * dim
         stop = num
     elif len(args) == 1:
         # start is start, args[0] is stop, step is 1
@@ -108,7 +109,10 @@ def reshape_for_broadcast(
                 x.shape[1],
                 x.shape[-1],
             ), f"freqs_cis shape {freqs_cis[0].shape} does not match x shape {x.shape}"
-            shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
+            shape = [
+                d if i == 1 or i == ndim - 1 else 1
+                for i, d in enumerate(x.shape)
+            ]
         return freqs_cis[0].view(*shape), freqs_cis[1].view(*shape)
     else:
         # freqs_cis: values in complex space
@@ -126,14 +130,16 @@ def reshape_for_broadcast(
                 x.shape[1],
                 x.shape[-1],
             ), f"freqs_cis shape {freqs_cis.shape} does not match x shape {x.shape}"
-            shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
+            shape = [
+                d if i == 1 or i == ndim - 1 else 1
+                for i, d in enumerate(x.shape)
+            ]
         return freqs_cis.view(*shape)
 
 
 def rotate_half(x):
-    x_real, x_imag = (
-        x.float().reshape(*x.shape[:-1], -1, 2).unbind(-1)
-    )  # [B, S, H, D//2]
+    x_real, x_imag = (x.float().reshape(*x.shape[:-1], -1,
+                                        2).unbind(-1))  # [B, S, H, D//2]
     return torch.stack([-x_imag, x_real], dim=-1).flatten(3)
 
 
@@ -171,18 +177,15 @@ def apply_rotary_emb(
         xk_out = (xk.float() * cos + rotate_half(xk.float()) * sin).type_as(xk)
     else:
         # view_as_complex will pack [..., D/2, 2](real) to [..., D/2](complex)
-        xq_ = torch.view_as_complex(
-            xq.float().reshape(*xq.shape[:-1], -1, 2)
-        )  # [B, S, H, D//2]
+        xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1,
+                                                       2))  # [B, S, H, D//2]
         freqs_cis = reshape_for_broadcast(freqs_cis, xq_, head_first).to(
-            xq.device
-        )  # [S, D//2] --> [1, S, 1, D//2]
+            xq.device)  # [S, D//2] --> [1, S, 1, D//2]
         # (real, imag) * (cos, sin) = (real * cos - imag * sin, imag * cos + real * sin)
         # view_as_real will expand [..., D/2](complex) to [..., D/2, 2](real)
         xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3).type_as(xq)
-        xk_ = torch.view_as_complex(
-            xk.float().reshape(*xk.shape[:-1], -1, 2)
-        )  # [B, S, H, D//2]
+        xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1,
+                                                       2))  # [B, S, H, D//2]
         xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3).type_as(xk)
 
     return xq_out, xk_out
@@ -216,21 +219,24 @@ def get_nd_rotary_pos_embed(
         pos_embed (torch.Tensor): [HW, D/2]
     """
 
-    grid = get_meshgrid_nd(
-        start, *args, dim=len(rope_dim_list)
-    )  # [3, W, H, D] / [2, W, H]
+    grid = get_meshgrid_nd(start, *args,
+                           dim=len(rope_dim_list))  # [3, W, H, D] / [2, W, H]
 
-    if isinstance(theta_rescale_factor, int) or isinstance(theta_rescale_factor, float):
+    if isinstance(theta_rescale_factor, int) or isinstance(
+            theta_rescale_factor, float):
         theta_rescale_factor = [theta_rescale_factor] * len(rope_dim_list)
-    elif isinstance(theta_rescale_factor, list) and len(theta_rescale_factor) == 1:
+    elif isinstance(theta_rescale_factor,
+                    list) and len(theta_rescale_factor) == 1:
         theta_rescale_factor = [theta_rescale_factor[0]] * len(rope_dim_list)
     assert len(theta_rescale_factor) == len(
         rope_dim_list
     ), "len(theta_rescale_factor) should equal to len(rope_dim_list)"
 
-    if isinstance(interpolation_factor, int) or isinstance(interpolation_factor, float):
+    if isinstance(interpolation_factor, int) or isinstance(
+            interpolation_factor, float):
         interpolation_factor = [interpolation_factor] * len(rope_dim_list)
-    elif isinstance(interpolation_factor, list) and len(interpolation_factor) == 1:
+    elif isinstance(interpolation_factor,
+                    list) and len(interpolation_factor) == 1:
         interpolation_factor = [interpolation_factor[0]] * len(rope_dim_list)
     assert len(interpolation_factor) == len(
         rope_dim_list
@@ -292,11 +298,10 @@ def get_1d_rotary_pos_embed(
     # proposed by reddit user bloc97, to rescale rotary embeddings to longer sequence length without fine-tuning
     # has some connection to NTK literature
     if theta_rescale_factor != 1.0:
-        theta *= theta_rescale_factor ** (dim / (dim - 2))
+        theta *= theta_rescale_factor**(dim / (dim - 2))
 
-    freqs = 1.0 / (
-        theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim)
-    )  # [D/2]
+    freqs = 1.0 / (theta**(torch.arange(0, dim, 2)[:(dim // 2)].float() / dim)
+                   )  # [D/2]
     # assert interpolation_factor == 1.0, f"interpolation_factor: {interpolation_factor}"
     freqs = torch.outer(pos * interpolation_factor, freqs)  # [S, D/2]
     if use_real:
@@ -304,7 +309,6 @@ def get_1d_rotary_pos_embed(
         freqs_sin = freqs.sin().repeat_interleave(2, dim=1)  # [S, D]
         return freqs_cos, freqs_sin
     else:
-        freqs_cis = torch.polar(
-            torch.ones_like(freqs), freqs
-        )  # complex64     # [S, D/2]
+        freqs_cis = torch.polar(torch.ones_like(freqs),
+                                freqs)  # complex64     # [S, D/2]
         return freqs_cis

fastvideo/models/hunyuan/modules/token_refiner.py

@@ -1,19 +1,19 @@
 from typing import Optional
 
-from einops import rearrange
 import torch
 import torch.nn as nn
+from einops import rearrange
 
 from .activation_layers import get_activation_layer
 from .attenion import attention
-from .norm_layers import get_norm_layer
-from .embed_layers import TimestepEmbedder, TextProjection
-from .attenion import attention
+from .embed_layers import TextProjection, TimestepEmbedder
 from .mlp_layers import MLP
-from .modulate_layers import modulate, apply_gate
+from .modulate_layers import apply_gate
+from .norm_layers import get_norm_layer
 
 
 class IndividualTokenRefinerBlock(nn.Module):
+
     def __init__(
         self,
         hidden_size,
@@ -33,30 +33,30 @@ class IndividualTokenRefinerBlock(nn.Module):
         head_dim = hidden_size // heads_num
         mlp_hidden_dim = int(hidden_size * mlp_width_ratio)
 
-        self.norm1 = nn.LayerNorm(
-            hidden_size, elementwise_affine=True, eps=1e-6, **factory_kwargs
-        )
-        self.self_attn_qkv = nn.Linear(
-            hidden_size, hidden_size * 3, bias=qkv_bias, **factory_kwargs
-        )
+        self.norm1 = nn.LayerNorm(hidden_size,
+                                  elementwise_affine=True,
+                                  eps=1e-6,
+                                  **factory_kwargs)
+        self.self_attn_qkv = nn.Linear(hidden_size,
+                                       hidden_size * 3,
+                                       bias=qkv_bias,
+                                       **factory_kwargs)
         qk_norm_layer = get_norm_layer(qk_norm_type)
-        self.self_attn_q_norm = (
-            qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs)
-            if qk_norm
-            else nn.Identity()
-        )
-        self.self_attn_k_norm = (
-            qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs)
-            if qk_norm
-            else nn.Identity()
-        )
-        self.self_attn_proj = nn.Linear(
-            hidden_size, hidden_size, bias=qkv_bias, **factory_kwargs
-        )
+        self.self_attn_q_norm = (qk_norm_layer(
+            head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs)
+                                 if qk_norm else nn.Identity())
+        self.self_attn_k_norm = (qk_norm_layer(
+            head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs)
+                                 if qk_norm else nn.Identity())
+        self.self_attn_proj = nn.Linear(hidden_size,
+                                        hidden_size,
+                                        bias=qkv_bias,
+                                        **factory_kwargs)
 
-        self.norm2 = nn.LayerNorm(
-            hidden_size, elementwise_affine=True, eps=1e-6, **factory_kwargs
-        )
+        self.norm2 = nn.LayerNorm(hidden_size,
+                                  elementwise_affine=True,
+                                  eps=1e-6,
+                                  **factory_kwargs)
         act_layer = get_activation_layer(act_type)
         self.mlp = MLP(
             in_channels=hidden_size,
@@ -68,7 +68,10 @@ class IndividualTokenRefinerBlock(nn.Module):
 
         self.adaLN_modulation = nn.Sequential(
             act_layer(),
-            nn.Linear(hidden_size, 2 * hidden_size, bias=True, **factory_kwargs),
+            nn.Linear(hidden_size,
+                      2 * hidden_size,
+                      bias=True,
+                      **factory_kwargs),
         )
         # Zero-initialize the modulation
         nn.init.zeros_(self.adaLN_modulation[1].weight)
@@ -77,14 +80,18 @@ class IndividualTokenRefinerBlock(nn.Module):
     def forward(
         self,
         x: torch.Tensor,
-        c: torch.Tensor,  # timestep_aware_representations + context_aware_representations
+        c: torch.
+        Tensor,  # timestep_aware_representations + context_aware_representations
         attn_mask: torch.Tensor = None,
     ):
         gate_msa, gate_mlp = self.adaLN_modulation(c).chunk(2, dim=1)
 
         norm_x = self.norm1(x)
         qkv = self.self_attn_qkv(norm_x)
-        q, k, v = rearrange(qkv, "B L (K H D) -> K B L H D", K=3, H=self.heads_num)
+        q, k, v = rearrange(qkv,
+                            "B L (K H D) -> K B L H D",
+                            K=3,
+                            H=self.heads_num)
         # Apply QK-Norm if needed
         q = self.self_attn_q_norm(q).to(v)
         k = self.self_attn_k_norm(k).to(v)
@@ -101,6 +108,7 @@ class IndividualTokenRefinerBlock(nn.Module):
 
 
 class IndividualTokenRefiner(nn.Module):
+
     def __init__(
         self,
         hidden_size,
@@ -117,22 +125,19 @@ class IndividualTokenRefiner(nn.Module):
     ):
         factory_kwargs = {"device": device, "dtype": dtype}
         super().__init__()
-        self.blocks = nn.ModuleList(
-            [
-                IndividualTokenRefinerBlock(
-                    hidden_size=hidden_size,
-                    heads_num=heads_num,
-                    mlp_width_ratio=mlp_width_ratio,
-                    mlp_drop_rate=mlp_drop_rate,
-                    act_type=act_type,
-                    qk_norm=qk_norm,
-                    qk_norm_type=qk_norm_type,
-                    qkv_bias=qkv_bias,
-                    **factory_kwargs,
-                )
-                for _ in range(depth)
-            ]
-        )
+        self.blocks = nn.ModuleList([
+            IndividualTokenRefinerBlock(
+                hidden_size=hidden_size,
+                heads_num=heads_num,
+                mlp_width_ratio=mlp_width_ratio,
+                mlp_drop_rate=mlp_drop_rate,
+                act_type=act_type,
+                qk_norm=qk_norm,
+                qk_norm_type=qk_norm_type,
+                qkv_bias=qkv_bias,
+                **factory_kwargs,
+            ) for _ in range(depth)
+        ])
 
     def forward(
         self,
@@ -174,17 +179,18 @@ class SingleTokenRefiner(nn.Module):
         self.attn_mode = attn_mode
         assert self.attn_mode == "torch", "Only support 'torch' mode for token refiner."
 
-        self.input_embedder = nn.Linear(
-            in_channels, hidden_size, bias=True, **factory_kwargs
-        )
+        self.input_embedder = nn.Linear(in_channels,
+                                        hidden_size,
+                                        bias=True,
+                                        **factory_kwargs)
 
         act_layer = get_activation_layer(act_type)
         # Build timestep embedding layer
-        self.t_embedder = TimestepEmbedder(hidden_size, act_layer, **factory_kwargs)
+        self.t_embedder = TimestepEmbedder(hidden_size, act_layer,
+                                           **factory_kwargs)
         # Build context embedding layer
-        self.c_embedder = TextProjection(
-            in_channels, hidden_size, act_layer, **factory_kwargs
-        )
+        self.c_embedder = TextProjection(in_channels, hidden_size, act_layer,
+                                         **factory_kwargs)
 
         self.individual_token_refiner = IndividualTokenRefiner(
             hidden_size=hidden_size,
@@ -212,9 +218,9 @@ class SingleTokenRefiner(nn.Module):
         else:
             mask_float = mask.float().unsqueeze(-1)  # [b, s1, 1]
             context_aware_representations = (x * mask_float).sum(
-                dim=1
-            ) / mask_float.sum(dim=1)
-        context_aware_representations = self.c_embedder(context_aware_representations)
+                dim=1) / mask_float.sum(dim=1)
+        context_aware_representations = self.c_embedder(
+            context_aware_representations)
         c = timestep_aware_representations + context_aware_representations
 
         x = self.input_embedder(x)

fastvideo/models/hunyuan/prompt_rewrite.py

@@ -16,7 +16,6 @@ Given Input:
 input: "{input}"
 """
 
-
 master_mode_prompt = """Master mode - Video Recaption Task:
 
 You are a large language model specialized in rewriting video descriptions. Your task is to modify the input description.

fastvideo/models/hunyuan/text_encoder/__init__.py

@@ -1,14 +1,12 @@
 from dataclasses import dataclass
 from typing import Optional, Tuple
-from copy import deepcopy
 
 import torch
 import torch.nn as nn
-from transformers import CLIPTextModel, CLIPTokenizer, AutoTokenizer, AutoModel
+from transformers import AutoModel, AutoTokenizer, CLIPTextModel, CLIPTokenizer
 from transformers.utils import ModelOutput
 
-from ..constants import TEXT_ENCODER_PATH, TOKENIZER_PATH
-from ..constants import PRECISION_TO_TYPE
+from ..constants import PRECISION_TO_TYPE, TEXT_ENCODER_PATH, TOKENIZER_PATH
 
 
 def use_default(value, default):
@@ -33,16 +31,16 @@ def load_text_encoder(
         text_encoder = CLIPTextModel.from_pretrained(text_encoder_path)
         text_encoder.final_layer_norm = text_encoder.text_model.final_layer_norm
     elif text_encoder_type == "llm":
-        text_encoder = AutoModel.from_pretrained(
-            text_encoder_path, low_cpu_mem_usage=True
-        )
+        text_encoder = AutoModel.from_pretrained(text_encoder_path,
+                                                 low_cpu_mem_usage=True)
         text_encoder.final_layer_norm = text_encoder.norm
     else:
         raise ValueError(f"Unsupported text encoder type: {text_encoder_type}")
     # from_pretrained will ensure that the model is in eval mode.
 
     if text_encoder_precision is not None:
-        text_encoder = text_encoder.to(dtype=PRECISION_TO_TYPE[text_encoder_precision])
+        text_encoder = text_encoder.to(
+            dtype=PRECISION_TO_TYPE[text_encoder_precision])
 
     text_encoder.requires_grad_(False)
 
@@ -55,20 +53,22 @@ def load_text_encoder(
     return text_encoder, text_encoder_path
 
 
-def load_tokenizer(
-    tokenizer_type, tokenizer_path=None, padding_side="right", logger=None
-):
+def load_tokenizer(tokenizer_type,
+                   tokenizer_path=None,
+                   padding_side="right",
+                   logger=None):
     if tokenizer_path is None:
         tokenizer_path = TOKENIZER_PATH[tokenizer_type]
     if logger is not None:
-        logger.info(f"Loading tokenizer ({tokenizer_type}) from: {tokenizer_path}")
+        logger.info(
+            f"Loading tokenizer ({tokenizer_type}) from: {tokenizer_path}")
 
     if tokenizer_type == "clipL":
-        tokenizer = CLIPTokenizer.from_pretrained(tokenizer_path, max_length=77)
+        tokenizer = CLIPTokenizer.from_pretrained(tokenizer_path,
+                                                  max_length=77)
     elif tokenizer_type == "llm":
-        tokenizer = AutoTokenizer.from_pretrained(
-            tokenizer_path, padding_side=padding_side
-        )
+        tokenizer = AutoTokenizer.from_pretrained(tokenizer_path,
+                                                  padding_side=padding_side)
     else:
         raise ValueError(f"Unsupported tokenizer type: {tokenizer_type}")
 
@@ -100,6 +100,7 @@ class TextEncoderModelOutput(ModelOutput):
 
 
 class TextEncoder(nn.Module):
+
     def __init__(
         self,
         text_encoder_type: str,
@@ -124,20 +125,16 @@ class TextEncoder(nn.Module):
         self.max_length = max_length
         self.precision = text_encoder_precision
         self.model_path = text_encoder_path
-        self.tokenizer_type = (
-            tokenizer_type if tokenizer_type is not None else text_encoder_type
-        )
-        self.tokenizer_path = (
-            tokenizer_path if tokenizer_path is not None else text_encoder_path
-        )
+        self.tokenizer_type = (tokenizer_type if tokenizer_type is not None
+                               else text_encoder_type)
+        self.tokenizer_path = (tokenizer_path if tokenizer_path is not None
+                               else text_encoder_path)
         self.use_attention_mask = use_attention_mask
         if prompt_template_video is not None:
-            assert (
-                use_attention_mask is True
-            ), "Attention mask is True required when training videos."
-        self.input_max_length = (
-            input_max_length if input_max_length is not None else max_length
-        )
+            assert (use_attention_mask is True
+                    ), "Attention mask is True required when training videos."
+        self.input_max_length = (input_max_length if input_max_length
+                                 is not None else max_length)
         self.prompt_template = prompt_template
         self.prompt_template_video = prompt_template_video
         self.hidden_state_skip_layer = hidden_state_skip_layer
@@ -153,8 +150,7 @@ class TextEncoder(nn.Module):
             ), f"`prompt_template` must be a dictionary with a key 'template', got {self.prompt_template}"
             assert "{}" in str(self.prompt_template["template"]), (
                 "`prompt_template['template']` must contain a placeholder `{}` for the input text, "
-                f"got {self.prompt_template['template']}"
-            )
+                f"got {self.prompt_template['template']}")
 
         self.use_video_template = self.prompt_template_video is not None
         if self.use_video_template:
@@ -165,8 +161,7 @@ class TextEncoder(nn.Module):
                 ), f"`prompt_template_video` must be a dictionary with a key 'template', got {self.prompt_template_video}"
             assert "{}" in str(self.prompt_template_video["template"]), (
                 "`prompt_template_video['template']` must contain a placeholder `{}` for the input text, "
-                f"got {self.prompt_template_video['template']}"
-            )
+                f"got {self.prompt_template_video['template']}")
 
         if "t5" in text_encoder_type:
             self.output_key = output_key or "last_hidden_state"
@@ -175,7 +170,8 @@ class TextEncoder(nn.Module):
         elif "llm" in text_encoder_type or "glm" in text_encoder_type:
             self.output_key = output_key or "last_hidden_state"
         else:
-            raise ValueError(f"Unsupported text encoder type: {text_encoder_type}")
+            raise ValueError(
+                f"Unsupported text encoder type: {text_encoder_type}")
 
         self.model, self.model_path = load_text_encoder(
             text_encoder_type=self.text_encoder_type,
@@ -205,7 +201,7 @@ class TextEncoder(nn.Module):
         Args:
             text (str): Input text.
             template (str or list): Template string or list of chat conversation.
-            prevent_empty_text (bool): If Ture, we will prevent the user text from being empty
+            prevent_empty_text (bool): If True, we will prevent the user text from being empty
                 by adding a space. Defaults to True.
         """
         if isinstance(template, str):
@@ -266,7 +262,8 @@ class TextEncoder(nn.Module):
                 **kwargs,
             )
         else:
-            raise ValueError(f"Unsupported tokenize_input_type: {tokenize_input_type}")
+            raise ValueError(
+                f"Unsupported tokenize_input_type: {tokenize_input_type}")
 
     def encode(
         self,
@@ -294,14 +291,13 @@ class TextEncoder(nn.Module):
             return_texts (bool): Whether to return the decoded texts. Defaults to False.
         """
         device = self.model.device if device is None else device
-        use_attention_mask = use_default(use_attention_mask, self.use_attention_mask)
-        hidden_state_skip_layer = use_default(
-            hidden_state_skip_layer, self.hidden_state_skip_layer
-        )
+        use_attention_mask = use_default(use_attention_mask,
+                                         self.use_attention_mask)
+        hidden_state_skip_layer = use_default(hidden_state_skip_layer,
+                                              self.hidden_state_skip_layer)
         do_sample = use_default(do_sample, not self.reproduce)
-        attention_mask = (
-            batch_encoding["attention_mask"].to(device) if use_attention_mask else None
-        )
+        attention_mask = (batch_encoding["attention_mask"].to(device)
+                          if use_attention_mask else None)
         outputs = self.model(
             input_ids=batch_encoding["input_ids"].to(device),
             attention_mask=attention_mask,
@@ -309,11 +305,13 @@ class TextEncoder(nn.Module):
             or hidden_state_skip_layer is not None,
         )
         if hidden_state_skip_layer is not None:
-            last_hidden_state = outputs.hidden_states[-(hidden_state_skip_layer + 1)]
+            last_hidden_state = outputs.hidden_states[-(
+                hidden_state_skip_layer + 1)]
             # Real last hidden state already has layer norm applied. So here we only apply it
             # for intermediate layers.
             if hidden_state_skip_layer > 0 and self.apply_final_norm:
-                last_hidden_state = self.model.final_layer_norm(last_hidden_state)
+                last_hidden_state = self.model.final_layer_norm(
+                    last_hidden_state)
         else:
             last_hidden_state = outputs[self.output_key]
 
@@ -327,14 +325,12 @@ class TextEncoder(nn.Module):
                 raise ValueError(f"Unsupported data type: {data_type}")
             if crop_start > 0:
                 last_hidden_state = last_hidden_state[:, crop_start:]
-                attention_mask = (
-                    attention_mask[:, crop_start:] if use_attention_mask else None
-                )
+                attention_mask = (attention_mask[:, crop_start:]
+                                  if use_attention_mask else None)
 
         if output_hidden_states:
-            return TextEncoderModelOutput(
-                last_hidden_state, attention_mask, outputs.hidden_states
-            )
+            return TextEncoderModelOutput(last_hidden_state, attention_mask,
+                                          outputs.hidden_states)
         return TextEncoderModelOutput(last_hidden_state, attention_mask)
 
     def forward(

fastvideo/models/hunyuan/utils/data_utils.py

@@ -1,9 +1,8 @@
-import numpy as np
 import math
 
 
 def align_to(value, alignment):
-    """align hight, width according to alignment
+    """align height, width according to alignment
 
     Args:
         value (int): height or width

fastvideo/models/hunyuan/utils/file_utils.py

@@ -1,11 +1,11 @@
 import os
 from pathlib import Path
-from einops import rearrange
 
+import imageio
+import numpy as np
 import torch
 import torchvision
-import numpy as np
-import imageio
+from einops import rearrange
 
 CODE_SUFFIXES = {
     ".py",  # Python codes
@@ -45,7 +45,11 @@ def safe_file(path):
     return path
 
 
-def save_videos_grid(videos: torch.Tensor, path: str, rescale=False, n_rows=1, fps=24):
+def save_videos_grid(videos: torch.Tensor,
+                     path: str,
+                     rescale=False,
+                     n_rows=1,
+                     fps=24):
     """save videos by video tensor
        copy from https://github.com/guoyww/AnimateDiff/blob/e92bd5671ba62c0d774a32951453e328018b7c5b/animatediff/utils/util.py#L61
 

fastvideo/models/hunyuan/utils/helpers.py

@@ -1,9 +1,9 @@
 import collections.abc
-
 from itertools import repeat
 
 
 def _ntuple(n):
+
     def parse(x):
         if isinstance(x, collections.abc.Iterable) and not isinstance(x, str):
             x = tuple(x)
@@ -25,7 +25,7 @@ def as_tuple(x):
     if isinstance(x, collections.abc.Iterable) and not isinstance(x, str):
         return tuple(x)
     if x is None or isinstance(x, (int, float, str)):
-        return (x,)
+        return (x, )
     else:
         raise ValueError(f"Unknown type {type(x)}")
 

fastvideo/models/hunyuan/utils/preprocess_text_encoder_tokenizer_utils.py

@@ -1,9 +1,7 @@
 import argparse
+
 import torch
-from transformers import (
-    AutoProcessor,
-    LlavaForConditionalGeneration,
-)
+from transformers import AutoProcessor, LlavaForConditionalGeneration
 
 
 def preprocess_text_encoder_tokenizer(args):

fastvideo/models/hunyuan/vae/__init__.py

@@ -2,8 +2,8 @@ from pathlib import Path
 
 import torch
 
+from ..constants import PRECISION_TO_TYPE, VAE_PATH
 from .autoencoder_kl_causal_3d import AutoencoderKLCausal3D
-from ..constants import VAE_PATH, PRECISION_TO_TYPE
 
 
 def load_vae(
@@ -14,7 +14,7 @@ def load_vae(
     logger=None,
     device=None,
 ):
-    """the fucntion to load the 3D VAE model
+    """the function to load the 3D VAE model
 
     Args:
         vae_type (str): the type of the 3D VAE model. Defaults to "884-16c-hy".
@@ -31,7 +31,8 @@ def load_vae(
         logger.info(f"Loading 3D VAE model ({vae_type}) from: {vae_path}")
     config = AutoencoderKLCausal3D.load_config(vae_path)
     if sample_size:
-        vae = AutoencoderKLCausal3D.from_config(config, sample_size=sample_size)
+        vae = AutoencoderKLCausal3D.from_config(config,
+                                                sample_size=sample_size)
     else:
         vae = AutoencoderKLCausal3D.from_config(config)
 
@@ -43,7 +44,8 @@ def load_vae(
         ckpt = ckpt["state_dict"]
     if any(k.startswith("vae.") for k in ckpt.keys()):
         ckpt = {
-            k.replace("vae.", ""): v for k, v in ckpt.items() if k.startswith("vae.")
+            k.replace("vae.", ""): v
+            for k, v in ckpt.items() if k.startswith("vae.")
         }
     vae.load_state_dict(ckpt)
 

fastvideo/models/hunyuan/vae/autoencoder_kl_causal_3d.py

@@ -16,40 +16,34 @@
 # Modified from diffusers==0.29.2
 #
 # ==============================================================================
-from typing import Dict, Optional, Tuple, Union
 from dataclasses import dataclass
+from math import prod
+from typing import Dict, Optional, Tuple, Union
 
 import torch
+import torch.distributed as dist
 import torch.nn as nn
-
 from diffusers.configuration_utils import ConfigMixin, register_to_config
 
+from fastvideo.utils.parallel_states import nccl_info
+
 try:
     # This diffusers is modified and packed in the mirror.
     from diffusers.loaders import FromOriginalVAEMixin
 except ImportError:
     # Use this to be compatible with the original diffusers.
     from diffusers.loaders.single_file_model import (
-        FromOriginalModelMixin as FromOriginalVAEMixin,
-    )
-from diffusers.utils.accelerate_utils import apply_forward_hook
+        FromOriginalModelMixin as FromOriginalVAEMixin, )
+
 from diffusers.models.attention_processor import (
-    ADDED_KV_ATTENTION_PROCESSORS,
-    CROSS_ATTENTION_PROCESSORS,
-    Attention,
-    AttentionProcessor,
-    AttnAddedKVProcessor,
-    AttnProcessor,
-)
+    ADDED_KV_ATTENTION_PROCESSORS, CROSS_ATTENTION_PROCESSORS, Attention,
+    AttentionProcessor, AttnAddedKVProcessor, AttnProcessor)
 from diffusers.models.modeling_outputs import AutoencoderKLOutput
 from diffusers.models.modeling_utils import ModelMixin
-from .vae import (
-    DecoderCausal3D,
-    BaseOutput,
-    DecoderOutput,
-    DiagonalGaussianDistribution,
-    EncoderCausal3D,
-)
+from diffusers.utils.accelerate_utils import apply_forward_hook
+
+from .vae import (BaseOutput, DecoderCausal3D, DecoderOutput,
+                  DiagonalGaussianDistribution, EncoderCausal3D)
 
 
 @dataclass
@@ -73,9 +67,9 @@ class AutoencoderKLCausal3D(ModelMixin, ConfigMixin, FromOriginalVAEMixin):
         self,
         in_channels: int = 3,
         out_channels: int = 3,
-        down_block_types: Tuple[str] = ("DownEncoderBlockCausal3D",),
-        up_block_types: Tuple[str] = ("UpDecoderBlockCausal3D",),
-        block_out_channels: Tuple[int] = (64,),
+        down_block_types: Tuple[str] = ("DownEncoderBlockCausal3D", ),
+        up_block_types: Tuple[str] = ("UpDecoderBlockCausal3D", ),
+        block_out_channels: Tuple[int] = (64, ),
         layers_per_block: int = 1,
         act_fn: str = "silu",
         latent_channels: int = 4,
@@ -119,30 +113,28 @@ class AutoencoderKLCausal3D(ModelMixin, ConfigMixin, FromOriginalVAEMixin):
             mid_block_add_attention=mid_block_add_attention,
         )
 
-        self.quant_conv = nn.Conv3d(
-            2 * latent_channels, 2 * latent_channels, kernel_size=1
-        )
-        self.post_quant_conv = nn.Conv3d(
-            latent_channels, latent_channels, kernel_size=1
-        )
+        self.quant_conv = nn.Conv3d(2 * latent_channels,
+                                    2 * latent_channels,
+                                    kernel_size=1)
+        self.post_quant_conv = nn.Conv3d(latent_channels,
+                                         latent_channels,
+                                         kernel_size=1)
 
         self.use_slicing = False
         self.use_spatial_tiling = False
         self.use_temporal_tiling = False
+        self.use_parallel = False
 
         # only relevant if vae tiling is enabled
         self.tile_sample_min_tsize = sample_tsize
         self.tile_latent_min_tsize = sample_tsize // time_compression_ratio
 
         self.tile_sample_min_size = self.config.sample_size
-        sample_size = (
-            self.config.sample_size[0]
-            if isinstance(self.config.sample_size, (list, tuple))
-            else self.config.sample_size
-        )
+        sample_size = (self.config.sample_size[0] if isinstance(
+            self.config.sample_size,
+            (list, tuple)) else self.config.sample_size)
         self.tile_latent_min_size = int(
-            sample_size / (2 ** (len(self.config.block_out_channels) - 1))
-        )
+            sample_size / (2**(len(self.config.block_out_channels) - 1)))
         self.tile_overlap_factor = 0.25
 
     def _set_gradient_checkpointing(self, module, value=False):
@@ -178,6 +170,12 @@ class AutoencoderKLCausal3D(ModelMixin, ConfigMixin, FromOriginalVAEMixin):
         self.disable_spatial_tiling()
         self.disable_temporal_tiling()
 
+    def enable_parallel(self):
+        r"""
+        Enable sequence parallelism for the model. This will allow the vae to decode (with tiling) in parallel.
+        """
+        self.use_parallel = True
+
     def enable_slicing(self):
         r"""
         Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
@@ -210,11 +208,11 @@ class AutoencoderKLCausal3D(ModelMixin, ConfigMixin, FromOriginalVAEMixin):
         ):
             if hasattr(module, "get_processor"):
                 processors[f"{name}.processor"] = module.get_processor(
-                    return_deprecated_lora=True
-                )
+                    return_deprecated_lora=True)
 
             for sub_name, child in module.named_children():
-                fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
+                fn_recursive_add_processors(f"{name}.{sub_name}", child,
+                                            processors)
 
             return processors
 
@@ -249,17 +247,18 @@ class AutoencoderKLCausal3D(ModelMixin, ConfigMixin, FromOriginalVAEMixin):
                 f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
             )
 
-        def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
+        def fn_recursive_attn_processor(name: str, module: torch.nn.Module,
+                                        processor):
             if hasattr(module, "set_processor"):
                 if not isinstance(processor, dict):
                     module.set_processor(processor, _remove_lora=_remove_lora)
                 else:
-                    module.set_processor(
-                        processor.pop(f"{name}.processor"), _remove_lora=_remove_lora
-                    )
+                    module.set_processor(processor.pop(f"{name}.processor"),
+                                         _remove_lora=_remove_lora)
 
             for sub_name, child in module.named_children():
-                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
+                fn_recursive_attn_processor(f"{name}.{sub_name}", child,
+                                            processor)
 
         for name, module in self.named_children():
             fn_recursive_attn_processor(name, module, processor)
@@ -269,15 +268,11 @@ class AutoencoderKLCausal3D(ModelMixin, ConfigMixin, FromOriginalVAEMixin):
         """
         Disables custom attention processors and sets the default attention implementation.
         """
-        if all(
-            proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS
-            for proc in self.attn_processors.values()
-        ):
+        if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS
+               for proc in self.attn_processors.values()):
             processor = AttnAddedKVProcessor()
-        elif all(
-            proc.__class__ in CROSS_ATTENTION_PROCESSORS
-            for proc in self.attn_processors.values()
-        ):
+        elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS
+                 for proc in self.attn_processors.values()):
             processor = AttnProcessor()
         else:
             raise ValueError(
@@ -288,7 +283,9 @@ class AutoencoderKLCausal3D(ModelMixin, ConfigMixin, FromOriginalVAEMixin):
 
     @apply_forward_hook
     def encode(
-        self, x: torch.FloatTensor, return_dict: bool = True
+        self,
+        x: torch.FloatTensor,
+        return_dict: bool = True
     ) -> Union[AutoencoderKLOutput, Tuple[DiagonalGaussianDistribution]]:
         """
         Encode a batch of images/videos into latents.
@@ -308,9 +305,8 @@ class AutoencoderKLCausal3D(ModelMixin, ConfigMixin, FromOriginalVAEMixin):
             return self.temporal_tiled_encode(x, return_dict=return_dict)
 
         if self.use_spatial_tiling and (
-            x.shape[-1] > self.tile_sample_min_size
-            or x.shape[-2] > self.tile_sample_min_size
-        ):
+                x.shape[-1] > self.tile_sample_min_size
+                or x.shape[-2] > self.tile_sample_min_size):
             return self.spatial_tiled_encode(x, return_dict=return_dict)
 
         if self.use_slicing and x.shape[0] > 1:
@@ -323,36 +319,41 @@ class AutoencoderKLCausal3D(ModelMixin, ConfigMixin, FromOriginalVAEMixin):
         posterior = DiagonalGaussianDistribution(moments)
 
         if not return_dict:
-            return (posterior,)
+            return (posterior, )
 
         return AutoencoderKLOutput(latent_dist=posterior)
 
     def _decode(
-        self, z: torch.FloatTensor, return_dict: bool = True
+            self,
+            z: torch.FloatTensor,
+            return_dict: bool = True
     ) -> Union[DecoderOutput, torch.FloatTensor]:
         assert len(z.shape) == 5, "The input tensor should have 5 dimensions."
 
+        if self.use_parallel:
+            return self.parallel_tiled_decode(z, return_dict=return_dict)
+
         if self.use_temporal_tiling and z.shape[2] > self.tile_latent_min_tsize:
             return self.temporal_tiled_decode(z, return_dict=return_dict)
 
         if self.use_spatial_tiling and (
-            z.shape[-1] > self.tile_latent_min_size
-            or z.shape[-2] > self.tile_latent_min_size
-        ):
+                z.shape[-1] > self.tile_latent_min_size
+                or z.shape[-2] > self.tile_latent_min_size):
             return self.spatial_tiled_decode(z, return_dict=return_dict)
 
         z = self.post_quant_conv(z)
         dec = self.decoder(z)
 
         if not return_dict:
-            return (dec,)
+            return (dec, )
 
         return DecoderOutput(sample=dec)
 
     @apply_forward_hook
-    def decode(
-        self, z: torch.FloatTensor, return_dict: bool = True, generator=None
-    ) -> Union[DecoderOutput, torch.FloatTensor]:
+    def decode(self,
+               z: torch.FloatTensor,
+               return_dict: bool = True,
+               generator=None) -> Union[DecoderOutput, torch.FloatTensor]:
         """
         Decode a batch of images/videos.
 
@@ -368,44 +369,40 @@ class AutoencoderKLCausal3D(ModelMixin, ConfigMixin, FromOriginalVAEMixin):
 
         """
         if self.use_slicing and z.shape[0] > 1:
-            decoded_slices = [self._decode(z_slice).sample for z_slice in z.split(1)]
+            decoded_slices = [
+                self._decode(z_slice).sample for z_slice in z.split(1)
+            ]
             decoded = torch.cat(decoded_slices)
         else:
             decoded = self._decode(z).sample
 
         if not return_dict:
-            return (decoded,)
+            return (decoded, )
 
         return DecoderOutput(sample=decoded)
 
-    def blend_v(
-        self, a: torch.Tensor, b: torch.Tensor, blend_extent: int
-    ) -> torch.Tensor:
+    def blend_v(self, a: torch.Tensor, b: torch.Tensor,
+                blend_extent: int) -> torch.Tensor:
         blend_extent = min(a.shape[-2], b.shape[-2], blend_extent)
         for y in range(blend_extent):
             b[:, :, :, y, :] = a[:, :, :, -blend_extent + y, :] * (
-                1 - y / blend_extent
-            ) + b[:, :, :, y, :] * (y / blend_extent)
+                1 - y / blend_extent) + b[:, :, :, y, :] * (y / blend_extent)
         return b
 
-    def blend_h(
-        self, a: torch.Tensor, b: torch.Tensor, blend_extent: int
-    ) -> torch.Tensor:
+    def blend_h(self, a: torch.Tensor, b: torch.Tensor,
+                blend_extent: int) -> torch.Tensor:
         blend_extent = min(a.shape[-1], b.shape[-1], blend_extent)
         for x in range(blend_extent):
             b[:, :, :, :, x] = a[:, :, :, :, -blend_extent + x] * (
-                1 - x / blend_extent
-            ) + b[:, :, :, :, x] * (x / blend_extent)
+                1 - x / blend_extent) + b[:, :, :, :, x] * (x / blend_extent)
         return b
 
-    def blend_t(
-        self, a: torch.Tensor, b: torch.Tensor, blend_extent: int
-    ) -> torch.Tensor:
+    def blend_t(self, a: torch.Tensor, b: torch.Tensor,
+                blend_extent: int) -> torch.Tensor:
         blend_extent = min(a.shape[-3], b.shape[-3], blend_extent)
         for x in range(blend_extent):
             b[:, :, x, :, :] = a[:, :, -blend_extent + x, :, :] * (
-                1 - x / blend_extent
-            ) + b[:, :, x, :, :] * (x / blend_extent)
+                1 - x / blend_extent) + b[:, :, x, :, :] * (x / blend_extent)
         return b
 
     def spatial_tiled_encode(
@@ -432,8 +429,10 @@ class AutoencoderKLCausal3D(ModelMixin, ConfigMixin, FromOriginalVAEMixin):
                 If return_dict is True, a [`~models.autoencoder_kl.AutoencoderKLOutput`] is returned, otherwise a plain
                 `tuple` is returned.
         """
-        overlap_size = int(self.tile_sample_min_size * (1 - self.tile_overlap_factor))
-        blend_extent = int(self.tile_latent_min_size * self.tile_overlap_factor)
+        overlap_size = int(self.tile_sample_min_size *
+                           (1 - self.tile_overlap_factor))
+        blend_extent = int(self.tile_latent_min_size *
+                           self.tile_overlap_factor)
         row_limit = self.tile_latent_min_size - blend_extent
 
         # Split video into tiles and encode them separately.
@@ -441,13 +440,8 @@ class AutoencoderKLCausal3D(ModelMixin, ConfigMixin, FromOriginalVAEMixin):
         for i in range(0, x.shape[-2], overlap_size):
             row = []
             for j in range(0, x.shape[-1], overlap_size):
-                tile = x[
-                    :,
-                    :,
-                    :,
-                    i : i + self.tile_sample_min_size,
-                    j : j + self.tile_sample_min_size,
-                ]
+                tile = x[:, :, :, i:i + self.tile_sample_min_size,
+                         j:j + self.tile_sample_min_size, ]
                 tile = self.encoder(tile)
                 tile = self.quant_conv(tile)
                 row.append(tile)
@@ -471,13 +465,14 @@ class AutoencoderKLCausal3D(ModelMixin, ConfigMixin, FromOriginalVAEMixin):
 
         posterior = DiagonalGaussianDistribution(moments)
         if not return_dict:
-            return (posterior,)
+            return (posterior, )
 
         return AutoencoderKLOutput(latent_dist=posterior)
 
-    def spatial_tiled_decode(
-        self, z: torch.FloatTensor, return_dict: bool = True
-    ) -> Union[DecoderOutput, torch.FloatTensor]:
+    def spatial_tiled_decode(self,
+                             z: torch.FloatTensor,
+                             return_dict: bool = True
+                             ) -> Union[DecoderOutput, torch.FloatTensor]:
         r"""
         Decode a batch of images/videos using a tiled decoder.
 
@@ -491,8 +486,10 @@ class AutoencoderKLCausal3D(ModelMixin, ConfigMixin, FromOriginalVAEMixin):
                 If return_dict is True, a [`~models.vae.DecoderOutput`] is returned, otherwise a plain `tuple` is
                 returned.
         """
-        overlap_size = int(self.tile_latent_min_size * (1 - self.tile_overlap_factor))
-        blend_extent = int(self.tile_sample_min_size * self.tile_overlap_factor)
+        overlap_size = int(self.tile_latent_min_size *
+                           (1 - self.tile_overlap_factor))
+        blend_extent = int(self.tile_sample_min_size *
+                           self.tile_overlap_factor)
         row_limit = self.tile_sample_min_size - blend_extent
 
         # Split z into overlapping tiles and decode them separately.
@@ -501,13 +498,8 @@ class AutoencoderKLCausal3D(ModelMixin, ConfigMixin, FromOriginalVAEMixin):
         for i in range(0, z.shape[-2], overlap_size):
             row = []
             for j in range(0, z.shape[-1], overlap_size):
-                tile = z[
-                    :,
-                    :,
-                    :,
-                    i : i + self.tile_latent_min_size,
-                    j : j + self.tile_latent_min_size,
-                ]
+                tile = z[:, :, :, i:i + self.tile_latent_min_size,
+                         j:j + self.tile_latent_min_size, ]
                 tile = self.post_quant_conv(tile)
                 decoded = self.decoder(tile)
                 row.append(decoded)
@@ -527,27 +519,28 @@ class AutoencoderKLCausal3D(ModelMixin, ConfigMixin, FromOriginalVAEMixin):
 
         dec = torch.cat(result_rows, dim=-2)
         if not return_dict:
-            return (dec,)
+            return (dec, )
 
         return DecoderOutput(sample=dec)
 
-    def temporal_tiled_encode(
-        self, x: torch.FloatTensor, return_dict: bool = True
-    ) -> AutoencoderKLOutput:
+    def temporal_tiled_encode(self,
+                              x: torch.FloatTensor,
+                              return_dict: bool = True) -> AutoencoderKLOutput:
 
         B, C, T, H, W = x.shape
-        overlap_size = int(self.tile_sample_min_tsize * (1 - self.tile_overlap_factor))
-        blend_extent = int(self.tile_latent_min_tsize * self.tile_overlap_factor)
+        overlap_size = int(self.tile_sample_min_tsize *
+                           (1 - self.tile_overlap_factor))
+        blend_extent = int(self.tile_latent_min_tsize *
+                           self.tile_overlap_factor)
         t_limit = self.tile_latent_min_tsize - blend_extent
 
         # Split the video into tiles and encode them separately.
         row = []
         for i in range(0, T, overlap_size):
-            tile = x[:, :, i : i + self.tile_sample_min_tsize + 1, :, :]
+            tile = x[:, :, i:i + self.tile_sample_min_tsize + 1, :, :]
             if self.use_spatial_tiling and (
-                tile.shape[-1] > self.tile_sample_min_size
-                or tile.shape[-2] > self.tile_sample_min_size
-            ):
+                    tile.shape[-1] > self.tile_sample_min_size
+                    or tile.shape[-2] > self.tile_sample_min_size):
                 tile = self.spatial_tiled_encode(tile, return_moments=True)
             else:
                 tile = self.encoder(tile)
@@ -561,34 +554,37 @@ class AutoencoderKLCausal3D(ModelMixin, ConfigMixin, FromOriginalVAEMixin):
                 tile = self.blend_t(row[i - 1], tile, blend_extent)
                 result_row.append(tile[:, :, :t_limit, :, :])
             else:
-                result_row.append(tile[:, :, : t_limit + 1, :, :])
+                result_row.append(tile[:, :, :t_limit + 1, :, :])
 
         moments = torch.cat(result_row, dim=2)
         posterior = DiagonalGaussianDistribution(moments)
 
         if not return_dict:
-            return (posterior,)
+            return (posterior, )
 
         return AutoencoderKLOutput(latent_dist=posterior)
 
-    def temporal_tiled_decode(
-        self, z: torch.FloatTensor, return_dict: bool = True
-    ) -> Union[DecoderOutput, torch.FloatTensor]:
+    def temporal_tiled_decode(self,
+                              z: torch.FloatTensor,
+                              return_dict: bool = True
+                              ) -> Union[DecoderOutput, torch.FloatTensor]:
         # Split z into overlapping tiles and decode them separately.
 
