modify default path

add init file for chatbot

readme.md

@@ -18,7 +18,11 @@ SCEPTER offers 3 core components:
 
 
 ## 🎉 News
-- [🔥🔥🔥2024.10]: We are pleased to announce the release of the code for [ACE](https://arxiv.org/abs/2410.00086), supporting Customized Training / Comfy UI Workflow / gradio-based ChatBot Interface. The detailed documents can be found at [ACE repo](https://github.com/ali-vilab/ACE.git).
+- [🔥🔥🔥2024.11]: We're excited to announce the upcoming release of the [ACE-0.6b-1024px](https://huggingface.co/scepter-studio/ACE-0.6B-1024px) model, 
+which significantly enhances image generation quality compared with [ACE-0.6b-512px](https://huggingface.co/scepter-studio/ACE-0.6B-512px). The detailed documents can be found at [ACE repo](https://github.com/ali-vilab/ACE.git).
+At the same time, based on the editing results of ACE, combined with the powerful text-to-image capabilities of the [FLUX-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev) model through SDEdit as an image quality refiner, the quality of image editing can be further enhanced.
+- [🔥2024.11]: Supports video files, video annotation, caption translation in data management, and inference & training of the [CogVideoX](https://arxiv.org/abs/2408.06072).
+- [2024.10]: We are pleased to announce the release of the code for [ACE](https://arxiv.org/abs/2410.00086), supporting Customized Training / Comfy UI Workflow / gradio-based ChatBot Interface. 
 - [2024.10]: Support for inference and tuning with [FLUX](https://huggingface.co/black-forest-labs/FLUX.1-dev), as well as for building [ComfyUI](https://github.com/comfyanonymous/ComfyUI) workflows using this framework.
 - [2024.09]: We introduce **ACE**, an **A**ll-round **C**reator and **E**ditor adept at executing a diverse array of image editing tasks tailored to your specifications. Built upon the cutting-edge Diffusion Transformer architecture, ACE has been extensively trained on a comprehensive dataset to seamlessly interpret and execute any natural language instruction. For further information, please consult the [project page](https://ali-vilab.github.io/ace-page/).
 - [2024.07]: Support the inference and training of open-source generative models based on the [DiT](https://arxiv.org/abs/2212.09748) architecture, such as [SD3](https://arxiv.org/pdf/2403.03206) and [PixArt](https://arxiv.org/abs/2310.00426).
@@ -32,19 +36,25 @@ SCEPTER offers 3 core components:
 - [2023.12]: We release [🪄SCEPTER](https://github.com/modelscope/scepter/) library.
 
 
-## 🖼 Gallery for Recent Works
 
-### ACE
+
+## 🪄ACE
 
 ACE is a unified foundational model framework that supports a wide range of visual generation tasks. By defining CU for unifying multi-modal inputs across different tasks and incorporating long-context CU, we introduce historical contextual information into visual generation tasks, paving the way for ChatGPT-like dialog systems in visual generation.
 
 [![Watch the demo](https://ali-vilab.github.io/ace-page/static/images/tasks.png)](https://ali-vilab.github.io/ace-page/)
 
-#### ACE Training
+### ACE Models
+|    **Model**     |                                                                                                                                                                                                            **Status**                                                                                                                                                                                                             | 
+|:----------------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
+|  ACE-0.6B-512px  |          [![Demo link](https://img.shields.io/badge/Demo-ACE_Chat-purple)](https://huggingface.co/spaces/scepter-studio/ACE-Chat)<br>[![ModelScope link](https://img.shields.io/badge/ModelScope-Model-blue)](https://www.modelscope.cn/models/iic/ACE-0.6B-512px)  [![HuggingFace link](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model-yellow)](https://huggingface.co/scepter-studio/ACE-0.6B-512px)          |
+| ACE-0.6B-1024px  | [![Demo link](https://img.shields.io/badge/Demo-ACE_Refiner_Chat-purple)](https://huggingface.co/spaces/scepter-studio/ACE-Refiner-Chat)<br>[![ModelScope link](https://img.shields.io/badge/ModelScope-Model-blue)](https://www.modelscope.cn/models/iic/ACE-0.6B-1024px)  [![HuggingFace link](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model-yellow)](https://huggingface.co/scepter-studio/ACE-0.6B-1024px) |             |
+| ACE-12B-FLUX-dev |                                                                                                                                                                                                            Coming Soon                                                                                                                                                                                                            |
+### ACE Training
 
 We offer a demonstration training YAML that enables the end-to-end training of ACE using a toy dataset. For a comprehensive overview of the hyperparameter configurations, please consult `scepter/methods/edit/dit_ace_0.6b_512.yaml`.
 
-##### Prepare datasets
+#### Prepare datasets
 
 Please find the dataset class located in `scepter/modules/data/dataset/ms_dataset.py`,
 designed to facilitate end-to-end training using an open-source toy dataset.
@@ -52,7 +62,7 @@ Download a dataset zip file from [modelscope](https://www.modelscope.cn/models/i
 
 Should you wish to prepare your own datasets, we recommend consulting `scepter/modules/data/dataset/ms_dataset.py` for detailed guidance on the required data format.
 
-##### Prepare initial weight
+#### Prepare initial weight
 The ACE checkpoint has been uploaded to both ModelScope and HuggingFace platforms:
 * [ModelScope](https://www.modelscope.cn/models/iic/ACE-0.6B-512px)
 * [HuggingFace](https://huggingface.co/scepter-studio/ACE-0.6B-512px)
@@ -60,22 +70,25 @@ The ACE checkpoint has been uploaded to both ModelScope and HuggingFace platform
 In the provided training YAML configuration, we have designated the Modelscope URL as the default checkpoint URL. Should you wish to transition to Hugging Face, you can effortlessly achieve this by modifying the PRETRAINED_MODEL value within the YAML file (replace the prefix "ms://iic" to "hf://scepter-studio").
 
 
-##### Start training
+#### Start training
 
 You can easily start training procedure by executing the following command:
 ```bash
+# ACE-0.6B-512px
 PYTHONPATH=. python scepter/tools/run_train.py --cfg scepter/methods/edit/dit_ace_0.6b_512.yaml
+# ACE-0.6B-1024px
+PYTHONPATH=. python scepter/tools/run_train.py --cfg scepter/methods/edit/dit_ace_0.6b_1024.yaml
 ```
 
-#### ACE Chat Bot
+### ACE Chat Bot
 
 We have developed a chatbot interface utilizing Gradio, designed to convert user input in natural language into visually captivating images that align semantically with the specified instructions. You can easily access this functionality by launching Scepter Studio with the following command:
 ```bash
-PYTHONPATH=. python scepter/tools/webui.py --cfg scepter/methods/studio/scepter_ui.yaml --language zh
+PYTHONPATH=. python scepter/tools/webui.py --cfg scepter/methods/studio/scepter_ui.yaml --language zh --tab chatbot
 ```
 Upon starting, you will find a "ChatBot" tab within the Gradio application, which serves as a chat-based interface to handle any requests related to image editing or generation.
 
-#### ACE ComfyUI Workflow
+### ACE ComfyUI Workflow
 
 ![Workflow](https://github.com/ali-vilab/ace-page/raw/main/assets/comfyui/ace_example.jpg)
 
@@ -108,6 +121,8 @@ Upon starting, you will find a "ChatBot" tab within the Gradio application, whic
 </tbody>
 </table>
 
+## 🖼 Gallery for Recent Works
+
 ### FLUX Tuners
 
 <table><tbody>
@@ -258,18 +273,20 @@ We deploy a work studio on Modelscope that includes only the inference tab, plea
 
 ## ⚙️️ ComfyUI Workflow
 
-### Launch
+We support the use of all models in the ComfyUI Workflow through the following methods:
 
-Manually install by moving custom_nodes to ComfyUI.
+1) Automatic installation directly via the ComfyUI Manager by searching for the **ComfyUI-Scepter** node.
+2) Manually install by moving custom_nodes from Scepter to ComfyUI.
 ```shell
+git clone https://github.com/modelscope/scepter.git
 cd path/to/scepter
 pip install -e .
 cp -r path/to/scepter/workflow/ path/to/ComfyUI/custom_nodes/ComfyUI-Scepter
 cd path/to/ComfyUI
 python main.py
 ```
-In addition, we also support installation and usage through the ComfyUI Manager.
 
+**Note**: You can use the nodes by dragging the sample images into ComfyUI. Additionally, our nodes can automatically pull models from ModelScope or HuggingFace by selecting the *model_source* field, or you can place the already downloaded models in a local path.
 
 ## 🔍 Learn More
 

requirements/framework.txt

@@ -2,7 +2,7 @@ albumentations
 beautifulsoup4
 bezier
 einops
-modelscope
+modelscope[framework]
 ms-swift
 numpy
 open_clip_torch
@@ -12,6 +12,7 @@ oss2>=2.15.0
 pycocotools
 pyyaml>=5.3.1
 scikit-image
+scikit-learn
+sentencepiece
 torchsde
 transformers
-scikit-learn

requirements/recommended.txt

@@ -1,4 +1,5 @@
 git+https://github.com/cocodataset/panopticapi.git
-torch==2.0.1
-torchvision==0.15.2
-xformers==0.0.21
+torch==2.4.1
+torchvision==.19.1
+flash-attn==2.5.8
+xformers==0.0.28

requirements/scepter_studio.txt

@@ -1,5 +1,5 @@
 bitsandbytes
-gradio==4.44.1
+gradio
 gradio_imageslider
 imagehash
 psutil

scepter/methods/edit/dit_ace_0.6b_1024.yaml

@@ -0,0 +1,161 @@
+ENV:
+  BACKEND: nccl
+  SEED: 2024
+#
+SOLVER:
+  NAME: ACESolver
+  RESUME_FROM:
+  LOAD_MODEL_ONLY: True
+  USE_FSDP: False
+  SHARDING_STRATEGY:
+  USE_AMP: True
+  DTYPE: float16
+  CHANNELS_LAST: True
+  MAX_STEPS: 500
+  MAX_EPOCHS: -1
+  NUM_FOLDS: 1
+  ACCU_STEP: 1
+  EVAL_INTERVAL: 50
+  RESCALE_LR: False
+  #
+  WORK_DIR: ./cache/save_data/ace_0.6b_1024
+  LOG_FILE: std_log.txt
+  #
+  FILE_SYSTEM:
+    - NAME: "HuggingfaceFs"
+      TEMP_DIR: ./cache/cache_data
+    - NAME: "LocalFs"
+      TEMP_DIR: ./cache/cache_data
+    - NAME: "ModelscopeFs"
+      TEMP_DIR: ./cache/cache_data
+
+  #
+  MODEL:
+    NAME: LatentDiffusionACE
+    PRETRAINED_MODEL:
+    IGNORE_KEYS: [ ]
+    SCALE_FACTOR: 0.18215
+    SIZE_FACTOR: 8
+    DECODER_BIAS: 0.5
+    DEFAULT_N_PROMPT:
+    USE_EMA: True
+    EVAL_EMA: False
+    TEXT_IDENTIFIER: [ '{image}', '{image1}', '{image2}', '{image3}', '{image4}', '{image5}', '{image6}', '{image7}', '{image8}', '{image9}' ]
+    USE_TEXT_POS_EMBEDDINGS: True
+    #
+    DIFFUSION:
+      NAME: BaseDiffusion
+      PREDICTION_TYPE: eps
+      MIN_SNR_GAMMA:
+      NOISE_SCHEDULER:
+        NAME: LinearScheduler
+        NUM_TIMESTEPS: 1000
+        BETA_MIN: 0.0001
+        BETA_MAX: 0.02
+    #
+    DIFFUSION_MODEL:
+      NAME: ACE
+      PRETRAINED_MODEL: ms://iic/ACE-0.6B-1024px@models/dit/ace_0.6b_1024px.pth
+      IGNORE_KEYS: [ ]
+      PATCH_SIZE: 2
+      IN_CHANNELS: 4
+      HIDDEN_SIZE: 1152
+      DEPTH: 28
+      NUM_HEADS: 16
+      MLP_RATIO: 4.0
+      PRED_SIGMA: True
+      DROP_PATH: 0.0
+      WINDOW_DIZE: 0
+      Y_CHANNELS: 4096
+      MAX_SEQ_LEN: 4096
+      QK_NORM: True
+      USE_GRAD_CHECKPOINT: True
+      ATTENTION_BACKEND: flash_attn
+    #
+    FIRST_STAGE_MODEL:
+      NAME: AutoencoderKL
+      EMBED_DIM: 4
+      PRETRAINED_MODEL: ms://iic/ACE-0.6B-1024px@models/vae/vae.bin
+      IGNORE_KEYS: []
+      #
+      ENCODER:
+        NAME: Encoder
+        CH: 128
+        OUT_CH: 3
+        NUM_RES_BLOCKS: 2
+        IN_CHANNELS: 3
+        ATTN_RESOLUTIONS: [ ]
+        CH_MULT: [ 1, 2, 4, 4 ]
+        Z_CHANNELS: 4
+        DOUBLE_Z: True
+        DROPOUT: 0.0
+        RESAMP_WITH_CONV: True
+      #
+      DECODER:
+        NAME: Decoder
+        CH: 128
+        OUT_CH: 3
+        NUM_RES_BLOCKS: 2
+        IN_CHANNELS: 3
+        ATTN_RESOLUTIONS: [ ]
+        CH_MULT: [ 1, 2, 4, 4 ]
+        Z_CHANNELS: 4
+        DROPOUT: 0.0
+        RESAMP_WITH_CONV: True
+        GIVE_PRE_END: False
+        TANH_OUT: False
+    #
+    COND_STAGE_MODEL:
+      NAME: T5EmbedderHF
+      PRETRAINED_MODEL: ms://iic/ACE-0.6B-1024px@models/text_encoder/t5-v1_1-xxl/
+      TOKENIZER_PATH: ms://iic/ACE-0.6B-1024px@models/tokenizer/t5-v1_1-xxl
+      LENGTH: 120
+      T5_DTYPE: bfloat16
+      ADDED_IDENTIFIER: [ '{image}', '{caption}', '{mask}', '{ref_image}', '{image1}', '{image2}', '{image3}', '{image4}', '{image5}', '{image6}', '{image7}', '{image8}', '{image9}' ]
+      CLEAN: whitespace
+      USE_GRAD: False
+    LOSS:
+      NAME: ReconstructLoss
+      LOSS_TYPE: l2
+  #
+  SAMPLE_ARGS:
+    SAMPLER: ddim
+    SAMPLE_STEPS: 20
+    GUIDE_SCALE: 4.5
+    GUIDE_RESCALE: 0.5
+  #
+  OPTIMIZER:
+    NAME: AdamW
+    LEARNING_RATE: 1e-7
+    EPS: 1e-10
+    WEIGHT_DECAY: 5e-4
+  #
+  TRAIN_DATA:
+    NAME: ImageTextPairMSDatasetForACE
+    MODE: train
+    MS_DATASET_NAME: cache/datasets/hed_pair
+    MS_DATASET_NAMESPACE: ""
+    MS_DATASET_SPLIT: "train"
+    MS_DATASET_SUBNAME: ""
+    PROMPT_PREFIX: ""
+    REPLACE_STYLE: False
+    MAX_SEQ_LEN: 4096
+    PIN_MEMORY: True
+    BATCH_SIZE: 1
+    NUM_WORKERS: 1
+    SAMPLER:
+      NAME: LoopSampler
+  #
+  TRAIN_HOOKS:
+    -
+      NAME: BackwardHook
+      PRIORITY: 0
+    -
+      NAME: LogHook
+      LOG_INTERVAL: 50
+    -
+      NAME: CheckpointHook
+      INTERVAL: 100
+    -
+      NAME: ProbeDataHook
+      PROB_INTERVAL: 100

scepter/methods/examples/generation/dit_cogvideox_2b_lora.yaml

@@ -0,0 +1,235 @@
+ENV:
+  BACKEND: nccl
+  SEED: 42
+  TENSOR_PARALLEL_SIZE: 1
+  PIPELINE_PARALLEL_SIZE: 1
+  SYS_ENVS:
+    TORCH_CUDNN_V8_API_ENABLED: '1'
+    TOKENIZERS_PARALLELISM: 'false'
+    TF_CPP_MIN_LOG_LEVEL: '3'
+    PYTORCH_CUDA_ALLOC_CONF: 'expandable_segments:True'
+#
+SOLVER:
+  NAME: LatentDiffusionVideoSolver
+  MAX_STEPS: 2000
+  USE_AMP: True
+  DTYPE: bfloat16
+  USE_FAIRSCALE: False
+  USE_FSDP: True
+  LOAD_MODEL_ONLY: False
+  RESUME_FROM:
+  WORK_DIR: ./cache/save_data/dit_cogvideox_2b_lora
+  LOG_FILE: std_log.txt
+  EVAL_INTERVAL: 100
+  LOG_TRAIN_NUM: 4
+  ENABLE_GRADSCALER: False
+  USE_SCALER: False
+  FPS: 8
+  SHARDING_STRATEGY: full_shard
+  FSDP_REDUCE_DTYPE: float32
+  FSDP_BUFFER_DTYPE: float32
+  FSDP_SHARD_MODULES: [ 'model', 'cond_stage_model.model']
+  SAVE_MODULES: [ 'model', 'cond_stage_model.model']
+  TRAIN_MODULES: ['model']
+  #
+  FILE_SYSTEM:
+    NAME: "ModelscopeFs"
+    TEMP_DIR: "./cache/cache_data"
+  #
+  TUNER:
+    - NAME: SwiftLoRA
+      R: 64
+      LORA_ALPHA: 64
+      LORA_DROPOUT: 0.0
+      BIAS: "none"
+      TARGET_MODULES: "model.*(.to_k|.to_q|.to_v|.to_out.0)$"
+  #
+  MODEL:
+    NAME: LatentDiffusionCogVideoX
+    PRETRAINED_MODEL:
+    PARAMETERIZATION: v
+    TIMESTEPS: 1000
+    MIN_SNR_GAMMA: 3.0
+    ZERO_TERMINAL_SNR: True
+    SCALE_FACTOR_SPATIAL: 8
+    SCALE_FACTOR_TEMPORAL: 4
+    SCALING_FACTOR_IMAGE: 1.15258426
+    IGNORE_KEYS: [ ]
+    DEFAULT_N_PROMPT:
+    USE_EMA: False
+    EVAL_EMA: False
+    DIFFUSION:
+      NAME: BaseDiffusion
+      PREDICTION_TYPE: v
+      NOISE_SCHEDULER:
+        NAME: ScaledLinearScheduler
+        BETA_MIN: 0.00085
+        BETA_MAX: 0.012
+        SNR_SHIFT_SCALE: 3.0
+        RESCALE_BETAS_ZERO_SNR: True
+      DIFFUSION_SAMPLERS:
+        NAME: DDIMSampler
+        DISCRETIZATION_TYPE: trailing
+        ETA: 0.0
+    #
+    DIFFUSION_MODEL:
+      NAME: CogVideoXTransformer3DModel
+      DTYPE: bfloat16
+      PRETRAINED_MODEL: ms://AI-ModelScope/CogVideoX-2b@transformer/diffusion_pytorch_model.safetensors
+      NUM_ATTENTION_HEADS: 30
+      ATTENTION_HEAD_DIM: 64
+      IN_CHANNELS: 16
+      OUT_CHANNELS: 16
+      FLIP_SIN_TO_COS: True
+      FREQ_SHIFT: 0
+      TIME_EMBED_DIM: 512
+      TEXT_EMBED_DIM: 4096
+      NUM_LAYERS: 30
+      DROPOUT: 0.0
+      ATTENTION_BIAS: True
+      SAMPLE_WIDTH: 90
+      SAMPLE_HEIGHT: 60
+      SAMPLE_FRAMES: 49
+      PATCH_SIZE: 2
+      TEMPORAL_COMPRESSION_RATIO: 4
+      MAX_TEXT_SEQ_LENGTH: 226
+      ACTIVATION_FN: "gelu-approximate"
+      TIMESTEP_ACTIVATION_FN: "silu"
+      NORM_ELEMENTWISE_AFFINE: True
+      NORM_EPS: 1e-5
+      SPATIAL_INTERPOLATION_SCALE: 1.875
+      TEMPORAL_INTERPOLATION_SCALE: 1.0
+      USE_ROTARY_POSITIONAL_EMBEDDINGS: False
+      USE_LEARNED_POSITIONAL_EMBEDDINGS: False
+      GRADIENT_CHECKPOINTING: False
+    #
+    FIRST_STAGE_MODEL:
+      NAME: AutoencoderKLCogVideoX
+      DTYPE: bfloat16
+      PRETRAINED_MODEL: ms://AI-ModelScope/CogVideoX-2b@vae/diffusion_pytorch_model.safetensors
+      SAMPLE_HEIGHT: 480
+      SAMPLE_WIDTH: 720
+      USE_QUANT_CONV: False
+      USE_POST_QUANT_CONV: False
+      USE_SLICING: True
+      USE_TILING: True
+      GRADIENT_CHECKPOINTING: False
+      ENCODER:
+        NAME: CogVideoXEncoder3D
+        IN_CHANNELS: 3
+        OUT_CHANNELS: 16
+        UP_BLOCK_TYPES: [ "CogVideoXDownBlock3D", "CogVideoXDownBlock3D", "CogVideoXDownBlock3D", "CogVideoXDownBlock3D" ]
+        BLOCK_OUT_CHANNELS: [ 128, 256, 256, 512 ]
+        LAYERS_PER_BLOCK: 3
+        ACT_FN: "silu"
+        NORM_EPS: 1e-6
+        NORM_NUM_GROUPS: 32
+        DROPOUT: 0.0
+        PAD_MODE: "first"
+        TEMPORAL_COMPRESSION_RATIO: 4
+        GRADIENT_CHECKPOINTING: False
+      DECODER:
+        NAME: CogVideoXDecoder3D
+        IN_CHANNELS: 16
+        OUT_CHANNELS: 3
+        UP_BLOCK_TYPES: [ "CogVideoXUpBlock3D", "CogVideoXUpBlock3D", "CogVideoXUpBlock3D", "CogVideoXUpBlock3D" ]
+        BLOCK_OUT_CHANNELS: [ 128, 256, 256, 512 ]
+        LAYERS_PER_BLOCK: 3
+        ACT_FN: "silu"
+        NORM_EPS: 1e-6
+        NORM_NUM_GROUPS: 32
+        DROPOUT: 0.0
+        PAD_MODE: "first"
+        TEMPORAL_COMPRESSION_RATIO: 4
+        GRADIENT_CHECKPOINTING: False
+    #
+    COND_STAGE_MODEL:
+      NAME: T5EmbedderHF
+      PRETRAINED_MODEL: ms://AI-ModelScope/t5-v1_1-xxl
+      TOKENIZER_PATH: ms://AI-ModelScope/t5-v1_1-xxl
+      LENGTH: 226
+      CLEAN:
+      USE_GRAD: False
+    #
+    LOSS:
+      NAME: ReconstructLoss
+      LOSS_TYPE: l2
+  #
+  SAMPLE_ARGS:
+    SAMPLER: ddim
+    SAMPLE_STEPS: 50
+    SEED: 42
+    GUIDE_SCALE: 6.0
+    GUIDE_RESCALE: 0.0
+    NUM_FRAMES: 49
+  #
+  OPTIMIZER:
+    NAME: Adam
+    LEARNING_RATE: 1e-3
+    BETAS: [ 0.9, 0.95 ]
+    EPS: 1e-8
+    WEIGHT_DECAY: 0.0
+    AMSGRAD: False
+  #
+#  LR_SCHEDULER:
+#    NAME: StepAnnealingLR
+#    WARMUP_STEPS: 200
+#    TOTAL_STEPS: 2000
+#    DECAY_MODE: 'cosine'
+  #
+  TRAIN_DATA:
+    NAME: VideoGenDataset
+    MODE: train
+    PIN_MEMORY: True
+    BATCH_SIZE: 1
+    NUM_WORKERS: 4
+    PROMPT_PREFIX: 'DISNEY '
+    SAMPLER:
+      NAME: MixtureOfSamplers
+      SUB_SAMPLERS:
+        - NAME: MultiLevelBatchSampler
+          PROB: 1.0
+          FIELDS: [ "video_path", "prompt" ]
+          DELIMITER: '#;#'
+          PATH_PREFIX: cache/datasets/Disney-VideoGeneration-Dataset/
+          INDEX_FILE: cache/datasets/Disney-VideoGeneration-Dataset/index.jsonl
+    TRANSFORMS:
+      - NAME: Select
+        KEYS: [ 'video', "prompt" ]
+        META_KEYS: [ ]
+  #
+  EVAL_DATA:
+    NAME: Text2ImageDataset
+    MODE: eval
+    PROMPT_FILE:
+    PROMPT_DATA: [ "A girl riding a bike.", "A panda, dressed in a small, red jacket and a tiny hat, sits on a wooden stool in a serene bamboo forest. The panda's fluffy paws strum a miniature acoustic guitar, producing soft, melodic tunes. Nearby, a few other pandas gather, watching curiously and some clapping in rhythm. Sunlight filters through the tall bamboo, casting a gentle glow on the scene. The panda's face is expressive, showing concentration and joy as it plays. The background includes a small, flowing stream and vibrant green foliage, enhancing the peaceful and magical atmosphere of this unique musical performance." ]
+    IMAGE_SIZE: [ 480, 720 ]
+    FIELDS: [ "prompt" ]
+    DELIMITER: '#;#'
+    PROMPT_PREFIX: 'DISNEY '
+    PIN_MEMORY: True
+    BATCH_SIZE: 1
+    USE_NUM: 8
+    NUM_WORKERS: 4
+    TRANSFORMS:
+      - NAME: Select
+        KEYS: [ 'index', 'prompt' ]
+        META_KEYS: [ 'image_size' ]
+  #
+  TRAIN_HOOKS:
+    - NAME: ProbeDataHook
+      PROB_INTERVAL: 100
+      PRIORITY: 0
+    - NAME: BackwardHook
+      PRIORITY: 10
+    - NAME: LogHook
+      LOG_INTERVAL: 10
+      PRIORITY: 20
+    - NAME: CheckpointHook
+      INTERVAL: 1000
+      PRIORITY: 40
+  #
+  EVAL_HOOKS:
+    - NAME: ProbeDataHook
+      PROB_INTERVAL: 100
+      PRIORITY: 0

scepter/methods/examples/generation/dit_cogvideox_5b_i2v_lora.yaml

@@ -0,0 +1,266 @@
+ENV:
+  BACKEND: nccl
+  SEED: 42
+  TENSOR_PARALLEL_SIZE: 1
+  PIPELINE_PARALLEL_SIZE: 1
+  SYS_ENVS:
+    TORCH_CUDNN_V8_API_ENABLED: '1'
+    TOKENIZERS_PARALLELISM: 'false'
+    TF_CPP_MIN_LOG_LEVEL: '3'
+    PYTORCH_CUDA_ALLOC_CONF: 'expandable_segments:True'
+#
+SOLVER:
+  NAME: LatentDiffusionVideoSolver
+  MAX_STEPS: 2000
+  USE_AMP: True
+  DTYPE: bfloat16
+  USE_FAIRSCALE: False
+  USE_FSDP: True
+  LOAD_MODEL_ONLY: False
+  ENABLE_GRADSCALER: False
+  USE_SCALER: False
+  RESUME_FROM:
+  WORK_DIR: ./cache/save_data/dit_cogvideox_5b_i2v_lora
+  LOG_FILE: std_log.txt
+  EVAL_INTERVAL: 100
+  LOG_TRAIN_NUM: 4
+  FPS: 8
+  SHARDING_STRATEGY: full_shard
+  FSDP_REDUCE_DTYPE: float32
+  FSDP_BUFFER_DTYPE: float32
+  FSDP_SHARD_MODULES: [ 'model', 'cond_stage_model.model']
+  SAVE_MODULES: [ 'model', 'cond_stage_model.model']
+  TRAIN_MODULES: ['model']
+  #
+  FILE_SYSTEM:
+    NAME: "ModelscopeFs"
+    TEMP_DIR: "./cache/cache_data"
+  #
+  TUNER:
+    - NAME: SwiftLoRA
+      R: 64
+      LORA_ALPHA: 64
+      LORA_DROPOUT: 0.0
+      BIAS: "none"
+      TARGET_MODULES: "model.*(.to_k|.to_q|.to_v|.to_out.0)$"
+  #
+  MODEL:
+    NAME: LatentDiffusionCogVideoX
+    PRETRAINED_MODEL:
+    PARAMETERIZATION: v
+    TIMESTEPS: 1000
+    MIN_SNR_GAMMA: 3.0
+    ZERO_TERMINAL_SNR: True
+    SCALE_FACTOR_SPATIAL: 8
+    SCALE_FACTOR_TEMPORAL: 4
+    SCALING_FACTOR_IMAGE: 0.7  # 5b diff
+    NOISED_IMAGE_DROPOUT: 0.05
+    IGNORE_KEYS: [ ]
+    DEFAULT_N_PROMPT:
+    USE_EMA: False
+    EVAL_EMA: False
+    DIFFUSION:
+      NAME: BaseDiffusion
+      PREDICTION_TYPE: v
+      NOISE_SCHEDULER:
+        NAME: ScaledLinearScheduler
+        BETA_MIN: 0.00085
+        BETA_MAX: 0.012
+        SNR_SHIFT_SCALE: 1.0  # 5b diff
+        RESCALE_BETAS_ZERO_SNR: True
+      DIFFUSION_SAMPLERS:
+        NAME: DDIMSampler
+        DISCRETIZATION_TYPE: trailing
+        ETA: 0.0
+    #
+    DIFFUSION_MODEL:
+      NAME: CogVideoXTransformer3DModel
+      DTYPE: bfloat16
+      PRETRAINED_MODEL: # 5b-I2V diff
+        - ms://AI-ModelScope/CogVideoX-5b-I2V@transformer/diffusion_pytorch_model-00001-of-00003.safetensors
+        - ms://AI-ModelScope/CogVideoX-5b-I2V@transformer/diffusion_pytorch_model-00002-of-00003.safetensors
+        - ms://AI-ModelScope/CogVideoX-5b-I2V@transformer/diffusion_pytorch_model-00003-of-00003.safetensors
+      NUM_ATTENTION_HEADS: 48  # 5b diff
+      ATTENTION_HEAD_DIM: 64
+      IN_CHANNELS: 32  # 5b-I2V diff
+      LATENT_CHANNELS: 16
+      OUT_CHANNELS: 16
+      FLIP_SIN_TO_COS: True
+      FREQ_SHIFT: 0
+      TIME_EMBED_DIM: 512
+      TEXT_EMBED_DIM: 4096
+      NUM_LAYERS: 42  # 5b diff
+      DROPOUT: 0.0
+      ATTENTION_BIAS: True
+      SAMPLE_WIDTH: 90
+      SAMPLE_HEIGHT: 60
+      SAMPLE_FRAMES: 49
+      PATCH_SIZE: 2
+      TEMPORAL_COMPRESSION_RATIO: 4
+      MAX_TEXT_SEQ_LENGTH: 226
+      ACTIVATION_FN: "gelu-approximate"
+      TIMESTEP_ACTIVATION_FN: "silu"
+      NORM_ELEMENTWISE_AFFINE: True
+      NORM_EPS: 1e-5
+      SPATIAL_INTERPOLATION_SCALE: 1.875
+      TEMPORAL_INTERPOLATION_SCALE: 1.0
+      USE_ROTARY_POSITIONAL_EMBEDDINGS: True # 5b diff
+      USE_LEARNED_POSITIONAL_EMBEDDINGS: True  # 5b-I2V diff
+      GRADIENT_CHECKPOINTING: True
+    #
+    FIRST_STAGE_MODEL:
+      NAME: AutoencoderKLCogVideoX
+      DTYPE: bfloat16
+      PRETRAINED_MODEL: ms://AI-ModelScope/CogVideoX-5b-I2V@vae/diffusion_pytorch_model.safetensors # 5b diff
+      SAMPLE_HEIGHT: 480
+      SAMPLE_WIDTH: 720
+      USE_QUANT_CONV: False
+      USE_POST_QUANT_CONV: False
+      USE_SLICING: True
+      USE_TILING: True
+      GRADIENT_CHECKPOINTING: True
+      ENCODER:
+        NAME: CogVideoXEncoder3D
+        IN_CHANNELS: 3
+        OUT_CHANNELS: 16
+        UP_BLOCK_TYPES: [ "CogVideoXDownBlock3D", "CogVideoXDownBlock3D", "CogVideoXDownBlock3D", "CogVideoXDownBlock3D" ]
+        BLOCK_OUT_CHANNELS: [ 128, 256, 256, 512 ]
+        LAYERS_PER_BLOCK: 3
+        ACT_FN: "silu"
+        NORM_EPS: 1e-6
+        NORM_NUM_GROUPS: 32
+        DROPOUT: 0.0
+        PAD_MODE: "first"
+        TEMPORAL_COMPRESSION_RATIO: 4
+        GRADIENT_CHECKPOINTING: True
+      DECODER:
+        NAME: CogVideoXDecoder3D
+        IN_CHANNELS: 16
+        OUT_CHANNELS: 3
+        UP_BLOCK_TYPES: [ "CogVideoXUpBlock3D", "CogVideoXUpBlock3D", "CogVideoXUpBlock3D", "CogVideoXUpBlock3D" ]
+        BLOCK_OUT_CHANNELS: [ 128, 256, 256, 512 ]
+        LAYERS_PER_BLOCK: 3
+        ACT_FN: "silu"
+        NORM_EPS: 1e-6
+        NORM_NUM_GROUPS: 32
+        DROPOUT: 0.0
+        PAD_MODE: "first"
+        TEMPORAL_COMPRESSION_RATIO: 4
+        GRADIENT_CHECKPOINTING: True
+    #
+    COND_STAGE_MODEL:
+      NAME: T5EmbedderHF
+      PRETRAINED_MODEL: ms://AI-ModelScope/t5-v1_1-xxl
+      TOKENIZER_PATH: ms://AI-ModelScope/t5-v1_1-xxl
+      LENGTH: 226
+      CLEAN:
+      USE_GRAD: False
+    #
+    LOSS:
+      NAME: ReconstructLoss
+      LOSS_TYPE: l2
+  #
+  SAMPLE_ARGS:
+    SAMPLER: ddim
+    SAMPLE_STEPS: 50
+    SEED: 42
+    GUIDE_SCALE: 6.0
+    GUIDE_RESCALE: 0.0
+    NUM_FRAMES: 49
+  #
+  OPTIMIZER:
+    NAME: Adam
+    LEARNING_RATE: 1e-3
+    BETAS: [ 0.9, 0.95 ]
+    EPS: 1e-8
+    WEIGHT_DECAY: 0.0
+    AMSGRAD: False
+  #
+#  LR_SCHEDULER:
+#    NAME: StepAnnealingLR
+#    WARMUP_STEPS: 200
+#    TOTAL_STEPS: 2000
+#    DECAY_MODE: 'cosine'
+  #
+  TRAIN_DATA:
+    NAME: VideoGenDataset
+    MODE: train
+    PIN_MEMORY: True
+    BATCH_SIZE: 1
+    NUM_WORKERS: 0
+    PROMPT_PREFIX: 'DISNEY '
+    DATA_TYPE: 'i2v'
+    SAMPLER:
+      NAME: MixtureOfSamplers
+      SUB_SAMPLERS:
+        - NAME: MultiLevelBatchSampler
+          PROB: 1.0
+          FIELDS: [ "video_path", "prompt" ]
+          DELIMITER: '#;#'
+          PATH_PREFIX: cache/datasets/Disney-VideoGeneration-Dataset/
+          INDEX_FILE: cache/datasets/Disney-VideoGeneration-Dataset/index.jsonl
+    TRANSFORMS:
+      - NAME: Select
+        KEYS: [ "video", "image", "prompt" ]
+        META_KEYS: [ ]
+  #
+#  EVAL_DATA:
+#    NAME: Text2ImageDataset
+#    MODE: eval
+#    PROMPT_FILE:
+#    PROMPT_DATA: [ "A cat running.#;#asset/images/edit_tuner/cat_512.jpg" ]
+#    FIELDS: [ "prompt", "img_path" ]
+#    DELIMITER: '#;#'
+#    PROMPT_PREFIX: ''
+#    PIN_MEMORY: True
+#    BATCH_SIZE: 1
+#    USE_NUM: 8
+#    NUM_WORKERS: 0
+#    IMAGE_SIZE: [ 480, 720 ]
+#    TRANSFORMS:
+#      - NAME: LoadImageFromFileList
+#        FILE_KEYS: [ 'img_path' ]
+#        RGB_ORDER: RGB
+#        BACKEND: pillow
+#      - NAME: FlexibleResize
+#        INTERPOLATION: bilinear
+#        SIZE: [ 480, 720 ]
+#        INPUT_KEY: [ 'img' ]
+#        OUTPUT_KEY: [ 'img' ]
+#        BACKEND: pillow
+#      - NAME: FlexibleCenterCrop
+#        SIZE: [ 480, 720 ]
+#        INPUT_KEY: [ 'img' ]
+#        OUTPUT_KEY: [ 'img' ]
+#        BACKEND: pillow
+#      - NAME: ImageToTensor
+#        INPUT_KEY: [ 'img' ]
+#        OUTPUT_KEY: [ 'img' ]
+#        BACKEND: pillow
+#      - NAME: Normalize
+#        MEAN: [ 0.5,  0.5,  0.5 ]
+#        STD: [ 0.5,  0.5,  0.5 ]
+#        INPUT_KEY: [ 'img' ]
+#        OUTPUT_KEY: [ 'image' ]
+#        BACKEND: torchvision
+#      - NAME: Select
+#        KEYS: [ 'image', 'prompt' ]
+#        META_KEYS: [ 'image_size' ]
+  #
+  TRAIN_HOOKS:
+    - NAME: ProbeDataHook
+      PROB_INTERVAL: 100
+      PRIORITY: 0
+    - NAME: BackwardHook
+      PRIORITY: 10
+    - NAME: LogHook
+      LOG_INTERVAL: 10
+      PRIORITY: 20
+    - NAME: CheckpointHook
+      INTERVAL: 1000
+      PRIORITY: 40
+  #
+#  EVAL_HOOKS:
+#    - NAME: ProbeDataHook
+#      PROB_INTERVAL: 100
+#      PRIORITY: 0