         B, C, T, H, W = z.shape
-        overlap_size = int(self.tile_latent_min_tsize * (1 - self.tile_overlap_factor))
-        blend_extent = int(self.tile_sample_min_tsize * self.tile_overlap_factor)
+        overlap_size = int(self.tile_latent_min_tsize *
+                           (1 - self.tile_overlap_factor))
+        blend_extent = int(self.tile_sample_min_tsize *
+                           self.tile_overlap_factor)
         t_limit = self.tile_sample_min_tsize - blend_extent
 
         row = []
         for i in range(0, T, overlap_size):
-            tile = z[:, :, i : i + self.tile_latent_min_tsize + 1, :, :]
+            tile = z[:, :, i:i + self.tile_latent_min_tsize + 1, :, :]
             if self.use_spatial_tiling and (
-                tile.shape[-1] > self.tile_latent_min_size
-                or tile.shape[-2] > self.tile_latent_min_size
-            ):
-                decoded = self.spatial_tiled_decode(tile, return_dict=True).sample
+                    tile.shape[-1] > self.tile_latent_min_size
+                    or tile.shape[-2] > self.tile_latent_min_size):
+                decoded = self.spatial_tiled_decode(tile,
+                                                    return_dict=True).sample
             else:
                 tile = self.post_quant_conv(tile)
                 decoded = self.decoder(tile)
@@ -601,14 +597,165 @@ class AutoencoderKLCausal3D(ModelMixin, ConfigMixin, FromOriginalVAEMixin):
                 tile = self.blend_t(row[i - 1], tile, blend_extent)
                 result_row.append(tile[:, :, :t_limit, :, :])
             else:
-                result_row.append(tile[:, :, : t_limit + 1, :, :])
+                result_row.append(tile[:, :, :t_limit + 1, :, :])
 
         dec = torch.cat(result_row, dim=2)
         if not return_dict:
-            return (dec,)
+            return (dec, )
 
         return DecoderOutput(sample=dec)
 
+    def _parallel_data_generator(self, gathered_results,
+                                 gathered_dim_metadata):
+        global_idx = 0
+        for i, per_rank_metadata in enumerate(gathered_dim_metadata):
+            _start_shape = 0
+            for shape in per_rank_metadata:
+                mul_shape = prod(shape)
+                yield (gathered_results[i, _start_shape:_start_shape +
+                                        mul_shape].reshape(shape), global_idx)
+                _start_shape += mul_shape
+                global_idx += 1
+
+    def parallel_tiled_decode(self,
+                              z: torch.FloatTensor,
+                              return_dict: bool = True
+                              ) -> Union[DecoderOutput, torch.FloatTensor]:
+        """
+        Parallel version of tiled_decode that distributes both temporal and spatial computation across GPUs
+        """
+        world_size, rank = nccl_info.sp_size, nccl_info.rank_within_group
+        B, C, T, H, W = z.shape
+
+        # Calculate parameters
+        t_overlap_size = int(self.tile_latent_min_tsize *
+                             (1 - self.tile_overlap_factor))
+        t_blend_extent = int(self.tile_sample_min_tsize *
+                             self.tile_overlap_factor)
+        t_limit = self.tile_sample_min_tsize - t_blend_extent
+
+        s_overlap_size = int(self.tile_latent_min_size *
+                             (1 - self.tile_overlap_factor))
+        s_blend_extent = int(self.tile_sample_min_size *
+                             self.tile_overlap_factor)
+        s_row_limit = self.tile_sample_min_size - s_blend_extent
+
+        # Calculate tile dimensions
+        num_t_tiles = (T + t_overlap_size - 1) // t_overlap_size
+        num_h_tiles = (H + s_overlap_size - 1) // s_overlap_size
+        num_w_tiles = (W + s_overlap_size - 1) // s_overlap_size
+        total_spatial_tiles = num_h_tiles * num_w_tiles
+        total_tiles = num_t_tiles * total_spatial_tiles
+
+        # Calculate tiles per rank and padding
+        tiles_per_rank = (total_tiles + world_size - 1) // world_size
+        start_tile_idx = rank * tiles_per_rank
+        end_tile_idx = min((rank + 1) * tiles_per_rank, total_tiles)
+
+        local_results = []
+        local_dim_metadata = []
+        # Process assigned tiles
+        for local_idx, global_idx in enumerate(
+                range(start_tile_idx, end_tile_idx)):
+            # Convert flat index to 3D indices
+            t_idx = global_idx // total_spatial_tiles
+            spatial_idx = global_idx % total_spatial_tiles
+            h_idx = spatial_idx // num_w_tiles
+            w_idx = spatial_idx % num_w_tiles
+
+            # Calculate positions
+            t_start = t_idx * t_overlap_size
+            h_start = h_idx * s_overlap_size
+            w_start = w_idx * s_overlap_size
+
+            # Extract and process tile
+            tile = z[:, :, t_start:t_start + self.tile_latent_min_tsize + 1,
+                     h_start:h_start + self.tile_latent_min_size,
+                     w_start:w_start + self.tile_latent_min_size]
+
+            # Process tile
+            tile = self.post_quant_conv(tile)
+            decoded = self.decoder(tile)
+
+            if t_start > 0:
+                decoded = decoded[:, :, 1:, :, :]
+
+            # Store metadata
+            shape = decoded.shape
+            # Store decoded data (flattened)
+            decoded_flat = decoded.reshape(-1)
+            local_results.append(decoded_flat)
+            local_dim_metadata.append(shape)
+
+        results = torch.cat(local_results, dim=0).contiguous()
+        del local_results
+        torch.cuda.empty_cache()
+        # first gather size to pad the results
+        local_size = torch.tensor([results.size(0)],
+                                  device=results.device,
+                                  dtype=torch.int64)
+        all_sizes = [
+            torch.zeros(1, device=results.device, dtype=torch.int64)
+            for _ in range(world_size)
+        ]
+        dist.all_gather(all_sizes, local_size)
+        max_size = max(size.item() for size in all_sizes)
+        padded_results = torch.zeros(max_size, device=results.device)
+        padded_results[:results.size(0)] = results
+        del results
+        torch.cuda.empty_cache()
+        # Gather all results
+        gathered_dim_metadata = [None] * world_size
+        gathered_results = torch.zeros_like(padded_results).repeat(
+            world_size, *[1] * len(padded_results.shape)
+        ).contiguous(
+        )  # use contiguous to make sure it won't copy data in the following operations
+        dist.all_gather_into_tensor(gathered_results, padded_results)
+        dist.all_gather_object(gathered_dim_metadata, local_dim_metadata)
+        # Process gathered results
+        data = [[[[] for _ in range(num_w_tiles)] for _ in range(num_h_tiles)]
+                for _ in range(num_t_tiles)]
+        for current_data, global_idx in self._parallel_data_generator(
+                gathered_results, gathered_dim_metadata):
+            t_idx = global_idx // total_spatial_tiles
+            spatial_idx = global_idx % total_spatial_tiles
+            h_idx = spatial_idx // num_w_tiles
+            w_idx = spatial_idx % num_w_tiles
+            data[t_idx][h_idx][w_idx] = current_data
+        # Merge results
+        result_slices = []
+        last_slice_data = None
+        for i, tem_data in enumerate(data):
+            slice_data = self._merge_spatial_tiles(tem_data, s_blend_extent,
+                                                   s_row_limit)
+            if i > 0:
+                slice_data = self.blend_t(last_slice_data, slice_data,
+                                          t_blend_extent)
+                result_slices.append(slice_data[:, :, :t_limit, :, :])
+            else:
+                result_slices.append(slice_data[:, :, :t_limit + 1, :, :])
+            last_slice_data = slice_data
+        dec = torch.cat(result_slices, dim=2)
+
+        if not return_dict:
+            return (dec, )
+        return DecoderOutput(sample=dec)
+
+    def _merge_spatial_tiles(self, spatial_rows, blend_extent, row_limit):
+        """Helper function to merge spatial tiles with blending"""
+        result_rows = []
+        for i, row in enumerate(spatial_rows):
+            result_row = []
+            for j, tile in enumerate(row):
+                if i > 0:
+                    tile = self.blend_v(spatial_rows[i - 1][j], tile,
+                                        blend_extent)
+                if j > 0:
+                    tile = self.blend_h(row[j - 1], tile, blend_extent)
+                result_row.append(tile[:, :, :, :row_limit, :row_limit])
+            result_rows.append(torch.cat(result_row, dim=-1))
+        return torch.cat(result_rows, dim=-2)
+
     def forward(
         self,
         sample: torch.FloatTensor,
@@ -637,7 +784,7 @@ class AutoencoderKLCausal3D(ModelMixin, ConfigMixin, FromOriginalVAEMixin):
             if return_posterior:
                 return (dec, posterior)
             else:
-                return (dec,)
+                return (dec, )
         if return_posterior:
             return DecoderOutput2(sample=dec, posterior=posterior)
         else:

fastvideo/models/hunyuan/vae/unet_causal_3d_blocks.py

@@ -21,27 +21,29 @@ from typing import Optional, Tuple, Union
 
 import torch
 import torch.nn.functional as F
-from torch import nn
-from einops import rearrange
-
-from diffusers.utils import logging
 from diffusers.models.activations import get_activation
-from diffusers.models.attention_processor import SpatialNorm
-from diffusers.models.attention_processor import Attention
-from diffusers.models.normalization import AdaGroupNorm
-from diffusers.models.normalization import RMSNorm
+from diffusers.models.attention_processor import Attention, SpatialNorm
+from diffusers.models.normalization import AdaGroupNorm, RMSNorm
+from diffusers.utils import logging
+from einops import rearrange
+from torch import nn
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
-def prepare_causal_attention_mask(
-    n_frame: int, n_hw: int, dtype, device, batch_size: int = None
-):
+def prepare_causal_attention_mask(n_frame: int,
+                                  n_hw: int,
+                                  dtype,
+                                  device,
+                                  batch_size: int = None):
     seq_len = n_frame * n_hw
-    mask = torch.full((seq_len, seq_len), float("-inf"), dtype=dtype, device=device)
+    mask = torch.full((seq_len, seq_len),
+                      float("-inf"),
+                      dtype=dtype,
+                      device=device)
     for i in range(seq_len):
         i_frame = i // n_hw
-        mask[i, : (i_frame + 1) * n_hw] = 0
+        mask[i, :(i_frame + 1) * n_hw] = 0
     if batch_size is not None:
         mask = mask.unsqueeze(0).expand(batch_size, -1, -1)
     return mask
@@ -76,9 +78,12 @@ class CausalConv3d(nn.Module):
         )  # W, H, T
         self.time_causal_padding = padding
 
-        self.conv = nn.Conv3d(
-            chan_in, chan_out, kernel_size, stride=stride, dilation=dilation, **kwargs
-        )
+        self.conv = nn.Conv3d(chan_in,
+                              chan_out,
+                              kernel_size,
+                              stride=stride,
+                              dilation=dilation,
+                              **kwargs)
 
     def forward(self, x):
         x = F.pad(x, self.time_causal_padding, mode=self.pad_mode)
@@ -91,20 +96,20 @@ class UpsampleCausal3D(nn.Module):
     """
 
     def __init__(
-        self,
-        channels: int,
-        use_conv: bool = False,
-        use_conv_transpose: bool = False,
-        out_channels: Optional[int] = None,
-        name: str = "conv",
-        kernel_size: Optional[int] = None,
-        padding=1,
-        norm_type=None,
-        eps=None,
-        elementwise_affine=None,
-        bias=True,
-        interpolate=True,
-        upsample_factor=(2, 2, 2),
+            self,
+            channels: int,
+            use_conv: bool = False,
+            use_conv_transpose: bool = False,
+            out_channels: Optional[int] = None,
+            name: str = "conv",
+            kernel_size: Optional[int] = None,
+            padding=1,
+            norm_type=None,
+            eps=None,
+            elementwise_affine=None,
+            bias=True,
+            interpolate=True,
+            upsample_factor=(2, 2, 2),
     ):
         super().__init__()
         self.channels = channels
@@ -130,9 +135,10 @@ class UpsampleCausal3D(nn.Module):
         elif use_conv:
             if kernel_size is None:
                 kernel_size = 3
-            conv = CausalConv3d(
-                self.channels, self.out_channels, kernel_size=kernel_size, bias=bias
-            )
+            conv = CausalConv3d(self.channels,
+                                self.out_channels,
+                                kernel_size=kernel_size,
+                                bias=bias)
 
         if name == "conv":
             self.conv = conv
@@ -169,14 +175,14 @@ class UpsampleCausal3D(nn.Module):
             first_h, other_h = hidden_states.split((1, T - 1), dim=2)
             if output_size is None:
                 if T > 1:
-                    other_h = F.interpolate(
-                        other_h, scale_factor=self.upsample_factor, mode="nearest"
-                    )
+                    other_h = F.interpolate(other_h,
+                                            scale_factor=self.upsample_factor,
+                                            mode="nearest")
 
                 first_h = first_h.squeeze(2)
-                first_h = F.interpolate(
-                    first_h, scale_factor=self.upsample_factor[1:], mode="nearest"
-                )
+                first_h = F.interpolate(first_h,
+                                        scale_factor=self.upsample_factor[1:],
+                                        mode="nearest")
                 first_h = first_h.unsqueeze(2)
             else:
                 raise NotImplementedError
@@ -254,15 +260,15 @@ class DownsampleCausal3D(nn.Module):
         else:
             self.conv = conv
 
-    def forward(
-        self, hidden_states: torch.FloatTensor, scale: float = 1.0
-    ) -> torch.FloatTensor:
+    def forward(self,
+                hidden_states: torch.FloatTensor,
+                scale: float = 1.0) -> torch.FloatTensor:
         assert hidden_states.shape[1] == self.channels
 
         if self.norm is not None:
-            hidden_states = self.norm(hidden_states.permute(0, 2, 3, 1)).permute(
-                0, 3, 1, 2
-            )
+            hidden_states = self.norm(hidden_states.permute(0, 2, 3,
+                                                            1)).permute(
+                                                                0, 3, 1, 2)
 
         assert hidden_states.shape[1] == self.channels
 
@@ -319,25 +325,31 @@ class ResnetBlockCausal3D(nn.Module):
             groups_out = groups
 
         if self.time_embedding_norm == "ada_group":
-            self.norm1 = AdaGroupNorm(temb_channels, in_channels, groups, eps=eps)
+            self.norm1 = AdaGroupNorm(temb_channels,
+                                      in_channels,
+                                      groups,
+                                      eps=eps)
         elif self.time_embedding_norm == "spatial":
             self.norm1 = SpatialNorm(in_channels, temb_channels)
         else:
-            self.norm1 = torch.nn.GroupNorm(
-                num_groups=groups, num_channels=in_channels, eps=eps, affine=True
-            )
+            self.norm1 = torch.nn.GroupNorm(num_groups=groups,
+                                            num_channels=in_channels,
+                                            eps=eps,
+                                            affine=True)
 
-        self.conv1 = CausalConv3d(in_channels, out_channels, kernel_size=3, stride=1)
+        self.conv1 = CausalConv3d(in_channels,
+                                  out_channels,
+                                  kernel_size=3,
+                                  stride=1)
 
         if temb_channels is not None:
             if self.time_embedding_norm == "default":
                 self.time_emb_proj = linear_cls(temb_channels, out_channels)
             elif self.time_embedding_norm == "scale_shift":
-                self.time_emb_proj = linear_cls(temb_channels, 2 * out_channels)
-            elif (
-                self.time_embedding_norm == "ada_group"
-                or self.time_embedding_norm == "spatial"
-            ):
+                self.time_emb_proj = linear_cls(temb_channels,
+                                                2 * out_channels)
+            elif (self.time_embedding_norm == "ada_group"
+                  or self.time_embedding_norm == "spatial"):
                 self.time_emb_proj = None
             else:
                 raise ValueError(
@@ -347,19 +359,24 @@ class ResnetBlockCausal3D(nn.Module):
             self.time_emb_proj = None
 
         if self.time_embedding_norm == "ada_group":
-            self.norm2 = AdaGroupNorm(temb_channels, out_channels, groups_out, eps=eps)
+            self.norm2 = AdaGroupNorm(temb_channels,
+                                      out_channels,
+                                      groups_out,
+                                      eps=eps)
         elif self.time_embedding_norm == "spatial":
             self.norm2 = SpatialNorm(out_channels, temb_channels)
         else:
-            self.norm2 = torch.nn.GroupNorm(
-                num_groups=groups_out, num_channels=out_channels, eps=eps, affine=True
-            )
+            self.norm2 = torch.nn.GroupNorm(num_groups=groups_out,
+                                            num_channels=out_channels,
+                                            eps=eps,
+                                            affine=True)
 
         self.dropout = torch.nn.Dropout(dropout)
         conv_3d_out_channels = conv_3d_out_channels or out_channels
-        self.conv2 = CausalConv3d(
-            out_channels, conv_3d_out_channels, kernel_size=3, stride=1
-        )
+        self.conv2 = CausalConv3d(out_channels,
+                                  conv_3d_out_channels,
+                                  kernel_size=3,
+                                  stride=1)
 
         self.nonlinearity = get_activation(non_linearity)
 
@@ -367,13 +384,12 @@ class ResnetBlockCausal3D(nn.Module):
         if self.up:
             self.upsample = UpsampleCausal3D(in_channels, use_conv=False)
         elif self.down:
-            self.downsample = DownsampleCausal3D(in_channels, use_conv=False, name="op")
+            self.downsample = DownsampleCausal3D(in_channels,
+                                                 use_conv=False,
+                                                 name="op")
 
-        self.use_in_shortcut = (
-            self.in_channels != conv_3d_out_channels
-            if use_in_shortcut is None
-            else use_in_shortcut
-        )
+        self.use_in_shortcut = (self.in_channels != conv_3d_out_channels if
+                                use_in_shortcut is None else use_in_shortcut)
 
         self.conv_shortcut = None
         if self.use_in_shortcut:
@@ -393,10 +409,8 @@ class ResnetBlockCausal3D(nn.Module):
     ) -> torch.FloatTensor:
         hidden_states = input_tensor
 
-        if (
-            self.time_embedding_norm == "ada_group"
-            or self.time_embedding_norm == "spatial"
-        ):
+        if (self.time_embedding_norm == "ada_group"
+                or self.time_embedding_norm == "spatial"):
             hidden_states = self.norm1(hidden_states, temb)
         else:
             hidden_states = self.norm1(hidden_states)
@@ -424,10 +438,8 @@ class ResnetBlockCausal3D(nn.Module):
         if temb is not None and self.time_embedding_norm == "default":
             hidden_states = hidden_states + temb
 
-        if (
-            self.time_embedding_norm == "ada_group"
-            or self.time_embedding_norm == "spatial"
-        ):
+        if (self.time_embedding_norm == "ada_group"
+                or self.time_embedding_norm == "spatial"):
             hidden_states = self.norm2(hidden_states, temb)
         else:
             hidden_states = self.norm2(hidden_states)
@@ -444,7 +456,8 @@ class ResnetBlockCausal3D(nn.Module):
         if self.conv_shortcut is not None:
             input_tensor = self.conv_shortcut(input_tensor)
 
-        output_tensor = (input_tensor + hidden_states) / self.output_scale_factor
+        output_tensor = (input_tensor +
+                         hidden_states) / self.output_scale_factor
 
         return output_tensor
 
@@ -484,11 +497,9 @@ def get_down_block3d(
         )
         attention_head_dim = num_attention_heads
 
-    down_block_type = (
-        down_block_type[7:]
-        if down_block_type.startswith("UNetRes")
-        else down_block_type
-    )
+    down_block_type = (down_block_type[7:]
+                       if down_block_type.startswith("UNetRes") else
+                       down_block_type)
     if down_block_type == "DownEncoderBlockCausal3D":
         return DownEncoderBlockCausal3D(
             num_layers=num_layers,
@@ -542,9 +553,8 @@ def get_up_block3d(
         )
         attention_head_dim = num_attention_heads
 
-    up_block_type = (
-        up_block_type[7:] if up_block_type.startswith("UNetRes") else up_block_type
-    )
+    up_block_type = (up_block_type[7:]
+                     if up_block_type.startswith("UNetRes") else up_block_type)
     if up_block_type == "UpDecoderBlockCausal3D":
         return UpDecoderBlockCausal3D(
             num_layers=num_layers,
@@ -585,15 +595,13 @@ class UNetMidBlockCausal3D(nn.Module):
         output_scale_factor: float = 1.0,
     ):
         super().__init__()
-        resnet_groups = (
-            resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
-        )
+        resnet_groups = (resnet_groups if resnet_groups is not None else min(
+            in_channels // 4, 32))
         self.add_attention = add_attention
 
         if attn_groups is None:
-            attn_groups = (
-                resnet_groups if resnet_time_scale_shift == "default" else None
-            )
+            attn_groups = (resnet_groups
+                           if resnet_time_scale_shift == "default" else None)
 
         # there is always at least one resnet
         resnets = [
@@ -628,17 +636,14 @@ class UNetMidBlockCausal3D(nn.Module):
                         rescale_output_factor=output_scale_factor,
                         eps=resnet_eps,
                         norm_num_groups=attn_groups,
-                        spatial_norm_dim=(
-                            temb_channels
-                            if resnet_time_scale_shift == "spatial"
-                            else None
-                        ),
+                        spatial_norm_dim=(temb_channels
+                                          if resnet_time_scale_shift
+                                          == "spatial" else None),
                         residual_connection=True,
                         bias=True,
                         upcast_softmax=True,
                         _from_deprecated_attn_block=True,
-                    )
-                )
+                    ))
             else:
                 attentions.append(None)
 
@@ -654,35 +659,41 @@ class UNetMidBlockCausal3D(nn.Module):
                     non_linearity=resnet_act_fn,
                     output_scale_factor=output_scale_factor,
                     pre_norm=resnet_pre_norm,
-                )
-            )
+                ))
 
         self.attentions = nn.ModuleList(attentions)
         self.resnets = nn.ModuleList(resnets)
 
-    def forward(
-        self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None
-    ) -> torch.FloatTensor:
+    def forward(self,
+                hidden_states: torch.FloatTensor,
+                temb: Optional[torch.FloatTensor] = None) -> torch.FloatTensor:
         hidden_states = self.resnets[0](hidden_states, temb)
         for attn, resnet in zip(self.attentions, self.resnets[1:]):
             if attn is not None:
                 B, C, T, H, W = hidden_states.shape
-                hidden_states = rearrange(hidden_states, "b c f h w -> b (f h w) c")
+                hidden_states = rearrange(hidden_states,
+                                          "b c f h w -> b (f h w) c")
                 attention_mask = prepare_causal_attention_mask(
-                    T, H * W, hidden_states.dtype, hidden_states.device, batch_size=B
-                )
-                hidden_states = attn(
-                    hidden_states, temb=temb, attention_mask=attention_mask
-                )
-                hidden_states = rearrange(
-                    hidden_states, "b (f h w) c -> b c f h w", f=T, h=H, w=W
-                )
+                    T,
+                    H * W,
+                    hidden_states.dtype,
+                    hidden_states.device,
+                    batch_size=B)
+                hidden_states = attn(hidden_states,
+                                     temb=temb,
+                                     attention_mask=attention_mask)
+                hidden_states = rearrange(hidden_states,
+                                          "b (f h w) c -> b c f h w",
+                                          f=T,
+                                          h=H,
+                                          w=W)
             hidden_states = resnet(hidden_states, temb)
 
         return hidden_states
 
 
 class DownEncoderBlockCausal3D(nn.Module):
+
     def __init__(
         self,
         in_channels: int,
@@ -716,30 +727,27 @@ class DownEncoderBlockCausal3D(nn.Module):
                     non_linearity=resnet_act_fn,
                     output_scale_factor=output_scale_factor,
                     pre_norm=resnet_pre_norm,
-                )
-            )
+                ))
 
         self.resnets = nn.ModuleList(resnets)
 
         if add_downsample:
-            self.downsamplers = nn.ModuleList(
-                [
-                    DownsampleCausal3D(
-                        out_channels,
-                        use_conv=True,
-                        out_channels=out_channels,
-                        padding=downsample_padding,
-                        name="op",
-                        stride=downsample_stride,
-                    )
-                ]
-            )
+            self.downsamplers = nn.ModuleList([
+                DownsampleCausal3D(
+                    out_channels,
+                    use_conv=True,
+                    out_channels=out_channels,
+                    padding=downsample_padding,
+                    name="op",
+                    stride=downsample_stride,
+                )
+            ])
         else:
             self.downsamplers = None
 
-    def forward(
-        self, hidden_states: torch.FloatTensor, scale: float = 1.0
-    ) -> torch.FloatTensor:
+    def forward(self,
+                hidden_states: torch.FloatTensor,
+                scale: float = 1.0) -> torch.FloatTensor:
         for resnet in self.resnets:
             hidden_states = resnet(hidden_states, temb=None, scale=scale)
 
@@ -751,6 +759,7 @@ class DownEncoderBlockCausal3D(nn.Module):
 
 
 class UpDecoderBlockCausal3D(nn.Module):
+
     def __init__(
         self,
         in_channels: int,
@@ -786,22 +795,19 @@ class UpDecoderBlockCausal3D(nn.Module):
                     non_linearity=resnet_act_fn,
                     output_scale_factor=output_scale_factor,
                     pre_norm=resnet_pre_norm,
-                )
-            )
+                ))
 
         self.resnets = nn.ModuleList(resnets)
 
         if add_upsample:
-            self.upsamplers = nn.ModuleList(
-                [
-                    UpsampleCausal3D(
-                        out_channels,
-                        use_conv=True,
-                        out_channels=out_channels,
-                        upsample_factor=upsample_scale_factor,
-                    )
-                ]
-            )
+            self.upsamplers = nn.ModuleList([
+                UpsampleCausal3D(
+                    out_channels,
+                    use_conv=True,
+                    out_channels=out_channels,
+                    upsample_factor=upsample_scale_factor,
+                )
+            ])
         else:
             self.upsamplers = None
 

fastvideo/models/hunyuan/vae/vae.py

@@ -4,16 +4,12 @@ from typing import Optional, Tuple
 import numpy as np
 import torch
 import torch.nn as nn
-
+from diffusers.models.attention_processor import SpatialNorm
 from diffusers.utils import BaseOutput, is_torch_version
 from diffusers.utils.torch_utils import randn_tensor
-from diffusers.models.attention_processor import SpatialNorm
-from .unet_causal_3d_blocks import (
-    CausalConv3d,
-    UNetMidBlockCausal3D,
-    get_down_block3d,
-    get_up_block3d,
-)
+
+from .unet_causal_3d_blocks import (CausalConv3d, UNetMidBlockCausal3D,
+                                    get_down_block3d, get_up_block3d)
 
 
 @dataclass
@@ -38,8 +34,8 @@ class EncoderCausal3D(nn.Module):
         self,
         in_channels: int = 3,
         out_channels: int = 3,
-        down_block_types: Tuple[str, ...] = ("DownEncoderBlockCausal3D",),
-        block_out_channels: Tuple[int, ...] = (64,),
+        down_block_types: Tuple[str, ...] = ("DownEncoderBlockCausal3D", ),
+        block_out_channels: Tuple[int, ...] = (64, ),
         layers_per_block: int = 2,
         norm_num_groups: int = 32,
         act_fn: str = "silu",
@@ -51,9 +47,10 @@ class EncoderCausal3D(nn.Module):
         super().__init__()
         self.layers_per_block = layers_per_block
 
-        self.conv_in = CausalConv3d(
-            in_channels, block_out_channels[0], kernel_size=3, stride=1
-        )
+        self.conv_in = CausalConv3d(in_channels,
+                                    block_out_channels[0],
+                                    kernel_size=3,
+                                    stride=1)
         self.mid_block = None
         self.down_blocks = nn.ModuleList([])
 
@@ -63,29 +60,33 @@ class EncoderCausal3D(nn.Module):
             input_channel = output_channel
             output_channel = block_out_channels[i]
             is_final_block = i == len(block_out_channels) - 1
-            num_spatial_downsample_layers = int(np.log2(spatial_compression_ratio))
+            num_spatial_downsample_layers = int(
+                np.log2(spatial_compression_ratio))
             num_time_downsample_layers = int(np.log2(time_compression_ratio))
 
             if time_compression_ratio == 4:
-                add_spatial_downsample = bool(i < num_spatial_downsample_layers)
+                add_spatial_downsample = bool(
+                    i < num_spatial_downsample_layers)
                 add_time_downsample = bool(
-                    i >= (len(block_out_channels) - 1 - num_time_downsample_layers)
-                    and not is_final_block
-                )
+                    i >=
+                    (len(block_out_channels) - 1 - num_time_downsample_layers)
+                    and not is_final_block)
             else:
                 raise ValueError(
                     f"Unsupported time_compression_ratio: {time_compression_ratio}."
                 )
 
             downsample_stride_HW = (2, 2) if add_spatial_downsample else (1, 1)
-            downsample_stride_T = (2,) if add_time_downsample else (1,)
-            downsample_stride = tuple(downsample_stride_T + downsample_stride_HW)
+            downsample_stride_T = (2, ) if add_time_downsample else (1, )
+            downsample_stride = tuple(downsample_stride_T +
+                                      downsample_stride_HW)
             down_block = get_down_block3d(
                 down_block_type,
                 num_layers=self.layers_per_block,
                 in_channels=input_channel,
                 out_channels=output_channel,
-                add_downsample=bool(add_spatial_downsample or add_time_downsample),
+                add_downsample=bool(add_spatial_downsample
+                                    or add_time_downsample),
                 downsample_stride=downsample_stride,
                 resnet_eps=1e-6,
                 downsample_padding=0,
@@ -110,19 +111,20 @@ class EncoderCausal3D(nn.Module):
         )
 
         # out
-        self.conv_norm_out = nn.GroupNorm(
-            num_channels=block_out_channels[-1], num_groups=norm_num_groups, eps=1e-6
-        )
+        self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[-1],
+                                          num_groups=norm_num_groups,
+                                          eps=1e-6)
         self.conv_act = nn.SiLU()
 
         conv_out_channels = 2 * out_channels if double_z else out_channels
-        self.conv_out = CausalConv3d(
-            block_out_channels[-1], conv_out_channels, kernel_size=3
-        )
+        self.conv_out = CausalConv3d(block_out_channels[-1],
+                                     conv_out_channels,
+                                     kernel_size=3)
 
     def forward(self, sample: torch.FloatTensor) -> torch.FloatTensor:
         r"""The forward method of the `EncoderCausal3D` class."""
-        assert len(sample.shape) == 5, "The input tensor should have 5 dimensions"
+        assert len(
+            sample.shape) == 5, "The input tensor should have 5 dimensions"
 
         sample = self.conv_in(sample)
 
@@ -150,8 +152,8 @@ class DecoderCausal3D(nn.Module):
         self,
         in_channels: int = 3,
         out_channels: int = 3,
-        up_block_types: Tuple[str, ...] = ("UpDecoderBlockCausal3D",),
-        block_out_channels: Tuple[int, ...] = (64,),
+        up_block_types: Tuple[str, ...] = ("UpDecoderBlockCausal3D", ),
+        block_out_channels: Tuple[int, ...] = (64, ),
         layers_per_block: int = 2,
         norm_num_groups: int = 32,
         act_fn: str = "silu",
@@ -163,9 +165,10 @@ class DecoderCausal3D(nn.Module):
         super().__init__()
         self.layers_per_block = layers_per_block
 
-        self.conv_in = CausalConv3d(
-            in_channels, block_out_channels[-1], kernel_size=3, stride=1
-        )
+        self.conv_in = CausalConv3d(in_channels,
+                                    block_out_channels[-1],
+                                    kernel_size=3,
+                                    stride=1)
         self.mid_block = None
         self.up_blocks = nn.ModuleList([])
 
@@ -177,7 +180,8 @@ class DecoderCausal3D(nn.Module):
             resnet_eps=1e-6,
             resnet_act_fn=act_fn,
             output_scale_factor=1,
-            resnet_time_scale_shift="default" if norm_type == "group" else norm_type,
+            resnet_time_scale_shift="default"
+            if norm_type == "group" else norm_type,
             attention_head_dim=block_out_channels[-1],
             resnet_groups=norm_num_groups,
             temb_channels=temb_channels,
@@ -191,25 +195,25 @@ class DecoderCausal3D(nn.Module):
             prev_output_channel = output_channel
             output_channel = reversed_block_out_channels[i]
             is_final_block = i == len(block_out_channels) - 1
-            num_spatial_upsample_layers = int(np.log2(spatial_compression_ratio))
+            num_spatial_upsample_layers = int(
+                np.log2(spatial_compression_ratio))
             num_time_upsample_layers = int(np.log2(time_compression_ratio))
 
             if time_compression_ratio == 4:
                 add_spatial_upsample = bool(i < num_spatial_upsample_layers)
                 add_time_upsample = bool(
                     i >= len(block_out_channels) - 1 - num_time_upsample_layers
-                    and not is_final_block
-                )
+                    and not is_final_block)
             else:
                 raise ValueError(
                     f"Unsupported time_compression_ratio: {time_compression_ratio}."
                 )
 
-            upsample_scale_factor_HW = (2, 2) if add_spatial_upsample else (1, 1)
-            upsample_scale_factor_T = (2,) if add_time_upsample else (1,)
-            upsample_scale_factor = tuple(
-                upsample_scale_factor_T + upsample_scale_factor_HW
-            )
+            upsample_scale_factor_HW = (2, 2) if add_spatial_upsample else (1,
+                                                                            1)
+            upsample_scale_factor_T = (2, ) if add_time_upsample else (1, )
+            upsample_scale_factor = tuple(upsample_scale_factor_T +
+                                          upsample_scale_factor_HW)
             up_block = get_up_block3d(
                 up_block_type,
                 num_layers=self.layers_per_block + 1,
@@ -230,13 +234,17 @@ class DecoderCausal3D(nn.Module):
 
         # out
         if norm_type == "spatial":
-            self.conv_norm_out = SpatialNorm(block_out_channels[0], temb_channels)
+            self.conv_norm_out = SpatialNorm(block_out_channels[0],
+                                             temb_channels)
         else:
             self.conv_norm_out = nn.GroupNorm(
-                num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=1e-6
-            )
+                num_channels=block_out_channels[0],
+                num_groups=norm_num_groups,
+                eps=1e-6)
         self.conv_act = nn.SiLU()
-        self.conv_out = CausalConv3d(block_out_channels[0], out_channels, kernel_size=3)
+        self.conv_out = CausalConv3d(block_out_channels[0],
+                                     out_channels,
+                                     kernel_size=3)
 
         self.gradient_checkpointing = False
 
@@ -246,7 +254,8 @@ class DecoderCausal3D(nn.Module):
         latent_embeds: Optional[torch.FloatTensor] = None,
     ) -> torch.FloatTensor:
         r"""The forward method of the `DecoderCausal3D` class."""
-        assert len(sample.shape) == 5, "The input tensor should have 5 dimensions."
+        assert len(
+            sample.shape) == 5, "The input tensor should have 5 dimensions."
 
         sample = self.conv_in(sample)
 
@@ -254,6 +263,7 @@ class DecoderCausal3D(nn.Module):
         if self.training and self.gradient_checkpointing:
 
             def create_custom_forward(module):
+
                 def custom_forward(*inputs):
                     return module(*inputs)
 
@@ -280,15 +290,14 @@ class DecoderCausal3D(nn.Module):
             else:
                 # middle
                 sample = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(self.mid_block), sample, latent_embeds
-                )
+                    create_custom_forward(self.mid_block), sample,
+                    latent_embeds)
                 sample = sample.to(upscale_dtype)
 
                 # up
                 for up_block in self.up_blocks:
                     sample = torch.utils.checkpoint.checkpoint(
-                        create_custom_forward(up_block), sample, latent_embeds
-                    )
+                        create_custom_forward(up_block), sample, latent_embeds)
         else:
             # middle
             sample = self.mid_block(sample, latent_embeds)
@@ -310,6 +319,7 @@ class DecoderCausal3D(nn.Module):
 
 
 class DiagonalGaussianDistribution(object):
+
     def __init__(self, parameters: torch.Tensor, deterministic: bool = False):
         if parameters.ndim == 3:
             dim = 2  # (B, L, C)
@@ -325,10 +335,13 @@ class DiagonalGaussianDistribution(object):
         self.var = torch.exp(self.logvar)
         if self.deterministic:
             self.var = self.std = torch.zeros_like(
-                self.mean, device=self.parameters.device, dtype=self.parameters.dtype
-            )
+                self.mean,
+                device=self.parameters.device,
+                dtype=self.parameters.dtype)
 
-    def sample(self, generator: Optional[torch.Generator] = None) -> torch.FloatTensor:
+    def sample(
+            self,
+            generator: Optional[torch.Generator] = None) -> torch.FloatTensor:
         # make sure sample is on the same device as the parameters and has same dtype
         sample = randn_tensor(
             self.mean.shape,
@@ -351,22 +364,20 @@ class DiagonalGaussianDistribution(object):
                 )
             else:
                 return 0.5 * torch.sum(
-                    torch.pow(self.mean - other.mean, 2) / other.var
-                    + self.var / other.var
-                    - 1.0
-                    - self.logvar
-                    + other.logvar,
+                    torch.pow(self.mean - other.mean, 2) / other.var +
+                    self.var / other.var - 1.0 - self.logvar + other.logvar,
                     dim=reduce_dim,
                 )
 
-    def nll(
-        self, sample: torch.Tensor, dims: Tuple[int, ...] = [1, 2, 3]
-    ) -> torch.Tensor:
+    def nll(self,
+            sample: torch.Tensor,
+            dims: Tuple[int, ...] = [1, 2, 3]) -> torch.Tensor:
         if self.deterministic:
             return torch.Tensor([0.0])
         logtwopi = np.log(2.0 * np.pi)
         return 0.5 * torch.sum(
-            logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
+            logtwopi + self.logvar +
+            torch.pow(sample - self.mean, 2) / self.var,
             dim=dims,
         )
 

fastvideo/models/hunyuan_hf/modeling_hunyuan.py

@@ -0,0 +1,952 @@
+# Copyright 2024 The Hunyuan Team and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.loaders import FromOriginalModelMixin, PeftAdapterMixin
+from diffusers.models.attention import FeedForward
+from diffusers.models.attention_processor import Attention, AttentionProcessor
+from diffusers.models.embeddings import (
+    CombinedTimestepGuidanceTextProjEmbeddings,
+    CombinedTimestepTextProjEmbeddings, get_1d_rotary_pos_embed)
+from diffusers.models.modeling_outputs import Transformer2DModelOutput
+from diffusers.models.modeling_utils import ModelMixin
+from diffusers.models.normalization import (AdaLayerNormContinuous,
+                                            AdaLayerNormZero,
+                                            AdaLayerNormZeroSingle)
+from diffusers.utils import (USE_PEFT_BACKEND, is_torch_version, logging,
+                             scale_lora_layers, unscale_lora_layers)
+
+from fastvideo.models.flash_attn_no_pad import flash_attn_no_pad
+from fastvideo.utils.communications import all_gather, all_to_all_4D
+from fastvideo.utils.parallel_states import (get_sequence_parallel_state,
+                                             nccl_info)
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+def shrink_head(encoder_state, dim):
+    local_heads = encoder_state.shape[dim] // nccl_info.sp_size
+    return encoder_state.narrow(dim, nccl_info.rank_within_group * local_heads,
+                                local_heads)
+
+
+class HunyuanVideoAttnProcessor2_0:
+
+    def __init__(self):
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError(
+                "HunyuanVideoAttnProcessor2_0 requires PyTorch 2.0. To use it, please upgrade PyTorch to 2.0."
+            )
+
+    def __call__(
+        self,
+        attn: Attention,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        image_rotary_emb: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+
+        sequence_length = hidden_states.size(1)
+        encoder_sequence_length = encoder_hidden_states.size(1)
+        if attn.add_q_proj is None and encoder_hidden_states is not None:
+            hidden_states = torch.cat([hidden_states, encoder_hidden_states],
+                                      dim=1)
+
+        # 1. QKV projections
+        query = attn.to_q(hidden_states)
+        key = attn.to_k(hidden_states)
+        value = attn.to_v(hidden_states)
+
+        query = query.unflatten(2, (attn.heads, -1)).transpose(1, 2)
+        key = key.unflatten(2, (attn.heads, -1)).transpose(1, 2)
+        value = value.unflatten(2, (attn.heads, -1)).transpose(1, 2)
+
+        # 2. QK normalization
+        if attn.norm_q is not None:
+            query = attn.norm_q(query).to(value)
+        if attn.norm_k is not None:
+            key = attn.norm_k(key).to(value)
+
+        image_rotary_emb = (
+            shrink_head(image_rotary_emb[0], dim=0),
+            shrink_head(image_rotary_emb[1], dim=0),
+        )
+
+        # 3. Rotational positional embeddings applied to latent stream
+        if image_rotary_emb is not None:
+            from diffusers.models.embeddings import apply_rotary_emb
+
+            if attn.add_q_proj is None and encoder_hidden_states is not None:
+                query = torch.cat(
+                    [
+                        apply_rotary_emb(
+                            query[:, :, :-encoder_hidden_states.shape[1]],
+                            image_rotary_emb),
+                        query[:, :, -encoder_hidden_states.shape[1]:],
+                    ],
+                    dim=2,
+                )
+                key = torch.cat(
+                    [
+                        apply_rotary_emb(
+                            key[:, :, :-encoder_hidden_states.shape[1]],
+                            image_rotary_emb),
+                        key[:, :, -encoder_hidden_states.shape[1]:],
+                    ],
+                    dim=2,
+                )
+            else:
+                query = apply_rotary_emb(query, image_rotary_emb)
+                key = apply_rotary_emb(key, image_rotary_emb)
+
+        # 4. Encoder condition QKV projection and normalization
+        if attn.add_q_proj is not None and encoder_hidden_states is not None:
+            encoder_query = attn.add_q_proj(encoder_hidden_states)
+            encoder_key = attn.add_k_proj(encoder_hidden_states)
+            encoder_value = attn.add_v_proj(encoder_hidden_states)
+
+            encoder_query = encoder_query.unflatten(
+                2, (attn.heads, -1)).transpose(1, 2)
+            encoder_key = encoder_key.unflatten(2, (attn.heads, -1)).transpose(
+                1, 2)
+            encoder_value = encoder_value.unflatten(
+                2, (attn.heads, -1)).transpose(1, 2)
+
+            if attn.norm_added_q is not None:
+                encoder_query = attn.norm_added_q(encoder_query).to(
+                    encoder_value)
+            if attn.norm_added_k is not None:
+                encoder_key = attn.norm_added_k(encoder_key).to(encoder_value)
+
+            query = torch.cat([query, encoder_query], dim=2)
+            key = torch.cat([key, encoder_key], dim=2)
+            value = torch.cat([value, encoder_value], dim=2)
+
+        if get_sequence_parallel_state():
+            query_img, query_txt = query[:, :, :
+                                         sequence_length, :], query[:, :,
+                                                                    sequence_length:, :]
+            key_img, key_txt = key[:, :, :
+                                   sequence_length, :], key[:, :,
+                                                            sequence_length:, :]
+            value_img, value_txt = value[:, :, :
+                                         sequence_length, :], value[:, :,
+                                                                    sequence_length:, :]
+            query_img = all_to_all_4D(query_img, scatter_dim=1,
+                                      gather_dim=2)  #
+            key_img = all_to_all_4D(key_img, scatter_dim=1, gather_dim=2)
+            value_img = all_to_all_4D(value_img, scatter_dim=1, gather_dim=2)
+
+            query_txt = shrink_head(query_txt, dim=1)
+            key_txt = shrink_head(key_txt, dim=1)
+            value_txt = shrink_head(value_txt, dim=1)
+            query = torch.cat([query_img, query_txt], dim=2)
+            key = torch.cat([key_img, key_txt], dim=2)
+            value = torch.cat([value_img, value_txt], dim=2)
+
+        query = query.unsqueeze(2)
+        key = key.unsqueeze(2)
+        value = value.unsqueeze(2)
+        qkv = torch.cat([query, key, value], dim=2)
+        qkv = qkv.transpose(1, 3)
+
+        # 5. Attention
+        attention_mask = attention_mask[:, 0, :]
+        seq_len = qkv.shape[1]
+        attn_len = attention_mask.shape[1]
+        attention_mask = F.pad(attention_mask, (seq_len - attn_len, 0),
+                               value=True)
+
+        hidden_states = flash_attn_no_pad(qkv,
+                                          attention_mask,
+                                          causal=False,
+                                          dropout_p=0.0,
+                                          softmax_scale=None)
+
+        if get_sequence_parallel_state():
+            hidden_states, encoder_hidden_states = hidden_states.split_with_sizes(
+                (sequence_length * nccl_info.sp_size, encoder_sequence_length),
+                dim=1)
+            hidden_states = all_to_all_4D(hidden_states,
+                                          scatter_dim=1,
+                                          gather_dim=2)
+            encoder_hidden_states = all_gather(encoder_hidden_states,
+                                               dim=2).contiguous()
+            hidden_states = hidden_states.flatten(2, 3)
+            hidden_states = hidden_states.to(query.dtype)
+            encoder_hidden_states = encoder_hidden_states.flatten(2, 3)
+            encoder_hidden_states = encoder_hidden_states.to(query.dtype)
+        else:
+            hidden_states = hidden_states.flatten(2, 3)
+            hidden_states = hidden_states.to(query.dtype)
+
+            # 6. Output projection
+            if encoder_hidden_states is not None:
+                hidden_states, encoder_hidden_states = (
+                    hidden_states[:, :-encoder_hidden_states.shape[1]],
+                    hidden_states[:, -encoder_hidden_states.shape[1]:],
+                )
+
+        if encoder_hidden_states is not None:
+            if getattr(attn, "to_out", None) is not None:
+                hidden_states = attn.to_out[0](hidden_states)
+                hidden_states = attn.to_out[1](hidden_states)
+
+            if getattr(attn, "to_add_out", None) is not None:
+                encoder_hidden_states = attn.to_add_out(encoder_hidden_states)
+
+        return hidden_states, encoder_hidden_states
+
+
+class HunyuanVideoPatchEmbed(nn.Module):
+
+    def __init__(
+        self,
+        patch_size: Union[int, Tuple[int, int, int]] = 16,
+        in_chans: int = 3,
+        embed_dim: int = 768,
+    ) -> None:
+        super().__init__()
+
+        patch_size = (patch_size, patch_size, patch_size) if isinstance(
+            patch_size, int) else patch_size
+        self.proj = nn.Conv3d(in_chans,
+                              embed_dim,
+                              kernel_size=patch_size,
+                              stride=patch_size)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.proj(hidden_states)
+        hidden_states = hidden_states.flatten(2).transpose(1,
+                                                           2)  # BCFHW -> BNC
+        return hidden_states
+
+
+class HunyuanVideoAdaNorm(nn.Module):
+
+    def __init__(self,
+                 in_features: int,
+                 out_features: Optional[int] = None) -> None:
+        super().__init__()
+
+        out_features = out_features or 2 * in_features
+        self.linear = nn.Linear(in_features, out_features)
+        self.nonlinearity = nn.SiLU()
+
+    def forward(
+        self, temb: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor,
+               torch.Tensor]:
+        temb = self.linear(self.nonlinearity(temb))
+        gate_msa, gate_mlp = temb.chunk(2, dim=1)
+        gate_msa, gate_mlp = gate_msa.unsqueeze(1), gate_mlp.unsqueeze(1)
+        return gate_msa, gate_mlp
+
+
+class HunyuanVideoIndividualTokenRefinerBlock(nn.Module):
+
+    def __init__(
+        self,
+        num_attention_heads: int,
+        attention_head_dim: int,
+        mlp_width_ratio: str = 4.0,
+        mlp_drop_rate: float = 0.0,
+        attention_bias: bool = True,
+    ) -> None:
+        super().__init__()
+
+        hidden_size = num_attention_heads * attention_head_dim
+
+        self.norm1 = nn.LayerNorm(hidden_size,
+                                  elementwise_affine=True,
+                                  eps=1e-6)
+        self.attn = Attention(
+            query_dim=hidden_size,
+            cross_attention_dim=None,
+            heads=num_attention_heads,
+            dim_head=attention_head_dim,
+            bias=attention_bias,
+        )
+
+        self.norm2 = nn.LayerNorm(hidden_size,
+                                  elementwise_affine=True,
+                                  eps=1e-6)
+        self.ff = FeedForward(hidden_size,
+                              mult=mlp_width_ratio,
+                              activation_fn="linear-silu",
+                              dropout=mlp_drop_rate)
+
+        self.norm_out = HunyuanVideoAdaNorm(hidden_size, 2 * hidden_size)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        temb: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        norm_hidden_states = self.norm1(hidden_states)
+
+        attn_output = self.attn(
+            hidden_states=norm_hidden_states,
+            encoder_hidden_states=None,
+            attention_mask=attention_mask,
+        )
+
+        gate_msa, gate_mlp = self.norm_out(temb)
+        hidden_states = hidden_states + attn_output * gate_msa
+
+        ff_output = self.ff(self.norm2(hidden_states))
+        hidden_states = hidden_states + ff_output * gate_mlp
+
+        return hidden_states
+
+
+class HunyuanVideoIndividualTokenRefiner(nn.Module):
+
+    def __init__(
+        self,
+        num_attention_heads: int,
+        attention_head_dim: int,
+        num_layers: int,
+        mlp_width_ratio: float = 4.0,
+        mlp_drop_rate: float = 0.0,
+        attention_bias: bool = True,
+    ) -> None:
+        super().__init__()
+
+        self.refiner_blocks = nn.ModuleList([
+            HunyuanVideoIndividualTokenRefinerBlock(
+                num_attention_heads=num_attention_heads,
+                attention_head_dim=attention_head_dim,
+                mlp_width_ratio=mlp_width_ratio,
+                mlp_drop_rate=mlp_drop_rate,
+                attention_bias=attention_bias,
+            ) for _ in range(num_layers)
+        ])
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        temb: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+    ) -> None:
+        self_attn_mask = None
+        if attention_mask is not None:
+            batch_size = attention_mask.shape[0]
+            seq_len = attention_mask.shape[1]
+            attention_mask = attention_mask.to(hidden_states.device).bool()
+            self_attn_mask_1 = attention_mask.view(batch_size, 1, 1,
+                                                   seq_len).repeat(
+                                                       1, 1, seq_len, 1)
+            self_attn_mask_2 = self_attn_mask_1.transpose(2, 3)
+            self_attn_mask = (self_attn_mask_1 & self_attn_mask_2).bool()
+            self_attn_mask[:, :, :, 0] = True
+
+        for block in self.refiner_blocks:
+            hidden_states = block(hidden_states, temb, self_attn_mask)
+
+        return hidden_states
+
+
+class HunyuanVideoTokenRefiner(nn.Module):
+
+    def __init__(
+        self,
+        in_channels: int,
+        num_attention_heads: int,
+        attention_head_dim: int,
+        num_layers: int,
+        mlp_ratio: float = 4.0,
+        mlp_drop_rate: float = 0.0,
+        attention_bias: bool = True,
+    ) -> None:
+        super().__init__()
+
+        hidden_size = num_attention_heads * attention_head_dim
+
+        self.time_text_embed = CombinedTimestepTextProjEmbeddings(
+            embedding_dim=hidden_size, pooled_projection_dim=in_channels)
+        self.proj_in = nn.Linear(in_channels, hidden_size, bias=True)
+        self.token_refiner = HunyuanVideoIndividualTokenRefiner(
+            num_attention_heads=num_attention_heads,
+            attention_head_dim=attention_head_dim,
+            num_layers=num_layers,
+            mlp_width_ratio=mlp_ratio,
+            mlp_drop_rate=mlp_drop_rate,
+            attention_bias=attention_bias,
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        timestep: torch.LongTensor,
+        attention_mask: Optional[torch.LongTensor] = None,
+    ) -> torch.Tensor:
+        if attention_mask is None:
+            pooled_projections = hidden_states.mean(dim=1)
+        else:
+            original_dtype = hidden_states.dtype
+            mask_float = attention_mask.float().unsqueeze(-1)
+            pooled_projections = (hidden_states * mask_float).sum(
+                dim=1) / mask_float.sum(dim=1)
+            pooled_projections = pooled_projections.to(original_dtype)
+
+        temb = self.time_text_embed(timestep, pooled_projections)
+        hidden_states = self.proj_in(hidden_states)
+        hidden_states = self.token_refiner(hidden_states, temb, attention_mask)
+
+        return hidden_states
+
+
+class HunyuanVideoRotaryPosEmbed(nn.Module):
+
+    def __init__(self,
+                 patch_size: int,
+                 patch_size_t: int,
+                 rope_dim: List[int],
+                 theta: float = 256.0) -> None:
+        super().__init__()
+
+        self.patch_size = patch_size
+        self.patch_size_t = patch_size_t
+        self.rope_dim = rope_dim
+        self.theta = theta
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        batch_size, num_channels, num_frames, height, width = hidden_states.shape
+        rope_sizes = [
+            num_frames * nccl_info.sp_size // self.patch_size_t,
+            height // self.patch_size, width // self.patch_size
+        ]
+
+        axes_grids = []
+        for i in range(3):
+            # Note: The following line diverges from original behaviour. We create the grid on the device, whereas
+            # original implementation creates it on CPU and then moves it to device. This results in numerical
+            # differences in layerwise debugging outputs, but visually it is the same.
+            grid = torch.arange(0,
+                                rope_sizes[i],
+                                device=hidden_states.device,
+                                dtype=torch.float32)
+            axes_grids.append(grid)
+        grid = torch.meshgrid(*axes_grids, indexing="ij")  # [W, H, T]
+        grid = torch.stack(grid, dim=0)  # [3, W, H, T]
+
+        freqs = []
+        for i in range(3):
+            freq = get_1d_rotary_pos_embed(self.rope_dim[i],
+                                           grid[i].reshape(-1),
+                                           self.theta,
+                                           use_real=True)
+            freqs.append(freq)
+
+        freqs_cos = torch.cat([f[0] for f in freqs],
+                              dim=1)  # (W * H * T, D / 2)
+        freqs_sin = torch.cat([f[1] for f in freqs],
+                              dim=1)  # (W * H * T, D / 2)
+        return freqs_cos, freqs_sin
+
+
+class HunyuanVideoSingleTransformerBlock(nn.Module):
+
+    def __init__(
+        self,
+        num_attention_heads: int,
+        attention_head_dim: int,
+        mlp_ratio: float = 4.0,
+        qk_norm: str = "rms_norm",
+    ) -> None:
+        super().__init__()
+
+        hidden_size = num_attention_heads * attention_head_dim
+        mlp_dim = int(hidden_size * mlp_ratio)
+
+        self.attn = Attention(
+            query_dim=hidden_size,
+            cross_attention_dim=None,
+            dim_head=attention_head_dim,
+            heads=num_attention_heads,
+            out_dim=hidden_size,
+            bias=True,
+            processor=HunyuanVideoAttnProcessor2_0(),
+            qk_norm=qk_norm,
+            eps=1e-6,
+            pre_only=True,
+        )
+
+        self.norm = AdaLayerNormZeroSingle(hidden_size, norm_type="layer_norm")
+        self.proj_mlp = nn.Linear(hidden_size, mlp_dim)
+        self.act_mlp = nn.GELU(approximate="tanh")
+        self.proj_out = nn.Linear(hidden_size + mlp_dim, hidden_size)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor,
+        temb: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+    ) -> torch.Tensor:
+        text_seq_length = encoder_hidden_states.shape[1]
+        hidden_states = torch.cat([hidden_states, encoder_hidden_states],
+                                  dim=1)
+
+        residual = hidden_states
+
+        # 1. Input normalization
+        norm_hidden_states, gate = self.norm(hidden_states, emb=temb)
+        mlp_hidden_states = self.act_mlp(self.proj_mlp(norm_hidden_states))
+
+        norm_hidden_states, norm_encoder_hidden_states = (
+            norm_hidden_states[:, :-text_seq_length, :],
+            norm_hidden_states[:, -text_seq_length:, :],
+        )
+
+        # 2. Attention
+        attn_output, context_attn_output = self.attn(
+            hidden_states=norm_hidden_states,
+            encoder_hidden_states=norm_encoder_hidden_states,
+            attention_mask=attention_mask,
+            image_rotary_emb=image_rotary_emb,
+        )
+        attn_output = torch.cat([attn_output, context_attn_output], dim=1)
+
+        # 3. Modulation and residual connection
+        hidden_states = torch.cat([attn_output, mlp_hidden_states], dim=2)
+        hidden_states = gate.unsqueeze(1) * self.proj_out(hidden_states)
+        hidden_states = hidden_states + residual
+
+        hidden_states, encoder_hidden_states = (
+            hidden_states[:, :-text_seq_length, :],
+            hidden_states[:, -text_seq_length:, :],
+        )
+        return hidden_states, encoder_hidden_states
+
+
+class HunyuanVideoTransformerBlock(nn.Module):
+
+    def __init__(
+        self,
+        num_attention_heads: int,
+        attention_head_dim: int,
+        mlp_ratio: float,
+        qk_norm: str = "rms_norm",
+    ) -> None:
+        super().__init__()
+
+        hidden_size = num_attention_heads * attention_head_dim
+
+        self.norm1 = AdaLayerNormZero(hidden_size, norm_type="layer_norm")
+        self.norm1_context = AdaLayerNormZero(hidden_size,
+                                              norm_type="layer_norm")
+
+        self.attn = Attention(
+            query_dim=hidden_size,
+            cross_attention_dim=None,
+            added_kv_proj_dim=hidden_size,
+            dim_head=attention_head_dim,
+            heads=num_attention_heads,
+            out_dim=hidden_size,
+            context_pre_only=False,
+            bias=True,
+            processor=HunyuanVideoAttnProcessor2_0(),
+            qk_norm=qk_norm,
+            eps=1e-6,
+        )
+
+        self.norm2 = nn.LayerNorm(hidden_size,
+                                  elementwise_affine=False,
+                                  eps=1e-6)
+        self.ff = FeedForward(hidden_size,
+                              mult=mlp_ratio,
+                              activation_fn="gelu-approximate")
+
+        self.norm2_context = nn.LayerNorm(hidden_size,
+                                          elementwise_affine=False,
+                                          eps=1e-6)
+        self.ff_context = FeedForward(hidden_size,
+                                      mult=mlp_ratio,
+                                      activation_fn="gelu-approximate")
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor,
+        temb: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        freqs_cis: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        # 1. Input normalization
+        norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(
+            hidden_states, emb=temb)
+        norm_encoder_hidden_states, c_gate_msa, c_shift_mlp, c_scale_mlp, c_gate_mlp = self.norm1_context(
+            encoder_hidden_states, emb=temb)
+
+        # 2. Joint attention
+        attn_output, context_attn_output = self.attn(
+            hidden_states=norm_hidden_states,
+            encoder_hidden_states=norm_encoder_hidden_states,
+            attention_mask=attention_mask,
+            image_rotary_emb=freqs_cis,
+        )
+
+        # 3. Modulation and residual connection
+        hidden_states = hidden_states + attn_output * gate_msa.unsqueeze(1)
+        encoder_hidden_states = encoder_hidden_states + context_attn_output * c_gate_msa.unsqueeze(
+            1)
+
+        norm_hidden_states = self.norm2(hidden_states)
+        norm_encoder_hidden_states = self.norm2_context(encoder_hidden_states)
+
+        norm_hidden_states = norm_hidden_states * (
+            1 + scale_mlp[:, None]) + shift_mlp[:, None]
+        norm_encoder_hidden_states = norm_encoder_hidden_states * (
+            1 + c_scale_mlp[:, None]) + c_shift_mlp[:, None]
+
+        # 4. Feed-forward
+        ff_output = self.ff(norm_hidden_states)
+        context_ff_output = self.ff_context(norm_encoder_hidden_states)
+
+        hidden_states = hidden_states + gate_mlp.unsqueeze(1) * ff_output
+        encoder_hidden_states = encoder_hidden_states + c_gate_mlp.unsqueeze(
+            1) * context_ff_output
+
+        return hidden_states, encoder_hidden_states
+
+
+class HunyuanVideoTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin,
+                                     FromOriginalModelMixin):
+    r"""
+    A Transformer model for video-like data used in [HunyuanVideo](https://huggingface.co/tencent/HunyuanVideo).
+
+    Args:
+        in_channels (`int`, defaults to `16`):
+            The number of channels in the input.
+        out_channels (`int`, defaults to `16`):
+            The number of channels in the output.
+        num_attention_heads (`int`, defaults to `24`):
+            The number of heads to use for multi-head attention.
+        attention_head_dim (`int`, defaults to `128`):
+            The number of channels in each head.
+        num_layers (`int`, defaults to `20`):
+            The number of layers of dual-stream blocks to use.
+        num_single_layers (`int`, defaults to `40`):
+            The number of layers of single-stream blocks to use.
+        num_refiner_layers (`int`, defaults to `2`):
+            The number of layers of refiner blocks to use.
+        mlp_ratio (`float`, defaults to `4.0`):
+            The ratio of the hidden layer size to the input size in the feedforward network.
+        patch_size (`int`, defaults to `2`):
+            The size of the spatial patches to use in the patch embedding layer.
+        patch_size_t (`int`, defaults to `1`):
+            The size of the tmeporal patches to use in the patch embedding layer.
+        qk_norm (`str`, defaults to `rms_norm`):
+            The normalization to use for the query and key projections in the attention layers.
+        guidance_embeds (`bool`, defaults to `True`):
+            Whether to use guidance embeddings in the model.
+        text_embed_dim (`int`, defaults to `4096`):
+            Input dimension of text embeddings from the text encoder.
+        pooled_projection_dim (`int`, defaults to `768`):
+            The dimension of the pooled projection of the text embeddings.
+        rope_theta (`float`, defaults to `256.0`):
+            The value of theta to use in the RoPE layer.
+        rope_axes_dim (`Tuple[int]`, defaults to `(16, 56, 56)`):
+            The dimensions of the axes to use in the RoPE layer.
+    """
+
+    _supports_gradient_checkpointing = True
+
+    @register_to_config
+    def __init__(
+            self,
+            in_channels: int = 16,
+            out_channels: int = 16,
+            num_attention_heads: int = 24,
+            attention_head_dim: int = 128,
+            num_layers: int = 20,
+            num_single_layers: int = 40,
+            num_refiner_layers: int = 2,
+            mlp_ratio: float = 4.0,
+            patch_size: int = 2,
+            patch_size_t: int = 1,
+            qk_norm: str = "rms_norm",
+            guidance_embeds: bool = True,
+            text_embed_dim: int = 4096,
+            pooled_projection_dim: int = 768,
+            rope_theta: float = 256.0,
+            rope_axes_dim: Tuple[int] = (16, 56, 56),
+    ) -> None:
+        super().__init__()
+
+        inner_dim = num_attention_heads * attention_head_dim
+        out_channels = out_channels or in_channels
+
+        # 1. Latent and condition embedders
+        self.x_embedder = HunyuanVideoPatchEmbed(
+            (patch_size_t, patch_size, patch_size), in_channels, inner_dim)
+        self.context_embedder = HunyuanVideoTokenRefiner(
+            text_embed_dim,
+            num_attention_heads,
+            attention_head_dim,
+            num_layers=num_refiner_layers)
+        self.time_text_embed = CombinedTimestepGuidanceTextProjEmbeddings(
+            inner_dim, pooled_projection_dim)
+
+        # 2. RoPE
+        self.rope = HunyuanVideoRotaryPosEmbed(patch_size, patch_size_t,
+                                               rope_axes_dim, rope_theta)
+
+        # 3. Dual stream transformer blocks
+        self.transformer_blocks = nn.ModuleList([
+            HunyuanVideoTransformerBlock(num_attention_heads,
+                                         attention_head_dim,
+                                         mlp_ratio=mlp_ratio,
+                                         qk_norm=qk_norm)
+            for _ in range(num_layers)
+        ])
+
+        # 4. Single stream transformer blocks
+        self.single_transformer_blocks = nn.ModuleList([
+            HunyuanVideoSingleTransformerBlock(num_attention_heads,
+                                               attention_head_dim,
+                                               mlp_ratio=mlp_ratio,
+                                               qk_norm=qk_norm)
+            for _ in range(num_single_layers)
+        ])
+
+        # 5. Output projection
+        self.norm_out = AdaLayerNormContinuous(inner_dim,
+                                               inner_dim,
+                                               elementwise_affine=False,
+                                               eps=1e-6)
+        self.proj_out = nn.Linear(
+            inner_dim, patch_size_t * patch_size * patch_size * out_channels)
+
+        self.gradient_checkpointing = False
+
+    @property
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
+    def attn_processors(self) -> Dict[str, AttentionProcessor]:
+        r"""
+        Returns:
+            `dict` of attention processors: A dictionary containing all attention processors used in the model with
+            indexed by its weight name.
+        """
+        # set recursively
+        processors = {}
+
+        def fn_recursive_add_processors(name: str, module: torch.nn.Module,
+                                        processors: Dict[str,
+                                                         AttentionProcessor]):
+            if hasattr(module, "get_processor"):
+                processors[f"{name}.processor"] = module.get_processor()
+
+            for sub_name, child in module.named_children():
+                fn_recursive_add_processors(f"{name}.{sub_name}", child,
+                                            processors)
+
+            return processors
+
+        for name, module in self.named_children():
+            fn_recursive_add_processors(name, module, processors)
+
+        return processors
+
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
+    def set_attn_processor(self, processor: Union[AttentionProcessor,
+                                                  Dict[str,
+                                                       AttentionProcessor]]):
+        r"""
+        Sets the attention processor to use to compute attention.
+
+        Parameters:
+            processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
+                The instantiated processor class or a dictionary of processor classes that will be set as the processor
+                for **all** `Attention` layers.
+
+                If `processor` is a dict, the key needs to define the path to the corresponding cross attention
+                processor. This is strongly recommended when setting trainable attention processors.
+
+        """
+        count = len(self.attn_processors.keys())
+
+        if isinstance(processor, dict) and len(processor) != count:
+            raise ValueError(
+                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
+                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
+            )
+
+        def fn_recursive_attn_processor(name: str, module: torch.nn.Module,
+                                        processor):
+            if hasattr(module, "set_processor"):
+                if not isinstance(processor, dict):
+                    module.set_processor(processor)
+                else:
+                    module.set_processor(processor.pop(f"{name}.processor"))
+
+            for sub_name, child in module.named_children():
+                fn_recursive_attn_processor(f"{name}.{sub_name}", child,
+                                            processor)
+
+        for name, module in self.named_children():
+            fn_recursive_attn_processor(name, module, processor)
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if hasattr(module, "gradient_checkpointing"):
+            module.gradient_checkpointing = value
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor,
+        timestep: torch.LongTensor,
+        encoder_attention_mask: torch.Tensor,
+        guidance: torch.Tensor = None,
+        attention_kwargs: Optional[Dict[str, Any]] = None,
+        return_dict: bool = True,
+    ) -> Union[torch.Tensor, Dict[str, torch.Tensor]]:
+        if guidance is None:
+            guidance = torch.tensor([6016.0],
+                                    device=hidden_states.device,
+                                    dtype=torch.bfloat16)
+
+        if attention_kwargs is not None:
+            attention_kwargs = attention_kwargs.copy()
+            lora_scale = attention_kwargs.pop("scale", 1.0)
+        else:
+            lora_scale = 1.0
+
+        if USE_PEFT_BACKEND:
+            # weight the lora layers by setting `lora_scale` for each PEFT layer
+            scale_lora_layers(self, lora_scale)
+        else:
+            if attention_kwargs is not None and attention_kwargs.get(
+                    "scale", None) is not None:
+                logger.warning(
+                    "Passing `scale` via `attention_kwargs` when not using the PEFT backend is ineffective."
+                )
+
+        batch_size, num_channels, num_frames, height, width = hidden_states.shape
+        p, p_t = self.config.patch_size, self.config.patch_size_t
+        post_patch_num_frames = num_frames // p_t
+        post_patch_height = height // p
+        post_patch_width = width // p
+
+        pooled_projections = encoder_hidden_states[:, 0, :self.config.
+                                                   pooled_projection_dim]
+        encoder_hidden_states = encoder_hidden_states[:, 1:]
+
+        # 1. RoPE
+        image_rotary_emb = self.rope(hidden_states)
+
+        # 2. Conditional embeddings
+        temb = self.time_text_embed(timestep, guidance, pooled_projections)
+        hidden_states = self.x_embedder(hidden_states)
+        encoder_hidden_states = self.context_embedder(encoder_hidden_states,
+                                                      timestep,
+                                                      encoder_attention_mask)
+
+        # 3. Attention mask preparation
+        latent_sequence_length = hidden_states.shape[1]
+        condition_sequence_length = encoder_hidden_states.shape[1]
+        sequence_length = latent_sequence_length + condition_sequence_length
+        attention_mask = torch.zeros(batch_size,
+                                     sequence_length,
+                                     sequence_length,
+                                     device=hidden_states.device,
+                                     dtype=torch.bool)  # [B, N, N]
+
+        effective_condition_sequence_length = encoder_attention_mask.sum(
+            dim=1, dtype=torch.int)
+        effective_sequence_length = latent_sequence_length + effective_condition_sequence_length
+
+        for i in range(batch_size):
+            attention_mask[i, :effective_sequence_length[i], :
+                           effective_sequence_length[i]] = True
+
+        # 4. Transformer blocks
+        if torch.is_grad_enabled() and self.gradient_checkpointing:
+
+            def create_custom_forward(module, return_dict=None):
+
+                def custom_forward(*inputs):
+                    if return_dict is not None:
+                        return module(*inputs, return_dict=return_dict)
+                    else:
+                        return module(*inputs)
+
+                return custom_forward
+
+            ckpt_kwargs: Dict[str, Any] = {
+                "use_reentrant": False
+            } if is_torch_version(">=", "1.11.0") else {}
+
+            for block in self.transformer_blocks:
+                hidden_states, encoder_hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(block),
+                    hidden_states,
+                    encoder_hidden_states,
+                    temb,
+                    attention_mask,
+                    image_rotary_emb,
+                    **ckpt_kwargs,
+                )
+
+            for block in self.single_transformer_blocks:
+                hidden_states, encoder_hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(block),
+                    hidden_states,
+                    encoder_hidden_states,
+                    temb,
+                    attention_mask,
+                    image_rotary_emb,
+                    **ckpt_kwargs,
+                )
+
+        else:
+            for block in self.transformer_blocks:
+                hidden_states, encoder_hidden_states = block(
+                    hidden_states, encoder_hidden_states, temb, attention_mask,
+                    image_rotary_emb)
+
+            for block in self.single_transformer_blocks:
+                hidden_states, encoder_hidden_states = block(
+                    hidden_states, encoder_hidden_states, temb, attention_mask,
+                    image_rotary_emb)
+
+        # 5. Output projection
+        hidden_states = self.norm_out(hidden_states, temb)
+        hidden_states = self.proj_out(hidden_states)
+
+        hidden_states = hidden_states.reshape(batch_size,
+                                              post_patch_num_frames,
+                                              post_patch_height,
+                                              post_patch_width, -1, p_t, p, p)
+        hidden_states = hidden_states.permute(0, 4, 1, 5, 2, 6, 3, 7)
+        hidden_states = hidden_states.flatten(6, 7).flatten(4, 5).flatten(2, 3)
+
+        if USE_PEFT_BACKEND:
+            # remove `lora_scale` from each PEFT layer
+            unscale_lora_layers(self, lora_scale)
+
+        if not return_dict:
+            return (hidden_states, )
+
+        return Transformer2DModelOutput(sample=hidden_states)

fastvideo/models/hunyuan_hf/pipeline_hunyuan.py

@@ -0,0 +1,756 @@
+# Copyright 2024 The HunyuanVideo Team and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import inspect
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback
+from diffusers.loaders import HunyuanVideoLoraLoaderMixin
+from diffusers.models import (AutoencoderKLHunyuanVideo,
+                              HunyuanVideoTransformer3DModel)
+from diffusers.pipelines.hunyuan_video.pipeline_output import \
+    HunyuanVideoPipelineOutput
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline
+from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
+from diffusers.utils import logging, replace_example_docstring
+from diffusers.utils.torch_utils import randn_tensor
+from diffusers.video_processor import VideoProcessor
+from einops import rearrange
+from transformers import (CLIPTextModel, CLIPTokenizer, LlamaModel,
+                          LlamaTokenizerFast)
+
+from fastvideo.utils.communications import all_gather
+from fastvideo.utils.parallel_states import (get_sequence_parallel_state,
+                                             nccl_info)
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```python
+        >>> import torch
+        >>> from diffusers import HunyuanVideoPipeline, HunyuanVideoTransformer3DModel
+        >>> from diffusers.utils import export_to_video
+
+        >>> model_id = "tencent/HunyuanVideo"
+        >>> transformer = HunyuanVideoTransformer3DModel.from_pretrained(
+        ...     model_id, subfolder="transformer", torch_dtype=torch.bfloat16
+        ... )
+        >>> pipe = HunyuanVideoPipeline.from_pretrained(model_id, transformer=transformer, torch_dtype=torch.float16)
+        >>> pipe.vae.enable_tiling()
+        >>> pipe.to("cuda")
+
+        >>> output = pipe(
+        ...     prompt="A cat walks on the grass, realistic",
+        ...     height=320,
+        ...     width=512,
+        ...     num_frames=61,
+        ...     num_inference_steps=30,
+        ... ).frames[0]
+        >>> export_to_video(output, "output.mp4", fps=15)
+        ```
+"""
+
+DEFAULT_PROMPT_TEMPLATE = {
+    "template":
+    ("<|start_header_id|>system<|end_header_id|>\n\nDescribe the video by detailing the following aspects: "
+     "1. The main content and theme of the video."
+     "2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects."
+     "3. Actions, events, behaviors temporal relationships, physical movement changes of the objects."
+     "4. background environment, light, style and atmosphere."
+     "5. camera angles, movements, and transitions used in the video:<|eot_id|>"
+     "<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>"),
+    "crop_start":
+    95,
+}
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
+def retrieve_timesteps(
+    scheduler,
+    num_inference_steps: Optional[int] = None,
+    device: Optional[Union[str, torch.device]] = None,
+    timesteps: Optional[List[int]] = None,
+    sigmas: Optional[List[float]] = None,
+    **kwargs,
+):
+    r"""
+    Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
+    custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
+
+    Args:
+        scheduler (`SchedulerMixin`):
+            The scheduler to get timesteps from.
+        num_inference_steps (`int`):
+            The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
+            must be `None`.
+        device (`str` or `torch.device`, *optional*):
+            The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
+        timesteps (`List[int]`, *optional*):
+            Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
+            `num_inference_steps` and `sigmas` must be `None`.
+        sigmas (`List[float]`, *optional*):
+            Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
+            `num_inference_steps` and `timesteps` must be `None`.
+
+    Returns:
+        `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
+        second element is the number of inference steps.
+    """
+    if timesteps is not None and sigmas is not None:
+        raise ValueError(
+            "Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values"
+        )
+    if timesteps is not None:
+        accepts_timesteps = "timesteps" in set(
+            inspect.signature(scheduler.set_timesteps).parameters.keys())
+        if not accepts_timesteps:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" timestep schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    elif sigmas is not None:
+        accept_sigmas = "sigmas" in set(
+            inspect.signature(scheduler.set_timesteps).parameters.keys())
+        if not accept_sigmas:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" sigmas schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    else:
+        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+    return timesteps, num_inference_steps
+
+
+class HunyuanVideoPipeline(DiffusionPipeline, HunyuanVideoLoraLoaderMixin):
+    r"""
+    Pipeline for text-to-video generation using HunyuanVideo.
+
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
+    implemented for all pipelines (downloading, saving, running on a particular device, etc.).
+
+    Args:
+        text_encoder ([`LlamaModel`]):
+            [Llava Llama3-8B](https://huggingface.co/xtuner/llava-llama-3-8b-v1_1-transformers).
+        tokenizer_2 (`LlamaTokenizer`):
+            Tokenizer from [Llava Llama3-8B](https://huggingface.co/xtuner/llava-llama-3-8b-v1_1-transformers).
+        transformer ([`HunyuanVideoTransformer3DModel`]):
+            Conditional Transformer to denoise the encoded image latents.
+        scheduler ([`FlowMatchEulerDiscreteScheduler`]):
+            A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
+        vae ([`AutoencoderKLHunyuanVideo`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations.
+        text_encoder_2 ([`CLIPTextModel`]):
+            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
+            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
+        tokenizer_2 (`CLIPTokenizer`):
+            Tokenizer of class
+            [CLIPTokenizer](https://huggingface.co/docs/transformers/en/model_doc/clip#transformers.CLIPTokenizer).
+    """
+
+    model_cpu_offload_seq = "text_encoder->text_encoder_2->transformer->vae"
+    _callback_tensor_inputs = ["latents", "prompt_embeds"]
+
+    def __init__(
+        self,
+        text_encoder: LlamaModel,
+        tokenizer: LlamaTokenizerFast,
+        transformer: HunyuanVideoTransformer3DModel,
+        vae: AutoencoderKLHunyuanVideo,
+        scheduler: FlowMatchEulerDiscreteScheduler,
+        text_encoder_2: CLIPTextModel,
+        tokenizer_2: CLIPTokenizer,
+    ):
+        super().__init__()
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            transformer=transformer,
+            scheduler=scheduler,
+            text_encoder_2=text_encoder_2,
+            tokenizer_2=tokenizer_2,
+        )
+
+        self.vae_scale_factor_temporal = (self.vae.temporal_compression_ratio
+                                          if hasattr(self, "vae")
+                                          and self.vae is not None else 4)
+        self.vae_scale_factor_spatial = (self.vae.spatial_compression_ratio
+                                         if hasattr(self, "vae")
+                                         and self.vae is not None else 8)
+        self.video_processor = VideoProcessor(
+            vae_scale_factor=self.vae_scale_factor_spatial)
+
+    def _get_llama_prompt_embeds(
+        self,
+        prompt: Union[str, List[str]],
+        prompt_template: Dict[str, Any],
+        num_videos_per_prompt: int = 1,
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+        max_sequence_length: int = 256,
+        num_hidden_layers_to_skip: int = 2,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        device = device or self._execution_device
+        dtype = dtype or self.text_encoder.dtype
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+        batch_size = len(prompt)
+
+        prompt = [prompt_template["template"].format(p) for p in prompt]
+
+        crop_start = prompt_template.get("crop_start", None)
+        if crop_start is None:
+            prompt_template_input = self.tokenizer(
+                prompt_template["template"],
+                padding="max_length",
+                return_tensors="pt",
+                return_length=False,
+                return_overflowing_tokens=False,
+                return_attention_mask=False,
+            )
+            crop_start = prompt_template_input["input_ids"].shape[-1]
+            # Remove <|eot_id|> token and placeholder {}
+            crop_start -= 2
+
+        max_sequence_length += crop_start
+        text_inputs = self.tokenizer(
+            prompt,
+            max_length=max_sequence_length,
+            padding="max_length",
+            truncation=True,
+            return_tensors="pt",
+            return_length=False,
+            return_overflowing_tokens=False,
+            return_attention_mask=True,
+        )
+        text_input_ids = text_inputs.input_ids.to(device=device)
+        prompt_attention_mask = text_inputs.attention_mask.to(device=device)
+
+        prompt_embeds = self.text_encoder(
+            input_ids=text_input_ids,
+            attention_mask=prompt_attention_mask,
+            output_hidden_states=True,
+        ).hidden_states[-(num_hidden_layers_to_skip + 1)]
+        prompt_embeds = prompt_embeds.to(dtype=dtype)
+
+        if crop_start is not None and crop_start > 0:
+            prompt_embeds = prompt_embeds[:, crop_start:]
+            prompt_attention_mask = prompt_attention_mask[:, crop_start:]
+
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        _, seq_len, _ = prompt_embeds.shape
+        prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1)
+        prompt_embeds = prompt_embeds.view(batch_size * num_videos_per_prompt,
+                                           seq_len, -1)
+        prompt_attention_mask = prompt_attention_mask.repeat(
+            1, num_videos_per_prompt)
+        prompt_attention_mask = prompt_attention_mask.view(
+            batch_size * num_videos_per_prompt, seq_len)
+
+        return prompt_embeds, prompt_attention_mask
+
+    def _get_clip_prompt_embeds(
+        self,
+        prompt: Union[str, List[str]],
+        num_videos_per_prompt: int = 1,
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+        max_sequence_length: int = 77,
+    ) -> torch.Tensor:
+        device = device or self._execution_device
+        dtype = dtype or self.text_encoder_2.dtype
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+        batch_size = len(prompt)
+
+        text_inputs = self.tokenizer_2(
+            prompt,
+            padding="max_length",
+            max_length=max_sequence_length,
+            truncation=True,
+            return_tensors="pt",
+        )
+
+        text_input_ids = text_inputs.input_ids
+        untruncated_ids = self.tokenizer_2(prompt,
+                                           padding="longest",
+                                           return_tensors="pt").input_ids
+        if untruncated_ids.shape[-1] >= text_input_ids.shape[
+                -1] and not torch.equal(text_input_ids, untruncated_ids):
+            removed_text = self.tokenizer_2.batch_decode(
+                untruncated_ids[:, max_sequence_length - 1:-1])
+            logger.warning(
+                "The following part of your input was truncated because CLIP can only handle sequences up to"
+                f" {max_sequence_length} tokens: {removed_text}")
+
+        prompt_embeds = self.text_encoder_2(
+            text_input_ids.to(device),
+            output_hidden_states=False).pooler_output
+
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt)
+        prompt_embeds = prompt_embeds.view(batch_size * num_videos_per_prompt,
+                                           -1)
+
+        return prompt_embeds
+
+    def encode_prompt(
+        self,
+        prompt: Union[str, List[str]],
+        prompt_2: Union[str, List[str]] = None,
+        prompt_template: Dict[str, Any] = DEFAULT_PROMPT_TEMPLATE,
+        num_videos_per_prompt: int = 1,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        pooled_prompt_embeds: Optional[torch.Tensor] = None,
+        prompt_attention_mask: Optional[torch.Tensor] = None,
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+        max_sequence_length: int = 256,
+    ):
+
+        if prompt_embeds is None:
+            prompt_embeds, prompt_attention_mask = self._get_llama_prompt_embeds(
+                prompt,
+                prompt_template,
+                num_videos_per_prompt,
+                device=device,
+                dtype=dtype,
+                max_sequence_length=max_sequence_length,
+            )
+
+        if pooled_prompt_embeds is None:
+            if prompt_2 is None and pooled_prompt_embeds is None:
+                prompt_2 = prompt
+            pooled_prompt_embeds = self._get_clip_prompt_embeds(
+                prompt,
+                num_videos_per_prompt,
+                device=device,
+                dtype=dtype,
+                max_sequence_length=77,
+            )
+
+        return prompt_embeds, pooled_prompt_embeds, prompt_attention_mask
+
+    def check_inputs(
+        self,
+        prompt,
+        prompt_2,
+        height,
+        width,
+        prompt_embeds=None,
+        callback_on_step_end_tensor_inputs=None,
+        prompt_template=None,
+    ):
+        if height % 16 != 0 or width % 16 != 0:
+            raise ValueError(
+                f"`height` and `width` have to be divisible by 16 but are {height} and {width}."
+            )
+
+        if callback_on_step_end_tensor_inputs is not None and not all(
+                k in self._callback_tensor_inputs
+                for k in callback_on_step_end_tensor_inputs):
+            raise ValueError(
+                f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
+            )
+
+        if prompt is not None and prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+                " only forward one of the two.")
+        elif prompt_2 is not None and prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+                " only forward one of the two.")
+        elif prompt is None and prompt_embeds is None:
+            raise ValueError(
+                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
+            )
+        elif prompt is not None and (not isinstance(prompt, str)
+                                     and not isinstance(prompt, list)):
+            raise ValueError(
+                f"`prompt` has to be of type `str` or `list` but is {type(prompt)}"
+            )
+        elif prompt_2 is not None and (not isinstance(prompt_2, str)
+                                       and not isinstance(prompt_2, list)):
+            raise ValueError(
+                f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}"
+            )
+
+        if prompt_template is not None:
+            if not isinstance(prompt_template, dict):
+                raise ValueError(
+                    f"`prompt_template` has to be of type `dict` but is {type(prompt_template)}"
+                )
+            if "template" not in prompt_template:
+                raise ValueError(
+                    f"`prompt_template` has to contain a key `template` but only found {prompt_template.keys()}"
+                )
+
+    def prepare_latents(
+        self,
+        batch_size: int,
+        num_channels_latents: 32,
+        height: int = 720,
+        width: int = 1280,
+        num_frames: int = 129,
+        dtype: Optional[torch.dtype] = None,
+        device: Optional[torch.device] = None,
+        generator: Optional[Union[torch.Generator,
+                                  List[torch.Generator]]] = None,
+        latents: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        if latents is not None:
+            return latents.to(device=device, dtype=dtype)
+
+        shape = (
+            batch_size,
+            num_channels_latents,
+            num_frames,
+            int(height) // self.vae_scale_factor_spatial,
+            int(width) // self.vae_scale_factor_spatial,
+        )
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+
+        latents = randn_tensor(shape,
+                               generator=generator,
+                               device=device,
+                               dtype=dtype)
+        return latents
+
+    def enable_vae_slicing(self):
+        r"""
+        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
+        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
+        """
+        self.vae.enable_slicing()
+
+    def disable_vae_slicing(self):
+        r"""
+        Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
+        computing decoding in one step.
+        """
+        self.vae.disable_slicing()
+
+    def enable_vae_tiling(self):
+        r"""
+        Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
+        compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
+        processing larger images.
+        """
+        self.vae.enable_tiling()
+
+    def disable_vae_tiling(self):
+        r"""
+        Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
+        computing decoding in one step.
+        """
+        self.vae.disable_tiling()
+
+    @property
+    def guidance_scale(self):
+        return self._guidance_scale
+
+    @property
+    def num_timesteps(self):
+        return self._num_timesteps
+
+    @property
+    def attention_kwargs(self):
+        return self._attention_kwargs
+
+    @property
+    def interrupt(self):
+        return self._interrupt
+
+    @torch.no_grad()
+    @replace_example_docstring(EXAMPLE_DOC_STRING)
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        prompt_2: Union[str, List[str]] = None,
+        height: int = 720,
+        width: int = 1280,
+        num_frames: int = 129,
+        num_inference_steps: int = 50,
+        sigmas: List[float] = None,
+        guidance_scale: float = 6.0,
+        num_videos_per_prompt: Optional[int] = 1,
+        generator: Optional[Union[torch.Generator,
+                                  List[torch.Generator]]] = None,
+        latents: Optional[torch.Tensor] = None,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        pooled_prompt_embeds: Optional[torch.Tensor] = None,
+        prompt_attention_mask: Optional[torch.Tensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        attention_kwargs: Optional[Dict[str, Any]] = None,
+        callback_on_step_end: Optional[Union[Callable[[int, int, Dict],
+                                                      None], PipelineCallback,
+                                             MultiPipelineCallbacks]] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+        prompt_template: Dict[str, Any] = DEFAULT_PROMPT_TEMPLATE,
+        max_sequence_length: int = 256,
+    ):
+        r"""
+        The call function to the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
+                instead.
+            prompt_2 (`str` or `List[str]`, *optional*):
+                The prompt or prompts to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
+                will be used instead.
+            height (`int`, defaults to `720`):
+                The height in pixels of the generated image.
+            width (`int`, defaults to `1280`):
+                The width in pixels of the generated image.
+            num_frames (`int`, defaults to `129`):
+                The number of frames in the generated video.
+            num_inference_steps (`int`, defaults to `50`):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            sigmas (`List[float]`, *optional*):
+                Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
+                their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
+                will be used.
+            guidance_scale (`float`, defaults to `6.0`):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality. Note that the only available HunyuanVideo model is
+                CFG-distilled, which means that traditional guidance between unconditional and conditional latent is
+                not applied.
+            num_videos_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
+                generation deterministic.
+            latents (`torch.Tensor`, *optional*):
+                Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor is generated by sampling using the supplied random `generator`.
+            prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
+                provided, text embeddings are generated from the `prompt` input argument.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generated image. Choose between `PIL.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`HunyuanVideoPipelineOutput`] instead of a plain tuple.
+            attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+                `self.processor` in
+                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+            clip_skip (`int`, *optional*):
+                Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
+                the output of the pre-final layer will be used for computing the prompt embeddings.
+            callback_on_step_end (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*):
+                A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of
+                each denoising step during the inference. with the following arguments: `callback_on_step_end(self:
+                DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a
+                list of all tensors as specified by `callback_on_step_end_tensor_inputs`.
+            callback_on_step_end_tensor_inputs (`List`, *optional*):
+                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
+                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
+                `._callback_tensor_inputs` attribute of your pipeline class.
+
+        Examples:
+
+        Returns:
+            [`~HunyuanVideoPipelineOutput`] or `tuple`:
+                If `return_dict` is `True`, [`HunyuanVideoPipelineOutput`] is returned, otherwise a `tuple` is returned
+                where the first element is a list with the generated images and the second element is a list of `bool`s
+                indicating whether the corresponding generated image contains "not-safe-for-work" (nsfw) content.
+        """
+
+        if isinstance(callback_on_step_end,
+                      (PipelineCallback, MultiPipelineCallbacks)):
+            callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            prompt_2,
+            height,
+            width,
+            prompt_embeds,
+            callback_on_step_end_tensor_inputs,
+            prompt_template,
+        )
+
+        self._guidance_scale = guidance_scale
+        self._attention_kwargs = attention_kwargs
+        self._interrupt = False
+
+        device = self._execution_device
+
+        # 2. Define call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        # 3. Encode input prompt
+        prompt_embeds, pooled_prompt_embeds, prompt_attention_mask = self.encode_prompt(
+            prompt=prompt,
+            prompt_2=prompt,
+            prompt_template=prompt_template,
+            num_videos_per_prompt=num_videos_per_prompt,
+            prompt_embeds=prompt_embeds,
+            pooled_prompt_embeds=pooled_prompt_embeds,
+            prompt_attention_mask=prompt_attention_mask,
+            device=device,
+            max_sequence_length=max_sequence_length,
+        )
+
+        transformer_dtype = self.transformer.dtype
+        prompt_embeds = prompt_embeds.to(transformer_dtype)
+        prompt_attention_mask = prompt_attention_mask.to(transformer_dtype)
+        if pooled_prompt_embeds is not None:
+            pooled_prompt_embeds = pooled_prompt_embeds.to(transformer_dtype)
+
+        # 4. Prepare timesteps
+        sigmas = np.linspace(1.0, 0.0, num_inference_steps +
+                             1)[:-1] if sigmas is None else sigmas
+        timesteps, num_inference_steps = retrieve_timesteps(
+            self.scheduler,
+            num_inference_steps,
+            device,
+            sigmas=sigmas,
+        )
+
+        # 5. Prepare latent variables
+        num_channels_latents = self.transformer.config.in_channels
+        num_latent_frames = (num_frames -
+                             1) // self.vae_scale_factor_temporal + 1
+
+        latents = self.prepare_latents(
+            batch_size * num_videos_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            num_latent_frames,
+            torch.float32,
+            device,
+            generator,
+            latents,
+        )
+        # check sequence_parallel
+        world_size, rank = nccl_info.sp_size, nccl_info.rank_within_group
+        if get_sequence_parallel_state():
+            latents = rearrange(latents,
+                                "b t (n s) h w -> b t n s h w",
+                                n=world_size).contiguous()
+            latents = latents[:, :, rank, :, :, :]
+
+        # 6. Prepare guidance condition
+        guidance = torch.tensor([guidance_scale] * latents.shape[0],
+                                dtype=transformer_dtype,
+                                device=device) * 1000.0
+
+        # 7. Denoising loop
+        num_warmup_steps = len(
+            timesteps) - num_inference_steps * self.scheduler.order
+        self._num_timesteps = len(timesteps)
+
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                if self.interrupt:
+                    continue
+
+                latent_model_input = latents.to(transformer_dtype)
+                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+                timestep = t.expand(latents.shape[0]).to(latents.dtype)
+                if pooled_prompt_embeds.shape[-1] != prompt_embeds.shape[-1]:
+                    pooled_prompt_embeds_padding = F.pad(
+                        pooled_prompt_embeds,
+                        (0, prompt_embeds.shape[2] -
+                         pooled_prompt_embeds.shape[1]),
+                        value=0,
+                    ).unsqueeze(1)
+                encoder_hidden_states = torch.cat(
+                    [pooled_prompt_embeds_padding, prompt_embeds], dim=1)
+
+                noise_pred = self.transformer(
+                    hidden_states=latent_model_input,
+                    encoder_hidden_states=
+                    encoder_hidden_states,  # [1, 257, 4096]
+                    timestep=timestep,
+                    encoder_attention_mask=prompt_attention_mask,
+                    guidance=guidance,
+                    attention_kwargs=attention_kwargs,
+                    return_dict=False,
+                )[0]
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents = self.scheduler.step(noise_pred,
+                                              t,
+                                              latents,
+                                              return_dict=False)[0]
+
+                if callback_on_step_end is not None:
+                    callback_kwargs = {}
+                    for k in callback_on_step_end_tensor_inputs:
+                        callback_kwargs[k] = locals()[k]
+                    callback_outputs = callback_on_step_end(
+                        self, i, t, callback_kwargs)
+
+                    latents = callback_outputs.pop("latents", latents)
+                    prompt_embeds = callback_outputs.pop(
+                        "prompt_embeds", prompt_embeds)
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or (
+                    (i + 1) > num_warmup_steps and
+                    (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+
+        if get_sequence_parallel_state():
+            latents = all_gather(latents, dim=2)
+
+        if not output_type == "latent":
+            latents = latents.to(
+                self.vae.dtype) / self.vae.config.scaling_factor
+            video = self.vae.decode(latents, return_dict=False)[0]
+            video = self.video_processor.postprocess_video(
+                video, output_type=output_type)
+        else:
+            video = latents
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (video, )
+
+        return HunyuanVideoPipelineOutput(frames=video)

fastvideo/models/mochi_hf/convert_diffusers_to_mochi.py

@@ -1,19 +1,23 @@
-import torch
 import argparse
-from safetensors.torch import save_file
 import os
 
+import torch
+from safetensors.torch import save_file
+
 parser = argparse.ArgumentParser()
 parser.add_argument("--diffusers_path", required=True, type=str)
-parser.add_argument(
-    "--transformer_path", type=str, default=None, help="Path to save transformer model"
-)
-parser.add_argument(
-    "--vae_encoder_path", type=str, default=None, help="Path to save VAE encoder model"
-)
-parser.add_argument(
-    "--vae_decoder_path", type=str, default=None, help="Path to save VAE decoder model"
-)
+parser.add_argument("--transformer_path",
+                    type=str,
+                    default=None,
+                    help="Path to save transformer model")
+parser.add_argument("--vae_encoder_path",
+                    type=str,
+                    default=None,
+                    help="Path to save VAE encoder model")
+parser.add_argument("--vae_decoder_path",
+                    type=str,
+                    default=None,
+                    help="Path to save VAE decoder model")
 
 args = parser.parse_args()
 
@@ -36,49 +40,35 @@ def convert_diffusers_transformer_to_mochi(state_dict):
 
     # Convert patch_embed
     new_state_dict["x_embedder.proj.weight"] = original_state_dict.pop(
-        "patch_embed.proj.weight"
-    )
+        "patch_embed.proj.weight")
     new_state_dict["x_embedder.proj.bias"] = original_state_dict.pop(
-        "patch_embed.proj.bias"
-    )
+        "patch_embed.proj.bias")
 
     # Convert time_embed
     new_state_dict["t_embedder.mlp.0.weight"] = original_state_dict.pop(
-        "time_embed.timestep_embedder.linear_1.weight"
-    )
+        "time_embed.timestep_embedder.linear_1.weight")
     new_state_dict["t_embedder.mlp.0.bias"] = original_state_dict.pop(
-        "time_embed.timestep_embedder.linear_1.bias"
-    )
+        "time_embed.timestep_embedder.linear_1.bias")
     new_state_dict["t_embedder.mlp.2.weight"] = original_state_dict.pop(
-        "time_embed.timestep_embedder.linear_2.weight"
-    )
+        "time_embed.timestep_embedder.linear_2.weight")
     new_state_dict["t_embedder.mlp.2.bias"] = original_state_dict.pop(
-        "time_embed.timestep_embedder.linear_2.bias"
-    )
+        "time_embed.timestep_embedder.linear_2.bias")
     new_state_dict["t5_y_embedder.to_kv.weight"] = original_state_dict.pop(
-        "time_embed.pooler.to_kv.weight"
-    )
+        "time_embed.pooler.to_kv.weight")
     new_state_dict["t5_y_embedder.to_kv.bias"] = original_state_dict.pop(
-        "time_embed.pooler.to_kv.bias"
-    )
+        "time_embed.pooler.to_kv.bias")
     new_state_dict["t5_y_embedder.to_q.weight"] = original_state_dict.pop(
-        "time_embed.pooler.to_q.weight"
-    )
+        "time_embed.pooler.to_q.weight")
     new_state_dict["t5_y_embedder.to_q.bias"] = original_state_dict.pop(
-        "time_embed.pooler.to_q.bias"
-    )
+        "time_embed.pooler.to_q.bias")
     new_state_dict["t5_y_embedder.to_out.weight"] = original_state_dict.pop(
-        "time_embed.pooler.to_out.weight"
-    )
+        "time_embed.pooler.to_out.weight")
     new_state_dict["t5_y_embedder.to_out.bias"] = original_state_dict.pop(
-        "time_embed.pooler.to_out.bias"
-    )
+        "time_embed.pooler.to_out.bias")
     new_state_dict["t5_yproj.weight"] = original_state_dict.pop(
-        "time_embed.caption_proj.weight"
-    )
+        "time_embed.caption_proj.weight")
     new_state_dict["t5_yproj.bias"] = original_state_dict.pop(
-        "time_embed.caption_proj.bias"
-    )
+        "time_embed.caption_proj.bias")
 
     # Convert transformer blocks
     num_layers = 48
@@ -88,26 +78,24 @@ def convert_diffusers_transformer_to_mochi(state_dict):
 
         # norm1
         new_state_dict[new_prefix + "mod_x.weight"] = original_state_dict.pop(
-            block_prefix + "norm1.linear.weight"
-        )
+            block_prefix + "norm1.linear.weight")
         new_state_dict[new_prefix + "mod_x.bias"] = original_state_dict.pop(
-            block_prefix + "norm1.linear.bias"
-        )
+            block_prefix + "norm1.linear.bias")
 
         if i < num_layers - 1:
-            new_state_dict[new_prefix + "mod_y.weight"] = original_state_dict.pop(
-                block_prefix + "norm1_context.linear.weight"
-            )
-            new_state_dict[new_prefix + "mod_y.bias"] = original_state_dict.pop(
-                block_prefix + "norm1_context.linear.bias"
-            )
+            new_state_dict[new_prefix +
+                           "mod_y.weight"] = original_state_dict.pop(
+                               block_prefix + "norm1_context.linear.weight")
+            new_state_dict[new_prefix +
+                           "mod_y.bias"] = original_state_dict.pop(
+                               block_prefix + "norm1_context.linear.bias")
         else:
-            new_state_dict[new_prefix + "mod_y.weight"] = original_state_dict.pop(
-                block_prefix + "norm1_context.linear_1.weight"
-            )
-            new_state_dict[new_prefix + "mod_y.bias"] = original_state_dict.pop(
-                block_prefix + "norm1_context.linear_1.bias"
-            )
+            new_state_dict[new_prefix +
+                           "mod_y.weight"] = original_state_dict.pop(
+                               block_prefix + "norm1_context.linear_1.weight")
+            new_state_dict[new_prefix +
+                           "mod_y.bias"] = original_state_dict.pop(
+                               block_prefix + "norm1_context.linear_1.bias")
 
         # Visual attention
         q = original_state_dict.pop(block_prefix + "attn1.to_q.weight")
@@ -116,18 +104,18 @@ def convert_diffusers_transformer_to_mochi(state_dict):
         qkv_weight = torch.cat([q, k, v], dim=0)
         new_state_dict[new_prefix + "attn.qkv_x.weight"] = qkv_weight
 
-        new_state_dict[new_prefix + "attn.q_norm_x.weight"] = original_state_dict.pop(
-            block_prefix + "attn1.norm_q.weight"
-        )
-        new_state_dict[new_prefix + "attn.k_norm_x.weight"] = original_state_dict.pop(
-            block_prefix + "attn1.norm_k.weight"
-        )
-        new_state_dict[new_prefix + "attn.proj_x.weight"] = original_state_dict.pop(
-            block_prefix + "attn1.to_out.0.weight"
-        )
-        new_state_dict[new_prefix + "attn.proj_x.bias"] = original_state_dict.pop(
-            block_prefix + "attn1.to_out.0.bias"
-        )
+        new_state_dict[new_prefix +
+                       "attn.q_norm_x.weight"] = original_state_dict.pop(
+                           block_prefix + "attn1.norm_q.weight")
+        new_state_dict[new_prefix +
+                       "attn.k_norm_x.weight"] = original_state_dict.pop(
+                           block_prefix + "attn1.norm_k.weight")
+        new_state_dict[new_prefix +
+                       "attn.proj_x.weight"] = original_state_dict.pop(
+                           block_prefix + "attn1.to_out.0.weight")
+        new_state_dict[new_prefix +
+                       "attn.proj_x.bias"] = original_state_dict.pop(
+                           block_prefix + "attn1.to_out.0.bias")
 
         # Context attention
         q = original_state_dict.pop(block_prefix + "attn1.add_q_proj.weight")
@@ -136,48 +124,46 @@ def convert_diffusers_transformer_to_mochi(state_dict):
         qkv_weight = torch.cat([q, k, v], dim=0)
         new_state_dict[new_prefix + "attn.qkv_y.weight"] = qkv_weight
 
-        new_state_dict[new_prefix + "attn.q_norm_y.weight"] = original_state_dict.pop(
-            block_prefix + "attn1.norm_added_q.weight"
-        )
-        new_state_dict[new_prefix + "attn.k_norm_y.weight"] = original_state_dict.pop(
-            block_prefix + "attn1.norm_added_k.weight"
-        )
+        new_state_dict[new_prefix +
+                       "attn.q_norm_y.weight"] = original_state_dict.pop(
+                           block_prefix + "attn1.norm_added_q.weight")
+        new_state_dict[new_prefix +
+                       "attn.k_norm_y.weight"] = original_state_dict.pop(
+                           block_prefix + "attn1.norm_added_k.weight")
         if i < num_layers - 1:
-            new_state_dict[new_prefix + "attn.proj_y.weight"] = original_state_dict.pop(
-                block_prefix + "attn1.to_add_out.weight"
-            )
-            new_state_dict[new_prefix + "attn.proj_y.bias"] = original_state_dict.pop(
-                block_prefix + "attn1.to_add_out.bias"
-            )
+            new_state_dict[new_prefix +
+                           "attn.proj_y.weight"] = original_state_dict.pop(
+                               block_prefix + "attn1.to_add_out.weight")
+            new_state_dict[new_prefix +
+                           "attn.proj_y.bias"] = original_state_dict.pop(
+                               block_prefix + "attn1.to_add_out.bias")
 
         # MLP
         new_state_dict[new_prefix + "mlp_x.w1.weight"] = reverse_proj_gate(
-            original_state_dict.pop(block_prefix + "ff.net.0.proj.weight")
-        )
-        new_state_dict[new_prefix + "mlp_x.w2.weight"] = original_state_dict.pop(
-            block_prefix + "ff.net.2.weight"
-        )
+            original_state_dict.pop(block_prefix + "ff.net.0.proj.weight"))
+        new_state_dict[new_prefix +
+                       "mlp_x.w2.weight"] = original_state_dict.pop(
+                           block_prefix + "ff.net.2.weight")
         if i < num_layers - 1:
             new_state_dict[new_prefix + "mlp_y.w1.weight"] = reverse_proj_gate(
-                original_state_dict.pop(block_prefix + "ff_context.net.0.proj.weight")
-            )
-            new_state_dict[new_prefix + "mlp_y.w2.weight"] = original_state_dict.pop(
-                block_prefix + "ff_context.net.2.weight"
-            )
+                original_state_dict.pop(block_prefix +
+                                        "ff_context.net.0.proj.weight"))
+            new_state_dict[new_prefix +
+                           "mlp_y.w2.weight"] = original_state_dict.pop(
+                               block_prefix + "ff_context.net.2.weight")
 
     # Output layers
     new_state_dict["final_layer.mod.weight"] = reverse_scale_shift(
-        original_state_dict.pop("norm_out.linear.weight"), dim=0
-    )
+        original_state_dict.pop("norm_out.linear.weight"), dim=0)
     new_state_dict["final_layer.mod.bias"] = reverse_scale_shift(
-        original_state_dict.pop("norm_out.linear.bias"), dim=0
-    )
+        original_state_dict.pop("norm_out.linear.bias"), dim=0)
     new_state_dict["final_layer.linear.weight"] = original_state_dict.pop(
-        "proj_out.weight"
-    )
-    new_state_dict["final_layer.linear.bias"] = original_state_dict.pop("proj_out.bias")
+        "proj_out.weight")
+    new_state_dict["final_layer.linear.bias"] = original_state_dict.pop(
+        "proj_out.bias")
 
-    new_state_dict["pos_frequencies"] = original_state_dict.pop("pos_frequencies")
+    new_state_dict["pos_frequencies"] = original_state_dict.pop(
+        "pos_frequencies")
 
     print("Remaining Keys:", original_state_dict.keys())
 
@@ -193,307 +179,270 @@ def convert_diffusers_vae_to_mochi(state_dict):
     prefix = "encoder."
 
     encoder_state_dict["layers.0.weight"] = original_state_dict.pop(
-        f"{prefix}proj_in.weight"
-    )
+        f"{prefix}proj_in.weight")
     encoder_state_dict["layers.0.bias"] = original_state_dict.pop(
-        f"{prefix}proj_in.bias"
-    )
+        f"{prefix}proj_in.bias")
 
     # Convert block_in
     for i in range(3):
-        encoder_state_dict[f"layers.{i+1}.stack.0.weight"] = original_state_dict.pop(
-            f"{prefix}block_in.resnets.{i}.norm1.norm_layer.weight"
-        )
-        encoder_state_dict[f"layers.{i+1}.stack.0.bias"] = original_state_dict.pop(
-            f"{prefix}block_in.resnets.{i}.norm1.norm_layer.bias"
-        )
-        encoder_state_dict[f"layers.{i+1}.stack.2.weight"] = original_state_dict.pop(
-            f"{prefix}block_in.resnets.{i}.conv1.conv.weight"
-        )
-        encoder_state_dict[f"layers.{i+1}.stack.2.bias"] = original_state_dict.pop(
-            f"{prefix}block_in.resnets.{i}.conv1.conv.bias"
-        )
-        encoder_state_dict[f"layers.{i+1}.stack.3.weight"] = original_state_dict.pop(
-            f"{prefix}block_in.resnets.{i}.norm2.norm_layer.weight"
-        )
-        encoder_state_dict[f"layers.{i+1}.stack.3.bias"] = original_state_dict.pop(
-            f"{prefix}block_in.resnets.{i}.norm2.norm_layer.bias"
-        )
-        encoder_state_dict[f"layers.{i+1}.stack.5.weight"] = original_state_dict.pop(
-            f"{prefix}block_in.resnets.{i}.conv2.conv.weight"
-        )
-        encoder_state_dict[f"layers.{i+1}.stack.5.bias"] = original_state_dict.pop(
-            f"{prefix}block_in.resnets.{i}.conv2.conv.bias"
-        )
+        encoder_state_dict[
+            f"layers.{i+1}.stack.0.weight"] = original_state_dict.pop(
+                f"{prefix}block_in.resnets.{i}.norm1.norm_layer.weight")
+        encoder_state_dict[
+            f"layers.{i+1}.stack.0.bias"] = original_state_dict.pop(
+                f"{prefix}block_in.resnets.{i}.norm1.norm_layer.bias")
+        encoder_state_dict[
+            f"layers.{i+1}.stack.2.weight"] = original_state_dict.pop(
+                f"{prefix}block_in.resnets.{i}.conv1.conv.weight")
+        encoder_state_dict[
+            f"layers.{i+1}.stack.2.bias"] = original_state_dict.pop(
+                f"{prefix}block_in.resnets.{i}.conv1.conv.bias")
+        encoder_state_dict[
+            f"layers.{i+1}.stack.3.weight"] = original_state_dict.pop(
+                f"{prefix}block_in.resnets.{i}.norm2.norm_layer.weight")
+        encoder_state_dict[
+            f"layers.{i+1}.stack.3.bias"] = original_state_dict.pop(
+                f"{prefix}block_in.resnets.{i}.norm2.norm_layer.bias")
+        encoder_state_dict[
+            f"layers.{i+1}.stack.5.weight"] = original_state_dict.pop(
+                f"{prefix}block_in.resnets.{i}.conv2.conv.weight")
+        encoder_state_dict[
+            f"layers.{i+1}.stack.5.bias"] = original_state_dict.pop(
+                f"{prefix}block_in.resnets.{i}.conv2.conv.bias")
 
     # Convert down_blocks
     down_block_layers = [3, 4, 6]
     for block in range(3):
-        encoder_state_dict[f"layers.{block+4}.layers.0.weight"] = (
-            original_state_dict.pop(f"{prefix}down_blocks.{block}.conv_in.conv.weight")
-        )
-        encoder_state_dict[f"layers.{block+4}.layers.0.bias"] = original_state_dict.pop(
-            f"{prefix}down_blocks.{block}.conv_in.conv.bias"
-        )
+        encoder_state_dict[
+            f"layers.{block+4}.layers.0.weight"] = original_state_dict.pop(
+                f"{prefix}down_blocks.{block}.conv_in.conv.weight")
+        encoder_state_dict[
+            f"layers.{block+4}.layers.0.bias"] = original_state_dict.pop(
+                f"{prefix}down_blocks.{block}.conv_in.conv.bias")
 
         for i in range(down_block_layers[block]):
             # Convert resnets
-            encoder_state_dict[f"layers.{block+4}.layers.{i+1}.stack.0.weight"] = (
-                original_state_dict.pop(
+            encoder_state_dict[
+                f"layers.{block+4}.layers.{i+1}.stack.0.weight"] = original_state_dict.pop(
                     f"{prefix}down_blocks.{block}.resnets.{i}.norm1.norm_layer.weight"
                 )
-            )
-            encoder_state_dict[f"layers.{block+4}.layers.{i+1}.stack.0.bias"] = (
-                original_state_dict.pop(
+            encoder_state_dict[
+                f"layers.{block+4}.layers.{i+1}.stack.0.bias"] = original_state_dict.pop(
                     f"{prefix}down_blocks.{block}.resnets.{i}.norm1.norm_layer.bias"
                 )
-            )
-            encoder_state_dict[f"layers.{block+4}.layers.{i+1}.stack.2.weight"] = (
-                original_state_dict.pop(
+            encoder_state_dict[
+                f"layers.{block+4}.layers.{i+1}.stack.2.weight"] = original_state_dict.pop(
                     f"{prefix}down_blocks.{block}.resnets.{i}.conv1.conv.weight"
                 )
-            )
-            encoder_state_dict[f"layers.{block+4}.layers.{i+1}.stack.2.bias"] = (
-                original_state_dict.pop(
-                    f"{prefix}down_blocks.{block}.resnets.{i}.conv1.conv.bias"
-                )
-            )
-            encoder_state_dict[f"layers.{block+4}.layers.{i+1}.stack.3.weight"] = (
-                original_state_dict.pop(
+            encoder_state_dict[
+                f"layers.{block+4}.layers.{i+1}.stack.2.bias"] = original_state_dict.pop(
+                    f"{prefix}down_blocks.{block}.resnets.{i}.conv1.conv.bias")
+            encoder_state_dict[
+                f"layers.{block+4}.layers.{i+1}.stack.3.weight"] = original_state_dict.pop(
                     f"{prefix}down_blocks.{block}.resnets.{i}.norm2.norm_layer.weight"
                 )
-            )
-            encoder_state_dict[f"layers.{block+4}.layers.{i+1}.stack.3.bias"] = (
-                original_state_dict.pop(
+            encoder_state_dict[
+                f"layers.{block+4}.layers.{i+1}.stack.3.bias"] = original_state_dict.pop(
                     f"{prefix}down_blocks.{block}.resnets.{i}.norm2.norm_layer.bias"
                 )
-            )
-            encoder_state_dict[f"layers.{block+4}.layers.{i+1}.stack.5.weight"] = (
-                original_state_dict.pop(
+            encoder_state_dict[
+                f"layers.{block+4}.layers.{i+1}.stack.5.weight"] = original_state_dict.pop(
                     f"{prefix}down_blocks.{block}.resnets.{i}.conv2.conv.weight"
                 )
-            )
-            encoder_state_dict[f"layers.{block+4}.layers.{i+1}.stack.5.bias"] = (
-                original_state_dict.pop(
-                    f"{prefix}down_blocks.{block}.resnets.{i}.conv2.conv.bias"
-                )
-            )
+            encoder_state_dict[
+                f"layers.{block+4}.layers.{i+1}.stack.5.bias"] = original_state_dict.pop(
+                    f"{prefix}down_blocks.{block}.resnets.{i}.conv2.conv.bias")
 
             # Convert attentions
             q = original_state_dict.pop(
-                f"{prefix}down_blocks.{block}.attentions.{i}.to_q.weight"
-            )
+                f"{prefix}down_blocks.{block}.attentions.{i}.to_q.weight")
             k = original_state_dict.pop(
-                f"{prefix}down_blocks.{block}.attentions.{i}.to_k.weight"
-            )
+                f"{prefix}down_blocks.{block}.attentions.{i}.to_k.weight")
             v = original_state_dict.pop(
-                f"{prefix}down_blocks.{block}.attentions.{i}.to_v.weight"
-            )
+                f"{prefix}down_blocks.{block}.attentions.{i}.to_v.weight")
             qkv_weight = torch.cat([q, k, v], dim=0)
             encoder_state_dict[
-                f"layers.{block+4}.layers.{i+1}.attn_block.attn.qkv.weight"
-            ] = qkv_weight
+                f"layers.{block+4}.layers.{i+1}.attn_block.attn.qkv.weight"] = qkv_weight
 
             encoder_state_dict[
-                f"layers.{block+4}.layers.{i+1}.attn_block.attn.out.weight"
-            ] = original_state_dict.pop(
-                f"{prefix}down_blocks.{block}.attentions.{i}.to_out.0.weight"
-            )
+                f"layers.{block+4}.layers.{i+1}.attn_block.attn.out.weight"] = original_state_dict.pop(
+                    f"{prefix}down_blocks.{block}.attentions.{i}.to_out.0.weight"
+                )
             encoder_state_dict[
-                f"layers.{block+4}.layers.{i+1}.attn_block.attn.out.bias"
-            ] = original_state_dict.pop(
-                f"{prefix}down_blocks.{block}.attentions.{i}.to_out.0.bias"
-            )
+                f"layers.{block+4}.layers.{i+1}.attn_block.attn.out.bias"] = original_state_dict.pop(
+                    f"{prefix}down_blocks.{block}.attentions.{i}.to_out.0.bias"
+                )
             encoder_state_dict[
-                f"layers.{block+4}.layers.{i+1}.attn_block.norm.weight"
-            ] = original_state_dict.pop(
-                f"{prefix}down_blocks.{block}.norms.{i}.norm_layer.weight"
-            )
+                f"layers.{block+4}.layers.{i+1}.attn_block.norm.weight"] = original_state_dict.pop(
+                    f"{prefix}down_blocks.{block}.norms.{i}.norm_layer.weight")
             encoder_state_dict[
-                f"layers.{block+4}.layers.{i+1}.attn_block.norm.bias"
-            ] = original_state_dict.pop(
-                f"{prefix}down_blocks.{block}.norms.{i}.norm_layer.bias"
-            )
+                f"layers.{block+4}.layers.{i+1}.attn_block.norm.bias"] = original_state_dict.pop(
+                    f"{prefix}down_blocks.{block}.norms.{i}.norm_layer.bias")
 
     # Convert block_out
     for i in range(3):
-        encoder_state_dict[f"layers.{i+7}.stack.0.weight"] = original_state_dict.pop(
-            f"{prefix}block_out.resnets.{i}.norm1.norm_layer.weight"
-        )
-        encoder_state_dict[f"layers.{i+7}.stack.0.bias"] = original_state_dict.pop(
-            f"{prefix}block_out.resnets.{i}.norm1.norm_layer.bias"
-        )
-        encoder_state_dict[f"layers.{i+7}.stack.2.weight"] = original_state_dict.pop(
-            f"{prefix}block_out.resnets.{i}.conv1.conv.weight"
-        )
-        encoder_state_dict[f"layers.{i+7}.stack.2.bias"] = original_state_dict.pop(
-            f"{prefix}block_out.resnets.{i}.conv1.conv.bias"
-        )
-        encoder_state_dict[f"layers.{i+7}.stack.3.weight"] = original_state_dict.pop(
-            f"{prefix}block_out.resnets.{i}.norm2.norm_layer.weight"
-        )
-        encoder_state_dict[f"layers.{i+7}.stack.3.bias"] = original_state_dict.pop(
-            f"{prefix}block_out.resnets.{i}.norm2.norm_layer.bias"
-        )
-        encoder_state_dict[f"layers.{i+7}.stack.5.weight"] = original_state_dict.pop(
-            f"{prefix}block_out.resnets.{i}.conv2.conv.weight"
-        )
-        encoder_state_dict[f"layers.{i+7}.stack.5.bias"] = original_state_dict.pop(
-            f"{prefix}block_out.resnets.{i}.conv2.conv.bias"
-        )
+        encoder_state_dict[
+            f"layers.{i+7}.stack.0.weight"] = original_state_dict.pop(
+                f"{prefix}block_out.resnets.{i}.norm1.norm_layer.weight")
+        encoder_state_dict[
+            f"layers.{i+7}.stack.0.bias"] = original_state_dict.pop(
+                f"{prefix}block_out.resnets.{i}.norm1.norm_layer.bias")
+        encoder_state_dict[
+            f"layers.{i+7}.stack.2.weight"] = original_state_dict.pop(
+                f"{prefix}block_out.resnets.{i}.conv1.conv.weight")
+        encoder_state_dict[
+            f"layers.{i+7}.stack.2.bias"] = original_state_dict.pop(
+                f"{prefix}block_out.resnets.{i}.conv1.conv.bias")
+        encoder_state_dict[
+            f"layers.{i+7}.stack.3.weight"] = original_state_dict.pop(
+                f"{prefix}block_out.resnets.{i}.norm2.norm_layer.weight")
+        encoder_state_dict[
+            f"layers.{i+7}.stack.3.bias"] = original_state_dict.pop(
+                f"{prefix}block_out.resnets.{i}.norm2.norm_layer.bias")
+        encoder_state_dict[
+            f"layers.{i+7}.stack.5.weight"] = original_state_dict.pop(
+                f"{prefix}block_out.resnets.{i}.conv2.conv.weight")
+        encoder_state_dict[
+            f"layers.{i+7}.stack.5.bias"] = original_state_dict.pop(
+                f"{prefix}block_out.resnets.{i}.conv2.conv.bias")
 
-        q = original_state_dict.pop(f"{prefix}block_out.attentions.{i}.to_q.weight")
-        k = original_state_dict.pop(f"{prefix}block_out.attentions.{i}.to_k.weight")
-        v = original_state_dict.pop(f"{prefix}block_out.attentions.{i}.to_v.weight")
+        q = original_state_dict.pop(
+            f"{prefix}block_out.attentions.{i}.to_q.weight")
+        k = original_state_dict.pop(
+            f"{prefix}block_out.attentions.{i}.to_k.weight")
+        v = original_state_dict.pop(
+            f"{prefix}block_out.attentions.{i}.to_v.weight")