scepter/methods/examples/generation/dit_cogvideox_5b_lora.yaml

@@ -0,0 +1,273 @@
+ENV:
+  BACKEND: nccl
+  SEED: 42
+  TENSOR_PARALLEL_SIZE: 1
+  PIPELINE_PARALLEL_SIZE: 1
+  SYS_ENVS:
+    TORCH_CUDNN_V8_API_ENABLED: '1'
+    TOKENIZERS_PARALLELISM: 'false'
+    TF_CPP_MIN_LOG_LEVEL: '3'
+    PYTORCH_CUDA_ALLOC_CONF: 'expandable_segments:True'
+#
+SOLVER:
+  NAME: LatentDiffusionVideoSolver
+  MAX_STEPS: 2000
+  USE_AMP: True
+  DTYPE: bfloat16
+  USE_FAIRSCALE: False
+  USE_FSDP: True
+  LOAD_MODEL_ONLY: False
+  ENABLE_GRADSCALER: False
+  USE_SCALER: False
+  RESUME_FROM:
+  WORK_DIR: ./cache/save_data/dit_cogvideox_5b_lora
+  LOG_FILE: std_log.txt
+  EVAL_INTERVAL: 100
+  LOG_TRAIN_NUM: 4
+  FPS: 8
+  SHARDING_STRATEGY: full_shard
+  FSDP_REDUCE_DTYPE: float32
+  FSDP_BUFFER_DTYPE: float32
+  FSDP_SHARD_MODULES: [ 'model', 'cond_stage_model.model']
+  SAVE_MODULES: [ 'model', 'cond_stage_model.model']
+  TRAIN_MODULES: ['model']
+  #
+  FILE_SYSTEM:
+    NAME: "ModelscopeFs"
+    TEMP_DIR: "./cache/cache_data"
+  #
+  TUNER:
+    - NAME: SwiftLoRA
+      R: 64
+      LORA_ALPHA: 64
+      LORA_DROPOUT: 0.0
+      BIAS: "none"
+      TARGET_MODULES: "model.*(.to_k|.to_q|.to_v|.to_out.0)$"
+  #
+  MODEL:
+    NAME: LatentDiffusionCogVideoX
+    PRETRAINED_MODEL:
+    PARAMETERIZATION: v
+    TIMESTEPS: 1000
+    MIN_SNR_GAMMA: 3.0
+    ZERO_TERMINAL_SNR: True
+    SCALE_FACTOR_SPATIAL: 8
+    SCALE_FACTOR_TEMPORAL: 4
+    SCALING_FACTOR_IMAGE: 0.7  # 5b diff
+    IGNORE_KEYS: [ ]
+    DEFAULT_N_PROMPT:
+    USE_EMA: False
+    EVAL_EMA: False
+    DIFFUSION:
+      NAME: BaseDiffusion
+      PREDICTION_TYPE: v
+      NOISE_SCHEDULER:
+        NAME: ScaledLinearScheduler
+        BETA_MIN: 0.00085
+        BETA_MAX: 0.012
+        SNR_SHIFT_SCALE: 1.0  # 5b diff
+        RESCALE_BETAS_ZERO_SNR: True
+      DIFFUSION_SAMPLERS:
+        NAME: DDIMSampler
+        DISCRETIZATION_TYPE: trailing
+        ETA: 0.0
+    #
+    DIFFUSION_MODEL:
+      NAME: CogVideoXTransformer3DModel
+      DTYPE: bfloat16
+      PRETRAINED_MODEL: # 5b diff
+        - ms://AI-ModelScope/CogVideoX-5b@transformer/diffusion_pytorch_model-00001-of-00002.safetensors
+        - ms://AI-ModelScope/CogVideoX-5b@transformer/diffusion_pytorch_model-00002-of-00002.safetensors
+      NUM_ATTENTION_HEADS: 48  # 5b diff
+      ATTENTION_HEAD_DIM: 64
+      IN_CHANNELS: 16
+      OUT_CHANNELS: 16
+      FLIP_SIN_TO_COS: True
+      FREQ_SHIFT: 0
+      TIME_EMBED_DIM: 512
+      TEXT_EMBED_DIM: 4096
+      NUM_LAYERS: 42  # 5b diff
+      DROPOUT: 0.0
+      ATTENTION_BIAS: True
+      SAMPLE_WIDTH: 90
+      SAMPLE_HEIGHT: 60
+      SAMPLE_FRAMES: 49
+      PATCH_SIZE: 2
+      TEMPORAL_COMPRESSION_RATIO: 4
+      MAX_TEXT_SEQ_LENGTH: 226
+      ACTIVATION_FN: "gelu-approximate"
+      TIMESTEP_ACTIVATION_FN: "silu"
+      NORM_ELEMENTWISE_AFFINE: True
+      NORM_EPS: 1e-5
+      SPATIAL_INTERPOLATION_SCALE: 1.875
+      TEMPORAL_INTERPOLATION_SCALE: 1.0
+      USE_ROTARY_POSITIONAL_EMBEDDINGS: True # 5b diff
+      USE_LEARNED_POSITIONAL_EMBEDDINGS: False
+      GRADIENT_CHECKPOINTING: True
+    #
+    FIRST_STAGE_MODEL:
+      NAME: AutoencoderKLCogVideoX
+      DTYPE: bfloat16
+      PRETRAINED_MODEL: ms://AI-ModelScope/CogVideoX-5b@vae/diffusion_pytorch_model.safetensors # 5b diff
+      SAMPLE_HEIGHT: 480
+      SAMPLE_WIDTH: 720
+      USE_QUANT_CONV: False
+      USE_POST_QUANT_CONV: False
+      USE_SLICING: True
+      USE_TILING: True
+      GRADIENT_CHECKPOINTING: True
+      ENCODER:
+        NAME: CogVideoXEncoder3D
+        IN_CHANNELS: 3
+        OUT_CHANNELS: 16
+        UP_BLOCK_TYPES: [ "CogVideoXDownBlock3D", "CogVideoXDownBlock3D", "CogVideoXDownBlock3D", "CogVideoXDownBlock3D" ]
+        BLOCK_OUT_CHANNELS: [ 128, 256, 256, 512 ]
+        LAYERS_PER_BLOCK: 3
+        ACT_FN: "silu"
+        NORM_EPS: 1e-6
+        NORM_NUM_GROUPS: 32
+        DROPOUT: 0.0
+        PAD_MODE: "first"
+        TEMPORAL_COMPRESSION_RATIO: 4
+        GRADIENT_CHECKPOINTING: True
+      DECODER:
+        NAME: CogVideoXDecoder3D
+        IN_CHANNELS: 16
+        OUT_CHANNELS: 3
+        UP_BLOCK_TYPES: [ "CogVideoXUpBlock3D", "CogVideoXUpBlock3D", "CogVideoXUpBlock3D", "CogVideoXUpBlock3D" ]
+        BLOCK_OUT_CHANNELS: [ 128, 256, 256, 512 ]
+        LAYERS_PER_BLOCK: 3
+        ACT_FN: "silu"
+        NORM_EPS: 1e-6
+        NORM_NUM_GROUPS: 32
+        DROPOUT: 0.0
+        PAD_MODE: "first"
+        TEMPORAL_COMPRESSION_RATIO: 4
+        GRADIENT_CHECKPOINTING: True
+    #
+    COND_STAGE_MODEL:
+      NAME: T5EmbedderHF
+      PRETRAINED_MODEL: ms://AI-ModelScope/t5-v1_1-xxl
+      TOKENIZER_PATH: ms://AI-ModelScope/t5-v1_1-xxl
+      LENGTH: 226
+      CLEAN:
+      USE_GRAD: False
+    #
+    LOSS:
+      NAME: ReconstructLoss
+      LOSS_TYPE: l2
+  #
+  SAMPLE_ARGS:
+    SAMPLER: ddim
+    SAMPLE_STEPS: 50
+    SEED: 42
+    GUIDE_SCALE: 6.0
+    GUIDE_RESCALE: 0.0
+    NUM_FRAMES: 49
+  #
+  OPTIMIZER:
+    NAME: Adam
+    LEARNING_RATE: 1e-3
+    BETAS: [ 0.9, 0.95 ]
+    EPS: 1e-8
+    WEIGHT_DECAY: 0.0
+    AMSGRAD: False
+  #
+#  LR_SCHEDULER:
+#    NAME: StepAnnealingLR
+#    WARMUP_STEPS: 200
+#    TOTAL_STEPS: 2000
+#    DECAY_MODE: 'cosine'
+  #
+  TRAIN_DATA:
+    NAME: VideoGenDatasetOTF
+    MODE: train
+    PIN_MEMORY: True
+    BATCH_SIZE: 1
+    NUM_WORKERS: 4
+    PROMPT_PREFIX: 'DISNEY '
+    DELIMITER: '#;#'
+    FIELDS: [ 'video_path', 'prompt' ]
+    PATH_PREFIX: cache/datasets/Disney-VideoGeneration-Dataset/
+    DATA_FILE: cache/datasets/Disney-VideoGeneration-Dataset/index.jsonl
+    SAMPLER:
+      NAME: LoopSampler
+    TRANSFORMS:
+      - NAME: Select
+        KEYS: [ 'video', 'video_latent', "prompt" ]
+        META_KEYS: [ ]
+    MODEL:
+      NAME: AutoencoderKLCogVideoX
+      DTYPE: bfloat16
+      PRETRAINED_MODEL: ms://AI-ModelScope/CogVideoX-5b@vae/diffusion_pytorch_model.safetensors
+      SAMPLE_HEIGHT: 480
+      SAMPLE_WIDTH: 720
+      USE_QUANT_CONV: False
+      USE_POST_QUANT_CONV: False
+      USE_SLICING: True
+      USE_TILING: True
+      GRADIENT_CHECKPOINTING: True
+      ENCODER:
+        NAME: CogVideoXEncoder3D
+        IN_CHANNELS: 3
+        OUT_CHANNELS: 16
+        UP_BLOCK_TYPES: [ "CogVideoXDownBlock3D", "CogVideoXDownBlock3D", "CogVideoXDownBlock3D", "CogVideoXDownBlock3D" ]
+        BLOCK_OUT_CHANNELS: [ 128, 256, 256, 512 ]
+        LAYERS_PER_BLOCK: 3
+        ACT_FN: "silu"
+        NORM_EPS: 1e-6
+        NORM_NUM_GROUPS: 32
+        DROPOUT: 0.0
+        PAD_MODE: "first"
+        TEMPORAL_COMPRESSION_RATIO: 4
+        GRADIENT_CHECKPOINTING: True
+      DECODER:
+        NAME: CogVideoXDecoder3D
+        IN_CHANNELS: 16
+        OUT_CHANNELS: 3
+        UP_BLOCK_TYPES: [ "CogVideoXUpBlock3D", "CogVideoXUpBlock3D", "CogVideoXUpBlock3D", "CogVideoXUpBlock3D" ]
+        BLOCK_OUT_CHANNELS: [ 128, 256, 256, 512 ]
+        LAYERS_PER_BLOCK: 3
+        ACT_FN: "silu"
+        NORM_EPS: 1e-6
+        NORM_NUM_GROUPS: 32
+        DROPOUT: 0.0
+        PAD_MODE: "first"
+        TEMPORAL_COMPRESSION_RATIO: 4
+        GRADIENT_CHECKPOINTING: True
+  #
+  EVAL_DATA:
+    NAME: Text2ImageDataset
+    MODE: eval
+    PROMPT_FILE:
+    PROMPT_DATA: [ "A girl riding a bike.", "A panda, dressed in a small, red jacket and a tiny hat, sits on a wooden stool in a serene bamboo forest. The panda's fluffy paws strum a miniature acoustic guitar, producing soft, melodic tunes. Nearby, a few other pandas gather, watching curiously and some clapping in rhythm. Sunlight filters through the tall bamboo, casting a gentle glow on the scene. The panda's face is expressive, showing concentration and joy as it plays. The background includes a small, flowing stream and vibrant green foliage, enhancing the peaceful and magical atmosphere of this unique musical performance." ]
+    IMAGE_SIZE: [ 480, 720 ]
+    FIELDS: [ "prompt" ]
+    DELIMITER: '#;#'
+    PROMPT_PREFIX: 'DISNEY '
+    PIN_MEMORY: True
+    BATCH_SIZE: 1
+    USE_NUM: 8
+    NUM_WORKERS: 4
+    TRANSFORMS:
+      - NAME: Select
+        KEYS: [ 'index', 'prompt' ]
+        META_KEYS: [ 'image_size' ]
+  #
+  TRAIN_HOOKS:
+    - NAME: ProbeDataHook
+      PROB_INTERVAL: 100
+      PRIORITY: 0
+    - NAME: BackwardHook
+      PRIORITY: 10
+    - NAME: LogHook
+      LOG_INTERVAL: 10
+      PRIORITY: 20
+    - NAME: CheckpointHook
+      INTERVAL: 1000
+      PRIORITY: 40
+  #
+  EVAL_HOOKS:
+    - NAME: ProbeDataHook
+      PROB_INTERVAL: 100
+      PRIORITY: 0

scepter/methods/examples/generation/dit_flux1.0_dev_1024_lora.yaml

@@ -2,33 +2,22 @@ ENV:
   BACKEND: nccl
   SEED: 166666
 SOLVER:
-  # NAME DESCRIPTION:  TYPE:  default: 'LatentUfitSolver'
   NAME: LatentDiffusionSolver
-  # MAX_STEPS DESCRIPTION: The total steps for training. TYPE: int default: 100000
   MAX_STEPS: 100000
-  # USE_AMP DESCRIPTION: Use amp to surpport mix precision or not, default is False. TYPE: bool default: False
   USE_AMP: True
-  # DTYPE DESCRIPTION: The precision for training. TYPE: str default: 'float32'
   DTYPE: bfloat16
-  # USE_FAIRSCALE DESCRIPTION: Use fairscale as the backend of ddp, default False. TYPE: bool default: False
   USE_FAIRSCALE: False
-  # USE_FSDP DESCRIPTION: Use fsdp as the backend of ddp, default False. TYPE: bool default: False
   USE_FSDP: True
-  # LOAD_MODEL_ONLY DESCRIPTION: Only load the model rather than the optimizer and schedule, default is False. TYPE: bool default: False
   LOAD_MODEL_ONLY: False
-  # RESUME_FROM DESCRIPTION: Resume from some state of training! TYPE: str default: ''
+  ENABLE_GRADSCALER: False
+  USE_SCALER: False
   RESUME_FROM:
   WORK_DIR: ./cache/save_data/dit_flux_dev_1024_lora
   LOG_FILE: std_log.txt
-  # EVAL_INTERVAL DESCRIPTION: Eval the model interval. TYPE: int default: 1
   EVAL_INTERVAL: 100
-  # LOG_TRAIN_NUM DESCRIPTION: The number samples used to log in training phase. TYPE: int default: -1
   LOG_TRAIN_NUM: 16
-  # FSDP_REDUCE_DTYPE DESCRIPTION: The dtype of reduce in FSDP. TYPE: str default: 'float16'
   FSDP_REDUCE_DTYPE: float32
-  # FSDP_BUFFER_DTYPE DESCRIPTION: The dtype of buffer in FSDP. TYPE: str default: 'float16'
   FSDP_BUFFER_DTYPE: float32
-  # FSDP_SHARD_MODULES DESCRIPTION: The modules to be sharded in FSDP. TYPE: list default: ['model']
   FSDP_SHARD_MODULES: [ 'model', 'cond_stage_model.t5_model' ] #
   SAVE_MODULES: [ 'model'] #
   TRAIN_MODULES: ['model']
@@ -58,61 +47,36 @@ SOLVER:
     USE_EMA: False
     EVAL_EMA: False
     DIFFUSION:
-      # NAME DESCRIPTION:  TYPE:  default: 'DiffusionFluxRF'
       NAME: DiffusionFluxRF
       PREDICTION_TYPE: raw
-      # NOISE_SCHEDULER DESCRIPTION:  TYPE:  default: ''
       NOISE_SCHEDULER:
-        # NAME DESCRIPTION:  TYPE:  default: 'FlowMatchSigmaScheduler'
         NAME: FlowMatchSigmaScheduler
-        # WEIGHTING_SCHEME DESCRIPTION: The weighting scheme for sampling timesteps, choose from ['sigma_sqrt', 'logit_normal', 'mode', 'cosmap', 'none']. TYPE: str default: 'logit_normal'
         WEIGHTING_SCHEME: logit_normal
         SHIFT: 3.0
-        # LOGIT_MEAN DESCRIPTION: The mean of the logit distribution for sampling timesteps. TYPE: float default: 0.0
         LOGIT_MEAN: 0.0
-        # LOGIT_STD DESCRIPTION: The standard deviation of the logit distribution for sampling timesteps. TYPE: float default: 1.0
         LOGIT_STD: 1.0
-        # MODE_SCALE DESCRIPTION: The scale factor for the mode of the logit distribution for sampling timesteps. TYPE: float default: 1.29
         MODE_SCALE: 1.29
       SAMPLER_SCHEDULER:
-        # NAME DESCRIPTION:  TYPE:  default: 'FlowMatchFluxShiftScheduler'
         NAME: FlowMatchFluxShiftScheduler
-        # SHIFT DESCRIPTION: Use timestamp shift or not, default is True. TYPE: bool default: True
         SHIFT: False
-        # SIGMOID_SCALE DESCRIPTION: The scale of sigmoid function for sampling timesteps. TYPE: int default: 1
         SIGMOID_SCALE: 1
-        # BASE_SHIFT DESCRIPTION: The base shift factor for the timestamp. TYPE: float default: 0.5
         BASE_SHIFT: 0.5
-        # MAX_SHIFT DESCRIPTION: The max shift factor for the timestamp. TYPE: float default: 1.15
         MAX_SHIFT: 1.15
         #
     DIFFUSION_MODEL:
-      # NAME DESCRIPTION:  TYPE:  default: 'Flux'
       NAME: Flux
       PRETRAINED_MODEL: ms://AI-ModelScope/FLUX.1-dev@flux1-dev.safetensors
-      # IN_CHANNELS DESCRIPTION: model's input channels. TYPE: int default: 64
       IN_CHANNELS: 64
-      # HIDDEN_SIZE DESCRIPTION: model's hidden size. TYPE: int default: 1024
       HIDDEN_SIZE: 3072
-      # NUM_HEADS DESCRIPTION: number of heads in the transformer. TYPE: int default: 16
       NUM_HEADS: 24
-      # AXES_DIM DESCRIPTION: dimensions of the axes of the positional encoding. TYPE: list default: [16, 56, 56]
       AXES_DIM: [ 16, 56, 56 ]
-      # THETA DESCRIPTION: theta for positional encoding. TYPE: int default: 10000
       THETA: 10000
-      # VEC_IN_DIM DESCRIPTION: dimension of the vector input. TYPE: int default: 768
       VEC_IN_DIM: 768
-      # GUIDANCE_EMBED DESCRIPTION: whether to use guidance embedding. TYPE: bool default: False
       GUIDANCE_EMBED: True
-      # CONTEXT_IN_DIM DESCRIPTION: dimension of the context input. TYPE: int default: 4096
       CONTEXT_IN_DIM: 4096
-      # MLP_RATIO DESCRIPTION: ratio of mlp hidden size to hidden size. TYPE: float default: 4.0
       MLP_RATIO: 4.0
-      # QKV_BIAS DESCRIPTION: whether to use bias in qkv projection. TYPE: bool default: True
       QKV_BIAS: True
-      # DEPTH DESCRIPTION: number of transformer blocks. TYPE: int default: 19
       DEPTH: 19
-      # DEPTH_SINGLE_BLOCKS DESCRIPTION: number of transformer blocks in the single stream block. TYPE: int default: 38
       DEPTH_SINGLE_BLOCKS: 38
       USE_GRAD_CHECKPOINT: True
 
@@ -157,55 +121,34 @@ SOLVER:
         TANH_OUT: False
     #
     COND_STAGE_MODEL:
-      # NAME DESCRIPTION:  TYPE:  default: 'T5PlusClipFluxEmbedder'
       NAME: T5PlusClipFluxEmbedder
-      # T5_MODEL DESCRIPTION:  TYPE:  default: ''
       T5_MODEL:
-        # NAME DESCRIPTION:  TYPE:  default: 'HFEmbedder'
         NAME: HFEmbedder
-        # HF_MODEL_CLS DESCRIPTION: huggingface cls in transfomer TYPE: NoneType default: None
         HF_MODEL_CLS: T5EncoderModel
-        # MODEL_PATH DESCRIPTION: model folder path TYPE: NoneType default: None
         MODEL_PATH: ms://AI-ModelScope/FLUX.1-dev@text_encoder_2/
-        # HF_TOKENIZER_CLS DESCRIPTION: huggingface cls in transfomer TYPE: NoneType default: None
         HF_TOKENIZER_CLS: T5Tokenizer
-        # TOKENIZER_PATH DESCRIPTION: tokenizer folder path TYPE: NoneType default: None
         TOKENIZER_PATH: ms://AI-ModelScope/FLUX.1-dev@tokenizer_2/
-        # MAX_LENGTH DESCRIPTION: max length of input TYPE: int default: 77
         MAX_LENGTH: 512
-        # OUTPUT_KEY DESCRIPTION: output key TYPE: str default: 'last_hidden_state'
         OUTPUT_KEY: last_hidden_state
-        # D_TYPE DESCRIPTION: dtype TYPE: str default: 'bfloat16'
         D_TYPE: bfloat16
-        # BATCH_INFER DESCRIPTION: batch infer TYPE: bool default: False
         BATCH_INFER: False
         CLEAN: whitespace
-      # CLIP_MODEL DESCRIPTION:  TYPE:  default: ''
       CLIP_MODEL:
-        # NAME DESCRIPTION:  TYPE:  default: 'HFEmbedder'
         NAME: HFEmbedder
-        # HF_MODEL_CLS DESCRIPTION: huggingface cls in transfomer TYPE: NoneType default: None
         HF_MODEL_CLS: CLIPTextModel
-        # MODEL_PATH DESCRIPTION: model folder path TYPE: NoneType default: None
         MODEL_PATH: ms://AI-ModelScope/FLUX.1-dev@text_encoder/
-        # HF_TOKENIZER_CLS DESCRIPTION: huggingface cls in transfomer TYPE: NoneType default: None
         HF_TOKENIZER_CLS: CLIPTokenizer
-        # TOKENIZER_PATH DESCRIPTION: tokenizer folder path TYPE: NoneType default: None
         TOKENIZER_PATH: ms://AI-ModelScope/FLUX.1-dev@tokenizer/
-        # MAX_LENGTH DESCRIPTION: max length of input TYPE: int default: 77
         MAX_LENGTH: 77
-        # OUTPUT_KEY DESCRIPTION: output key TYPE: str default: 'last_hidden_state'
         OUTPUT_KEY: pooler_output
-        # D_TYPE DESCRIPTION: dtype TYPE: str default: 'bfloat16'
         D_TYPE: bfloat16
-        # BATCH_INFER DESCRIPTION: batch infer TYPE: bool default: False
         BATCH_INFER: True
         CLEAN: whitespace
         USE_GRAD_CHECKPOINT: True
   #
   SAMPLE_ARGS:
     SAMPLE_STEPS: 50
-    SAMPLER: flow_eluer
+    SAMPLER: flow_euler
     SEED: 2024
     IMAGE_SIZE: [ 1024, 1024 ]
     GUIDE_SCALE: 3.5

scepter/methods/examples/generation/dit_flux1.0_schnell_1024_lora.yaml

@@ -2,35 +2,24 @@ ENV:
   BACKEND: nccl
   SEED: 166666
 SOLVER:
-  # NAME DESCRIPTION:  TYPE:  default: 'LatentUfitSolver'
   NAME: LatentDiffusionSolver
-  # MAX_STEPS DESCRIPTION: The total steps for training. TYPE: int default: 100000
   MAX_STEPS: 100000
-  # USE_AMP DESCRIPTION: Use amp to surpport mix precision or not, default is False. TYPE: bool default: False
   USE_AMP: True
-  # DTYPE DESCRIPTION: The precision for training. TYPE: str default: 'float32'
   DTYPE: bfloat16
-  # USE_FAIRSCALE DESCRIPTION: Use fairscale as the backend of ddp, default False. TYPE: bool default: False
   USE_FAIRSCALE: False
-  # USE_FSDP DESCRIPTION: Use fsdp as the backend of ddp, default False. TYPE: bool default: False
   USE_FSDP: True
-  # LOAD_MODEL_ONLY DESCRIPTION: Only load the model rather than the optimizer and schedule, default is False. TYPE: bool default: False
   LOAD_MODEL_ONLY: False
-  # RESUME_FROM DESCRIPTION: Resume from some state of training! TYPE: str default: ''
+  ENABLE_GRADSCALER: False
+  USE_SCALER: False
   RESUME_FROM:
   WORK_DIR: ./cache/save_data/dit_flux_schnell_1024_lora
   LOG_FILE: std_log.txt
-  # EVAL_INTERVAL DESCRIPTION: Eval the model interval. TYPE: int default: 1
   EVAL_INTERVAL: 100
-  # LOG_TRAIN_NUM DESCRIPTION: The number samples used to log in training phase. TYPE: int default: -1
   LOG_TRAIN_NUM: 16
-  # FSDP_REDUCE_DTYPE DESCRIPTION: The dtype of reduce in FSDP. TYPE: str default: 'float16'
   FSDP_REDUCE_DTYPE: float32
-  # FSDP_BUFFER_DTYPE DESCRIPTION: The dtype of buffer in FSDP. TYPE: str default: 'float16'
   FSDP_BUFFER_DTYPE: float32
-  # FSDP_SHARD_MODULES DESCRIPTION: The modules to be sharded in FSDP. TYPE: list default: ['model']
   FSDP_SHARD_MODULES: [ 'model', 'cond_stage_model.t5_model' ] #
-  SAVE_MODULES: [ 'model'] #
+  SAVE_MODULES: [ 'model']
   TRAIN_MODULES: ['model']
   #
   FILE_SYSTEM:
@@ -58,12 +47,9 @@ SOLVER:
     USE_EMA: False
     EVAL_EMA: False
     DIFFUSION:
-      # NAME DESCRIPTION:  TYPE:  default: 'DiffusionFluxRF'
       NAME: DiffusionFluxRF
       PREDICTION_TYPE: raw
-      # NOISE_SCHEDULER DESCRIPTION:  TYPE:  default: ''
       NOISE_SCHEDULER:
-        # NAME DESCRIPTION:  TYPE:  default: 'FlowMatchSigmaScheduler'
         NAME: FlowMatchSigmaScheduler
         # WEIGHTING_SCHEME DESCRIPTION: The weighting scheme for sampling timesteps, choose from ['sigma_sqrt', 'logit_normal', 'mode', 'cosmap', 'none']. TYPE: str default: 'logit_normal'
         WEIGHTING_SCHEME: logit_normal
@@ -157,54 +143,33 @@ SOLVER:
         TANH_OUT: False
     #
     COND_STAGE_MODEL:
-      # NAME DESCRIPTION:  TYPE:  default: 'T5PlusClipFluxEmbedder'
       NAME: T5PlusClipFluxEmbedder
-      # T5_MODEL DESCRIPTION:  TYPE:  default: ''
       T5_MODEL:
-        # NAME DESCRIPTION:  TYPE:  default: 'HFEmbedder'
         NAME: HFEmbedder
-        # HF_MODEL_CLS DESCRIPTION: huggingface cls in transfomer TYPE: NoneType default: None
         HF_MODEL_CLS: T5EncoderModel
-        # MODEL_PATH DESCRIPTION: model folder path TYPE: NoneType default: None
         MODEL_PATH: ms://AI-ModelScope/FLUX.1-schnell@text_encoder_2/
-        # HF_TOKENIZER_CLS DESCRIPTION: huggingface cls in transfomer TYPE: NoneType default: None
         HF_TOKENIZER_CLS: T5Tokenizer
-        # TOKENIZER_PATH DESCRIPTION: tokenizer folder path TYPE: NoneType default: None
         TOKENIZER_PATH: ms://AI-ModelScope/FLUX.1-schnell@tokenizer_2/
-        # MAX_LENGTH DESCRIPTION: max length of input TYPE: int default: 77
         MAX_LENGTH: 256
-        # OUTPUT_KEY DESCRIPTION: output key TYPE: str default: 'last_hidden_state'
         OUTPUT_KEY: last_hidden_state
-        # D_TYPE DESCRIPTION: dtype TYPE: str default: 'bfloat16'
         D_TYPE: bfloat16
-        # BATCH_INFER DESCRIPTION: batch infer TYPE: bool default: False
         BATCH_INFER: False
         CLEAN: whitespace
-      # CLIP_MODEL DESCRIPTION:  TYPE:  default: ''
       CLIP_MODEL:
-        # NAME DESCRIPTION:  TYPE:  default: 'HFEmbedder'
         NAME: HFEmbedder
-        # HF_MODEL_CLS DESCRIPTION: huggingface cls in transfomer TYPE: NoneType default: None
         HF_MODEL_CLS: CLIPTextModel
-        # MODEL_PATH DESCRIPTION: model folder path TYPE: NoneType default: None
         MODEL_PATH: ms://AI-ModelScope/FLUX.1-schnell@text_encoder/
-        # HF_TOKENIZER_CLS DESCRIPTION: huggingface cls in transfomer TYPE: NoneType default: None
         HF_TOKENIZER_CLS: CLIPTokenizer
-        # TOKENIZER_PATH DESCRIPTION: tokenizer folder path TYPE: NoneType default: None
         TOKENIZER_PATH: ms://AI-ModelScope/FLUX.1-schnell@tokenizer/
-        # MAX_LENGTH DESCRIPTION: max length of input TYPE: int default: 77
         MAX_LENGTH: 77
-        # OUTPUT_KEY DESCRIPTION: output key TYPE: str default: 'last_hidden_state'
         OUTPUT_KEY: pooler_output
-        # D_TYPE DESCRIPTION: dtype TYPE: str default: 'bfloat16'
         D_TYPE: bfloat16
-        # BATCH_INFER DESCRIPTION: batch infer TYPE: bool default: False
         BATCH_INFER: True
         CLEAN: whitespace
   #
   SAMPLE_ARGS:
     SAMPLE_STEPS: 4
-    SAMPLER: flow_eluer
+    SAMPLER: flow_euler
     SEED: 2024
     IMAGE_SIZE: [ 1024, 1024 ]
     GUIDE_SCALE: 3.5

scepter/methods/studio/chatbot/chatbot.yaml

@@ -8,11 +8,11 @@ FILE_SYSTEM:
     TEMP_DIR: ./cache/cache_data
 #
 ENABLE_I2V: False
+SKIP_EXAMPLES: True
 #
 MODEL:
   EDIT_MODEL:
     MODEL_CFG_DIR: scepter/methods/studio/chatbot/models/
-    DEFAULT: ace_0.6b_512
   I2V:
     MODEL_NAME: CogVideoX-5b-I2V
     MODEL_DIR: ms://ZhipuAI/CogVideoX-5b-I2V/

scepter/methods/studio/chatbot/models/ace_0.6b_1024.yaml

@@ -0,0 +1,128 @@
+NAME: ACE_0.6B_1024
+IS_DEFAULT: False
+USE_DYNAMIC_MODEL: True
+DEFAULT_PARAS:
+  PARAS:
+  #
+  INPUT:
+    INPUT_IMAGE:
+    INPUT_MASK:
+    TASK:
+    PROMPT: ""
+    NEGATIVE_PROMPT: ""
+    OUTPUT_HEIGHT: 1024
+    OUTPUT_WIDTH: 1024
+    SAMPLER: ddim
+    SAMPLE_STEPS: 50
+    GUIDE_SCALE: 4.5
+    GUIDE_RESCALE: 0.5
+    SEED: -1
+    TAR_INDEX: 0
+  OUTPUT:
+    LATENT:
+    IMAGES:
+    SEED:
+  MODULES_PARAS:
+    FIRST_STAGE_MODEL:
+      FUNCTION:
+        - NAME: encode
+          DTYPE: float16
+          INPUT: ["IMAGE"]
+        - NAME: decode
+          DTYPE: float16
+          INPUT: ["LATENT"]
+    #
+    DIFFUSION_MODEL:
+      FUNCTION:
+        - NAME: forward
+          DTYPE: float16
+          INPUT: ["SAMPLE_STEPS", "SAMPLE", "GUIDE_SCALE"]
+    #
+    COND_STAGE_MODEL:
+      FUNCTION:
+        - NAME: encode_list_of_list
+          DTYPE: bfloat16
+          INPUT: ["PROMPT"]
+#
+MODEL:
+  NAME: LatentDiffusionACE
+  PRETRAINED_MODEL:
+  IGNORE_KEYS: [ ]
+  SCALE_FACTOR: 0.18215
+  SIZE_FACTOR: 8
+  DECODER_BIAS: 0.5
+  DEFAULT_N_PROMPT: ""
+  TEXT_IDENTIFIER: [ '{image}', '{image1}', '{image2}', '{image3}', '{image4}', '{image5}', '{image6}', '{image7}', '{image8}', '{image9}' ]
+  USE_TEXT_POS_EMBEDDINGS: True
+  #
+  DIFFUSION:
+    NAME: BaseDiffusion
+    PREDICTION_TYPE: eps
+    MIN_SNR_GAMMA:
+    NOISE_SCHEDULER:
+      NAME: LinearScheduler
+      NUM_TIMESTEPS: 1000
+      BETA_MIN: 0.0001
+      BETA_MAX: 0.02
+  #
+  DIFFUSION_MODEL:
+    NAME: ACE
+    PRETRAINED_MODEL: ms://iic/ACE-0.6B-1024px@models/dit/ace_0.6b_1024px.pth
+    IGNORE_KEYS: [ ]
+    PATCH_SIZE: 2
+    IN_CHANNELS: 4
+    HIDDEN_SIZE: 1152
+    DEPTH: 28
+    NUM_HEADS: 16
+    MLP_RATIO: 4.0
+    PRED_SIGMA: True
+    DROP_PATH: 0.0
+    WINDOW_DIZE: 0
+    Y_CHANNELS: 4096
+    MAX_SEQ_LEN: 4096
+    QK_NORM: True
+    USE_GRAD_CHECKPOINT: True
+    ATTENTION_BACKEND: flash_attn
+  #
+  FIRST_STAGE_MODEL:
+    NAME: AutoencoderKL
+    EMBED_DIM: 4
+    PRETRAINED_MODEL: ms://iic/ACE-0.6B-1024px@models/vae/vae.bin
+    IGNORE_KEYS: []
+    #
+    ENCODER:
+      NAME: Encoder
+      CH: 128
+      OUT_CH: 3
+      NUM_RES_BLOCKS: 2
+      IN_CHANNELS: 3
+      ATTN_RESOLUTIONS: [ ]
+      CH_MULT: [ 1, 2, 4, 4 ]
+      Z_CHANNELS: 4
+      DOUBLE_Z: True
+      DROPOUT: 0.0
+      RESAMP_WITH_CONV: True
+    #
+    DECODER:
+      NAME: Decoder
+      CH: 128
+      OUT_CH: 3
+      NUM_RES_BLOCKS: 2
+      IN_CHANNELS: 3
+      ATTN_RESOLUTIONS: [ ]
+      CH_MULT: [ 1, 2, 4, 4 ]
+      Z_CHANNELS: 4
+      DROPOUT: 0.0
+      RESAMP_WITH_CONV: True
+      GIVE_PRE_END: False
+      TANH_OUT: False
+  #
+  COND_STAGE_MODEL:
+    NAME: T5EmbedderHF
+    PRETRAINED_MODEL: ms://iic/ACE-0.6B-1024px@models/text_encoder/t5-v1_1-xxl/
+    TOKENIZER_PATH: ms://iic/ACE-0.6B-1024px@models/tokenizer/t5-v1_1-xxl
+    LENGTH: 120
+    T5_DTYPE: bfloat16
+    ADDED_IDENTIFIER: [ '{image}', '{caption}', '{mask}', '{ref_image}', '{image1}', '{image2}', '{image3}', '{image4}', '{image5}', '{image6}', '{image7}', '{image8}', '{image9}' ]
+    CLEAN: whitespace
+    USE_GRAD: False

scepter/methods/studio/chatbot/models/ace_0.6b_1024_refiner.yaml

@@ -0,0 +1,284 @@
+NAME: ACE_0.6B_1024_REFINER
+IS_DEFAULT: False
+USE_DYNAMIC_MODEL: True
+DEFAULT_PARAS:
+  PARAS:
+  #
+  INPUT:
+    INPUT_IMAGE:
+    INPUT_MASK:
+    TASK:
+    PROMPT: ""
+    NEGATIVE_PROMPT: ""
+    OUTPUT_HEIGHT: 1024
+    OUTPUT_WIDTH: 1024
+    SAMPLER: ddim
+    SAMPLE_STEPS: 50
+    GUIDE_SCALE: 4.5
+    GUIDE_RESCALE: 0.5
+    SEED: -1
+    TAR_INDEX: 0
+    REFINER_SCALE: 0.2
+    USE_ACE: True
+    #REFINER_PROMPT: "High Resolution, Sharpness, Clarity, Detail Enhancement, Noise Reduction, HD, 4k, Image Restoration, HDR"
+    REFINER_PROMPT: "High Resolution, Sharpness, Clarity, Detail Enhancement, Noise Reduction, HD, 4k, Image Restoration, HDR"
+  OUTPUT:
+    LATENT:
+    IMAGES:
+    SEED:
+  MODULES_PARAS:
+    FIRST_STAGE_MODEL:
+      FUNCTION:
+        - NAME: encode
+          DTYPE: float16
+          INPUT: ["IMAGE"]
+        - NAME: decode
+          DTYPE: float16
+          INPUT: ["LATENT"]
+    #
+    DIFFUSION_MODEL:
+      FUNCTION:
+        - NAME: forward
+          DTYPE: float16
+          INPUT: ["SAMPLE_STEPS", "SAMPLE", "GUIDE_SCALE"]
+    #
+    COND_STAGE_MODEL:
+      FUNCTION:
+        - NAME: encode_list_of_list
+          DTYPE: bfloat16
+          INPUT: ["PROMPT"]
+#
+MODEL:
+  NAME: LatentDiffusionACE
+  PRETRAINED_MODEL:
+  IGNORE_KEYS: [ ]
+  SCALE_FACTOR: 0.18215
+  SIZE_FACTOR: 8
+  DECODER_BIAS: 0.5
+  DEFAULT_N_PROMPT: ""
+  TEXT_IDENTIFIER: [ '{image}', '{image1}', '{image2}', '{image3}', '{image4}', '{image5}', '{image6}', '{image7}', '{image8}', '{image9}' ]
+  USE_TEXT_POS_EMBEDDINGS: True
+  #
+  DIFFUSION:
+    NAME: BaseDiffusion
+    PREDICTION_TYPE: eps
+    MIN_SNR_GAMMA:
+    NOISE_SCHEDULER:
+      NAME: LinearScheduler
+      NUM_TIMESTEPS: 1000
+      BETA_MIN: 0.0001
+      BETA_MAX: 0.02
+  #
+  DIFFUSION_MODEL:
+    NAME: ACE
+    PRETRAINED_MODEL: ms://iic/ACE-0.6B-1024px@models/dit/ace_0.6b_1024px.pth
+    IGNORE_KEYS: [ ]
+    PATCH_SIZE: 2
+    IN_CHANNELS: 4
+    HIDDEN_SIZE: 1152
+    DEPTH: 28
+    NUM_HEADS: 16
+    MLP_RATIO: 4.0
+    PRED_SIGMA: True
+    DROP_PATH: 0.0
+    WINDOW_DIZE: 0
+    Y_CHANNELS: 4096
+    MAX_SEQ_LEN: 4096
+    QK_NORM: True
+    USE_GRAD_CHECKPOINT: True
+    ATTENTION_BACKEND: flash_attn
+  #
+  FIRST_STAGE_MODEL:
+    NAME: AutoencoderKL
+    EMBED_DIM: 4
+    PRETRAINED_MODEL: ms://iic/ACE-0.6B-1024px@models/vae/vae.bin
+    IGNORE_KEYS: []
+    #
+    ENCODER:
+      NAME: Encoder
+      CH: 128
+      OUT_CH: 3
+      NUM_RES_BLOCKS: 2
+      IN_CHANNELS: 3
+      ATTN_RESOLUTIONS: [ ]
+      CH_MULT: [ 1, 2, 4, 4 ]
+      Z_CHANNELS: 4
+      DOUBLE_Z: True
+      DROPOUT: 0.0
+      RESAMP_WITH_CONV: True
+    #
+    DECODER:
+      NAME: Decoder
+      CH: 128
+      OUT_CH: 3
+      NUM_RES_BLOCKS: 2
+      IN_CHANNELS: 3
+      ATTN_RESOLUTIONS: [ ]
+      CH_MULT: [ 1, 2, 4, 4 ]
+      Z_CHANNELS: 4
+      DROPOUT: 0.0
+      RESAMP_WITH_CONV: True
+      GIVE_PRE_END: False
+      TANH_OUT: False
+  #
+  COND_STAGE_MODEL:
+    NAME: T5EmbedderHF
+    PRETRAINED_MODEL: ms://iic/ACE-0.6B-1024px@models/text_encoder/t5-v1_1-xxl/
+    TOKENIZER_PATH: ms://iic/ACE-0.6B-1024px@models/tokenizer/t5-v1_1-xxl
+    LENGTH: 120
+    T5_DTYPE: bfloat16
+    ADDED_IDENTIFIER: [ '{image}', '{caption}', '{mask}', '{ref_image}', '{image1}', '{image2}', '{image3}', '{image4}', '{image5}', '{image6}', '{image7}', '{image8}', '{image9}' ]
+    CLEAN: whitespace
+    USE_GRAD: False
+
+ACE_PROMPT: [
+  "A cute cartoon rabbit holding a whiteboard that says 'ACE Refiner', standing in a sunny meadow filled with flowers, with a big smile and bright colors.",
+  "A beautiful young woman with long flowing hair, wearing a summer dress, holding a whiteboard that reads 'ACE Refiner' while sitting on a park bench surrounded by cherry blossoms.",
+  "An adorable cartoon cat wearing oversized glasses, holding a whiteboard that says 'ACE Refiner', perched on a stack of colorful books in a cozy library setting.",
+  "A charming girl with pigtails, wearing a cute school uniform, enthusiastically holding a whiteboard that has 'ACE Refiner' written on it, in a bright and cheerful classroom full of educational posters.",
+  "A friendly cartoon dog with floppy ears, sitting in front of a doghouse, proudly holding a whiteboard that says 'ACE Refiner', with a playful expression and a blue sky in the background.",
+  "A cute anime girl with big expressive eyes, dressed in a colorful outfit, holding a whiteboard that reads 'ACE Refiner' in a fantastical landscape filled with mythical creatures.",
+  "A vibrant cartoon fox holding a whiteboard that says 'ACE Refiner', standing on a rock by a sparkling stream, surrounded by lush greenery and butterflies.",
+  "A stylish young woman in a business outfit, smiling as she holds a whiteboard written with 'ACE Refiner', in a modern office filled with plants and natural light.",
+  "A cute cartoon unicorn holding a sparkling whiteboard that says 'ACE Refiner', frolicking in a magical forest, with rainbows and stars in the background.",
+  "A happy family, consisting of a cute little girl and her playful puppy, holding a whiteboard that says 'ACE Refiner', together in their backyard on a sunny day."
+]
+REFINER_MODEL:
+  NAME: ""
+  IS_DEFAULT: False
+  DEFAULT_PARAS:
+    PARAS:
+      RESOLUTIONS: [ [ 1024, 1024 ] ]
+    INPUT:
+      INPUT_IMAGE:
+      INPUT_MASK:
+      TASK:
+      PROMPT: ""
+      NEGATIVE_PROMPT: ""
+      OUTPUT_HEIGHT: 1024
+      OUTPUT_WIDTH: 1024
+      SAMPLER: flow_euler
+      SAMPLE_STEPS: 30
+      GUIDE_SCALE: 3.5
+      GUIDE_RESCALE:
+    OUTPUT:
+      LATENT:
+      IMAGES:
+      SEED:
+    MODULES_PARAS:
+      FIRST_STAGE_MODEL:
+        FUNCTION:
+          - NAME: encode
+            DTYPE: bfloat16
+            INPUT: [ "IMAGE" ]
+          - NAME: decode
+            DTYPE: bfloat16
+            INPUT: [ "LATENT" ]
+        PARAS:
+          SCALE_FACTOR: 1.5305
+          SHIFT_FACTOR: 0.0609
+          SIZE_FACTOR: 8
+      DIFFUSION_MODEL:
+        FUNCTION:
+          - NAME: forward
+            DTYPE: bfloat16
+            INPUT: [ "SAMPLE_STEPS", "SAMPLE", "GUIDE_SCALE" ]
+      COND_STAGE_MODEL:
+        FUNCTION:
+          - NAME: encode
+            DTYPE: bfloat16
+            INPUT: [ "PROMPT" ]
+
+  MODEL:
+    DIFFUSION:
+      NAME: DiffusionFluxRF
+      PREDICTION_TYPE: raw
+      NOISE_SCHEDULER:
+        NAME: FlowMatchSigmaScheduler
+        WEIGHTING_SCHEME: logit_normal
+        SHIFT: 3.0
+        LOGIT_MEAN: 0.0
+        LOGIT_STD: 1.0
+        MODE_SCALE: 1.29
+    DIFFUSION_MODEL:
+      NAME: FluxMR
+      PRETRAINED_MODEL: ms://AI-ModelScope/FLUX.1-dev@flux1-dev.safetensors
+      IN_CHANNELS: 64
+      OUT_CHANNELS: 64
+      HIDDEN_SIZE: 3072
+      NUM_HEADS: 24
+      AXES_DIM: [ 16, 56, 56 ]
+      THETA: 10000
+      VEC_IN_DIM: 768
+      GUIDANCE_EMBED: True
+      CONTEXT_IN_DIM: 4096
+      MLP_RATIO: 4.0
+      QKV_BIAS: True
+      DEPTH: 19
+      DEPTH_SINGLE_BLOCKS: 38
+      USE_GRAD_CHECKPOINT: True
+      ATTN_BACKEND: flash_attn
+    #
+    FIRST_STAGE_MODEL:
+      NAME: AutoencoderKLFlux
+      EMBED_DIM: 16
+      PRETRAINED_MODEL:  ms://AI-ModelScope/FLUX.1-dev@ae.safetensors
+      IGNORE_KEYS: [ ]
+      BATCH_SIZE: 8
+      USE_CONV: False
+      SCALE_FACTOR: 0.3611
+      SHIFT_FACTOR: 0.1159
+      #
+      ENCODER:
+        NAME: Encoder
+        USE_CHECKPOINT: False
+        CH: 128
+        OUT_CH: 3
+        NUM_RES_BLOCKS: 2
+        IN_CHANNELS: 3
+        ATTN_RESOLUTIONS: [ ]
+        CH_MULT: [ 1, 2, 4, 4 ]
+        Z_CHANNELS: 16
+        DOUBLE_Z: True
+        DROPOUT: 0.0
+        RESAMP_WITH_CONV: True
+      #
+      DECODER:
+        NAME: Decoder
+        USE_CHECKPOINT: False
+        CH: 128
+        OUT_CH: 3
+        NUM_RES_BLOCKS: 2
+        IN_CHANNELS: 3
+        ATTN_RESOLUTIONS: [ ]
+        CH_MULT: [ 1, 2, 4, 4 ]
+        Z_CHANNELS: 16
+        DROPOUT: 0.0
+        RESAMP_WITH_CONV: True
+        GIVE_PRE_END: False
+        TANH_OUT: False
+    #
+    COND_STAGE_MODEL:
+      NAME: T5PlusClipFluxEmbedder
+      T5_MODEL:
+        NAME: HFEmbedder
+        HF_MODEL_CLS: T5EncoderModel
+        MODEL_PATH: ms://AI-ModelScope/FLUX.1-dev@text_encoder_2/
+        HF_TOKENIZER_CLS: T5Tokenizer
+        TOKENIZER_PATH: ms://AI-ModelScope/FLUX.1-dev@tokenizer_2/
+        MAX_LENGTH: 512
+        OUTPUT_KEY: last_hidden_state
+        D_TYPE: bfloat16
+        BATCH_INFER: False
+        CLEAN: whitespace
+      CLIP_MODEL:
+        NAME: HFEmbedder
+        HF_MODEL_CLS: CLIPTextModel
+        MODEL_PATH: ms://AI-ModelScope/FLUX.1-dev@text_encoder/
+        HF_TOKENIZER_CLS: CLIPTokenizer
+        TOKENIZER_PATH: ms://AI-ModelScope/FLUX.1-dev@tokenizer/
+        MAX_LENGTH: 77
+        OUTPUT_KEY: pooler_output
+        D_TYPE: bfloat16
+        BATCH_INFER: True
+        CLEAN: whitespace