         qkv_weight = torch.cat([q, k, v], dim=0)
-        encoder_state_dict[f"layers.{i+7}.attn_block.attn.qkv.weight"] = qkv_weight
+        encoder_state_dict[
+            f"layers.{i+7}.attn_block.attn.qkv.weight"] = qkv_weight
 
-        encoder_state_dict[f"layers.{i+7}.attn_block.attn.out.weight"] = (
-            original_state_dict.pop(f"{prefix}block_out.attentions.{i}.to_out.0.weight")
-        )
-        encoder_state_dict[f"layers.{i+7}.attn_block.attn.out.bias"] = (
-            original_state_dict.pop(f"{prefix}block_out.attentions.{i}.to_out.0.bias")
-        )
-        encoder_state_dict[f"layers.{i+7}.attn_block.norm.weight"] = (
-            original_state_dict.pop(f"{prefix}block_out.norms.{i}.norm_layer.weight")
-        )
-        encoder_state_dict[f"layers.{i+7}.attn_block.norm.bias"] = (
-            original_state_dict.pop(f"{prefix}block_out.norms.{i}.norm_layer.bias")
-        )
+        encoder_state_dict[
+            f"layers.{i+7}.attn_block.attn.out.weight"] = original_state_dict.pop(
+                f"{prefix}block_out.attentions.{i}.to_out.0.weight")
+        encoder_state_dict[
+            f"layers.{i+7}.attn_block.attn.out.bias"] = original_state_dict.pop(
+                f"{prefix}block_out.attentions.{i}.to_out.0.bias")
+        encoder_state_dict[
+            f"layers.{i+7}.attn_block.norm.weight"] = original_state_dict.pop(
+                f"{prefix}block_out.norms.{i}.norm_layer.weight")
+        encoder_state_dict[
+            f"layers.{i+7}.attn_block.norm.bias"] = original_state_dict.pop(
+                f"{prefix}block_out.norms.{i}.norm_layer.bias")
 
     # Convert output layers
     encoder_state_dict["output_norm.weight"] = original_state_dict.pop(
-        f"{prefix}norm_out.norm_layer.weight"
-    )
+        f"{prefix}norm_out.norm_layer.weight")
     encoder_state_dict["output_norm.bias"] = original_state_dict.pop(
-        f"{prefix}norm_out.norm_layer.bias"
-    )
+        f"{prefix}norm_out.norm_layer.bias")
     encoder_state_dict["output_proj.weight"] = original_state_dict.pop(
-        f"{prefix}proj_out.weight"
-    )
+        f"{prefix}proj_out.weight")
 
     # Convert decoder
     prefix = "decoder."
 
     decoder_state_dict["blocks.0.0.weight"] = original_state_dict.pop(
-        f"{prefix}conv_in.weight"
-    )
+        f"{prefix}conv_in.weight")
     decoder_state_dict["blocks.0.0.bias"] = original_state_dict.pop(
-        f"{prefix}conv_in.bias"
-    )
+        f"{prefix}conv_in.bias")
 
     # Convert block_in
     for i in range(3):
-        decoder_state_dict[f"blocks.0.{i+1}.stack.0.weight"] = original_state_dict.pop(
-            f"{prefix}block_in.resnets.{i}.norm1.norm_layer.weight"
-        )
-        decoder_state_dict[f"blocks.0.{i+1}.stack.0.bias"] = original_state_dict.pop(
-            f"{prefix}block_in.resnets.{i}.norm1.norm_layer.bias"
-        )
-        decoder_state_dict[f"blocks.0.{i+1}.stack.2.weight"] = original_state_dict.pop(
-            f"{prefix}block_in.resnets.{i}.conv1.conv.weight"
-        )
-        decoder_state_dict[f"blocks.0.{i+1}.stack.2.bias"] = original_state_dict.pop(
-            f"{prefix}block_in.resnets.{i}.conv1.conv.bias"
-        )
-        decoder_state_dict[f"blocks.0.{i+1}.stack.3.weight"] = original_state_dict.pop(
-            f"{prefix}block_in.resnets.{i}.norm2.norm_layer.weight"
-        )
-        decoder_state_dict[f"blocks.0.{i+1}.stack.3.bias"] = original_state_dict.pop(
-            f"{prefix}block_in.resnets.{i}.norm2.norm_layer.bias"
-        )
-        decoder_state_dict[f"blocks.0.{i+1}.stack.5.weight"] = original_state_dict.pop(
-            f"{prefix}block_in.resnets.{i}.conv2.conv.weight"
-        )
-        decoder_state_dict[f"blocks.0.{i+1}.stack.5.bias"] = original_state_dict.pop(
-            f"{prefix}block_in.resnets.{i}.conv2.conv.bias"
-        )
+        decoder_state_dict[
+            f"blocks.0.{i+1}.stack.0.weight"] = original_state_dict.pop(
+                f"{prefix}block_in.resnets.{i}.norm1.norm_layer.weight")
+        decoder_state_dict[
+            f"blocks.0.{i+1}.stack.0.bias"] = original_state_dict.pop(
+                f"{prefix}block_in.resnets.{i}.norm1.norm_layer.bias")
+        decoder_state_dict[
+            f"blocks.0.{i+1}.stack.2.weight"] = original_state_dict.pop(
+                f"{prefix}block_in.resnets.{i}.conv1.conv.weight")
+        decoder_state_dict[
+            f"blocks.0.{i+1}.stack.2.bias"] = original_state_dict.pop(
+                f"{prefix}block_in.resnets.{i}.conv1.conv.bias")
+        decoder_state_dict[
+            f"blocks.0.{i+1}.stack.3.weight"] = original_state_dict.pop(
+                f"{prefix}block_in.resnets.{i}.norm2.norm_layer.weight")
+        decoder_state_dict[
+            f"blocks.0.{i+1}.stack.3.bias"] = original_state_dict.pop(
+                f"{prefix}block_in.resnets.{i}.norm2.norm_layer.bias")
+        decoder_state_dict[
+            f"blocks.0.{i+1}.stack.5.weight"] = original_state_dict.pop(
+                f"{prefix}block_in.resnets.{i}.conv2.conv.weight")
+        decoder_state_dict[
+            f"blocks.0.{i+1}.stack.5.bias"] = original_state_dict.pop(
+                f"{prefix}block_in.resnets.{i}.conv2.conv.bias")
 
     # Convert up_blocks
     up_block_layers = [6, 4, 3]
     for block in range(3):
         for i in range(up_block_layers[block]):
-            decoder_state_dict[f"blocks.{block+1}.blocks.{i}.stack.0.weight"] = (
-                original_state_dict.pop(
+            decoder_state_dict[
+                f"blocks.{block+1}.blocks.{i}.stack.0.weight"] = original_state_dict.pop(
                     f"{prefix}up_blocks.{block}.resnets.{i}.norm1.norm_layer.weight"
                 )
-            )
-            decoder_state_dict[f"blocks.{block+1}.blocks.{i}.stack.0.bias"] = (
-                original_state_dict.pop(
+            decoder_state_dict[
+                f"blocks.{block+1}.blocks.{i}.stack.0.bias"] = original_state_dict.pop(
                     f"{prefix}up_blocks.{block}.resnets.{i}.norm1.norm_layer.bias"
                 )
-            )
-            decoder_state_dict[f"blocks.{block+1}.blocks.{i}.stack.2.weight"] = (
-                original_state_dict.pop(
-                    f"{prefix}up_blocks.{block}.resnets.{i}.conv1.conv.weight"
-                )
-            )
-            decoder_state_dict[f"blocks.{block+1}.blocks.{i}.stack.2.bias"] = (
-                original_state_dict.pop(
-                    f"{prefix}up_blocks.{block}.resnets.{i}.conv1.conv.bias"
-                )
-            )
-            decoder_state_dict[f"blocks.{block+1}.blocks.{i}.stack.3.weight"] = (
-                original_state_dict.pop(
+            decoder_state_dict[
+                f"blocks.{block+1}.blocks.{i}.stack.2.weight"] = original_state_dict.pop(
+                    f"{prefix}up_blocks.{block}.resnets.{i}.conv1.conv.weight")
+            decoder_state_dict[
+                f"blocks.{block+1}.blocks.{i}.stack.2.bias"] = original_state_dict.pop(
+                    f"{prefix}up_blocks.{block}.resnets.{i}.conv1.conv.bias")
+            decoder_state_dict[
+                f"blocks.{block+1}.blocks.{i}.stack.3.weight"] = original_state_dict.pop(
                     f"{prefix}up_blocks.{block}.resnets.{i}.norm2.norm_layer.weight"
                 )
-            )
-            decoder_state_dict[f"blocks.{block+1}.blocks.{i}.stack.3.bias"] = (
-                original_state_dict.pop(
+            decoder_state_dict[
+                f"blocks.{block+1}.blocks.{i}.stack.3.bias"] = original_state_dict.pop(
                     f"{prefix}up_blocks.{block}.resnets.{i}.norm2.norm_layer.bias"
                 )
-            )
-            decoder_state_dict[f"blocks.{block+1}.blocks.{i}.stack.5.weight"] = (
-                original_state_dict.pop(
-                    f"{prefix}up_blocks.{block}.resnets.{i}.conv2.conv.weight"
-                )
-            )
-            decoder_state_dict[f"blocks.{block+1}.blocks.{i}.stack.5.bias"] = (
-                original_state_dict.pop(
-                    f"{prefix}up_blocks.{block}.resnets.{i}.conv2.conv.bias"
-                )
-            )
-        decoder_state_dict[f"blocks.{block+1}.proj.weight"] = original_state_dict.pop(
-            f"{prefix}up_blocks.{block}.proj.weight"
-        )
-        decoder_state_dict[f"blocks.{block+1}.proj.bias"] = original_state_dict.pop(
-            f"{prefix}up_blocks.{block}.proj.bias"
-        )
+            decoder_state_dict[
+                f"blocks.{block+1}.blocks.{i}.stack.5.weight"] = original_state_dict.pop(
+                    f"{prefix}up_blocks.{block}.resnets.{i}.conv2.conv.weight")
+            decoder_state_dict[
+                f"blocks.{block+1}.blocks.{i}.stack.5.bias"] = original_state_dict.pop(
+                    f"{prefix}up_blocks.{block}.resnets.{i}.conv2.conv.bias")
+        decoder_state_dict[
+            f"blocks.{block+1}.proj.weight"] = original_state_dict.pop(
+                f"{prefix}up_blocks.{block}.proj.weight")
+        decoder_state_dict[
+            f"blocks.{block+1}.proj.bias"] = original_state_dict.pop(
+                f"{prefix}up_blocks.{block}.proj.bias")
 
     # Convert block_out
     for i in range(3):
-        decoder_state_dict[f"blocks.4.{i}.stack.0.weight"] = original_state_dict.pop(
-            f"{prefix}block_out.resnets.{i}.norm1.norm_layer.weight"
-        )
-        decoder_state_dict[f"blocks.4.{i}.stack.0.bias"] = original_state_dict.pop(
-            f"{prefix}block_out.resnets.{i}.norm1.norm_layer.bias"
-        )
-        decoder_state_dict[f"blocks.4.{i}.stack.2.weight"] = original_state_dict.pop(
-            f"{prefix}block_out.resnets.{i}.conv1.conv.weight"
-        )
-        decoder_state_dict[f"blocks.4.{i}.stack.2.bias"] = original_state_dict.pop(
-            f"{prefix}block_out.resnets.{i}.conv1.conv.bias"
-        )
-        decoder_state_dict[f"blocks.4.{i}.stack.3.weight"] = original_state_dict.pop(
-            f"{prefix}block_out.resnets.{i}.norm2.norm_layer.weight"
-        )
-        decoder_state_dict[f"blocks.4.{i}.stack.3.bias"] = original_state_dict.pop(
-            f"{prefix}block_out.resnets.{i}.norm2.norm_layer.bias"
-        )
-        decoder_state_dict[f"blocks.4.{i}.stack.5.weight"] = original_state_dict.pop(
-            f"{prefix}block_out.resnets.{i}.conv2.conv.weight"
-        )
-        decoder_state_dict[f"blocks.4.{i}.stack.5.bias"] = original_state_dict.pop(
-            f"{prefix}block_out.resnets.{i}.conv2.conv.bias"
-        )
+        decoder_state_dict[
+            f"blocks.4.{i}.stack.0.weight"] = original_state_dict.pop(
+                f"{prefix}block_out.resnets.{i}.norm1.norm_layer.weight")
+        decoder_state_dict[
+            f"blocks.4.{i}.stack.0.bias"] = original_state_dict.pop(
+                f"{prefix}block_out.resnets.{i}.norm1.norm_layer.bias")
+        decoder_state_dict[
+            f"blocks.4.{i}.stack.2.weight"] = original_state_dict.pop(
+                f"{prefix}block_out.resnets.{i}.conv1.conv.weight")
+        decoder_state_dict[
+            f"blocks.4.{i}.stack.2.bias"] = original_state_dict.pop(
+                f"{prefix}block_out.resnets.{i}.conv1.conv.bias")
+        decoder_state_dict[
+            f"blocks.4.{i}.stack.3.weight"] = original_state_dict.pop(
+                f"{prefix}block_out.resnets.{i}.norm2.norm_layer.weight")
+        decoder_state_dict[
+            f"blocks.4.{i}.stack.3.bias"] = original_state_dict.pop(
+                f"{prefix}block_out.resnets.{i}.norm2.norm_layer.bias")
+        decoder_state_dict[
+            f"blocks.4.{i}.stack.5.weight"] = original_state_dict.pop(
+                f"{prefix}block_out.resnets.{i}.conv2.conv.weight")
+        decoder_state_dict[
+            f"blocks.4.{i}.stack.5.bias"] = original_state_dict.pop(
+                f"{prefix}block_out.resnets.{i}.conv2.conv.bias")
 
     # Convert output layers
     decoder_state_dict["output_proj.weight"] = original_state_dict.pop(
-        f"{prefix}proj_out.weight"
-    )
+        f"{prefix}proj_out.weight")
     decoder_state_dict["output_proj.bias"] = original_state_dict.pop(
-        f"{prefix}proj_out.bias"
-    )
+        f"{prefix}proj_out.bias")
 
     return encoder_state_dict, decoder_state_dict
 
@@ -519,10 +468,9 @@ def main(args):
         transformer_path = ensure_safetensors_extension(args.transformer_path)
         ensure_directory_exists(transformer_path)
 
-        print(f"Converting transformer model...")
+        print("Converting transformer model...")
         transformer_state_dict = convert_diffusers_transformer_to_mochi(
-            pipe.transformer.state_dict()
-        )
+            pipe.transformer.state_dict())
         save_file(transformer_state_dict, transformer_path)
         print(f"Saved transformer to {transformer_path}")
 
@@ -533,10 +481,9 @@ def main(args):
         ensure_directory_exists(encoder_path)
         ensure_directory_exists(decoder_path)
 
-        print(f"Converting VAE models...")
+        print("Converting VAE models...")
         encoder_state_dict, decoder_state_dict = convert_diffusers_vae_to_mochi(
-            pipe.vae.state_dict()
-        )
+            pipe.vae.state_dict())
 
         save_file(encoder_state_dict, encoder_path)
         print(f"Saved VAE encoder to {encoder_path}")

fastvideo/models/mochi_hf/mochi_latents_utils.py

@@ -1,37 +1,33 @@
 import torch
 
-mochi_latents_mean = torch.tensor(
-    [
-        -0.06730895953510081,
-        -0.038011381506090416,
-        -0.07477820912866141,
-        -0.05565264470995561,
-        0.012767231469026969,
-        -0.04703542746246419,
-        0.043896967884726704,
-        -0.09346305707025976,
-        -0.09918314763016893,
-        -0.008729793427399178,
-        -0.011931556316503654,
-        -0.0321993391887285,
-    ]
-).view(1, 12, 1, 1, 1)
-mochi_latents_std = torch.tensor(
-    [
-        0.9263795028493863,
-        0.9248894543193766,
-        0.9393059390890617,
-        0.959253732819592,
-        0.8244560132752793,
-        0.917259975397747,
-        0.9294154431013696,
-        1.3720942357788521,
-        0.881393668867029,
-        0.9168315692124348,
-        0.9185249279345552,
-        0.9274757570805041,
-    ]
-).view(1, 12, 1, 1, 1)
+mochi_latents_mean = torch.tensor([
+    -0.06730895953510081,
+    -0.038011381506090416,
+    -0.07477820912866141,
+    -0.05565264470995561,
+    0.012767231469026969,
+    -0.04703542746246419,
+    0.043896967884726704,
+    -0.09346305707025976,
+    -0.09918314763016893,
+    -0.008729793427399178,
+    -0.011931556316503654,
+    -0.0321993391887285,
+]).view(1, 12, 1, 1, 1)
+mochi_latents_std = torch.tensor([
+    0.9263795028493863,
+    0.9248894543193766,
+    0.9393059390890617,
+    0.959253732819592,
+    0.8244560132752793,
+    0.917259975397747,
+    0.9294154431013696,
+    1.3720942357788521,
+    0.881393668867029,
+    0.9168315692124348,
+    0.9185249279345552,
+    0.9274757570805041,
+]).view(1, 12, 1, 1, 1)
 mochi_scaling_factor = 1.0
 
 
@@ -41,6 +37,8 @@ def normalize_dit_input(model_type, latents):
         latents_std = mochi_latents_std.to(latents.device, latents.dtype)
         latents = (latents - latents_mean) / latents_std
         return latents
+    elif model_type == "hunyuan_hf":
+        return latents * 0.476986
     elif model_type == "hunyuan":
         return latents * 0.476986
     else:

fastvideo/models/mochi_hf/modeling_mochi.py

@@ -16,47 +16,33 @@ from typing import Any, Dict, Optional, Tuple
 
 import torch
 import torch.nn as nn
-import diffusers
+import torch.nn.functional as F
 from diffusers.configuration_utils import ConfigMixin, register_to_config
-from diffusers.utils import is_torch_version, logging
-from diffusers.utils import (
-    USE_PEFT_BACKEND,
-    is_torch_version,
-    logging,
-    scale_lora_layers,
-    unscale_lora_layers,
-)
-from diffusers.utils.torch_utils import maybe_allow_in_graph
+from diffusers.loaders import PeftAdapterMixin
 from diffusers.models.attention import FeedForward as HF_FeedForward
 from diffusers.models.attention_processor import Attention
-from diffusers.models.embeddings import (
-    MochiCombinedTimestepCaptionEmbedding,
-    PatchEmbed,
-)
-from diffusers.models.modeling_outputs import Transformer2DModelOutput
+from diffusers.models.embeddings import (MochiCombinedTimestepCaptionEmbedding,
+                                         PatchEmbed)
 from diffusers.models.modeling_utils import ModelMixin
-from diffusers.loaders import PeftAdapterMixin
-from fastvideo.models.mochi_hf.norm import (
-    MochiLayerNormContinuous,
-    MochiRMSNormZero,
-    MochiModulatedRMSNorm,
-    MochiRMSNorm,
-)
 from diffusers.models.normalization import AdaLayerNormContinuous
-
-from fastvideo.utils.parallel_states import get_sequence_parallel_state, nccl_info
-from fastvideo.utils.communications import all_gather, all_to_all_4D
-import torch.nn.functional as F
-from diffusers.utils.torch_utils import is_torch_version, maybe_allow_in_graph
+from diffusers.utils import (USE_PEFT_BACKEND, is_torch_version, logging,
+                             scale_lora_layers, unscale_lora_layers)
+from diffusers.utils.torch_utils import maybe_allow_in_graph
+from liger_kernel.ops.swiglu import LigerSiLUMulFunction
 
 from fastvideo.models.flash_attn_no_pad import flash_attn_no_pad
-
-from liger_kernel.ops.swiglu import LigerSiLUMulFunction
+from fastvideo.models.mochi_hf.norm import (MochiLayerNormContinuous,
+                                            MochiModulatedRMSNorm,
+                                            MochiRMSNorm, MochiRMSNormZero)
+from fastvideo.utils.communications import all_gather, all_to_all_4D
+from fastvideo.utils.parallel_states import (get_sequence_parallel_state,
+                                             nccl_info)
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
 class FeedForward(HF_FeedForward):
+
     def __init__(
         self,
         dim: int,
@@ -68,9 +54,8 @@ class FeedForward(HF_FeedForward):
         inner_dim=None,
         bias: bool = True,
     ):
-        super().__init__(
-            dim, dim_out, mult, dropout, activation_fn, final_dropout, inner_dim, bias
-        )
+        super().__init__(dim, dim_out, mult, dropout, activation_fn,
+                         final_dropout, inner_dim, bias)
         assert activation_fn == "swiglu"
 
     def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
@@ -81,6 +66,7 @@ class FeedForward(HF_FeedForward):
 
 
 class MochiAttention(nn.Module):
+
     def __init__(
         self,
         query_dim: int,
@@ -114,25 +100,26 @@ class MochiAttention(nn.Module):
         self.to_k = nn.Linear(query_dim, self.inner_dim, bias=bias)
         self.to_v = nn.Linear(query_dim, self.inner_dim, bias=bias)
 
-        self.add_k_proj = nn.Linear(
-            added_kv_proj_dim, self.inner_dim, bias=added_proj_bias
-        )
-        self.add_v_proj = nn.Linear(
-            added_kv_proj_dim, self.inner_dim, bias=added_proj_bias
-        )
+        self.add_k_proj = nn.Linear(added_kv_proj_dim,
+                                    self.inner_dim,
+                                    bias=added_proj_bias)
+        self.add_v_proj = nn.Linear(added_kv_proj_dim,
+                                    self.inner_dim,
+                                    bias=added_proj_bias)
         if self.context_pre_only is not None:
-            self.add_q_proj = nn.Linear(
-                added_kv_proj_dim, self.inner_dim, bias=added_proj_bias
-            )
+            self.add_q_proj = nn.Linear(added_kv_proj_dim,
+                                        self.inner_dim,
+                                        bias=added_proj_bias)
 
         self.to_out = nn.ModuleList([])
-        self.to_out.append(nn.Linear(self.inner_dim, self.out_dim, bias=out_bias))
+        self.to_out.append(
+            nn.Linear(self.inner_dim, self.out_dim, bias=out_bias))
         self.to_out.append(nn.Dropout(dropout))
 
         if not self.context_pre_only:
-            self.to_add_out = nn.Linear(
-                self.inner_dim, self.out_context_dim, bias=out_bias
-            )
+            self.to_add_out = nn.Linear(self.inner_dim,
+                                        self.out_context_dim,
+                                        bias=out_bias)
 
         self.processor = processor
 
@@ -212,8 +199,8 @@ class MochiAttnProcessor2_0:
             def shrink_head(encoder_state, dim):
                 local_heads = encoder_state.shape[dim] // nccl_info.sp_size
                 return encoder_state.narrow(
-                    dim, nccl_info.rank_within_group * local_heads, local_heads
-                )
+                    dim, nccl_info.rank_within_group * local_heads,
+                    local_heads)
 
             encoder_query = shrink_head(encoder_query, dim=2)
             encoder_key = shrink_head(encoder_key, dim=2)
@@ -254,9 +241,11 @@ class MochiAttnProcessor2_0:
 
         attn_mask = encoder_attention_mask[:, :].bool()
         attn_mask = F.pad(attn_mask, (sequence_length, 0), value=True)
-        hidden_states = flash_attn_no_pad(
-            qkv, attn_mask, causal=False, dropout_p=0.0, softmax_scale=None
-        )
+        hidden_states = flash_attn_no_pad(qkv,
+                                          attn_mask,
+                                          causal=False,
+                                          dropout_p=0.0,
+                                          softmax_scale=None)
 
         # hidden_states = F.scaled_dot_product_attention(query, key, value, attn_mask = None, dropout_p=0.0, is_causal=False)
 
@@ -267,13 +256,13 @@ class MochiAttnProcessor2_0:
         # hidden_states = flex_attention(query, key, value, score_mod=no_padding_mask)
         if get_sequence_parallel_state():
             hidden_states, encoder_hidden_states = hidden_states.split_with_sizes(
-                (sequence_length, encoder_sequence_length), dim=1
-            )
+                (sequence_length, encoder_sequence_length), dim=1)
             # B, S, H, D
-            hidden_states = all_to_all_4D(hidden_states, scatter_dim=1, gather_dim=2)
-            encoder_hidden_states = all_gather(
-                encoder_hidden_states, dim=2
-            ).contiguous()
+            hidden_states = all_to_all_4D(hidden_states,
+                                          scatter_dim=1,
+                                          gather_dim=2)
+            encoder_hidden_states = all_gather(encoder_hidden_states,
+                                               dim=2).contiguous()
             hidden_states = hidden_states.flatten(2, 3)
             hidden_states = hidden_states.to(query.dtype)
             encoder_hidden_states = encoder_hidden_states.flatten(2, 3)
@@ -283,8 +272,7 @@ class MochiAttnProcessor2_0:
             hidden_states = hidden_states.to(query.dtype)
 
             hidden_states, encoder_hidden_states = hidden_states.split_with_sizes(
-                (sequence_length, encoder_sequence_length), dim=1
-            )
+                (sequence_length, encoder_sequence_length), dim=1)
 
         # linear proj
         hidden_states = attn.to_out[0](hidden_states)
@@ -336,12 +324,16 @@ class MochiTransformerBlock(nn.Module):
         self.ff_inner_dim = (4 * dim * 2) // 3
         self.ff_context_inner_dim = (4 * pooled_projection_dim * 2) // 3
 
-        self.norm1 = MochiRMSNormZero(dim, 4 * dim, eps=eps, elementwise_affine=False)
+        self.norm1 = MochiRMSNormZero(dim,
+                                      4 * dim,
+                                      eps=eps,
+                                      elementwise_affine=False)
 
         if not context_pre_only:
-            self.norm1_context = MochiRMSNormZero(
-                dim, 4 * pooled_projection_dim, eps=eps, elementwise_affine=False
-            )
+            self.norm1_context = MochiRMSNormZero(dim,
+                                                  4 * pooled_projection_dim,
+                                                  eps=eps,
+                                                  elementwise_affine=False)
         else:
             self.norm1_context = MochiLayerNormContinuous(
                 embedding_dim=pooled_projection_dim,
@@ -365,18 +357,17 @@ class MochiTransformerBlock(nn.Module):
 
         # TODO(aryan): norm_context layers are not needed when `context_pre_only` is True
         self.norm2 = MochiModulatedRMSNorm(eps=eps)
-        self.norm2_context = (
-            MochiModulatedRMSNorm(eps=eps) if not self.context_pre_only else None
-        )
+        self.norm2_context = (MochiModulatedRMSNorm(
+            eps=eps) if not self.context_pre_only else None)
 
         self.norm3 = MochiModulatedRMSNorm(eps)
-        self.norm3_context = (
-            MochiModulatedRMSNorm(eps=eps) if not self.context_pre_only else None
-        )
+        self.norm3_context = (MochiModulatedRMSNorm(
+            eps=eps) if not self.context_pre_only else None)
 
-        self.ff = FeedForward(
-            dim, inner_dim=self.ff_inner_dim, activation_fn=activation_fn, bias=False
-        )
+        self.ff = FeedForward(dim,
+                              inner_dim=self.ff_inner_dim,
+                              activation_fn=activation_fn,
+                              bias=False)
         self.ff_context = None
         if not context_pre_only:
             self.ff_context = FeedForward(
@@ -399,8 +390,7 @@ class MochiTransformerBlock(nn.Module):
         output_attn=False,
     ) -> Tuple[torch.Tensor, torch.Tensor]:
         norm_hidden_states, gate_msa, scale_mlp, gate_mlp = self.norm1(
-            hidden_states, temb
-        )
+            hidden_states, temb)
 
         if not self.context_pre_only:
             (
@@ -410,7 +400,8 @@ class MochiTransformerBlock(nn.Module):
                 enc_gate_mlp,
             ) = self.norm1_context(encoder_hidden_states, temb)
         else:
-            norm_encoder_hidden_states = self.norm1_context(encoder_hidden_states, temb)
+            norm_encoder_hidden_states = self.norm1_context(
+                encoder_hidden_states, temb)
 
         attn_hidden_states, context_attn_hidden_states = self.attn1(
             hidden_states=norm_hidden_states,
@@ -420,28 +411,27 @@ class MochiTransformerBlock(nn.Module):
         )
 
         hidden_states = hidden_states + self.norm2(
-            attn_hidden_states, torch.tanh(gate_msa).unsqueeze(1)
-        )
+            attn_hidden_states,
+            torch.tanh(gate_msa).unsqueeze(1))
         norm_hidden_states = self.norm3(
-            hidden_states, (1 + scale_mlp.unsqueeze(1).to(torch.float32))
-        )
+            hidden_states, (1 + scale_mlp.unsqueeze(1).to(torch.float32)))
         ff_output = self.ff(norm_hidden_states)
         hidden_states = hidden_states + self.norm4(
-            ff_output, torch.tanh(gate_mlp).unsqueeze(1)
-        )
+            ff_output,
+            torch.tanh(gate_mlp).unsqueeze(1))
 
         if not self.context_pre_only:
             encoder_hidden_states = encoder_hidden_states + self.norm2_context(
-                context_attn_hidden_states, torch.tanh(enc_gate_msa).unsqueeze(1)
-            )
+                context_attn_hidden_states,
+                torch.tanh(enc_gate_msa).unsqueeze(1))
             norm_encoder_hidden_states = self.norm3_context(
                 encoder_hidden_states,
                 (1 + enc_scale_mlp.unsqueeze(1).to(torch.float32)),
             )
             context_ff_output = self.ff_context(norm_encoder_hidden_states)
             encoder_hidden_states = encoder_hidden_states + self.norm4_context(
-                context_ff_output, torch.tanh(enc_gate_mlp).unsqueeze(1)
-            )
+                context_ff_output,
+                torch.tanh(enc_gate_mlp).unsqueeze(1))
 
         if not output_attn:
             attn_hidden_states = None
@@ -465,7 +455,11 @@ class MochiRoPE(nn.Module):
         self.target_area = base_height * base_width
 
     def _centers(self, start, stop, num, device, dtype) -> torch.Tensor:
-        edges = torch.linspace(start, stop, num + 1, device=device, dtype=dtype)
+        edges = torch.linspace(start,
+                               stop,
+                               num + 1,
+                               device=device,
+                               dtype=dtype)
         return (edges[:-1] + edges[1:]) / 2
 
     def _get_positions(
@@ -476,24 +470,28 @@ class MochiRoPE(nn.Module):
         device: Optional[torch.device] = None,
         dtype: Optional[torch.dtype] = None,
     ) -> torch.Tensor:
-        scale = (self.target_area / (height * width)) ** 0.5
-        t = torch.arange(num_frames * nccl_info.sp_size, device=device, dtype=dtype)
-        h = self._centers(
-            -height * scale / 2, height * scale / 2, height, device, dtype
-        )
-        w = self._centers(-width * scale / 2, width * scale / 2, width, device, dtype)
+        scale = (self.target_area / (height * width))**0.5
+        t = torch.arange(num_frames * nccl_info.sp_size,
+                         device=device,
+                         dtype=dtype)
+        h = self._centers(-height * scale / 2, height * scale / 2, height,
+                          device, dtype)
+        w = self._centers(-width * scale / 2, width * scale / 2, width, device,
+                          dtype)
 
         grid_t, grid_h, grid_w = torch.meshgrid(t, h, w, indexing="ij")
 
         positions = torch.stack([grid_t, grid_h, grid_w], dim=-1).view(-1, 3)
         return positions
 
-    def _create_rope(self, freqs: torch.Tensor, pos: torch.Tensor) -> torch.Tensor:
+    def _create_rope(self, freqs: torch.Tensor,
+                     pos: torch.Tensor) -> torch.Tensor:
         with torch.autocast(freqs.device.type, enabled=False):
             # Always run ROPE freqs computation in FP32
             freqs = torch.einsum(
-                "nd,dhf->nhf", pos.to(torch.float32), freqs.to(torch.float32)
-            )
+                "nd,dhf->nhf",  # codespell:ignore
+                pos.to(torch.float32),  # codespell:ignore
+                freqs.to(torch.float32))
         freqs_cos = torch.cos(freqs)
         freqs_sin = torch.sin(freqs)
         return freqs_cos, freqs_sin
@@ -581,24 +579,20 @@ class MochiTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
         )
 
         self.pos_frequencies = nn.Parameter(
-            torch.full((3, num_attention_heads, attention_head_dim // 2), 0.0)
-        )
+            torch.full((3, num_attention_heads, attention_head_dim // 2), 0.0))
         self.rope = MochiRoPE()
 
-        self.transformer_blocks = nn.ModuleList(
-            [
-                MochiTransformerBlock(
-                    dim=inner_dim,
-                    num_attention_heads=num_attention_heads,
-                    attention_head_dim=attention_head_dim,
-                    pooled_projection_dim=pooled_projection_dim,
-                    qk_norm=qk_norm,
-                    activation_fn=activation_fn,
-                    context_pre_only=i == num_layers - 1,
-                )
-                for i in range(num_layers)
-            ]
-        )
+        self.transformer_blocks = nn.ModuleList([
+            MochiTransformerBlock(
+                dim=inner_dim,
+                num_attention_heads=num_attention_heads,
+                attention_head_dim=attention_head_dim,
+                pooled_projection_dim=pooled_projection_dim,
+                qk_norm=qk_norm,
+                activation_fn=activation_fn,
+                context_pre_only=i == num_layers - 1,
+            ) for i in range(num_layers)
+        ])
 
         self.norm_out = AdaLayerNormContinuous(
             inner_dim,
@@ -607,7 +601,8 @@ class MochiTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
             eps=1e-6,
             norm_type="layer_norm",
         )
-        self.proj_out = nn.Linear(inner_dim, patch_size * patch_size * out_channels)
+        self.proj_out = nn.Linear(inner_dim,
+                                  patch_size * patch_size * out_channels)
 
         self.gradient_checkpointing = False
 
@@ -622,13 +617,12 @@ class MochiTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
         timestep: torch.LongTensor,
         encoder_attention_mask: torch.Tensor,
         output_features=False,
-        output_features_stride = 8,
+        output_features_stride=8,
         attention_kwargs: Optional[Dict[str, Any]] = None,
         return_dict: bool = False,
     ) -> torch.Tensor:
-        assert (
-            return_dict is False
-        ), "return_dict is not supported in MochiTransformer3DModel"
+        assert (return_dict is False
+                ), "return_dict is not supported in MochiTransformer3DModel"
 
         if attention_kwargs is not None:
             attention_kwargs = attention_kwargs.copy()
@@ -640,10 +634,8 @@ class MochiTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
             # weight the lora layers by setting `lora_scale` for each PEFT layer
             scale_lora_layers(self, lora_scale)
         else:
-            if (
-                attention_kwargs is not None
-                and attention_kwargs.get("scale", None) is not None
-            ):
+            if (attention_kwargs is not None
+                    and attention_kwargs.get("scale", None) is not None):
                 logger.warning(
                     "Passing `scale` via `attention_kwargs` when not using the PEFT backend is ineffective."
                 )
@@ -664,7 +656,8 @@ class MochiTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
 
         hidden_states = hidden_states.permute(0, 2, 1, 3, 4).flatten(0, 1)
         hidden_states = self.patch_embed(hidden_states)
-        hidden_states = hidden_states.unflatten(0, (batch_size, -1)).flatten(1, 2)
+        hidden_states = hidden_states.unflatten(0, (batch_size, -1)).flatten(
+            1, 2)
 
         image_rotary_emb = self.rope(
             self.pos_frequencies,
@@ -679,14 +672,15 @@ class MochiTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
             if self.gradient_checkpointing:
 
                 def create_custom_forward(module):
+
                     def custom_forward(*inputs):
                         return module(*inputs)
 
                     return custom_forward
 
-                ckpt_kwargs: Dict[str, Any] = (
-                    {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
-                )
+                ckpt_kwargs: Dict[str, Any] = ({
+                    "use_reentrant": False
+                } if is_torch_version(">=", "1.11.0") else {})
                 (
                     hidden_states,
                     encoder_hidden_states,
@@ -716,11 +710,12 @@ class MochiTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
         hidden_states = self.norm_out(hidden_states, temb)
         hidden_states = self.proj_out(hidden_states)
 
-        hidden_states = hidden_states.reshape(
-            batch_size, num_frames, post_patch_height, post_patch_width, p, p, -1
-        )
+        hidden_states = hidden_states.reshape(batch_size, num_frames,
+                                              post_patch_height,
+                                              post_patch_width, p, p, -1)
         hidden_states = hidden_states.permute(0, 6, 1, 2, 4, 3, 5)
-        output = hidden_states.reshape(batch_size, -1, num_frames, height, width)
+        output = hidden_states.reshape(batch_size, -1, num_frames, height,
+                                       width)
 
         if USE_PEFT_BACKEND:
             # remove `lora_scale` from each PEFT layer

fastvideo/models/mochi_hf/norm.py

@@ -13,15 +13,14 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import numbers
-from typing import Dict, Optional, Tuple
+from typing import Tuple
 
 import torch
 import torch.nn as nn
-import torch.nn.functional as F
 
 
 class MochiModulatedRMSNorm(nn.Module):
+
     def __init__(self, eps: float):
         super().__init__()
 
@@ -41,6 +40,7 @@ class MochiModulatedRMSNorm(nn.Module):
 
 
 class MochiRMSNorm(nn.Module):
+
     def __init__(self, dim, eps: float, elementwise_affine=True):
         super().__init__()
 
@@ -66,6 +66,7 @@ class MochiRMSNorm(nn.Module):
 
 
 class MochiLayerNormContinuous(nn.Module):
+
     def __init__(
         self,
         embedding_dim: int,
@@ -77,7 +78,9 @@ class MochiLayerNormContinuous(nn.Module):
 
         # AdaLN
         self.silu = nn.SiLU()
-        self.linear_1 = nn.Linear(conditioning_embedding_dim, embedding_dim, bias=bias)
+        self.linear_1 = nn.Linear(conditioning_embedding_dim,
+                                  embedding_dim,
+                                  bias=bias)
         self.norm = MochiModulatedRMSNorm(eps=eps)
 
     def forward(
@@ -122,9 +125,8 @@ class MochiRMSNormZero(nn.Module):
         emb = self.linear(self.silu(emb))
         scale_msa, gate_msa, scale_mlp, gate_mlp = emb.chunk(4, dim=1)
 
-        hidden_states = self.norm(
-            hidden_states, (1 + scale_msa[:, None].to(torch.float32))
-        )
+        hidden_states = self.norm(hidden_states,
+                                  (1 + scale_msa[:, None].to(torch.float32)))
         hidden_states = hidden_states.to(hidden_states_dtype)
 
         return hidden_states, gate_msa, scale_mlp, gate_mlp

fastvideo/models/mochi_hf/pipeline_mochi.py

@@ -12,30 +12,29 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import inspect
-from typing import Callable, Dict, List, Optional, Union, Any
 import copy
+import inspect
+from typing import Any, Callable, Dict, List, Optional, Union
+
 import numpy as np
 import torch
-from transformers import T5EncoderModel, T5TokenizerFast
-
 from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback
+from diffusers.loaders import Mochi1LoraLoaderMixin
 from diffusers.models.autoencoders import AutoencoderKL
-from fastvideo.models.mochi_hf.modeling_mochi import MochiTransformer3DModel
-
+from diffusers.pipelines.mochi.pipeline_output import MochiPipelineOutput
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline
 from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
-from diffusers.utils import (
-    is_torch_xla_available,
-    logging,
-    replace_example_docstring,
-)
+from diffusers.utils import (is_torch_xla_available, logging,
+                             replace_example_docstring)
 from diffusers.utils.torch_utils import randn_tensor
 from diffusers.video_processor import VideoProcessor
-from diffusers.pipelines.pipeline_utils import DiffusionPipeline
-from diffusers.pipelines.mochi.pipeline_output import MochiPipelineOutput
 from einops import rearrange
-from fastvideo.utils.parallel_states import get_sequence_parallel_state, nccl_info
+from transformers import T5EncoderModel, T5TokenizerFast
+
+from fastvideo.models.mochi_hf.modeling_mochi import MochiTransformer3DModel
 from fastvideo.utils.communications import all_gather
+from fastvideo.utils.parallel_states import (get_sequence_parallel_state,
+                                             nccl_info)
 
 if is_torch_xla_available():
     import torch_xla.core.xla_model as xm
@@ -44,7 +43,6 @@ if is_torch_xla_available():
 else:
     XLA_AVAILABLE = False
 
-
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 EXAMPLE_DOC_STRING = """
@@ -85,10 +83,10 @@ def linear_quadratic_schedule(num_steps, threshold_noise, linear_steps=None):
     ]
     threshold_noise_step_diff = linear_steps - threshold_noise * num_steps
     quadratic_steps = num_steps - linear_steps
-    quadratic_coef = threshold_noise_step_diff / (linear_steps * quadratic_steps**2)
+    quadratic_coef = threshold_noise_step_diff / (linear_steps *
+                                                  quadratic_steps**2)
     linear_coef = threshold_noise / linear_steps - 2 * threshold_noise_step_diff / (
-        quadratic_steps**2
-    )
+        quadratic_steps**2)
     const = quadratic_coef * (linear_steps**2)
     quadratic_sigma_schedule = [
         quadratic_coef * (i**2) + linear_coef * i + const
@@ -137,8 +135,7 @@ def retrieve_timesteps(
         )
     if timesteps is not None:
         accepts_timesteps = "timesteps" in set(
-            inspect.signature(scheduler.set_timesteps).parameters.keys()
-        )
+            inspect.signature(scheduler.set_timesteps).parameters.keys())
         if not accepts_timesteps:
             raise ValueError(
                 f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
@@ -149,8 +146,7 @@ def retrieve_timesteps(
         num_inference_steps = len(timesteps)
     elif sigmas is not None:
         accept_sigmas = "sigmas" in set(
-            inspect.signature(scheduler.set_timesteps).parameters.keys()
-        )
+            inspect.signature(scheduler.set_timesteps).parameters.keys())
         if not accept_sigmas:
             raise ValueError(
                 f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
@@ -165,7 +161,7 @@ def retrieve_timesteps(
     return timesteps, num_inference_steps
 
 
-class MochiPipeline(DiffusionPipeline):
+class MochiPipeline(DiffusionPipeline, Mochi1LoraLoaderMixin):
     r"""
     The mochi pipeline for text-to-video generation.
 
@@ -191,7 +187,9 @@ class MochiPipeline(DiffusionPipeline):
 
     model_cpu_offload_seq = "text_encoder->transformer->vae"
     _optional_components = []
-    _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
+    _callback_tensor_inputs = [
+        "latents", "prompt_embeds", "negative_prompt_embeds"
+    ]
 
     def __init__(
         self,
@@ -215,13 +213,10 @@ class MochiPipeline(DiffusionPipeline):
         self.patch_size = 2
 
         self.video_processor = VideoProcessor(
-            vae_scale_factor=self.vae_spatial_scale_factor
-        )
-        self.tokenizer_max_length = (
-            self.tokenizer.model_max_length
-            if hasattr(self, "tokenizer") and self.tokenizer is not None
-            else 77
-        )
+            vae_scale_factor=self.vae_spatial_scale_factor)
+        self.tokenizer_max_length = (self.tokenizer.model_max_length
+                                     if hasattr(self, "tokenizer")
+                                     and self.tokenizer is not None else 77)
         self.default_height = 480
         self.default_width = 848
 
@@ -252,35 +247,31 @@ class MochiPipeline(DiffusionPipeline):
         prompt_attention_mask = text_inputs.attention_mask
         prompt_attention_mask = prompt_attention_mask.bool().to(device)
 
-        untruncated_ids = self.tokenizer(
-            prompt, padding="longest", return_tensors="pt"
-        ).input_ids
+        untruncated_ids = self.tokenizer(prompt,
+                                         padding="longest",
+                                         return_tensors="pt").input_ids
 
-        if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
-            text_input_ids, untruncated_ids
-        ):
+        if untruncated_ids.shape[-1] >= text_input_ids.shape[
+                -1] and not torch.equal(text_input_ids, untruncated_ids):
             removed_text = self.tokenizer.batch_decode(
-                untruncated_ids[:, max_sequence_length - 1 : -1]
-            )
+                untruncated_ids[:, max_sequence_length - 1:-1])
             logger.warning(
                 "The following part of your input was truncated because `max_sequence_length` is set to "
-                f" {max_sequence_length} tokens: {removed_text}"
-            )
+                f" {max_sequence_length} tokens: {removed_text}")
 
         prompt_embeds = self.text_encoder(
-            text_input_ids.to(device), attention_mask=prompt_attention_mask
-        )[0]
+            text_input_ids.to(device), attention_mask=prompt_attention_mask)[0]
         prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
 
         # duplicate text embeddings for each generation per prompt, using mps friendly method
         _, seq_len, _ = prompt_embeds.shape
         prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1)
-        prompt_embeds = prompt_embeds.view(
-            batch_size * num_videos_per_prompt, seq_len, -1
-        )
+        prompt_embeds = prompt_embeds.view(batch_size * num_videos_per_prompt,
+                                           seq_len, -1)
 
         prompt_attention_mask = prompt_attention_mask.view(batch_size, -1)
-        prompt_attention_mask = prompt_attention_mask.repeat(num_videos_per_prompt, 1)
+        prompt_attention_mask = prompt_attention_mask.repeat(
+            num_videos_per_prompt, 1)
 
         return prompt_embeds, prompt_attention_mask
 
@@ -344,23 +335,19 @@ class MochiPipeline(DiffusionPipeline):
 
         if do_classifier_free_guidance and negative_prompt_embeds is None:
             negative_prompt = negative_prompt or ""
-            negative_prompt = (
-                batch_size * [negative_prompt]
-                if isinstance(negative_prompt, str)
-                else negative_prompt
-            )
+            negative_prompt = (batch_size * [negative_prompt] if isinstance(
+                negative_prompt, str) else negative_prompt)
 
-            if prompt is not None and type(prompt) is not type(negative_prompt):
+            if prompt is not None and type(prompt) is not type(
+                    negative_prompt):
                 raise TypeError(
                     f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
-                    f" {type(prompt)}."
-                )
+                    f" {type(prompt)}.")
             elif batch_size != len(negative_prompt):
                 raise ValueError(
                     f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
                     f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
-                    " the batch size of `prompt`."
-                )
+                    " the batch size of `prompt`.")
 
             (
                 negative_prompt_embeds,
@@ -397,9 +384,8 @@ class MochiPipeline(DiffusionPipeline):
             )
 
         if callback_on_step_end_tensor_inputs is not None and not all(
-            k in self._callback_tensor_inputs
-            for k in callback_on_step_end_tensor_inputs
-        ):
+                k in self._callback_tensor_inputs
+                for k in callback_on_step_end_tensor_inputs):
             raise ValueError(
                 f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
             )
@@ -407,15 +393,13 @@ class MochiPipeline(DiffusionPipeline):
         if prompt is not None and prompt_embeds is not None:
             raise ValueError(
                 f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
-                " only forward one of the two."
-            )
+                " only forward one of the two.")
         elif prompt is None and prompt_embeds is None:
             raise ValueError(
                 "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
             )
-        elif prompt is not None and (
-            not isinstance(prompt, str) and not isinstance(prompt, list)
-        ):
+        elif prompt is not None and (not isinstance(prompt, str)
+                                     and not isinstance(prompt, list)):
             raise ValueError(
                 f"`prompt` has to be of type `str` or `list` but is {type(prompt)}"
             )
@@ -425,10 +409,8 @@ class MochiPipeline(DiffusionPipeline):
                 "Must provide `prompt_attention_mask` when specifying `prompt_embeds`."
             )
 
-        if (
-            negative_prompt_embeds is not None
-            and negative_prompt_attention_mask is None
-        ):
+        if (negative_prompt_embeds is not None
+                and negative_prompt_attention_mask is None):
             raise ValueError(
                 "Must provide `negative_prompt_attention_mask` when specifying `negative_prompt_embeds`."
             )
@@ -438,14 +420,12 @@ class MochiPipeline(DiffusionPipeline):
                 raise ValueError(
                     "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
                     f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
-                    f" {negative_prompt_embeds.shape}."
-                )
+                    f" {negative_prompt_embeds.shape}.")
             if prompt_attention_mask.shape != negative_prompt_attention_mask.shape:
                 raise ValueError(
                     "`prompt_attention_mask` and `negative_prompt_attention_mask` must have the same shape when passed directly, but"
                     f" got: `prompt_attention_mask` {prompt_attention_mask.shape} != `negative_prompt_attention_mask`"
-                    f" {negative_prompt_attention_mask.shape}."
-                )
+                    f" {negative_prompt_attention_mask.shape}.")
 
     def enable_vae_slicing(self):
         r"""
@@ -502,7 +482,11 @@ class MochiPipeline(DiffusionPipeline):
                 f" size of {batch_size}. Make sure the batch size matches the length of the generators."
             )
 
-        latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+        latents = randn_tensor(shape,
+                               generator=generator,
+                               device=device,
+                               dtype=torch.float32)
+        latents = latents.to(dtype)
         return latents
 
     @property
@@ -533,12 +517,13 @@ class MochiPipeline(DiffusionPipeline):
         negative_prompt: Optional[Union[str, List[str]]] = None,
         height: Optional[int] = None,
         width: Optional[int] = None,
-        num_frames: int = 16,
-        num_inference_steps: int = 28,
+        num_frames: int = 19,
+        num_inference_steps: int = 64,
         timesteps: List[int] = None,
         guidance_scale: float = 4.5,
         num_videos_per_prompt: Optional[int] = 1,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        generator: Optional[Union[torch.Generator,
+                                  List[torch.Generator]]] = None,
         latents: Optional[torch.Tensor] = None,
         prompt_embeds: Optional[torch.Tensor] = None,
         prompt_attention_mask: Optional[torch.Tensor] = None,
@@ -547,7 +532,8 @@ class MochiPipeline(DiffusionPipeline):
         output_type: Optional[str] = "pil",
         return_dict: bool = True,
         attention_kwargs: Optional[Dict[str, Any]] = None,
-        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
+        callback_on_step_end: Optional[Callable[[int, int, Dict],
+                                                None]] = None,
         callback_on_step_end_tensor_inputs: List[str] = ["latents"],
         max_sequence_length: int = 256,
         return_all_states=False,
@@ -626,7 +612,8 @@ class MochiPipeline(DiffusionPipeline):
                 is returned where the first element is a list with the generated images.
         """
 
-        if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
+        if isinstance(callback_on_step_end,
+                      (PipelineCallback, MultiPipelineCallbacks)):
             callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
 
         height = height or self.default_height
@@ -637,7 +624,8 @@ class MochiPipeline(DiffusionPipeline):
             prompt=prompt,
             height=height,
             width=width,
-            callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
+            callback_on_step_end_tensor_inputs=
+            callback_on_step_end_tensor_inputs,
             prompt_embeds=prompt_embeds,
             negative_prompt_embeds=negative_prompt_embeds,
             prompt_attention_mask=prompt_attention_mask,
@@ -677,10 +665,10 @@ class MochiPipeline(DiffusionPipeline):
             device=device,
         )
         if self.do_classifier_free_guidance:
-            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
+            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds],
+                                      dim=0)
             prompt_attention_mask = torch.cat(
-                [negative_prompt_attention_mask, prompt_attention_mask], dim=0
-            )
+                [negative_prompt_attention_mask, prompt_attention_mask], dim=0)
 
         # 4. Prepare latent variables
         num_channels_latents = self.transformer.config.in_channels
@@ -697,16 +685,17 @@ class MochiPipeline(DiffusionPipeline):
         )
         world_size, rank = nccl_info.sp_size, nccl_info.rank_within_group
         if get_sequence_parallel_state():
-            latents = rearrange(
-                latents, "b t (n s) h w -> b t n s h w", n=world_size
-            ).contiguous()
+            latents = rearrange(latents,
+                                "b t (n s) h w -> b t n s h w",
+                                n=world_size).contiguous()
             latents = latents[:, :, rank, :, :, :]
 
         original_noise = copy.deepcopy(latents)
         # 5. Prepare timestep
         # from https://github.com/genmoai/models/blob/075b6e36db58f1242921deff83a1066887b9c9e1/src/mochi_preview/infer.py#L77
         threshold_noise = 0.025
-        sigmas = linear_quadratic_schedule(num_inference_steps, threshold_noise)
+        sigmas = linear_quadratic_schedule(num_inference_steps,
+                                           threshold_noise)
         sigmas = np.array(sigmas)
         # check if of type FlowMatchEulerDiscreteScheduler
         if isinstance(self.scheduler, FlowMatchEulerDiscreteScheduler):
@@ -724,23 +713,24 @@ class MochiPipeline(DiffusionPipeline):
                 device,
             )
         num_warmup_steps = max(
-            len(timesteps) - num_inference_steps * self.scheduler.order, 0
-        )
+            len(timesteps) - num_inference_steps * self.scheduler.order, 0)
         self._num_timesteps = len(timesteps)
 
         # 6. Denoising loop
+        self._progress_bar_config = {
+            "disable": nccl_info.rank_within_group != 0
+        }
         with self.progress_bar(total=num_inference_steps) as progress_bar:
             for i, t in enumerate(timesteps):
                 if self.interrupt:
                     continue
 
-                latent_model_input = (
-                    torch.cat([latents] * 2)
-                    if self.do_classifier_free_guidance
-                    else latents
-                )
+                latent_model_input = (torch.cat(
+                    [latents] *
+                    2) if self.do_classifier_free_guidance else latents)
                 # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
-                timestep = t.expand(latent_model_input.shape[0]).to(latents.dtype)
+                timestep = t.expand(latent_model_input.shape[0]).to(
+                    latents.dtype)
 
                 noise_pred = self.transformer(
                     hidden_states=latent_model_input,
@@ -756,14 +746,14 @@ class MochiPipeline(DiffusionPipeline):
                 if self.do_classifier_free_guidance:
                     noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
                     noise_pred = noise_pred_uncond + self.guidance_scale * (
-                        noise_pred_text - noise_pred_uncond
-                    )
+                        noise_pred_text - noise_pred_uncond)
 
                 # compute the previous noisy sample x_t -> x_t-1
                 latents_dtype = latents.dtype
-                latents = self.scheduler.step(
-                    noise_pred, t, latents.to(torch.float32), return_dict=False
-                )[0]
+                latents = self.scheduler.step(noise_pred,
+                                              t,
+                                              latents.to(torch.float32),
+                                              return_dict=False)[0]
                 latents = latents.to(latents_dtype)
 
                 if latents.dtype != latents_dtype:
@@ -775,15 +765,17 @@ class MochiPipeline(DiffusionPipeline):
                     callback_kwargs = {}
                     for k in callback_on_step_end_tensor_inputs:
                         callback_kwargs[k] = locals()[k]
-                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+                    callback_outputs = callback_on_step_end(
+                        self, i, t, callback_kwargs)
 
                     latents = callback_outputs.pop("latents", latents)
-                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+                    prompt_embeds = callback_outputs.pop(
+                        "prompt_embeds", prompt_embeds)
 
                 # call the callback, if provided
                 if i == len(timesteps) - 1 or (
-                    (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0
-                ):
+                    (i + 1) > num_warmup_steps and
+                    (i + 1) % self.scheduler.order == 0):
                     progress_bar.update()
 
                 if XLA_AVAILABLE:
@@ -803,36 +795,25 @@ class MochiPipeline(DiffusionPipeline):
         else:
             # unscale/denormalize the latents
             # denormalize with the mean and std if available and not None
-            has_latents_mean = (
-                hasattr(self.vae.config, "latents_mean")
-                and self.vae.config.latents_mean is not None
-            )
-            has_latents_std = (
-                hasattr(self.vae.config, "latents_std")
-                and self.vae.config.latents_std is not None
-            )
+            has_latents_mean = (hasattr(self.vae.config, "latents_mean")
+                                and self.vae.config.latents_mean is not None)
+            has_latents_std = (hasattr(self.vae.config, "latents_std")
+                               and self.vae.config.latents_std is not None)
             if has_latents_mean and has_latents_std:
-                latents_mean = (
-                    torch.tensor(self.vae.config.latents_mean)
-                    .view(1, 12, 1, 1, 1)
-                    .to(latents.device, latents.dtype)
-                )
-                latents_std = (
-                    torch.tensor(self.vae.config.latents_std)
-                    .view(1, 12, 1, 1, 1)
-                    .to(latents.device, latents.dtype)
-                )
+                latents_mean = (torch.tensor(
+                    self.vae.config.latents_mean).view(1, 12, 1, 1, 1).to(
+                        latents.device, latents.dtype))
+                latents_std = (torch.tensor(self.vae.config.latents_std).view(
+                    1, 12, 1, 1, 1).to(latents.device, latents.dtype))
                 latents = (
-                    latents * latents_std / self.vae.config.scaling_factor
-                    + latents_mean
-                )
+                    latents * latents_std / self.vae.config.scaling_factor +
+                    latents_mean)
             else:
                 latents = latents / self.vae.config.scaling_factor
 
             video = self.vae.decode(latents, return_dict=False)[0]
             video = self.video_processor.postprocess_video(
-                video, output_type=output_type
-            )
+                video, output_type=output_type)
 
         # Offload all models
         self.maybe_free_model_hooks()
@@ -843,6 +824,6 @@ class MochiPipeline(DiffusionPipeline):
             return original_noise, video, latents, prompt_embeds, prompt_attention_mask
 
         if not return_dict:
-            return (video,)
+            return (video, )
 
         return MochiPipelineOutput(frames=video)

fastvideo/ops/modulate/k_modulate.py

@@ -0,0 +1,102 @@
+import triton
+import triton.language as tl
+
+CONFIG_LIST = [
+    triton.Config({"BLOCK_M": 256, "BLOCK_N": 32}, num_stages=2, num_warps=4),
+    triton.Config({"BLOCK_M": 128, "BLOCK_N": 64}, num_stages=2, num_warps=4),
+    triton.Config({"BLOCK_M": 128, "BLOCK_N": 32}, num_stages=2, num_warps=4),
+    triton.Config({"BLOCK_M": 64, "BLOCK_N": 128}, num_stages=2, num_warps=4),
+    triton.Config({"BLOCK_M": 64, "BLOCK_N": 64}, num_stages=2, num_warps=4),
+    triton.Config({"BLOCK_M": 64, "BLOCK_N": 32}, num_stages=2, num_warps=4),
+    triton.Config({"BLOCK_M": 32, "BLOCK_N": 64}, num_stages=2, num_warps=4),
+    triton.Config({"BLOCK_M": 32, "BLOCK_N": 128}, num_stages=2, num_warps=4),
+    triton.Config({"BLOCK_M": 32, "BLOCK_N": 256}, num_stages=2, num_warps=4),
+]
+
+
+@triton.autotune(
+    configs=CONFIG_LIST,
+    key=["M", "N"],
+)
+@triton.jit
+def _modulate_fwd(
+        x_ptr,  # *Pointer* to first input vector.
+        output_ptr,  # *Pointer* to output vector.
+        scale_ptr,
+        shift_ptr,
+        m_stride,
+        s_stride,
+        M,
+        N,
+        seq_len,
+        BLOCK_M: tl.constexpr,  # Number of elements each program should process.
+        BLOCK_N: tl.constexpr,
+        # NOTE: `constexpr` so it can be used as a shape value.
+):
+    row_id = tl.program_id(axis=0)  # We use a 1D launch grid so axis is 0.
+    rows = row_id * BLOCK_M + tl.arange(0, BLOCK_M)
+    s_rows = (row_id // seq_len) * BLOCK_M
+    col_id = tl.program_id(axis=1)
+    cols = col_id * BLOCK_N + tl.arange(0, BLOCK_N)
+
+    x_ptrs = x_ptr + rows[:, None] * m_stride + cols[None, :]
+    scale_ptrs = scale_ptr + s_rows * s_stride + cols[None, :]
+    shift_ptrs = shift_ptr + s_rows * s_stride + cols[None, :]
+
+    col_mask = cols[None, :] < N
+    block_mask = (rows[:, None] < M) & col_mask
+    s_block_mask = col_mask
+    x = tl.load(x_ptrs, mask=block_mask, other=0.0)
+    scale = tl.load(scale_ptrs, mask=s_block_mask, other=0.0)
+    shift = tl.load(shift_ptrs, mask=s_block_mask, other=0.0)
+
+    output = x * (1 + scale) + shift
+    # Write x + y back to DRAM.
+    tl.store(output_ptr + rows[:, None] * m_stride + cols[None, :], output, mask=block_mask)
+
+
+@triton.autotune(
+    configs=CONFIG_LIST,
+    key=["M", "N"],
+)
+@triton.jit
+def _modulate_bwd(
+        dx_ptr,  # *Pointer* to first input vector.
+        x_ptr,
+        dy_ptr,  # *Pointer* to output vector.
+        scale_ptr,
+        dscale_ptr,
+        m_stride,
+        s_stride,
+        M,
+        N,
+        seq_len,
+        BLOCK_M: tl.constexpr,  # Number of elements each program should process.
+        BLOCK_N: tl.constexpr,
+        # NOTE: `constexpr` so it can be used as a shape value.
+):
+    row_id = tl.program_id(axis=0)  # We use a 1D launch grid so axis is 0.
+    rows = row_id * BLOCK_M + tl.arange(0, BLOCK_M)
+    s_rows = (row_id // seq_len) * BLOCK_M
+    col_id = tl.program_id(axis=1)
+    cols = col_id * BLOCK_N + tl.arange(0, BLOCK_N)
+
+    x_ptrs = x_ptr + rows[:, None] * m_stride + cols[None, :]
+    dy_ptrs = dy_ptr + rows[:, None] * m_stride + cols[None, :]
+    dx_ptrs = dx_ptr + rows[:, None] * m_stride + cols[None, :]
+    dscale_ptrs = dscale_ptr + rows[:, None] * m_stride + cols[None, :]
+
+    scale_ptrs = scale_ptr + s_rows * s_stride + cols[None, :]
+
+    col_mask = cols[None, :] < N
+    block_mask = (rows[:, None] < M) & col_mask
+    s_block_mask = col_mask
+    x = tl.load(x_ptrs, mask=block_mask, other=0.0)
+    dy = tl.load(dy_ptrs, mask=block_mask, other=0.0)
+    scale = tl.load(scale_ptrs, mask=s_block_mask, other=0.0)
+
+    dx = dy * (1 + scale)
+    dscale = dy * x
+    # Write x + y back to DRAM.
+    tl.store(dx_ptrs, dx, mask=block_mask)
+    tl.store(dscale_ptrs, dscale, mask=block_mask)

fastvideo/ops/modulate/modulate.py

@@ -0,0 +1,63 @@
+import torch
+import triton
+
+from fastvideo.ops.modulate.k_modulate import _modulate_fwd, _modulate_bwd
+
+
+class _FusedModulate(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, x, scale, shift):
+        y = torch.empty_like(x)
+        batch, seq_len, dim = x.shape
+        M = batch * seq_len
+        N = dim
+        x = x.view(-1, dim).contiguous()
+        scale = scale.view(-1, dim).contiguous()
+        shift = shift.view(-1, dim).contiguous()
+
+        def grid(meta):
+            return (
+                triton.cdiv(batch * seq_len, meta["BLOCK_M"]),
+                triton.cdiv(dim, meta["BLOCK_N"]),
+            )
+
+        _modulate_fwd[grid](x, y, scale, shift, x.stride(0), scale.stride(0), M, N, seq_len)
+
+        ctx.save_for_backward(x, scale)
+        ctx.batch = batch
+        ctx.seq_len = seq_len
+        ctx.dim = dim
+        return y
+
+    @staticmethod
+    def backward(ctx, dy):  # pragma: no cover  # this is covered, but called directly from C++
+        x, scale = ctx.saved_tensors
+
+        batch, seq_len, dim = ctx.batch, ctx.seq_len, ctx.dim
+        M = batch * seq_len
+        N = dim
+
+        # allocate output
+        dy = dy.contiguous()
+        dx = torch.empty_like(dy)
+        dscale = torch.empty_like(dy)
+        dshift = torch.sum(dy, dim=1)
+
+        def grid(meta):
+            return (
+                triton.cdiv(batch * seq_len, meta["BLOCK_M"]),
+                triton.cdiv(dim, meta["BLOCK_N"]),
+            )
+
+        _modulate_bwd[grid](dx, x, dy, scale, dscale, x.stride(0), scale.stride(0), M, N, seq_len)
+
+        dscale = torch.sum(dscale, dim=1)
+        return dx, dscale, dshift
+
+
+def fused_modulate(
+        x: torch.Tensor,
+        scale: torch.Tensor,
+        shift: torch.Tensor,
+) -> torch.Tensor:
+    return _FusedModulate.apply(x, scale, shift)

fastvideo/sample/generate_synthetic.py

@@ -1,16 +1,16 @@
-import json
-
-import torch.distributed as dist
-import torch
-from fastvideo.models.mochi_hf.pipeline_mochi import MochiPipeline
-import os
-from diffusers.utils import export_to_video
 import argparse
+import json
+import os
+
+import torch
+import torch.distributed as dist
+from diffusers.utils import export_to_video
+
+from fastvideo.models.mochi_hf.pipeline_mochi import MochiPipeline
 
 
-def generate_video_and_latent(
-    pipe, prompt, height, width, num_frames, num_inference_steps, guidance_scale
-):
+def generate_video_and_latent(pipe, prompt, height, width, num_frames,
+                              num_inference_steps, guidance_scale):
     # Set the random seed for reproducibility
     generator = torch.Generator("cuda").manual_seed(12345)
     # Generate videos from the input prompt
@@ -25,7 +25,8 @@ def generate_video_and_latent(
         output_type="latent_and_video",
     )
     # prompt_embed has negative prompt at index 0
-    return noise[0], video[0], latent[0], prompt_embed[1], prompt_attention_mask[1]
+    return noise[0], video[0], latent[0], prompt_embed[
+        1], prompt_attention_mask[1]
 
     # return dummy tensor to debug first
     # return torch.zeros(1, 3, 480, 848), torch.zeros(1, 256, 16, 16)
@@ -39,19 +40,22 @@ if __name__ == "__main__":
     parser.add_argument("--num_inference_steps", type=int, default=64)
     parser.add_argument("--guidance_scale", type=float, default=4.5)
     parser.add_argument("--model_path", type=str, default="data/mochi")
-    parser.add_argument(
-        "--prompt_path", type=str, default="data/dummyVid/videos2caption.json"
-    )
-    parser.add_argument("--dataset_output_dir", type=str, default="data/dummySynthetic")
+    parser.add_argument("--prompt_path",
+                        type=str,
+                        default="data/dummyVid/videos2caption.json")
+    parser.add_argument("--dataset_output_dir",
+                        type=str,
+                        default="data/dummySynthetic")
     args = parser.parse_args()
 
     local_rank = int(os.getenv("RANK", 0))
     world_size = int(os.getenv("WORLD_SIZE", 1))
     print("world_size", world_size, "local rank", local_rank)
     torch.cuda.set_device(local_rank)
-    dist.init_process_group(
-        backend="nccl", init_method="env://", world_size=world_size, rank=local_rank
-    )
+    dist.init_process_group(backend="nccl",
+                            init_method="env://",
+                            world_size=world_size,
+                            rank=local_rank)
 
     if not isinstance(args.prompt_path, list):
         args.prompt_path = [args.prompt_path]
@@ -59,7 +63,8 @@ if __name__ == "__main__":
         text_prompt = open(args.prompt_path[0], "r").readlines()
         text_prompt = [i.strip() for i in text_prompt]
 
-    pipe = MochiPipeline.from_pretrained(args.model_path, torch_dtype=torch.bfloat16)
+    pipe = MochiPipeline.from_pretrained(args.model_path,
+                                         torch_dtype=torch.bfloat16)
     pipe.enable_vae_tiling()
     pipe.enable_model_cpu_offload(gpu_id=local_rank)
     # make dir if not exist
@@ -68,10 +73,10 @@ if __name__ == "__main__":
     os.makedirs(os.path.join(args.dataset_output_dir, "noise"), exist_ok=True)
     os.makedirs(os.path.join(args.dataset_output_dir, "video"), exist_ok=True)
     os.makedirs(os.path.join(args.dataset_output_dir, "latent"), exist_ok=True)
-    os.makedirs(os.path.join(args.dataset_output_dir, "prompt_embed"), exist_ok=True)
-    os.makedirs(
-        os.path.join(args.dataset_output_dir, "prompt_attention_mask"), exist_ok=True
-    )
+    os.makedirs(os.path.join(args.dataset_output_dir, "prompt_embed"),
+                exist_ok=True)
+    os.makedirs(os.path.join(args.dataset_output_dir, "prompt_attention_mask"),
+                exist_ok=True)
     data = []
     for i, prompt in enumerate(text_prompt):
         if i % world_size != local_rank:
@@ -93,17 +98,17 @@ if __name__ == "__main__":
         )
         # save latent
         video_name = str(i)
-        noise_path = os.path.join(args.dataset_output_dir, "noise", video_name + ".pt")
-        latent_path = os.path.join(
-            args.dataset_output_dir, "latent", video_name + ".pt"
-        )
-        prompt_embed_path = os.path.join(
-            args.dataset_output_dir, "prompt_embed", video_name + ".pt"
-        )
-        video_path = os.path.join(args.dataset_output_dir, "video", video_name + ".mp4")
-        prompt_attention_mask_path = os.path.join(
-            args.dataset_output_dir, "prompt_attention_mask", video_name + ".pt"
-        )
+        noise_path = os.path.join(args.dataset_output_dir, "noise",
+                                  video_name + ".pt")
+        latent_path = os.path.join(args.dataset_output_dir, "latent",
+                                   video_name + ".pt")
+        prompt_embed_path = os.path.join(args.dataset_output_dir,
+                                         "prompt_embed", video_name + ".pt")
+        video_path = os.path.join(args.dataset_output_dir, "video",
+                                  video_name + ".mp4")
+        prompt_attention_mask_path = os.path.join(args.dataset_output_dir,
+                                                  "prompt_attention_mask",
+                                                  video_name + ".pt")
         # save latent
         torch.save(noise, noise_path)
         torch.save(latent, latent_path)
@@ -127,7 +132,6 @@ if __name__ == "__main__":
     # save json
     if local_rank == 0:
         all_data = [item for sublist in gathered_data for item in sublist]
-        with open(
-            os.path.join(args.dataset_output_dir, "videos2caption.json"), "w"
-        ) as f:
+        with open(os.path.join(args.dataset_output_dir, "videos2caption.json"),
+                  "w") as f:
             json.dump(all_data, f, indent=4)

fastvideo/sample/sample_t2v_cogvideo.py

@@ -0,0 +1,306 @@
+"""
+This script demonstrates how to generate a video using the CogVideoX model with the Hugging Face `diffusers` pipeline.
+The script supports different types of video generation, including text-to-video (t2v), image-to-video (i2v),
+and video-to-video (v2v), depending on the input data and different weight.
+
+- text-to-video: THUDM/CogVideoX-5b, THUDM/CogVideoX-2b or THUDM/CogVideoX1.5-5b
+- video-to-video: THUDM/CogVideoX-5b, THUDM/CogVideoX-2b or THUDM/CogVideoX1.5-5b
+- image-to-video: THUDM/CogVideoX-5b-I2V or THUDM/CogVideoX1.5-5b-I2V
+
+Running the Script:
+To run the script, use the following command with appropriate arguments:
+
+```bash
+$ python cli_demo.py --prompt "A girl riding a bike." --model_path THUDM/CogVideoX1.5-5b --generate_type "t2v"
+```
+
+Additional options are available to specify the model path, guidance scale, number of inference steps, video generation type, and output paths.
+"""
+
+import argparse
+import logging
+import os
+from typing import Literal, Optional
+
+import torch
+from diffusers import (
+    CogVideoXDPMScheduler,
+    CogVideoXImageToVideoPipeline,
+    CogVideoXPipeline,
+    CogVideoXVideoToVideoPipeline,
+)
+from diffusers.utils import export_to_video, load_image, load_video
+
+
+logging.basicConfig(level=logging.INFO)
+
+# Recommended resolution for each model (width, height)
+RESOLUTION_MAP = {
+    # cogvideox1.5-*
+    "cogvideox1.5-5b-i2v": (1360, 768),
+    "cogvideox1.5-5b": (1360, 768),
+    # cogvideox-*
+    "cogvideox-5b-i2v": (720, 480),
+    "cogvideox-5b": (720, 480),
+    "cogvideox-2b": (720, 480),
+}
+
+
+def generate_video(
+    prompt: str,
+    model_path: str,
+    lora_path: str = None,
+    lora_rank: int = 128,
+    num_frames: int = 81,
+    width: Optional[int] = None,
+    height: Optional[int] = None,
+    output_path: str = "./output.mp4",
+    image_or_video_path: str = "",
+    num_inference_steps: int = 50,
+    guidance_scale: float = 6.0,
+    num_videos_per_prompt: int = 1,
+    dtype: torch.dtype = torch.bfloat16,
+    generate_type: str = Literal[
+        "t2v", "i2v", "v2v"
+    ],  # i2v: image to video, v2v: video to video
+    seed: int = 42,
+    fps: int = 16,
+):
+    """
+    Generates a video based on the given prompt and saves it to the specified path.
+
+    Parameters:
+    - prompt (str): The description of the video to be generated.
+    - model_path (str): The path of the pre-trained model to be used.
+    - lora_path (str): The path of the LoRA weights to be used.
+    - lora_rank (int): The rank of the LoRA weights.
+    - output_path (str): The path where the generated video will be saved.
+    - num_inference_steps (int): Number of steps for the inference process. More steps can result in better quality.
+    - num_frames (int): Number of frames to generate. CogVideoX1.0 generates 49 frames for 6 seconds at 8 fps, while CogVideoX1.5 produces either 81 or 161 frames, corresponding to 5 seconds or 10 seconds at 16 fps.
+    - width (int): The width of the generated video, applicable only for CogVideoX1.5-5B-I2V
+    - height (int): The height of the generated video, applicable only for CogVideoX1.5-5B-I2V
+    - guidance_scale (float): The scale for classifier-free guidance. Higher values can lead to better alignment with the prompt.
+    - num_videos_per_prompt (int): Number of videos to generate per prompt.
+    - dtype (torch.dtype): The data type for computation (default is torch.bfloat16).
+    - generate_type (str): The type of video generation (e.g., 't2v', 'i2v', 'v2v').·
+    - seed (int): The seed for reproducibility.
+    - fps (int): The frames per second for the generated video.
+    """
+
+    # 1.  Load the pre-trained CogVideoX pipeline with the specified precision (bfloat16).
+    # add device_map="balanced" in the from_pretrained function and remove the enable_model_cpu_offload()
+    # function to use Multi GPUs.
+
+    image = None
+    video = None
+
+    model_name = model_path.split("/")[-1].lower()
+    desired_resolution = RESOLUTION_MAP[model_name]
+    if width is None or height is None:
+        width, height = desired_resolution
+        logging.info(
+            f"\033[1mUsing default resolution {desired_resolution} for {model_name}\033[0m"
+        )
+    elif (width, height) != desired_resolution:
+        if generate_type == "i2v":
+            # For i2v models, use user-defined width and height
+            logging.warning(
+                f"\033[1;31mThe width({width}) and height({height}) are not recommended for {model_name}. The best resolution is {desired_resolution}.\033[0m"
+            )
+        else:
+            # Otherwise, use the recommended width and height
+            logging.warning(
+                f"\033[1;31m{model_name} is not supported for custom resolution. Setting back to default resolution {desired_resolution}.\033[0m"
+            )
+            width, height = desired_resolution
+
+    if generate_type == "i2v":
+        pipe = CogVideoXImageToVideoPipeline.from_pretrained(
+            model_path, torch_dtype=dtype
+        )
+        image = load_image(image=image_or_video_path)
+    elif generate_type == "t2v":
+        pipe = CogVideoXPipeline.from_pretrained(model_path, torch_dtype=dtype)
+    else:
+        pipe = CogVideoXVideoToVideoPipeline.from_pretrained(
+            model_path, torch_dtype=dtype
+        )
+        video = load_video(image_or_video_path)
+
+    # If you're using with lora, add this code
+    if lora_path:
+        pipe.load_lora_weights(
+            lora_path,
+            weight_name="pytorch_lora_weights.safetensors",
+            adapter_name="test_1",
+        )
+        pipe.fuse_lora(lora_scale=1 / lora_rank)
+
+    # 2. Set Scheduler.
+    # Can be changed to `CogVideoXDPMScheduler` or `CogVideoXDDIMScheduler`.
+    # We recommend using `CogVideoXDDIMScheduler` for CogVideoX-2B.
+    # using `CogVideoXDPMScheduler` for CogVideoX-5B / CogVideoX-5B-I2V.
+
+    # pipe.scheduler = CogVideoXDDIMScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
+    pipe.scheduler = CogVideoXDPMScheduler.from_config(
+        pipe.scheduler.config, timestep_spacing="trailing"
+    )
+
+    # 3. Enable CPU offload for the model.
+    # turn off if you have multiple GPUs or enough GPU memory(such as H100) and it will cost less time in inference
+    # and enable to("cuda")
+
+    # pipe.to("cuda")
+    pipe.enable_sequential_cpu_offload()
+    pipe.vae.enable_slicing()
+    pipe.vae.enable_tiling()
+
+    # 4. Generate the video frames based on the prompt.
+    # `num_frames` is the Number of frames to generate.
+    if generate_type == "i2v":
+        video_generate = pipe(
+            height=height,
+            width=width,
+            prompt=prompt,
+            image=image,
+            # The path of the image, the resolution of video will be the same as the image for CogVideoX1.5-5B-I2V, otherwise it will be 720 * 480
+            num_videos_per_prompt=num_videos_per_prompt,  # Number of videos to generate per prompt
+            num_inference_steps=num_inference_steps,  # Number of inference steps
+            num_frames=num_frames,  # Number of frames to generate
+            use_dynamic_cfg=True,  # This id used for DPM scheduler, for DDIM scheduler, it should be False
+            guidance_scale=guidance_scale,
+            generator=torch.Generator().manual_seed(
+                seed
+            ),  # Set the seed for reproducibility
+        ).frames[0]
+    elif generate_type == "t2v":
+        video_generate = pipe(
+            height=height,
+            width=width,
+            prompt=prompt,
+            num_videos_per_prompt=num_videos_per_prompt,
+            num_inference_steps=num_inference_steps,
+            num_frames=num_frames,
+            use_dynamic_cfg=True,
+            guidance_scale=guidance_scale,
+            generator=torch.Generator().manual_seed(seed),
+        ).frames[0]
+    else:
+        video_generate = pipe(
+            height=height,
+            width=width,
+            prompt=prompt,
+            video=video,  # The path of the video to be used as the background of the video
+            num_videos_per_prompt=num_videos_per_prompt,
+            num_inference_steps=num_inference_steps,
+            num_frames=num_frames,
+            use_dynamic_cfg=True,
+            guidance_scale=guidance_scale,
+            generator=torch.Generator().manual_seed(
+                seed
+            ),  # Set the seed for reproducibility
+        ).frames[0]
+    export_to_video(video_generate, output_path, fps=fps)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Generate a video from a text prompt using CogVideoX"
+    )
+    parser.add_argument(
+        "--prompt",
+        type=str,
+        required=True,
+        help="The description of the video to be generated",
+    )
+    parser.add_argument(
+        "--image_or_video_path",
+        type=str,
+        default=None,
+        help="The path of the image to be used as the background of the video",
+    )
+    parser.add_argument(
+        "--model_path",
+        type=str,
+        default="THUDM/CogVideoX1.5-5B",
+        help="Path of the pre-trained model use",
+    )
+    parser.add_argument(
+        "--lora_path",
+        type=str,
+        default=None,
+        help="The path of the LoRA weights to be used",
+    )
+    parser.add_argument(
+        "--lora_rank", type=int, default=128, help="The rank of the LoRA weights"
+    )
+    parser.add_argument(
+        "--output_path",
+        type=str,
+        default="./output.mp4",
+        help="The path save generated video",
+    )
+    parser.add_argument(
+        "--guidance_scale",
+        type=float,
+        default=6.0,
+        help="The scale for classifier-free guidance",
+    )
+    parser.add_argument(
+        "--num_inference_steps", type=int, default=50, help="Inference steps"
+    )
+    parser.add_argument(
+        "--num_frames",
+        type=int,
+        default=81,
+        help="Number of steps for the inference process",
+    )
+    parser.add_argument(
+        "--width", type=int, default=None, help="The width of the generated video"
+    )
+    parser.add_argument(
+        "--height", type=int, default=None, help="The height of the generated video"
+    )
+    parser.add_argument(
+        "--fps",
+        type=int,
+        default=16,
+        help="The frames per second for the generated video",
+    )
+    parser.add_argument(
+        "--num_videos_per_prompt",
+        type=int,
+        default=1,
+        help="Number of videos to generate per prompt",
+    )
+    parser.add_argument(
+        "--generate_type", type=str, default="t2v", help="The type of video generation"
+    )
+    parser.add_argument(
+        "--dtype", type=str, default="bfloat16", help="The data type for computation"
+    )
+    parser.add_argument(
+        "--seed", type=int, default=42, help="The seed for reproducibility"
+    )
+
+    args = parser.parse_args()
+    dtype = torch.float16 if args.dtype == "float16" else torch.bfloat16
+    os.makedirs(os.path.dirname(args.output_path), exist_ok=True)
+    generate_video(
+        prompt=args.prompt,
+        model_path=args.model_path,
+        lora_path=args.lora_path,
+        lora_rank=args.lora_rank,
+        output_path=args.output_path,
+        num_frames=args.num_frames,
+        width=args.width,
+        height=args.height,
+        image_or_video_path=args.image_or_video_path,
+        num_inference_steps=args.num_inference_steps,
+        guidance_scale=args.guidance_scale,
+        num_videos_per_prompt=args.num_videos_per_prompt,
+        dtype=dtype,
+        generate_type=args.generate_type,
+        seed=args.seed,
+        fps=args.fps,
+    )

fastvideo/sample/sample_t2v_hunyuan.py

@@ -1,26 +1,17 @@
+import argparse
 import os
+from pathlib import Path
+
 import imageio
-import time
+import numpy as np
+import torch
+import torch.distributed as dist
+import torchvision
 from einops import rearrange
 
-import torch
-import torchvision
-import numpy as np
-from pathlib import Path
-from loguru import logger
-from datetime import datetime
-import argparse
-from diffusers.utils import export_to_video
-
-from fastvideo.models.hunyuan.utils.file_utils import save_videos_grid
 from fastvideo.models.hunyuan.inference import HunyuanVideoSampler
-
-import torch.distributed as dist
-
 from fastvideo.utils.parallel_states import (
-    initialize_sequence_parallel_state,
-    nccl_info,
-)
+    initialize_sequence_parallel_state, nccl_info)
 
 
 def initialize_distributed():
@@ -28,16 +19,16 @@ def initialize_distributed():
     world_size = int(os.getenv("WORLD_SIZE", 1))
     print("world_size", world_size)
     torch.cuda.set_device(local_rank)
-    dist.init_process_group(
-        backend="nccl", init_method="env://", world_size=world_size, rank=local_rank
-    )
+    dist.init_process_group(backend="nccl",
+                            init_method="env://",
+                            world_size=world_size,
+                            rank=local_rank)
     initialize_sequence_parallel_state(world_size)
 
 
 def main(args):
     initialize_distributed()
     print(nccl_info.sp_size)
-    device = torch.cuda.current_device()
 
     print(args)
     models_root_path = Path(args.model_path)
@@ -50,15 +41,15 @@ def main(args):
 
     # Load models
     hunyuan_video_sampler = HunyuanVideoSampler.from_pretrained(
-        models_root_path, args=args
-    )
+        models_root_path, args=args)
 
     # Get the updated args
     args = hunyuan_video_sampler.args
 
-    # Start sampling
-    samples = []
-    for prompt in args.prompts:
+    with open(args.prompt) as f:
+        prompts = f.readlines()
+
+    for prompt in prompts:
         outputs = hunyuan_video_sampler.predict(
             prompt=prompt,
             height=args.height,
@@ -73,24 +64,23 @@ def main(args):
             batch_size=args.batch_size,
             embedded_guidance_scale=args.embedded_cfg_scale,
         )
-        samples.append(outputs["samples"][0])
-
-    for prompt, video in zip(args.prompts, samples):
-        videos = rearrange(video.unsqueeze(0), "b c t h w -> t b c h w")
+        videos = rearrange(outputs["samples"], "b c t h w -> t b c h w")
         outputs = []
         for x in videos:
             x = torchvision.utils.make_grid(x, nrow=6)
             x = x.transpose(0, 1).transpose(1, 2).squeeze(-1)
             outputs.append((x * 255).numpy().astype(np.uint8))
         os.makedirs(os.path.dirname(args.output_path), exist_ok=True)
-        imageio.mimsave(args.output_path + f"{prompt[:100]}.mp4", outputs, fps=args.fps)
+        imageio.mimsave(os.path.join(args.output_path, f"{prompt[:100]}.mp4"),
+                        outputs,
+                        fps=args.fps)
 
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
 
     # Basic parameters
-    parser.add_argument("--prompts", nargs="+", default=[])
+    parser.add_argument("--prompt", type=str, help="prompt file for inference")
     parser.add_argument("--num_frames", type=int, default=16)
     parser.add_argument("--height", type=int, default=256)
     parser.add_argument("--width", type=int, default=256)
@@ -106,10 +96,14 @@ if __name__ == "__main__":
         default="flow",
         help="Denoise type for noised inputs.",
     )
-    parser.add_argument("--seed", type=int, default=None, help="Seed for evaluation.")
-    parser.add_argument(
-        "--neg_prompt", type=str, default=None, help="Negative prompt for sampling."
-    )
+    parser.add_argument("--seed",
+                        type=int,
+                        default=None,
+                        help="Seed for evaluation.")
+    parser.add_argument("--neg_prompt",
+                        type=str,
+                        default=None,
+                        help="Negative prompt for sampling.")
     parser.add_argument(
         "--guidance_scale",
         type=float,
@@ -122,12 +116,14 @@ if __name__ == "__main__":
         default=6.0,
         help="Embedded classifier free guidance scale.",
     )
-    parser.add_argument(
-        "--flow_shift", type=int, default=7, help="Flow shift parameter."
-    )
-    parser.add_argument(
-        "--batch_size", type=int, default=1, help="Batch size for inference."
-    )
+    parser.add_argument("--flow_shift",
+                        type=int,
+                        default=7,
+                        help="Flow shift parameter.")
+    parser.add_argument("--batch_size",
+                        type=int,
+                        default=1,
+                        help="Batch size for inference.")
     parser.add_argument(
         "--num_videos",
         type=int,
@@ -138,7 +134,8 @@ if __name__ == "__main__":
         "--load-key",
         type=str,
         default="module",
-        help="Key to load the model states. 'module' for the main model, 'ema' for the EMA model.",
+        help=
+        "Key to load the model states. 'module' for the main model, 'ema' for the EMA model.",
     )
     parser.add_argument(
         "--use-cpu-offload",
@@ -148,17 +145,20 @@ if __name__ == "__main__":
     parser.add_argument(
         "--dit-weight",
         type=str,
-        default="data/hunyuan/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt",
+        default=
+        "data/hunyuan/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt",
     )
     parser.add_argument(
         "--reproduce",
         action="store_true",
-        help="Enable reproducibility by setting random seeds and deterministic algorithms.",
+        help=
+        "Enable reproducibility by setting random seeds and deterministic algorithms.",
     )
     parser.add_argument(
         "--disable-autocast",
         action="store_true",
-        help="Disable autocast for denoising loop and vae decoding in pipeline sampling.",
+        help=
+        "Disable autocast for denoising loop and vae decoding in pipeline sampling.",
     )
 
     # Flow Matching
@@ -167,13 +167,15 @@ if __name__ == "__main__":
         action="store_true",
         help="If reverse, learning/sampling from t=1 -> t=0.",
     )
-    parser.add_argument(
-        "--flow-solver", type=str, default="euler", help="Solver for flow matching."
-    )
+    parser.add_argument("--flow-solver",
+                        type=str,
+                        default="euler",
+                        help="Solver for flow matching.")
     parser.add_argument(
         "--use-linear-quadratic-schedule",
         action="store_true",
-        help="Use linear quadratic schedule for flow matching. Following MovieGen (https://ai.meta.com/static-resource/movie-gen-research-paper)",
+        help=
+        "Use linear quadratic schedule for flow matching. Following MovieGen (https://ai.meta.com/static-resource/movie-gen-research-paper)",
     )
     parser.add_argument(
         "--linear-schedule-end",
@@ -185,18 +187,22 @@ if __name__ == "__main__":
     # Model parameters
     parser.add_argument("--model", type=str, default="HYVideo-T/2-cfgdistill")
     parser.add_argument("--latent-channels", type=int, default=16)
-    parser.add_argument(
-        "--precision", type=str, default="bf16", choices=["fp32", "fp16", "bf16"]
-    )
-    parser.add_argument(
-        "--rope-theta", type=int, default=256, help="Theta used in RoPE."
-    )
+    parser.add_argument("--precision",
+                        type=str,
+                        default="bf16",
+                        choices=["fp32", "fp16", "bf16"])
+    parser.add_argument("--rope-theta",
+                        type=int,
+                        default=256,
+                        help="Theta used in RoPE.")
 
     parser.add_argument("--vae", type=str, default="884-16c-hy")
-    parser.add_argument(
-        "--vae-precision", type=str, default="fp16", choices=["fp32", "fp16", "bf16"]
-    )
+    parser.add_argument("--vae-precision",
+                        type=str,
+                        default="fp16",
+                        choices=["fp32", "fp16", "bf16"])
     parser.add_argument("--vae-tiling", action="store_true", default=True)
+    parser.add_argument("--vae-sp", action="store_true", default=False)
 
     parser.add_argument("--text-encoder", type=str, default="llm")
     parser.add_argument(
@@ -208,10 +214,12 @@ if __name__ == "__main__":
     parser.add_argument("--text-states-dim", type=int, default=4096)
     parser.add_argument("--text-len", type=int, default=256)
     parser.add_argument("--tokenizer", type=str, default="llm")
-    parser.add_argument("--prompt-template", type=str, default="dit-llm-encode")
-    parser.add_argument(
-        "--prompt-template-video", type=str, default="dit-llm-encode-video"
-    )
+    parser.add_argument("--prompt-template",
+                        type=str,
+                        default="dit-llm-encode")
+    parser.add_argument("--prompt-template-video",
+                        type=str,
+                        default="dit-llm-encode-video")
     parser.add_argument("--hidden-state-skip-layer", type=int, default=2)
     parser.add_argument("--apply-final-norm", action="store_true")
 
@@ -227,4 +235,9 @@ if __name__ == "__main__":
     parser.add_argument("--text-len-2", type=int, default=77)
 
     args = parser.parse_args()
+    # process for vae sequence parallel
+    if args.vae_sp and not args.vae_tiling:
+        raise ValueError(
+            "Currently enabling vae_sp requires enabling vae_tiling, please set --vae-tiling to True."
+        )
     main(args)

fastvideo/sample/sample_t2v_hunyuan_hf.py

@@ -0,0 +1,372 @@
+import argparse
+import json
+import os
+import time
+
+import torch
+import torch.distributed as dist
+from diffusers import BitsAndBytesConfig
+from diffusers.utils import export_to_video
+
+from fastvideo.models.hunyuan_hf.modeling_hunyuan import \
+    HunyuanVideoTransformer3DModel
+from fastvideo.models.hunyuan_hf.pipeline_hunyuan import HunyuanVideoPipeline
+from fastvideo.utils.parallel_states import (
+    initialize_sequence_parallel_state, nccl_info)
+
+
+def initialize_distributed():
+    os.environ["TOKENIZERS_PARALLELISM"] = "false"
+    local_rank = int(os.getenv("RANK", 0))
+    world_size = int(os.getenv("WORLD_SIZE", 1))
+    print("world_size", world_size)
+    torch.cuda.set_device(local_rank)
+    dist.init_process_group(backend="nccl",
+                            init_method="env://",
+                            world_size=world_size,
+                            rank=local_rank)
+    initialize_sequence_parallel_state(world_size)
+
+
+def inference(args):
+    initialize_distributed()
+    print(nccl_info.sp_size)
+    device = torch.cuda.current_device()
+    # Peiyuan: GPU seed will cause A100 and H100 to produce different results .....
+    weight_dtype = torch.bfloat16
+
+    if args.transformer_path is not None:
+        transformer = HunyuanVideoTransformer3DModel.from_pretrained(
+            args.transformer_path)
+    else:
+        transformer = HunyuanVideoTransformer3DModel.from_pretrained(
+            args.model_path,
+            subfolder="transformer/",
+            torch_dtype=weight_dtype)
+
+    pipe = HunyuanVideoPipeline.from_pretrained(args.model_path,
+                                                transformer=transformer,
+                                                torch_dtype=weight_dtype)
+
+    pipe.enable_vae_tiling()
+
+    if args.lora_checkpoint_dir is not None:
+        print(f"Loading LoRA weights from {args.lora_checkpoint_dir}")
+        config_path = os.path.join(args.lora_checkpoint_dir,
+                                   "lora_config.json")
+        with open(config_path, "r") as f:
+            lora_config_dict = json.load(f)
+        rank = lora_config_dict["lora_params"]["lora_rank"]
+        lora_alpha = lora_config_dict["lora_params"]["lora_alpha"]
+        lora_scaling = lora_alpha / rank
+        pipe.load_lora_weights(args.lora_checkpoint_dir,
+                               adapter_name="default")
+        pipe.set_adapters(["default"], [lora_scaling])
+        print(
+            f"Successfully Loaded LoRA weights from {args.lora_checkpoint_dir}"
+        )
+    if args.cpu_offload:
+        pipe.enable_model_cpu_offload(device)
+    else:
+        pipe.to(device)
+
+    # Generate videos from the input prompt
+
+    if args.prompt_embed_path is not None:
+        prompt_embeds = (torch.load(args.prompt_embed_path,
+                                    map_location="cpu",
+                                    weights_only=True).to(device).unsqueeze(0))
+        encoder_attention_mask = (torch.load(
+            args.encoder_attention_mask_path,
+            map_location="cpu",
+            weights_only=True).to(device).unsqueeze(0))
+        prompts = None
+    elif args.prompt_path is not None:
+        prompts = [line.strip() for line in open(args.prompt_path, "r")]
+        prompt_embeds = None
+        encoder_attention_mask = None
+    else:
+        prompts = args.prompts
+        prompt_embeds = None
+        encoder_attention_mask = None
+
+    if prompts is not None:
+        with torch.autocast("cuda", dtype=torch.bfloat16):
+            for prompt in prompts:
+                generator = torch.Generator("cpu").manual_seed(args.seed)
+                video = pipe(
+                    prompt=[prompt],
+                    height=args.height,
+                    width=args.width,
+                    num_frames=args.num_frames,
+                    num_inference_steps=args.num_inference_steps,
+                    generator=generator,
+                ).frames
+                if nccl_info.global_rank <= 0:
+                    os.makedirs(args.output_path, exist_ok=True)
+                    suffix = prompt.split(".")[0]
+                    export_to_video(
+                        video[0],
+                        os.path.join(args.output_path, f"{suffix}.mp4"),
+                        fps=24,
+                    )
+    else:
+        with torch.autocast("cuda", dtype=torch.bfloat16):
+            generator = torch.Generator("cpu").manual_seed(args.seed)
+            videos = pipe(
+                prompt_embeds=prompt_embeds,
+                prompt_attention_mask=encoder_attention_mask,
+                height=args.height,
+                width=args.width,
+                num_frames=args.num_frames,
+                num_inference_steps=args.num_inference_steps,
+                generator=generator,
+            ).frames
+
+        if nccl_info.global_rank <= 0:
+            export_to_video(videos[0], args.output_path + ".mp4", fps=24)
+
+
+def inference_quantization(args):
+    torch.manual_seed(args.seed)
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    model_id = args.model_path
+
+    if args.quantization == "nf4":
+        quantization_config = BitsAndBytesConfig(
+            load_in_4bit=True,
+            bnb_4bit_compute_dtype=torch.bfloat16,
+            bnb_4bit_quant_type="nf4",
+            llm_int8_skip_modules=["proj_out", "norm_out"])
+        transformer = HunyuanVideoTransformer3DModel.from_pretrained(
+            model_id,
+            subfolder="transformer/",
+            torch_dtype=torch.bfloat16,
+            quantization_config=quantization_config)
+    if args.quantization == "int8":
+        quantization_config = BitsAndBytesConfig(
+            load_in_8bit=True, llm_int8_skip_modules=["proj_out", "norm_out"])
+        transformer = HunyuanVideoTransformer3DModel.from_pretrained(
+            model_id,
+            subfolder="transformer/",
+            torch_dtype=torch.bfloat16,
+            quantization_config=quantization_config)
+    elif not args.quantization:
+        transformer = HunyuanVideoTransformer3DModel.from_pretrained(
+            model_id, subfolder="transformer/",
+            torch_dtype=torch.bfloat16).to(device)
+
+    print("Max vram for read transformer:",
+          round(torch.cuda.max_memory_allocated(device="cuda") / 1024**3, 3),
+          "GiB")
+    torch.cuda.reset_max_memory_allocated(device)
+
+    if not args.cpu_offload:
+        pipe = HunyuanVideoPipeline.from_pretrained(
+            model_id, torch_dtype=torch.bfloat16).to(device)
+        pipe.transformer = transformer
+    else:
+        pipe = HunyuanVideoPipeline.from_pretrained(model_id,
+                                                    transformer=transformer,
+                                                    torch_dtype=torch.bfloat16)
+    torch.cuda.reset_max_memory_allocated(device)
+    pipe.scheduler._shift = args.flow_shift
+    pipe.vae.enable_tiling()
+    if args.cpu_offload:
+        pipe.enable_model_cpu_offload()
+    print("Max vram for init pipeline:",
+          round(torch.cuda.max_memory_allocated(device="cuda") / 1024**3, 3),
+          "GiB")
+    with open(args.prompt) as f:
+        prompts = f.readlines()
+
+    generator = torch.Generator("cpu").manual_seed(args.seed)
+    os.makedirs(os.path.dirname(args.output_path), exist_ok=True)
+    torch.cuda.reset_max_memory_allocated(device)
+    for prompt in prompts:
+        start_time = time.perf_counter()
+        output = pipe(
+            prompt=prompt,
+            height=args.height,
+            width=args.width,
+            num_frames=args.num_frames,
+            num_inference_steps=args.num_inference_steps,
+            generator=generator,
+        ).frames[0]
+        export_to_video(output,
+                        os.path.join(args.output_path, f"{prompt[:100]}.mp4"),
+                        fps=args.fps)
+        print("Time:", round(time.perf_counter() - start_time, 2), "seconds")
+        print(
+            "Max vram for denoise:",
+            round(torch.cuda.max_memory_allocated(device="cuda") / 1024**3, 3),
+            "GiB")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+
+    # Basic parameters
+    parser.add_argument("--prompt", type=str, help="prompt file for inference")
+    parser.add_argument("--prompt_embed_path", type=str, default=None)
+    parser.add_argument("--prompt_path", type=str, default=None)
+    parser.add_argument("--num_frames", type=int, default=16)
+    parser.add_argument("--height", type=int, default=256)
+    parser.add_argument("--width", type=int, default=256)
+    parser.add_argument("--num_inference_steps", type=int, default=50)
+    parser.add_argument("--model_path", type=str, default="data/hunyuan")
+    parser.add_argument("--transformer_path", type=str, default=None)
+    parser.add_argument("--output_path", type=str, default="./outputs/video")
+    parser.add_argument("--fps", type=int, default=24)
+    parser.add_argument("--quantization", type=str, default=None)
+    parser.add_argument("--cpu_offload", action="store_true")
+    parser.add_argument(
+        "--lora_checkpoint_dir",
+        type=str,
+        default=None,
+        help="Path to the directory containing LoRA checkpoints",
+    )
+    # Additional parameters
+    parser.add_argument(
+        "--denoise-type",
+        type=str,
+        default="flow",
+        help="Denoise type for noised inputs.",
+    )
+    parser.add_argument("--seed",
+                        type=int,
+                        default=None,
+                        help="Seed for evaluation.")
+    parser.add_argument("--neg_prompt",
+                        type=str,
+                        default=None,
+                        help="Negative prompt for sampling.")
+    parser.add_argument(
+        "--guidance_scale",
+        type=float,
+        default=1.0,
+        help="Classifier free guidance scale.",
+    )
+    parser.add_argument(
+        "--embedded_cfg_scale",
+        type=float,
+        default=6.0,
+        help="Embedded classifier free guidance scale.",
+    )
+    parser.add_argument("--flow_shift",
+                        type=int,
+                        default=7,
+                        help="Flow shift parameter.")
+    parser.add_argument("--batch_size",
+                        type=int,
+                        default=1,
+                        help="Batch size for inference.")
+    parser.add_argument(
+        "--num_videos",
+        type=int,
+        default=1,
+        help="Number of videos to generate per prompt.",
+    )
+    parser.add_argument(
+        "--load-key",
+        type=str,
+        default="module",
+        help=
+        "Key to load the model states. 'module' for the main model, 'ema' for the EMA model.",
+    )
+    parser.add_argument(
+        "--dit-weight",
+        type=str,
+        default=
+        "data/hunyuan/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt",
+    )
+    parser.add_argument(
+        "--reproduce",
+        action="store_true",
+        help=
+        "Enable reproducibility by setting random seeds and deterministic algorithms.",
+    )
+    parser.add_argument(
+        "--disable-autocast",
+        action="store_true",
+        help=
+        "Disable autocast for denoising loop and vae decoding in pipeline sampling.",
+    )
+
+    # Flow Matching
+    parser.add_argument(
+        "--flow-reverse",
+        action="store_true",
+        help="If reverse, learning/sampling from t=1 -> t=0.",
+    )
+    parser.add_argument("--flow-solver",
+                        type=str,
+                        default="euler",
+                        help="Solver for flow matching.")
+    parser.add_argument(
+        "--use-linear-quadratic-schedule",
+        action="store_true",
+        help=
+        "Use linear quadratic schedule for flow matching. Following MovieGen (https://ai.meta.com/static-resource/movie-gen-research-paper)",
+    )
+    parser.add_argument(
+        "--linear-schedule-end",
+        type=int,
+        default=25,
+        help="End step for linear quadratic schedule for flow matching.",
+    )
+
+    # Model parameters
+    parser.add_argument("--model", type=str, default="HYVideo-T/2-cfgdistill")
+    parser.add_argument("--latent-channels", type=int, default=16)
+    parser.add_argument("--precision",
+                        type=str,
+                        default="bf16",
+                        choices=["fp32", "fp16", "bf16", "fp8"])