scepter/methods/studio/chatbot/models/ace_0.6b_512.yaml

@@ -1,5 +1,6 @@
 NAME: ACE_0.6B_512
-IS_DEFAULT: False
+IS_DEFAULT: True
+USE_DYNAMIC_MODEL: True
 DEFAULT_PARAS:
   PARAS:
   #
@@ -39,7 +40,7 @@ DEFAULT_PARAS:
     #
     COND_STAGE_MODEL:
       FUNCTION:
-        - NAME: encode_list
+        - NAME: encode_list_of_list
           DTYPE: bfloat16
           INPUT: ["PROMPT"]
 #

scepter/methods/studio/inference/dit/cogvideox_2b_pro.yaml

@@ -0,0 +1,151 @@
+NAME: COGVIDEOX_2B
+IS_DEFAULT: False
+DEFAULT_PARAS:
+  PARAS:
+    RESOLUTIONS: [[480, 720]]
+  INPUT:
+    IMAGE:
+    ORIGINAL_SIZE_AS_TUPLE: [480, 720]
+    TARGET_SIZE_AS_TUPLE: [480, 720]
+    PROMPT: ""
+    NEGATIVE_PROMPT: ""
+    PROMPT_PREFIX: ""
+    SAMPLE: ddim
+    SAMPLE_STEPS: 50
+    GUIDE_SCALE: 6.0
+    GUIDE_RESCALE: 0.0
+    DISCRETIZATION: trailing
+    NUM_FRAMES:
+      DEFAULT: 49
+      VISIBLE: True
+    FPS:
+      DEFAULT: 8
+      VISIBLE: True
+  OUTPUT:
+    VIDEOS:
+    SEED:
+  MODULES_PARAS:
+    FIRST_STAGE_MODEL:
+      FUNCTION:
+        -
+          NAME: decode
+          DTYPE: bfloat16
+          INPUT: ["LATENT"]
+      PARAS:
+          SCALING_FACTOR_IMAGE: 1.15258426
+    DIFFUSION_MODEL:
+      FUNCTION:
+        -
+          NAME: forward
+          DTYPE: bfloat16
+          INPUT: ["SAMPLE_STEPS", "SAMPLE", "GUIDE_SCALE", "GUIDE_RESCALE", "DISCRETIZATION", "NUM_FRAMES", "FPS"]
+      PARAS:
+        USE_ROTARY_POSITIONAL_EMBEDDINGS: False
+        PATCH_SIZE: 2
+        LATENT_CHANNELS: 16
+        SCALE_FACTOR_SPATIAL: 8
+        SCALE_FACTOR_TEMPORAL: 4
+        ATTENTION_HEAD_DIM: 64
+        SAMPLE_HEIGHT: 480
+        SAMPLE_WIDTH: 720
+    COND_STAGE_MODEL:
+      FUNCTION:
+        -
+          NAME: encode
+          DTYPE: bfloat16
+          INPUT: ["PROMPT"]
+#
+MODEL:
+  PRETRAINED_MODEL:
+  DIFFUSION:
+    NAME: BaseDiffusion
+    PREDICTION_TYPE: v
+    NOISE_SCHEDULER:
+      NAME: ScaledLinearScheduler
+      BETA_MIN: 0.00085
+      BETA_MAX: 0.012
+      SNR_SHIFT_SCALE: 3.0
+      RESCALE_BETAS_ZERO_SNR: True
+    DIFFUSION_SAMPLERS:
+      NAME: DDIMSampler
+      DISCRETIZATION_TYPE: trailing
+      ETA: 0.0
+  #
+  DIFFUSION_MODEL:
+    NAME: CogVideoXTransformer3DModel
+    DTYPE: bfloat16
+    PRETRAINED_MODEL: ms://AI-ModelScope/CogVideoX-2b@transformer/diffusion_pytorch_model.safetensors
+    NUM_ATTENTION_HEADS: 30
+    ATTENTION_HEAD_DIM: 64
+    IN_CHANNELS: 16
+    OUT_CHANNELS: 16
+    FLIP_SIN_TO_COS: True
+    FREQ_SHIFT: 0
+    TIME_EMBED_DIM: 512
+    TEXT_EMBED_DIM: 4096
+    NUM_LAYERS: 30
+    DROPOUT: 0.0
+    ATTENTION_BIAS: True
+    SAMPLE_WIDTH: 90
+    SAMPLE_HEIGHT: 60
+    SAMPLE_FRAMES: 49
+    PATCH_SIZE: 2
+    TEMPORAL_COMPRESSION_RATIO: 4
+    MAX_TEXT_SEQ_LENGTH: 226
+    ACTIVATION_FN: "gelu-approximate"
+    TIMESTEP_ACTIVATION_FN: "silu"
+    NORM_ELEMENTWISE_AFFINE: True
+    NORM_EPS: 1e-5
+    SPATIAL_INTERPOLATION_SCALE: 1.875
+    TEMPORAL_INTERPOLATION_SCALE: 1.0
+    USE_ROTARY_POSITIONAL_EMBEDDINGS: False
+    USE_LEARNED_POSITIONAL_EMBEDDINGS: False
+    GRADIENT_CHECKPOINTING: False
+  #
+  FIRST_STAGE_MODEL:
+    NAME: AutoencoderKLCogVideoX
+    DTYPE: bfloat16
+    PRETRAINED_MODEL: ms://AI-ModelScope/CogVideoX-2b@vae/diffusion_pytorch_model.safetensors
+    SAMPLE_HEIGHT: 480
+    SAMPLE_WIDTH: 720
+    USE_QUANT_CONV: False
+    USE_POST_QUANT_CONV: False
+    USE_SLICING: True
+    USE_TILING: True
+    GRADIENT_CHECKPOINTING: False
+    ENCODER:
+      NAME: CogVideoXEncoder3D
+      IN_CHANNELS: 3
+      OUT_CHANNELS: 16
+      UP_BLOCK_TYPES: [ "CogVideoXDownBlock3D", "CogVideoXDownBlock3D", "CogVideoXDownBlock3D", "CogVideoXDownBlock3D" ]
+      BLOCK_OUT_CHANNELS: [ 128, 256, 256, 512 ]
+      LAYERS_PER_BLOCK: 3
+      ACT_FN: "silu"
+      NORM_EPS: 1e-6
+      NORM_NUM_GROUPS: 32
+      DROPOUT: 0.0
+      PAD_MODE: "first"
+      TEMPORAL_COMPRESSION_RATIO: 4
+      GRADIENT_CHECKPOINTING: False
+    DECODER:
+      NAME: CogVideoXDecoder3D
+      IN_CHANNELS: 16
+      OUT_CHANNELS: 3
+      UP_BLOCK_TYPES: [ "CogVideoXUpBlock3D", "CogVideoXUpBlock3D", "CogVideoXUpBlock3D", "CogVideoXUpBlock3D" ]
+      BLOCK_OUT_CHANNELS: [ 128, 256, 256, 512 ]
+      LAYERS_PER_BLOCK: 3
+      ACT_FN: "silu"
+      NORM_EPS: 1e-6
+      NORM_NUM_GROUPS: 32
+      DROPOUT: 0.0
+      PAD_MODE: "first"
+      TEMPORAL_COMPRESSION_RATIO: 4
+      GRADIENT_CHECKPOINTING: False
+  #
+  COND_STAGE_MODEL:
+    NAME: T5EmbedderHF
+    PRETRAINED_MODEL: ms://AI-ModelScope/t5-v1_1-xxl
+    TOKENIZER_PATH: ms://AI-ModelScope/t5-v1_1-xxl
+    LENGTH: 226
+    CLEAN:
+    USE_GRAD: False

scepter/methods/studio/inference/dit/cogvideox_5b_pro.yaml

@@ -0,0 +1,153 @@
+NAME: COGVIDEOX_5B
+IS_DEFAULT: False
+DEFAULT_PARAS:
+  PARAS:
+    RESOLUTIONS: [[480, 720]]
+  INPUT:
+    IMAGE:
+    ORIGINAL_SIZE_AS_TUPLE: [480, 720]
+    TARGET_SIZE_AS_TUPLE: [480, 720]
+    PROMPT: ""
+    NEGATIVE_PROMPT: ""
+    PROMPT_PREFIX: ""
+    SAMPLE: ddim
+    SAMPLE_STEPS: 50
+    GUIDE_SCALE: 6.0
+    GUIDE_RESCALE: 0.0
+    DISCRETIZATION: trailing
+    NUM_FRAMES:
+      DEFAULT: 49
+      VISIBLE: True
+    FPS:
+      DEFAULT: 8
+      VISIBLE: True
+  OUTPUT:
+    VIDEOS:
+    SEED:
+  MODULES_PARAS:
+    FIRST_STAGE_MODEL:
+      FUNCTION:
+        -
+          NAME: decode
+          DTYPE: bfloat16
+          INPUT: ["LATENT"]
+      PARAS:
+          SCALING_FACTOR_IMAGE: 0.7  # 5b diff
+    DIFFUSION_MODEL:
+      FUNCTION:
+        -
+          NAME: forward
+          DTYPE: bfloat16
+          INPUT: ["SAMPLE_STEPS", "SAMPLE", "GUIDE_SCALE", "GUIDE_RESCALE", "DISCRETIZATION", "NUM_FRAMES", "FPS"]
+      PARAS:
+        USE_ROTARY_POSITIONAL_EMBEDDINGS: True
+        PATCH_SIZE: 2
+        LATENT_CHANNELS: 16
+        SCALE_FACTOR_SPATIAL: 8
+        SCALE_FACTOR_TEMPORAL: 4
+        ATTENTION_HEAD_DIM: 64
+        SAMPLE_HEIGHT: 480
+        SAMPLE_WIDTH: 720
+    COND_STAGE_MODEL:
+      FUNCTION:
+        -
+          NAME: encode
+          DTYPE: bfloat16
+          INPUT: ["PROMPT"]
+#
+MODEL:
+  PRETRAINED_MODEL:
+  DIFFUSION:
+    NAME: BaseDiffusion
+    PREDICTION_TYPE: v
+    NOISE_SCHEDULER:
+      NAME: ScaledLinearScheduler
+      BETA_MIN: 0.00085
+      BETA_MAX: 0.012
+      SNR_SHIFT_SCALE: 1.0  # 5b diff
+      RESCALE_BETAS_ZERO_SNR: True
+    DIFFUSION_SAMPLERS:
+      NAME: DDIMSampler
+      DISCRETIZATION_TYPE: trailing
+      ETA: 0.0
+  #
+  DIFFUSION_MODEL:
+    NAME: CogVideoXTransformer3DModel
+    DTYPE: bfloat16
+    PRETRAINED_MODEL: # 5b diff
+      - ms://AI-ModelScope/CogVideoX-5b@transformer/diffusion_pytorch_model-00001-of-00002.safetensors
+      - ms://AI-ModelScope/CogVideoX-5b@transformer/diffusion_pytorch_model-00002-of-00002.safetensors
+    NUM_ATTENTION_HEADS: 48  # 5b diff
+    ATTENTION_HEAD_DIM: 64
+    IN_CHANNELS: 16
+    OUT_CHANNELS: 16
+    FLIP_SIN_TO_COS: True
+    FREQ_SHIFT: 0
+    TIME_EMBED_DIM: 512
+    TEXT_EMBED_DIM: 4096
+    NUM_LAYERS: 42  # 5b diff
+    DROPOUT: 0.0
+    ATTENTION_BIAS: True
+    SAMPLE_WIDTH: 90
+    SAMPLE_HEIGHT: 60
+    SAMPLE_FRAMES: 49
+    PATCH_SIZE: 2
+    TEMPORAL_COMPRESSION_RATIO: 4
+    MAX_TEXT_SEQ_LENGTH: 226
+    ACTIVATION_FN: "gelu-approximate"
+    TIMESTEP_ACTIVATION_FN: "silu"
+    NORM_ELEMENTWISE_AFFINE: True
+    NORM_EPS: 1e-5
+    SPATIAL_INTERPOLATION_SCALE: 1.875
+    TEMPORAL_INTERPOLATION_SCALE: 1.0
+    USE_ROTARY_POSITIONAL_EMBEDDINGS: True # 5b diff
+    USE_LEARNED_POSITIONAL_EMBEDDINGS: False
+    GRADIENT_CHECKPOINTING: True
+  #
+  FIRST_STAGE_MODEL:
+    NAME: AutoencoderKLCogVideoX
+    DTYPE: bfloat16
+    PRETRAINED_MODEL: ms://AI-ModelScope/CogVideoX-5b@vae/diffusion_pytorch_model.safetensors # 5b diff
+    SAMPLE_HEIGHT: 480
+    SAMPLE_WIDTH: 720
+    USE_QUANT_CONV: False
+    USE_POST_QUANT_CONV: False
+    USE_SLICING: True
+    USE_TILING: True
+    GRADIENT_CHECKPOINTING: True
+    ENCODER:
+      NAME: CogVideoXEncoder3D
+      IN_CHANNELS: 3
+      OUT_CHANNELS: 16
+      UP_BLOCK_TYPES: [ "CogVideoXDownBlock3D", "CogVideoXDownBlock3D", "CogVideoXDownBlock3D", "CogVideoXDownBlock3D" ]
+      BLOCK_OUT_CHANNELS: [ 128, 256, 256, 512 ]
+      LAYERS_PER_BLOCK: 3
+      ACT_FN: "silu"
+      NORM_EPS: 1e-6
+      NORM_NUM_GROUPS: 32
+      DROPOUT: 0.0
+      PAD_MODE: "first"
+      TEMPORAL_COMPRESSION_RATIO: 4
+      GRADIENT_CHECKPOINTING: True
+    DECODER:
+      NAME: CogVideoXDecoder3D
+      IN_CHANNELS: 16
+      OUT_CHANNELS: 3
+      UP_BLOCK_TYPES: [ "CogVideoXUpBlock3D", "CogVideoXUpBlock3D", "CogVideoXUpBlock3D", "CogVideoXUpBlock3D" ]
+      BLOCK_OUT_CHANNELS: [ 128, 256, 256, 512 ]
+      LAYERS_PER_BLOCK: 3
+      ACT_FN: "silu"
+      NORM_EPS: 1e-6
+      NORM_NUM_GROUPS: 32
+      DROPOUT: 0.0
+      PAD_MODE: "first"
+      TEMPORAL_COMPRESSION_RATIO: 4
+      GRADIENT_CHECKPOINTING: True
+  #
+  COND_STAGE_MODEL:
+    NAME: T5EmbedderHF
+    PRETRAINED_MODEL: ms://AI-ModelScope/t5-v1_1-xxl
+    TOKENIZER_PATH: ms://AI-ModelScope/t5-v1_1-xxl
+    LENGTH: 226
+    CLEAN:
+    USE_GRAD: False

scepter/methods/studio/inference/dit/flux1.0_dev_pro.yaml

@@ -13,8 +13,8 @@ DEFAULT_PARAS:
       VISIBLE: False
     PROMPT_PREFIX: ""
     SAMPLE:
-      VALUES: ["flow_eluer"]
-      DEFAULT: "flow_eluer"
+      VALUES: ["flow_euler"]
+      DEFAULT: "flow_euler"
     SAMPLE_STEPS: 50
     GUIDE_SCALE: 3.5
     GUIDE_RESCALE:

scepter/methods/studio/inference/dit/flux1.0_schnell_pro.yaml

@@ -13,8 +13,8 @@ DEFAULT_PARAS:
       VISIBLE: False
     PROMPT_PREFIX: ""
     SAMPLE:
-      VALUES: ["flow_eluer"]
-      DEFAULT: "flow_eluer"
+      VALUES: ["flow_euler"]
+      DEFAULT: "flow_euler"
     SAMPLE_STEPS: 4
     GUIDE_SCALE: 3.5
     GUIDE_RESCALE:

scepter/methods/studio/inference/inference.yaml

@@ -18,6 +18,16 @@ DIFFUSION_PARAS:
     MAX: 4
     DEFAULT: 1
     VISIBLE: True
+  NUM_FRAMES:
+    MIN: 1
+    MAX: 100
+    DEFAULT: 49
+    VISIBLE: False
+  FPS:
+    MIN: 1
+    MAX: 50
+    DEFAULT: 8
+    VISIBLE: False
   SAMPLE_STEPS:
     MIN: 1
     MAX: 100
@@ -93,7 +103,8 @@ DIFFUSION_PARAS:
              [1664, 576], [1728, 576],
              [2048, 2048], [2048, 1920], [1920, 2048],
              [1536, 2560], [2560, 1536], [2560, 1440],
-             [2560, 1440]
+             [2560, 1440],
+             [480, 720], [720, 480]
             ]
     DEFAULT: [1024, 1024]
     VISIBLE: True

scepter/methods/studio/preprocess/preprocess.yaml

@@ -450,3 +450,28 @@ PROCESSORS:
       SRC_IMAGE_TOOL: sketch
       SRC_IMAGE_INTERACTIVE: True
       CAPTION_INTERACTIVE: False
+
+VIDEO_PROCESSORS:
+  - NAME: CogVLM2Llama3Caption
+    TYPE: caption
+    MODEL_PATH: ms://ZhipuAI/cogvlm2-llama3-caption
+    DEVICE: "gpu"
+    MEMORY: 20000
+    PROMPT: Please describe this video in detail.
+    TEMPERATURE: 0.1
+    MAX_NEW_TOKENS: 2048
+    PAD_TOKEN_ID: 128002
+    TOP_K: 1
+    TOP_P: 0.1
+
+TRANSLATION_PROCESSORS:
+  - NAME: OpusMtZhEn
+    TYPE: caption
+    MODEL_PATH: ms://cubeai/trans-opus-mt-zh-en
+    DEVICE: "gpu"
+    MEMORY: 5000
+  - NAME: OpusMtEnZh
+    TYPE: caption
+    MODEL_PATH: ms://cubeai/trans-opus-mt-en-zh
+    DEVICE: "gpu"
+    MEMORY: 5000

scepter/methods/studio/scepter_ui.yaml

@@ -89,5 +89,5 @@ INTERFACE:
     CONFIG: scepter/methods/studio/inference/inference.yaml
   - NAME: 对话式编辑
     NAME_EN: ChatBot
-    IFID: ChatBot
+    IFID: chatbot
     CONFIG: scepter/methods/studio/chatbot/chatbot.yaml

scepter/methods/studio/self_train/dit/cogvideox_2b_pro.yaml

@@ -0,0 +1,315 @@
+ENV:
+  BACKEND: nccl
+  SEED: 42
+  TENSOR_PARALLEL_SIZE: 1
+  PIPELINE_PARALLEL_SIZE: 1
+  SYS_ENVS:
+    TORCH_CUDNN_V8_API_ENABLED: '1'
+    TOKENIZERS_PARALLELISM: 'false'
+    TF_CPP_MIN_LOG_LEVEL: '3'
+    PYTORCH_CUDA_ALLOC_CONF: 'expandable_segments:True'
+META:
+  VERSION: 'COGVIDEOX_2B'
+  DESCRIPTION: "cogvideox 2b"
+  IS_DEFAULT: False
+  IS_SHARE: True
+  INFERENCE_PARAS:
+    INFERENCE_BATCH_SIZE: 1
+    INFERENCE_PREFIX: ""
+    DEFAULT_SAMPLER: "ddim"
+    DEFAULT_SAMPLE_STEPS: 50
+    INFERENCE_N_PROMPT: ""
+    RESOLUTION: [ 480, 720 ]
+  PARAS:
+    - TRAIN_BATCH_SIZE: 1
+      TRAIN_PREFIX: ""
+      TRAIN_N_PROMPT: ""
+      RESOLUTION: [ 480, 720 ]
+      MEMORY: 89000
+      EPOCHS: 50
+      SAVE_INTERVAL: 25
+      EPSEC: 0.818
+      LEARNING_RATE: 4e-4
+      IS_DEFAULT: False
+      TUNER: FULL
+    - TRAIN_BATCH_SIZE: 1
+      TRAIN_PREFIX: ""
+      TRAIN_N_PROMPT: ""
+      RESOLUTION: [ 480, 720 ]
+      MEMORY: 89000
+      EPOCHS: 50
+      SAVE_INTERVAL: 25
+      EPSEC: 0.818
+      LEARNING_RATE: 4e-4
+      IS_DEFAULT: True
+      TUNER: LORA
+  #
+  TUNERS:
+    LORA:
+      - NAME: SwiftLoRA
+        R: 64
+        LORA_ALPHA: 64
+        LORA_DROPOUT: 0.0
+        BIAS: "none"
+        TARGET_MODULES: "model.*(.to_k|.to_q|.to_v|.to_out.0)$"
+#
+SOLVER:
+  NAME: LatentDiffusionVideoSolver
+  MAX_STEPS: 2000
+  USE_AMP: True
+  DTYPE: bfloat16
+  USE_FAIRSCALE: False
+  USE_FSDP: True
+  LOAD_MODEL_ONLY: False
+  ENABLE_GRADSCALER: False
+  USE_SCALER: False
+  RESUME_FROM:
+  WORK_DIR: ./cache/save_data/dit_cogvideox_2b_lora
+  LOG_FILE: std_log.txt
+  EVAL_INTERVAL: 100
+  LOG_TRAIN_NUM: 4
+  FPS: 8
+  SHARDING_STRATEGY: full_shard
+  FSDP_REDUCE_DTYPE: float32
+  FSDP_BUFFER_DTYPE: float32
+  FSDP_SHARD_MODULES: [ 'model', 'cond_stage_model.model']
+  SAVE_MODULES: [ 'model', 'cond_stage_model.model']
+  TRAIN_MODULES: ['model']
+  #
+  FILE_SYSTEM:
+    NAME: "ModelscopeFs"
+    TEMP_DIR: "./cache/cache_data"
+  #
+  TUNER:
+  #
+  MODEL:
+    NAME: LatentDiffusionCogVideoX
+    PRETRAINED_MODEL:
+    PARAMETERIZATION: v
+    TIMESTEPS: 1000
+    MIN_SNR_GAMMA: 3.0
+    ZERO_TERMINAL_SNR: True
+    SCALE_FACTOR_SPATIAL: 8
+    SCALE_FACTOR_TEMPORAL: 4
+    SCALING_FACTOR_IMAGE: 1.15258426
+    IGNORE_KEYS: [ ]
+    DEFAULT_N_PROMPT:
+    USE_EMA: False
+    EVAL_EMA: False
+    DIFFUSION:
+      NAME: BaseDiffusion
+      PREDICTION_TYPE: v
+      NOISE_SCHEDULER:
+        NAME: ScaledLinearScheduler
+        BETA_MIN: 0.00085
+        BETA_MAX: 0.012
+        SNR_SHIFT_SCALE: 3.0
+        RESCALE_BETAS_ZERO_SNR: True
+      DIFFUSION_SAMPLERS:
+        NAME: DDIMSampler
+        DISCRETIZATION_TYPE: trailing
+        ETA: 0.0
+    #
+    DIFFUSION_MODEL:
+      NAME: CogVideoXTransformer3DModel
+      DTYPE: bfloat16
+      PRETRAINED_MODEL: ms://AI-ModelScope/CogVideoX-2b@transformer/diffusion_pytorch_model.safetensors
+      NUM_ATTENTION_HEADS: 30
+      ATTENTION_HEAD_DIM: 64
+      IN_CHANNELS: 16
+      OUT_CHANNELS: 16
+      FLIP_SIN_TO_COS: True
+      FREQ_SHIFT: 0
+      TIME_EMBED_DIM: 512
+      TEXT_EMBED_DIM: 4096
+      NUM_LAYERS: 30
+      DROPOUT: 0.0
+      ATTENTION_BIAS: True
+      SAMPLE_WIDTH: 90
+      SAMPLE_HEIGHT: 60
+      SAMPLE_FRAMES: 49
+      PATCH_SIZE: 2
+      TEMPORAL_COMPRESSION_RATIO: 4
+      MAX_TEXT_SEQ_LENGTH: 226
+      ACTIVATION_FN: "gelu-approximate"
+      TIMESTEP_ACTIVATION_FN: "silu"
+      NORM_ELEMENTWISE_AFFINE: True
+      NORM_EPS: 1e-5
+      SPATIAL_INTERPOLATION_SCALE: 1.875
+      TEMPORAL_INTERPOLATION_SCALE: 1.0
+      USE_ROTARY_POSITIONAL_EMBEDDINGS: False
+      USE_LEARNED_POSITIONAL_EMBEDDINGS: False
+      GRADIENT_CHECKPOINTING: False
+    #
+    FIRST_STAGE_MODEL:
+      NAME: AutoencoderKLCogVideoX
+      DTYPE: bfloat16
+      PRETRAINED_MODEL: ms://AI-ModelScope/CogVideoX-2b@vae/diffusion_pytorch_model.safetensors
+      SAMPLE_HEIGHT: 480
+      SAMPLE_WIDTH: 720
+      USE_QUANT_CONV: False
+      USE_POST_QUANT_CONV: False
+      USE_SLICING: True
+      USE_TILING: True
+      GRADIENT_CHECKPOINTING: False
+      ENCODER:
+        NAME: CogVideoXEncoder3D
+        IN_CHANNELS: 3
+        OUT_CHANNELS: 16
+        UP_BLOCK_TYPES: [ "CogVideoXDownBlock3D", "CogVideoXDownBlock3D", "CogVideoXDownBlock3D", "CogVideoXDownBlock3D" ]
+        BLOCK_OUT_CHANNELS: [ 128, 256, 256, 512 ]
+        LAYERS_PER_BLOCK: 3
+        ACT_FN: "silu"
+        NORM_EPS: 1e-6
+        NORM_NUM_GROUPS: 32
+        DROPOUT: 0.0
+        PAD_MODE: "first"
+        TEMPORAL_COMPRESSION_RATIO: 4
+        GRADIENT_CHECKPOINTING: False
+      DECODER:
+        NAME: CogVideoXDecoder3D
+        IN_CHANNELS: 16
+        OUT_CHANNELS: 3
+        UP_BLOCK_TYPES: [ "CogVideoXUpBlock3D", "CogVideoXUpBlock3D", "CogVideoXUpBlock3D", "CogVideoXUpBlock3D" ]
+        BLOCK_OUT_CHANNELS: [ 128, 256, 256, 512 ]
+        LAYERS_PER_BLOCK: 3
+        ACT_FN: "silu"
+        NORM_EPS: 1e-6
+        NORM_NUM_GROUPS: 32
+        DROPOUT: 0.0
+        PAD_MODE: "first"
+        TEMPORAL_COMPRESSION_RATIO: 4
+        GRADIENT_CHECKPOINTING: False
+    #
+    COND_STAGE_MODEL:
+      NAME: T5EmbedderHF
+      PRETRAINED_MODEL: ms://AI-ModelScope/t5-v1_1-xxl
+      TOKENIZER_PATH: ms://AI-ModelScope/t5-v1_1-xxl
+      LENGTH: 226
+      CLEAN:
+      USE_GRAD: False
+    #
+    LOSS:
+      NAME: ReconstructLoss
+      LOSS_TYPE: l2
+  #
+  SAMPLE_ARGS:
+    SAMPLER: ddim
+    SAMPLE_STEPS: 50
+    SEED: 42
+    GUIDE_SCALE: 6.0
+    GUIDE_RESCALE: 0.0
+    NUM_FRAMES: 49
+  #
+  OPTIMIZER:
+    NAME: Adam
+    LEARNING_RATE: 1e-3
+    BETAS: [ 0.9, 0.95 ]
+    EPS: 1e-8
+    WEIGHT_DECAY: 0.0
+    AMSGRAD: False
+  #
+#  LR_SCHEDULER:
+#    NAME: StepAnnealingLR
+#    WARMUP_STEPS: 200
+#    TOTAL_STEPS: 2000
+#    DECAY_MODE: 'cosine'
+  #
+  TRAIN_DATA:
+    NAME: VideoGenDatasetOTF
+    MODE: train
+    PIN_MEMORY: True
+    BATCH_SIZE: 1
+    NUM_WORKERS: 4
+    PROMPT_PREFIX: ''
+    DELIMITER: '#;#'
+    FIELDS: [ 'video_path', 'width', 'height', 'prompt' ]
+    PATH_PREFIX:
+    DATA_FILE:
+    SAMPLER:
+      NAME: LoopSampler
+    TRANSFORMS:
+      - NAME: Select
+        KEYS: [ 'video', 'video_latent', "prompt" ]
+        META_KEYS: [ ]
+    MODEL:
+      NAME: AutoencoderKLCogVideoX
+      DTYPE: bfloat16
+      PRETRAINED_MODEL: ms://AI-ModelScope/CogVideoX-2b@vae/diffusion_pytorch_model.safetensors
+      SAMPLE_HEIGHT: 480
+      SAMPLE_WIDTH: 720
+      USE_QUANT_CONV: False
+      USE_POST_QUANT_CONV: False
+      USE_SLICING: True
+      USE_TILING: True
+      GRADIENT_CHECKPOINTING: True
+      ENCODER:
+        NAME: CogVideoXEncoder3D
+        IN_CHANNELS: 3
+        OUT_CHANNELS: 16
+        UP_BLOCK_TYPES: [ "CogVideoXDownBlock3D", "CogVideoXDownBlock3D", "CogVideoXDownBlock3D", "CogVideoXDownBlock3D" ]
+        BLOCK_OUT_CHANNELS: [ 128, 256, 256, 512 ]
+        LAYERS_PER_BLOCK: 3
+        ACT_FN: "silu"
+        NORM_EPS: 1e-6
+        NORM_NUM_GROUPS: 32
+        DROPOUT: 0.0
+        PAD_MODE: "first"
+        TEMPORAL_COMPRESSION_RATIO: 4
+        GRADIENT_CHECKPOINTING: True
+      DECODER:
+        NAME: CogVideoXDecoder3D
+        IN_CHANNELS: 16
+        OUT_CHANNELS: 3
+        UP_BLOCK_TYPES: [ "CogVideoXUpBlock3D", "CogVideoXUpBlock3D", "CogVideoXUpBlock3D", "CogVideoXUpBlock3D" ]
+        BLOCK_OUT_CHANNELS: [ 128, 256, 256, 512 ]
+        LAYERS_PER_BLOCK: 3
+        ACT_FN: "silu"
+        NORM_EPS: 1e-6
+        NORM_NUM_GROUPS: 32
+        DROPOUT: 0.0
+        PAD_MODE: "first"
+        TEMPORAL_COMPRESSION_RATIO: 4
+        GRADIENT_CHECKPOINTING: True
+  #
+  EVAL_DATA:
+    NAME: Text2ImageDataset
+    MODE: eval
+    PROMPT_FILE:
+    PROMPT_DATA: [ "A girl riding a bike.", "A panda, dressed in a small, red jacket and a tiny hat, sits on a wooden stool in a serene bamboo forest. The panda's fluffy paws strum a miniature acoustic guitar, producing soft, melodic tunes. Nearby, a few other pandas gather, watching curiously and some clapping in rhythm. Sunlight filters through the tall bamboo, casting a gentle glow on the scene. The panda's face is expressive, showing concentration and joy as it plays. The background includes a small, flowing stream and vibrant green foliage, enhancing the peaceful and magical atmosphere of this unique musical performance." ]
+    IMAGE_SIZE: [ 480, 720 ]
+    FIELDS: [ "prompt" ]
+    DELIMITER: '#;#'
+    PROMPT_PREFIX: ''
+    PIN_MEMORY: True
+    BATCH_SIZE: 1
+#    USE_NUM: 8
+    NUM_WORKERS: 4
+    TRANSFORMS:
+      - NAME: Select
+        KEYS: [ 'index', 'prompt' ]
+        META_KEYS: [ 'image_size' ]
+  #
+  TRAIN_HOOKS:
+    - NAME: ProbeDataHook
+      PROB_INTERVAL: 100
+      PRIORITY: 0
+    - NAME: BackwardHook
+      PRIORITY: 10
+    - NAME: LogHook
+      LOG_INTERVAL: 10
+      PRIORITY: 20
+    - NAME: CheckpointHook
+      INTERVAL: 1000
+      PRIORITY: 40
+      SAVE_LAST: True
+      SAVE_NAME_PREFIX: 'step'
+      DISABLE_SNAPSHOT: True
+  #
+  EVAL_HOOKS:
+    - NAME: ProbeDataHook
+      PROB_INTERVAL: 100
+      PRIORITY: 0
+      SAVE_LAST: True
+      SAVE_NAME_PREFIX: 'step'
+      SAVE_PROBE_PREFIX: 'image'