+    parser.add_argument("--rope-theta",
+                        type=int,
+                        default=256,
+                        help="Theta used in RoPE.")
+
+    parser.add_argument("--vae", type=str, default="884-16c-hy")
+    parser.add_argument("--vae-precision",
+                        type=str,
+                        default="fp16",
+                        choices=["fp32", "fp16", "bf16"])
+    parser.add_argument("--vae-tiling", action="store_true", default=True)
+
+    parser.add_argument("--text-encoder", type=str, default="llm")
+    parser.add_argument(
+        "--text-encoder-precision",
+        type=str,
+        default="fp16",
+        choices=["fp32", "fp16", "bf16"],
+    )
+    parser.add_argument("--text-states-dim", type=int, default=4096)
+    parser.add_argument("--text-len", type=int, default=256)
+    parser.add_argument("--tokenizer", type=str, default="llm")
+    parser.add_argument("--prompt-template",
+                        type=str,
+                        default="dit-llm-encode")
+    parser.add_argument("--prompt-template-video",
+                        type=str,
+                        default="dit-llm-encode-video")
+    parser.add_argument("--hidden-state-skip-layer", type=int, default=2)
+    parser.add_argument("--apply-final-norm", action="store_true")
+
+    parser.add_argument("--text-encoder-2", type=str, default="clipL")
+    parser.add_argument(
+        "--text-encoder-precision-2",
+        type=str,
+        default="fp16",
+        choices=["fp32", "fp16", "bf16"],
+    )
+    parser.add_argument("--text-states-dim-2", type=int, default=768)
+    parser.add_argument("--tokenizer-2", type=str, default="clipL")
+    parser.add_argument("--text-len-2", type=int, default=77)
+
+    args = parser.parse_args()
+    if args.quantization:
+        inference_quantization(args)
+    else:
+        inference(args)

fastvideo/sample/sample_t2v_hunyuan_no_sp.py

@@ -1,208 +0,0 @@
-import os
-import imageio
-import time
-from einops import rearrange
-
-import torch
-import torchvision
-import numpy as np
-from pathlib import Path
-from loguru import logger
-from datetime import datetime
-import argparse
-from diffusers.utils import export_to_video
-
-from fastvideo.models.hunyuan.utils.file_utils import save_videos_grid
-from fastvideo.models.hunyuan.inference import HunyuanVideoSampler
-
-
-def main(args):
-    print(args)
-    models_root_path = Path(args.model_path)
-    if not models_root_path.exists():
-        raise ValueError(f"`models_root` not exists: {models_root_path}")
-
-    # Create save folder to save the samples
-    save_path = args.output_path
-    os.makedirs(os.path.dirname(save_path), exist_ok=True)
-
-    # Load models
-    hunyuan_video_sampler = HunyuanVideoSampler.from_pretrained(
-        models_root_path, args=args
-    )
-
-    # Get the updated args
-    args = hunyuan_video_sampler.args
-
-    # Start sampling
-    samples = []
-    for prompt in args.prompts:
-        outputs = hunyuan_video_sampler.predict(
-            prompt=prompt,
-            height=args.height,
-            width=args.width,
-            video_length=args.num_frames,
-            seed=args.seed,
-            negative_prompt=args.neg_prompt,
-            infer_steps=args.num_inference_steps,
-            guidance_scale=args.guidance_scale,
-            num_videos_per_prompt=args.num_videos,
-            flow_shift=args.flow_shift,
-            batch_size=args.batch_size,
-            embedded_guidance_scale=args.embedded_cfg_scale,
-        )
-        samples.append(outputs["samples"][0])
-
-    for prompt, video in zip(args.prompts, samples):
-        videos = rearrange(video.unsqueeze(0), "b c t h w -> t b c h w")
-        outputs = []
-        for x in videos:
-            x = torchvision.utils.make_grid(x, nrow=6)
-            x = x.transpose(0, 1).transpose(1, 2).squeeze(-1)
-            outputs.append((x * 255).numpy().astype(np.uint8))
-        os.makedirs(os.path.dirname(args.output_path), exist_ok=True)
-        imageio.mimsave(args.output_path + f"{prompt[:100]}.mp4", outputs, fps=args.fps)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-
-    # Basic parameters
-    parser.add_argument("--prompts", nargs="+", default=[])
-    parser.add_argument("--num_frames", type=int, default=16)
-    parser.add_argument("--height", type=int, default=256)
-    parser.add_argument("--width", type=int, default=256)
-    parser.add_argument("--num_inference_steps", type=int, default=50)
-    parser.add_argument("--model_path", type=str, default="data/hunyuan")
-    parser.add_argument("--output_path", type=str, default="./outputs/video")
-    parser.add_argument("--fps", type=int, default=24)
-
-    # Additional parameters
-    parser.add_argument(
-        "--denoise-type",
-        type=str,
-        default="flow",
-        help="Denoise type for noised inputs.",
-    )
-    parser.add_argument("--seed", type=int, default=None, help="Seed for evaluation.")
-    parser.add_argument(
-        "--neg_prompt", type=str, default=None, help="Negative prompt for sampling."
-    )
-    parser.add_argument(
-        "--guidance_scale",
-        type=float,
-        default=1.0,
-        help="Classifier free guidance scale.",
-    )
-    parser.add_argument(
-        "--embedded_cfg_scale",
-        type=float,
-        default=6.0,
-        help="Embedded classifier free guidance scale.",
-    )
-    parser.add_argument(
-        "--flow_shift", type=int, default=7, help="Flow shift parameter."
-    )
-    parser.add_argument(
-        "--batch_size", type=int, default=1, help="Batch size for inference."
-    )
-    parser.add_argument(
-        "--num_videos",
-        type=int,
-        default=1,
-        help="Number of videos to generate per prompt.",
-    )
-    parser.add_argument(
-        "--load-key",
-        type=str,
-        default="module",
-        help="Key to load the model states. 'module' for the main model, 'ema' for the EMA model.",
-    )
-    parser.add_argument(
-        "--use-cpu-offload",
-        action="store_true",
-        help="Use CPU offload for the model load.",
-    )
-    parser.add_argument(
-        "--dit-weight",
-        type=str,
-        default="data/hunyuan/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt",
-    )
-    parser.add_argument(
-        "--reproduce",
-        action="store_true",
-        help="Enable reproducibility by setting random seeds and deterministic algorithms.",
-    )
-    parser.add_argument(
-        "--disable-autocast",
-        action="store_true",
-        help="Disable autocast for denoising loop and vae decoding in pipeline sampling.",
-    )
-
-    # Flow Matching
-    parser.add_argument(
-        "--flow-reverse",
-        action="store_true",
-        help="If reverse, learning/sampling from t=1 -> t=0.",
-    )
-    parser.add_argument(
-        "--flow-solver", type=str, default="euler", help="Solver for flow matching."
-    )
-    parser.add_argument(
-        "--use-linear-quadratic-schedule",
-        action="store_true",
-        help="Use linear quadratic schedule for flow matching. Following MovieGen (https://ai.meta.com/static-resource/movie-gen-research-paper)",
-    )
-    parser.add_argument(
-        "--linear-schedule-end",
-        type=int,
-        default=25,
-        help="End step for linear quadratic schedule for flow matching.",
-    )
-
-    # Model parameters
-    parser.add_argument("--model", type=str, default="HYVideo-T/2-cfgdistill")
-    parser.add_argument("--latent-channels", type=int, default=16)
-    parser.add_argument(
-        "--precision", type=str, default="bf16", choices=["fp32", "fp16", "bf16"]
-    )
-    parser.add_argument(
-        "--rope-theta", type=int, default=256, help="Theta used in RoPE."
-    )
-
-    parser.add_argument("--vae", type=str, default="884-16c-hy")
-    parser.add_argument(
-        "--vae-precision", type=str, default="fp16", choices=["fp32", "fp16", "bf16"]
-    )
-    parser.add_argument("--vae-tiling", action="store_true", default=True)
-
-    parser.add_argument("--text-encoder", type=str, default="llm")
-    parser.add_argument(
-        "--text-encoder-precision",
-        type=str,
-        default="fp16",
-        choices=["fp32", "fp16", "bf16"],
-    )
-    parser.add_argument("--text-states-dim", type=int, default=4096)
-    parser.add_argument("--text-len", type=int, default=256)
-    parser.add_argument("--tokenizer", type=str, default="llm")
-    parser.add_argument("--prompt-template", type=str, default="dit-llm-encode")
-    parser.add_argument(
-        "--prompt-template-video", type=str, default="dit-llm-encode-video"
-    )
-    parser.add_argument("--hidden-state-skip-layer", type=int, default=2)
-    parser.add_argument("--apply-final-norm", action="store_true")
-
-    parser.add_argument("--text-encoder-2", type=str, default="clipL")
-    parser.add_argument(
-        "--text-encoder-precision-2",
-        type=str,
-        default="fp16",
-        choices=["fp32", "fp16", "bf16"],
-    )
-    parser.add_argument("--text-states-dim-2", type=int, default=768)
-    parser.add_argument("--tokenizer-2", type=str, default="clipL")
-    parser.add_argument("--text-len-2", type=int, default=77)
-
-    args = parser.parse_args()
-    main(args)

fastvideo/sample/sample_t2v_mochi.py

@@ -1,27 +1,17 @@
-import torch
-from fastvideo.models.mochi_hf.pipeline_mochi import MochiPipeline
-import torch.distributed as dist
-
-from diffusers.utils import export_to_video
-from fastvideo.utils.parallel_states import (
-    initialize_sequence_parallel_state,
-    nccl_info,
-)
 import argparse
-import os
-from fastvideo.models.mochi_hf.modeling_mochi import MochiTransformer3DModel
 import json
-from typing import Optional
-from safetensors.torch import save_file, load_file
-from peft import set_peft_model_state_dict, inject_adapter_in_model, load_peft_weights
-from peft import LoraConfig
-import sys
-import pdb
-import copy
-from typing import Dict
+import os
+
+import torch
+import torch.distributed as dist
 from diffusers import FlowMatchEulerDiscreteScheduler
-from diffusers.utils import convert_unet_state_dict_to_peft
+from diffusers.utils import export_to_video
+
 from fastvideo.distill.solver import PCMFMScheduler
+from fastvideo.models.mochi_hf.modeling_mochi import MochiTransformer3DModel
+from fastvideo.models.mochi_hf.pipeline_mochi import MochiPipeline
+from fastvideo.utils.parallel_states import (
+    initialize_sequence_parallel_state, nccl_info)
 
 
 def initialize_distributed():
@@ -29,9 +19,10 @@ def initialize_distributed():
     world_size = int(os.getenv("WORLD_SIZE", 1))
     print("world_size", world_size)
     torch.cuda.set_device(local_rank)
-    dist.init_process_group(
-        backend="nccl", init_method="env://", world_size=world_size, rank=local_rank
-    )
+    dist.init_process_group(backend="nccl",
+                            init_method="env://",
+                            world_size=world_size,
+                            rank=local_rank)
     initialize_sequence_parallel_state(world_size)
 
 
@@ -39,8 +30,8 @@ def main(args):
     initialize_distributed()
     print(nccl_info.sp_size)
     device = torch.cuda.current_device()
-    generator = torch.Generator(device).manual_seed(args.seed)
-    weight_dtype = torch.bfloat16
+    # Peiyuan: GPU seed will cause A100 and H100 to produce different results .....
+
     if args.scheduler_type == "euler":
         scheduler = FlowMatchEulerDiscreteScheduler()
     else:
@@ -54,29 +45,33 @@ def main(args):
             args.linear_range,
         )
     if args.transformer_path is not None:
-        transformer = MochiTransformer3DModel.from_pretrained(args.transformer_path)
+        transformer = MochiTransformer3DModel.from_pretrained(
+            args.transformer_path)
     else:
         transformer = MochiTransformer3DModel.from_pretrained(
-            args.model_path, subfolder="transformer/"
-        )
+            args.model_path, subfolder="transformer/")
 
-    pipe = MochiPipeline.from_pretrained(
-        args.model_path, transformer=transformer, scheduler=scheduler
-    )
+    pipe = MochiPipeline.from_pretrained(args.model_path,
+                                         transformer=transformer,
+                                         scheduler=scheduler)
 
     pipe.enable_vae_tiling()
 
     if args.lora_checkpoint_dir is not None:
         print(f"Loading LoRA weights from {args.lora_checkpoint_dir}")
-        config_path = os.path.join(args.lora_checkpoint_dir, "lora_config.json")
+        config_path = os.path.join(args.lora_checkpoint_dir,
+                                   "lora_config.json")
         with open(config_path, "r") as f:
             lora_config_dict = json.load(f)
         rank = lora_config_dict["lora_params"]["lora_rank"]
         lora_alpha = lora_config_dict["lora_params"]["lora_alpha"]
         lora_scaling = lora_alpha / rank
-        pipe.load_lora_weights(args.lora_checkpoint_dir, adapter_name="default")
+        pipe.load_lora_weights(args.lora_checkpoint_dir,
+                               adapter_name="default")
         pipe.set_adapters(["default"], [lora_scaling])
-        print(f"Successfully Loaded LoRA weights from {args.lora_checkpoint_dir}")
+        print(
+            f"Successfully Loaded LoRA weights from {args.lora_checkpoint_dir}"
+        )
     # pipe.to(device)
 
     pipe.enable_model_cpu_offload(device)
@@ -84,18 +79,13 @@ def main(args):
     # Generate videos from the input prompt
 
     if args.prompt_embed_path is not None:
-        prompt_embeds = (
-            torch.load(args.prompt_embed_path, map_location="cpu", weights_only=True)
-            .to(device)
-            .unsqueeze(0)
-        )
-        encoder_attention_mask = (
-            torch.load(
-                args.encoder_attention_mask_path, map_location="cpu", weights_only=True
-            )
-            .to(device)
-            .unsqueeze(0)
-        )
+        prompt_embeds = (torch.load(args.prompt_embed_path,
+                                    map_location="cpu",
+                                    weights_only=True).to(device).unsqueeze(0))
+        encoder_attention_mask = (torch.load(
+            args.encoder_attention_mask_path,
+            map_location="cpu",
+            weights_only=True).to(device).unsqueeze(0))
         prompts = None
     elif args.prompt_path is not None:
         prompts = [line.strip() for line in open(args.prompt_path, "r")]
@@ -107,9 +97,9 @@ def main(args):
         encoder_attention_mask = None
 
     if prompts is not None:
-        videos = []
         with torch.autocast("cuda", dtype=torch.bfloat16):
             for prompt in prompts:
+                generator = torch.Generator("cpu").manual_seed(args.seed)
                 video = pipe(
                     prompt=[prompt],
                     height=args.height,
@@ -119,9 +109,17 @@ def main(args):
                     guidance_scale=args.guidance_scale,
                     generator=generator,
                 ).frames
-                videos.append(video[0])
+                if nccl_info.global_rank <= 0:
+                    os.makedirs(args.output_path, exist_ok=True)
+                    suffix = prompt.split(".")[0]
+                    export_to_video(
+                        video[0],
+                        os.path.join(args.output_path, f"{suffix}.mp4"),
+                        fps=30,
+                    )
     else:
         with torch.autocast("cuda", dtype=torch.bfloat16):
+            generator = torch.Generator("cpu").manual_seed(args.seed)
             videos = pipe(
                 prompt_embeds=prompt_embeds,
                 prompt_attention_mask=encoder_attention_mask,
@@ -133,16 +131,7 @@ def main(args):
                 generator=generator,
             ).frames
 
-    if nccl_info.global_rank <= 0:
-        if prompts is not None:
-            # mkdir
-            os.makedirs(args.output_path, exist_ok=True)
-            for video, prompt in zip(videos, prompts):
-                suffix = prompt.split(".")[0]
-                export_to_video(
-                    video, os.path.join(args.output_path, f"{suffix}.mp4"), fps=30
-                )
-        else:
+        if nccl_info.global_rank <= 0:
             export_to_video(videos[0], args.output_path + ".mp4", fps=30)
 
 
@@ -162,7 +151,9 @@ if __name__ == "__main__":
     parser.add_argument("--prompt_embed_path", type=str, default=None)
     parser.add_argument("--prompt_path", type=str, default=None)
     parser.add_argument("--scheduler_type", type=str, default="euler")
-    parser.add_argument("--encoder_attention_mask_path", type=str, default=None)
+    parser.add_argument("--encoder_attention_mask_path",
+                        type=str,
+                        default=None)
     parser.add_argument(
         "--lora_checkpoint_dir",
         type=str,

fastvideo/sample/sample_t2v_mochi_no_sp.py

@@ -1,9 +1,11 @@
-import torch
-from fastvideo.models.mochi_hf.pipeline_mochi import MochiPipeline
-from fastvideo.models.mochi_hf.modeling_mochi import MochiTransformer3DModel
-from diffusers.utils import export_to_video, load_image, load_video
 import argparse
+
+import torch
 from diffusers import FlowMatchEulerDiscreteScheduler
+from diffusers.utils import export_to_video
+
+from fastvideo.models.mochi_hf.modeling_mochi import MochiTransformer3DModel
+from fastvideo.models.mochi_hf.pipeline_mochi import MochiPipeline
 
 
 def main(args):
@@ -12,14 +14,14 @@ def main(args):
     # do not invert
     scheduler = FlowMatchEulerDiscreteScheduler()
     if args.transformer_path is not None:
-        transformer = MochiTransformer3DModel.from_pretrained(args.transformer_path)
+        transformer = MochiTransformer3DModel.from_pretrained(
+            args.transformer_path)
     else:
         transformer = MochiTransformer3DModel.from_pretrained(
-            args.model_path, subfolder="transformer/"
-        )
-    pipe = MochiPipeline.from_pretrained(
-        args.model_path, transformer=transformer, scheduler=scheduler
-    )
+            args.model_path, subfolder="transformer/")
+    pipe = MochiPipeline.from_pretrained(args.model_path,
+                                         transformer=transformer,
+                                         scheduler=scheduler)
     pipe.enable_vae_tiling()
     # pipe.to("cuda:1")
     pipe.enable_model_cpu_offload()

fastvideo/train.py

@@ -1,60 +1,47 @@
+# !/bin/python3
+# isort: skip_file
 import argparse
-from email.policy import strict
-import logging
 import math
 import os
-import shutil
-from pathlib import Path
-from fastvideo.utils.parallel_states import (
-    initialize_sequence_parallel_state,
-    destroy_sequence_parallel_group,
-    get_sequence_parallel_state,
-    nccl_info,
-)
-from fastvideo.utils.communications import sp_parallel_dataloader_wrapper, broadcast
-from fastvideo.models.mochi_hf.mochi_latents_utils import normalize_dit_input
-from fastvideo.utils.validation import log_validation
 import time
-from torch.utils.data import DataLoader
+from collections import deque
+
 import torch
-from torch.distributed.fsdp import (
-    FullyShardedDataParallel as FSDP,
-    StateDictType,
-    FullStateDictConfig,
-)
-import json
-from torch.utils.data.distributed import DistributedSampler
-from fastvideo.utils.dataset_utils import LengthGroupedSampler
+import torch.distributed as dist
 import wandb
 from accelerate.utils import set_seed
-from tqdm.auto import tqdm
-from fastvideo.utils.fsdp_util import get_dit_fsdp_kwargs, apply_fsdp_checkpointing
-from diffusers.utils import convert_unet_state_dict_to_peft
-from diffusers import (
-    FlowMatchEulerDiscreteScheduler,
-)
+from diffusers import FlowMatchEulerDiscreteScheduler
 from diffusers.optimization import get_scheduler
-from fastvideo.models.mochi_hf.modeling_mochi import MochiTransformer3DModel
-from diffusers.utils import check_min_version
-from fastvideo.dataset.latent_datasets import LatentDataset, latent_collate_function
-import torch.distributed as dist
-from safetensors.torch import save_file, load_file
-from peft import LoraConfig, get_peft_model_state_dict, set_peft_model_state_dict
-from torch.distributed.fsdp import (
-    FullyShardedDataParallel as FSDP,
-)
-from fastvideo.utils.checkpoint import (
-    save_checkpoint,
-    save_lora_checkpoint,
-    resume_lora_optimizer,
-)
-from fastvideo.utils.logging_ import main_print
+from diffusers.utils import check_min_version, convert_unet_state_dict_to_peft
+from peft import LoraConfig, set_peft_model_state_dict
+from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
+from torch.utils.data import DataLoader
+from torch.utils.data.distributed import DistributedSampler
+from tqdm.auto import tqdm
+
+from fastvideo.dataset.latent_datasets import (LatentDataset,
+                                               latent_collate_function)
+from fastvideo.models.mochi_hf.mochi_latents_utils import normalize_dit_input
 from fastvideo.models.mochi_hf.pipeline_mochi import MochiPipeline
+from fastvideo.models.hunyuan_hf.pipeline_hunyuan import HunyuanVideoPipeline
+
+from fastvideo.utils.checkpoint import (resume_lora_optimizer, save_checkpoint,
+                                        save_lora_checkpoint)
+from fastvideo.utils.communications import (broadcast,
+                                            sp_parallel_dataloader_wrapper)
+from fastvideo.utils.dataset_utils import LengthGroupedSampler
+from fastvideo.utils.fsdp_util import (apply_fsdp_checkpointing,
+                                       get_dit_fsdp_kwargs)
+from fastvideo.utils.load import load_transformer
+from fastvideo.utils.logging_ import main_print
+from fastvideo.utils.parallel_states import (destroy_sequence_parallel_group,
+                                             get_sequence_parallel_state,
+                                             initialize_sequence_parallel_state
+                                             )
+from fastvideo.utils.validation import log_validation
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
 check_min_version("0.31.0")
-import time
-from collections import deque
 
 
 def compute_density_for_timestep_sampling(
@@ -77,24 +64,29 @@ def compute_density_for_timestep_sampling(
         u = torch.normal(
             mean=logit_mean,
             std=logit_std,
-            size=(batch_size,),
+            size=(batch_size, ),
             device="cpu",
             generator=generator,
         )
         u = torch.nn.functional.sigmoid(u)
     elif weighting_scheme == "mode":
-        u = torch.rand(size=(batch_size,), device="cpu", generator=generator)
-        u = 1 - u - mode_scale * (torch.cos(math.pi * u / 2) ** 2 - 1 + u)
+        u = torch.rand(size=(batch_size, ), device="cpu", generator=generator)
+        u = 1 - u - mode_scale * (torch.cos(math.pi * u / 2)**2 - 1 + u)
     else:
-        u = torch.rand(size=(batch_size,), device="cpu", generator=generator)
+        u = torch.rand(size=(batch_size, ), device="cpu", generator=generator)
     return u
 
 
-def get_sigmas(noise_scheduler, device, timesteps, n_dim=4, dtype=torch.float32):
+def get_sigmas(noise_scheduler,
+               device,
+               timesteps,
+               n_dim=4,
+               dtype=torch.float32):
     sigmas = noise_scheduler.sigmas.to(device=device, dtype=dtype)
     schedule_timesteps = noise_scheduler.timesteps.to(device)
     timesteps = timesteps.to(device)
-    step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps]
+    step_indices = [(schedule_timesteps == t).nonzero().item()
+                    for t in timesteps]
 
     sigma = sigmas[step_indices].flatten()
     while len(sigma.shape) < n_dim:
@@ -102,8 +94,9 @@ def get_sigmas(noise_scheduler, device, timesteps, n_dim=4, dtype=torch.float32)
     return sigma
 
 
-def train_one_step_mochi(
+def train_one_step(
     transformer,
+    model_type,
     optimizer,
     lr_scheduler,
     loader,
@@ -127,7 +120,7 @@ def train_one_step_mochi(
             latents_attention_mask,
             encoder_attention_mask,
         ) = next(loader)
-        latents = normalize_mochi_dit_input(latents)
+        latents = normalize_dit_input(model_type, latents)
         batch_size = latents.shape[0]
         noise = torch.randn_like(latents)
         u = compute_density_for_timestep_sampling(
@@ -139,7 +132,8 @@ def train_one_step_mochi(
             mode_scale=mode_scale,
         )
         indices = (u * noise_scheduler.config.num_train_timesteps).long()
-        timesteps = noise_scheduler.timesteps[indices].to(device=latents.device)
+        timesteps = noise_scheduler.timesteps[indices].to(
+            device=latents.device)
         if sp_size > 1:
             # Make sure that the timesteps are the same across all sp processes.
             broadcast(timesteps)
@@ -151,20 +145,21 @@ def train_one_step_mochi(
             dtype=latents.dtype,
         )
         noisy_model_input = (1.0 - sigmas) * latents + sigmas * noise
-        # if rank<=0:
-        #     print("2222222222222222222222222222222222222222222222")
-        # print(type(latents_attention_mask))
-        # print(latents_attention_mask)
-        with torch.autocast("cuda", torch.bfloat16):
-            model_pred = transformer(
-                noisy_model_input,
-                encoder_hidden_states,
-                timesteps,
-                encoder_attention_mask,  # B, L
-                return_dict=False,
-            )[0]
-        # if rank<=0:
-        #     print("333333333333333333333333333333333333333333333333")
+        with torch.autocast("cuda", dtype=torch.bfloat16):
+            input_kwargs = {
+                "hidden_states": noisy_model_input,
+                "encoder_hidden_states": encoder_hidden_states,
+                "timestep": timesteps,
+                "encoder_attention_mask": encoder_attention_mask,  # B, L
+                "return_dict": False,
+            }
+            if 'hunyuan' in model_type:
+                input_kwargs["guidance"] = torch.tensor(
+                    [1000.0],
+                    device=noisy_model_input.device,
+                    dtype=torch.bfloat16)
+            model_pred = transformer(**input_kwargs)[0]
+
         if precondition_outputs:
             model_pred = noisy_model_input - model_pred * sigmas
         if precondition_outputs:
@@ -172,10 +167,8 @@ def train_one_step_mochi(
         else:
             target = noise - latents
 
-        loss = (
-            torch.mean((model_pred.float() - target.float()) ** 2)
-            / gradient_accumulation_steps
-        )
+        loss = (torch.mean((model_pred.float() - target.float())**2) /
+                gradient_accumulation_steps)
 
         loss.backward()
 
@@ -211,22 +204,26 @@ def main(args):
     if rank <= 0 and args.output_dir is not None:
         os.makedirs(args.output_dir, exist_ok=True)
 
-    # For mixed precision training we cast all non-trainable weigths to half-precision
+    # For mixed precision training we cast all non-trainable weights to half-precision
     # as these weights are only used for inference, keeping weights in full precision is not required.
 
     # Create model:
 
     main_print(f"--> loading model from {args.pretrained_model_name_or_path}")
     # keep the master weight to float32
-    transformer = MochiTransformer3DModel.from_pretrained(
+    transformer = load_transformer(
+        args.model_type,
+        args.dit_model_name_or_path,
         args.pretrained_model_name_or_path,
-        subfolder="transformer",
-        torch_dtype=(
-            torch.float32 if args.master_weight_type == "fp32" else torch.bfloat16
-        ),
+        torch.float32 if args.master_weight_type == "fp32" else torch.bfloat16,
     )
 
     if args.use_lora:
+        assert args.model_type != "hunyuan", "LoRA is only supported for huggingface model. Please use hunyuan_hf for lora finetuning"
+        if args.model_type == "mochi":
+            pipe = MochiPipeline
+        elif args.model_type == "hunyuan_hf":
+            pipe = HunyuanVideoPipeline
         transformer.requires_grad_(False)
         transformer_lora_config = LoraConfig(
             r=args.lora_rank,
@@ -237,26 +234,25 @@ def main(args):
         transformer.add_adapter(transformer_lora_config)
 
     if args.resume_from_lora_checkpoint:
-        lora_state_dict = MochiPipeline.lora_state_dict(
-            args.resume_from_lora_checkpoint
-        )
+        lora_state_dict = pipe.lora_state_dict(
+            args.resume_from_lora_checkpoint)
         transformer_state_dict = {
             f'{k.replace("transformer.", "")}': v
-            for k, v in lora_state_dict.items()
-            if k.startswith("transformer.")
+            for k, v in lora_state_dict.items() if k.startswith("transformer.")
         }
-        transformer_state_dict = convert_unet_state_dict_to_peft(transformer_state_dict)
-        incompatible_keys = set_peft_model_state_dict(
-            transformer, transformer_state_dict, adapter_name="default"
-        )
+        transformer_state_dict = convert_unet_state_dict_to_peft(
+            transformer_state_dict)
+        incompatible_keys = set_peft_model_state_dict(transformer,
+                                                      transformer_state_dict,
+                                                      adapter_name="default")
         if incompatible_keys is not None:
             # check only for unexpected keys
-            unexpected_keys = getattr(incompatible_keys, "unexpected_keys", None)
+            unexpected_keys = getattr(incompatible_keys, "unexpected_keys",
+                                      None)
             if unexpected_keys:
                 main_print(
                     f"Loading adapter weights from state_dict led to unexpected keys not found in the model: "
-                    f" {unexpected_keys}. "
-                )
+                    f" {unexpected_keys}. ")
 
     main_print(
         f"  Total training parameters = {sum(p.numel() for p in transformer.parameters() if p.requires_grad) / 1e6} M"
@@ -264,7 +260,8 @@ def main(args):
     main_print(
         f"--> Initializing FSDP with sharding strategy: {args.fsdp_sharding_startegy}"
     )
-    fsdp_kwargs = get_dit_fsdp_kwargs(
+    fsdp_kwargs, no_split_modules = get_dit_fsdp_kwargs(
+        transformer,
         args.fsdp_sharding_startegy,
         args.use_lora,
         args.use_cpu_offload,
@@ -274,18 +271,24 @@ def main(args):
     if args.use_lora:
         transformer.config.lora_rank = args.lora_rank
         transformer.config.lora_alpha = args.lora_alpha
-        transformer.config.lora_target_modules = ["to_k", "to_q", "to_v", "to_out.0"]
-        transformer._no_split_modules = ["MochiTransformerBlock"]
-        fsdp_kwargs["auto_wrap_policy"] = fsdp_kwargs["auto_wrap_policy"](transformer)
+        transformer.config.lora_target_modules = [
+            "to_k", "to_q", "to_v", "to_out.0"
+        ]
+        transformer._no_split_modules = [
+            no_split_module.__name__ for no_split_module in no_split_modules
+        ]
+        fsdp_kwargs["auto_wrap_policy"] = fsdp_kwargs["auto_wrap_policy"](
+            transformer)
 
     transformer = FSDP(
         transformer,
         **fsdp_kwargs,
     )
-    main_print(f"--> model loaded")
+    main_print("--> model loaded")
 
     if args.gradient_checkpointing:
-        apply_fsdp_checkpointing(transformer, args.selective_checkpointing)
+        apply_fsdp_checkpointing(transformer, no_split_modules,
+                                 args.selective_checkpointing)
 
     # Set model as trainable.
     transformer.train()
@@ -293,7 +296,8 @@ def main(args):
     noise_scheduler = FlowMatchEulerDiscreteScheduler()
 
     params_to_optimize = transformer.parameters()
-    params_to_optimize = list(filter(lambda p: p.requires_grad, params_to_optimize))
+    params_to_optimize = list(
+        filter(lambda p: p.requires_grad, params_to_optimize))
 
     optimizer = torch.optim.AdamW(
         params_to_optimize,
@@ -306,8 +310,7 @@ def main(args):
     init_steps = 0
     if args.resume_from_lora_checkpoint:
         transformer, optimizer, init_steps = resume_lora_optimizer(
-            transformer, args.resume_from_lora_checkpoint, optimizer
-        )
+            transformer, args.resume_from_lora_checkpoint, optimizer)
     main_print(f"optimizer: {optimizer}")
 
     lr_scheduler = get_scheduler(
@@ -320,21 +323,17 @@ def main(args):
         last_epoch=init_steps - 1,
     )
 
-    train_dataset = LatentDataset(args.data_json_path, args.num_latent_t, args.cfg)
-    sampler = (
-        LengthGroupedSampler(
-            args.train_batch_size,
-            rank=rank,
-            world_size=world_size,
-            lengths=train_dataset.lengths,
-            group_frame=args.group_frame,
-            group_resolution=args.group_resolution,
-        )
-        if (args.group_frame or args.group_resolution)
-        else DistributedSampler(
-            train_dataset, rank=rank, num_replicas=world_size, shuffle=False
-        )
-    )
+    train_dataset = LatentDataset(args.data_json_path, args.num_latent_t,
+                                  args.cfg)
+    sampler = (LengthGroupedSampler(
+        args.train_batch_size,
+        rank=rank,
+        world_size=world_size,
+        lengths=train_dataset.lengths,
+        group_frame=args.group_frame,
+        group_resolution=args.group_resolution,
+    ) if (args.group_frame or args.group_resolution) else DistributedSampler(
+        train_dataset, rank=rank, num_replicas=world_size, shuffle=False))
 
     train_dataloader = DataLoader(
         train_dataset,
@@ -347,45 +346,42 @@ def main(args):
     )
 
     num_update_steps_per_epoch = math.ceil(
-        len(train_dataloader)
-        / args.gradient_accumulation_steps
-        * args.sp_size
-        / args.train_sp_batch_size
-    )
-    args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
+        len(train_dataloader) / args.gradient_accumulation_steps *
+        args.sp_size / args.train_sp_batch_size)
+    args.num_train_epochs = math.ceil(args.max_train_steps /
+                                      num_update_steps_per_epoch)
 
     if rank <= 0:
         project = args.tracker_project_name or "fastvideo"
         wandb.init(project=project, config=args)
 
     # Train!
-    total_batch_size = (
-        args.train_batch_size
-        * world_size
-        * args.gradient_accumulation_steps
-        / args.sp_size
-        * args.train_sp_batch_size
-    )
+    total_batch_size = (world_size * args.gradient_accumulation_steps /
+                        args.sp_size * args.train_sp_batch_size)
     main_print("***** Running training *****")
     main_print(f"  Num examples = {len(train_dataset)}")
     main_print(f"  Dataloader size = {len(train_dataloader)}")
     main_print(f"  Num Epochs = {args.num_train_epochs}")
     main_print(f"  Resume training from step {init_steps}")
-    main_print(f"  Instantaneous batch size per device = {args.train_batch_size}")
+    main_print(
+        f"  Instantaneous batch size per device = {args.train_batch_size}")
     main_print(
         f"  Total train batch size (w. data & sequence parallel, accumulation) = {total_batch_size}"
     )
-    main_print(f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
+    main_print(
+        f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
     main_print(f"  Total optimization steps = {args.max_train_steps}")
     main_print(
         f"  Total training parameters per FSDP shard = {sum(p.numel() for p in transformer.parameters() if p.requires_grad) / 1e9} B"
     )
     # print dtype
-    main_print(f"  Master weight dtype: {transformer.parameters().__next__().dtype}")
+    main_print(
+        f"  Master weight dtype: {transformer.parameters().__next__().dtype}")
 
     # Potentially load in the weights and states from a previous save
     if args.resume_from_checkpoint:
-        assert NotImplementedError("resume_from_checkpoint is not supported now.")
+        assert NotImplementedError(
+            "resume_from_checkpoint is not supported now.")
         # TODO
 
     progress_bar = tqdm(
@@ -411,8 +407,9 @@ def main(args):
         next(loader)
     for step in range(init_steps + 1, args.max_train_steps + 1):
         start_time = time.time()
-        loss, grad_norm = train_one_step_mochi(
+        loss, grad_norm = train_one_step(
             transformer,
+            args.model_type,
             optimizer,
             lr_scheduler,
             loader,
@@ -432,13 +429,11 @@ def main(args):
         step_times.append(step_time)
         avg_step_time = sum(step_times) / len(step_times)
 
-        progress_bar.set_postfix(
-            {
-                "loss": f"{loss:.4f}",
-                "step_time": f"{step_time:.2f}s",
-                "grad_norm": grad_norm,
-            }
-        )
+        progress_bar.set_postfix({
+            "loss": f"{loss:.4f}",
+            "step_time": f"{step_time:.2f}s",
+            "grad_norm": grad_norm,
+        })
         progress_bar.update(1)
         if rank <= 0:
             wandb.log(
@@ -454,24 +449,26 @@ def main(args):
         if step % args.checkpointing_steps == 0:
             if args.use_lora:
                 # Save LoRA weights
-                save_lora_checkpoint(
-                    transformer, optimizer, rank, args.output_dir, step
-                )
+                save_lora_checkpoint(transformer, optimizer, rank,
+                                     args.output_dir, step, pipe)
             else:
                 # Your existing checkpoint saving code
-                save_checkpoint(transformer, optimizer, rank, args.output_dir, step)
+                save_checkpoint(transformer, rank, args.output_dir, step)
             dist.barrier()
         if args.log_validation and step % args.validation_steps == 0:
-            log_validation(args, transformer, device, torch.bfloat16, step)
+            log_validation(args,
+                           transformer,
+                           device,
+                           torch.bfloat16,
+                           step,
+                           shift=args.shift)
 
     if args.use_lora:
-        save_lora_checkpoint(
-            transformer, optimizer, rank, args.output_dir, args.max_train_steps
-        )
+        save_lora_checkpoint(transformer, optimizer, rank, args.output_dir,
+                             args.max_train_steps, pipe)
     else:
-        save_checkpoint(
-            transformer, optimizer, rank, args.output_dir, args.max_train_steps
-        )
+        save_checkpoint(transformer, rank, args.output_dir,
+                        args.max_train_steps)
 
     if get_sequence_parallel_state():
         destroy_sequence_parallel_group()
@@ -479,15 +476,24 @@ def main(args):
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
-
+    parser.add_argument(
+        "--model_type",
+        type=str,
+        default="mochi",
+        help=
+        "The type of model to train. Currentlt support [mochi, hunyuan_hf, hunyuan]"
+    )
     # dataset & dataloader
     parser.add_argument("--data_json_path", type=str, required=True)
+    parser.add_argument("--num_height", type=int, default=480)
+    parser.add_argument("--num_width", type=int, default=848)
     parser.add_argument("--num_frames", type=int, default=163)
     parser.add_argument(
         "--dataloader_num_workers",
         type=int,
         default=10,
-        help="Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.",
+        help=
+        "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.",
     )
     parser.add_argument(
         "--train_batch_size",
@@ -495,14 +501,16 @@ if __name__ == "__main__":
         default=16,
         help="Batch size (per device) for the training dataloader.",
     )
-    parser.add_argument(
-        "--num_latent_t", type=int, default=28, help="Number of latent timesteps."
-    )
+    parser.add_argument("--num_latent_t",
+                        type=int,
+                        default=28,
+                        help="Number of latent timesteps.")
     parser.add_argument("--group_frame", action="store_true")  # TODO
     parser.add_argument("--group_resolution", action="store_true")  # TODO
 
     # text encoder & vae & diffusion model
     parser.add_argument("--pretrained_model_name_or_path", type=str)
+    parser.add_argument("--dit_model_name_or_path", type=str, default=None)
     parser.add_argument("--cache_dir", type=str, default="./cache_dir")
 
     # diffusion setting
@@ -533,14 +541,16 @@ if __name__ == "__main__":
     parser.add_argument("--validation_steps", type=int, default=50)
     parser.add_argument("--log_validation", action="store_true")
     parser.add_argument("--tracker_project_name", type=str, default=None)
-    parser.add_argument(
-        "--seed", type=int, default=None, help="A seed for reproducible training."
-    )
+    parser.add_argument("--seed",
+                        type=int,
+                        default=None,
+                        help="A seed for reproducible training.")
     parser.add_argument(
         "--output_dir",
         type=str,
         default=None,
-        help="The output directory where the model predictions and checkpoints will be written.",
+        help=
+        "The output directory where the model predictions and checkpoints will be written.",
     )
     parser.add_argument(
         "--checkpoints_total_limit",
@@ -552,38 +562,40 @@ if __name__ == "__main__":
         "--checkpointing_steps",
         type=int,
         default=500,
-        help=(
-            "Save a checkpoint of the training state every X updates. These checkpoints can be used both as final"
-            " checkpoints in case they are better than the last checkpoint, and are also suitable for resuming"
-            " training using `--resume_from_checkpoint`."
-        ),
+        help=
+        ("Save a checkpoint of the training state every X updates. These checkpoints can be used both as final"
+         " checkpoints in case they are better than the last checkpoint, and are also suitable for resuming"
+         " training using `--resume_from_checkpoint`."),
     )
+    parser.add_argument("--shift",
+                        type=float,
+                        default=1.0,
+                        help=("Set shift to 7 for hunyuan model."))
     parser.add_argument(
         "--resume_from_checkpoint",
         type=str,
         default=None,
-        help=(
-            "Whether training should be resumed from a previous checkpoint. Use a path saved by"
-            ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
-        ),
+        help=
+        ("Whether training should be resumed from a previous checkpoint. Use a path saved by"
+         ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
+         ),
     )
     parser.add_argument(
         "--resume_from_lora_checkpoint",
         type=str,
         default=None,
-        help=(
-            "Whether training should be resumed from a previous lora checkpoint. Use a path saved by"
-            ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
-        ),
+        help=
+        ("Whether training should be resumed from a previous lora checkpoint. Use a path saved by"
+         ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
+         ),
     )
     parser.add_argument(
         "--logging_dir",
         type=str,
         default="logs",
-        help=(
-            "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
-            " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
-        ),
+        help=
+        ("[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
+         " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."),
     )
 
     # optimizer & scheduler & Training
@@ -592,25 +604,29 @@ if __name__ == "__main__":
         "--max_train_steps",
         type=int,
         default=None,
-        help="Total number of training steps to perform.  If provided, overrides num_train_epochs.",
+        help=
+        "Total number of training steps to perform.  If provided, overrides num_train_epochs.",
     )
     parser.add_argument(
         "--gradient_accumulation_steps",
         type=int,
         default=1,
-        help="Number of updates steps to accumulate before performing a backward/update pass.",
+        help=
+        "Number of updates steps to accumulate before performing a backward/update pass.",
     )
     parser.add_argument(
         "--learning_rate",
         type=float,
         default=1e-4,
-        help="Initial learning rate (after the potential warmup period) to use.",
+        help=
+        "Initial learning rate (after the potential warmup period) to use.",
     )
     parser.add_argument(
         "--scale_lr",
         action="store_true",
         default=False,
-        help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
+        help=
+        "Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
     )
     parser.add_argument(
         "--lr_warmup_steps",
@@ -618,41 +634,47 @@ if __name__ == "__main__":
         default=10,
         help="Number of steps for the warmup in the lr scheduler.",
     )
-    parser.add_argument(
-        "--max_grad_norm", default=1.0, type=float, help="Max gradient norm."
-    )
+    parser.add_argument("--max_grad_norm",
+                        default=1.0,
+                        type=float,
+                        help="Max gradient norm.")
     parser.add_argument(
         "--gradient_checkpointing",
         action="store_true",
-        help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
+        help=
+        "Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
     )
     parser.add_argument("--selective_checkpointing", type=float, default=1.0)
     parser.add_argument(
         "--allow_tf32",
         action="store_true",
-        help=(
-            "Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
-            " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
-        ),
+        help=
+        ("Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
+         " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
+         ),
     )
     parser.add_argument(
         "--mixed_precision",
         type=str,
         default=None,
         choices=["no", "fp16", "bf16"],
-        help=(
-            "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
-            " 1.10.and an Nvidia Ampere GPU.  Default to the value of accelerate config of the current system or the"
-            " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
-        ),
+        help=
+        ("Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
+         " 1.10.and an Nvidia Ampere GPU.  Default to the value of accelerate config of the current system or the"
+         " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
+         ),
     )
     parser.add_argument(
         "--use_cpu_offload",
         action="store_true",
-        help="Whether to use CPU offload for param & gradient & optimizer states.",
+        help=
+        "Whether to use CPU offload for param & gradient & optimizer states.",
     )
 
-    parser.add_argument("--sp_size", type=int, default=1, help="For sequence parallel")
+    parser.add_argument("--sp_size",
+                        type=int,
+                        default=1,
+                        help="For sequence parallel")
     parser.add_argument(
         "--train_sp_batch_size",
         type=int,
@@ -666,12 +688,14 @@ if __name__ == "__main__":
         default=False,
         help="Whether to use LoRA for finetuning.",
     )
-    parser.add_argument(
-        "--lora_alpha", type=int, default=256, help="Alpha parameter for LoRA."
-    )
-    parser.add_argument(
-        "--lora_rank", type=int, default=128, help="LoRA rank parameter. "
-    )
+    parser.add_argument("--lora_alpha",
+                        type=int,
+                        default=256,
+                        help="Alpha parameter for LoRA.")
+    parser.add_argument("--lora_rank",
+                        type=int,
+                        default=128,
+                        help="LoRA rank parameter. ")
     parser.add_argument("--fsdp_sharding_startegy", default="full")
 
     parser.add_argument(
@@ -696,17 +720,17 @@ if __name__ == "__main__":
         "--mode_scale",
         type=float,
         default=1.29,
-        help="Scale of mode weighting scheme. Only effective when using the `'mode'` as the `weighting_scheme`.",
+        help=
+        "Scale of mode weighting scheme. Only effective when using the `'mode'` as the `weighting_scheme`.",
     )
     # lr_scheduler
     parser.add_argument(
         "--lr_scheduler",
         type=str,
         default="constant",
-        help=(
-            'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
-            ' "constant", "constant_with_warmup"]'
-        ),
+        help=
+        ('The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
+         ' "constant", "constant_with_warmup"]'),
     )
     parser.add_argument(
         "--lr_num_cycles",
@@ -720,9 +744,10 @@ if __name__ == "__main__":
         default=1.0,
         help="Power factor of the polynomial scheduler.",
     )
-    parser.add_argument(
-        "--weight_decay", type=float, default=0.01, help="Weight decay to apply."
-    )
+    parser.add_argument("--weight_decay",
+                        type=float,
+                        default=0.01,
+                        help="Weight decay to apply.")
     parser.add_argument(
         "--master_weight_type",
         type=str,

fastvideo/utils/checkpoint.py

@@ -1,31 +1,34 @@
 # import
-import os
 import json
+import os
+
 import torch
-from fastvideo.utils.logging_ import main_print
-from torch.distributed.fsdp import (
-    FullyShardedDataParallel as FSDP,
-    StateDictType,
-    FullStateDictConfig,
-)
-from safetensors.torch import save_file, load_file
 import torch.distributed.checkpoint as dist_cp
-from torch.distributed.checkpoint.default_planner import (
-    DefaultSavePlanner,
-    DefaultLoadPlanner,
-)
-from torch.distributed.checkpoint.optimizer import load_sharded_optimizer_state_dict
-from torch.distributed.fsdp import FullOptimStateDictConfig
-from peft import LoraConfig, get_peft_model_state_dict, set_peft_model_state_dict
-from fastvideo.models.mochi_hf.pipeline_mochi import MochiPipeline
+from peft import get_peft_model_state_dict
+from safetensors.torch import load_file, save_file
+from torch.distributed.checkpoint.default_planner import (DefaultLoadPlanner,
+                                                          DefaultSavePlanner)
+from torch.distributed.checkpoint.optimizer import \
+    load_sharded_optimizer_state_dict
+from torch.distributed.fsdp import (FullOptimStateDictConfig,
+                                    FullStateDictConfig)
+from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
+from torch.distributed.fsdp import StateDictType
+
+from fastvideo.utils.logging_ import main_print
 
 
-def save_checkpoint(model, optimizer, rank, output_dir, step, discriminator=False):
+def save_checkpoint_optimizer(model,
+                              optimizer,
+                              rank,
+                              output_dir,
+                              step,
+                              discriminator=False):
     with FSDP.state_dict_type(
-        model,
-        StateDictType.FULL_STATE_DICT,
-        FullStateDictConfig(offload_to_cpu=True, rank0_only=True),
-        FullOptimStateDictConfig(offload_to_cpu=True, rank0_only=True),
+            model,
+            StateDictType.FULL_STATE_DICT,
+            FullStateDictConfig(offload_to_cpu=True, rank0_only=True),
+            FullOptimStateDictConfig(offload_to_cpu=True, rank0_only=True),
     ):
         cpu_state = model.state_dict()
         optim_state = FSDP.optim_state_dict(
@@ -38,9 +41,11 @@ def save_checkpoint(model, optimizer, rank, output_dir, step, discriminator=Fals
     os.makedirs(save_dir, exist_ok=True)
     # save using safetensors
     if rank <= 0 and not discriminator:
-        weight_path = os.path.join(save_dir, "diffusion_pytorch_model.safetensors")
+        weight_path = os.path.join(save_dir,
+                                   "diffusion_pytorch_model.safetensors")
         save_file(cpu_state, weight_path)
         config_dict = dict(model.config)
+        config_dict.pop('dtype')
         config_path = os.path.join(save_dir, "config.json")
         # save dict as json
         with open(config_path, "w") as f:
@@ -48,10 +53,38 @@ def save_checkpoint(model, optimizer, rank, output_dir, step, discriminator=Fals
         optimizer_path = os.path.join(save_dir, "optimizer.pt")
         torch.save(optim_state, optimizer_path)
     else:
-        weight_path = os.path.join(save_dir, "discriminator_pytorch_model.safetensors")
+        weight_path = os.path.join(save_dir,
+                                   "discriminator_pytorch_model.safetensors")
         save_file(cpu_state, weight_path)
         optimizer_path = os.path.join(save_dir, "discriminator_optimizer.pt")
         torch.save(optim_state, optimizer_path)
+    main_print(f"--> checkpoint saved at step {step}")
+
+
+def save_checkpoint(transformer, rank, output_dir, step):
+    main_print(f"--> saving checkpoint at step {step}")
+    with FSDP.state_dict_type(
+            transformer,
+            StateDictType.FULL_STATE_DICT,
+            FullStateDictConfig(offload_to_cpu=True, rank0_only=True),
+    ):
+        cpu_state = transformer.state_dict()
+    # todo move to get_state_dict
+    if rank <= 0:
+        save_dir = os.path.join(output_dir, f"checkpoint-{step}")
+        os.makedirs(save_dir, exist_ok=True)
+        # save using safetensors
+        weight_path = os.path.join(save_dir,
+                                   "diffusion_pytorch_model.safetensors")
+        save_file(cpu_state, weight_path)
+        config_dict = dict(transformer.config)
+        if "dtype" in config_dict:
+            del config_dict["dtype"]  # TODO
+        config_path = os.path.join(save_dir, "config.json")
+        # save dict as json
+        with open(config_path, "w") as f:
+            json.dump(config_dict, f, indent=4)
+    main_print(f"--> checkpoint saved at step {step}")
 
 
 def save_checkpoint_generator_discriminator(
@@ -64,9 +97,9 @@ def save_checkpoint_generator_discriminator(
     step,
 ):
     with FSDP.state_dict_type(
-        model,
-        StateDictType.FULL_STATE_DICT,
-        FullStateDictConfig(offload_to_cpu=True, rank0_only=True),
+            model,
+            StateDictType.FULL_STATE_DICT,
+            FullStateDictConfig(offload_to_cpu=True, rank0_only=True),
     ):
         cpu_state = model.state_dict()
 
@@ -82,7 +115,8 @@ def save_checkpoint_generator_discriminator(
         # save dict as json
         with open(config_path, "w") as f:
             json.dump(config_dict, f, indent=4)
-        weight_path = os.path.join(hf_weight_dir, "diffusion_pytorch_model.safetensors")
+        weight_path = os.path.join(hf_weight_dir,
+                                   "diffusion_pytorch_model.safetensors")
         save_file(cpu_state, weight_path)
 
     main_print(f"--> saved HF weight checkpoint at path {hf_weight_dir}")
@@ -106,21 +140,22 @@ def save_checkpoint_generator_discriminator(
             planner=DefaultSavePlanner(),
         )
 
-    discriminator_fsdp_state_dir = os.path.join(save_dir, "discriminator_fsdp_state")
+    discriminator_fsdp_state_dir = os.path.join(save_dir,
+                                                "discriminator_fsdp_state")
     os.makedirs(discriminator_fsdp_state_dir, exist_ok=True)
     with FSDP.state_dict_type(
-        discriminator,
-        StateDictType.FULL_STATE_DICT,
-        FullStateDictConfig(offload_to_cpu=True, rank0_only=True),
-        FullOptimStateDictConfig(offload_to_cpu=True, rank0_only=True),
+            discriminator,
+            StateDictType.FULL_STATE_DICT,
+            FullStateDictConfig(offload_to_cpu=True, rank0_only=True),
+            FullOptimStateDictConfig(offload_to_cpu=True, rank0_only=True),
     ):
-        optim_state = FSDP.optim_state_dict(discriminator, discriminator_optimizer)
+        optim_state = FSDP.optim_state_dict(discriminator,
+                                            discriminator_optimizer)
         model_state = discriminator.state_dict()
         state_dict = {"optimizer": optim_state, "model": model_state}
         if rank <= 0:
             discriminator_fsdp_state_fil = os.path.join(
-                discriminator_fsdp_state_dir, "discriminator_state.pt"
-            )
+                discriminator_fsdp_state_dir, "discriminator_state.pt")
             torch.save(state_dict, discriminator_fsdp_state_fil)
 
     main_print("--> saved FSDP state checkpoint")
@@ -137,8 +172,7 @@ def load_sharded_model(model, optimizer, model_dir, optimizer_dir):
         )
         optim_state = optim_state["optimizer"]
         flattened_osd = FSDP.optim_state_dict_to_load(
-            model=model, optim=optimizer, optim_state_dict=optim_state
-        )
+            model=model, optim=optimizer, optim_state_dict=optim_state)
         optimizer.load_state_dict(flattened_osd)
         dist_cp.load_state_dict(
             state_dict=weight_state_dict,
@@ -153,10 +187,10 @@ def load_sharded_model(model, optimizer, model_dir, optimizer_dir):
 
 def load_full_state_model(model, optimizer, checkpoint_file, rank):
     with FSDP.state_dict_type(
-        model,
-        StateDictType.FULL_STATE_DICT,
-        FullStateDictConfig(offload_to_cpu=True, rank0_only=True),
-        FullOptimStateDictConfig(offload_to_cpu=True, rank0_only=True),
+            model,
+            StateDictType.FULL_STATE_DICT,
+            FullStateDictConfig(offload_to_cpu=True, rank0_only=True),
+            FullOptimStateDictConfig(offload_to_cpu=True, rank0_only=True),
     ):
         discriminator_state = torch.load(checkpoint_file)
         model_state = discriminator_state["model"]
@@ -166,8 +200,7 @@ def load_full_state_model(model, optimizer, checkpoint_file, rank):
             optim_state = None
         model.load_state_dict(model_state)
         discriminator_optim_state = FSDP.optim_state_dict_to_load(
-            model=model, optim=optimizer, optim_state_dict=optim_state
-        )
+            model=model, optim=optimizer, optim_state_dict=optim_state)
         optimizer.load_state_dict(discriminator_optim_state)
     main_print(
         f"--> loaded discriminator and discriminator optimizer from path {checkpoint_file}"
@@ -175,37 +208,35 @@ def load_full_state_model(model, optimizer, checkpoint_file, rank):
     return model, optimizer
 
 
-def resume_training_generator_discriminator(
-    model, optimizer, discriminator, discriminator_optimizer, checkpoint_dir, rank
-):
+def resume_training_generator_discriminator(model, optimizer, discriminator,
+                                            discriminator_optimizer,
+                                            checkpoint_dir, rank):
     step = int(checkpoint_dir.split("-")[-1])
     model_weight_dir = os.path.join(checkpoint_dir, "model_weights_state")
     model_optimizer_dir = os.path.join(checkpoint_dir, "model_optimizer_state")
-    model, optimizer = load_sharded_model(
-        model, optimizer, model_weight_dir, model_optimizer_dir
-    )
-    discriminator_ckpt_file = os.path.join(
-        checkpoint_dir, "discriminator_fsdp_state", "discriminator_state.pt"
-    )
+    model, optimizer = load_sharded_model(model, optimizer, model_weight_dir,
+                                          model_optimizer_dir)
+    discriminator_ckpt_file = os.path.join(checkpoint_dir,
+                                           "discriminator_fsdp_state",
+                                           "discriminator_state.pt")
     discriminator, discriminator_optimizer = load_full_state_model(
-        discriminator, discriminator_optimizer, discriminator_ckpt_file, rank
-    )
+        discriminator, discriminator_optimizer, discriminator_ckpt_file, rank)
     return model, optimizer, discriminator, discriminator_optimizer, step
 
 
 def resume_training(model, optimizer, checkpoint_dir, discriminator=False):
-    weight_path = os.path.join(checkpoint_dir, "diffusion_pytorch_model.safetensors")
+    weight_path = os.path.join(checkpoint_dir,
+                               "diffusion_pytorch_model.safetensors")
     if discriminator:
-        weight_path = os.path.join(
-            checkpoint_dir, "discriminator_pytorch_model.safetensors"
-        )
+        weight_path = os.path.join(checkpoint_dir,
+                                   "discriminator_pytorch_model.safetensors")
     model_weights = load_file(weight_path)
 
     with FSDP.state_dict_type(
-        model,
-        StateDictType.FULL_STATE_DICT,
-        FullStateDictConfig(offload_to_cpu=True, rank0_only=True),
-        FullOptimStateDictConfig(offload_to_cpu=True, rank0_only=True),
+            model,
+            StateDictType.FULL_STATE_DICT,
+            FullStateDictConfig(offload_to_cpu=True, rank0_only=True),
+            FullOptimStateDictConfig(offload_to_cpu=True, rank0_only=True),
     ):
         current_state = model.state_dict()
         current_state.update(model_weights)
@@ -216,18 +247,18 @@ def resume_training(model, optimizer, checkpoint_dir, discriminator=False):
         optim_path = os.path.join(checkpoint_dir, "optimizer.pt")
     optimizer_state_dict = torch.load(optim_path, weights_only=False)
     optim_state = FSDP.optim_state_dict_to_load(
-        model=model, optim=optimizer, optim_state_dict=optimizer_state_dict
-    )
+        model=model, optim=optimizer, optim_state_dict=optimizer_state_dict)
     optimizer.load_state_dict(optim_state)
     step = int(checkpoint_dir.split("-")[-1])
     return model, optimizer, step
 
 
-def save_lora_checkpoint(transformer, optimizer, rank, output_dir, step):
+def save_lora_checkpoint(transformer, optimizer, rank, output_dir, step,
+                         pipeline):
     with FSDP.state_dict_type(
-        transformer,
-        StateDictType.FULL_STATE_DICT,
-        FullStateDictConfig(offload_to_cpu=True, rank0_only=True),
+            transformer,
+            StateDictType.FULL_STATE_DICT,
+            FullStateDictConfig(offload_to_cpu=True, rank0_only=True),
     ):
         full_state_dict = transformer.state_dict()
         lora_optim_state = FSDP.optim_state_dict(
@@ -245,9 +276,8 @@ def save_lora_checkpoint(transformer, optimizer, rank, output_dir, step):
         # save lora weight
         main_print(f"--> saving LoRA checkpoint at step {step}")
         transformer_lora_layers = get_peft_model_state_dict(
-            model=transformer, state_dict=full_state_dict
-        )
-        MochiPipeline.save_lora_weights(
+            model=transformer, state_dict=full_state_dict)
+        pipeline.save_lora_weights(
             save_directory=save_dir,
             transformer_lora_layers=transformer_lora_layers,
             is_main_process=True,
@@ -274,8 +304,9 @@ def resume_lora_optimizer(transformer, checkpoint_dir, optimizer):
     optim_path = os.path.join(checkpoint_dir, "lora_optimizer.pt")
     optimizer_state_dict = torch.load(optim_path, weights_only=False)
     optim_state = FSDP.optim_state_dict_to_load(
-        model=transformer, optim=optimizer, optim_state_dict=optimizer_state_dict
-    )
+        model=transformer,
+        optim=optimizer,
+        optim_state_dict=optimizer_state_dict)
     optimizer.load_state_dict(optim_state)
     step = config_dict["step"]
     main_print(f"-->  Successfully resuming LoRA optimizer from step {step}")

fastvideo/utils/communications.py

@@ -3,12 +3,13 @@
 
 # DeepSpeed Team
 
+from typing import Any, Tuple
+
 import torch
 import torch.distributed as dist
-from fastvideo.utils.parallel_states import nccl_info
-from typing import Any, Tuple
 from torch import Tensor
-from torch.nn import Module
+
+from fastvideo.utils.parallel_states import nccl_info
 
 
 def broadcast(input_: torch.Tensor):
@@ -16,9 +17,10 @@ def broadcast(input_: torch.Tensor):
     dist.broadcast(input_, src=src, group=nccl_info.group)
 
 
-def _all_to_all_4D(
-    input: torch.tensor, scatter_idx: int = 2, gather_idx: int = 1, group=None
-) -> torch.tensor:
+def _all_to_all_4D(input: torch.tensor,
+                   scatter_idx: int = 2,
+                   gather_idx: int = 1,
+                   group=None) -> torch.tensor:
     """
     all-to-all for QKV
 
@@ -45,11 +47,8 @@ def _all_to_all_4D(
 
         # transpose groups of heads with the seq-len parallel dimension, so that we can scatter them!
         # (bs, seqlen/P, hc, hs) -reshape-> (bs, seq_len/P, P, hc/P, hs) -transpose(0,2)-> (P, seq_len/P, bs, hc/P, hs)
-        input_t = (
-            input.reshape(bs, shard_seqlen, seq_world_size, shard_hc, hs)
-            .transpose(0, 2)
-            .contiguous()
-        )
+        input_t = (input.reshape(bs, shard_seqlen, seq_world_size, shard_hc,
+                                 hs).transpose(0, 2).contiguous())
 
         output = torch.empty_like(input_t)
         # https://pytorch.org/docs/stable/distributed.html#torch.distributed.all_to_all_single
@@ -63,7 +62,8 @@ def _all_to_all_4D(
         output = output.reshape(seqlen, bs, shard_hc, hs)
 
         # (seq_len, bs, hc/P, hs) -reshape-> (bs, seq_len, hc/P, hs)
-        output = output.transpose(0, 1).contiguous().reshape(bs, seqlen, shard_hc, hs)
+        output = output.transpose(0, 1).contiguous().reshape(
+            bs, seqlen, shard_hc, hs)
 
         return output
 
@@ -76,13 +76,10 @@ def _all_to_all_4D(
 
         # transpose groups of heads with the seq-len parallel dimension, so that we can scatter them!
         # (bs, seqlen, hc/P, hs) -reshape-> (bs, P, seq_len/P, hc/P, hs) -transpose(0, 3)-> (hc/P, P, seqlen/P, bs, hs) -transpose(0, 1) -> (P, hc/P, seqlen/P, bs, hs)
-        input_t = (
-            input.reshape(bs, seq_world_size, shard_seqlen, shard_hc, hs)
-            .transpose(0, 3)
-            .transpose(0, 1)
-            .contiguous()
-            .reshape(seq_world_size, shard_hc, shard_seqlen, bs, hs)
-        )
+        input_t = (input.reshape(
+            bs, seq_world_size, shard_seqlen, shard_hc,
+            hs).transpose(0, 3).transpose(0, 1).contiguous().reshape(
+                seq_world_size, shard_hc, shard_seqlen, bs, hs))
 
         output = torch.empty_like(input_t)
         # https://pytorch.org/docs/stable/distributed.html#torch.distributed.all_to_all_single
@@ -97,14 +94,17 @@ def _all_to_all_4D(
         output = output.reshape(hc, shard_seqlen, bs, hs)
 
         # (hc, seqlen/N, bs, hs) -tranpose(0,2)-> (bs, seqlen/N, hc, hs)
-        output = output.transpose(0, 2).contiguous().reshape(bs, shard_seqlen, hc, hs)
+        output = output.transpose(0, 2).contiguous().reshape(
+            bs, shard_seqlen, hc, hs)
 
         return output
     else:
-        raise RuntimeError("scatter_idx must be 1 or 2 and gather_idx must be 1 or 2")
+        raise RuntimeError(
+            "scatter_idx must be 1 or 2 and gather_idx must be 1 or 2")
 
 
 class SeqAllToAll4D(torch.autograd.Function):
+
     @staticmethod
     def forward(
         ctx: Any,
@@ -120,12 +120,12 @@ class SeqAllToAll4D(torch.autograd.Function):
         return _all_to_all_4D(input, scatter_idx, gather_idx, group=group)
 
     @staticmethod
-    def backward(ctx: Any, *grad_output: Tensor) -> Tuple[None, Tensor, None, None]:
+    def backward(ctx: Any,
+                 *grad_output: Tensor) -> Tuple[None, Tensor, None, None]:
         return (
             None,
-            SeqAllToAll4D.apply(
-                ctx.group, *grad_output, ctx.gather_idx, ctx.scatter_idx
-            ),
+            SeqAllToAll4D.apply(ctx.group, *grad_output, ctx.gather_idx,
+                                ctx.scatter_idx),
             None,
             None,
         )
@@ -136,7 +136,8 @@ def all_to_all_4D(
     scatter_dim: int = 2,
     gather_dim: int = 1,
 ):
-    return SeqAllToAll4D.apply(nccl_info.group, input_, scatter_dim, gather_dim)
+    return SeqAllToAll4D.apply(nccl_info.group, input_, scatter_dim,
+                               gather_dim)
 
 
 def _all_to_all(
@@ -147,7 +148,8 @@ def _all_to_all(
     gather_dim: int,
 ):
     input_list = [
-        t.contiguous() for t in torch.tensor_split(input_, world_size, scatter_dim)
+        t.contiguous()
+        for t in torch.tensor_split(input_, world_size, scatter_dim)
     ]
     output_list = [torch.empty_like(input_list[0]) for _ in range(world_size)]
     dist.all_to_all(output_list, input_list, group=group)
@@ -170,9 +172,8 @@ class _AllToAll(torch.autograd.Function):
         ctx.scatter_dim = scatter_dim
         ctx.gather_dim = gather_dim
         ctx.world_size = dist.get_world_size(process_group)
-        output = _all_to_all(
-            input_, ctx.world_size, process_group, scatter_dim, gather_dim
-        )
+        output = _all_to_all(input_, ctx.world_size, process_group,
+                             scatter_dim, gather_dim)
         return output
 
     @staticmethod
@@ -252,9 +253,8 @@ def all_gather(input_: torch.Tensor, dim: int = 1):
     return _AllGather.apply(input_, dim)
 
 
-def prepare_sequence_parallel_data(
-    hidden_states, encoder_hidden_states, attention_mask, encoder_attention_mask
-):
+def prepare_sequence_parallel_data(hidden_states, encoder_hidden_states,
+                                   attention_mask, encoder_attention_mask):
     if nccl_info.sp_size == 1:
         return (
             hidden_states,
@@ -263,17 +263,18 @@ def prepare_sequence_parallel_data(
             encoder_attention_mask,
         )
 
-    def prepare(
-        hidden_states, encoder_hidden_states, attention_mask, encoder_attention_mask
-    ):
+    def prepare(hidden_states, encoder_hidden_states, attention_mask,
+                encoder_attention_mask):
         hidden_states = all_to_all(hidden_states, scatter_dim=2, gather_dim=0)
-        encoder_hidden_states = all_to_all(
-            encoder_hidden_states, scatter_dim=1, gather_dim=0
-        )
-        attention_mask = all_to_all(attention_mask, scatter_dim=1, gather_dim=0)
-        encoder_attention_mask = all_to_all(
-            encoder_attention_mask, scatter_dim=1, gather_dim=0
-        )
+        encoder_hidden_states = all_to_all(encoder_hidden_states,
+                                           scatter_dim=1,
+                                           gather_dim=0)
+        attention_mask = all_to_all(attention_mask,
+                                    scatter_dim=1,
+                                    gather_dim=0)
+        encoder_attention_mask = all_to_all(encoder_attention_mask,
+                                            scatter_dim=1,
+                                            gather_dim=0)
         return (
             hidden_states,
             encoder_hidden_states,
@@ -300,9 +301,8 @@ def prepare_sequence_parallel_data(
     return hidden_states, encoder_hidden_states, attention_mask, encoder_attention_mask
 
 
-def sp_parallel_dataloader_wrapper(
-    dataloader, device, train_batch_size, sp_size, train_sp_batch_size
-):
+def sp_parallel_dataloader_wrapper(dataloader, device, train_batch_size,
+                                   sp_size, train_sp_batch_size):
     while True:
         for data_item in dataloader:
             latents, cond, attn_mask, cond_mask = data_item
@@ -315,12 +315,12 @@ def sp_parallel_dataloader_wrapper(
                 yield latents, cond, attn_mask, cond_mask
             else:
                 latents, cond, attn_mask, cond_mask = prepare_sequence_parallel_data(
-                    latents, cond, attn_mask, cond_mask
-                )
+                    latents, cond, attn_mask, cond_mask)
                 assert (
                     train_batch_size * sp_size >= train_sp_batch_size
                 ), "train_batch_size * sp_size should be greater than train_sp_batch_size"
-                for iter in range(train_batch_size * sp_size // train_sp_batch_size):
+                for iter in range(train_batch_size * sp_size //
+                                  train_sp_batch_size):
                     st_idx = iter * train_sp_batch_size
                     ed_idx = (iter + 1) * train_sp_batch_size
                     encoder_hidden_states = cond[st_idx:ed_idx]

fastvideo/utils/dataset_utils.py

@@ -1,17 +1,14 @@
 import math
-from einops import rearrange
+import random
+from collections import Counter
+from typing import List, Optional
+
 import decord
-from torch.nn import functional as F
 import torch
-from typing import Optional
 import torch.utils
 import torch.utils.data
-import torch
+from torch.nn import functional as F
 from torch.utils.data import Sampler
-from typing import List
-from collections import Counter
-import random
-
 
 IMG_EXTENSIONS = [".jpg", ".JPG", ".jpeg", ".JPEG", ".png", ".PNG"]
 
@@ -33,17 +30,15 @@ class DecordInit(object):
             results (dict): The resulting dict to be modified and passed
                 to the next transform in pipeline.
         """
-        reader = decord.VideoReader(
-            filename, ctx=self.ctx, num_threads=self.num_threads
-        )
+        reader = decord.VideoReader(filename,
+                                    ctx=self.ctx,
+                                    num_threads=self.num_threads)
         return reader
 
     def __repr__(self):
-        repr_str = (
-            f"{self.__class__.__name__}("
-            f"sr={self.sr},"
-            f"num_threads={self.num_threads})"
-        )
+        repr_str = (f"{self.__class__.__name__}("
+                    f"sr={self.sr},"
+                    f"num_threads={self.num_threads})")
         return repr_str
 
 
@@ -58,6 +53,7 @@ def pad_to_multiple(number, ds_stride):
 
 # TODO
 class Collate:
+
     def __init__(self, args):
         self.batch_size = args.train_batch_size
         self.group_frame = args.group_frame
@@ -98,7 +94,8 @@ class Collate:
             self.max_thw,
             self.ae_stride_thw,
         )
-        assert not torch.any(torch.isnan(pad_batch_tubes)), "after pad_batch_tubes"
+        assert not torch.any(
+            torch.isnan(pad_batch_tubes)), "after pad_batch_tubes"
         return pad_batch_tubes, attention_mask, input_ids, cond_mask
 
     def process(
@@ -112,18 +109,20 @@ class Collate:
         ae_stride_thw,
     ):
         # pad to max multiple of ds_stride
-        batch_input_size = [i.shape for i in batch_tubes]  # [(c t h w), (c t h w)]
+        batch_input_size = [i.shape
+                            for i in batch_tubes]  # [(c t h w), (c t h w)]
         assert len(batch_input_size) == self.batch_size
         if self.group_frame or self.group_resolution or self.batch_size == 1:  #
             len_each_batch = batch_input_size
-            idx_length_dict = dict([*zip(list(range(self.batch_size)), len_each_batch)])
+            idx_length_dict = dict(
+                [*zip(list(range(self.batch_size)), len_each_batch)])
             count_dict = Counter(len_each_batch)
             if len(count_dict) != 1:
-                sorted_by_value = sorted(count_dict.items(), key=lambda item: item[1])
+                sorted_by_value = sorted(count_dict.items(),
+                                         key=lambda item: item[1])
                 pick_length = sorted_by_value[-1][0]  # the highest frequency
                 candidate_batch = [
-                    idx
-                    for idx, length in idx_length_dict.items()
+                    idx for idx, length in idx_length_dict.items()
                     if length == pick_length
                 ]
                 random_select_batch = [
@@ -142,9 +141,8 @@ class Collate:
                 pick_idx = candidate_batch + random_select_batch
 
                 batch_tubes = [batch_tubes[i] for i in pick_idx]
-                batch_input_size = [
-                    i.shape for i in batch_tubes
-                ]  # [(c t h w), (c t h w)]
+                batch_input_size = [i.shape for i in batch_tubes
+                                    ]  # [(c t h w), (c t h w)]
                 input_ids = [input_ids[i] for i in pick_idx]  # b [1, l]
                 cond_mask = [cond_mask[i] for i in pick_idx]  # b [1, l]
 
@@ -161,10 +159,10 @@ class Collate:
             pad_to_multiple(max_w, ds_stride),
         )
         pad_max_t = pad_max_t + 1 - self.ae_stride_t
-        each_pad_t_h_w = [
-            [pad_max_t - i.shape[1], pad_max_h - i.shape[2], pad_max_w - i.shape[3]]
-            for i in batch_tubes
-        ]
+        each_pad_t_h_w = [[
+            pad_max_t - i.shape[1], pad_max_h - i.shape[2],
+            pad_max_w - i.shape[3]
+        ] for i in batch_tubes]
         pad_batch_tubes = [
             F.pad(im, (0, pad_w, 0, pad_h, 0, pad_t), value=0)
             for (pad_t, pad_h, pad_w), im in zip(each_pad_t_h_w, batch_tubes)
@@ -177,14 +175,11 @@ class Collate:
             max_tube_size[1] // ae_stride_thw[1],
             max_tube_size[2] // ae_stride_thw[2],
         ]
-        valid_latent_size = [
-            [
-                int(math.ceil((i[1] - 1) / ae_stride_thw[0])) + 1,
-                int(math.ceil(i[2] / ae_stride_thw[1])),
-                int(math.ceil(i[3] / ae_stride_thw[2])),
-            ]
-            for i in batch_input_size
-        ]
+        valid_latent_size = [[
+            int(math.ceil((i[1] - 1) / ae_stride_thw[0])) + 1,
+            int(math.ceil(i[2] / ae_stride_thw[1])),
+            int(math.ceil(i[3] / ae_stride_thw[2])),
+        ] for i in batch_input_size]
         attention_mask = [
             F.pad(
                 torch.ones(i, dtype=pad_batch_tubes.dtype),
@@ -197,8 +192,7 @@ class Collate:
                     max_latent_size[0] - i[0],
                 ),
                 value=0,
-            )
-            for i in valid_latent_size
+            ) for i in valid_latent_size
         ]
         attention_mask = torch.stack(attention_mask)  # b t h w
         if self.batch_size == 1 or self.group_frame or self.group_resolution:
@@ -236,7 +230,8 @@ def split_to_even_chunks(indices, lengths, num_chunks, batch_size):
             assert batch_size > len(chunk)
             if len(chunk) != 0:
                 chunk = chunk + [
-                    random.choice(chunk) for _ in range(batch_size - len(chunk))
+                    random.choice(chunk)
+                    for _ in range(batch_size - len(chunk))
                 ]
             else:
                 chunk = random.choice(pad_chunks)
@@ -261,10 +256,12 @@ def megabatch_frame_alignment(megabatches, lengths):
 
         # mixed frame length, align megabatch inside
         if len(count_dict) != 1:
-            sorted_by_value = sorted(count_dict.items(), key=lambda item: item[1])
+            sorted_by_value = sorted(count_dict.items(),
+                                     key=lambda item: item[1])
             pick_length = sorted_by_value[-1][0]  # the highest frequency
             candidate_batch = [
-                idx for idx, length in idx_length_dict.items() if length == pick_length
+                idx for idx, length in idx_length_dict.items()
+                if length == pick_length
             ]
             random_select_batch = [
                 random.choice(candidate_batch)
@@ -291,8 +288,7 @@ def get_length_grouped_indices(
     # We need to use torch for the random part as a distributed sampler will set the random seed for torch.
     if generator is None:
         generator = torch.Generator().manual_seed(
-            seed
-        )  # every rank will generate a fixed order but random index
+            seed)  # every rank will generate a fixed order but random index
 
     indices = torch.randperm(len(lengths), generator=generator).tolist()
 
@@ -302,7 +298,8 @@ def get_length_grouped_indices(
     # chunk dataset to megabatches
     megabatch_size = world_size * batch_size
     megabatches = [
-        indices[i : i + megabatch_size] for i in range(0, len(lengths), megabatch_size)
+        indices[i:i + megabatch_size]
+        for i in range(0, len(lengths), megabatch_size)
     ]
 
     # make sure the length in each magabatch is align with each other
@@ -320,7 +317,8 @@ def get_length_grouped_indices(
 
     # expand indices and return
     return [
-        i for megabatch in shuffled_megabatches for batch in megabatch for i in batch
+        i for megabatch in shuffled_megabatches for batch in megabatch
+        for i in batch
     ]
 
 
@@ -368,11 +366,10 @@ class LengthGroupedSampler(Sampler):
             result = []
             index = rank * batch_size
             while index < len(lst):
-                result.extend(lst[index : index + batch_size])
+                result.extend(lst[index:index + batch_size])
                 index += batch_size * world_size
             return result
 
-        indices = distributed_sampler(
-            indices, self.rank, self.batch_size, self.world_size
-        )
+        indices = distributed_sampler(indices, self.rank, self.batch_size,
+                                      self.world_size)
         return iter(indices)

fastvideo/utils/env_utils.py

@@ -0,0 +1,40 @@
+import platform
+
+import accelerate
+import peft
+import torch
+import transformers
+from transformers.utils import is_torch_cuda_available, is_torch_npu_available
+
+VERSION = "1.2.0"
+
+if __name__ == "__main__":
+    info = {
+        "FastVideo version": VERSION,
+        "Platform": platform.platform(),
+        "Python version": platform.python_version(),
+        "PyTorch version": torch.__version__,
+        "Transformers version": transformers.__version__,
+        "Accelerate version": accelerate.__version__,
+        "PEFT version": peft.__version__,
+    }
+
+    if is_torch_cuda_available():
+        info["PyTorch version"] += " (GPU)"
+        info["GPU type"] = torch.cuda.get_device_name()
+
+    if is_torch_npu_available():
+        info["PyTorch version"] += " (NPU)"
+        info["NPU type"] = torch.npu.get_device_name()
+        info["CANN version"] = torch.version.cann  # codespell:ignore
+
+    try:
+        import bitsandbytes
+
+        info["Bitsandbytes version"] = bitsandbytes.__version__
+    except Exception:
+        pass
+
+    print("\n" +
+          "\n".join([f"- {key}: {value}"
+                     for key, value in info.items()]) + "\n")

fastvideo/utils/fsdp_util.py

@@ -1,32 +1,16 @@
-from sympy import use
-import torch
-import os
-import torch.distributed as dist
-from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import (
-    checkpoint_wrapper,
-    CheckpointImpl,
-    apply_activation_checkpointing,
-)
-from peft.utils.other import fsdp_auto_wrap_policy
-
-from torch.distributed.fsdp import (
-    FullyShardedDataParallel as FSDP,
-    StateDictType,
-    FullStateDictConfig,  # general model non-sharded, non-flattened params
-    LocalStateDictConfig,  # flattened params, usable only by FSDP
-    # ShardedStateDictConfig, # un-flattened param but shards, usable by other parallel schemes.
-)
-
-from fastvideo.utils.load import get_no_split_modules
-from fastvideo.models.mochi_hf.modeling_mochi import MochiTransformerBlock
-
+# ruff: noqa: E731
+import functools
 from functools import partial
 
+import torch
+from peft.utils.other import fsdp_auto_wrap_policy
+from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import (
+    CheckpointImpl, apply_activation_checkpointing, checkpoint_wrapper)
+from torch.distributed.fsdp import MixedPrecision, ShardingStrategy
 from torch.distributed.fsdp.wrap import transformer_auto_wrap_policy
 
-from torch.distributed.fsdp import MixedPrecision, ShardingStrategy
-import functools
-
+from fastvideo.models.mochi_hf.modeling_mochi import MochiTransformerBlock
+from fastvideo.utils.load import get_no_split_modules
 
 non_reentrant_wrapper = partial(
     checkpoint_wrapper,
@@ -41,7 +25,7 @@ def apply_fsdp_checkpointing(model, no_split_modules, p=1):
     """apply activation checkpointing to model
     returns None as model is updated directly
     """
-    print(f"--> applying fdsp activation checkpointing...")
+    print("--> applying fdsp activation checkpointing...")
     block_idx = 0
     cut_off = 1 / 2
     # when passing p as a fraction number (e.g. 1/3), it will be interpreted
@@ -109,9 +93,8 @@ def get_dit_fsdp_kwargs(
         sharding_strategy = ShardingStrategy._HYBRID_SHARD_ZERO2
 
     device_id = torch.cuda.current_device()
-    cpu_offload = (
-        torch.distributed.fsdp.CPUOffload(offload_params=True) if cpu_offload else None
-    )
+    cpu_offload = (torch.distributed.fsdp.CPUOffload(
+        offload_params=True) if cpu_offload else None)
     fsdp_kwargs = {
         "auto_wrap_policy": auto_wrap_policy,
         "mixed_precision": mixed_precision,
@@ -123,12 +106,10 @@ def get_dit_fsdp_kwargs(
 
     # Add LoRA-specific settings when LoRA is enabled
     if use_lora:
-        fsdp_kwargs.update(
-            {
-                "use_orig_params": False,  # Required for LoRA memory savings
-                "sync_module_states": True,
-            }
-        )
+        fsdp_kwargs.update({
+            "use_orig_params": False,  # Required for LoRA memory savings
+            "sync_module_states": True,
+        })
 
     return fsdp_kwargs, no_split_modules
 

fastvideo/utils/load.py

@@ -1,23 +1,22 @@
-import torch
-from fastvideo.models.mochi_hf.modeling_mochi import (
-    MochiTransformer3DModel,
-    MochiTransformerBlock,
-)
-from fastvideo.models.hunyuan.modules.models import (
-    HYVideoDiffusionTransformer,
-    MMDoubleStreamBlock,
-    MMSingleStreamBlock,
-)
-from fastvideo.models.hunyuan.vae.autoencoder_kl_causal_3d import AutoencoderKLCausal3D
-from diffusers import AutoencoderKLMochi
-from transformers import T5EncoderModel, AutoTokenizer
 import os
-from torch import nn
-
-# Path
 from pathlib import Path
+
+import torch
 import torch.nn.functional as F
+from diffusers import AutoencoderKLHunyuanVideo, AutoencoderKLMochi
+from torch import nn
+from transformers import AutoTokenizer, T5EncoderModel
+
+from fastvideo.models.hunyuan.modules.models import (
+    HYVideoDiffusionTransformer, MMDoubleStreamBlock, MMSingleStreamBlock)
 from fastvideo.models.hunyuan.text_encoder import TextEncoder
+from fastvideo.models.hunyuan.vae.autoencoder_kl_causal_3d import \
+    AutoencoderKLCausal3D
+from fastvideo.models.hunyuan_hf.modeling_hunyuan import (
+    HunyuanVideoSingleTransformerBlock, HunyuanVideoTransformer3DModel,
+    HunyuanVideoTransformerBlock)
+from fastvideo.models.mochi_hf.modeling_mochi import (MochiTransformer3DModel,
+                                                      MochiTransformerBlock)
 from fastvideo.utils.logging_ import main_print
 
 hunyuan_config = {
@@ -30,12 +29,10 @@ hunyuan_config = {
     "guidance_embed": True,
 }
 
-
 PROMPT_TEMPLATE_ENCODE = (
     "<|start_header_id|>system<|end_header_id|>\n\nDescribe the image by detailing the color, shape, size, texture, "
     "quantity, text, spatial relationships of the objects and background:<|eot_id|>"
-    "<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>"
-)
+    "<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>")
 PROMPT_TEMPLATE_ENCODE_VIDEO = (
     "<|start_header_id|>system<|end_header_id|>\n\nDescribe the video by detailing the following aspects: "
     "1. The main content and theme of the video."
@@ -43,8 +40,7 @@ PROMPT_TEMPLATE_ENCODE_VIDEO = (
     "3. Actions, events, behaviors temporal relationships, physical movement changes of the objects."
     "4. background environment, light, style and atmosphere."
     "5. camera angles, movements, and transitions used in the video:<|eot_id|>"
-    "<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>"
-)
+    "<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>")
 
 NEGATIVE_PROMPT = "Aerial view, aerial view, overexposed, low quality, deformation, a poor composition, bad hands, bad teeth, bad eyes, bad limbs, distortion"
 
@@ -61,11 +57,13 @@ PROMPT_TEMPLATE = {
 
 
 class HunyuanTextEncoderWrapper(nn.Module):
+
     def __init__(self, pretrained_model_name_or_path, device):
         super().__init__()
 
         text_len = 256
-        crop_start = PROMPT_TEMPLATE["dit-llm-encode-video"].get("crop_start", 0)
+        crop_start = PROMPT_TEMPLATE["dit-llm-encode-video"].get(
+            "crop_start", 0)
 
         max_length = text_len + crop_start
 
@@ -74,7 +72,8 @@ class HunyuanTextEncoderWrapper(nn.Module):
 
         # prompt_template_video
         prompt_template_video = PROMPT_TEMPLATE["dit-llm-encode-video"]
-        text_encoder_path = os.path.join(pretrained_model_name_or_path, "text_encoder")
+        text_encoder_path = os.path.join(pretrained_model_name_or_path,
+                                         "text_encoder")
         self.text_encoder = TextEncoder(
             text_encoder_type="llm",
             text_encoder_path=text_encoder_path,
@@ -89,9 +88,8 @@ class HunyuanTextEncoderWrapper(nn.Module):
             logger=None,
             device=device,
         )
-        text_encoder_path_2 = os.path.join(
-            pretrained_model_name_or_path, "text_encoder_2"
-        )
+        text_encoder_path_2 = os.path.join(pretrained_model_name_or_path,
+                                           "text_encoder_2")
         self.text_encoder_2 = TextEncoder(
             text_encoder_type="clipL",
             text_encoder_path=text_encoder_path_2,
@@ -112,9 +110,9 @@ class HunyuanTextEncoderWrapper(nn.Module):
         text_inputs = text_encoder.text2tokens(prompt, data_type=data_type)
 
         if clip_skip is None:
-            prompt_outputs = text_encoder.encode(
-                text_inputs, data_type="video", device=device
-            )
+            prompt_outputs = text_encoder.encode(text_inputs,
+                                                 data_type="video",
+                                                 device=device)
             prompt_embeds = prompt_outputs.hidden_state
         else:
             prompt_outputs = text_encoder.encode(
@@ -126,8 +124,7 @@ class HunyuanTextEncoderWrapper(nn.Module):
             prompt_embeds = prompt_outputs.hidden_states_list[-(clip_skip + 1)]
 
             prompt_embeds = text_encoder.model.text_model.final_layer_norm(
-                prompt_embeds
-            )
+                prompt_embeds)
 
         attention_mask = prompt_outputs.attention_mask
         if attention_mask is not None:
@@ -135,8 +132,7 @@ class HunyuanTextEncoderWrapper(nn.Module):
             bs_embed, seq_len = attention_mask.shape
             attention_mask = attention_mask.repeat(1, num_videos_per_prompt)
             attention_mask = attention_mask.view(
-                bs_embed * num_videos_per_prompt, seq_len
-            )
+                bs_embed * num_videos_per_prompt, seq_len)
 
         if text_encoder is not None:
             prompt_embeds_dtype = text_encoder.dtype
@@ -145,25 +141,27 @@ class HunyuanTextEncoderWrapper(nn.Module):
         else:
             prompt_embeds_dtype = prompt_embeds.dtype
 
-        prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
+        prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype,
+                                         device=device)
 
         if prompt_embeds.ndim == 2:
             bs_embed, _ = prompt_embeds.shape
             # duplicate text embeddings for each generation per prompt, using mps friendly method
             prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt)
-            prompt_embeds = prompt_embeds.view(bs_embed * num_videos_per_prompt, -1)
+            prompt_embeds = prompt_embeds.view(
+                bs_embed * num_videos_per_prompt, -1)
         else:
             bs_embed, seq_len, _ = prompt_embeds.shape
             # duplicate text embeddings for each generation per prompt, using mps friendly method
             prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1)
             prompt_embeds = prompt_embeds.view(
-                bs_embed * num_videos_per_prompt, seq_len, -1
-            )
+                bs_embed * num_videos_per_prompt, seq_len, -1)
         return (prompt_embeds, attention_mask)
 
     def encode_prompt(self, prompt):
         prompt_embeds, attention_mask = self.encode_(prompt, self.text_encoder)
-        prompt_embeds_2, attention_mask_2 = self.encode_(prompt, self.text_encoder_2)
+        prompt_embeds_2, attention_mask_2 = self.encode_(
+            prompt, self.text_encoder_2)
         prompt_embeds_2 = F.pad(
             prompt_embeds_2,
             (0, prompt_embeds.shape[2] - prompt_embeds_2.shape[1]),
@@ -174,19 +172,19 @@ class HunyuanTextEncoderWrapper(nn.Module):
 
 
 class MochiTextEncoderWrapper(nn.Module):
+
     def __init__(self, pretrained_model_name_or_path, device):
         super().__init__()
         self.text_encoder = T5EncoderModel.from_pretrained(
-            os.path.join(pretrained_model_name_or_path, "text_encoder")
-        ).to(device)
+            os.path.join(pretrained_model_name_or_path,
+                         "text_encoder")).to(device)
         self.tokenizer = AutoTokenizer.from_pretrained(
-            os.path.join(pretrained_model_name_or_path, "text_encoder")
-        )
+            os.path.join(pretrained_model_name_or_path, "tokenizer"))
         self.max_sequence_length = 256
 
     def encode_prompt(self, prompt):
         device = self.text_encoder.device
-        dtype = self.dtype
+        dtype = self.text_encoder.dtype
 
         prompt = [prompt] if isinstance(prompt, str) else prompt
         batch_size = len(prompt)
@@ -203,22 +201,19 @@ class MochiTextEncoderWrapper(nn.Module):
         prompt_attention_mask = text_inputs.attention_mask
         prompt_attention_mask = prompt_attention_mask.bool().to(device)
 
-        untruncated_ids = self.tokenizer(
-            prompt, padding="longest", return_tensors="pt"
-        ).input_ids
+        untruncated_ids = self.tokenizer(prompt,
+                                         padding="longest",
+                                         return_tensors="pt").input_ids
 
-        if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
-            text_input_ids, untruncated_ids
-        ):
+        if untruncated_ids.shape[-1] >= text_input_ids.shape[
+                -1] and not torch.equal(text_input_ids, untruncated_ids):
             removed_text = self.tokenizer.batch_decode(
-                untruncated_ids[:, self.max_sequence_length - 1 : -1]
-            )
+                untruncated_ids[:, self.max_sequence_length - 1:-1])
             main_print(
                 f"Truncated text input: {prompt} to: {removed_text} for model input."
             )
         prompt_embeds = self.text_encoder(
-            text_input_ids.to(device), attention_mask=prompt_attention_mask
-        )[0]
+            text_input_ids.to(device), attention_mask=prompt_attention_mask)[0]
         prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
 
         # duplicate text embeddings for each generation per prompt, using mps friendly method
@@ -234,11 +229,12 @@ def load_hunyuan_state_dict(model, dit_model_name_or_path):
     model_path = dit_model_name_or_path
     bare_model = "unknown"
 
-    state_dict = torch.load(
-        model_path, map_location=lambda storage, loc: storage, weights_only=True
-    )
+    state_dict = torch.load(model_path,
+                            map_location=lambda storage, loc: storage,
+                            weights_only=True)
 
-    if bare_model == "unknown" and ("ema" in state_dict or "module" in state_dict):
+    if bare_model == "unknown" and ("ema" in state_dict
+                                    or "module" in state_dict):
         bare_model = False
     if bare_model is False:
         if load_key in state_dict:
@@ -246,8 +242,7 @@ def load_hunyuan_state_dict(model, dit_model_name_or_path):
         else:
             raise KeyError(
                 f"Missing key: `{load_key}` in the checkpoint: {model_path}. The keys in the checkpoint "
-                f"are: {list(state_dict.keys())}."
-            )
+                f"are: {list(state_dict.keys())}.")
     model.load_state_dict(state_dict, strict=True)
     return model
 
@@ -272,6 +267,20 @@ def load_transformer(
                 torch_dtype=master_weight_type,
                 # torch_dtype=torch.bfloat16 if args.use_lora else torch.float32,
             )
+    elif model_type == "hunyuan_hf":
+        if dit_model_name_or_path:
+            transformer = HunyuanVideoTransformer3DModel.from_pretrained(
+                dit_model_name_or_path,
+                torch_dtype=master_weight_type,
+                # torch_dtype=torch.bfloat16 if args.use_lora else torch.float32,
+            )
+        else:
+            transformer = HunyuanVideoTransformer3DModel.from_pretrained(
+                pretrained_model_name_or_path,
+                subfolder="transformer",
+                torch_dtype=master_weight_type,
+                # torch_dtype=torch.bfloat16 if args.use_lora else torch.float32,
+            )
     elif model_type == "hunyuan":
         transformer = HYVideoDiffusionTransformer(
             in_channels=16,
@@ -279,7 +288,10 @@ def load_transformer(
             **hunyuan_config,
             dtype=master_weight_type,
         )
-        transformer = load_hunyuan_state_dict(transformer, dit_model_name_or_path)
+        transformer = load_hunyuan_state_dict(transformer,
+                                              dit_model_name_or_path)
+        if master_weight_type == torch.bfloat16:
+            transformer = transformer.bfloat16()
     else:
         raise ValueError(f"Unsupported model type: {model_type}")
     return transformer
@@ -289,15 +301,22 @@ def load_vae(model_type, pretrained_model_name_or_path):
     weight_dtype = torch.float32
     if model_type == "mochi":
         vae = AutoencoderKLMochi.from_pretrained(
-            pretrained_model_name_or_path, subfolder="vae", torch_dtype=weight_dtype
-        ).to("cuda")
+            pretrained_model_name_or_path,
+            subfolder="vae",
+            torch_dtype=weight_dtype).to("cuda")
         autocast_type = torch.bfloat16
         fps = 30
+    elif model_type == "hunyuan_hf":
+        vae = AutoencoderKLHunyuanVideo.from_pretrained(
+            pretrained_model_name_or_path,
+            subfolder="vae",
+            torch_dtype=weight_dtype).to("cuda")
+        autocast_type = torch.bfloat16
+        fps = 24
     elif model_type == "hunyuan":
         vae_precision = torch.float32
-        vae_path = os.path.join(
-            pretrained_model_name_or_path, "hunyuan-video-t2v-720p/vae"
-        )
+        vae_path = os.path.join(pretrained_model_name_or_path,
+                                "hunyuan-video-t2v-720p/vae")
 
         config = AutoencoderKLCausal3D.load_config(vae_path)
         vae = AutoencoderKLCausal3D.from_config(config)
@@ -311,8 +330,7 @@ def load_vae(model_type, pretrained_model_name_or_path):
         if any(k.startswith("vae.") for k in ckpt.keys()):
             ckpt = {
                 k.replace("vae.", ""): v
-                for k, v in ckpt.items()
-                if k.startswith("vae.")
+                for k, v in ckpt.items() if k.startswith("vae.")
             }
         vae.load_state_dict(ckpt)
         vae = vae.to(dtype=vae_precision)
@@ -326,9 +344,11 @@ def load_vae(model_type, pretrained_model_name_or_path):
 
 def load_text_encoder(model_type, pretrained_model_name_or_path, device):
     if model_type == "mochi":
-        text_encoder = MochiTextEncoderWrapper(pretrained_model_name_or_path, device)
-    elif model_type == "hunyuan":
-        text_encoder = HunyuanTextEncoderWrapper(pretrained_model_name_or_path, device)
+        text_encoder = MochiTextEncoderWrapper(pretrained_model_name_or_path,
+                                               device)
+    elif model_type == "hunyuan" or "hunyuan_hf":
+        text_encoder = HunyuanTextEncoderWrapper(pretrained_model_name_or_path,
+                                                 device)
     else:
         raise ValueError(f"Unsupported model type: {model_type}")
     return text_encoder
@@ -337,7 +357,10 @@ def load_text_encoder(model_type, pretrained_model_name_or_path, device):
 def get_no_split_modules(transformer):
     # if of type MochiTransformer3DModel
     if isinstance(transformer, MochiTransformer3DModel):
-        return (MochiTransformerBlock,)
+        return (MochiTransformerBlock, )
+    elif isinstance(transformer, HunyuanVideoTransformer3DModel):
+        return (HunyuanVideoSingleTransformerBlock,
+                HunyuanVideoTransformerBlock)
     elif isinstance(transformer, HYVideoDiffusionTransformer):
         return (MMDoubleStreamBlock, MMSingleStreamBlock)
     else:
@@ -348,6 +371,7 @@ if __name__ == "__main__":
     # test encode prompt
     device = torch.cuda.current_device()
     pretrained_model_name_or_path = "data/hunyuan"
-    text_encoder = load_text_encoder("hunyuan", pretrained_model_name_or_path, device)
+    text_encoder = load_text_encoder("hunyuan", pretrained_model_name_or_path,
+                                     device)
     prompt = "A man on stage claps his hands together while facing the audience. The audience, visible in the foreground, holds up mobile devices to record the event, capturing the moment from various angles. The background features a large banner with text identifying the man on stage. Throughout the sequence, the man's expression remains engaged and directed towards the audience. The camera angle remains constant, focusing on capturing the interaction between the man on stage and the audience."
     prompt_embeds, attention_mask = text_encoder.encode_prompt(prompt)

fastvideo/utils/logging_.py

@@ -1,6 +1,6 @@
-import sys
-import pdb
 import os
+import pdb
+import sys
 
 
 def main_print(content):

fastvideo/utils/optimizer.py

@@ -1,5 +1,5 @@
-from accelerate.logging import get_logger
 import torch
+from accelerate.logging import get_logger
 
 logger = get_logger(__name__)
 
@@ -13,11 +13,11 @@ def get_optimizer(args, params_to_optimize, use_deepspeed: bool = False):
         )
         args.optimizer = "adamw"
 
-    if args.use_8bit_adam and not (args.optimizer.lower() not in ["adam", "adamw"]):
+    if args.use_8bit_adam and not (args.optimizer.lower()
+                                   not in ["adam", "adamw"]):
         logger.warning(
             f"use_8bit_adam is ignored when optimizer is not set to 'Adam' or 'AdamW'. Optimizer was "
-            f"set to {args.optimizer.lower()}"
-        )
+            f"set to {args.optimizer.lower()}")
 
     if args.use_8bit_adam:
         try:
@@ -28,9 +28,8 @@ def get_optimizer(args, params_to_optimize, use_deepspeed: bool = False):
             )
 
     if args.optimizer.lower() == "adamw":
-        optimizer_class = (
-            bnb.optim.AdamW8bit if args.use_8bit_adam else torch.optim.AdamW
-        )
+        optimizer_class = (bnb.optim.AdamW8bit
+                           if args.use_8bit_adam else torch.optim.AdamW)
 
         optimizer = optimizer_class(
             params_to_optimize,

fastvideo/utils/parallel_states.py

@@ -1,9 +1,10 @@
-import torch
-import torch.distributed as dist
 import os
 
+import torch.distributed as dist
+
 
 class COMM_INFO:
+
     def __init__(self):
         self.group = None
         self.sp_size = 1
@@ -44,13 +45,13 @@ def initialize_sequence_parallel_group(sequence_parallel_size):
     assert (
         world_size % sequence_parallel_size == 0
     ), "world_size must be divisible by sequence_parallel_size, but got world_size: {}, sequence_parallel_size: {}".format(
-        world_size, sequence_parallel_size
-    )
+        world_size, sequence_parallel_size)
     nccl_info.sp_size = sequence_parallel_size
     nccl_info.global_rank = rank
     num_sequence_parallel_groups: int = world_size // sequence_parallel_size
     for i in range(num_sequence_parallel_groups):
-        ranks = range(i * sequence_parallel_size, (i + 1) * sequence_parallel_size)
+        ranks = range(i * sequence_parallel_size,
+                      (i + 1) * sequence_parallel_size)
         group = dist.new_group(ranks)
         if rank in ranks:
             nccl_info.group = group

fastvideo/utils/validation.py

@@ -1,27 +1,24 @@
-from typing import Optional, Union, List
+import gc
+import os
+from typing import List, Optional, Union
+
 import numpy as np
 import torch
-from einops import rearrange
-from fastvideo.utils.parallel_states import get_sequence_parallel_state, nccl_info
-from fastvideo.utils.communications import all_gather
-from diffusers.utils.torch_utils import randn_tensor
-from fastvideo.models.mochi_hf.pipeline_mochi import (
-    linear_quadratic_schedule,
-    retrieve_timesteps,
-)
-from tqdm import tqdm
-from diffusers.video_processor import VideoProcessor
-from diffusers import (
-    FlowMatchEulerDiscreteScheduler,
-    AutoencoderKLMochi,
-)
-from fastvideo.utils.logging_ import main_print
-from fastvideo.distill.solver import PCMFMScheduler
+from diffusers import FlowMatchEulerDiscreteScheduler
 from diffusers.utils import export_to_video