scepter/methods/studio/self_train/dit/cogvideox_5b_pro.yaml

@@ -0,0 +1,317 @@
+ENV:
+  BACKEND: nccl
+  SEED: 42
+  TENSOR_PARALLEL_SIZE: 1
+  PIPELINE_PARALLEL_SIZE: 1
+  SYS_ENVS:
+    TORCH_CUDNN_V8_API_ENABLED: '1'
+    TOKENIZERS_PARALLELISM: 'false'
+    TF_CPP_MIN_LOG_LEVEL: '3'
+    PYTORCH_CUDA_ALLOC_CONF: 'expandable_segments:True'
+META:
+  VERSION: 'COGVIDEOX_5B'
+  DESCRIPTION: "cogvideox 5b"
+  IS_DEFAULT: False
+  IS_SHARE: True
+  INFERENCE_PARAS:
+    INFERENCE_BATCH_SIZE: 1
+    INFERENCE_PREFIX: ""
+    DEFAULT_SAMPLER: "ddim"
+    DEFAULT_SAMPLE_STEPS: 50
+    INFERENCE_N_PROMPT: ""
+    RESOLUTION: [ 480, 720 ]
+  PARAS:
+    - TRAIN_BATCH_SIZE: 1
+      TRAIN_PREFIX: ""
+      TRAIN_N_PROMPT: ""
+      RESOLUTION: [ 480, 720 ]
+      MEMORY: 89000
+      EPOCHS: 50
+      SAVE_INTERVAL: 25
+      EPSEC: 0.818
+      LEARNING_RATE: 4e-4
+      IS_DEFAULT: False
+      TUNER: FULL
+    - TRAIN_BATCH_SIZE: 1
+      TRAIN_PREFIX: ""
+      TRAIN_N_PROMPT: ""
+      RESOLUTION: [ 480, 720 ]
+      MEMORY: 89000
+      EPOCHS: 50
+      SAVE_INTERVAL: 25
+      EPSEC: 0.818
+      LEARNING_RATE: 4e-4
+      IS_DEFAULT: True
+      TUNER: LORA
+  #
+  TUNERS:
+    LORA:
+      - NAME: SwiftLoRA
+        R: 64
+        LORA_ALPHA: 64
+        LORA_DROPOUT: 0.0
+        BIAS: "none"
+        TARGET_MODULES: "model.*(.to_k|.to_q|.to_v|.to_out.0)$"
+#
+SOLVER:
+  NAME: LatentDiffusionVideoSolver
+  MAX_STEPS: 2000
+  USE_AMP: True
+  DTYPE: bfloat16
+  USE_FAIRSCALE: False
+  USE_FSDP: True
+  LOAD_MODEL_ONLY: False
+  ENABLE_GRADSCALER: False
+  USE_SCALER: False
+  RESUME_FROM:
+  WORK_DIR: ./cache/save_data/dit_cogvideox_5b_lora
+  LOG_FILE: std_log.txt
+  EVAL_INTERVAL: 100
+  LOG_TRAIN_NUM: 4
+  FPS: 8
+  SHARDING_STRATEGY: full_shard
+  FSDP_REDUCE_DTYPE: float32
+  FSDP_BUFFER_DTYPE: float32
+  FSDP_SHARD_MODULES: [ 'model', 'cond_stage_model.model']
+  SAVE_MODULES: [ 'model', 'cond_stage_model.model']
+  TRAIN_MODULES: ['model']
+  #
+  FILE_SYSTEM:
+    NAME: "ModelscopeFs"
+    TEMP_DIR: "./cache/cache_data"
+  #
+  TUNER:
+  #
+  MODEL:
+    NAME: LatentDiffusionCogVideoX
+    PRETRAINED_MODEL:
+    PARAMETERIZATION: v
+    TIMESTEPS: 1000
+    MIN_SNR_GAMMA: 3.0
+    ZERO_TERMINAL_SNR: True
+    SCALE_FACTOR_SPATIAL: 8
+    SCALE_FACTOR_TEMPORAL: 4
+    SCALING_FACTOR_IMAGE: 0.7  # 5b diff
+    IGNORE_KEYS: [ ]
+    DEFAULT_N_PROMPT:
+    USE_EMA: False
+    EVAL_EMA: False
+    DIFFUSION:
+      NAME: BaseDiffusion
+      PREDICTION_TYPE: v
+      NOISE_SCHEDULER:
+        NAME: ScaledLinearScheduler
+        BETA_MIN: 0.00085
+        BETA_MAX: 0.012
+        SNR_SHIFT_SCALE: 1.0  # 5b diff
+        RESCALE_BETAS_ZERO_SNR: True
+      DIFFUSION_SAMPLERS:
+        NAME: DDIMSampler
+        DISCRETIZATION_TYPE: trailing
+        ETA: 0.0
+    #
+    DIFFUSION_MODEL:
+      NAME: CogVideoXTransformer3DModel
+      DTYPE: bfloat16
+      PRETRAINED_MODEL: # 5b diff
+        - ms://AI-ModelScope/CogVideoX-5b@transformer/diffusion_pytorch_model-00001-of-00002.safetensors
+        - ms://AI-ModelScope/CogVideoX-5b@transformer/diffusion_pytorch_model-00002-of-00002.safetensors
+      NUM_ATTENTION_HEADS: 48  # 5b diff
+      ATTENTION_HEAD_DIM: 64
+      IN_CHANNELS: 16
+      OUT_CHANNELS: 16
+      FLIP_SIN_TO_COS: True
+      FREQ_SHIFT: 0
+      TIME_EMBED_DIM: 512
+      TEXT_EMBED_DIM: 4096
+      NUM_LAYERS: 42  # 5b diff
+      DROPOUT: 0.0
+      ATTENTION_BIAS: True
+      SAMPLE_WIDTH: 90
+      SAMPLE_HEIGHT: 60
+      SAMPLE_FRAMES: 49
+      PATCH_SIZE: 2
+      TEMPORAL_COMPRESSION_RATIO: 4
+      MAX_TEXT_SEQ_LENGTH: 226
+      ACTIVATION_FN: "gelu-approximate"
+      TIMESTEP_ACTIVATION_FN: "silu"
+      NORM_ELEMENTWISE_AFFINE: True
+      NORM_EPS: 1e-5
+      SPATIAL_INTERPOLATION_SCALE: 1.875
+      TEMPORAL_INTERPOLATION_SCALE: 1.0
+      USE_ROTARY_POSITIONAL_EMBEDDINGS: True # 5b diff
+      USE_LEARNED_POSITIONAL_EMBEDDINGS: False
+      GRADIENT_CHECKPOINTING: True
+    #
+    FIRST_STAGE_MODEL:
+      NAME: AutoencoderKLCogVideoX
+      DTYPE: bfloat16
+      PRETRAINED_MODEL: ms://AI-ModelScope/CogVideoX-5b@vae/diffusion_pytorch_model.safetensors # 5b diff
+      SAMPLE_HEIGHT: 480
+      SAMPLE_WIDTH: 720
+      USE_QUANT_CONV: False
+      USE_POST_QUANT_CONV: False
+      USE_SLICING: True
+      USE_TILING: True
+      GRADIENT_CHECKPOINTING: True
+      ENCODER:
+        NAME: CogVideoXEncoder3D
+        IN_CHANNELS: 3
+        OUT_CHANNELS: 16
+        UP_BLOCK_TYPES: [ "CogVideoXDownBlock3D", "CogVideoXDownBlock3D", "CogVideoXDownBlock3D", "CogVideoXDownBlock3D" ]
+        BLOCK_OUT_CHANNELS: [ 128, 256, 256, 512 ]
+        LAYERS_PER_BLOCK: 3
+        ACT_FN: "silu"
+        NORM_EPS: 1e-6
+        NORM_NUM_GROUPS: 32
+        DROPOUT: 0.0
+        PAD_MODE: "first"
+        TEMPORAL_COMPRESSION_RATIO: 4
+        GRADIENT_CHECKPOINTING: True
+      DECODER:
+        NAME: CogVideoXDecoder3D
+        IN_CHANNELS: 16
+        OUT_CHANNELS: 3
+        UP_BLOCK_TYPES: [ "CogVideoXUpBlock3D", "CogVideoXUpBlock3D", "CogVideoXUpBlock3D", "CogVideoXUpBlock3D" ]
+        BLOCK_OUT_CHANNELS: [ 128, 256, 256, 512 ]
+        LAYERS_PER_BLOCK: 3
+        ACT_FN: "silu"
+        NORM_EPS: 1e-6
+        NORM_NUM_GROUPS: 32
+        DROPOUT: 0.0
+        PAD_MODE: "first"
+        TEMPORAL_COMPRESSION_RATIO: 4
+        GRADIENT_CHECKPOINTING: True
+    #
+    COND_STAGE_MODEL:
+      NAME: T5EmbedderHF
+      PRETRAINED_MODEL: ms://AI-ModelScope/t5-v1_1-xxl
+      TOKENIZER_PATH: ms://AI-ModelScope/t5-v1_1-xxl
+      LENGTH: 226
+      CLEAN:
+      USE_GRAD: False
+    #
+    LOSS:
+      NAME: ReconstructLoss
+      LOSS_TYPE: l2
+  #
+  SAMPLE_ARGS:
+    SAMPLER: ddim
+    SAMPLE_STEPS: 50
+    SEED: 42
+    GUIDE_SCALE: 6.0
+    GUIDE_RESCALE: 0.0
+    NUM_FRAMES: 49
+  #
+  OPTIMIZER:
+    NAME: Adam
+    LEARNING_RATE: 1e-3
+    BETAS: [ 0.9, 0.95 ]
+    EPS: 1e-8
+    WEIGHT_DECAY: 0.0
+    AMSGRAD: False
+  #
+#  LR_SCHEDULER:
+#    NAME: StepAnnealingLR
+#    WARMUP_STEPS: 200
+#    TOTAL_STEPS: 2000
+#    DECAY_MODE: 'cosine'
+  #
+  TRAIN_DATA:
+    NAME: VideoGenDatasetOTF
+    MODE: train
+    PIN_MEMORY: True
+    BATCH_SIZE: 1
+    NUM_WORKERS: 4
+    PROMPT_PREFIX: ''
+    DELIMITER: '#;#'
+    FIELDS: [ 'video_path', 'width', 'height', 'prompt' ]
+    PATH_PREFIX:
+    DATA_FILE:
+    SAMPLER:
+      NAME: LoopSampler
+    TRANSFORMS:
+      - NAME: Select
+        KEYS: [ 'video', 'video_latent', "prompt" ]
+        META_KEYS: [ ]
+    MODEL:
+      NAME: AutoencoderKLCogVideoX
+      DTYPE: bfloat16
+      PRETRAINED_MODEL: ms://AI-ModelScope/CogVideoX-5b@vae/diffusion_pytorch_model.safetensors
+      SAMPLE_HEIGHT: 480
+      SAMPLE_WIDTH: 720
+      USE_QUANT_CONV: False
+      USE_POST_QUANT_CONV: False
+      USE_SLICING: True
+      USE_TILING: True
+      GRADIENT_CHECKPOINTING: True
+      ENCODER:
+        NAME: CogVideoXEncoder3D
+        IN_CHANNELS: 3
+        OUT_CHANNELS: 16
+        UP_BLOCK_TYPES: [ "CogVideoXDownBlock3D", "CogVideoXDownBlock3D", "CogVideoXDownBlock3D", "CogVideoXDownBlock3D" ]
+        BLOCK_OUT_CHANNELS: [ 128, 256, 256, 512 ]
+        LAYERS_PER_BLOCK: 3
+        ACT_FN: "silu"
+        NORM_EPS: 1e-6
+        NORM_NUM_GROUPS: 32
+        DROPOUT: 0.0
+        PAD_MODE: "first"
+        TEMPORAL_COMPRESSION_RATIO: 4
+        GRADIENT_CHECKPOINTING: True
+      DECODER:
+        NAME: CogVideoXDecoder3D
+        IN_CHANNELS: 16
+        OUT_CHANNELS: 3
+        UP_BLOCK_TYPES: [ "CogVideoXUpBlock3D", "CogVideoXUpBlock3D", "CogVideoXUpBlock3D", "CogVideoXUpBlock3D" ]
+        BLOCK_OUT_CHANNELS: [ 128, 256, 256, 512 ]
+        LAYERS_PER_BLOCK: 3
+        ACT_FN: "silu"
+        NORM_EPS: 1e-6
+        NORM_NUM_GROUPS: 32
+        DROPOUT: 0.0
+        PAD_MODE: "first"
+        TEMPORAL_COMPRESSION_RATIO: 4
+        GRADIENT_CHECKPOINTING: True
+  #
+  EVAL_DATA:
+    NAME: Text2ImageDataset
+    MODE: eval
+    PROMPT_FILE:
+    PROMPT_DATA: [ "A girl riding a bike.", "A panda, dressed in a small, red jacket and a tiny hat, sits on a wooden stool in a serene bamboo forest. The panda's fluffy paws strum a miniature acoustic guitar, producing soft, melodic tunes. Nearby, a few other pandas gather, watching curiously and some clapping in rhythm. Sunlight filters through the tall bamboo, casting a gentle glow on the scene. The panda's face is expressive, showing concentration and joy as it plays. The background includes a small, flowing stream and vibrant green foliage, enhancing the peaceful and magical atmosphere of this unique musical performance." ]
+    IMAGE_SIZE: [ 480, 720 ]
+    FIELDS: [ "prompt" ]
+    DELIMITER: '#;#'
+    PROMPT_PREFIX: ''
+    PIN_MEMORY: True
+    BATCH_SIZE: 1
+    #    USE_NUM: 8
+    NUM_WORKERS: 4
+    TRANSFORMS:
+      - NAME: Select
+        KEYS: [ 'index', 'prompt' ]
+        META_KEYS: [ 'image_size' ]
+  #
+  TRAIN_HOOKS:
+    - NAME: ProbeDataHook
+      PROB_INTERVAL: 100
+      PRIORITY: 0
+    - NAME: BackwardHook
+      PRIORITY: 10
+    - NAME: LogHook
+      LOG_INTERVAL: 10
+      PRIORITY: 20
+    - NAME: CheckpointHook
+      INTERVAL: 1000
+      PRIORITY: 40
+      SAVE_LAST: True
+      SAVE_NAME_PREFIX: 'step'
+      DISABLE_SNAPSHOT: True
+  #
+  EVAL_HOOKS:
+    - NAME: ProbeDataHook
+      PROB_INTERVAL: 100
+      PRIORITY: 0
+      SAVE_LAST: True
+      SAVE_NAME_PREFIX: 'step'
+      SAVE_PROBE_PREFIX: 'image'

scepter/methods/studio/self_train/dit/flux1.0_dv_pro.yaml

@@ -3,7 +3,7 @@ ENV:
 META:
   VERSION: 'FLUX1.0_DEV'
   DESCRIPTION: "flux 1.0 dev"
-  IS_DEFAULT: False
+  IS_DEFAULT: True
   IS_SHARE: True
   INFERENCE_PARAS:
     INFERENCE_BATCH_SIZE: 1
@@ -50,43 +50,33 @@ META:
 #
 SOLVER:
   NAME: LatentDiffusionSolver
-  # MAX_STEPS DESCRIPTION: The total steps for training. TYPE: int default: 100000
   MAX_STEPS: 100000
-  # USE_AMP DESCRIPTION: Use amp to surpport mix precision or not, default is False. TYPE: bool default: False
   USE_AMP: True
-  # DTYPE DESCRIPTION: The precision for training. TYPE: str default: 'float32'
   DTYPE: bfloat16
-  # USE_FAIRSCALE DESCRIPTION: Use fairscale as the backend of ddp, default False. TYPE: bool default: False
   USE_FAIRSCALE: False
-  # USE_FSDP DESCRIPTION: Use fsdp as the backend of ddp, default False. TYPE: bool default: False
   USE_FSDP: True
-  # LOAD_MODEL_ONLY DESCRIPTION: Only load the model rather than the optimizer and schedule, default is False. TYPE: bool default: False
   LOAD_MODEL_ONLY: False
-  # RESUME_FROM DESCRIPTION: Resume from some state of training! TYPE: str default: ''
   RESUME_FROM:
   WORK_DIR: ./cache/save_data/dit_flux_dev_1024_lora
   LOG_FILE: std_log.txt
-  # EVAL_INTERVAL DESCRIPTION: Eval the model interval. TYPE: int default: 1
   EVAL_INTERVAL: 100
-  # LOG_TRAIN_NUM DESCRIPTION: The number samples used to log in training phase. TYPE: int default: -1
   LOG_TRAIN_NUM: 16
-  # FSDP_REDUCE_DTYPE DESCRIPTION: The dtype of reduce in FSDP. TYPE: str default: 'float16'
+  ENABLE_GRADSCALER: False
+  USE_SCALER: False
   FSDP_REDUCE_DTYPE: float32
-  # FSDP_BUFFER_DTYPE DESCRIPTION: The dtype of buffer in FSDP. TYPE: str default: 'float16'
   FSDP_BUFFER_DTYPE: float32
-  # FSDP_SHARD_MODULES DESCRIPTION: The modules to be sharded in FSDP. TYPE: list default: ['model']
   FSDP_SHARD_MODULES: [ 'model', 'cond_stage_model.t5_model' ] #
-  SAVE_MODULES: [ 'model'] #
+  SAVE_MODULES: [ 'model']
   TRAIN_MODULES: ['model']
-  #
+
   FILE_SYSTEM:
     NAME: "ModelscopeFs"
     TEMP_DIR: "./cache/cache_data"
-  #
+
   FREEZE:
-  #
+
   TUNER:
-  #
+
   MODEL:
     NAME: LatentDiffusionFlux
     PARAMETERIZATION: rf
@@ -99,65 +89,39 @@ SOLVER:
     USE_EMA: False
     EVAL_EMA: False
     DIFFUSION:
-      # NAME DESCRIPTION:  TYPE:  default: 'DiffusionFluxRF'
       NAME: DiffusionFluxRF
       PREDICTION_TYPE: raw
-      # NOISE_SCHEDULER DESCRIPTION:  TYPE:  default: ''
       NOISE_SCHEDULER:
-        # NAME DESCRIPTION:  TYPE:  default: 'FlowMatchSigmaScheduler'
         NAME: FlowMatchSigmaScheduler
-        # WEIGHTING_SCHEME DESCRIPTION: The weighting scheme for sampling timesteps, choose from ['sigma_sqrt', 'logit_normal', 'mode', 'cosmap', 'none']. TYPE: str default: 'logit_normal'
         WEIGHTING_SCHEME: logit_normal
         SHIFT: 3.0
-        # LOGIT_MEAN DESCRIPTION: The mean of the logit distribution for sampling timesteps. TYPE: float default: 0.0
         LOGIT_MEAN: 0.0
-        # LOGIT_STD DESCRIPTION: The standard deviation of the logit distribution for sampling timesteps. TYPE: float default: 1.0
         LOGIT_STD: 1.0
-        # MODE_SCALE DESCRIPTION: The scale factor for the mode of the logit distribution for sampling timesteps. TYPE: float default: 1.29
         MODE_SCALE: 1.29
       SAMPLER_SCHEDULER:
-        # NAME DESCRIPTION:  TYPE:  default: 'FlowMatchFluxShiftScheduler'
         NAME: FlowMatchFluxShiftScheduler
-        # SHIFT DESCRIPTION: Use timestamp shift or not, default is True. TYPE: bool default: True
         SHIFT: False
-        # SIGMOID_SCALE DESCRIPTION: The scale of sigmoid function for sampling timesteps. TYPE: int default: 1
         SIGMOID_SCALE: 1
-        # BASE_SHIFT DESCRIPTION: The base shift factor for the timestamp. TYPE: float default: 0.5
         BASE_SHIFT: 0.5
-        # MAX_SHIFT DESCRIPTION: The max shift factor for the timestamp. TYPE: float default: 1.15
         MAX_SHIFT: 1.15
-        #
+
     DIFFUSION_MODEL:
-      # NAME DESCRIPTION:  TYPE:  default: 'Flux'
       NAME: Flux
       PRETRAINED_MODEL: ms://AI-ModelScope/FLUX.1-dev@flux1-dev.safetensors
-      # IN_CHANNELS DESCRIPTION: model's input channels. TYPE: int default: 64
       IN_CHANNELS: 64
-      # HIDDEN_SIZE DESCRIPTION: model's hidden size. TYPE: int default: 1024
       HIDDEN_SIZE: 3072
-      # NUM_HEADS DESCRIPTION: number of heads in the transformer. TYPE: int default: 16
       NUM_HEADS: 24
-      # AXES_DIM DESCRIPTION: dimensions of the axes of the positional encoding. TYPE: list default: [16, 56, 56]
       AXES_DIM: [ 16, 56, 56 ]
-      # THETA DESCRIPTION: theta for positional encoding. TYPE: int default: 10000
       THETA: 10000
-      # VEC_IN_DIM DESCRIPTION: dimension of the vector input. TYPE: int default: 768
       VEC_IN_DIM: 768
-      # GUIDANCE_EMBED DESCRIPTION: whether to use guidance embedding. TYPE: bool default: False
       GUIDANCE_EMBED: False
-      # CONTEXT_IN_DIM DESCRIPTION: dimension of the context input. TYPE: int default: 4096
       CONTEXT_IN_DIM: 4096
-      # MLP_RATIO DESCRIPTION: ratio of mlp hidden size to hidden size. TYPE: float default: 4.0
       MLP_RATIO: 4.0
-      # QKV_BIAS DESCRIPTION: whether to use bias in qkv projection. TYPE: bool default: True
       QKV_BIAS: True
-      # DEPTH DESCRIPTION: number of transformer blocks. TYPE: int default: 19
       DEPTH: 19
-      # DEPTH_SINGLE_BLOCKS DESCRIPTION: number of transformer blocks in the single stream block. TYPE: int default: 38
       DEPTH_SINGLE_BLOCKS: 38
       USE_GRAD_CHECKPOINT: True
 
-    #
     FIRST_STAGE_MODEL:
       NAME: AutoencoderKLFlux
       EMBED_DIM: 16
@@ -167,7 +131,7 @@ SOLVER:
       USE_CONV: False
       SCALE_FACTOR: 0.3611
       SHIFT_FACTOR: 0.1159
-      #
+
       ENCODER:
         NAME: Encoder
         USE_CHECKPOINT: True
@@ -181,7 +145,7 @@ SOLVER:
         DOUBLE_Z: True
         DROPOUT: 0.0
         RESAMP_WITH_CONV: True
-      #
+
       DECODER:
         NAME: Decoder
         USE_CHECKPOINT: True
@@ -196,61 +160,40 @@ SOLVER:
         RESAMP_WITH_CONV: True
         GIVE_PRE_END: False
         TANH_OUT: False
-    #
+
     COND_STAGE_MODEL:
-      # NAME DESCRIPTION:  TYPE:  default: 'T5PlusClipFluxEmbedder'
       NAME: T5PlusClipFluxEmbedder
-      # T5_MODEL DESCRIPTION:  TYPE:  default: ''
       T5_MODEL:
-        # NAME DESCRIPTION:  TYPE:  default: 'HFEmbedder'
         NAME: HFEmbedder
-        # HF_MODEL_CLS DESCRIPTION: huggingface cls in transfomer TYPE: NoneType default: None
         HF_MODEL_CLS: T5EncoderModel
-        # MODEL_PATH DESCRIPTION: model folder path TYPE: NoneType default: None
         MODEL_PATH: ms://AI-ModelScope/FLUX.1-dev@text_encoder_2/
-        # HF_TOKENIZER_CLS DESCRIPTION: huggingface cls in transfomer TYPE: NoneType default: None
         HF_TOKENIZER_CLS: T5Tokenizer
-        # TOKENIZER_PATH DESCRIPTION: tokenizer folder path TYPE: NoneType default: None
         TOKENIZER_PATH: ms://AI-ModelScope/FLUX.1-dev@tokenizer_2/
-        # MAX_LENGTH DESCRIPTION: max length of input TYPE: int default: 77
         MAX_LENGTH: 512
-        # OUTPUT_KEY DESCRIPTION: output key TYPE: str default: 'last_hidden_state'
         OUTPUT_KEY: last_hidden_state
-        # D_TYPE DESCRIPTION: dtype TYPE: str default: 'bfloat16'
         D_TYPE: bfloat16
-        # BATCH_INFER DESCRIPTION: batch infer TYPE: bool default: False
         BATCH_INFER: False
         CLEAN: whitespace
-      # CLIP_MODEL DESCRIPTION:  TYPE:  default: ''
       CLIP_MODEL:
-        # NAME DESCRIPTION:  TYPE:  default: 'HFEmbedder'
         NAME: HFEmbedder
-        # HF_MODEL_CLS DESCRIPTION: huggingface cls in transfomer TYPE: NoneType default: None
         HF_MODEL_CLS: CLIPTextModel
-        # MODEL_PATH DESCRIPTION: model folder path TYPE: NoneType default: None
         MODEL_PATH: ms://AI-ModelScope/FLUX.1-dev@text_encoder/
-        # HF_TOKENIZER_CLS DESCRIPTION: huggingface cls in transfomer TYPE: NoneType default: None
         HF_TOKENIZER_CLS: CLIPTokenizer
-        # TOKENIZER_PATH DESCRIPTION: tokenizer folder path TYPE: NoneType default: None
         TOKENIZER_PATH: ms://AI-ModelScope/FLUX.1-dev@tokenizer/
-        # MAX_LENGTH DESCRIPTION: max length of input TYPE: int default: 77
         MAX_LENGTH: 77
-        # OUTPUT_KEY DESCRIPTION: output key TYPE: str default: 'last_hidden_state'
         OUTPUT_KEY: pooler_output
-        # D_TYPE DESCRIPTION: dtype TYPE: str default: 'bfloat16'
         D_TYPE: bfloat16
-        # BATCH_INFER DESCRIPTION: batch infer TYPE: bool default: False
         BATCH_INFER: True
         CLEAN: whitespace
-  #
+
   SAMPLE_ARGS:
     SAMPLE_STEPS: 50
-    SAMPLER: flow_eluer
+    SAMPLER: flow_euler
     SEED: 2024
     IMAGE_SIZE: [ 1024, 1024 ]
     SHIFT: True
     GUIDE_SCALE: 3.5
-  #
+
   OPTIMIZER:
     NAME: AdamW
     LEARNING_RATE: 4e-4
@@ -258,7 +201,7 @@ SOLVER:
     EPS: 1e-8
     WEIGHT_DECAY: 1e-2
     AMSGRAD: False
-  #
+
   TRAIN_DATA:
     NAME: ImageTextPairMSDataset
     MODE: train
@@ -302,7 +245,7 @@ SOLVER:
       - NAME: Select
         KEYS: [ 'image', 'prompt' ]
         META_KEYS: [ 'data_key' ]
-  #
+
   EVAL_DATA:
     NAME: Text2ImageDataset
     MODE: eval
@@ -319,13 +262,12 @@ SOLVER:
       - NAME: Select
         KEYS: [ 'index', 'prompt' ]
         META_KEYS: [ 'image_size' ]
-  #
+
   TRAIN_HOOKS:
     - NAME: ProbeDataHook
       PROB_INTERVAL: 100
       PRIORITY: 0
     - NAME: BackwardHook
-#      GRADIENT_CLIP: 1.0
       PRIORITY: 10
     - NAME: LogHook
       LOG_INTERVAL: 10

scepter/methods/studio/self_train/dit/flux1.0_schnell_pro.yaml

@@ -50,43 +50,33 @@ META:
 #
 SOLVER:
   NAME: LatentDiffusionSolver
-  # MAX_STEPS DESCRIPTION: The total steps for training. TYPE: int default: 100000
   MAX_STEPS: 100000
-  # USE_AMP DESCRIPTION: Use amp to surpport mix precision or not, default is False. TYPE: bool default: False
   USE_AMP: True
-  # DTYPE DESCRIPTION: The precision for training. TYPE: str default: 'float32'
   DTYPE: bfloat16
-  # USE_FAIRSCALE DESCRIPTION: Use fairscale as the backend of ddp, default False. TYPE: bool default: False
   USE_FAIRSCALE: False
-  # USE_FSDP DESCRIPTION: Use fsdp as the backend of ddp, default False. TYPE: bool default: False
   USE_FSDP: True
-  # LOAD_MODEL_ONLY DESCRIPTION: Only load the model rather than the optimizer and schedule, default is False. TYPE: bool default: False
   LOAD_MODEL_ONLY: False
-  # RESUME_FROM DESCRIPTION: Resume from some state of training! TYPE: str default: ''
   RESUME_FROM:
   WORK_DIR: ./cache/save_data/dit_flux_schnell_1024_lora
   LOG_FILE: std_log.txt
-  # EVAL_INTERVAL DESCRIPTION: Eval the model interval. TYPE: int default: 1
   EVAL_INTERVAL: 100
-  # LOG_TRAIN_NUM DESCRIPTION: The number samples used to log in training phase. TYPE: int default: -1
   LOG_TRAIN_NUM: 16
-  # FSDP_REDUCE_DTYPE DESCRIPTION: The dtype of reduce in FSDP. TYPE: str default: 'float16'
+  ENABLE_GRADSCALER: False
+  USE_SCALER: False
   FSDP_REDUCE_DTYPE: float32
-  # FSDP_BUFFER_DTYPE DESCRIPTION: The dtype of buffer in FSDP. TYPE: str default: 'float16'
   FSDP_BUFFER_DTYPE: float32
-  # FSDP_SHARD_MODULES DESCRIPTION: The modules to be sharded in FSDP. TYPE: list default: ['model']
-  FSDP_SHARD_MODULES: [ 'model', 'cond_stage_model.t5_model' ] #
-  SAVE_MODULES: [ 'model'] #
+  FSDP_SHARD_MODULES: [ 'model', 'cond_stage_model.t5_model' ]
+  SAVE_MODULES: [ 'model']
   TRAIN_MODULES: ['model']
   #
   FILE_SYSTEM:
     NAME: "ModelscopeFs"
     TEMP_DIR: "./cache/cache_data"
-  #
+
   FREEZE:
-  #
+
   TUNER:
-  #
+
   MODEL:
     NAME: LatentDiffusionFlux
     PARAMETERIZATION: rf
@@ -99,65 +89,39 @@ SOLVER:
     USE_EMA: False
     EVAL_EMA: False
     DIFFUSION:
-      # NAME DESCRIPTION:  TYPE:  default: 'DiffusionFluxRF'
       NAME: DiffusionFluxRF
       PREDICTION_TYPE: raw
-      # NOISE_SCHEDULER DESCRIPTION:  TYPE:  default: ''
       NOISE_SCHEDULER:
-        # NAME DESCRIPTION:  TYPE:  default: 'FlowMatchSigmaScheduler'
         NAME: FlowMatchSigmaScheduler
-        # WEIGHTING_SCHEME DESCRIPTION: The weighting scheme for sampling timesteps, choose from ['sigma_sqrt', 'logit_normal', 'mode', 'cosmap', 'none']. TYPE: str default: 'logit_normal'
         WEIGHTING_SCHEME: logit_normal
         SHIFT: 3.0
-        # LOGIT_MEAN DESCRIPTION: The mean of the logit distribution for sampling timesteps. TYPE: float default: 0.0
         LOGIT_MEAN: 0.0
-        # LOGIT_STD DESCRIPTION: The standard deviation of the logit distribution for sampling timesteps. TYPE: float default: 1.0
         LOGIT_STD: 1.0
-        # MODE_SCALE DESCRIPTION: The scale factor for the mode of the logit distribution for sampling timesteps. TYPE: float default: 1.29
         MODE_SCALE: 1.29
       SAMPLER_SCHEDULER:
-        # NAME DESCRIPTION:  TYPE:  default: 'FlowMatchFluxShiftScheduler'
         NAME: FlowMatchFluxShiftScheduler
-        # SHIFT DESCRIPTION: Use timestamp shift or not, default is True. TYPE: bool default: True
         SHIFT: False
-        # SIGMOID_SCALE DESCRIPTION: The scale of sigmoid function for sampling timesteps. TYPE: int default: 1
         SIGMOID_SCALE: 1
-        # BASE_SHIFT DESCRIPTION: The base shift factor for the timestamp. TYPE: float default: 0.5
         BASE_SHIFT: 0.5
-        # MAX_SHIFT DESCRIPTION: The max shift factor for the timestamp. TYPE: float default: 1.15
         MAX_SHIFT: 1.15
-        #
+
     DIFFUSION_MODEL:
-      # NAME DESCRIPTION:  TYPE:  default: 'Flux'
       NAME: Flux
       PRETRAINED_MODEL: ms://AI-ModelScope/FLUX.1-schnell@flux1-schnell.safetensors
-      # IN_CHANNELS DESCRIPTION: model's input channels. TYPE: int default: 64
       IN_CHANNELS: 64
-      # HIDDEN_SIZE DESCRIPTION: model's hidden size. TYPE: int default: 1024
       HIDDEN_SIZE: 3072
-      # NUM_HEADS DESCRIPTION: number of heads in the transformer. TYPE: int default: 16
       NUM_HEADS: 24
-      # AXES_DIM DESCRIPTION: dimensions of the axes of the positional encoding. TYPE: list default: [16, 56, 56]
       AXES_DIM: [ 16, 56, 56 ]
-      # THETA DESCRIPTION: theta for positional encoding. TYPE: int default: 10000
       THETA: 10000
-      # VEC_IN_DIM DESCRIPTION: dimension of the vector input. TYPE: int default: 768
       VEC_IN_DIM: 768
-      # GUIDANCE_EMBED DESCRIPTION: whether to use guidance embedding. TYPE: bool default: False
       GUIDANCE_EMBED: False
-      # CONTEXT_IN_DIM DESCRIPTION: dimension of the context input. TYPE: int default: 4096
       CONTEXT_IN_DIM: 4096
-      # MLP_RATIO DESCRIPTION: ratio of mlp hidden size to hidden size. TYPE: float default: 4.0
       MLP_RATIO: 4.0
-      # QKV_BIAS DESCRIPTION: whether to use bias in qkv projection. TYPE: bool default: True
       QKV_BIAS: True
-      # DEPTH DESCRIPTION: number of transformer blocks. TYPE: int default: 19
       DEPTH: 19
-      # DEPTH_SINGLE_BLOCKS DESCRIPTION: number of transformer blocks in the single stream block. TYPE: int default: 38
       DEPTH_SINGLE_BLOCKS: 38
       USE_GRAD_CHECKPOINT: True
 
-    #
     FIRST_STAGE_MODEL:
       NAME: AutoencoderKLFlux
       EMBED_DIM: 16
@@ -167,7 +131,7 @@ SOLVER:
       USE_CONV: False
       SCALE_FACTOR: 0.3611
       SHIFT_FACTOR: 0.1159
-      #
+
       ENCODER:
         NAME: Encoder
         USE_CHECKPOINT: True
@@ -181,7 +145,7 @@ SOLVER:
         DOUBLE_Z: True
         DROPOUT: 0.0
         RESAMP_WITH_CONV: True
-      #
+
       DECODER:
         NAME: Decoder
         USE_CHECKPOINT: True
@@ -196,60 +160,39 @@ SOLVER:
         RESAMP_WITH_CONV: True
         GIVE_PRE_END: False
         TANH_OUT: False
-    #
+
     COND_STAGE_MODEL:
-      # NAME DESCRIPTION:  TYPE:  default: 'T5PlusClipFluxEmbedder'
       NAME: T5PlusClipFluxEmbedder
-      # T5_MODEL DESCRIPTION:  TYPE:  default: ''
       T5_MODEL:
-        # NAME DESCRIPTION:  TYPE:  default: 'HFEmbedder'
         NAME: HFEmbedder
-        # HF_MODEL_CLS DESCRIPTION: huggingface cls in transfomer TYPE: NoneType default: None
         HF_MODEL_CLS: T5EncoderModel
-        # MODEL_PATH DESCRIPTION: model folder path TYPE: NoneType default: None
         MODEL_PATH: ms://AI-ModelScope/FLUX.1-schnell@text_encoder_2/
-        # HF_TOKENIZER_CLS DESCRIPTION: huggingface cls in transfomer TYPE: NoneType default: None
         HF_TOKENIZER_CLS: T5Tokenizer
-        # TOKENIZER_PATH DESCRIPTION: tokenizer folder path TYPE: NoneType default: None
         TOKENIZER_PATH: ms://AI-ModelScope/FLUX.1-schnell@tokenizer_2/
-        # MAX_LENGTH DESCRIPTION: max length of input TYPE: int default: 77
         MAX_LENGTH: 256
-        # OUTPUT_KEY DESCRIPTION: output key TYPE: str default: 'last_hidden_state'
         OUTPUT_KEY: last_hidden_state
-        # D_TYPE DESCRIPTION: dtype TYPE: str default: 'bfloat16'
         D_TYPE: bfloat16
-        # BATCH_INFER DESCRIPTION: batch infer TYPE: bool default: False
         BATCH_INFER: False
         CLEAN: whitespace
-      # CLIP_MODEL DESCRIPTION:  TYPE:  default: ''
       CLIP_MODEL:
-        # NAME DESCRIPTION:  TYPE:  default: 'HFEmbedder'
         NAME: HFEmbedder
-        # HF_MODEL_CLS DESCRIPTION: huggingface cls in transfomer TYPE: NoneType default: None
         HF_MODEL_CLS: CLIPTextModel
-        # MODEL_PATH DESCRIPTION: model folder path TYPE: NoneType default: None
         MODEL_PATH: ms://AI-ModelScope/FLUX.1-schnell@text_encoder/
-        # HF_TOKENIZER_CLS DESCRIPTION: huggingface cls in transfomer TYPE: NoneType default: None
         HF_TOKENIZER_CLS: CLIPTokenizer
-        # TOKENIZER_PATH DESCRIPTION: tokenizer folder path TYPE: NoneType default: None
         TOKENIZER_PATH: ms://AI-ModelScope/FLUX.1-schnell@tokenizer/
-        # MAX_LENGTH DESCRIPTION: max length of input TYPE: int default: 77
         MAX_LENGTH: 77
-        # OUTPUT_KEY DESCRIPTION: output key TYPE: str default: 'last_hidden_state'
         OUTPUT_KEY: pooler_output
-        # D_TYPE DESCRIPTION: dtype TYPE: str default: 'bfloat16'
         D_TYPE: bfloat16
-        # BATCH_INFER DESCRIPTION: batch infer TYPE: bool default: False
         BATCH_INFER: True
         CLEAN: whitespace
-  #
+
   SAMPLE_ARGS:
     SAMPLE_STEPS: 4
-    SAMPLER: flow_eluer
+    SAMPLER: flow_euler
     SEED: 2024
     IMAGE_SIZE: [ 1024, 1024 ]
     GUIDE_SCALE: 3.5
-  #
+
   OPTIMIZER:
     NAME: AdamW
     LEARNING_RATE: 4e-4
@@ -257,7 +200,7 @@ SOLVER:
     EPS: 1e-8
     WEIGHT_DECAY: 1e-2
     AMSGRAD: False
-  #
+
   TRAIN_DATA:
     NAME: ImageTextPairMSDataset
     MODE: train
@@ -301,7 +244,7 @@ SOLVER:
       - NAME: Select
         KEYS: [ 'image', 'prompt' ]
         META_KEYS: [ 'data_key' ]
-  #
+
   EVAL_DATA:
     NAME: Text2ImageDataset
     MODE: eval
@@ -318,13 +261,12 @@ SOLVER:
       - NAME: Select
         KEYS: [ 'index', 'prompt' ]
         META_KEYS: [ 'image_size' ]
-  #
+
   TRAIN_HOOKS:
     - NAME: ProbeDataHook
       PROB_INTERVAL: 100
       PRIORITY: 0
     - NAME: BackwardHook
-#      GRADIENT_CLIP: 1.0
       PRIORITY: 10
     - NAME: LogHook
       LOG_INTERVAL: 10
@@ -339,4 +281,4 @@ SOLVER:
       PROB_INTERVAL: 100
       SAVE_LAST: True
       SAVE_NAME_PREFIX: 'step'
-      SAVE_PROBE_PREFIX: 'image'
+      SAVE_PROBE_PREFIX: 'image'

scepter/methods/studio/self_train/dit/pixart_alpha_pro.yaml

@@ -35,7 +35,7 @@ META:
       SAVE_INTERVAL: 25
       EPSEC: 0.818
       LEARNING_RATE: 0.0001
-      IS_DEFAULT: False
+      IS_DEFAULT: True
       TUNER: LORA
   #
   TUNERS:

scepter/methods/studio/self_train/self_train.yaml

@@ -13,8 +13,10 @@ TRAIN_PARAS:
     VALUES: [[256, 256], [320, 180], [180, 320],
              [512, 512], [640, 360], [360, 640],
              [768, 768], [960, 540], [540, 960],
-             [1024, 1024], [1280, 720], [720, 1280]]
+             [1024, 1024], [1280, 720], [720, 1280],
+             [720, 480], [480, 720]]
     DEFAULT: [1024, 1024]
   EVAL_PROMPTS:
     - a boy wearing a jacket
     - a dog running on the lawn
+SAVE_FILE_LOCAL_PATH: "cache/scepter_ui/datasets/train_data_from_list"

scepter/modules/data/dataset/__init__.py

@@ -7,6 +7,6 @@ from scepter.modules.data.dataset.dataset import (Image2ImageDataset,
                                                   ImageTextPairDataset,
                                                   Text2ImageDataset)
 from scepter.modules.data.dataset.ms_dataset import (
-    ImageTextPairFolderDataset, ImageTextPairMSDataset,
-    ImageTextPairMSDatasetForACE)
+    ImageTextPairFolderDataset, ImageTextPairMSDataset)
 from scepter.modules.data.dataset.registry import DATASETS
+from scepter.modules.data.dataset.video_gen_dataset import VideoGenDataset

scepter/modules/data/dataset/dataset.py

@@ -242,6 +242,8 @@ class Text2ImageDataset(BaseDataset):
         prompt_prefix = cfg.get('PROMPT_PREFIX', '')
         path_prefix = cfg.get('PATH_PREFIX', '')
         use_num = cfg.get('USE_NUM', -1)
+        meta_cfg = cfg.get('META_CFG', None)
+        meta_cfg = meta_cfg.get_lowercase_dict() if meta_cfg is not None else None
 
         image_size = cfg.get('IMAGE_SIZE', 1024)
         if isinstance(image_size, numbers.Number):
@@ -264,7 +266,12 @@ class Text2ImageDataset(BaseDataset):
 
         self.items = list()
         for i, row in enumerate(rows):
-            item = {'index': i, 'meta': {'image_size': image_size}}
+            if meta_cfg is not None:
+                meta_cfg_copy = copy.deepcopy(meta_cfg)
+                meta_cfg_copy['image_size'] = image_size
+                item = {'index': i, 'meta': meta_cfg_copy}
+            else:
+                item = {'index': i, 'meta': {'image_size': image_size}}
             for key, value in zip(fields, row):
                 if key in ['prompt', 'caption', 'text']:
                     item['ori_prompt'] = value

scepter/modules/data/dataset/video_gen_dataset.py

@@ -0,0 +1,184 @@
+import io
+import random
+import sys
+import os
+import warnings
+
+import torch
+import numpy as np
+from tqdm import tqdm
+
+from scepter.modules.utils.distribute import we
+from scepter.modules.data.dataset import DATASETS, BaseDataset
+from scepter.modules.utils.file_system import FS
+
+try:
+    import decord
+    decord.bridge.set_bridge("torch")
+except ImportError:
+    warnings.warn(
+        "The `decord` package is required for loading the video dataset. Install with `pip install decord`"
+    )
+
+
+@DATASETS.register_class()
+class VideoGenDataset(BaseDataset):
+    def __init__(self, cfg, logger = None):
+        super().__init__(cfg, logger=logger)
+        self.prompt_prefix = cfg.get('PROMPT_PREFIX', '')
+        self.path_prefix = cfg.get('PATH_PREFIX', '')
+        self.p_zero = cfg.get('P_ZERO', 0.0)
+        self.max_num_frames = cfg.get("NUM_FRAMES", 49)
+        self.fps = cfg.get("FPS", 8)
+        self.height = cfg.get("HEIGHT", 480)
+        self.width = cfg.get("WIDTH", 720)
+        self.skip_frames_start = cfg.get("SKIP_FRAMES_START", 0)
+        self.skip_frames_end = cfg.get("SKIP_FRAMES_END", 0)
+        self.data_type = cfg.get('DATA_TYPE', 't2v')
+
+    def worker_init_fn(self, worker_id, num_workers=1):
+        super().worker_init_fn(worker_id, num_workers=num_workers)
+        randseed = np.random.randint(0, 2 ** 32 - num_workers - 1)
+        workerseed = randseed + worker_id
+        random.seed(workerseed)
+        np.random.seed(workerseed)
+
+    def _preprocess_video_data(self, video_path):
+
+        with FS.get_object(video_path) as video_data:
+            video_reader = decord.VideoReader(io.BytesIO(video_data), width=self.width, height=self.height)
+        video_num_frames = len(video_reader)
+
+        start_frame = min(self.skip_frames_start, video_num_frames)
+        end_frame = max(0, video_num_frames - self.skip_frames_end)
+        if end_frame <= start_frame:
+            frames = video_reader.get_batch([start_frame])
+        elif end_frame - start_frame <= self.max_num_frames:
+            frames = video_reader.get_batch(list(range(start_frame, end_frame)))
+        else:
+            indices = list(range(start_frame, end_frame, (end_frame - start_frame) // self.max_num_frames))
+            frames = video_reader.get_batch(indices)
+
+        # Ensure that we don't go over the limit
+        frames = frames[: self.max_num_frames]
+        selected_num_frames = frames.shape[0]
+
+        # Choose first (4k + 1) frames as this is how many is required by the VAE
+        remainder = (3 + (selected_num_frames % 4)) % 4
+        if remainder != 0:
+            frames = frames[:-remainder]
+        selected_num_frames = frames.shape[0]
+
+        assert (selected_num_frames - 1) % 4 == 0
+
+        # Training transforms
+        frames = frames.float().div_(127.5).sub_(1.)
+        frames = frames.permute(3, 0, 1, 2).contiguous() # [C, F, H, W]
+        return frames
+
+    def _parse_index(self, index):
+        meta = dict()
+        for key, value in zip(index[-1], index[:-1]):
+            if key in ['oss_key', 'path', 'video_path']:
+                meta['video_path'] = value
+            elif key in ['prompt', 'caption', 'text']:
+                meta['prompt'] = value
+            elif key in ['width', 'height']:
+                meta[key] = int(value)
+            else:
+                meta[key] = value
+        return meta
+
+    def _get(self, index):
+        meta = self._parse_index(index)
+
+        video_path = os.path.join(self.path_prefix, meta.get('video_path', ''))
+        video = self._preprocess_video_data(video_path)
+
+        prompt = self.prompt_prefix + meta.get('prompt', '')
+        if self.mode == 'train' and np.random.uniform() < self.p_zero:
+            prompt = ''
+
+        item = {
+            'video': video,
+            'prompt': prompt,
+            'meta': meta,
+        }
+        if self.data_type == 'i2v':
+            item['image'] = item['video'][:, :1, :, :]
+        return item
+
+    def __len__(self):
+        return sys.maxsize
+
+    @staticmethod
+    def collate_fn(batch):
+        collect = {}
+        for sample in batch:
+            for k, v in sample.items():
+                if k not in collect:
+                    collect[k] = []
+                collect[k].append(v)
+        return collect
+
+
+
+@DATASETS.register_class()
+class VideoGenDatasetOTF(VideoGenDataset):
+    def __init__(self, cfg, logger=None):
+        super().__init__(cfg, logger)
+        self.data_file = cfg.DATA_FILE
+        self.delimiter = cfg.get('DELIMITER', '#;#')
+        self.fields = cfg.get('FIELDS', ['video_path', 'prompt'])
+        self.use_num = cfg.get('USE_NUM', -1)
+
+        from scepter.modules.model.registry import MODELS
+        model_cfg = cfg.get('MODEL', None)
+        if model_cfg is not None:
+            self.model = MODELS.build(cfg.MODEL, logger=logger).eval().requires_grad_(False).to(we.device_id)
+        self.items = self.parse_data(self.data_file, self.delimiter, self.fields)
+        if self.use_num and self.use_num > 0:
+            self.items = self.items[:self.use_num]
+        self.data = self.encode(self.items)
+        self.real_number = len(self.data)
+        if model_cfg is not None:
+            self.model.to('cpu')
+            del self.model
+            torch.cuda.empty_cache()
+
+    def parse_data(self, data_file, delimiter, fields):
+        items = list()
+        with FS.get_object(data_file) as local_data:
+            rows = [
+                i.split(delimiter,
+                        len(fields) - 1)
+                for i in local_data.decode('utf-8').strip().split('\n')
+            ]
+            for i, row in enumerate(rows):
+                item = {}
+                for key, value in zip(self.fields, row):
+                    if key in ['oss_key', 'path', 'video_path']:
+                        item['video_path'] = value
+                    elif key in ['prompt', 'caption', 'text']:
+                        item['prompt'] = value
+                    elif key in ['width', 'height']:
+                        item[key] = int(value)
+                    else:
+                        item[key] = value
+                items.append(item)
+        return items
+
+    def encode(self, items):
+        self.logger.info("Start to encode video data [{}]!".format(len(items)))
+        for item in tqdm(items):
+            video_path = os.path.join(self.path_prefix, item.get('video_path', ''))
+            video = self._preprocess_video_data(video_path)
+            latent = self.model.encode_first_stage(video.unsqueeze(0).to(we.device_id)).squeeze(0)
+            item['video_latent'] = latent.detach().cpu()
+            item['video'] = video
+            if self.data_type == 'i2v':
+                item['image'] = item['video'][:, :1, :, :]
+        return items
+
+    def _get(self, index):
+        return self.data[index % self.real_number]

scepter/modules/inference/ace_inference.py

@@ -10,7 +10,7 @@ import torch.nn as nn
 import torch.nn.functional as F
 import torchvision.transforms.functional as TF
 from PIL import Image
-
+import torchvision.transforms as T
 from scepter.modules.model.registry import DIFFUSIONS
 from scepter.modules.model.utils.basic_utils import check_list_of_list
 from scepter.modules.model.utils.basic_utils import \
@@ -85,6 +85,138 @@ class TextEmbedding(nn.Module):
         super().__init__()
         self.pos = nn.Parameter(data=torch.zeros(embedding_shape))
 
+class RefinerInference(DiffusionInference):
+    def init_from_cfg(self, cfg):
+        self.use_dynamic_model = cfg.get('USE_DYNAMIC_MODEL', True)
+        super().init_from_cfg(cfg)
+        self.diffusion = DIFFUSIONS.build(cfg.MODEL.DIFFUSION, logger=self.logger) \
+            if cfg.MODEL.have('DIFFUSION') else None
+        self.max_seq_length = cfg.MODEL.get("MAX_SEQ_LENGTH", 4096)
+        assert self.diffusion is not None
+        if not self.use_dynamic_model:
+            self.dynamic_load(self.first_stage_model, 'first_stage_model')
+            self.dynamic_load(self.cond_stage_model, 'cond_stage_model')
+            self.dynamic_load(self.diffusion_model, 'diffusion_model')
+    @torch.no_grad()
+    def encode_first_stage(self, x, **kwargs):
+        _, dtype = self.get_function_info(self.first_stage_model, 'encode')
+        with torch.autocast('cuda',
+                            enabled=dtype in ('float16', 'bfloat16'),
+                            dtype=getattr(torch, dtype)):
+            def run_one_image(u):
+                zu = get_model(self.first_stage_model).encode(u)
+                if isinstance(zu, (tuple, list)):
+                    zu = zu[0]
+                return zu
+            z = [run_one_image(u.unsqueeze(0) if u.dim == 3 else u) for u in x]
+            return z
+    def upscale_resize(self, image, interpolation=T.InterpolationMode.BILINEAR):
+        c, H, W = image.shape
+        scale = max(1.0, math.sqrt(self.max_seq_length / ((H / 16) * (W / 16))))
+        rH = int(H * scale) // 16 * 16  # ensure divisible by self.d
+        rW = int(W * scale) // 16 * 16
+        image = T.Resize((rH, rW), interpolation=interpolation, antialias=True)(image)
+        return image
+    @torch.no_grad()
+    def decode_first_stage(self, z):
+        _, dtype = self.get_function_info(self.first_stage_model, 'decode')
+        with torch.autocast('cuda',
+                            enabled=dtype in ('float16', 'bfloat16'),
+                            dtype=getattr(torch, dtype)):
+            return [get_model(self.first_stage_model).decode(zu) for zu in z]
+
+    def noise_sample(self, num_samples, h, w, seed, device = None, dtype = torch.bfloat16):
+        noise = torch.randn(
+            num_samples,
+            16,
+            # allow for packing
+            2 * math.ceil(h / 16),
+            2 * math.ceil(w / 16),
+            device=device,
+            dtype=dtype,
+            generator=torch.Generator(device=device).manual_seed(seed),
+        )
+        return noise
+    def refine(self,
+               x_samples=None,
+               prompt=None,
+               reverse_scale=-1.,
+               seed = 2024,
+               **kwargs
+               ):
+        print(prompt)
+        value_input = copy.deepcopy(self.input)
+        x_samples = [self.upscale_resize(x) for x in x_samples]
+
+        noise = []
+        for i, x in enumerate(x_samples):
+            noise_ = self.noise_sample(1, x.shape[1],
+                                       x.shape[2], seed,
+                                       device = x.device)
+            noise.append(noise_)
+        noise, x_shapes = pack_imagelist_into_tensor(noise)
+        if reverse_scale > 0:
+            self.dynamic_load(self.first_stage_model, 'first_stage_model')
+            x_samples = [x.unsqueeze(0) for x in x_samples]
+            x_start = self.encode_first_stage(x_samples, **kwargs)
+            self.dynamic_unload(self.first_stage_model,
+                                'first_stage_model',
+                                skip_loaded=not self.use_dynamic_model)
+            x_start, _ = pack_imagelist_into_tensor(x_start)
+        else:
+            x_start = None
+        # cond stage
+        self.dynamic_load(self.cond_stage_model, 'cond_stage_model')
+        function_name, dtype = self.get_function_info(self.cond_stage_model)
+        with torch.autocast('cuda',
+                            enabled=dtype == 'float16',
+                            dtype=getattr(torch, dtype)):
+            ctx = getattr(get_model(self.cond_stage_model),
+                          function_name)(prompt)
+            ctx["x_shapes"] = x_shapes
+        self.dynamic_unload(self.cond_stage_model,
+                            'cond_stage_model',
+                            skip_loaded=not self.use_dynamic_model)
+
+
+        self.dynamic_load(self.diffusion_model, 'diffusion_model')
+        # UNet use input n_prompt
+        function_name, dtype = self.get_function_info(
+            self.diffusion_model)
+        with torch.autocast('cuda',
+                            enabled=dtype in ('float16', 'bfloat16'),
+                            dtype=getattr(torch, dtype)):
+            solver_sample = value_input.get('sample', 'flow_euler')
+            sample_steps = value_input.get('sample_steps', 20)
+            guide_scale = value_input.get('guide_scale', 3.5)
+            if guide_scale is not None:
+                guide_scale = torch.full((noise.shape[0],), guide_scale, device=noise.device,
+                                         dtype=noise.dtype)
+            else:
+                guide_scale = None
+            latent = self.diffusion.sample(
+                noise=noise,
+                sampler=solver_sample,
+                model=get_model(self.diffusion_model),
+                model_kwargs={"cond": ctx, "guidance": guide_scale},
+                steps=sample_steps,
+                show_progress=True,
+                guide_scale=guide_scale,
+                return_intermediate=None,
+                reverse_scale=reverse_scale,
+                x=x_start,
+                **kwargs).float()
+        latent = unpack_tensor_into_imagelist(latent, x_shapes)
+        self.dynamic_unload(self.diffusion_model,
+                            'diffusion_model',
+                            skip_loaded=not self.use_dynamic_model)
+        self.dynamic_load(self.first_stage_model, 'first_stage_model')
+        x_samples = self.decode_first_stage(latent)
+        self.dynamic_unload(self.first_stage_model,
+                            'first_stage_model',
+                            skip_loaded=not self.use_dynamic_model)
+        return x_samples
+
 
 class ACEInference(DiffusionInference):
     def __init__(self, logger=None):
@@ -99,6 +231,7 @@ class ACEInference(DiffusionInference):
     def init_from_cfg(self, cfg):
         self.name = cfg.NAME
         self.is_default = cfg.get('IS_DEFAULT', False)
+        self.use_dynamic_model = cfg.get('USE_DYNAMIC_MODEL', True)
         module_paras = self.load_default(cfg.get('DEFAULT_PARAS', None))
         assert cfg.have('MODEL')
 
@@ -116,9 +249,22 @@ class ACEInference(DiffusionInference):
             module_paras.get(
                 'COND_STAGE_MODEL',
                 None)) if cfg.MODEL.have('COND_STAGE_MODEL') else None
+
+        self.refiner_model_cfg = cfg.get('REFINER_MODEL', None)
+        # self.refiner_scale = cfg.get('REFINER_SCALE', 0.)
+        # self.refiner_prompt = cfg.get('REFINER_PROMPT', "")
+        self.ace_prompt = cfg.get("ACE_PROMPT", [])
+        if self.refiner_model_cfg:
+            self.refiner_model_cfg.USE_DYNAMIC_MODEL = self.use_dynamic_model
+            self.refiner_module = RefinerInference(self.logger)
+            self.refiner_module.init_from_cfg(self.refiner_model_cfg)
+        else:
+            self.refiner_module = None
+
         self.diffusion = DIFFUSIONS.build(cfg.MODEL.DIFFUSION,
                                           logger=self.logger)
 
+
         self.interpolate_func = lambda x: (F.interpolate(
             x.unsqueeze(0),
             scale_factor=1 / self.size_factor,
@@ -137,6 +283,10 @@ class ACEInference(DiffusionInference):
         self.size_factor = cfg.get('SIZE_FACTOR', 8)
         self.decoder_bias = cfg.get('DECODER_BIAS', 0)
         self.default_n_prompt = cfg.get('DEFAULT_N_PROMPT', '')
+        if not self.use_dynamic_model:
+            self.dynamic_load(self.first_stage_model, 'first_stage_model')
+            self.dynamic_load(self.cond_stage_model, 'cond_stage_model')
+            self.dynamic_load(self.diffusion_model, 'diffusion_model')
 
     @torch.no_grad()
     def encode_first_stage(self, x, **kwargs):
@@ -163,6 +313,8 @@ class ACEInference(DiffusionInference):
             ]
         return x
 
+
+
     @torch.no_grad()
     def __call__(self,
                  image=None,
@@ -184,7 +336,6 @@ class ACEInference(DiffusionInference):
         g = torch.Generator(device=we.device_id)
         seed = seed if seed >= 0 else random.randint(0, 2**32 - 1)
         g.manual_seed(int(seed))
-
         if input_image is not None:
             # assert isinstance(input_image, list) and isinstance(input_mask, list)
             if task is None:
@@ -237,118 +388,142 @@ class ACEInference(DiffusionInference):
             assert isinstance(nn_p, list)
             n_prompt[nn_p_id][-1] = negative_prompt
 
-        ctx, null_ctx = {}, {}
-
-        # Get Noise Shape
-        self.dynamic_load(self.first_stage_model, 'first_stage_model')
+        is_txt_image = sum([len(e_i) for e_i in edit_image]) < 1
         image = to_device(image)
-        x = self.encode_first_stage(image)
-        self.dynamic_unload(self.first_stage_model,
-                            'first_stage_model',
-                            skip_loaded=True)
-        noise = [
-            torch.empty(*i.shape, device=we.device_id).normal_(generator=g)
-            for i in x
-        ]
-        noise, x_shapes = pack_imagelist_into_tensor(noise)
-        ctx['x_shapes'] = null_ctx['x_shapes'] = x_shapes
 
-        image_mask = to_device(image_mask, strict=False)
-        cond_mask = [self.interpolate_func(i) for i in image_mask
-                     ] if image_mask is not None else [None] * len(image)
-        ctx['x_mask'] = null_ctx['x_mask'] = cond_mask
+        refiner_scale = kwargs.pop("refiner_scale", 0.0)
+        refiner_prompt = kwargs.pop("refiner_prompt", "")
+        use_ace = kwargs.pop("use_ace", True)
+        # <= 0 use ace as the txt2img generator.
+        if use_ace and (not is_txt_image or refiner_scale <= 0):
+            ctx, null_ctx = {}, {}
+            # Get Noise Shape
+            self.dynamic_load(self.first_stage_model, 'first_stage_model')
+            x = self.encode_first_stage(image)
+            self.dynamic_unload(self.first_stage_model,
+                                'first_stage_model',
+                                skip_loaded=not self.use_dynamic_model)
+            noise = [
+                torch.empty(*i.shape, device=we.device_id).normal_(generator=g)
+                for i in x
+            ]
+            noise, x_shapes = pack_imagelist_into_tensor(noise)
+            ctx['x_shapes'] = null_ctx['x_shapes'] = x_shapes
 
-        # Encode Prompt
-        self.dynamic_load(self.cond_stage_model, 'cond_stage_model')
-        function_name, dtype = self.get_function_info(self.cond_stage_model)
-        cont, cont_mask = getattr(get_model(self.cond_stage_model),
-                                  function_name)(prompt)
-        cont, cont_mask = self.cond_stage_embeddings(prompt, edit_image, cont,
-                                                     cont_mask)
-        null_cont, null_cont_mask = getattr(get_model(self.cond_stage_model),
-                                            function_name)(n_prompt)
-        null_cont, null_cont_mask = self.cond_stage_embeddings(
-            prompt, edit_image, null_cont, null_cont_mask)
-        self.dynamic_unload(self.cond_stage_model,
-                            'cond_stage_model',
-                            skip_loaded=False)
-        ctx['crossattn'] = cont
-        null_ctx['crossattn'] = null_cont
+            image_mask = to_device(image_mask, strict=False)
+            cond_mask = [self.interpolate_func(i) for i in image_mask
+                         ] if image_mask is not None else [None] * len(image)
+            ctx['x_mask'] = null_ctx['x_mask'] = cond_mask
 
-        # Encode Edit Images
-        self.dynamic_load(self.first_stage_model, 'first_stage_model')
-        edit_image = [to_device(i, strict=False) for i in edit_image]
-        edit_image_mask = [to_device(i, strict=False) for i in edit_image_mask]
-        e_img, e_mask = [], []
-        for u, m in zip(edit_image, edit_image_mask):
-            if u is None:
-                continue
-            if m is None:
-                m = [None] * len(u)
-            e_img.append(self.encode_first_stage(u, **kwargs))
-            e_mask.append([self.interpolate_func(i) for i in m])
-        self.dynamic_unload(self.first_stage_model,
-                            'first_stage_model',
-                            skip_loaded=True)
-        null_ctx['edit'] = ctx['edit'] = e_img
-        null_ctx['edit_mask'] = ctx['edit_mask'] = e_mask
+            # Encode Prompt
+            self.dynamic_load(self.cond_stage_model, 'cond_stage_model')
+            function_name, dtype = self.get_function_info(self.cond_stage_model)
+            cont, cont_mask = getattr(get_model(self.cond_stage_model),
+                                      function_name)(prompt)
+            cont, cont_mask = self.cond_stage_embeddings(prompt, edit_image, cont,
+                                                         cont_mask)
+            null_cont, null_cont_mask = getattr(get_model(self.cond_stage_model),
+                                                function_name)(n_prompt)
+            null_cont, null_cont_mask = self.cond_stage_embeddings(
+                prompt, edit_image, null_cont, null_cont_mask)
+            self.dynamic_unload(self.cond_stage_model,
+                                'cond_stage_model',
+                                skip_loaded=not self.use_dynamic_model)
+            ctx['crossattn'] = cont
+            null_ctx['crossattn'] = null_cont
 
-        # Diffusion Process
-        self.dynamic_load(self.diffusion_model, 'diffusion_model')
-        function_name, dtype = self.get_function_info(self.diffusion_model)
-        with torch.autocast('cuda',
-                            enabled=dtype in ('float16', 'bfloat16'),
-                            dtype=getattr(torch, dtype)):
-            latent = self.diffusion.sample(
-                noise=noise,
-                sampler=sampler,
-                model=get_model(self.diffusion_model),
-                model_kwargs=[{
-                    'cond':
-                    ctx,
-                    'mask':
-                    cont_mask,
-                    'text_position_embeddings':
-                    self.text_position_embeddings.pos if hasattr(
-                        self.text_position_embeddings, 'pos') else None
-                }, {
-                    'cond':
-                    null_ctx,
-                    'mask':
-                    null_cont_mask,
-                    'text_position_embeddings':
-                    self.text_position_embeddings.pos if hasattr(
-                        self.text_position_embeddings, 'pos') else None
-                }] if guide_scale is not None and guide_scale > 1 else {
-                    'cond':
-                    null_ctx,
-                    'mask':
-                    cont_mask,
-                    'text_position_embeddings':
-                    self.text_position_embeddings.pos if hasattr(
-                        self.text_position_embeddings, 'pos') else None
-                },
-                steps=sample_steps,
-                show_progress=True,
-                seed=seed,
-                guide_scale=guide_scale,
-                guide_rescale=guide_rescale,
-                return_intermediate=None,
-                **kwargs)
-        self.dynamic_unload(self.diffusion_model,
-                            'diffusion_model',
-                            skip_loaded=False)
+            # Encode Edit Images
+            self.dynamic_load(self.first_stage_model, 'first_stage_model')
+            edit_image = [to_device(i, strict=False) for i in edit_image]
+            edit_image_mask = [to_device(i, strict=False) for i in edit_image_mask]
+            e_img, e_mask = [], []
+            for u, m in zip(edit_image, edit_image_mask):
+                if u is None:
+                    continue
+                if m is None:
+                    m = [None] * len(u)
+                e_img.append(self.encode_first_stage(u, **kwargs))
+                e_mask.append([self.interpolate_func(i) for i in m])
+            self.dynamic_unload(self.first_stage_model,
+                                'first_stage_model',
+                                skip_loaded=not self.use_dynamic_model)
+            null_ctx['edit'] = ctx['edit'] = e_img
+            null_ctx['edit_mask'] = ctx['edit_mask'] = e_mask
 
-        # Decode to Pixel Space
-        self.dynamic_load(self.first_stage_model, 'first_stage_model')
-        samples = unpack_tensor_into_imagelist(latent, x_shapes)
-        x_samples = self.decode_first_stage(samples)
-        self.dynamic_unload(self.first_stage_model,
-                            'first_stage_model',
-                            skip_loaded=False)
+            # Diffusion Process
+            self.dynamic_load(self.diffusion_model, 'diffusion_model')
+            function_name, dtype = self.get_function_info(self.diffusion_model)
+            with torch.autocast('cuda',
+                                enabled=dtype in ('float16', 'bfloat16'),
+                                dtype=getattr(torch, dtype)):
+                latent = self.diffusion.sample(
+                    noise=noise,
+                    sampler=sampler,
+                    model=get_model(self.diffusion_model),
+                    model_kwargs=[{
+                        'cond':
+                        ctx,
+                        'mask':
+                        cont_mask,
+                        'text_position_embeddings':
+                        self.text_position_embeddings.pos if hasattr(
+                            self.text_position_embeddings, 'pos') else None
+                    }, {
+                        'cond':
+                        null_ctx,
+                        'mask':
+                        null_cont_mask,
+                        'text_position_embeddings':
+                        self.text_position_embeddings.pos if hasattr(
+                            self.text_position_embeddings, 'pos') else None
+                    }] if guide_scale is not None and guide_scale > 1 else {
+                        'cond':
+                        null_ctx,
+                        'mask':
+                        cont_mask,
+                        'text_position_embeddings':
+                        self.text_position_embeddings.pos if hasattr(
+                            self.text_position_embeddings, 'pos') else None
+                    },
+                    steps=sample_steps,
+                    show_progress=True,
+                    seed=seed,
+                    guide_scale=guide_scale,
+                    guide_rescale=guide_rescale,
+                    return_intermediate=None,
+                    **kwargs)
+            self.dynamic_unload(self.diffusion_model,
+                                'diffusion_model',
+                                skip_loaded=not self.use_dynamic_model)
+
+            # Decode to Pixel Space
+            self.dynamic_load(self.first_stage_model, 'first_stage_model')
+            samples = unpack_tensor_into_imagelist(latent, x_shapes)
+            x_samples = self.decode_first_stage(samples)
+            self.dynamic_unload(self.first_stage_model,
+                                'first_stage_model',
+                                skip_loaded=not self.use_dynamic_model)
+            x_samples = [x.squeeze(0) for x in x_samples]
+        else:
+            x_samples = image
+        if self.refiner_module and refiner_scale > 0:
+            if is_txt_image:
+                random.shuffle(self.ace_prompt)
+                input_refine_prompt = [self.ace_prompt[0] + refiner_prompt if p[0] == "" else p[0] for p in prompt]
+                input_refine_scale = -1.
+            else:
+                input_refine_prompt = [p[0].replace("{image}", "") + " " + refiner_prompt for p in prompt]
+                input_refine_scale = refiner_scale
+                print(input_refine_prompt)
+
+            x_samples = self.refiner_module.refine(x_samples,
+                                                   reverse_scale = input_refine_scale,
+                                                   prompt= input_refine_prompt,
+                                                   seed=seed,
+                                                   use_dynamic_model=self.use_dynamic_model)
 
         imgs = [
-            torch.clamp((x_i + 1.0) / 2.0 + self.decoder_bias / 255,
+            torch.clamp((x_i.float() + 1.0) / 2.0 + self.decoder_bias / 255,
                         min=0.0,
                         max=1.0).squeeze(0).permute(1, 2, 0).cpu().numpy()
             for x_i in x_samples

scepter/modules/inference/cogvideox_inference.py

@@ -0,0 +1,181 @@
+# -*- coding: utf-8 -*-
+# Copyright (c) Alibaba, Inc. and its affiliates.
+
+import copy
+import numpy as np
+from typing import Tuple
+import random
+
+import torch
+
+from scepter.modules.utils.file_system import FS
+from scepter.modules.utils.distribute import we
+from scepter.modules.model.backbone.cogvideox.utils import get_3d_rotary_pos_embed, get_resize_crop_region_for_grid
+
+from .diffusion_inference import DiffusionInference, get_model
+from .tuner_inference import TunerInference
+
+class CogVideoXInference(DiffusionInference):
+    def __init__(self, logger=None):
+        self.logger = logger
+        self.is_redefine_paras = False
+        self.loaded_model = {}
+        self.loaded_model_name = [
+            'diffusion_model', 'first_stage_model', 'cond_stage_model'
+        ]
+        self.tuner_infer = TunerInference(self.logger)
+
+    @torch.no_grad()
+    def decode_first_stage(self, latents):
+        latents = latents.permute(0, 2, 1, 3, 4)
+        latents = 1 / self.first_stage_model['paras']['scaling_factor_image'] * latents
+        frames = get_model(self.first_stage_model).decode(latents)
+        return frames
+
+    def _prepare_rotary_positional_embeddings(
+        self,
+        height: int,
+        width: int,
+        num_frames: int,
+        device: torch.device,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+
+        grid_height = height // (self.diffusion_model['paras']['scale_factor_spatial'] * self.diffusion_model['paras']['patch_size'])
+        grid_width = width // (self.diffusion_model['paras']['scale_factor_spatial'] * self.diffusion_model['paras']['patch_size'])
+        base_size_width = self.diffusion_model['paras']['sample_width'] // (self.diffusion_model['paras']['scale_factor_spatial'] * self.diffusion_model['paras']['patch_size'])
+        base_size_height = self.diffusion_model['paras']['sample_height'] // (self.diffusion_model['paras']['scale_factor_spatial'] * self.diffusion_model['paras']['patch_size'])
+
+        grid_crops_coords = get_resize_crop_region_for_grid(
+            (grid_height, grid_width), base_size_width, base_size_height
+        )
+        freqs_cos, freqs_sin = get_3d_rotary_pos_embed(
+            embed_dim=self.diffusion_model['paras']['attention_head_dim'],
+            crops_coords=grid_crops_coords,
+            grid_size=(grid_height, grid_width),
+            temporal_size=num_frames,
+        )
+
+        freqs_cos = freqs_cos.to(device=device)
+        freqs_sin = freqs_sin.to(device=device)
+        return freqs_cos, freqs_sin
+
+    @torch.no_grad()
+    def __call__(self,
+                 input,
+                 num_samples=1,
+                 cat_uc=True,
+                 tuner_model=None,
+                 **kwargs):
+        value_input = copy.deepcopy(self.input)
+        value_input.update(input)
+        print(value_input)
+        height, width = value_input['target_size_as_tuple']
+        value_output = copy.deepcopy(self.output)
+
+        # register tuner
+        if tuner_model is not None and tuner_model != '' and len(
+                tuner_model) > 0:
+            if not isinstance(tuner_model, list):
+                tuner_model = [tuner_model]
+            self.dynamic_load(self.diffusion_model, 'diffusion_model')
+            self.tuner_infer.register_tuner(tuner_model, self.diffusion_model,
+                                            cond_stage_model=None)
+            self.dynamic_unload(self.diffusion_model,
+                                'diffusion_model',
+                                skip_loaded=True)
+
+        # cond stage
+        self.dynamic_load(self.cond_stage_model, 'cond_stage_model')
+        function_name, dtype = self.get_function_info(self.cond_stage_model)
+        with torch.autocast(device_type='cuda', enabled=True, dtype=torch.bfloat16):
+            cont = getattr(get_model(self.cond_stage_model),
+                                  function_name)(value_input['prompt'], return_mask=False, use_mask=False)
+            null_cont = getattr(get_model(self.cond_stage_model),
+                                        function_name)(value_input['negative_prompt'] * num_samples, return_mask=False, use_mask=False)
+        self.dynamic_unload(self.cond_stage_model,
+                            'cond_stage_model',
+                            skip_loaded=True)
+
+        # get noise
+        seed = kwargs.pop('seed', -1)
+        seed = seed if seed >= 0 else random.randint(0, 2**32 - 1)
+        generator = torch.Generator().manual_seed(seed)
+        if 'seed' in value_output:
+            value_output['seed'] = seed
+        for sample_id in range(num_samples):
+            if self.diffusion_model is not None:
+                noise_shape = (1,
+                         (value_input['num_frames'] - 1) // self.diffusion_model['paras']['scale_factor_temporal'] + 1,
+                         self.diffusion_model['paras']['latent_channels'],
+                         height // self.diffusion_model['paras']['scale_factor_spatial'],
+                         width // self.diffusion_model['paras']['scale_factor_spatial']
+                         )
+                noise = torch.randn(noise_shape, generator=generator, dtype=getattr(torch, dtype), device='cpu').to(we.device_id)
+
+                self.dynamic_load(self.diffusion_model, 'diffusion_model')
+
+                image_rotary_emb = (
+                    self._prepare_rotary_positional_embeddings(height, width, noise.size(1), we.device_id)
+                    if self.diffusion_model['paras']['use_rotary_positional_embeddings']
+                    else None
+                )
+                function_name, dtype = self.get_function_info(
+                    self.diffusion_model)
+                with torch.autocast('cuda',
+                                    enabled=dtype=='bfloat16',
+                                    dtype=getattr(torch, dtype)):
+                    solver_sample = value_input.get('sample', 'ddim')
+                    sample_steps = value_input.get('sample_steps', 50)
+                    guide_scale = value_input.get('guide_scale', 7.5)
+                    guide_rescale = value_input.get('guide_rescale', 0.5)
+
+                    latent = self.diffusion.sample(noise=noise,
+                                        sampler=solver_sample,
+                                        model=get_model(self.diffusion_model),
+                                        model_kwargs=[{
+                                            'cond': cont,
+                                            'image_latent': None,
+                                            'image_rotary_emb': image_rotary_emb,
+                                        }, {
+                                            'cond': null_cont,
+                                            'image_latent': None,
+                                            'image_rotary_emb': image_rotary_emb,
+                                        }],
+                                        steps=sample_steps,
+                                        show_progress=True,
+                                        use_dynamic_cfg=True,
+                                        guide_scale=guide_scale,
+                                        guide_rescale=guide_rescale,
+                                        return_intermediate=None,
+                                        **kwargs).float()
+                self.dynamic_unload(self.diffusion_model,
+                                    'diffusion_model',
+                                    skip_loaded=True)
+
+            self.dynamic_load(self.first_stage_model, 'first_stage_model')
+            x_samples = self.decode_first_stage(latent).float()  # [B, C, F, H, W]
+            self.dynamic_unload(self.first_stage_model,
+                                'first_stage_model',
+                                skip_loaded=True)
+
+            x_frames = torch.clamp(x_samples / 2 + 0.5, min=0.0, max=1.0)
+            if 'videos' in value_output:
+                if value_output['videos'] is None or (
+                        isinstance(value_output['videos'], list)
+                        and len(value_output['videos']) < 1):
+                    value_output['videos'] = []
+                value_output['videos'].append(x_frames)
+
+        for k, v in value_output.items():
+            if isinstance(v, list):
+                value_output[k] = torch.cat(v, dim=0)
+            if isinstance(v, torch.Tensor):
+                value_output[k] = v.cpu()
+
+        # unregister tuner
+        if tuner_model is not None and tuner_model != '' and len(
+                tuner_model) > 0:
+            self.tuner_infer.unregister_tuner(tuner_model,
+                                              self.diffusion_model,
+                                              cond_stage_model=None)
+        return value_output

scepter/modules/inference/diffusion_inference.py

@@ -14,6 +14,7 @@ from scepter.modules.model.registry import (BACKBONES, EMBEDDERS, MODELS,
                                             TOKENIZERS, DIFFUSIONS)
 from scepter.modules.utils.distribute import we
 from scepter.modules.utils.file_system import FS
+from scepter.modules.utils.config import Config
 from scepter.studio.utils.env import get_available_memory
 
 from .control_inference import ControlInference
@@ -316,7 +317,8 @@ class DiffusionInference():
         module_paras = {}
         if cfg is not None:
             self.paras = cfg.PARAS
-            self.input = {k.lower(): dict(v).get('DEFAULT', None) if isinstance(v, (dict, OrderedDict)) else v for k, v in cfg.INPUT.items()}
+            self.input_cfg = {k.lower(): v for k, v in cfg.INPUT.items()}
+            self.input = {k.lower(): dict(v).get('DEFAULT', None) if isinstance(v, (dict, OrderedDict, Config)) else v for k, v in cfg.INPUT.items()}
             self.output = {k.lower(): v for k, v in cfg.OUTPUT.items()}
             module_paras = cfg.MODULES_PARAS
         return module_paras

scepter/modules/inference/flux_inference.py

@@ -151,7 +151,7 @@ class FluxInference(DiffusionInference):
                 with torch.autocast('cuda',
                                     enabled= dtype in ('float16', 'bfloat16'),
                                     dtype=getattr(torch, dtype)):
-                    solver_sample = value_input.get('sample', 'flow_eluer')
+                    solver_sample = value_input.get('sample', 'flow_euler')
                     sample_steps = value_input.get('sample_steps', 20)
                     guide_scale = value_input.get('guide_scale', 3.5)
                     if guide_scale is not None:

scepter/modules/inference/tuner_inference.py

@@ -29,11 +29,11 @@ class TunerInference():
             warnings.warn(f'Import swift error, please deal with this problem: {e}')
 
         self.logger.info('Unloading tuner model')
-        if isinstance(diffusion_model['model'], SwiftModel):
+        if diffusion_model is not None and isinstance(diffusion_model['model'], SwiftModel):
             for adapter_name in diffusion_model['model'].adapters:
                 diffusion_model['model'].deactivate_adapter(adapter_name,
                                                             offload='cpu')
-        if isinstance(cond_stage_model['model'], SwiftModel):
+        if cond_stage_model is not None and isinstance(cond_stage_model['model'], SwiftModel):
             for adapter_name in cond_stage_model['model'].adapters:
                 cond_stage_model['model'].deactivate_adapter(adapter_name,
                                                              offload='cpu')

scepter/modules/model/backbone/__init__.py

@@ -1,4 +1,4 @@
 # -*- coding: utf-8 -*-
 # Copyright (c) Alibaba, Inc. and its affiliates.
-from scepter.modules.model.backbone import (ace, autoencoder, flux, image,
+from scepter.modules.model.backbone import (ace, autoencoder, flux, image, cogvideox,
                                             mmdit, pixart, unet, utils, video)

scepter/modules/model/backbone/cogvideox/__init__.py

@@ -0,0 +1,3 @@
+# -*- coding: utf-8 -*-
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from scepter.modules.model.backbone.cogvideox.cogvideox import CogVideoXTransformer3DModel