-import os
+from diffusers.utils.torch_utils import randn_tensor
+from diffusers.video_processor import VideoProcessor
+from einops import rearrange
+from tqdm import tqdm
+
 import wandb
-import gc
+from fastvideo.distill.solver import PCMFMScheduler
+from fastvideo.models.mochi_hf.pipeline_mochi import (
+    linear_quadratic_schedule, retrieve_timesteps)
+from fastvideo.utils.communications import all_gather
 from fastvideo.utils.load import load_vae
+from fastvideo.utils.parallel_states import (get_sequence_parallel_state,
+                                             nccl_info)
 
 
 def prepare_latents(
@@ -42,11 +39,15 @@ def prepare_latents(
 
     shape = (batch_size, num_channels_latents, num_frames, height, width)
 
-    latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+    latents = randn_tensor(shape,
+                           generator=generator,
+                           device=device,
+                           dtype=dtype)
     return latents
 
 
 def sample_validation_video(
+    model_type,
     transformer,
     vae,
     scheduler,
@@ -57,7 +58,6 @@ def sample_validation_video(
     num_inference_steps: int = 28,
     timesteps: List[int] = None,
     guidance_scale: float = 4.5,
-    embed_guidance_scale: float = None,
     num_videos_per_prompt: Optional[int] = 1,
     generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
     prompt_embeds: Optional[torch.Tensor] = None,
@@ -75,10 +75,10 @@ def sample_validation_video(
 
     do_classifier_free_guidance = guidance_scale > 1.0
     if do_classifier_free_guidance:
-        prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
+        prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds],
+                                  dim=0)
         prompt_attention_mask = torch.cat(
-            [negative_prompt_attention_mask, prompt_attention_mask], dim=0
-        )
+            [negative_prompt_attention_mask, prompt_attention_mask], dim=0)
 
     # 4. Prepare latent variables
     # TODO: Remove hardcore
@@ -96,9 +96,9 @@ def sample_validation_video(
     )
     world_size, rank = nccl_info.sp_size, nccl_info.rank_within_group
     if get_sequence_parallel_state():
-        latents = rearrange(
-            latents, "b t (n s) h w -> b t n s h w", n=world_size
-        ).contiguous()
+        latents = rearrange(latents,
+                            "b t (n s) h w -> b t n s h w",
+                            n=world_size).contiguous()
         latents = latents[:, :, rank, :, :, :]
 
     # 5. Prepare timestep
@@ -106,7 +106,7 @@ def sample_validation_video(
     threshold_noise = 0.025
     sigmas = linear_quadratic_schedule(num_inference_steps, threshold_noise)
     sigmas = np.array(sigmas)
-    if scheduler_type == "euler":
+    if scheduler_type == "euler" and model_type == "mochi":  #todo
         timesteps, num_inference_steps = retrieve_timesteps(
             scheduler,
             num_inference_steps,
@@ -120,7 +120,8 @@ def sample_validation_video(
             num_inference_steps,
             device,
         )
-    num_warmup_steps = max(len(timesteps) - num_inference_steps * scheduler.order, 0)
+    num_warmup_steps = max(
+        len(timesteps) - num_inference_steps * scheduler.order, 0)
 
     # 6. Denoising loop
     # with self.progress_bar(total=num_inference_steps) as progress_bar:
@@ -128,40 +129,37 @@ def sample_validation_video(
     # only enable if nccl_info.global_rank == 0
 
     with tqdm(
-        total=num_inference_steps,
-        disable=nccl_info.rank_within_group != 0,
-        desc="Validation sampling...",
+            total=num_inference_steps,
+            disable=nccl_info.rank_within_group != 0,
+            desc="Validation sampling...",
     ) as progress_bar:
         for i, t in enumerate(timesteps):
-            latent_model_input = (
-                torch.cat([latents] * 2) if do_classifier_free_guidance else latents
-            )
+            latent_model_input = (torch.cat([latents] * 2)
+                                  if do_classifier_free_guidance else latents)
             # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
             timestep = t.expand(latent_model_input.shape[0])
             with torch.autocast("cuda", dtype=torch.bfloat16):
-                kwargs = {
-                    "hidden_states": latent_model_input,
-                    "encoder_hidden_states": prompt_embeds,
-                    "timestep": timestep,
-                    "encoder_attention_mask": prompt_attention_mask,
-                    "return_dict": False,
-                }
-                if embed_guidance_scale > 0.0:
-                    kwargs["guidance"] = torch.tensor([embed_guidance_scale], device=device, dtype=torch.bfloat16) * 1000
-                noise_pred = transformer(**kwargs)[0]
+                noise_pred = transformer(
+                    hidden_states=latent_model_input,
+                    encoder_hidden_states=prompt_embeds,
+                    timestep=timestep,
+                    encoder_attention_mask=prompt_attention_mask,
+                    return_dict=False,
+                )[0]
+
             # Mochi CFG + Sampling runs in FP32
             noise_pred = noise_pred.to(torch.float32)
             if do_classifier_free_guidance:
                 noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
                 noise_pred = noise_pred_uncond + guidance_scale * (
-                    noise_pred_text - noise_pred_uncond
-                )
+                    noise_pred_text - noise_pred_uncond)
 
             # compute the previous noisy sample x_t -> x_t-1
             latents_dtype = latents.dtype
-            latents = scheduler.step(
-                noise_pred, t, latents.to(torch.float32), return_dict=False
-            )[0]
+            latents = scheduler.step(noise_pred,
+                                     t,
+                                     latents.to(torch.float32),
+                                     return_dict=False)[0]
             latents = latents.to(latents_dtype)
 
             if latents.dtype != latents_dtype:
@@ -169,9 +167,8 @@ def sample_validation_video(
                     # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
                     latents = latents.to(latents_dtype)
 
-            if i == len(timesteps) - 1 or (
-                (i + 1) > num_warmup_steps and (i + 1) % scheduler.order == 0
-            ):
+            if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and
+                                           (i + 1) % scheduler.order == 0):
                 progress_bar.update()
 
     if get_sequence_parallel_state():
@@ -182,32 +179,26 @@ def sample_validation_video(
     else:
         # unscale/denormalize the latents
         # denormalize with the mean and std if available and not None
-        has_latents_mean = (
-            hasattr(vae.config, "latents_mean") and vae.config.latents_mean is not None
-        )
-        has_latents_std = (
-            hasattr(vae.config, "latents_std") and vae.config.latents_std is not None
-        )
+        has_latents_mean = (hasattr(vae.config, "latents_mean")
+                            and vae.config.latents_mean is not None)
+        has_latents_std = (hasattr(vae.config, "latents_std")
+                           and vae.config.latents_std is not None)
         if has_latents_mean and has_latents_std:
-            latents_mean = (
-                torch.tensor(vae.config.latents_mean)
-                .view(1, 12, 1, 1, 1)
-                .to(latents.device, latents.dtype)
-            )
-            latents_std = (
-                torch.tensor(vae.config.latents_std)
-                .view(1, 12, 1, 1, 1)
-                .to(latents.device, latents.dtype)
-            )
+            latents_mean = (torch.tensor(vae.config.latents_mean).view(
+                1, 12, 1, 1, 1).to(latents.device, latents.dtype))
+            latents_std = (torch.tensor(vae.config.latents_std).view(
+                1, 12, 1, 1, 1).to(latents.device, latents.dtype))
             latents = latents * latents_std / vae.config.scaling_factor + latents_mean
         else:
             latents = latents / vae.config.scaling_factor
         with torch.autocast("cuda", dtype=vae.dtype):
             video = vae.decode(latents, return_dict=False)[0]
-        video_processor = VideoProcessor(vae_scale_factor=vae_spatial_scale_factor)
-        video = video_processor.postprocess_video(video, output_type=output_type)
+        video_processor = VideoProcessor(
+            vae_scale_factor=vae_spatial_scale_factor)
+        video = video_processor.postprocess_video(video,
+                                                  output_type=output_type)
 
-    return (video,)
+    return (video, )
 
 
 @torch.no_grad()
@@ -226,23 +217,22 @@ def log_validation(
     ema=False,
 ):
     # TODO
-    print(f"Running validation....\n")
+    print("Running validation....\n")
     if args.model_type == "mochi":
         vae_spatial_scale_factor = 8
         vae_temporal_scale_factor = 6
         num_channels_latents = 12
-    elif args.model_type == "hunyuan":
+    elif args.model_type == "hunyuan" or "hunyuan_hf":
         vae_spatial_scale_factor = 8
         vae_temporal_scale_factor = 4
         num_channels_latents = 16
     else:
         raise ValueError(f"Model type {args.model_type} not supported")
-    vae, autocast_type, fps = load_vae(
-        args.model_type, args.pretrained_model_name_or_path
-    )
+    vae, autocast_type, fps = load_vae(args.model_type,
+                                       args.pretrained_model_name_or_path)
     vae.enable_tiling()
     if scheduler_type == "euler":
-        scheduler = FlowMatchEulerDiscreteScheduler()
+        scheduler = FlowMatchEulerDiscreteScheduler(shift=shift)
     else:
         linear_quadraic = True if scheduler_type == "pcm_linear_quadratic" else False
         scheduler = PCMFMScheduler(
@@ -266,59 +256,55 @@ def log_validation(
             # prompt_embed are named embed0 to embedN
             # check how many embeds are there
             embe_dir = os.path.join(args.validation_prompt_dir, "prompt_embed")
-            mask_dir = os.path.join(args.validation_prompt_dir, "prompt_attention_mask")
+            mask_dir = os.path.join(args.validation_prompt_dir,
+                                    "prompt_attention_mask")
             embeds = sorted([f for f in os.listdir(embe_dir)])
             masks = sorted([f for f in os.listdir(mask_dir)])
             num_embeds = len(embeds)
             validation_prompt_ids = list(range(num_embeds))
-            num_sp_groups = int(os.getenv("WORLD_SIZE", "1")) // nccl_info.sp_size
+            num_sp_groups = int(os.getenv("WORLD_SIZE",
+                                          "1")) // nccl_info.sp_size
             # pad to multiple of groups
             if num_embeds % num_sp_groups != 0:
-                validation_prompt_ids += [0] * (
-                    num_sp_groups - num_embeds % num_sp_groups
-                )
+                validation_prompt_ids += [0] * (num_sp_groups -
+                                                num_embeds % num_sp_groups)
             num_embeds_per_group = len(validation_prompt_ids) // num_sp_groups
-            local_prompt_ids = validation_prompt_ids[
-                nccl_info.group_id
-                * num_embeds_per_group : (nccl_info.group_id + 1)
-                * num_embeds_per_group
-            ]
+            local_prompt_ids = validation_prompt_ids[nccl_info.group_id *
+                                                     num_embeds_per_group:
+                                                     (nccl_info.group_id + 1) *
+                                                     num_embeds_per_group]
 
             for i in local_prompt_ids:
                 prompt_embed_path = os.path.join(embe_dir, f"{embeds[i]}")
                 prompt_mask_path = os.path.join(mask_dir, f"{masks[i]}")
-                prompt_embeds = (
-                    torch.load(prompt_embed_path, map_location="cpu", weights_only=True)
-                    .to(device)
-                    .unsqueeze(0)
-                )
-                prompt_attention_mask = (
-                    torch.load(prompt_mask_path, map_location="cpu", weights_only=True)
-                    .to(device)
-                    .unsqueeze(0)
-                )
-                negative_prompt_embeds = torch.zeros(256, 4096).to(device).unsqueeze(0)
+                prompt_embeds = (torch.load(
+                    prompt_embed_path, map_location="cpu",
+                    weights_only=True).to(device).unsqueeze(0))
+                prompt_attention_mask = (torch.load(
+                    prompt_mask_path, map_location="cpu",
+                    weights_only=True).to(device).unsqueeze(0))
+                negative_prompt_embeds = torch.zeros(
+                    256, 4096).to(device).unsqueeze(0)
                 negative_prompt_attention_mask = (
-                    torch.zeros(256).bool().to(device).unsqueeze(0)
-                )
-                generator = torch.Generator(device="cuda").manual_seed(12345)
+                    torch.zeros(256).bool().to(device).unsqueeze(0))
+                generator = torch.Generator(device="cpu").manual_seed(12345)
                 video = sample_validation_video(
+                    args.model_type,
                     transformer,
                     vae,
                     scheduler,
                     scheduler_type=scheduler_type,
                     num_frames=args.num_frames,
-                    # Peiyuan TODO: remove hardcode
                     height=args.num_height,
                     width=args.num_width,
                     num_inference_steps=validation_sampling_step,
-                    guidance_scale=0.0 if args.model_type == "hunyuan" else validation_guidance_scale,
-                    embed_guidance_scale= validation_guidance_scale if args.model_type == "hunyuan" else 0.0,
+                    guidance_scale=validation_guidance_scale,
                     generator=generator,
                     prompt_embeds=prompt_embeds,
                     prompt_attention_mask=prompt_attention_mask,
                     negative_prompt_embeds=negative_prompt_embeds,
-                    negative_prompt_attention_mask=negative_prompt_attention_mask,
+                    negative_prompt_attention_mask=
+                    negative_prompt_attention_mask,
                     vae_spatial_scale_factor=vae_spatial_scale_factor,
                     vae_temporal_scale_factor=vae_temporal_scale_factor,
                     num_channels_latents=num_channels_latents,
@@ -350,7 +336,8 @@ def log_validation(
                     video_filenames.append(filename)
 
                 logs = {
-                    f"{'ema_' if ema else ''}validation_sample_{validation_sampling_step}_guidance_{validation_guidance_scale}": [
+                    f"{'ema_' if ema else ''}validation_sample_{validation_sampling_step}_guidance_{validation_guidance_scale}":
+                    [
                         wandb.Video(filename)
                         for i, filename in enumerate(video_filenames)
                     ]

format.sh

@@ -0,0 +1,237 @@
+#!/usr/bin/env bash
+# YAPF formatter, adapted from fastvideo.
+#
+# Usage:
+#    # Do work and commit your work.
+
+#    # Format files that differ from origin/main.
+#    bash format.sh
+
+#    # Commit changed files with message 'Run yapf and ruff'
+#
+#
+# This script formats all changed files from the last mergebase.
+# You are encouraged to run this locally before pushing changes for review.
+
+# Cause the script to exit if a single command fails
+set -eo pipefail
+
+# this stops git rev-parse from failing if we run this from the .git directory
+builtin cd "$(dirname "${BASH_SOURCE:-$0}")"
+ROOT="$(git rev-parse --show-toplevel)"
+builtin cd "$ROOT" || exit 1
+
+check_command() {
+    if ! command -v "$1" &> /dev/null; then
+        echo "❓❓$1 is not installed, please run \`bash env_setup.sh\`"
+        exit 1
+    fi
+}
+
+check_command yapf
+check_command ruff
+check_command codespell
+check_command isort
+
+YAPF_VERSION=$(yapf --version | awk '{print $2}')
+RUFF_VERSION=$(ruff --version | awk '{print $2}')
+CODESPELL_VERSION=$(codespell --version)
+ISORT_VERSION=$(isort --vn)
+SPHINX_LINT_VERSION=$(sphinx-lint --version | awk '{print $2}')
+
+
+# # params: tool name, tool version, required version
+tool_version_check() {
+    expected=$(grep "$1" requirements-lint.txt | cut -d'=' -f3)
+    if [[ "$2" != "$expected" ]]; then
+        echo "❓❓Wrong $1 version installed: $expected is required, not $2."
+        exit 1
+    fi
+}
+
+tool_version_check "yapf" "$YAPF_VERSION"
+tool_version_check "ruff" "$RUFF_VERSION"
+tool_version_check "isort" "$ISORT_VERSION"
+tool_version_check "codespell" "$CODESPELL_VERSION"
+tool_version_check "sphinx-lint" "$SPHINX_LINT_VERSION"
+
+YAPF_FLAGS=(
+    '--recursive'
+    '--parallel'
+)
+
+YAPF_EXCLUDES=(
+    '--exclude' 'data/**'
+)
+
+# Format specified files
+format() {
+    yapf --in-place "${YAPF_FLAGS[@]}" "$@"
+}
+
+# Format files that differ from main branch. Ignores dirs that are not slated
+# for autoformat yet.
+format_changed() {
+    # The `if` guard ensures that the list of filenames is not empty, which
+    # could cause yapf to receive 0 positional arguments, making it hang
+    # waiting for STDIN.
+    #
+    # `diff-filter=ACM` and $MERGEBASE is to ensure we only format files that
+    # exist on both branches.
+    MERGEBASE="$(git merge-base origin/main HEAD)"
+
+    if ! git diff --diff-filter=ACM --quiet --exit-code "$MERGEBASE" -- '*.py' '*.pyi' &>/dev/null; then
+        git diff --name-only --diff-filter=ACM "$MERGEBASE" -- '*.py' '*.pyi' | xargs -P 5 \
+             yapf --in-place "${YAPF_EXCLUDES[@]}" "${YAPF_FLAGS[@]}"
+    fi
+
+}
+
+# Format all files
+format_all() {
+    yapf --in-place "${YAPF_FLAGS[@]}" "${YAPF_EXCLUDES[@]}" .
+}
+
+## This flag formats individual files. --files *must* be the first command line
+## arg to use this option.
+if [[ "$1" == '--files' ]]; then
+   format "${@:2}"
+   # If `--all` is passed, then any further arguments are ignored and the
+   # entire python directory is formatted.
+elif [[ "$1" == '--all' ]]; then
+   format_all
+else
+   # Format only the files that changed in last commit.
+   format_changed
+fi
+echo 'FastVideo yapf: Done'
+
+
+# If git diff returns a file that is in the skip list, the file may be checked anyway:
+# https://github.com/codespell-project/codespell/issues/1915
+# Avoiding the "./" prefix and using "/**" globs for directories appears to solve the problem
+CODESPELL_EXCLUDES=(
+    '--skip' 'data/**,
+            fastvideo/distill.py,
+            fastvideo/models/hunyuan/modules/models.py,
+            fastvideo/models/mochi_hf/modeling_mochi.py,
+            fastvideo/utils/env_utils.py'
+)
+
+# check spelling of specified files
+spell_check() {
+    codespell "$@"
+}
+
+spell_check_all(){
+  codespell --toml pyproject.toml "${CODESPELL_EXCLUDES[@]}"
+}
+
+# Spelling check of files that differ from main branch.
+spell_check_changed() {
+    # The `if` guard ensures that the list of filenames is not empty, which
+    # could cause ruff to receive 0 positional arguments, making it hang
+    # waiting for STDIN.
+    #
+    # `diff-filter=ACM` and $MERGEBASE is to ensure we only lint files that
+    # exist on both branches.
+    MERGEBASE="$(git merge-base origin/main HEAD)"
+    if ! git diff --diff-filter=ACM --quiet --exit-code "$MERGEBASE" -- '*.py' '*.pyi' &>/dev/null; then
+        git diff --name-only --diff-filter=ACM "$MERGEBASE" -- '*.py' '*.pyi' | xargs \
+            codespell "${CODESPELL_EXCLUDES[@]}"
+    fi
+}
+
+# Run Codespell
+## This flag runs spell check of individual files. --files *must* be the first command line
+## arg to use this option.
+if [[ "$1" == '--files' ]]; then
+   spell_check "${@:2}"
+   # If `--all` is passed, then any further arguments are ignored and the
+   # entire python directory is linted.
+elif [[ "$1" == '--all' ]]; then
+   spell_check_all
+else
+   # Check spelling only of the files that changed in last commit.
+   spell_check_changed
+fi
+echo 'FastVideo codespell: Done'
+
+
+# Lint specified files
+lint() {
+    ruff check "$@"
+}
+
+# Lint files that differ from main branch. Ignores dirs that are not slated
+# for autolint yet.
+lint_changed() {
+    # The `if` guard ensures that the list of filenames is not empty, which
+    # could cause ruff to receive 0 positional arguments, making it hang
+    # waiting for STDIN.
+    #
+    # `diff-filter=ACM` and $MERGEBASE is to ensure we only lint files that
+    # exist on both branches.
+    MERGEBASE="$(git merge-base origin/main HEAD)"
+
+    if ! git diff --diff-filter=ACM --quiet --exit-code "$MERGEBASE" -- '*.py' '*.pyi' &>/dev/null; then
+        git diff --name-only --diff-filter=ACM "$MERGEBASE" -- '*.py' '*.pyi' | xargs \
+             ruff check
+    fi
+
+}
+
+# Run Ruff
+### This flag lints individual files. --files *must* be the first command line
+### arg to use this option.
+if [[ "$1" == '--files' ]]; then
+   lint "${@:2}"
+   # If `--all` is passed, then any further arguments are ignored and the
+   # entire python directory is linted.
+elif [[ "$1" == '--all' ]]; then
+   lint fastvideo scripts
+else
+   # Format only the files that changed in last commit.
+   lint_changed
+fi
+echo 'FastVideo ruff: Done'
+
+# check spelling of specified files
+isort_check() {
+    isort "$@"
+}
+
+isort_check_all(){
+  isort .
+}
+
+# Spelling  check of files that differ from main branch.
+isort_check_changed() {
+    # The `if` guard ensures that the list of filenames is not empty, which
+    # could cause ruff to receive 0 positional arguments, making it hang
+    # waiting for STDIN.
+    #
+    # `diff-filter=ACM` and $MERGEBASE is to ensure we only lint files that
+    # exist on both branches.
+    MERGEBASE="$(git merge-base origin/main HEAD)"
+
+    if ! git diff --diff-filter=ACM --quiet --exit-code "$MERGEBASE" -- '*.py' '*.pyi' &>/dev/null; then
+        git diff --name-only --diff-filter=ACM "$MERGEBASE" -- '*.py' '*.pyi' | xargs \
+             isort
+    fi
+}
+
+# Run Isort
+# This flag runs spell check of individual files. --files *must* be the first command line
+# arg to use this option.
+if [[ "$1" == '--files' ]]; then
+   isort_check "${@:2}"
+   # If `--all` is passed, then any further arguments are ignored and the
+   # entire python directory is linted.
+elif [[ "$1" == '--all' ]]; then
+   isort_check_all
+else
+   # Check spelling only of the files that changed in last commit.
+   isort_check_changed
+fi
+echo 'FastVideo isort: Done'

inference_hunyuan_no_sp.sh

@@ -1,12 +0,0 @@
-python3 fastvideo/sample/sample_t2v_hunyuan_no_sp.py \
-    --height 500 \
-    --width 700 \
-    --num_frames 29 \
-    --num_inference_steps 50 \
-    --guidance_scale 1 \
-    --embedded_cfg_scale 6 \
-    --flow-reverse \
-    --prompts "A cat walks on the grass, realistic style." \
-    --prompts "A dog runs in the park, realistic style." \
-    --seed 42 \
-    --output_path outputs_video/hunyuan/

inference_hunyuan_sp.sh

@@ -1,39 +0,0 @@
-num_gpus=4
-
-torchrun --nnodes=1 --nproc_per_node=$num_gpus --master_port 29503 \
-    fastvideo/sample/sample_t2v_hunyuan.py \
-    --height 512 \
-    --width 512 \
-    --num_frames 29 \
-    --num_inference_steps 4 \
-    --guidance_scale 1 \
-    --embedded_cfg_scale 6 \
-    --flow_shift 17 \
-    --flow-reverse \
-    --prompts "A man on stage claps his hands together while facing the audience. The audience, visible in the foreground, holds up mobile devices to record the event, capturing the moment from various angles. The background features a large banner with text identifying the man on stage. Throughout the sequence, the man's expression remains engaged and directed towards the audience. The camera angle remains constant, focusing on capturing the interaction between the man on stage and the audience."\
-    --seed 12345 \
-    --output_path outputs_video/hunyuan/
-
-
-tensor(-0.1065, device='cuda:0', dtype=torch.float16)
-tensor(-0.0034, device='cuda:2', dtype=torch.float16)
-
-tensor(-0.0230, device='cuda:0', dtype=torch.float16)
->>> weight[0, :768].mean()
-tensor(-0.0367, device='cuda:0', dtype=torch.float16)
-
-num_gpus=1
-
-torchrun --nnodes=1 --nproc_per_node=$num_gpus --master_port 29503 \
-    fastvideo/sample/sample_t2v_hunyuan.py \
-    --height 480 \
-    --width 848 \
-    --num_frames 93 \
-    --num_inference_steps 50 \
-    --guidance_scale 1 \
-    --embedded_cfg_scale 6 \
-    --flow_shift 17 \
-    --flow-reverse \
-    --prompts "A man on stage claps his hands together while facing the audience. The audience, visible in the foreground, holds up mobile devices to record the event, capturing the moment from various angles. The background features a large banner with text identifying the man on stage. Throughout the sequence, the man's expression remains engaged and directed towards the audience. The camera angle remains constant, focusing on capturing the interaction between the man on stage and the audience."\
-    --seed 12345 \
-    --output_path outputs_video/hunyuan/

inference_mochi.sh

@@ -1,20 +0,0 @@
-
-
-
-num_gpus=4
-
-torchrun --nnodes=1 --nproc_per_node=$num_gpus --master_port 29503 \
-    fastvideo/sample/sample_t2v_mochi.py \
-    --model_path data/mochi \
-    --prompt_embed_path "data/synthetic_debug2/prompt_embed/2.pt" \
-    --encoder_attention_mask_path "data/synthetic_debug2/prompt_attention_mask/1.pt" \
-    --num_frames  163 \
-    --height 480 \
-    --width 848 \
-    --num_inference_steps 32 \
-    --guidance_scale 4.5 \
-    --output_path outputs_video/debug \
-    --shift 8 \
-    --seed 12345 \
-    --scheduler_type "pcm_linear_quadratic" 
-

inference_mochi_no_sp.sh

@@ -1,11 +0,0 @@
-python fastvideo/sample/sample_t2v_mochi_no_sp.py \
-    --model_path data/mochi \
-    --prompts  "A hand enters the frame, pulling a sheet of plastic wrap over three balls of dough placed on a wooden surface. The plastic wrap is stretched to cover the dough more securely. The hand adjusts the wrap, ensuring that it is tight and smooth over the dough. The scene focuses on the hand's movements as it secures the edges of the plastic wrap. No new objects appear, and the camera remains stationary, focusing on the action of covering the dough."  \
-    --num_frames 163 \
-    --height 480 \
-    --width 848 \
-    --num_inference_steps 64 \
-    --guidance_scale 0.0 \
-    --seed 12346 \
-    --transformer_path data/outputs/debug/checkpoint-100/transformer \
-    --output_path outputs_video/single_no_guidance

predict.py

@@ -0,0 +1,167 @@
+# Prediction interface for Cog ⚙️
+# https://cog.run/python
+
+import argparse
+import os
+import subprocess
+import time
+
+import imageio
+import numpy as np
+import torch
+import torchvision
+from cog import BasePredictor, Input, Path
+from einops import rearrange
+
+from fastvideo.models.hunyuan.inference import HunyuanVideoSampler
+
+MODEL_CACHE = 'FastHunyuan'
+os.environ['MODEL_BASE'] = './' + MODEL_CACHE
+
+MODEL_URL = "https://weights.replicate.delivery/default/FastVideo/FastHunyuan/model.tar"
+
+
+def download_weights(url, dest):
+    start = time.time()
+    print("downloading url: ", url)
+    print("downloading to: ", dest)
+    subprocess.check_call(["pget", "-xf", url, dest], close_fds=False)
+    print("downloading took: ", time.time() - start)
+
+
+class Predictor(BasePredictor):
+
+    def setup(self):
+        """Load the model into memory"""
+        print("Model Base: " + os.environ['MODEL_BASE'])
+        # Download weights
+        if not os.path.exists(MODEL_CACHE):
+            download_weights(MODEL_URL, MODEL_CACHE)
+
+        self.device = torch.device(
+            "cuda" if torch.cuda.is_available() else "cpu")
+        args = argparse.Namespace(
+            num_frames=125,
+            height=720,
+            width=1280,
+            num_inference_steps=6,
+            fps=24,
+            denoise_type='flow',
+            seed=1024,
+            neg_prompt=None,
+            guidance_scale=1.0,
+            embedded_cfg_scale=6.0,
+            flow_shift=17,
+            batch_size=1,
+            num_videos=1,
+            load_key='module',
+            use_cpu_offload=False,
+            dit_weight=
+            'FastHunyuan/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt',
+            reproduce=True,
+            disable_autocast=False,
+            flow_reverse=True,
+            flow_solver='euler',
+            use_linear_quadratic_schedule=False,
+            linear_schedule_end=25,
+            model='HYVideo-T/2-cfgdistill',
+            latent_channels=16,
+            precision='bf16',
+            rope_theta=256,
+            vae='884-16c-hy',
+            vae_precision='fp16',
+            vae_tiling=True,
+            text_encoder='llm',
+            text_encoder_precision='fp16',
+            text_states_dim=4096,
+            text_len=256,
+            tokenizer='llm',
+            prompt_template='dit-llm-encode',
+            prompt_template_video='dit-llm-encode-video',
+            hidden_state_skip_layer=2,
+            apply_final_norm=False,
+            text_encoder_2='clipL',
+            text_encoder_precision_2='fp16',
+            text_states_dim_2=768,
+            tokenizer_2='clipL',
+            text_len_2=77,
+            model_path=MODEL_CACHE,
+        )
+        self.model = HunyuanVideoSampler.from_pretrained(MODEL_CACHE,
+                                                         args=args)
+
+    def predict(
+        self,
+        prompt: str = Input(
+            description="Text prompt for video generation",
+            default="A cat walks on the grass, realistic style."),
+        negative_prompt: str = Input(
+            description=
+            "Text prompt to specify what you don't want in the video.",
+            default=""),
+        width: int = Input(description="Width of output video",
+                           default=1280,
+                           ge=256),
+        height: int = Input(description="Height of output video",
+                            default=720,
+                            ge=256),
+        num_frames: int = Input(description="Number of frames to generate",
+                                default=125,
+                                ge=16),
+        num_inference_steps: int = Input(
+            description="Number of denoising steps", default=6, ge=1, le=50),
+        guidance_scale: float = Input(
+            description="Classifier free guidance scale",
+            default=1.0,
+            ge=0.1,
+            le=10.0),
+        embedded_cfg_scale: float = Input(
+            description="Embedded classifier free guidance scale",
+            default=6.0,
+            ge=0.1,
+            le=10.0),
+        flow_shift: int = Input(description="Flow shift parameter",
+                                default=17,
+                                ge=1,
+                                le=20),
+        fps: int = Input(description="Frames per second of output video",
+                         default=24,
+                         ge=1,
+                         le=60),
+        seed: int = Input(
+            description="0 for Random seed. Set for reproducible generation",
+            default=0),
+    ) -> Path:
+        """Run video generation"""
+        if seed <= 0:
+            seed = int.from_bytes(os.urandom(2), "big")
+        print(f"Using seed: {seed}")
+
+        outputs = self.model.predict(
+            prompt=prompt,
+            height=height,
+            width=width,
+            video_length=num_frames,
+            seed=seed,
+            negative_prompt=negative_prompt,
+            infer_steps=num_inference_steps,
+            guidance_scale=guidance_scale,
+            embedded_guidance_scale=embedded_cfg_scale,
+            flow_shift=flow_shift,
+            flow_reverse=True,
+            batch_size=1,
+            num_videos_per_prompt=1,
+        )
+
+        # Process output video
+        videos = rearrange(outputs["samples"], "b c t h w -> t b c h w")
+        frames = []
+        for x in videos:
+            x = torchvision.utils.make_grid(x, nrow=6)
+            x = x.transpose(0, 1).transpose(1, 2).squeeze(-1)
+            frames.append((x * 255).numpy().astype(np.uint8))
+
+        # Save video
+        output_path = Path("/tmp/output.mp4")
+        imageio.mimsave(str(output_path), frames, fps=fps)
+        return Path(output_path)

pyproject.toml

@@ -21,7 +21,7 @@ dependencies = [
 "timm==1.0.11", "torchdiffeq==0.2.4", "torchmetrics==1.5.1", "tqdm==4.66.5", "urllib3==2.2.0", "uvicorn==0.32.0", 
 "scikit-video==1.1.11", "imageio-ffmpeg==0.5.1", "sentencepiece==0.2.0", "beautifulsoup4==4.12.3", "ftfy==6.3.0", 
 "moviepy==1.0.3", "wandb==0.18.5", "tensorboard==2.18.0", "pydantic==2.9.2", "gradio==5.3.0", "huggingface_hub==0.26.1", "protobuf==5.28.3", 
-"watch", "gpustat", "peft==0.13.2", "liger_kernel==0.4.1", "einops==0.8.0", "wheel==0.44.0", "loguru"]
+"watch", "gpustat", "peft==0.13.2", "liger_kernel==0.4.1", "einops==0.8.0", "wheel==0.44.0", "loguru", "diffusers==0.32.0", "bitsandbytes"]
 
 
 [tool.setuptools.packages.find]

requirements-lint.txt

@@ -0,0 +1,14 @@
+# formatting
+yapf==0.32.0
+toml==0.10.2
+tomli==2.0.2
+ruff==0.6.5
+codespell==2.3.0
+isort==5.13.2
+sphinx-lint==1.0.0
+
+# type checking
+mypy==1.11.1
+types-PyYAML
+types-requests
+types-setuptools

scripts/dataset_preparation/prepare_json_file.py

@@ -0,0 +1,154 @@
+import json
+from pathlib import Path
+
+import cv2
+
+
+def get_video_info(video_path, prompt_text):
+    """Extract video information using OpenCV and corresponding prompt text"""
+    cap = cv2.VideoCapture(str(video_path))
+
+    if not cap.isOpened():
+        print(f"Error: Could not open video {video_path}")
+        return None
+
+    # Get video properties
+    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    fps = cap.get(cv2.CAP_PROP_FPS)
+    frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+    duration = frame_count / fps if fps > 0 else 0
+
+    cap.release()
+
+    return {
+        "path": video_path.name,
+        "resolution": {
+            "width": width,
+            "height": height
+        },
+        "fps": fps,
+        "duration": duration,
+        "cap": [prompt_text]
+    }
+
+
+def read_prompt_file(prompt_path):
+    """Read and return the content of a prompt file"""
+    try:
+        with open(prompt_path, 'r', encoding='utf-8') as f:
+            return f.read().strip()
+    except Exception as e:
+        print(f"Error reading prompt file {prompt_path}: {e}")
+        return None
+
+
+def process_videos_and_prompts(video_dir_path, prompt_dir_path, verbose=False):
+    """Process videos and their corresponding prompt files
+    
+    Args:
+        video_dir_path (str): Path to directory containing video files
+        prompt_dir_path (str): Path to directory containing prompt files
+        verbose (bool): Whether to print verbose processing information
+    """
+    video_dir = Path(video_dir_path)
+    prompt_dir = Path(prompt_dir_path)
+    processed_data = []
+
+    # Ensure directories exist
+    if not video_dir.exists() or not prompt_dir.exists():
+        print(
+            f"Error: One or both directories do not exist:\nVideos: {video_dir}\nPrompts: {prompt_dir}"
+        )
+        return []
+
+    # Process each video file
+    for video_file in video_dir.glob('*.mp4'):
+        video_name = video_file.stem
+        prompt_file = prompt_dir / f"{video_name}.txt"
+
+        # Check if corresponding prompt file exists
+        if not prompt_file.exists():
+            print(f"Warning: No prompt file found for video {video_name}")
+            continue
+
+        # Read prompt content
+        prompt_text = read_prompt_file(prompt_file)
+        if prompt_text is None:
+            continue
+
+        # Process video and add to results
+        video_info = get_video_info(video_file, prompt_text)
+        if video_info:
+            processed_data.append(video_info)
+
+    return processed_data
+
+
+def save_results(processed_data, output_path):
+    """Save processed data to JSON file
+    
+    Args:
+        processed_data (list): List of processed video information
+        output_path (str): Full path for output JSON file
+    """
+    output_path = Path(output_path)
+
+    # Create parent directories if they don't exist
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+
+    with open(output_path, 'w', encoding='utf-8') as f:
+        json.dump(processed_data, f, indent=2, ensure_ascii=False)
+
+    return output_path
+
+
+def parse_args():
+    """Parse command line arguments"""
+    import argparse
+
+    parser = argparse.ArgumentParser(
+        description='Process videos and their corresponding prompt files')
+    parser.add_argument('--video_dir',
+                        '-v',
+                        required=True,
+                        help='Directory containing video files')
+    parser.add_argument('--prompt_dir',
+                        '-p',
+                        required=True,
+                        help='Directory containing prompt text files')
+    parser.add_argument(
+        '--output_path',
+        '-o',
+        required=True,
+        help=
+        'Full path for output JSON file (e.g., /path/to/output/videos2caption.json)'
+    )
+    parser.add_argument('--verbose',
+                        action='store_true',
+                        help='Print verbose processing information')
+
+    return parser.parse_args()
+
+
+if __name__ == "__main__":
+    # Parse command line arguments
+    args = parse_args()
+
+    # Process videos and prompts
+    processed_videos = process_videos_and_prompts(args.video_dir,
+                                                  args.prompt_dir,
+                                                  args.verbose)
+
+    if processed_videos:
+        # Save results
+        output_path = save_results(processed_videos, args.output_path)
+
+        print(f"\nProcessed {len(processed_videos)} videos")
+        print(f"Results saved to: {output_path}")
+
+        # Print example of processed data
+        print("\nExample of processed video info:")
+        print(json.dumps(processed_videos[0], indent=2))
+    else:
+        print("No videos were processed successfully")

scripts/dataset_preparation/resize_videos.py

@@ -0,0 +1,174 @@
+import argparse
+import logging
+import time
+from concurrent.futures import ProcessPoolExecutor, as_completed
+from pathlib import Path
+
+import numpy as np
+from moviepy.editor import VideoFileClip
+from skimage.transform import resize
+from tqdm import tqdm
+
+# Configure logging
+logging.basicConfig(level=logging.INFO,
+                    format='%(asctime)s - %(levelname)s - %(message)s',
+                    handlers=[logging.FileHandler('video_processing.log')])
+
+
+def is_16_9_ratio(width: int, height: int, tolerance: float = 0.1) -> bool:
+    target_ratio = 16 / 9
+    actual_ratio = width / height
+    return abs(actual_ratio - target_ratio) <= (target_ratio * tolerance)
+
+
+def resize_video(args_tuple):
+    """
+    Resize a single video file.
+    args_tuple: (input_file, output_dir, width, height, fps)
+    """
+    input_file, output_dir, width, height, fps = args_tuple
+    video = None
+    resized = None
+    output_file = output_dir / f"{input_file.name}"
+
+    if output_file.exists():
+        output_file.unlink()
+
+    video = VideoFileClip(str(input_file))
+
+    if not is_16_9_ratio(video.w, video.h):
+        return (input_file.name, "skipped", "Not 16:9")
+
+    def process_frame(frame):
+        frame_float = frame.astype(float) / 255.0
+        resized = resize(frame_float, (height, width, 3),
+                         mode='reflect',
+                         anti_aliasing=True,
+                         preserve_range=True)
+        return (resized * 255).astype(np.uint8)
+
+    resized = video.fl_image(process_frame)
+    resized = resized.set_fps(fps)
+
+    resized.write_videofile(str(output_file),
+                            codec='libx264',
+                            audio_codec='aac',
+                            temp_audiofile=f'temp-audio-{input_file.stem}.m4a',
+                            remove_temp=True,
+                            verbose=False,
+                            logger=None,
+                            fps=fps)
+
+    return (input_file.name, "success", None)
+
+
+def process_folder(args):
+    input_path = Path(args.input_dir)
+    output_path = Path(args.output_dir)
+    output_path.mkdir(parents=True, exist_ok=True)
+
+    video_extensions = {'.mp4', '.avi', '.mov', '.mkv', '.webm'}
+    video_files = [
+        f for f in input_path.iterdir()
+        if f.is_file() and f.suffix.lower() in video_extensions
+    ]
+
+    if not video_files:
+        print(f"No video files found in {args.input_dir}")
+        return
+
+    print(f"Found {len(video_files)} videos")
+    print(f"Target: {args.width}x{args.height} at {args.fps}fps")
+
+    # Prepare arguments for parallel processing
+    process_args = [(video_file, output_path, args.width, args.height,
+                     args.fps) for video_file in video_files]
+
+    successful = 0
+    skipped = 0
+    failed = []
+
+    # Use ProcessPoolExecutor instead of ThreadPoolExecutor
+    with tqdm(total=len(video_files),
+              desc="Converting videos",
+              dynamic_ncols=True) as pbar:
+        # Use max_workers as specified or default to CPU count
+        max_workers = args.max_workers
+        with ProcessPoolExecutor(max_workers=max_workers) as executor:
+            # Submit all tasks
+            future_to_file = {
+                executor.submit(resize_video, arg): arg[0]
+                for arg in process_args
+            }
+
+            # Process completed tasks
+            for future in as_completed(future_to_file):
+                filename, status, message = future.result()
+                if status == "success":
+                    successful += 1
+                elif status == "skipped":
+                    skipped += 1
+                else:
+                    failed.append((filename, message))
+                pbar.update(1)
+
+    # Print final summary
+    print(
+        f"\nDone! Processed: {successful}, Skipped: {skipped}, Failed: {len(failed)}"
+    )
+    if failed:
+        print("Failed files:")
+        for fname, error in failed:
+            print(f"- {fname}: {error}")
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description=
+        'Batch resize videos to specified resolution and FPS (16:9 only)')
+    parser.add_argument('--input_dir',
+                        required=True,
+                        help='Input directory containing video files')
+    parser.add_argument('--output_dir',
+                        required=True,
+                        help='Output directory for processed videos')
+    parser.add_argument('--width',
+                        type=int,
+                        default=1280,
+                        help='Target width in pixels (default: 848)')
+    parser.add_argument('--height',
+                        type=int,
+                        default=720,
+                        help='Target height in pixels (default: 480)')
+    parser.add_argument('--fps',
+                        type=int,
+                        default=30,
+                        help='Target frames per second (default: 30)')
+    parser.add_argument(
+        '--max_workers',
+        type=int,
+        default=4,
+        help='Maximum number of concurrent processes (default: 4)')
+    parser.add_argument('--log-level',
+                        choices=['DEBUG', 'INFO', 'WARNING', 'ERROR'],
+                        default='INFO',
+                        help='Set the logging level (default: INFO)')
+    return parser.parse_args()
+
+
+def main():
+    args = parse_args()
+    logging.getLogger().setLevel(getattr(logging, args.log_level))
+
+    if not Path(args.input_dir).exists():
+        logging.error(f"Input directory not found: {args.input_dir}")
+        return
+
+    start_time = time.time()
+    process_folder(args)
+    duration = time.time() - start_time
+    logging.info(f"Batch processing completed in {duration:.2f} seconds")
+
+
+if __name__ == "__main__":
+    main()

scripts/distill/distill_hunyuan.sh

@@ -1,16 +1,10 @@
 export WANDB_BASE_URL="https://api.wandb.ai"
-export WANDB_DIR="$HOME"
 export WANDB_MODE=online
-export WANDB_API_KEY=4f6de3765d6464f43e0506ec7d785641af645e73
-export LD_LIBRARY_PATH=/opt/amazon/efa/lib:/opt/aws-ofi-nccl/lib:$LD_LIBRARY_PATH
-export FI_PROVIDER=efa
-export FI_EFA_USE_DEVICE_RDMA=1
-export NCCL_PROTO=simple
 
-DATA_DIR=/data
-IP=10.4.139.86
+DATA_DIR=./data
+IP=[MASTER NODE IP]
 
-torchrun --nnodes 2 --nproc_per_node 8\
+torchrun --nnodes 4 --nproc_per_node 8\
     --node_rank=0 \
     --rdzv_id=456 \
     --rdzv_backend=c10d \
@@ -21,12 +15,12 @@ torchrun --nnodes 2 --nproc_per_node 8\
     --dit_model_name_or_path $DATA_DIR/hunyuan/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt\
     --model_type "hunyuan" \
     --cache_dir "$DATA_DIR/.cache"\
-    --data_json_path "$DATA_DIR/Hunyuan-Distill-Data/videos2caption.json"\
-    --validation_prompt_dir "$DATA_DIR/Hunyuan-Distill-Data/validation"\
+    --data_json_path "$DATA_DIR/HD-Mixkit-Finetune-Hunyuan/videos2caption.json"\
+    --validation_prompt_dir "$DATA_DIR/HD-Mixkit-Finetune-Hunyuan/validation"\
     --gradient_checkpointing\
     --train_batch_size=1\
-    --num_latent_t 24\
-    --sp_size 1\
+    --num_latent_t 32 \
+    --sp_size 2 \
     --train_sp_batch_size 1\
     --dataloader_num_workers 4\
     --gradient_accumulation_steps=1\
@@ -41,9 +35,11 @@ torchrun --nnodes 2 --nproc_per_node 8\
     --ema_start_step 0\
     --cfg 0.0\
     --log_validation\
-    --output_dir="$DATA_DIR/outputs/hy_phase1_shift17_bs_16_nosp"\
+    --output_dir="$DATA_DIR/outputs/hy_phase1_shift17_bs_16_HD"\
     --tracker_project_name Hunyuan_Distill \
-    --num_frames  93 \
+    --num_height 720 \
+    --num_width 1280 \
+    --num_frames  125 \
     --shift 17 \
     --validation_guidance_scale "1.0" \
     --num_euler_timesteps 50 \
@@ -51,26 +47,23 @@ torchrun --nnodes 2 --nproc_per_node 8\
     --not_apply_cfg_solver 
 
 
-torchrun --nnodes 2 --nproc_per_node 8\
-    --node_rank=0 \
-    --rdzv_id=456 \
-    --rdzv_backend=c10d \
-    --rdzv_endpoint=$IP:29500 \
+# If you do not have 32 GPUs and to fit in memory, you can: 1. increase sp_size. 2. reduce num_latent_t
+torchrun --nnodes 1 --nproc_per_node 8\
     fastvideo/distill.py\
     --seed 42\
     --pretrained_model_name_or_path $DATA_DIR/hunyuan\
     --dit_model_name_or_path $DATA_DIR/hunyuan/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt\
     --model_type "hunyuan" \
     --cache_dir "$DATA_DIR/.cache"\
-    --data_json_path "$DATA_DIR/Hunyuan-Distill-Data/videos2caption.json"\
-    --validation_prompt_dir "$DATA_DIR/Hunyuan-Distill-Data/validation"\
+    --data_json_path "$DATA_DIR/HD-Mixkit-Finetune-Hunyuan/videos2caption.json"\
+    --validation_prompt_dir "$DATA_DIR/HD-Mixkit-Finetune-Hunyuan/validation"\
     --gradient_checkpointing\
     --train_batch_size=1\
-    --num_latent_t 24\
-    --sp_size 2\
+    --num_latent_t 32 \
+    --sp_size 2 \
     --train_sp_batch_size 1\
     --dataloader_num_workers 4\
-    --gradient_accumulation_steps=2\
+    --gradient_accumulation_steps=1\
     --max_train_steps=320\
     --learning_rate=1e-6\
     --mixed_precision="bf16"\
@@ -82,9 +75,11 @@ torchrun --nnodes 2 --nproc_per_node 8\
     --ema_start_step 0\
     --cfg 0.0\
     --log_validation\
-    --output_dir="$DATA_DIR/outputs/hy_phase1_shift17_bs_16_sp"\
+    --output_dir="$DATA_DIR/outputs/hy_phase1_shift17_bs_16_HD"\
     --tracker_project_name Hunyuan_Distill \
-    --num_frames  93 \
+    --num_height 720 \
+    --num_width 1280 \
+    --num_frames  125 \
     --shift 17 \
     --validation_guidance_scale "1.0" \
     --num_euler_timesteps 50 \

scripts/distill/distill_mochi.sh

@@ -0,0 +1,38 @@
+export WANDB_BASE_URL="https://api.wandb.ai"
+export WANDB_MODE=online
+
+torchrun --nnodes 1 --nproc_per_node 4 \
+    fastvideo/distill.py \
+    --seed 42 \
+    --pretrained_model_name_or_path data/mochi \
+    --model_type "mochi" \
+    --cache_dir data/.cache \
+    --data_json_path data/Merge-30k-Data/video2caption.json \
+    --validation_prompt_dir data/Image-Vid-Finetune-Mochi/validation \
+    --gradient_checkpointing \
+    --train_batch_size=1 \
+    --num_latent_t 28 \
+    --sp_size 4 \
+    --train_sp_batch_size 2 \
+    --dataloader_num_workers 4 \
+    --gradient_accumulation_steps=1 \
+    --max_train_steps=4000 \
+    --learning_rate=1e-6 \
+    --mixed_precision=bf16 \
+    --checkpointing_steps=64 \
+    --validation_steps 1 \
+    --validation_sampling_steps 8 \
+    --checkpoints_total_limit 3 \
+    --allow_tf32 \
+    --ema_start_step 0 \
+    --cfg 0.0 \
+    --log_validation \
+    --output_dir="data/outputs/lq_euler_50_thres0.1_lrg_0.75_phase1_lr1e-6_repro" \
+    --tracker_project_name PCM \
+    --num_frames 163 \
+    --scheduler_type pcm_linear_quadratic \
+    --validation_guidance_scale 0.5,1.5,2.5 \
+    --num_euler_timesteps 50 \
+    --linear_quadratic_threshold 0.1 \
+    --linear_range 0.75 \
+    --multi_phased_distill_schedule 4000-1

scripts/distill_hunyuan.md

@@ -1,5 +0,0 @@
-## How to Distill Hunyuan 
-
-python scripts/download_hf.py --repo_id FastVideo/hunyuan --local_dir data/hunyuan --repo_type model
-python scripts/download_hf.py --repo_id FastVideo/Hunyuan-Distill-Data --local_dir data/Hunyuan-Distill-Data --repo_type=dataset
-

scripts/distill_hunyuan.sh

@@ -1,40 +0,0 @@
-export WANDB_BASE_URL="https://api.wandb.ai"
-export WANDB_MODE=online
-export WANDB_API_KEY=4f6de3765d6464f43e0506ec7d785641af645e73
-
-DATA_DIR=./data
-torchrun --nnodes 1 --nproc_per_node 8\
-    fastvideo/distill_adv.py\
-    --seed 42\
-    --pretrained_model_name_or_path $DATA_DIR/hunyuan\
-    --dit_model_name_or_path $DATA_DIR/hunyuan/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt\
-    --model_type "hunyuan" \
-    --cache_dir "$DATA_DIR/.cache"\
-    --data_json_path "$DATA_DIR/Hunyuan-Distill-Data/videos2caption.json"\
-    --validation_prompt_dir "$DATA_DIR/Hunyuan-Distill-Data/validation"\
-    --gradient_checkpointing\
-    --train_batch_size=1\
-    --num_latent_t 24\
-    --sp_size 4\
-    --train_sp_batch_size 1\
-    --dataloader_num_workers 4\
-    --gradient_accumulation_steps=1\
-    --max_train_steps=640\
-    --learning_rate=1e-6\
-    --mixed_precision="bf16"\
-    --checkpointing_steps=64\
-    --validation_steps 64\
-    --validation_sampling_steps "2,4,8" \
-    --checkpoints_total_limit 3\
-    --allow_tf32\
-    --ema_start_step 0\
-    --cfg 0.0\
-    --log_validation\
-    --output_dir="$DATA_DIR/outputs/hy_phase1_shift17_bs_32_adv"\
-    --tracker_project_name Hunyuan_Distill \
-    --num_frames  93 \
-    --shift 17 \
-    --validation_guidance_scale "1.0" \
-    --num_euler_timesteps 50 \
-    --multi_phased_distill_schedule "4000-1" \
-    --not_apply_cfg_solver 

scripts/finetune/finetune_hunyuan.sh

@@ -0,0 +1,38 @@
+export WANDB_BASE_URL="https://api.wandb.ai"
+export WANDB_MODE=online
+
+torchrun --nnodes 1 --nproc_per_node 8 \
+    fastvideo/train.py \
+    --seed 42 \
+    --pretrained_model_name_or_path data/hunyuan \
+    --dit_model_name_or_path data/hunyuan/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt\
+    --model_type "hunyuan" \
+    --cache_dir data/.cache \
+    --data_json_path data/Image-Vid-Finetune-HunYuan/videos2caption.json \
+    --validation_prompt_dir data/Image-Vid-Finetune-HunYuan/validation \
+    --gradient_checkpointing \
+    --train_batch_size=1 \
+    --num_latent_t 32 \
+    --sp_size 4 \
+    --train_sp_batch_size 1 \
+    --dataloader_num_workers 4 \
+    --gradient_accumulation_steps=1 \
+    --max_train_steps=2000 \
+    --learning_rate=1e-5 \
+    --mixed_precision=bf16 \
+    --checkpointing_steps=200 \
+    --validation_steps 100 \
+    --validation_sampling_steps 50 \
+    --checkpoints_total_limit 3 \
+    --allow_tf32 \
+    --ema_start_step 0 \
+    --cfg 0.0 \
+    --ema_decay 0.999 \
+    --log_validation \
+    --output_dir=data/outputs/HSH-Taylor-Finetune-Hunyuan \
+    --tracker_project_name HSH-Taylor-Finetune-Hunyuan \
+    --num_frames 125 \
+    --num_height 720 \
+    --num_width 1280 \
+    --shift 7 \
+    --validation_guidance_scale "1.0" \