scepter/modules/model/backbone/cogvideox/cogvideox.py

@@ -0,0 +1,319 @@
+# -*- coding: utf-8 -*-
+
+# Copyright 2024 The CogVideoX team, Tsinghua University & ZhipuAI 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 collections import OrderedDict
+from typing import Any, Dict, Optional, Tuple, Union
+
+import torch
+from torch import nn
+
+from scepter.modules.model.base_model import BaseModel
+from scepter.modules.model.registry import BACKBONES
+from scepter.modules.utils.config import dict_to_yaml
+from scepter.modules.utils.distribute import we
+from scepter.modules.utils.file_system import FS
+
+from .layers import CogVideoXBlock, CogVideoXPatchEmbed, TimestepEmbedding, Timesteps, AdaLayerNorm
+
+
+@BACKBONES.register_class()
+class CogVideoXTransformer3DModel(BaseModel):
+    """
+    A Transformer model for video-like data in [CogVideoX](https://github.com/THUDM/CogVideo).
+
+    Parameters:
+        num_attention_heads (`int`, defaults to `30`):
+            The number of heads to use for multi-head attention.
+        attention_head_dim (`int`, defaults to `64`):
+            The number of channels in each head.
+        in_channels (`int`, defaults to `16`):
+            The number of channels in the input.
+        out_channels (`int`, *optional*, defaults to `16`):
+            The number of channels in the output.
+        flip_sin_to_cos (`bool`, defaults to `True`):
+            Whether to flip the sin to cos in the time embedding.
+        time_embed_dim (`int`, defaults to `512`):
+            Output dimension of timestep embeddings.
+        text_embed_dim (`int`, defaults to `4096`):
+            Input dimension of text embeddings from the text encoder.
+        num_layers (`int`, defaults to `30`):
+            The number of layers of Transformer blocks to use.
+        dropout (`float`, defaults to `0.0`):
+            The dropout probability to use.
+        attention_bias (`bool`, defaults to `True`):
+            Whether or not to use bias in the attention projection layers.
+        sample_width (`int`, defaults to `90`):
+            The width of the input latents.
+        sample_height (`int`, defaults to `60`):
+            The height of the input latents.
+        sample_frames (`int`, defaults to `49`):
+            The number of frames in the input latents. Note that this parameter was incorrectly initialized to 49
+            instead of 13 because CogVideoX processed 13 latent frames at once in its default and recommended settings,
+            but cannot be changed to the correct value to ensure backwards compatibility. To create a transformer with
+            K latent frames, the correct value to pass here would be: ((K - 1) * temporal_compression_ratio + 1).
+        patch_size (`int`, defaults to `2`):
+            The size of the patches to use in the patch embedding layer.
+        temporal_compression_ratio (`int`, defaults to `4`):
+            The compression ratio across the temporal dimension. See documentation for `sample_frames`.
+        max_text_seq_length (`int`, defaults to `226`):
+            The maximum sequence length of the input text embeddings.
+        activation_fn (`str`, defaults to `"gelu-approximate"`):
+            Activation function to use in feed-forward.
+        timestep_activation_fn (`str`, defaults to `"silu"`):
+            Activation function to use when generating the timestep embeddings.
+        norm_elementwise_affine (`bool`, defaults to `True`):
+            Whether or not to use elementwise affine in normalization layers.
+        norm_eps (`float`, defaults to `1e-5`):
+            The epsilon value to use in normalization layers.
+        spatial_interpolation_scale (`float`, defaults to `1.875`):
+            Scaling factor to apply in 3D positional embeddings across spatial dimensions.
+        temporal_interpolation_scale (`float`, defaults to `1.0`):
+            Scaling factor to apply in 3D positional embeddings across temporal dimensions.
+    """
+
+    def __init__(
+        self,
+        cfg,
+        logger=None
+    ):
+        super().__init__(cfg, logger=logger)
+        num_attention_heads = cfg.get("NUM_ATTENTION_HEADS", 30)
+        attention_head_dim = cfg.get("ATTENTION_HEAD_DIM", 64)
+        in_channels = cfg.get("IN_CHANNELS", 16)
+        out_channels = cfg.get("OUT_CHANNELS", 16)
+        flip_sin_to_cos = cfg.get("FLIP_SIN_TO_COS", True)
+        freq_shift = cfg.get("FREQ_SHIFT", 0)
+        time_embed_dim = cfg.get("TIME_EMBED_DIM", 512)
+        text_embed_dim = cfg.get("TEXT_EMBED_DIM", 4096)
+        num_layers = cfg.get("NUM_LAYERS", 30)
+        dropout = cfg.get("DROPOUT", 0.0)
+        attention_bias = cfg.get("ATTENTION_BIAS", True)
+        sample_width = cfg.get("SAMPLE_WIDTH", 90)
+        sample_height = cfg.get("SAMPLE_HEIGHT", 60)
+        sample_frames = cfg.get("SAMPLE_FRAMES", 49)
+        patch_size = cfg.get("PATCH_SIZE", 2)
+        temporal_compression_ratio = cfg.get("TEMPORAL_COMPRESSION_RATIO", 4)
+        max_text_seq_length = cfg.get("MAX_TEXT_SEQ_LENGTH", 226)
+        activation_fn = cfg.get("ACTIVATION_FN", "gelu-approximate")
+        timestep_activation_fn = cfg.get("TIMESTEP_ACTIVATION_FN", "silu")
+        norm_elementwise_affine = cfg.get("NORM_ELEMENTWISE_AFFINE", True)
+        norm_eps = cfg.get("NORM_EPS", 1e-5)
+        spatial_interpolation_scale = cfg.get("SPATIAL_INTERPOLATION_SCALE", 1.875)
+        temporal_interpolation_scale = cfg.get("TEMPORAL_INTERPOLATION_SCALE", 1.0)
+        use_rotary_positional_embeddings = cfg.get("USE_ROTARY_POSITIONAL_EMBEDDINGS", False)
+        use_learned_positional_embeddings = cfg.get("USE_LEARNED_POSITIONAL_EMBEDDINGS", False)
+        self.gradient_checkpointing = cfg.get("GRADIENT_CHECKPOINTING", False)
+        inner_dim = num_attention_heads * attention_head_dim
+        self.patch_size = patch_size
+        self.use_rotary_positional_embeddings = use_rotary_positional_embeddings
+
+        if not use_rotary_positional_embeddings and use_learned_positional_embeddings:
+            raise ValueError(
+                "There are no CogVideoX checkpoints available with disable rotary embeddings and learned positional "
+                "embeddings. If you're using a custom model and/or believe this should be supported, please open an "
+                "issue at https://github.com/huggingface/diffusers/issues."
+            )
+
+        # 1. Patch embedding
+        self.patch_embed = CogVideoXPatchEmbed(
+            patch_size=patch_size,
+            in_channels=in_channels,
+            embed_dim=inner_dim,
+            text_embed_dim=text_embed_dim,
+            bias=True,
+            sample_width=sample_width,
+            sample_height=sample_height,
+            sample_frames=sample_frames,
+            temporal_compression_ratio=temporal_compression_ratio,
+            max_text_seq_length=max_text_seq_length,
+            spatial_interpolation_scale=spatial_interpolation_scale,
+            temporal_interpolation_scale=temporal_interpolation_scale,
+            use_positional_embeddings=not use_rotary_positional_embeddings,
+            use_learned_positional_embeddings=use_learned_positional_embeddings,
+        )
+        self.embedding_dropout = nn.Dropout(dropout)
+
+        # 2. Time embeddings
+        self.time_proj = Timesteps(inner_dim, flip_sin_to_cos, freq_shift)
+        self.time_embedding = TimestepEmbedding(inner_dim, time_embed_dim, timestep_activation_fn)
+
+        # 3. Define spatio-temporal transformers blocks
+        self.transformer_blocks = nn.ModuleList(
+            [
+                CogVideoXBlock(
+                    dim=inner_dim,
+                    num_attention_heads=num_attention_heads,
+                    attention_head_dim=attention_head_dim,
+                    time_embed_dim=time_embed_dim,
+                    dropout=dropout,
+                    activation_fn=activation_fn,
+                    attention_bias=attention_bias,
+                    norm_elementwise_affine=norm_elementwise_affine,
+                    norm_eps=norm_eps,
+                )
+                for _ in range(num_layers)
+            ]
+        )
+        self.norm_final = nn.LayerNorm(inner_dim, norm_eps, norm_elementwise_affine)
+
+        # 4. Output blocks
+        self.norm_out = AdaLayerNorm(
+            embedding_dim=time_embed_dim,
+            output_dim=2 * inner_dim,
+            norm_elementwise_affine=norm_elementwise_affine,
+            norm_eps=norm_eps,
+            chunk_dim=1,
+        )
+        self.proj_out = nn.Linear(inner_dim, patch_size * patch_size * out_channels)
+
+    def forward(
+        self,
+        x: torch.Tensor = None,
+        t: Union[int, float, torch.LongTensor] = None,
+        cond: torch.Tensor = None,
+        timestep_cond: Optional[torch.Tensor] = None,
+        image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        **kwargs
+    ):
+        if 'image_latent' in kwargs and kwargs['image_latent'] is not None:
+            hidden_states = torch.cat([x, kwargs['image_latent']], dim=2)
+        else:
+            hidden_states = x
+        timestep = t
+        encoder_hidden_states = cond
+
+        batch_size, num_frames, channels, height, width = hidden_states.shape
+
+        # 1. Time embedding
+        timesteps = timestep
+        t_emb = self.time_proj(timesteps)
+
+        # timesteps does not contain any weights and will always return f32 tensors
+        # but time_embedding might actually be running in fp16. so we need to cast here.
+        # there might be better ways to encapsulate this.
+        t_emb = t_emb.to(dtype=encoder_hidden_states.dtype)
+        emb = self.time_embedding(t_emb, timestep_cond)
+
+        # 2. Patch embedding
+        hidden_states = self.patch_embed(encoder_hidden_states, hidden_states)
+        hidden_states = self.embedding_dropout(hidden_states)
+
+        text_seq_length = encoder_hidden_states.shape[1]
+        encoder_hidden_states = hidden_states[:, :text_seq_length]
+        hidden_states = hidden_states[:, text_seq_length:]
+
+        # 3. Transformer blocks
+        for i, block in enumerate(self.transformer_blocks):
+            if self.training and 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}
+                hidden_states, encoder_hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(block),
+                    hidden_states,
+                    encoder_hidden_states,
+                    emb,
+                    image_rotary_emb,
+                    **ckpt_kwargs,
+                )
+            else:
+                hidden_states, encoder_hidden_states = block(
+                    hidden_states=hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    temb=emb,
+                    image_rotary_emb=image_rotary_emb,
+                )
+
+        if not self.use_rotary_positional_embeddings:
+            # CogVideoX-2B
+            hidden_states = self.norm_final(hidden_states)
+        else:
+            # CogVideoX-5B
+            hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
+            hidden_states = self.norm_final(hidden_states)
+            hidden_states = hidden_states[:, text_seq_length:]
+
+        # 4. Final block
+        hidden_states = self.norm_out(hidden_states, temb=emb)
+        hidden_states = self.proj_out(hidden_states)
+
+        # 5. Unpatchify
+        # Note: we use `-1` instead of `channels`:
+        #   - It is okay to `channels` use for CogVideoX-2b and CogVideoX-5b (number of input channels is equal to output channels)
+        #   - However, for CogVideoX-5b-I2V also takes concatenated input image latents (number of input channels is twice the output channels)
+        p = self.patch_size
+        output = hidden_states.reshape(batch_size, num_frames, height // p, width // p, -1, p, p)
+        output = output.permute(0, 1, 4, 2, 5, 3, 6).flatten(5, 6).flatten(3, 4)
+
+        return output
+
+    def load_pretrained_model(self, pretrained_model):
+        if pretrained_model is not None:
+            pretrained_model_list = [pretrained_model] if isinstance(pretrained_model, str) else pretrained_model
+            ckpt_all = OrderedDict()
+            for pretrained_model in pretrained_model_list:
+                with FS.get_from(pretrained_model,
+                                 wait_finish=True) as local_model:
+                    if local_model.endswith('safetensors'):
+                        from safetensors.torch import load_file as load_safetensors
+                        ckpt = load_safetensors(local_model)
+                    else:
+                        ckpt = torch.load(local_model, map_location='cpu')
+                    ckpt_all.update(ckpt)
+            missing, unexpected = self.load_state_dict(ckpt_all, strict=False)
+            if we.rank == 0:
+                self.logger.info(
+                    f'Restored from {pretrained_model_list} with {len(missing)} missing and {len(unexpected)} unexpected keys'
+                )
+                if len(missing) > 0:
+                    self.logger.info(f'Missing Keys:\n {missing}')
+                if len(unexpected) > 0:
+                    self.logger.info(f'\nUnexpected Keys:\n {unexpected}')
+
+    @staticmethod
+    def get_config_template():
+        return dict_to_yaml('MODEL',
+                            __class__.__name__,
+                            CogVideoXTransformer3DModel.para_dict,
+                            set_name=True)
+
+
+if __name__ == "__main__":
+    import argparse
+    from scepter.modules.utils.file_system import FS
+    from scepter.modules.utils.config import Config
+    from scepter.modules.utils.logger import get_logger
+
+    parser = argparse.ArgumentParser()
+    cfg = Config(parser_ins=parser)
+    for file_sys in cfg.FILE_SYSTEM:
+        FS.init_fs_client(file_sys)
+    model = BACKBONES.build(cfg.DIFFUSION_MODEL, logger=get_logger()).eval().requires_grad_(False).to('cuda').to(torch.bfloat16)
+
+    hidden_states = torch.load(FS.get_from(cfg.HIDDEN_STATES))
+    encoder_hidden_states = torch.load(FS.get_from(cfg.ENCODER_HIDDEN_STATES))
+    timestep = torch.load(FS.get_from(cfg.TIMESTEP))
+    timestep_cond = None
+    image_rotary_emb = None
+    attention_kwargs = None
+    output = model(hidden_states, encoder_hidden_states, timestep, timestep_cond, image_rotary_emb, attention_kwargs)
+    print(output, torch.sum(output))

scepter/modules/model/backbone/cogvideox/layers.py

@@ -0,0 +1,554 @@
+# -*- coding: utf-8 -*-
+
+# Copyright 2024 The CogVideoX team, Tsinghua University & ZhipuAI 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 Optional, Tuple
+
+import torch
+from torch import nn
+import torch.nn.functional as F
+
+from .utils import get_activation, get_timestep_embedding, get_3d_sincos_pos_embed, apply_rotary_emb
+from .utils import GELU, GEGLU, ApproximateGELU, SwiGLU
+
+
+class TimestepEmbedding(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        time_embed_dim: int,
+        act_fn: str = "silu",
+        out_dim: int = None,
+        post_act_fn: Optional[str] = None,
+        cond_proj_dim=None,
+        sample_proj_bias=True,
+    ):
+        super().__init__()
+
+        self.linear_1 = nn.Linear(in_channels, time_embed_dim, sample_proj_bias)
+
+        if cond_proj_dim is not None:
+            self.cond_proj = nn.Linear(cond_proj_dim, in_channels, bias=False)
+        else:
+            self.cond_proj = None
+
+        self.act = get_activation(act_fn)
+
+        if out_dim is not None:
+            time_embed_dim_out = out_dim
+        else:
+            time_embed_dim_out = time_embed_dim
+        self.linear_2 = nn.Linear(time_embed_dim, time_embed_dim_out, sample_proj_bias)
+
+        if post_act_fn is None:
+            self.post_act = None
+        else:
+            self.post_act = get_activation(post_act_fn)
+
+    def forward(self, sample, condition=None):
+        if condition is not None:
+            sample = sample + self.cond_proj(condition)
+        sample = self.linear_1(sample)
+
+        if self.act is not None:
+            sample = self.act(sample)
+
+        sample = self.linear_2(sample)
+
+        if self.post_act is not None:
+            sample = self.post_act(sample)
+        return sample
+
+
+
+class Timesteps(nn.Module):
+    def __init__(self, num_channels: int, flip_sin_to_cos: bool, downscale_freq_shift: float, scale: int = 1):
+        super().__init__()
+        self.num_channels = num_channels
+        self.flip_sin_to_cos = flip_sin_to_cos
+        self.downscale_freq_shift = downscale_freq_shift
+        self.scale = scale
+
+    def forward(self, timesteps):
+        t_emb = get_timestep_embedding(
+            timesteps,
+            self.num_channels,
+            flip_sin_to_cos=self.flip_sin_to_cos,
+            downscale_freq_shift=self.downscale_freq_shift,
+            scale=self.scale,
+        )
+        return t_emb
+
+
+class CogVideoXLayerNormZero(nn.Module):
+    def __init__(
+        self,
+        conditioning_dim: int,
+        embedding_dim: int,
+        elementwise_affine: bool = True,
+        eps: float = 1e-5,
+        bias: bool = True,
+    ) -> None:
+        super().__init__()
+
+        self.silu = nn.SiLU()
+        self.linear = nn.Linear(conditioning_dim, 6 * embedding_dim, bias=bias)
+        self.norm = nn.LayerNorm(embedding_dim, eps=eps, elementwise_affine=elementwise_affine)
+
+    def forward(
+        self, hidden_states: torch.Tensor, encoder_hidden_states: torch.Tensor, temb: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        shift, scale, gate, enc_shift, enc_scale, enc_gate = self.linear(self.silu(temb)).chunk(6, dim=1)
+        hidden_states = self.norm(hidden_states) * (1 + scale)[:, None, :] + shift[:, None, :]
+        encoder_hidden_states = self.norm(encoder_hidden_states) * (1 + enc_scale)[:, None, :] + enc_shift[:, None, :]
+        return hidden_states, encoder_hidden_states, gate[:, None, :], enc_gate[:, None, :]
+
+
+class AdaLayerNorm(nn.Module):
+    r"""
+    Norm layer modified to incorporate timestep embeddings.
+
+    Parameters:
+        embedding_dim (`int`): The size of each embedding vector.
+        num_embeddings (`int`, *optional*): The size of the embeddings dictionary.
+        output_dim (`int`, *optional*):
+        norm_elementwise_affine (`bool`, defaults to `False):
+        norm_eps (`bool`, defaults to `False`):
+        chunk_dim (`int`, defaults to `0`):
+    """
+
+    def __init__(
+        self,
+        embedding_dim: int,
+        num_embeddings: Optional[int] = None,
+        output_dim: Optional[int] = None,
+        norm_elementwise_affine: bool = False,
+        norm_eps: float = 1e-5,
+        chunk_dim: int = 0,
+    ):
+        super().__init__()
+
+        self.chunk_dim = chunk_dim
+        output_dim = output_dim or embedding_dim * 2
+
+        if num_embeddings is not None:
+            self.emb = nn.Embedding(num_embeddings, embedding_dim)
+        else:
+            self.emb = None
+
+        self.silu = nn.SiLU()
+        self.linear = nn.Linear(embedding_dim, output_dim)
+        self.norm = nn.LayerNorm(output_dim // 2, norm_eps, norm_elementwise_affine)
+
+    def forward(
+        self, x: torch.Tensor, timestep: Optional[torch.Tensor] = None, temb: Optional[torch.Tensor] = None
+    ) -> torch.Tensor:
+        if self.emb is not None:
+            temb = self.emb(timestep)
+
+        temb = self.linear(self.silu(temb))
+
+        if self.chunk_dim == 1:
+            # This is a bit weird why we have the order of "shift, scale" here and "scale, shift" in the
+            # other if-branch. This branch is specific to CogVideoX for now.
+            shift, scale = temb.chunk(2, dim=1)
+            shift = shift[:, None, :]
+            scale = scale[:, None, :]
+        else:
+            scale, shift = temb.chunk(2, dim=0)
+
+        x = self.norm(x) * (1 + scale) + shift
+        return x
+
+
+class CogVideoXPatchEmbed(nn.Module):
+    def __init__(
+        self,
+        patch_size: int = 2,
+        in_channels: int = 16,
+        embed_dim: int = 1920,
+        text_embed_dim: int = 4096,
+        bias: bool = True,
+        sample_width: int = 90,
+        sample_height: int = 60,
+        sample_frames: int = 49,
+        temporal_compression_ratio: int = 4,
+        max_text_seq_length: int = 226,
+        spatial_interpolation_scale: float = 1.875,
+        temporal_interpolation_scale: float = 1.0,
+        use_positional_embeddings: bool = True,
+        use_learned_positional_embeddings: bool = True,
+    ) -> None:
+        super().__init__()
+
+        self.patch_size = patch_size
+        self.embed_dim = embed_dim
+        self.sample_height = sample_height
+        self.sample_width = sample_width
+        self.sample_frames = sample_frames
+        self.temporal_compression_ratio = temporal_compression_ratio
+        self.max_text_seq_length = max_text_seq_length
+        self.spatial_interpolation_scale = spatial_interpolation_scale
+        self.temporal_interpolation_scale = temporal_interpolation_scale
+        self.use_positional_embeddings = use_positional_embeddings
+        self.use_learned_positional_embeddings = use_learned_positional_embeddings
+
+        self.proj = nn.Conv2d(
+            in_channels, embed_dim, kernel_size=(patch_size, patch_size), stride=patch_size, bias=bias
+        )
+        self.text_proj = nn.Linear(text_embed_dim, embed_dim)
+
+        if use_positional_embeddings or use_learned_positional_embeddings:
+            persistent = use_learned_positional_embeddings
+            pos_embedding = self._get_positional_embeddings(sample_height, sample_width, sample_frames)
+            self.register_buffer("pos_embedding", pos_embedding, persistent=persistent)
+
+    def _get_positional_embeddings(self, sample_height: int, sample_width: int, sample_frames: int) -> torch.Tensor:
+        post_patch_height = sample_height // self.patch_size
+        post_patch_width = sample_width // self.patch_size
+        post_time_compression_frames = (sample_frames - 1) // self.temporal_compression_ratio + 1
+        num_patches = post_patch_height * post_patch_width * post_time_compression_frames
+
+        pos_embedding = get_3d_sincos_pos_embed(
+            self.embed_dim,
+            (post_patch_width, post_patch_height),
+            post_time_compression_frames,
+            self.spatial_interpolation_scale,
+            self.temporal_interpolation_scale,
+        )
+        pos_embedding = torch.from_numpy(pos_embedding).flatten(0, 1)
+        joint_pos_embedding = torch.zeros(
+            1, self.max_text_seq_length + num_patches, self.embed_dim, requires_grad=False
+        )
+        joint_pos_embedding.data[:, self.max_text_seq_length :].copy_(pos_embedding)
+
+        return joint_pos_embedding
+
+    def forward(self, text_embeds: torch.Tensor, image_embeds: torch.Tensor):
+        r"""
+        Args:
+            text_embeds (`torch.Tensor`):
+                Input text embeddings. Expected shape: (batch_size, seq_length, embedding_dim).
+            image_embeds (`torch.Tensor`):
+                Input image embeddings. Expected shape: (batch_size, num_frames, channels, height, width).
+        """
+        text_embeds = self.text_proj(text_embeds)
+
+        batch, num_frames, channels, height, width = image_embeds.shape
+        image_embeds = image_embeds.reshape(-1, channels, height, width)
+        image_embeds = self.proj(image_embeds)
+        image_embeds = image_embeds.view(batch, num_frames, *image_embeds.shape[1:])
+        image_embeds = image_embeds.flatten(3).transpose(2, 3)  # [batch, num_frames, height x width, channels]
+        image_embeds = image_embeds.flatten(1, 2)  # [batch, num_frames x height x width, channels]
+
+        embeds = torch.cat(
+            [text_embeds, image_embeds], dim=1
+        ).contiguous()  # [batch, seq_length + num_frames x height x width, channels]
+
+        if self.use_positional_embeddings or self.use_learned_positional_embeddings:
+            if self.use_learned_positional_embeddings and (self.sample_width != width or self.sample_height != height):
+                raise ValueError(
+                    "It is currently not possible to generate videos at a different resolution that the defaults. This should only be the case with 'THUDM/CogVideoX-5b-I2V'."
+                    "If you think this is incorrect, please open an issue at https://github.com/huggingface/diffusers/issues."
+                )
+
+            pre_time_compression_frames = (num_frames - 1) * self.temporal_compression_ratio + 1
+
+            if (
+                self.sample_height != height
+                or self.sample_width != width
+                or self.sample_frames != pre_time_compression_frames
+            ):
+                pos_embedding = self._get_positional_embeddings(height, width, pre_time_compression_frames)
+                pos_embedding = pos_embedding.to(embeds.device, dtype=embeds.dtype)
+            else:
+                pos_embedding = self.pos_embedding
+
+            embeds = embeds + pos_embedding
+
+        return embeds
+
+
+class FeedForward(nn.Module):
+    r"""
+    A feed-forward layer.
+
+    Parameters:
+        dim (`int`): The number of channels in the input.
+        dim_out (`int`, *optional*): The number of channels in the output. If not given, defaults to `dim`.
+        mult (`int`, *optional*, defaults to 4): The multiplier to use for the hidden dimension.
+        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
+        activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
+        final_dropout (`bool` *optional*, defaults to False): Apply a final dropout.
+        bias (`bool`, defaults to True): Whether to use a bias in the linear layer.
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        dim_out: Optional[int] = None,
+        mult: int = 4,
+        dropout: float = 0.0,
+        activation_fn: str = "geglu",
+        final_dropout: bool = False,
+        inner_dim=None,
+        bias: bool = True,
+    ):
+        super().__init__()
+        if inner_dim is None:
+            inner_dim = int(dim * mult)
+        dim_out = dim_out if dim_out is not None else dim
+
+        if activation_fn == "gelu":
+            act_fn = GELU(dim, inner_dim, bias=bias)
+        if activation_fn == "gelu-approximate":
+            act_fn = GELU(dim, inner_dim, approximate="tanh", bias=bias)
+        elif activation_fn == "geglu":
+            act_fn = GEGLU(dim, inner_dim, bias=bias)
+        elif activation_fn == "geglu-approximate":
+            act_fn = ApproximateGELU(dim, inner_dim, bias=bias)
+        elif activation_fn == "swiglu":
+            act_fn = SwiGLU(dim, inner_dim, bias=bias)
+
+        self.net = nn.ModuleList([])
+        # project in
+        self.net.append(act_fn)
+        # project dropout
+        self.net.append(nn.Dropout(dropout))
+        # project out
+        self.net.append(nn.Linear(inner_dim, dim_out, bias=bias))
+        # FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
+        if final_dropout:
+            self.net.append(nn.Dropout(dropout))
+
+    def forward(self, hidden_states: torch.Tensor, *args, **kwargs) -> torch.Tensor:
+        if len(args) > 0 or kwargs.get("scale", None) is not None:
+            deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`."
+            print(deprecation_message)
+        for module in self.net:
+            hidden_states = module(hidden_states)
+        return hidden_states
+
+
+class Attention(nn.Module):
+    def __init__(
+        self,
+        query_dim: int,
+        dim_head: int = 64,
+        heads: int = 8,
+        kv_heads: Optional[int] = None,
+        qk_norm: Optional[str] = None,
+        eps: float = 1e-5,
+        bias: bool = False,
+        out_bias: bool = True,
+        dropout: float = 0.0,
+        out_dim: int = None,
+        cross_attention_dim: Optional[int] = None,
+    ):
+        super().__init__()
+        self.inner_dim = out_dim if out_dim is not None else dim_head * heads
+        self.inner_kv_dim = self.inner_dim if kv_heads is None else dim_head * kv_heads
+        self.query_dim = query_dim
+        self.cross_attention_dim = cross_attention_dim if cross_attention_dim is not None else query_dim
+        self.is_cross_attention = cross_attention_dim is not None
+        self.out_dim = out_dim if out_dim is not None else query_dim
+        self.heads = out_dim // dim_head if out_dim is not None else heads
+
+        if qk_norm is None:
+            self.norm_q = None
+            self.norm_k = None
+        elif qk_norm == "layer_norm":
+            self.norm_q = nn.LayerNorm(dim_head, eps=eps)
+            self.norm_k = nn.LayerNorm(dim_head, eps=eps)
+
+        self.to_q = nn.Linear(query_dim, self.inner_dim, bias=bias)
+        self.to_k = nn.Linear(self.cross_attention_dim, self.inner_kv_dim, bias=bias)
+        self.to_v = nn.Linear(self.cross_attention_dim, self.inner_kv_dim, bias=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.Dropout(dropout))
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        image_rotary_emb: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+
+        text_seq_length = encoder_hidden_states.size(1)
+
+        hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
+
+        batch_size, sequence_length, _ = (
+            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+        )
+
+        query = self.to_q(hidden_states)
+        key = self.to_k(hidden_states)
+        value = self.to_v(hidden_states)
+
+        inner_dim = key.shape[-1]
+        head_dim = inner_dim // self.heads
+
+        query = query.view(batch_size, -1, self.heads, head_dim).transpose(1, 2)
+        key = key.view(batch_size, -1, self.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, self.heads, head_dim).transpose(1, 2)
+
+        if self.norm_q is not None:
+            query = self.norm_q(query)
+        if self.norm_k is not None:
+            key = self.norm_k(key)
+
+        # Apply RoPE if needed
+        if image_rotary_emb is not None:
+            query[:, :, text_seq_length:] = apply_rotary_emb(query[:, :, text_seq_length:], image_rotary_emb)
+            if not self.is_cross_attention:
+                key[:, :, text_seq_length:] = apply_rotary_emb(key[:, :, text_seq_length:], image_rotary_emb)
+
+        hidden_states = F.scaled_dot_product_attention(
+            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
+        )
+
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.heads * head_dim)
+
+        # linear proj
+        hidden_states = self.to_out[0](hidden_states)
+        # dropout
+        hidden_states = self.to_out[1](hidden_states)
+
+        encoder_hidden_states, hidden_states = hidden_states.split(
+            [text_seq_length, hidden_states.size(1) - text_seq_length], dim=1
+        )
+        return hidden_states, encoder_hidden_states
+
+
+class CogVideoXBlock(nn.Module):
+    r"""
+    Transformer block used in [CogVideoX](https://github.com/THUDM/CogVideo) model.
+
+    Parameters:
+        dim (`int`):
+            The number of channels in the input and output.
+        num_attention_heads (`int`):
+            The number of heads to use for multi-head attention.
+        attention_head_dim (`int`):
+            The number of channels in each head.
+        time_embed_dim (`int`):
+            The number of channels in timestep embedding.
+        dropout (`float`, defaults to `0.0`):
+            The dropout probability to use.
+        activation_fn (`str`, defaults to `"gelu-approximate"`):
+            Activation function to be used in feed-forward.
+        attention_bias (`bool`, defaults to `False`):
+            Whether or not to use bias in attention projection layers.
+        qk_norm (`bool`, defaults to `True`):
+            Whether or not to use normalization after query and key projections in Attention.
+        norm_elementwise_affine (`bool`, defaults to `True`):
+            Whether to use learnable elementwise affine parameters for normalization.
+        norm_eps (`float`, defaults to `1e-5`):
+            Epsilon value for normalization layers.
+        final_dropout (`bool` defaults to `False`):
+            Whether to apply a final dropout after the last feed-forward layer.
+        ff_inner_dim (`int`, *optional*, defaults to `None`):
+            Custom hidden dimension of Feed-forward layer. If not provided, `4 * dim` is used.
+        ff_bias (`bool`, defaults to `True`):
+            Whether or not to use bias in Feed-forward layer.
+        attention_out_bias (`bool`, defaults to `True`):
+            Whether or not to use bias in Attention output projection layer.
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        num_attention_heads: int,
+        attention_head_dim: int,
+        time_embed_dim: int,
+        dropout: float = 0.0,
+        activation_fn: str = "gelu-approximate",
+        attention_bias: bool = False,
+        qk_norm: bool = True,
+        norm_elementwise_affine: bool = True,
+        norm_eps: float = 1e-5,
+        final_dropout: bool = True,
+        ff_inner_dim: Optional[int] = None,
+        ff_bias: bool = True,
+        attention_out_bias: bool = True,
+    ):
+        super().__init__()
+
+        # 1. Self Attention
+        self.norm1 = CogVideoXLayerNormZero(time_embed_dim, dim, norm_elementwise_affine, norm_eps, bias=True)
+
+        self.attn1 = Attention(
+            query_dim=dim,
+            dim_head=attention_head_dim,
+            heads=num_attention_heads,
+            qk_norm="layer_norm" if qk_norm else None,
+            eps=1e-6,
+            bias=attention_bias,
+            out_bias=attention_out_bias
+        )
+
+        # 2. Feed Forward
+        self.norm2 = CogVideoXLayerNormZero(time_embed_dim, dim, norm_elementwise_affine, norm_eps, bias=True)
+
+        self.ff = FeedForward(
+            dim,
+            dropout=dropout,
+            activation_fn=activation_fn,
+            final_dropout=final_dropout,
+            inner_dim=ff_inner_dim,
+            bias=ff_bias,
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor,
+        temb: torch.Tensor,
+        image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+    ) -> torch.Tensor:
+        text_seq_length = encoder_hidden_states.size(1)
+
+        # norm & modulate
+        norm_hidden_states, norm_encoder_hidden_states, gate_msa, enc_gate_msa = self.norm1(
+            hidden_states, encoder_hidden_states, temb
+        )
+
+        # attention
+        attn_hidden_states, attn_encoder_hidden_states = self.attn1(
+            hidden_states=norm_hidden_states,
+            encoder_hidden_states=norm_encoder_hidden_states,
+            image_rotary_emb=image_rotary_emb,
+        )
+
+        hidden_states = hidden_states + gate_msa * attn_hidden_states
+        encoder_hidden_states = encoder_hidden_states + enc_gate_msa * attn_encoder_hidden_states
+
+        # norm & modulate
+        norm_hidden_states, norm_encoder_hidden_states, gate_ff, enc_gate_ff = self.norm2(
+            hidden_states, encoder_hidden_states, temb
+        )
+
+        # feed-forward
+        norm_hidden_states = torch.cat([norm_encoder_hidden_states, norm_hidden_states], dim=1)
+        ff_output = self.ff(norm_hidden_states)
+
+        hidden_states = hidden_states + gate_ff * ff_output[:, text_seq_length:]
+        encoder_hidden_states = encoder_hidden_states + enc_gate_ff * ff_output[:, :text_seq_length]
+
+        return hidden_states, encoder_hidden_states

scepter/modules/model/backbone/cogvideox/utils.py

@@ -0,0 +1,544 @@
+# -*- coding: utf-8 -*-
+
+# Copyright 2024 The CogVideoX team, Tsinghua University & ZhipuAI 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 math
+from typing import Optional, Tuple, Union, List
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+ACTIVATION_FUNCTIONS = {
+    "swish": nn.SiLU(),
+    "silu": nn.SiLU(),
+    "mish": nn.Mish(),
+    "gelu": nn.GELU(),
+    "relu": nn.ReLU(),
+}
+
+
+def get_activation(act_fn: str) -> nn.Module:
+    """Helper function to get activation function from string.
+
+    Args:
+        act_fn (str): Name of activation function.
+
+    Returns:
+        nn.Module: Activation function.
+    """
+
+    act_fn = act_fn.lower()
+    if act_fn in ACTIVATION_FUNCTIONS:
+        return ACTIVATION_FUNCTIONS[act_fn]
+    else:
+        raise ValueError(f"Unsupported activation function: {act_fn}")
+
+
+class FP32SiLU(nn.Module):
+    r"""
+    SiLU activation function with input upcasted to torch.float32.
+    """
+
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, inputs: torch.Tensor) -> torch.Tensor:
+        return F.silu(inputs.float(), inplace=False).to(inputs.dtype)
+
+
+class GELU(nn.Module):
+    r"""
+    GELU activation function with tanh approximation support with `approximate="tanh"`.
+
+    Parameters:
+        dim_in (`int`): The number of channels in the input.
+        dim_out (`int`): The number of channels in the output.
+        approximate (`str`, *optional*, defaults to `"none"`): If `"tanh"`, use tanh approximation.
+        bias (`bool`, defaults to True): Whether to use a bias in the linear layer.
+    """
+
+    def __init__(self, dim_in: int, dim_out: int, approximate: str = "none", bias: bool = True):
+        super().__init__()
+        self.proj = nn.Linear(dim_in, dim_out, bias=bias)
+        self.approximate = approximate
+
+    def gelu(self, gate: torch.Tensor) -> torch.Tensor:
+        if gate.device.type != "mps":
+            return F.gelu(gate, approximate=self.approximate)
+        # mps: gelu is not implemented for float16
+        return F.gelu(gate.to(dtype=torch.float32), approximate=self.approximate).to(dtype=gate.dtype)
+
+    def forward(self, hidden_states):
+        hidden_states = self.proj(hidden_states)
+        hidden_states = self.gelu(hidden_states)
+        return hidden_states
+
+
+class GEGLU(nn.Module):
+    r"""
+    A [variant](https://arxiv.org/abs/2002.05202) of the gated linear unit activation function.
+
+    Parameters:
+        dim_in (`int`): The number of channels in the input.
+        dim_out (`int`): The number of channels in the output.
+        bias (`bool`, defaults to True): Whether to use a bias in the linear layer.
+    """
+
+    def __init__(self, dim_in: int, dim_out: int, bias: bool = True):
+        super().__init__()
+        self.proj = nn.Linear(dim_in, dim_out * 2, bias=bias)
+
+    def gelu(self, gate: torch.Tensor) -> torch.Tensor:
+        if gate.device.type != "mps":
+            return F.gelu(gate)
+        # mps: gelu is not implemented for float16
+        return F.gelu(gate.to(dtype=torch.float32)).to(dtype=gate.dtype)
+
+    def forward(self, hidden_states, *args, **kwargs):
+        if len(args) > 0 or kwargs.get("scale", None) is not None:
+            deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`."
+            print("scale", "1.0.0", deprecation_message)
+        hidden_states = self.proj(hidden_states)
+        hidden_states, gate = hidden_states.chunk(2, dim=-1)
+        return hidden_states * self.gelu(gate)
+
+
+class SwiGLU(nn.Module):
+    r"""
+    A [variant](https://arxiv.org/abs/2002.05202) of the gated linear unit activation function. It's similar to `GEGLU`
+    but uses SiLU / Swish instead of GeLU.
+
+    Parameters:
+        dim_in (`int`): The number of channels in the input.
+        dim_out (`int`): The number of channels in the output.
+        bias (`bool`, defaults to True): Whether to use a bias in the linear layer.
+    """
+
+    def __init__(self, dim_in: int, dim_out: int, bias: bool = True):
+        super().__init__()
+        self.proj = nn.Linear(dim_in, dim_out * 2, bias=bias)
+        self.activation = nn.SiLU()
+
+    def forward(self, hidden_states):
+        hidden_states = self.proj(hidden_states)
+        hidden_states, gate = hidden_states.chunk(2, dim=-1)
+        return hidden_states * self.activation(gate)
+
+
+class ApproximateGELU(nn.Module):
+    r"""
+    The approximate form of the Gaussian Error Linear Unit (GELU). For more details, see section 2 of this
+    [paper](https://arxiv.org/abs/1606.08415).
+
+    Parameters:
+        dim_in (`int`): The number of channels in the input.
+        dim_out (`int`): The number of channels in the output.
+        bias (`bool`, defaults to True): Whether to use a bias in the linear layer.
+    """
+
+    def __init__(self, dim_in: int, dim_out: int, bias: bool = True):
+        super().__init__()
+        self.proj = nn.Linear(dim_in, dim_out, bias=bias)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.proj(x)
+        return x * torch.sigmoid(1.702 * x)
+
+
+def randn_tensor(
+    shape: Union[Tuple, List],
+    generator: Optional[Union[List["torch.Generator"], "torch.Generator"]] = None,
+    device: Optional["torch.device"] = None,
+    dtype: Optional["torch.dtype"] = None,
+    layout: Optional["torch.layout"] = None,
+):
+    """A helper function to create random tensors on the desired `device` with the desired `dtype`. When
+    passing a list of generators, you can seed each batch size individually. If CPU generators are passed, the tensor
+    is always created on the CPU.
+    """
+    # device on which tensor is created defaults to device
+    rand_device = device
+    batch_size = shape[0]
+
+    layout = layout or torch.strided
+    device = device or torch.device("cpu")
+
+    if generator is not None:
+        gen_device_type = generator.device.type if not isinstance(generator, list) else generator[0].device.type
+        if gen_device_type != device.type and gen_device_type == "cpu":
+            rand_device = "cpu"
+            if device != "mps":
+                print(
+                    f"The passed generator was created on 'cpu' even though a tensor on {device} was expected."
+                    f" Tensors will be created on 'cpu' and then moved to {device}. Note that one can probably"
+                    f" slighly speed up this function by passing a generator that was created on the {device} device."
+                )
+        elif gen_device_type != device.type and gen_device_type == "cuda":
+            raise ValueError(f"Cannot generate a {device} tensor from a generator of type {gen_device_type}.")
+
+    # make sure generator list of length 1 is treated like a non-list
+    if isinstance(generator, list) and len(generator) == 1:
+        generator = generator[0]
+
+    if isinstance(generator, list):
+        shape = (1,) + shape[1:]
+        latents = [
+            torch.randn(shape, generator=generator[i], device=rand_device, dtype=dtype, layout=layout)
+            for i in range(batch_size)
+        ]
+        latents = torch.cat(latents, dim=0).to(device)
+    else:
+        latents = torch.randn(shape, generator=generator, device=rand_device, dtype=dtype, layout=layout).to(device)
+
+    return latents
+
+
+def get_timestep_embedding(
+    timesteps: torch.Tensor,
+    embedding_dim: int,
+    flip_sin_to_cos: bool = False,
+    downscale_freq_shift: float = 1,
+    scale: float = 1,
+    max_period: int = 10000,
+):
+    """
+    This matches the implementation in Denoising Diffusion Probabilistic Models: Create sinusoidal timestep embeddings.
+
+    Args
+        timesteps (torch.Tensor):
+            a 1-D Tensor of N indices, one per batch element. These may be fractional.
+        embedding_dim (int):
+            the dimension of the output.
+        flip_sin_to_cos (bool):
+            Whether the embedding order should be `cos, sin` (if True) or `sin, cos` (if False)
+        downscale_freq_shift (float):
+            Controls the delta between frequencies between dimensions
+        scale (float):
+            Scaling factor applied to the embeddings.
+        max_period (int):
+            Controls the maximum frequency of the embeddings
+    Returns
+        torch.Tensor: an [N x dim] Tensor of positional embeddings.
+    """
+    assert len(timesteps.shape) == 1, "Timesteps should be a 1d-array"
+
+    half_dim = embedding_dim // 2
+    exponent = -math.log(max_period) * torch.arange(
+        start=0, end=half_dim, dtype=torch.float32, device=timesteps.device
+    )
+    exponent = exponent / (half_dim - downscale_freq_shift)
+
+    emb = torch.exp(exponent)
+    emb = timesteps[:, None].float() * emb[None, :]
+
+    # scale embeddings
+    emb = scale * emb
+
+    # concat sine and cosine embeddings
+    emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1)
+
+    # flip sine and cosine embeddings
+    if flip_sin_to_cos:
+        emb = torch.cat([emb[:, half_dim:], emb[:, :half_dim]], dim=-1)
+
+    # zero pad
+    if embedding_dim % 2 == 1:
+        emb = torch.nn.functional.pad(emb, (0, 1, 0, 0))
+    return emb
+
+
+
+def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
+    """
+    embed_dim: output dimension for each position pos: a list of positions to be encoded: size (M,) out: (M, D)
+    """
+    if embed_dim % 2 != 0:
+        raise ValueError("embed_dim must be divisible by 2")
+
+    omega = np.arange(embed_dim // 2, dtype=np.float64)
+    omega /= embed_dim / 2.0
+    omega = 1.0 / 10000**omega  # (D/2,)
+
+    pos = pos.reshape(-1)  # (M,)
+    out = np.einsum("m,d->md", pos, omega)  # (M, D/2), outer product
+
+    emb_sin = np.sin(out)  # (M, D/2)
+    emb_cos = np.cos(out)  # (M, D/2)
+
+    emb = np.concatenate([emb_sin, emb_cos], axis=1)  # (M, D)
+    return emb
+
+
+def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
+    if embed_dim % 2 != 0:
+        raise ValueError("embed_dim must be divisible by 2")
+
+    # use half of dimensions to encode grid_h
+    emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0])  # (H*W, D/2)
+    emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1])  # (H*W, D/2)
+
+    emb = np.concatenate([emb_h, emb_w], axis=1)  # (H*W, D)
+    return emb
+
+def get_3d_sincos_pos_embed(
+    embed_dim: int,
+    spatial_size: Union[int, Tuple[int, int]],
+    temporal_size: int,
+    spatial_interpolation_scale: float = 1.0,
+    temporal_interpolation_scale: float = 1.0,
+) -> np.ndarray:
+    r"""
+    Args:
+        embed_dim (`int`):
+        spatial_size (`int` or `Tuple[int, int]`):
+        temporal_size (`int`):
+        spatial_interpolation_scale (`float`, defaults to 1.0):
+        temporal_interpolation_scale (`float`, defaults to 1.0):
+    """
+    if embed_dim % 4 != 0:
+        raise ValueError("`embed_dim` must be divisible by 4")
+    if isinstance(spatial_size, int):
+        spatial_size = (spatial_size, spatial_size)
+
+    embed_dim_spatial = 3 * embed_dim // 4
+    embed_dim_temporal = embed_dim // 4
+
+    # 1. Spatial
+    grid_h = np.arange(spatial_size[1], dtype=np.float32) / spatial_interpolation_scale
+    grid_w = np.arange(spatial_size[0], dtype=np.float32) / spatial_interpolation_scale
+    grid = np.meshgrid(grid_w, grid_h)  # here w goes first
+    grid = np.stack(grid, axis=0)
+
+    grid = grid.reshape([2, 1, spatial_size[1], spatial_size[0]])
+    pos_embed_spatial = get_2d_sincos_pos_embed_from_grid(embed_dim_spatial, grid)
+
+    # 2. Temporal
+    grid_t = np.arange(temporal_size, dtype=np.float32) / temporal_interpolation_scale
+    pos_embed_temporal = get_1d_sincos_pos_embed_from_grid(embed_dim_temporal, grid_t)
+
+    # 3. Concat
+    pos_embed_spatial = pos_embed_spatial[np.newaxis, :, :]
+    pos_embed_spatial = np.repeat(pos_embed_spatial, temporal_size, axis=0)  # [T, H*W, D // 4 * 3]
+
+    pos_embed_temporal = pos_embed_temporal[:, np.newaxis, :]
+    pos_embed_temporal = np.repeat(pos_embed_temporal, spatial_size[0] * spatial_size[1], axis=1)  # [T, H*W, D // 4]
+
+    pos_embed = np.concatenate([pos_embed_temporal, pos_embed_spatial], axis=-1)  # [T, H*W, D]
+    return pos_embed
+
+
+def apply_rotary_emb(
+    x: torch.Tensor,
+    freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]],
+    use_real: bool = True,
+    use_real_unbind_dim: int = -1,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    """
+    Apply rotary embeddings to input tensors using the given frequency tensor. This function applies rotary embeddings
+    to the given query or key 'x' tensors using the provided frequency tensor 'freqs_cis'. The input tensors are
+    reshaped as complex numbers, and the frequency tensor is reshaped for broadcasting compatibility. The resulting
+    tensors contain rotary embeddings and are returned as real tensors.
+
+    Args:
+        x (`torch.Tensor`):
+            Query or key tensor to apply rotary embeddings. [B, H, S, D] xk (torch.Tensor): Key tensor to apply
+        freqs_cis (`Tuple[torch.Tensor]`): Precomputed frequency tensor for complex exponentials. ([S, D], [S, D],)
+
+    Returns:
+        Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor and key tensor with rotary embeddings.
+    """
+    if use_real:
+        cos, sin = freqs_cis  # [S, D]
+        cos = cos[None, None]
+        sin = sin[None, None]
+        cos, sin = cos.to(x.device), sin.to(x.device)
+
+        if use_real_unbind_dim == -1:
+            # Used for flux, cogvideox, hunyuan-dit
+            x_real, x_imag = x.reshape(*x.shape[:-1], -1, 2).unbind(-1)  # [B, S, H, D//2]
+            x_rotated = torch.stack([-x_imag, x_real], dim=-1).flatten(3)
+        elif use_real_unbind_dim == -2:
+            # Used for Stable Audio
+            x_real, x_imag = x.reshape(*x.shape[:-1], 2, -1).unbind(-2)  # [B, S, H, D//2]
+            x_rotated = torch.cat([-x_imag, x_real], dim=-1)
+        else:
+            raise ValueError(f"`use_real_unbind_dim={use_real_unbind_dim}` but should be -1 or -2.")
+
+        out = (x.float() * cos + x_rotated.float() * sin).to(x.dtype)
+
+        return out
+    else:
+        # used for lumina
+        x_rotated = torch.view_as_complex(x.float().reshape(*x.shape[:-1], -1, 2))
+        freqs_cis = freqs_cis.unsqueeze(2)
+        x_out = torch.view_as_real(x_rotated * freqs_cis).flatten(3)
+
+        return x_out.type_as(x)
+
+
+def get_1d_rotary_pos_embed(
+    dim: int,
+    pos: Union[np.ndarray, int],
+    theta: float = 10000.0,
+    use_real=False,
+    linear_factor=1.0,
+    ntk_factor=1.0,
+    repeat_interleave_real=True,
+    freqs_dtype=torch.float32,  #  torch.float32, torch.float64 (flux)
+):
+    """
+    Precompute the frequency tensor for complex exponentials (cis) with given dimensions.
+
+    This function calculates a frequency tensor with complex exponentials using the given dimension 'dim' and the end
+    index 'end'. The 'theta' parameter scales the frequencies. The returned tensor contains complex values in complex64
+    data type.
+
+    Args:
+        dim (`int`): Dimension of the frequency tensor.
+        pos (`np.ndarray` or `int`): Position indices for the frequency tensor. [S] or scalar
+        theta (`float`, *optional*, defaults to 10000.0):
+            Scaling factor for frequency computation. Defaults to 10000.0.
+        use_real (`bool`, *optional*):
+            If True, return real part and imaginary part separately. Otherwise, return complex numbers.
+        linear_factor (`float`, *optional*, defaults to 1.0):
+            Scaling factor for the context extrapolation. Defaults to 1.0.
+        ntk_factor (`float`, *optional*, defaults to 1.0):
+            Scaling factor for the NTK-Aware RoPE. Defaults to 1.0.
+        repeat_interleave_real (`bool`, *optional*, defaults to `True`):
+            If `True` and `use_real`, real part and imaginary part are each interleaved with themselves to reach `dim`.
+            Otherwise, they are concateanted with themselves.
+        freqs_dtype (`torch.float32` or `torch.float64`, *optional*, defaults to `torch.float32`):
+            the dtype of the frequency tensor.
+    Returns:
+        `torch.Tensor`: Precomputed frequency tensor with complex exponentials. [S, D/2]
+    """
+    assert dim % 2 == 0
+
+    if isinstance(pos, int):
+        pos = torch.arange(pos)
+    if isinstance(pos, np.ndarray):
+        pos = torch.from_numpy(pos)  # type: ignore  # [S]
+
+    theta = theta * ntk_factor
+    freqs = (
+        1.0
+        / (theta ** (torch.arange(0, dim, 2, dtype=freqs_dtype, device=pos.device)[: (dim // 2)] / dim))
+        / linear_factor
+    )  # [D/2]
+    freqs = torch.outer(pos, freqs)  # type: ignore   # [S, D/2]
+    if use_real and repeat_interleave_real:
+        # flux, hunyuan-dit, cogvideox
+        freqs_cos = freqs.cos().repeat_interleave(2, dim=1).float()  # [S, D]
+        freqs_sin = freqs.sin().repeat_interleave(2, dim=1).float()  # [S, D]
+        return freqs_cos, freqs_sin
+    elif use_real:
+        # stable audio
+        freqs_cos = torch.cat([freqs.cos(), freqs.cos()], dim=-1).float()  # [S, D]
+        freqs_sin = torch.cat([freqs.sin(), freqs.sin()], dim=-1).float()  # [S, D]
+        return freqs_cos, freqs_sin
+    else:
+        # lumina
+        freqs_cis = torch.polar(torch.ones_like(freqs), freqs)  # complex64     # [S, D/2]
+        return freqs_cis
+
+
+def get_3d_rotary_pos_embed(
+    embed_dim, crops_coords, grid_size, temporal_size, theta: int = 10000, use_real: bool = True
+) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+    """
+    RoPE for video tokens with 3D structure.
+
+    Args:
+    embed_dim: (`int`):
+        The embedding dimension size, corresponding to hidden_size_head.
+    crops_coords (`Tuple[int]`):
+        The top-left and bottom-right coordinates of the crop.
+    grid_size (`Tuple[int]`):
+        The grid size of the spatial positional embedding (height, width).
+    temporal_size (`int`):
+        The size of the temporal dimension.
+    theta (`float`):
+        Scaling factor for frequency computation.
+
+    Returns:
+        `torch.Tensor`: positional embedding with shape `(temporal_size * grid_size[0] * grid_size[1], embed_dim/2)`.
+    """
+    if use_real is not True:
+        raise ValueError(" `use_real = False` is not currently supported for get_3d_rotary_pos_embed")
+    start, stop = crops_coords
+    grid_size_h, grid_size_w = grid_size
+    grid_h = np.linspace(start[0], stop[0], grid_size_h, endpoint=False, dtype=np.float32)
+    grid_w = np.linspace(start[1], stop[1], grid_size_w, endpoint=False, dtype=np.float32)
+    grid_t = np.linspace(0, temporal_size, temporal_size, endpoint=False, dtype=np.float32)
+
+    # Compute dimensions for each axis
+    dim_t = embed_dim // 4
+    dim_h = embed_dim // 8 * 3
+    dim_w = embed_dim // 8 * 3
+
+    # Temporal frequencies
+    freqs_t = get_1d_rotary_pos_embed(dim_t, grid_t, use_real=True)
+    # Spatial frequencies for height and width
+    freqs_h = get_1d_rotary_pos_embed(dim_h, grid_h, use_real=True)
+    freqs_w = get_1d_rotary_pos_embed(dim_w, grid_w, use_real=True)
+
+    # BroadCast and concatenate temporal and spaial frequencie (height and width) into a 3d tensor
+    def combine_time_height_width(freqs_t, freqs_h, freqs_w):
+        freqs_t = freqs_t[:, None, None, :].expand(
+            -1, grid_size_h, grid_size_w, -1
+        )  # temporal_size, grid_size_h, grid_size_w, dim_t
+        freqs_h = freqs_h[None, :, None, :].expand(
+            temporal_size, -1, grid_size_w, -1
+        )  # temporal_size, grid_size_h, grid_size_2, dim_h
+        freqs_w = freqs_w[None, None, :, :].expand(
+            temporal_size, grid_size_h, -1, -1
+        )  # temporal_size, grid_size_h, grid_size_2, dim_w
+
+        freqs = torch.cat(
+            [freqs_t, freqs_h, freqs_w], dim=-1
+        )  # temporal_size, grid_size_h, grid_size_w, (dim_t + dim_h + dim_w)
+        freqs = freqs.view(
+            temporal_size * grid_size_h * grid_size_w, -1
+        )  # (temporal_size * grid_size_h * grid_size_w), (dim_t + dim_h + dim_w)
+        return freqs
+
+    t_cos, t_sin = freqs_t  # both t_cos and t_sin has shape: temporal_size, dim_t
+    h_cos, h_sin = freqs_h  # both h_cos and h_sin has shape: grid_size_h, dim_h
+    w_cos, w_sin = freqs_w  # both w_cos and w_sin has shape: grid_size_w, dim_w
+    cos = combine_time_height_width(t_cos, h_cos, w_cos)
+    sin = combine_time_height_width(t_sin, h_sin, w_sin)
+    return cos, sin
+
+
+def get_resize_crop_region_for_grid(src, tgt_width, tgt_height):
+    tw = tgt_width
+    th = tgt_height
+    h, w = src
+    r = h / w
+    if r > (th / tw):
+        resize_height = th
+        resize_width = int(round(th / h * w))
+    else:
+        resize_width = tw
+        resize_height = int(round(tw / w * h))
+
+    crop_top = int(round((th - resize_height) / 2.0))
+    crop_left = int(round((tw - resize_width) / 2.0))
+
+    return (crop_top, crop_left), (crop_top + resize_height, crop_left + resize_width)

scepter/modules/model/backbone/flux/flux.py

@@ -12,7 +12,7 @@ from scepter.modules.utils.distribute import we
 from scepter.modules.utils.file_system import FS
 from torch import Tensor, nn
 from torch.utils.checkpoint import checkpoint_sequential
-
+from torch.nn.utils.rnn import pad_sequence
 from .layers import (DoubleStreamBlock, EmbedND, LastLayer, MLPEmbedder,
                      SingleStreamBlock, timestep_embedding)
 
@@ -245,7 +245,133 @@ class Flux(BaseModel):
 
     @staticmethod
     def get_config_template():
-        return dict_to_yaml('MODEL',
+        return dict_to_yaml('BACKBONE',
                             __class__.__name__,
                             Flux.para_dict,
                             set_name=True)
+
+@BACKBONES.register_class()
+class FluxMR(Flux):
+    def prepare_input(self, x, cond):
+        context, y = cond["context"].to(x), cond["y"].to(x)
+        batch_frames, batch_frames_ids = [], []
+        for ix, shape in zip(x, cond["x_shapes"]):
+            # unpack image from sequence
+            ix = ix[:, :shape[0] * shape[1]].view(-1, shape[0], shape[1])
+            c, h, w = ix.shape
+            ix = rearrange(ix, "c (h ph) (w pw) -> (h w) (c ph pw)", ph=2, pw=2)
+            ix_id = torch.zeros(h // 2, w // 2, 3)
+            ix_id[..., 1] = ix_id[..., 1] + torch.arange(h // 2)[:, None]
+            ix_id[..., 2] = ix_id[..., 2] + torch.arange(w // 2)[None, :]
+            ix_id = rearrange(ix_id, "h w c -> (h w) c")
+            batch_frames.append([ix])
+            batch_frames_ids.append([ix_id])
+
+        x_list, x_id_list, mask_x_list, x_seq_length = [], [], [], []
+        for frames, frame_ids in zip(batch_frames, batch_frames_ids):
+            proj_frames = []
+            for idx, one_frame in enumerate(frames):
+                one_frame = self.img_in(one_frame)
+                proj_frames.append(one_frame)
+            ix = torch.cat(proj_frames, dim=0)
+            if_id = torch.cat(frame_ids, dim=0)
+            x_list.append(ix)
+            x_id_list.append(if_id)
+            mask_x_list.append(torch.ones(ix.shape[0]).to(ix.device, non_blocking=True).bool())
+            x_seq_length.append(ix.shape[0])
+        x = pad_sequence(tuple(x_list), batch_first=True)
+        x_ids = pad_sequence(tuple(x_id_list), batch_first=True).to(x)  # [b,pad_seq,2] pad (0.,0.) at dim2
+        mask_x = pad_sequence(tuple(mask_x_list), batch_first=True)
+
+        txt = self.txt_in(context)
+        txt_ids = torch.zeros(context.shape[0], context.shape[1], 3).to(x)
+        mask_txt = torch.ones(context.shape[0], context.shape[1]).to(x.device, non_blocking=True).bool()
+
+        return x, x_ids, txt, txt_ids, y, mask_x, mask_txt, x_seq_length
+
+    def unpack(self, x: Tensor, cond: dict = None, x_seq_length: list = None) -> Tensor:
+        x_list = []
+        image_shapes = cond["x_shapes"]
+        for u, shape, seq_length in zip(x, image_shapes, x_seq_length):
+            height, width = shape
+            h, w = math.ceil(height / 2), math.ceil(width / 2)
+            u = rearrange(
+                u[seq_length-h*w:seq_length, ...],
+                "(h w) (c ph pw) -> (h ph w pw) c",
+                h=h,
+                w=w,
+                ph=2,
+                pw=2,
+            )
+            x_list.append(u)
+        x = pad_sequence(tuple(x_list), batch_first=True).permute(0, 2, 1)
+        return x
+
+    def forward(
+            self,
+            x: Tensor,
+            t: Tensor,
+            cond: dict = {},
+            guidance: Tensor | None = None,
+            gc_seg: int = 0,
+            **kwargs
+    ) -> Tensor:
+        x, x_ids, txt, txt_ids, y, mask_x, mask_txt, seq_length_list = self.prepare_input(x, cond)
+        # running on sequences img
+        vec = self.time_in(timestep_embedding(t, 256))
+        if self.guidance_embed:
+            if guidance is None:
+                raise ValueError("Didn't get guidance strength for guidance distilled model.")
+            vec = vec + self.guidance_in(timestep_embedding(guidance, 256))
+        vec = vec + self.vector_in(y)
+        ids = torch.cat((txt_ids, x_ids), dim=1)
+        pe = self.pe_embedder(ids)
+
+        mask_aside = torch.cat((mask_txt, mask_x), dim=1)
+        mask = mask_aside[:, None, :] * mask_aside[:, :, None]
+
+        kwargs = dict(
+            vec=vec,
+            pe=pe,
+            mask=mask,
+            txt_length = txt.shape[1],
+        )
+        x = torch.cat((txt, x), 1)
+        if self.use_grad_checkpoint and gc_seg >= 0:
+            x = checkpoint_sequential(
+                functions=[partial(block, **kwargs) for block in self.double_blocks],
+                segments=gc_seg if gc_seg > 0 else len(self.double_blocks),
+                input=x,
+                use_reentrant=False
+            )
+        else:
+            for block in self.double_blocks:
+                x = block(x, **kwargs)
+
+        kwargs = dict(
+            vec=vec,
+            pe=pe,
+            mask=mask,
+        )
+
+        if self.use_grad_checkpoint and gc_seg >= 0:
+            x = checkpoint_sequential(
+                functions=[partial(block, **kwargs) for block in self.single_blocks],
+                segments=gc_seg if gc_seg > 0 else len(self.single_blocks),
+                input=x,
+                use_reentrant=False
+            )
+        else:
+            for block in self.single_blocks:
+                x = block(x, **kwargs)
+        x = x[:, txt.shape[1]:, ...]
+        x = self.final_layer(x, vec)  # (N, T, patch_size ** 2 * out_channels) 6 64 64
+        x = self.unpack(x, cond, seq_length_list)
+        return x
+
+    @staticmethod
+    def get_config_template():
+        return dict_to_yaml('BACKBONE',
+                            __class__.__name__,
+                            FluxMR.para_dict,
+                            set_name=True)

scepter/modules/model/backbone/flux/layers.py

@@ -4,24 +4,66 @@ from __future__ import annotations
 
 import math
 from dataclasses import dataclass
-
+from torch import Tensor, nn
 import torch
 from einops import rearrange, repeat
-from torch import Tensor, nn
+from torch import Tensor
+from torch.nn.utils.rnn import pad_sequence
 
+try:
+    from flash_attn import (
+        flash_attn_varlen_func
+    )
+    FLASHATTN_IS_AVAILABLE = True
+except ImportError:
+    FLASHATTN_IS_AVAILABLE = False
+    flash_attn_varlen_func = None
 
-def attention(q: Tensor,
-              k: Tensor,
-              v: Tensor,
-              pe: Tensor,
-              mask: Tensor | None = None) -> Tensor:
+def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, mask: Tensor | None = None, backend = 'pytorch') -> Tensor:
     q, k = apply_rope(q, k, pe)
-    x = torch.nn.functional.scaled_dot_product_attention(q,
-                                                         k,
-                                                         v,
-                                                         attn_mask=mask)
-    x = torch.nan_to_num(x, nan=0.0, posinf=1e10, neginf=-1e10)
-    x = rearrange(x, 'B H L D -> B L (H D)')
+    if backend == 'pytorch':
+        if mask is not None and mask.dtype == torch.bool:
+            mask = torch.zeros_like(mask).to(q).masked_fill_(mask.logical_not(), -1e20)
+        x = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask)
+        # x = torch.nan_to_num(x, nan=0.0, posinf=1e10, neginf=-1e10)
+        x = rearrange(x, "B H L D -> B L (H D)")
+    elif backend == 'flash_attn':
+        # q: (B, H, L, D)
+        # k: (B, H, S, D) now L = S
+        # v: (B, H, S, D)
+        b, h, lq, d = q.shape
+        _, _, lk, _ = k.shape
+        q = rearrange(q, "B H L D -> B L H D")
+        k = rearrange(k, "B H S D -> B S H D")
+        v = rearrange(v, "B H S D -> B S H D")
+        if mask is None:
+            q_lens = torch.tensor([lq] * b, dtype=torch.int32).to(q.device, non_blocking=True)
+            k_lens = torch.tensor([lk] * b, dtype=torch.int32).to(k.device, non_blocking=True)
+        else:
+            q_lens = torch.sum(mask[:, 0, :, 0], dim=1).int()
+            k_lens = torch.sum(mask[:, 0, 0, :], dim=1).int()
+        q = torch.cat([q_v[:q_l] for q_v, q_l in zip(q, q_lens)])
+        k = torch.cat([k_v[:k_l] for k_v, k_l in zip(k, k_lens)])
+        v = torch.cat([v_v[:v_l] for v_v, v_l in zip(v, k_lens)])
+        cu_seqlens_q = torch.cat([q_lens.new_zeros([1]), q_lens]).cumsum(0, dtype=torch.int32)
+        cu_seqlens_k = torch.cat([k_lens.new_zeros([1]), k_lens]).cumsum(0, dtype=torch.int32)
+        max_seqlen_q = q_lens.max()
+        max_seqlen_k = k_lens.max()
+
+        x = flash_attn_varlen_func(
+            q,
+            k,
+            v,
+            cu_seqlens_q=cu_seqlens_q,
+            cu_seqlens_k=cu_seqlens_k,
+            max_seqlen_q=max_seqlen_q,
+            max_seqlen_k=max_seqlen_k
+        )
+        x_list = [x[cu_seqlens_q[i]:cu_seqlens_q[i+1]] for i in range(b)]
+        x = pad_sequence(tuple(x_list), batch_first=True)
+        x = rearrange(x, "B L H D -> B L (H D)")
+    else:
+        raise NotImplementedError
     return x
 
 
@@ -173,11 +215,8 @@ class Modulation(nn.Module):
         self.multiplier = 6 if double else 3
         self.lin = nn.Linear(dim, self.multiplier * dim, bias=True)
 
-    def forward(self,
-                vec: Tensor) -> tuple[ModulationOut, ModulationOut | None]:
-        out = self.lin(nn.functional.silu(vec))[:,
-                                                None, :].chunk(self.multiplier,
-                                                               dim=-1)
+    def forward(self, vec: Tensor) -> tuple[ModulationOut, ModulationOut | None]:
+        out = self.lin(nn.functional.silu(vec))[:, None, :].chunk(self.multiplier, dim=-1)
 
         return (
             ModulationOut(*out[:3]),
@@ -186,56 +225,37 @@ class Modulation(nn.Module):
 
 
 class DoubleStreamBlock(nn.Module):
-    def __init__(self,
-                 hidden_size: int,
-                 num_heads: int,
-                 mlp_ratio: float,
-                 qkv_bias: bool = False):
+    def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False, backend = 'pytorch'):
         super().__init__()
 
         mlp_hidden_dim = int(hidden_size * mlp_ratio)
         self.num_heads = num_heads
         self.hidden_size = hidden_size
         self.img_mod = Modulation(hidden_size, double=True)
-        self.img_norm1 = nn.LayerNorm(hidden_size,
-                                      elementwise_affine=False,
-                                      eps=1e-6)
-        self.img_attn = SelfAttention(dim=hidden_size,
-                                      num_heads=num_heads,
-                                      qkv_bias=qkv_bias)
+        self.img_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.img_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias)
 
-        self.img_norm2 = nn.LayerNorm(hidden_size,
-                                      elementwise_affine=False,
-                                      eps=1e-6)
+        self.img_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
         self.img_mlp = nn.Sequential(
             nn.Linear(hidden_size, mlp_hidden_dim, bias=True),
-            nn.GELU(approximate='tanh'),
+            nn.GELU(approximate="tanh"),
             nn.Linear(mlp_hidden_dim, hidden_size, bias=True),
         )
 
+        self.backend = backend
+
         self.txt_mod = Modulation(hidden_size, double=True)
-        self.txt_norm1 = nn.LayerNorm(hidden_size,
-                                      elementwise_affine=False,
-                                      eps=1e-6)
-        self.txt_attn = SelfAttention(dim=hidden_size,
-                                      num_heads=num_heads,
-                                      qkv_bias=qkv_bias)
+        self.txt_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.txt_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias)
 
-        self.txt_norm2 = nn.LayerNorm(hidden_size,
-                                      elementwise_affine=False,
-                                      eps=1e-6)
+        self.txt_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
         self.txt_mlp = nn.Sequential(
             nn.Linear(hidden_size, mlp_hidden_dim, bias=True),
-            nn.GELU(approximate='tanh'),
+            nn.GELU(approximate="tanh"),
             nn.Linear(mlp_hidden_dim, hidden_size, bias=True),
         )
 
-    def forward(self,
-                x: Tensor,
-                vec: Tensor,
-                pe: Tensor,
-                mask: Tensor = None,
-                txt_length=None):
+    def forward(self, x: Tensor, vec: Tensor, pe: Tensor, mask: Tensor = None, txt_length = None):
         img_mod1, img_mod2 = self.img_mod(vec)
         txt_mod1, txt_mod2 = self.txt_mod(vec)
 
@@ -245,19 +265,13 @@ class DoubleStreamBlock(nn.Module):
         img_modulated = self.img_norm1(img)
         img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift
         img_qkv = self.img_attn.qkv(img_modulated)
-        img_q, img_k, img_v = rearrange(img_qkv,
-                                        'B L (K H D) -> K B H L D',
-                                        K=3,
-                                        H=self.num_heads)
+        img_q, img_k, img_v = rearrange(img_qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
         img_q, img_k = self.img_attn.norm(img_q, img_k, img_v)
         # prepare txt for attention
         txt_modulated = self.txt_norm1(txt)
         txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift
         txt_qkv = self.txt_attn.qkv(txt_modulated)
-        txt_q, txt_k, txt_v = rearrange(txt_qkv,
-                                        'B L (K H D) -> K B H L D',
-                                        K=3,
-                                        H=self.num_heads)
+        txt_q, txt_k, txt_v = rearrange(txt_qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
         txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v)
 
         # run actual attention
@@ -266,18 +280,16 @@ class DoubleStreamBlock(nn.Module):
         v = torch.cat((txt_v, img_v), dim=2)
         if mask is not None:
             mask = repeat(mask, 'B L S->  B H L S', H=self.num_heads)
-        attn = attention(q, k, v, pe=pe, mask=mask)
-        txt_attn, img_attn = attn[:, :txt.shape[1]], attn[:, txt.shape[1]:]
+        attn = attention(q, k, v, pe=pe, mask = mask, backend = self.backend)
+        txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :]
 
         # calculate the img bloks
         img = img + img_mod1.gate * self.img_attn.proj(img_attn)
-        img = img + img_mod2.gate * self.img_mlp(
-            (1 + img_mod2.scale) * self.img_norm2(img) + img_mod2.shift)
+        img = img + img_mod2.gate * self.img_mlp((1 + img_mod2.scale) * self.img_norm2(img) + img_mod2.shift)
 
         # calculate the txt bloks
         txt = txt + txt_mod1.gate * self.txt_attn.proj(txt_attn)
-        txt = txt + txt_mod2.gate * self.txt_mlp(
-            (1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift)
+        txt = txt + txt_mod2.gate * self.txt_mlp((1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift)
         x = torch.cat((txt, img), 1)
         return x
 
@@ -293,6 +305,7 @@ class SingleStreamBlock(nn.Module):
         num_heads: int,
         mlp_ratio: float = 4.0,
         qk_scale: float | None = None,
+        backend='pytorch'
     ):
         super().__init__()
         self.hidden_dim = hidden_size
@@ -317,6 +330,7 @@ class SingleStreamBlock(nn.Module):
 
         self.mlp_act = nn.GELU(approximate='tanh')
         self.modulation = Modulation(hidden_size, double=False)
+        self.backend = backend
 
     def forward(self,
                 x: Tensor,

scepter/modules/model/diffusion/diffusions.py

@@ -19,10 +19,6 @@ class BaseDiffusion(object):
     para_dict = {
         'NOISE_SCHEDULER': {},
         'SAMPLER_SCHEDULER': {},
-        'MIN_SNR_GAMMA': {
-            'value': None,
-            'description': 'The minimum SNR gamma value for the loss function.'
-        },
         'PREDICTION_TYPE': {
             'value': 'eps',
             'description':
@@ -37,7 +33,6 @@ class BaseDiffusion(object):
         self.init_params()
 
     def init_params(self):
-        self.min_snr_gamma = self.cfg.get('MIN_SNR_GAMMA', None)
         self.prediction_type = self.cfg.get('PREDICTION_TYPE', 'eps')
         self.noise_scheduler = NOISE_SCHEDULERS.build(self.cfg.NOISE_SCHEDULER,
                                                       logger=self.logger)
@@ -67,17 +62,19 @@ class BaseDiffusion(object):
                show_progress=False,
                return_intermediate=None,
                intermediate_callback=None,
+               reverse_scale = -1.,
+               x = None,
                **kwargs):
         assert isinstance(steps, (int, torch.LongTensor))
         assert return_intermediate in (None, 'x0', 'xt')
         assert isinstance(sampler, (str, dict, Config))
         intermediates = []
 
-        def callback_fn(x_t, t, sigma=None, alpha=None):
+        def callback_fn(x_t, t, sigma=None, alpha_bar=None):
             timestamp = t
             t = t.repeat(len(x_t)).round().long().to(x_t.device)
             sigma = sigma.repeat(len(x_t), *([1] * (len(sigma.shape) - 1)))
-            alpha = alpha.repeat(len(x_t), *([1] * (len(alpha.shape) - 1)))
+            alpha_bar = alpha_bar.repeat(len(x_t), *([1] * (len(alpha_bar.shape) - 1)))
 
             if guide_scale is None or guide_scale == 1.0:
                 out = model(x=x_t, t=t, **model_kwargs)
@@ -101,15 +98,12 @@ class BaseDiffusion(object):
             if self.prediction_type == 'x0':
                 x0 = out
             elif self.prediction_type == 'eps':
-                x0 = (x_t - sigma * out) / alpha
+                x0 = (x_t - sigma * out) / alpha_bar
             elif self.prediction_type == 'v':
-                x0 = alpha * x_t - sigma * out
+                x0 = alpha_bar * x_t - sigma * out
             else:
                 raise NotImplementedError(
                     f'prediction_type {self.prediction_type} not implemented')
-
-            # print("torch.sum(y_out):", torch.sum(y_out), "torch.sum(u_out):", torch.sum(u_out), "torch.sum(out):",
-            #       torch.sum(out), "torch.sum(x0):", torch.sum(x0), "sigmas", sigma, "alphas", alpha)
             return x0
 
         sampler_ins = self.get_sampler(sampler)
@@ -117,12 +111,14 @@ class BaseDiffusion(object):
         # this is ignored for schnell
         sampler_output = sampler_ins.preprare_sampler(
             noise,
+            x = x,
             steps=steps,
+            reverse_scale= reverse_scale,
             prediction_type=self.prediction_type,
             scheduler_ins=self.sampler_scheduler,
             callback_fn=callback_fn)
 
-        for _ in trange(steps, disable=not show_progress):
+        for _ in trange(sampler_output.steps, disable=not show_progress):
             trange.desc = sampler_output.msg
             sampler_output = sampler_ins.step(sampler_output)
             if return_intermediate == 'x_0':
@@ -145,30 +141,19 @@ class BaseDiffusion(object):
         if noise is None:
             noise = torch.randn_like(x_0)
         schedule_output = self.noise_scheduler.add_noise(x_0, noise, **kwargs)
-        x_t, t, sigma, alpha = schedule_output.x_t, schedule_output.t, schedule_output.sigma, schedule_output.alpha
+        x_t, t, sigma, alpha_bar = schedule_output.x_t, schedule_output.t, schedule_output.sigma, schedule_output.alpha_bar
         out = model(x=x_t, t=t, **model_kwargs)
 
         # mse loss
         target = {
             'eps': noise,
             'x0': x_0,
-            'v': alpha * noise - sigma * x_0
+            'v': alpha_bar * noise - sigma * x_0
         }[self.prediction_type]
 
         loss = (out - target).pow(2)
         if reduction == 'mean':
             loss = loss.flatten(1).mean(dim=1)
-
-        if self.min_snr_gamma is not None:
-            alphas = self.noise_scheduler.alphas.to(x_0.device)[t]
-            sigmas = self.noise_scheduler.sigmas.pow(2).to(x_0.device)[t]
-            snrs = (alphas / sigmas).clamp(min=1e-20)
-            min_snrs = snrs.clamp(max=self.min_snr_gamma)
-            weights = min_snrs / snrs
-        else:
-            weights = 1
-
-        loss = loss * weights
         return loss
 
     def get_sampler(self, sampler):
@@ -248,17 +233,6 @@ class DiffusionFluxRF(BaseDiffusion):
         loss = (target - out)**2
         if reduction == 'mean':
             loss = loss.flatten(1).mean(dim=1)
-
-        if self.min_snr_gamma is not None:
-            alphas = self.noise_scheduler.alphas.to(x_0.device)[t]
-            sigmas = self.noise_scheduler.sigmas.pow(2).to(x_0.device)[t]
-            snrs = (alphas / sigmas).clamp(min=1e-20)
-            min_snrs = snrs.clamp(max=self.min_snr_gamma)
-            weights = min_snrs / snrs
-        else:
-            weights = 1
-
-        loss = loss * weights
         return loss
 
     @torch.no_grad()
@@ -271,6 +245,8 @@ class DiffusionFluxRF(BaseDiffusion):
                show_progress=False,
                return_intermediate=None,
                intermediate_callback=None,
+               reverse_scale=-1.,
+               x=None,
                **kwargs):
         # sanity check
         assert isinstance(steps, (int, torch.LongTensor))
@@ -278,7 +254,7 @@ class DiffusionFluxRF(BaseDiffusion):
         assert isinstance(sampler, (str, dict, Config))
         intermediates = []
 
-        def callback_fn(x_t, t, sigma=None, alpha=None):
+        def callback_fn(x_t, t, sigma=None, alpha_bar=None):
             sigma = torch.full((x_t.shape[0], ),
                                sigma,
                                dtype=x_t.dtype,
@@ -291,12 +267,14 @@ class DiffusionFluxRF(BaseDiffusion):
         # this is ignored for schnell
         sampler_output = sampler_ins.preprare_sampler(
             noise,
+            x=x,
             steps=steps,
+            reverse_scale=reverse_scale,
             prediction_type=self.prediction_type,
             scheduler_ins=self.sampler_scheduler,
             callback_fn=callback_fn)
 
-        for _ in trange(steps, disable=not show_progress):
+        for _ in trange(sampler_output.steps, disable=not show_progress):
             trange.desc = sampler_output.msg
             sampler_output = sampler_ins.step(sampler_output)
             if return_intermediate == 'x_0':

scepter/modules/model/diffusion/samplers.py

@@ -15,15 +15,18 @@ class SamplerOutput(object):
     callback_fn: callable
     prediction_type: str
     alphas: torch.Tensor
+    alphas_bar: torch.Tensor
     betas: torch.Tensor
     sigmas: torch.Tensor
     alphas_init: torch.Tensor
+    alphas_bar_init: torch.Tensor
     betas_init: torch.Tensor
     sigmas_init: torch.Tensor
     ts: torch.Tensor
     x_t: torch.Tensor
     x_0: torch.Tensor
     step: int
+    steps: int
     msg: str
 
     def add_custom_field(self, key: str, value) -> None:
@@ -49,7 +52,7 @@ class BaseDiffusionSampler(object):
         self.t_max = self.cfg.get('T_MAX', None)
         self.t_min = self.cfg.get('T_MIN', None)
 
-    def discretization(self, steps=20, num_timesteps=1000, **kwargs):
+    def discretization(self, steps=20, num_timesteps=1000, reverse_scale = -1., **kwargs):
         # get timesteps
         if isinstance(steps, int):
             steps += 1 if self.discard_penultimate_step else 0
@@ -74,17 +77,23 @@ class BaseDiffusionSampler(object):
             steps = steps.clamp_(t_min, t_max)
         elif isinstance(steps, list):
             steps = torch.tensor(steps)
-        timesteps = torch.as_tensor(steps, dtype=torch.float32)
-        return timesteps
+        if reverse_scale >=0:
+            img2img_step = int((1 - reverse_scale) * len(steps))
+            timesteps = torch.as_tensor(steps[img2img_step:], dtype=torch.float32)
+            return timesteps
+        return torch.as_tensor(steps, dtype=torch.float32)
 
     def preprare_sampler(self,
                          noise,
+                         x=None,
                          steps=20,
+                         reverse_scale=-1.,
                          scheduler_ins=None,
                          prediction_type='',
                          sigmas=None,
                          betas=None,
                          alphas=None,
+                         alphas_bar=None,
                          callback_fn=None,
                          **kwargs):
         '''
@@ -96,36 +105,52 @@ class BaseDiffusionSampler(object):
             4. To ensure the safety of threading, use the instance of SamplerOutput as the manager,
             which manage all necessary information.
         '''
+        if reverse_scale >= 0:
+            assert x is not None
         num_timesteps = scheduler_ins.num_timesteps if scheduler_ins is not None else 1000
         timestamps = self.discretization(steps,
                                          num_timesteps=num_timesteps,
+                                         reverse_scale=reverse_scale,
                                          **kwargs)
         alphas = scheduler_ins.t_to_alpha(
             timestamps, **kwargs) if scheduler_ins is not None else alphas
+        alphas_bar = scheduler_ins.t_to_alpha_bar(
+            timestamps, **kwargs) if scheduler_ins is not None else alphas_bar
         betas = scheduler_ins.t_to_beta(
             timestamps, **kwargs) if scheduler_ins is not None else betas
         sigmas = scheduler_ins.t_to_sigma(
             timestamps, **kwargs) if scheduler_ins is not None else sigmas
         alphas_init = scheduler_ins.t_to_alpha_init(
             timestamps, **kwargs) if scheduler_ins is not None else alphas
+
+        alphas_bar_init = scheduler_ins.t_to_alpha_bar_init(
+            timestamps, **kwargs) if scheduler_ins is not None else alphas_bar
+
         betas_init = scheduler_ins.t_to_beta_init(
             timestamps, **kwargs) if scheduler_ins is not None else betas
         sigmas_init = scheduler_ins.t_to_sigma_init(
             timestamps, **kwargs) if scheduler_ins is not None else sigmas
-
+        if reverse_scale >= 0:
+            x_t = x_0 = scheduler_ins.add_noise(x, noise=noise, t=timestamps[0].repeat(x.size(0)).to(x.device)).x_t if len(timestamps) > 0 else x
+        else:
+            x_t = x_0 = noise
+        # Consider the sigma's list is from sigma_ to zero. the steps equal to len(timestamps)
         output = SamplerOutput(callback_fn=callback_fn,
                                prediction_type=prediction_type,
                                alphas=alphas,
+                               alphas_bar=alphas_bar,
                                betas=betas,
                                sigmas=sigmas,
                                alphas_init=alphas_init,
+                               alphas_bar_init=alphas_bar_init,
                                betas_init=betas_init,
                                sigmas_init=sigmas_init,
                                ts=timestamps,
-                               x_t=noise,
-                               x_0=noise,
+                               x_t=x_t,
+                               x_0=x_0,
                                step=0,
-                               msg='step 0')
+                               msg='step 0',
+                               steps=len(timestamps) - 1)
         return output
 
     def step(self, sampler_ouput):
@@ -159,22 +184,35 @@ class DDIMSampler(BaseDiffusionSampler):
 
     def preprare_sampler(self,
                          noise,
+                         x=None,
                          steps=20,
+                         reverse_scale = -1.,
                          scheduler_ins=None,
                          prediction_type='',
                          sigmas=None,
                          betas=None,
                          alphas=None,
+                         alphas_bar=None,
                          callback_fn=None,
                          **kwargs):
-        output = super().preprare_sampler(noise, steps, scheduler_ins,
-                                          prediction_type, sigmas, betas,
-                                          alphas, callback_fn, **kwargs)
+        output = super().preprare_sampler(noise,
+                                          x = x,
+                                          steps = steps,
+                                          reverse_scale = reverse_scale,
+                                          scheduler_ins = scheduler_ins,
+                                          prediction_type = prediction_type,
+                                          sigmas = sigmas,
+                                          betas = betas,
+                                          alphas = alphas,
+                                          alphas_bar = alphas_bar,
+                                          callback_fn = callback_fn,
+                                          **kwargs)
         sigmas = output.sigmas
         sigmas = torch.cat([sigmas, sigmas.new_zeros([1])])
         sigmas_vp = (sigmas**2 / (1 + sigmas**2))**0.5
         sigmas_vp[sigmas == float('inf')] = 1.
         output.add_custom_field('sigmas_vp', sigmas_vp)
+        output.steps += 1
         return output
 
     def step(self, sampler_output):
@@ -182,10 +220,10 @@ class DDIMSampler(BaseDiffusionSampler):
         step = sampler_output.step
         t = sampler_output.ts[step]
         sigmas_vp = sampler_output.sigmas_vp.to(x_t.device)
-        alpha_init = _i(sampler_output.alphas_init, step, x_t[:1])
+        alpha_bar_init = _i(sampler_output.alphas_bar_init, step, x_t[:1])
         sigma_init = _i(sampler_output.sigmas_init, step, x_t[:1])
 
-        x = sampler_output.callback_fn(x_t, t, sigma_init, alpha_init)
+        x = sampler_output.callback_fn(x_t, t, sigma_init, alpha_bar_init)
         noise_factor = self.eta * (sigmas_vp[step + 1]**2 /
                                    sigmas_vp[step]**2 *
                                    (1 - (1 - sigmas_vp[step]**2) /
@@ -202,16 +240,19 @@ class DDIMSampler(BaseDiffusionSampler):
         return sampler_output
 
 
-@DIFFUSION_SAMPLERS.register_class('flow_eluer')
+@DIFFUSION_SAMPLERS.register_class('flow_euler')
 class FlowEluerSampler(BaseDiffusionSampler):
     def preprare_sampler(self,
                          noise,
+                         x=None,
                          steps=20,
+                         reverse_scale = -1.,
                          scheduler_ins=None,
                          prediction_type='',
                          sigmas=None,
                          betas=None,
                          alphas=None,
+                         alphas_bar=None,
                          callback_fn=None,
                          **kwargs):
         if noise.ndim == 3:
@@ -220,9 +261,18 @@ class FlowEluerSampler(BaseDiffusionSampler):
             n, _, h, w = noise.shape
             seq_len = (h // 2 * w // 2)
         kwargs['seq_len'] = seq_len
-        output = super().preprare_sampler(noise, steps, scheduler_ins,
-                                          prediction_type, sigmas, betas,
-                                          alphas, callback_fn, **kwargs)
+        output = super().preprare_sampler(noise,
+                                          x = x,
+                                          steps = steps,
+                                          reverse_scale = reverse_scale,
+                                          scheduler_ins = scheduler_ins,
+                                          prediction_type = prediction_type,
+                                          sigmas = sigmas,
+                                          betas = betas,
+                                          alphas = alphas,
+                                          alphas_bar = alphas_bar,
+                                          callback_fn = callback_fn,
+                                          **kwargs)
         return output
 
     def step(self, sampler_output):
@@ -241,9 +291,13 @@ class FlowEluerSampler(BaseDiffusionSampler):
         sampler_output.msg = f'step {step}, sigma_curr: {sigma_curr}, sigma_prev: {sigma_prev}'
         return sampler_output
 
-    def discretization(self, steps=20, num_timesteps=1000, **kwargs):
+    def discretization(self, steps=20, num_timesteps=1000, reverse_scale=-1., **kwargs):
         # extra step for zero
         timesteps = torch.linspace(num_timesteps, 0, steps + 1)
+        if reverse_scale >= 0:
+            img2img_step = int((1 - reverse_scale) * len(timesteps))
+            timesteps = timesteps[img2img_step:]
+            return timesteps
         return timesteps
 
     @staticmethod

scepter/modules/model/diffusion/schedules.py

@@ -21,7 +21,7 @@ class ScheduleOutput(object):
     x_0: torch.Tensor
     t: torch.Tensor
     sigma: torch.Tensor
-    alpha: torch.Tensor
+    alpha_bar: torch.Tensor
     custom_fields: dict = field(default_factory=dict)
 
     def add_custom_field(self, key: str, value) -> None:
@@ -30,6 +30,21 @@ class ScheduleOutput(object):
 
 @NOISE_SCHEDULERS.register_class()
 class BaseNoiseScheduler(object):
+    '''
+    In the diffusion model, the parameters related to the noise schedule are alpha, beta,
+    and sigma. The following are the definitions of the above three parameters, which should
+    be the basic property for the instance of noise scheduler.
+        \alpha_{t} = \sqrt{1 - \beta_{t}^2} \alpha is the strength of signal and \beta is the strength of noise
+        \sigma_{t} = \sqrt{1 - \overline\alpha} = \sqrt{1 - \prod_{i=1}^{t}\alpha^2_{i}} (P(x_{t}|x_{0}) ~ N(\overline\alpha x_{0}, \sigma^2))
+        \alpha_bar_{t} = \sqrt{\overline\alpha} = \sqrt{\prod_{i=1}^{t}\alpha^2_{i}} (P(x_{t}|x_{0}) ~ N(\overline\alpha x_{0}, \sigma^2))
+
+    where sigma_{t} is the var of p(x_{t-1}|x_{t}, x_{0}).
+
+    (reference to https://arxiv.org/abs/2010.02502)
+    let sigma transfer to beta:
+        square_\beta = 1 - \frac{1 - square_\sigma_{t}}{1 - square_\sigma_{t - 1 }}
+
+    '''
     para_dict = {
         'NUM_TIMESTEPS': {
             'value': 1000,
@@ -48,7 +63,7 @@ class BaseNoiseScheduler(object):
         self.num_timesteps = self.cfg.get('NUM_TIMESTEPS', 1000)
         self._sample_steps = torch.arange(self.num_timesteps,
                                           dtype=torch.float32)
-        self._sigmas, self._betas, self._alphas, self._timesteps = None, None, None, None
+        self._sigmas, self._betas, self._alphas, self._alphas_bar, self._timesteps = None, None, None, None, None
 
     def check_function(self):
         try:
@@ -128,6 +143,10 @@ class BaseNoiseScheduler(object):
         square_beta = self.sigmas_to_square_betas(sigma)
         return torch.sqrt(1 - square_beta)
 
+    def t_to_alpha_bar(self, t, **kwargs):
+        sigma = self.t_to_sigma(t)
+        return torch.sqrt(1 - sigma**2)
+
     def t_to_beta(self, t, **kwargs):
         sigma = self.t_to_sigma(t)
         square_beta = self.sigmas_to_square_betas(sigma)
@@ -138,11 +157,11 @@ class BaseNoiseScheduler(object):
             t = torch.randint(0,
                               self.num_timesteps, (x_0.shape[0], ),
                               device=x_0.device).long()
-        alpha = _i(self.alphas, t, x_0)
+        alpha = _i(self.alphas_bar, t, x_0)
         sigma = _i(self.sigmas, t, x_0)
         x_t = alpha * x_0 + sigma * noise
 
-        return ScheduleOutput(x_0=x_0, x_t=x_t, t=t, alpha=alpha, sigma=sigma)
+        return ScheduleOutput(x_0=x_0, x_t=x_t, t=t, alpha_bar=alpha, sigma=sigma)
 
     def t_to_alpha_init(self, t, **kwargs):
         indices = t.long()
@@ -153,6 +172,16 @@ class BaseNoiseScheduler(object):
         alpha = self.alphas[step_indices].flatten().to(t)
         return alpha
 
+    def t_to_alpha_bar_init(self, t, **kwargs):
+        indices = t.long()
+        indices[indices >= self.num_timesteps] = self.num_timesteps - 1
+        timesteps = self.timesteps.to(t)[indices]
+        step_indices = [(self.timesteps.to(t) == t).nonzero().item()
+                        for t in timesteps]
+        alpha_bar = self.alphas_bar[step_indices].flatten().to(t)
+        return alpha_bar
+
+
     def t_to_beta_init(self, t, **kwargs):
         indices = t.long()
         indices[indices >= self.num_timesteps] = self.num_timesteps - 1
@@ -205,6 +234,10 @@ class BaseNoiseScheduler(object):
     def alphas(self):
         return self._alphas
 
+    @property
+    def alphas_bar(self):
+        return self._alphas_bar
+
     @property
     def timesteps(self):
         return self._timesteps
@@ -221,6 +254,10 @@ class BaseNoiseScheduler(object):
             'data': self._alphas.cpu().numpy(),
             'label': 'alphas'
         }, {
+            'data': self._alphas_bar.cpu().numpy(),
+            'label': 'alphas_bar'
+        },
+            {
             'data': self._timesteps.cpu().numpy() / self.num_timesteps,
             'label': 'timesteps'
         }]
@@ -280,7 +317,8 @@ class ScaledLinearScheduler(BaseNoiseScheduler):
                                                    self.snr_shift_scale,
                                                    self.rescale_betas_zero_snr)
         self._betas = torch.sqrt(square_betas)
-        self._alphas = torch.sqrt(1 - self._sigmas**2)
+        self._alphas = torch.sqrt(1 - square_betas)
+        self._alphas_bar = torch.sqrt(1 - self._sigmas**2)
         self._timesteps = torch.arange(len(self._sigmas), dtype=torch.float32)
 
 
@@ -304,7 +342,8 @@ class LinearScheduler(BaseNoiseScheduler):
         sigmas = self.betas_to_sigmas(betas)
         self._sigmas = sigmas
         self._betas = betas
-        self._alphas = torch.sqrt(1 - sigmas**2)
+        self._alphas = torch.sqrt(1 - betas**2)
+        self._alphas_bar = torch.sqrt(1 - sigmas**2)
         self._timesteps = torch.arange(len(sigmas), dtype=torch.float32)
 
 
@@ -319,7 +358,8 @@ class FlowMatchUniformScheduler(BaseNoiseScheduler):
         self._timesteps = timesteps
         self._sigmas = self.t_to_sigma(timesteps)
         self._betas = torch.sqrt(self.sigmas_to_square_betas(self._sigmas))
-        self._alphas = torch.sqrt(1 - self.betas**2)
+        self._alphas = torch.sqrt(1 - self._betas**2)
+        self._alphas_bar = torch.sqrt(1 - self._sigmas ** 2)
 
     def add_noise(self, x_0, noise=None, t=None, **kwargs):
         if t is None:
@@ -332,7 +372,7 @@ class FlowMatchUniformScheduler(BaseNoiseScheduler):
                               x_t=x_t,
                               t=t,
                               sigma=sigma,
-                              alpha=self.t_to_alpha(t))
+                              alpha_bar=self.t_to_alpha_bar(t))
 
     def sigma_to_t(self, sigma, **kwargs):
         return sigma * self.num_timesteps
@@ -406,7 +446,7 @@ class FlowMatchShiftScheduler(FlowMatchUniformScheduler):
                               x_t=x_t,
                               t=t,
                               sigma=sigma,
-                              alpha=self.t_to_alpha(t))
+                              alpha_bar=self.t_to_alpha_bar(t))
 
     def sigma_to_t(self, sigma, **kwargs):
         t = sigma / (sigma - self.shift * sigma + self.shift)
@@ -486,7 +526,7 @@ class FlowMatchFluxShiftScheduler(FlowMatchUniformScheduler):
                               x_t=x_t,
                               t=t,
                               sigma=sigma,
-                              alpha=self.t_to_alpha(t))
+                              alpha_bar=self.t_to_alpha_bar(t))
 
     def sigma_to_t(self, sigma, **kwargs):
         seq_len = kwargs.get('seq_len', 256)
@@ -570,6 +610,7 @@ class FlowMatchSigmaScheduler(FlowMatchUniformScheduler):
                                                  (self.shift - 1) * timesteps)
         self._betas = torch.sqrt(self.sigmas_to_square_betas(self._sigmas))
         self._alphas = torch.sqrt(1 - self.betas**2)
+        self._alphas_bar = torch.sqrt(1 - self._sigmas ** 2)
 
     def add_noise(self, x_0, noise=None, t=None, **kwargs):
         if t is None:
@@ -589,7 +630,7 @@ class FlowMatchSigmaScheduler(FlowMatchUniformScheduler):
                               x_t=x_t,
                               t=t,
                               sigma=sigma,
-                              alpha=self.t_to_alpha(t))
+                              alpha_bar=self.t_to_alpha_bar(t))
 
     def compute_density_for_timestep_sampling(self, t):
         """Compute the density for sampling the timesteps when doing SD3 training.

scepter/modules/model/embedder/embedder.py

@@ -832,22 +832,28 @@ class T5EmbedderHF(BaseEmbedder):
 
     def __init__(self, cfg, logger=None):
         super().__init__(cfg, logger=logger)
-        pretrained_path = cfg.get('PRETRAINED_MODEL', None)
-        self.t5_dtype = cfg.get('T5_DTYPE', 'float32')
-        assert pretrained_path
-        with FS.get_dir_to_local_dir(pretrained_path,
-                                     wait_finish=True) as local_path:
-            self.model = T5EncoderModel.from_pretrained(
-                local_path,
-                torch_dtype=getattr(
-                    torch,
-                    'float' if self.t5_dtype == 'float32' else self.t5_dtype))
-        tokenizer_path = cfg.get('TOKENIZER_PATH', None)
         self.length = cfg.get('LENGTH', 77)
-
+        self.t5_dtype = cfg.get('T5_DTYPE', 'bfloat16')
         self.use_grad = cfg.get('USE_GRAD', False)
         self.clean = cfg.get('CLEAN', 'whitespace')
         self.added_identifier = cfg.get('ADDED_IDENTIFIER', None)
+        tokenizer_path = cfg.get('TOKENIZER_PATH', None)
+        pretrained_path = cfg.get('PRETRAINED_MODEL', None)
+
+        if pretrained_path:
+            with FS.get_dir_to_local_dir(pretrained_path,
+                                         wait_finish=True) as local_path:
+                if self.t5_dtype is not None:
+                    self.model = T5EncoderModel.from_pretrained(
+                        local_path,
+                        torch_dtype=getattr(
+                            torch,
+                            'float' if self.t5_dtype == 'float32' else self.t5_dtype))
+                else:
+                    self.model = T5EncoderModel.from_pretrained(local_path)
+        else:
+            self.model = None
+
         if tokenizer_path:
             self.tokenize_kargs = {'return_tensors': 'pt'}
             with FS.get_dir_to_local_dir(tokenizer_path,
@@ -869,9 +875,6 @@ class T5EmbedderHF(BaseEmbedder):
             self.tokenizer = None
             self.tokenize_kargs = {}
 
-        self.use_grad = cfg.get('USE_GRAD', False)
-        self.clean = cfg.get('CLEAN', 'whitespace')
-
     def freeze(self):
         self.model = self.model.eval()
         for param in self.parameters():
@@ -888,14 +891,10 @@ class T5EmbedderHF(BaseEmbedder):
             else:
                 x = self.model(tokens.input_ids.to(we.device_id))
             x = x.last_hidden_state
-            # if not self.return_pooled:
-            #     return x.detach()
-            # else:
-            #     return x.detach(), self.pool(x, tokens.input_ids)
             if return_mask:
                 return x.detach() + 0.0, tokens.attention_mask.to(we.device_id)
             else:
-                return x.detach() + 0.0, None
+                return x.detach() + 0.0
 
     def pool(self, x, tokens):
         # take features from the eot embedding (eot_token is the highest number in each sequence)
@@ -921,6 +920,15 @@ class T5EmbedderHF(BaseEmbedder):
         return self(tokens, return_mask=return_mask)
 
     def encode(self, text, return_mask=False, use_mask=True):
+        if isinstance(text, str):
+            text = [text]
+        if self.clean:
+            text = [self._clean(u) for u in text]
+        assert self.tokenizer is not None
+        tokens = self.tokenizer(text, **self.tokenize_kargs)
+        return self(tokens, return_mask=return_mask, use_mask=use_mask)
+
+    def encode_list(self, text, return_mask=False, use_mask=True):
         if isinstance(text, str):
             text = [text]
         if self.clean:
@@ -942,62 +950,11 @@ class T5EmbedderHF(BaseEmbedder):
         else:
             return torch.cat(cont, dim=0)
 
-    def encode_longlist(self, text_list, return_mask=True):
-        text_max_len = max([len(p) for p in text_list]) * self.length
-        cont_list, cont_mask_list = [], []
-        for pp in text_list:
-            cont, cont_mask = self.encode(pp, return_mask=return_mask)
-            cont_channel, cont_dim = cont.shape[0] * cont.shape[1], cont.shape[
-                2]
-            cont = cont.view(cont_channel, cont_dim)
-            cont_mask_channel = cont_mask.shape[0] * cont_mask.shape[1]
-            cont_mask = cont_mask.view(cont_mask_channel)
-            select_cont = cont[cont_mask == 1]
-            select_cont_mask, _ = torch.sort(cont_mask, dim=0, descending=True)
-            if select_cont.shape[0] != text_max_len:
-                select_cont = F.pad(
-                    select_cont,
-                    (0, 0, 0, text_max_len - select_cont.shape[0]))
-            if select_cont_mask.shape[0] != text_max_len:
-                select_cont_mask = F.pad(
-                    select_cont_mask,
-                    (0, text_max_len - select_cont_mask.shape[0]))
-            cont_list.append(select_cont)
-            cont_mask_list.append(select_cont_mask)
-        return torch.stack(cont_list), torch.stack(cont_mask_list)
-
-    def encode_longlist_v1(self, text_list, return_mask=True):
-        cont_list = []
-        max_len = 0
-        for pp in text_list:
-            cont, cont_mask = self.encode(pp, return_mask=True)
-            txt_lens = cont_mask.flatten(start_dim=1).sum(dim=-1)
-            pp_cont = torch.cat(
-                [c[:txt_len] for c, txt_len in zip(cont, txt_lens)], dim=0)
-            max_len = pp_cont.size(0) if pp_cont.size(0) > max_len else max_len
-            cont_list.append(pp_cont)
-        cont = torch.cat([
-            torch.cat([c, c.new_zeros(max_len - c.size(0), c.size(1))],
-                      dim=0).unsqueeze(0) for c in cont_list
-        ],
-                         dim=0)
-        if return_mask:
-            cont_mask = torch.cat([
-                torch.cat(
-                    [c.new_ones(c.size(0)),
-                     c.new_zeros(max_len - c.size(0))],
-                    dim=-1).unsqueeze(0) for c in cont_list
-            ],
-                                  dim=0).type(torch.long, non_blocking=True)
-            return cont, cont_mask
-        else:
-            return cont
-
-    def encode_list(self, text_list, return_mask=True):
+    def encode_list_of_list(self, text_list, return_mask=True, use_mask=True):
         cont_list = []
         mask_list = []
         for pp in text_list:
-            cont, cont_mask = self.encode(pp, return_mask=return_mask)
+            cont, cont_mask = self.encode_list(pp, return_mask=return_mask, use_mask=use_mask)
             cont_list.append(cont)
             mask_list.append(cont_mask)
         if return_mask:

scepter/modules/model/network/autoencoder/__init__.py

@@ -1,3 +1,4 @@
 # -*- coding: utf-8 -*-
 # Copyright (c) Alibaba, Inc. and its affiliates.
 from scepter.modules.model.network.autoencoder.ae_kl import AutoencoderKL
+from scepter.modules.model.network.autoencoder.ae_kl_cogvideox import AutoencoderKLCogVideoX

scepter/modules/model/network/ldm/__init__.py

@@ -1,7 +1,8 @@
 # -*- coding: utf-8 -*-
 # Copyright (c) Alibaba, Inc. and its affiliates.
 from scepter.modules.model.network.ldm.ldm import LatentDiffusion
-from scepter.modules.model.network.ldm.ldm_ace import LatentDiffusionACE
+from scepter.modules.model.network.ldm.ldm_ace import (LatentDiffusionACE,
+                                                       LatentDiffusionACERefiner)
 from scepter.modules.model.network.ldm.ldm_edit import LatentDiffusionEdit
 from scepter.modules.model.network.ldm.ldm_pixart import LatentDiffusionPixart
 from scepter.modules.model.network.ldm.ldm_sce import (
@@ -9,3 +10,6 @@ from scepter.modules.model.network.ldm.ldm_sce import (
     LatentDiffusionXLSCEControl, LatentDiffusionXLSCETuning)
 from scepter.modules.model.network.ldm.ldm_sd3 import LatentDiffusionSD3
 from scepter.modules.model.network.ldm.ldm_xl import LatentDiffusionXL
+from scepter.modules.model.network.ldm.ldm_cogvideox import LatentDiffusionCogVideoX
+from scepter.modules.model.network.ldm.ldm_flux import (LatentDiffusionFlux,
+                                                        LatentDiffusionFluxMR)

scepter/modules/model/network/ldm/ldm_ace.py

@@ -1,6 +1,7 @@
 # -*- coding: utf-8 -*-
 # Copyright (c) Alibaba, Inc. and its affiliates.
 import copy
+import math
 import random
 from contextlib import nullcontext
 
@@ -10,6 +11,7 @@ from torch import nn
 
 from scepter.modules.model.network.ldm import LatentDiffusion
 from scepter.modules.model.registry import MODELS
+import torchvision.transforms as T
 from scepter.modules.model.utils.basic_utils import check_list_of_list
 from scepter.modules.model.utils.basic_utils import \
     pack_imagelist_into_tensor_v2 as pack_imagelist_into_tensor
@@ -67,10 +69,10 @@ class LatentDiffusionACE(LatentDiffusion):
         if self.use_text_pos_embeddings and not torch.sum(
                 self.text_position_embeddings.pos) > 0:
             identifier_cont, identifier_cont_mask = getattr(
-                self.cond_stage_model, 'encode')(self.text_indentifers,
+                self.cond_stage_model, 'encode_list_of_list')(self.text_indentifers,
                                                  return_mask=True)
             self.text_position_embeddings.load_state_dict(
-                {'pos': identifier_cont[:, 0, :]})
+                {'pos': torch.cat( [one_id[0][0, :].unsqueeze(0) for one_id in identifier_cont], dim=0)})
         cont_, cont_mask_ = [], []
         for pp, edit, c, cm in zip(prompt, edit_image, cont, cont_mask):
             if isinstance(pp, list):
@@ -138,7 +140,7 @@ class LatentDiffusionACE(LatentDiffusion):
         prompt_ = [[pp] if isinstance(pp, str) else pp for pp in prompt]
         try:
             cont, cont_mask = getattr(self.cond_stage_model,
-                                      'encode_list')(prompt_, return_mask=True)
+                                      'encode_list_of_list')(prompt_, return_mask=True)
         except Exception as e:
             print(e, prompt_)
         cont, cont_mask = self.cond_stage_embeddings(prompt, edit_image, cont,
@@ -240,11 +242,11 @@ class LatentDiffusionACE(LatentDiffusion):
         # with torch.autocast(device_type="cuda", enabled=True, dtype=torch.bfloat16):
         prompt_ = [[pp] if isinstance(pp, str) else pp for pp in prompt]
         cont, cont_mask = getattr(self.cond_stage_model,
-                                  'encode_list')(prompt_, return_mask=True)
+                                  'encode_list_of_list')(prompt_, return_mask=True)
         cont, cont_mask = self.cond_stage_embeddings(prompt, edit_image, cont,
                                                      cont_mask)
         null_cont, null_cont_mask = getattr(self.cond_stage_model,
-                                            'encode_list')(n_prompt,
+                                            'encode_list_of_list')(n_prompt,
                                                            return_mask=True)
         null_cont, null_cont_mask = self.cond_stage_embeddings(
             prompt, edit_image, null_cont, null_cont_mask)
@@ -349,3 +351,254 @@ class LatentDiffusionACE(LatentDiffusion):
                             __class__.__name__,
                             LatentDiffusionACE.para_dict,
                             set_name=True)
+
+@MODELS.register_class()
+class LatentDiffusionACERefiner(LatentDiffusionACE):
+    def init_params(self):
+        super().init_params()
+        self.enhence_model_cfg = self.cfg.get("ENHENCE_MODEL", None)
+        self.enhence_sampler_cfg = self.cfg.get("ENHENCE_SAMPLER_CFG", {})
+    def construct_network(self):
+        super().construct_network()
+        if self.enhence_model_cfg:
+            self.enhence_model = MODELS.build(self.enhence_model_cfg, logger=self.logger).eval().requires_grad_(False)
+            self.enhence_sampler_cfg = {key.lower(): value for key, value in self.enhence_sampler_cfg.items()}
+        else:
+            self.enhence_model = None
+            self.enhence_sampler_cfg = None
+
+    def forward_sample(self,
+                         edit_image=[],
+                         edit_mask=[],
+                         noise=None,
+                         cond_mask=[],
+                         x_shapes=[],
+                         prompt=[],
+                         n_prompt=[],
+                         sampler='ddim',
+                         sample_steps=20,
+                         seed=2023,
+                         guide_scale=4.5,
+                         guide_rescale=0.5,
+                         discretization='trailing',
+                         **kwargs
+                         ):
+        '''
+                Args:
+                    edit_image: list of list of edit_image
+                    edit_image_mask: list of list of edit_image_mask
+                    image: target image
+                    image_mask: target image mask
+                    prompt: list of list of text
+                    n_prompt: list of list of text
+                    sampler:
+                    sample_steps:
+                    seed:
+                    guide_scale:
+                    guide_rescale:
+                    discretization:
+                    log_num:
+                    **kwargs:
+
+                Returns:
+
+                '''
+
+        # prepare data
+        context, null_context = {}, {}
+        context['x_shapes'] = null_context['x_shapes'] = x_shapes
+        # process image mask
+
+        context['x_mask'] = null_context['x_mask'] = cond_mask
+        # process text
+        # with torch.autocast(device_type="cuda", enabled=True, dtype=torch.bfloat16):
+
+        cont, cont_mask = getattr(self.cond_stage_model, 'encode_list')(prompt, return_mask=True)
+        cont, cont_mask = self.cond_stage_embeddings(prompt, edit_image, cont, cont_mask)
+        null_cont, null_cont_mask = getattr(self.cond_stage_model, 'encode_list')(n_prompt, return_mask=True)
+        null_cont, null_cont_mask = self.cond_stage_embeddings(prompt, edit_image, null_cont, null_cont_mask)
+        context['crossattn'] = cont
+        null_context['crossattn'] = null_cont
+
+
+        null_context['edit'] = context['edit'] = edit_image
+        null_context['edit_mask'] = context['edit_mask'] = edit_mask
+
+        # process sample
+        model = self.model_ema if self.use_ema and self.eval_ema else self.model
+        embedding_context = model.no_sync if isinstance(model, torch.distributed.fsdp.FullyShardedDataParallel) \
+            else nullcontext
+        with embedding_context():
+            samples = self.diffusion.sample(solver=sampler,
+                                            noise=noise,
+                                            model=model,
+                                            model_kwargs=[{
+                                                'cond': context,
+                                                'mask': cont_mask,
+                                                'text_position_embeddings': self.text_position_embeddings.pos if hasattr(
+                                                    self.text_position_embeddings, 'pos') else None
+                                            }, {
+                                                'cond': null_context,
+                                                'mask': null_cont_mask,
+                                                'text_position_embeddings': self.text_position_embeddings.pos if hasattr(
+                                                    self.text_position_embeddings, 'pos') else None
+                                            }] if guide_scale is not None and guide_scale > 1 else {
+                                                'cond': context,
+                                                'mask': cont_mask,
+                                                'text_position_embeddings': self.text_position_embeddings.pos if hasattr(
+                                                    self.text_position_embeddings, 'pos') else None
+                                            },
+                                            cat_uc=False,
+                                            steps=sample_steps,
+                                            guide_scale=guide_scale,
+                                            guide_rescale=guide_rescale,
+                                            discretization=discretization,
+                                            show_progress=True,
+                                            seed=seed,
+                                            condition_fn=None,
+                                            clamp=None,
+                                            percentile=None,
+                                            t_max=None,
+                                            t_min=None,
+                                            discard_penultimate_step=None,
+                                            return_intermediate=None,
+                                            **kwargs)
+
+        samples = unpack_tensor_into_imagelist(samples, x_shapes)
+        x_samples = self.decode_first_stage(samples)
+        return x_samples
+
+    def upscale_resize(self, image, interpolation=T.InterpolationMode.BILINEAR):
+        _, c, H, W = image.shape
+        scale = max(1.0, math.sqrt(4096 / ((H / 16) * (W / 16))))
+        rH = int(H * scale) // 16 * 16  # ensure divisible by self.d
+        rW = int(W * scale) // 16 * 16
+        image = T.Resize((rH, rW), interpolation=interpolation, antialias=True)(image)
+        return image
+
+    @torch.no_grad()
+    def forward_test(self,
+                     edit_image=[],
+                     edit_image_mask=[],
+                     image=None,
+                     image_mask=None,
+                     prompt=[],
+                     n_prompt=[],
+                     sampler='ddim',
+                     sample_steps=20,
+                     seed=2023,
+                     guide_scale=4.5,
+                     guide_rescale=0.5,
+                     discretization='trailing',
+                     enhance_scale=0.99,
+                     log_num=-1,
+                     **kwargs):
+        assert check_list_of_list(prompt) and check_list_of_list(edit_image) and check_list_of_list(edit_image_mask)
+        assert len(edit_image) == len(edit_image_mask) == len(prompt)
+        assert self.cond_stage_model is not None
+        # gc_seg is unused
+        kwargs.pop("gc_seg", -1)
+        prompt, n_prompt, image, image_mask, edit_image, edit_image_mask = self.limit_batch_data(
+            [prompt, n_prompt, image, image_mask, edit_image, edit_image_mask], log_num)
+
+        prompt = [[pp] if isinstance(pp, str) else pp for pp in prompt]
+
+        g = torch.Generator(device=we.device_id)
+        seed = seed if seed >= 0 else random.randint(0, 2 ** 32 - 1)
+        g.manual_seed(seed)
+        n_prompt = copy.deepcopy(prompt)
+        # only modify the last prompt to be zero
+        for nn_p_id, nn_p in enumerate(n_prompt):
+            if isinstance(nn_p, str):
+                n_prompt[nn_p_id] = [""]
+            elif isinstance(nn_p, list):
+                n_prompt[nn_p_id][-1] = ""
+            else:
+                raise NotImplementedError
+        # process image
+        image = to_device(image)
+        x = self.encode_first_stage(image, **kwargs)
+        noise = [torch.empty(*i.shape, device=we.device_id).normal_(generator=g) for i in x]
+        noise, x_shapes = pack_imagelist_into_tensor(noise)
+        image_mask = to_device(image_mask, strict=False)
+        cond_mask = [self.interpolate_func(i) for i in image_mask] if image_mask is not None else [None] * len(image)
+
+        # processe edit image & edit image mask
+        edit_image = [to_device(i, strict=False) for i in edit_image]
+        edit_image_mask = [to_device(i, strict=False) for i in edit_image_mask]
+        e_img, e_mask = [], []
+        for u, m in zip(edit_image, edit_image_mask):
+            if u is None:
+                continue
+            if m is None:
+                m = [None] * len(u)
+            e_img.append(self.encode_first_stage(u, **kwargs))
+            e_mask.append([self.interpolate_func(i) for i in m])
+
+        x_samples = self.forward_sample(
+            edit_image=e_img,
+            edit_mask=e_mask,
+            noise=noise,
+            cond_mask=cond_mask,
+            x_shapes=x_shapes,
+            prompt=prompt,
+            n_prompt=n_prompt,
+            sampler=sampler,
+            sample_steps=sample_steps,
+            seed=seed,
+            guide_scale=guide_scale,
+            guide_rescale=guide_rescale,
+            discretization='trailing',
+            **kwargs)
+
+        if self.enhence_model and enhance_scale > 0:
+            x_samples = [self.upscale_resize(x) for x in x_samples]
+            x_start = self.enhence_model.encode_first_stage(x_samples, **kwargs)
+            noise = []
+            for i, x in enumerate(x_start):
+                noise_ = self.enhence_model.noise_sample(1, x_samples[i].shape[2], x_samples[i].shape[3], seed)
+                noise.append(noise_)
+            with torch.autocast(device_type="cuda", dtype=torch.bfloat16):
+                x_samples = self.enhence_model.forward_sample(noise = noise,
+                                                              x = x_start,
+                                                              reverse_scale = enhance_scale,
+                                                              prompt =[kwargs.pop("enhance_prompt", "") for _ in noise],
+                                                              **self.enhence_sampler_cfg)
+        outputs = list()
+        for i in range(len(prompt)):
+            rec_img = torch.clamp((x_samples[i].float() + 1.0) / 2.0 + self.decoder_bias / 255, min=0.0, max=1.0)
+            rec_img = rec_img.squeeze(0)
+            edit_imgs, edit_img_masks = [], []
+            if edit_image is not None and edit_image[i] is not None:
+                if edit_image_mask[i] is None:
+                    edit_image_mask[i] = [None] * len(edit_image[i])
+                for edit_img, edit_mask in zip(edit_image[i], edit_image_mask[i]):
+                    edit_img = torch.clamp((edit_img + 1.0) / 2.0, min=0.0, max=1.0)
+                    edit_imgs.append(edit_img.squeeze(0))
+                    if edit_mask is None:
+                        edit_mask = torch.ones_like(edit_img[[0], :, :])
+                    edit_img_masks.append(edit_mask)
+            one_tup = {
+                'reconstruct_image': rec_img,
+                'instruction': prompt[i],
+                'edit_image': edit_imgs if len(edit_imgs) > 0 else None,
+                'edit_mask': edit_img_masks if len(edit_imgs) > 0 else None
+            }
+            if image is not None:
+                if image_mask is None:
+                    image_mask = [None] * len(image)
+                ori_img = torch.clamp((image[i] + 1.0) / 2.0, min=0.0, max=1.0)
+                one_tup['target_image'] = ori_img.squeeze(0)
+                one_tup['target_mask'] = image_mask[i] if image_mask[i] is not None else torch.ones_like(
+                    ori_img[[0], :, :])
+            outputs.append(one_tup)
+
+        return outputs
+
+
+    @staticmethod
+    def get_config_template():
+        return dict_to_yaml('MODEL',
+                            __class__.__name__,
+                            LatentDiffusionACERefiner.para_dict,
+                            set_name=True)

scepter/modules/model/network/ldm/ldm_cogvideox.py

@@ -0,0 +1,225 @@
+# -*- coding: utf-8 -*-
+# Copyright (c) Alibaba, Inc. and its affiliates.
+
+import random
+import torch
+from typing import Tuple
+
+from scepter.modules.model.network.ldm import LatentDiffusion
+from scepter.modules.model.registry import MODELS
+from scepter.modules.model.utils.basic_utils import default
+from scepter.modules.utils.config import dict_to_yaml
+from scepter.modules.utils.distribute import we
+from scepter.modules.model.backbone.cogvideox.utils import get_3d_rotary_pos_embed, get_resize_crop_region_for_grid
+
+
+@MODELS.register_class()
+class LatentDiffusionCogVideoX(LatentDiffusion):
+    para_dict = LatentDiffusion.para_dict
+
+    def init_params(self):
+        super().init_params()
+        self.latent_channels = self.model_config.get('LATENT_CHANNELS', self.model_config.IN_CHANNELS)
+        self.scale_factor_spatial = self.cfg.get('SCALE_FACTOR_SPATIAL', 8)
+        self.scale_factor_temporal = self.cfg.get('SCALE_FACTOR_TEMPORAL', 4)
+        self.scaling_factor_image = self.cfg.get('SCALING_FACTOR_IMAGE', 0.7)
+        self.use_rotary_positional_embeddings = self.model_config.get('USE_ROTARY_POSITIONAL_EMBEDDINGS', False)
+        self.attention_head_dim = self.model_config.get('ATTENTION_HEAD_DIM', 64)
+        self.patch_size = self.model_config.get('PATCH_SIZE', 2)
+        self.sample_height = self.first_stage_config.get('SAMPLE_HEIGHT', 480)
+        self.sample_width = self.first_stage_config.get('SAMPLE_WIDTH', 720)
+        self.noised_image_dropout = self.cfg.get('NOISED_IMAGE_DROPOUT', 0.05)
+
+    def construct_network(self):
+        super().construct_network()
+        self.model = self.model.to(getattr(torch, self.model_config.DTYPE))
+
+    @torch.no_grad()
+    def encode_first_stage(self, x, **kwargs):
+        if isinstance(x, list):
+            x = torch.stack(x, dim=0)  # [B, C, F, H, W]
+        latents = self.scaling_factor_image * self.first_stage_model.encode(x).sample()
+        return latents
+
+    @torch.no_grad()
+    def decode_first_stage(self, latents):
+        latents = latents.permute(0, 2, 1, 3, 4)  # [batch_size, num_channels, num_frames, height, width]
+        latents = 1 / self.scaling_factor_image * latents
+        frames = self.first_stage_model.decode(latents)
+        return frames
+
+    def get_image_latent(self, image, video, noise):
+        latent = torch.zeros_like(noise)
+        if isinstance(image, list):
+            image = torch.stack(image, dim=0)  # [B, C, F, H, W]
+        if len(image.shape) == 4:  # [B, C, H, W]
+            image = image.unsqueeze(2)  # [B, C, F, H, W]
+        image_latent = self.encode_first_stage(image)  # [B, C, F, H, W]
+        image_latent = image_latent.permute(0, 2, 1, 3, 4)  # [B, F, C, H, W]
+        latent[:, :1, :, :, :] = image_latent
+        return latent, image
+
+    def noise_sample(self, batch_size, num_frames, height, width, generator, dtype=torch.bfloat16):
+        shape = (batch_size,
+                (num_frames - 1) // self.scale_factor_temporal + 1,
+                self.latent_channels,
+                height // self.scale_factor_spatial,
+                width // self.scale_factor_spatial
+                 )
+        noise = torch.randn(shape, generator=generator, dtype=dtype, device='cpu').to(we.device_id)
+        return noise
+
+    def _prepare_rotary_positional_embeddings(
+        self,
+        height: int,
+        width: int,
+        num_frames: int,
+        device: torch.device,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        grid_height = height // (self.scale_factor_spatial * self.patch_size)
+        grid_width = width // (self.scale_factor_spatial * self.patch_size)
+        base_size_width = self.sample_width // (self.scale_factor_spatial * self.patch_size)
+        base_size_height = self.sample_height // (self.scale_factor_spatial * self.patch_size)
+
+        grid_crops_coords = get_resize_crop_region_for_grid(
+            (grid_height, grid_width), base_size_width, base_size_height
+        )
+        freqs_cos, freqs_sin = get_3d_rotary_pos_embed(
+            embed_dim=self.attention_head_dim,
+            crops_coords=grid_crops_coords,
+            grid_size=(grid_height, grid_width),
+            temporal_size=num_frames,
+        )
+
+        freqs_cos = freqs_cos.to(device=device)
+        freqs_sin = freqs_sin.to(device=device)
+        return freqs_cos, freqs_sin
+
+
+    def forward_train(self, video=None, video_latent=None, image=None, noise=None, prompt=None, image_size=None, **kwargs):
+        # video: [B, C, F, H, W]
+        if image_size is None: image_size = [480, 720]
+        if video_latent is not None:
+            x_start = torch.stack(video_latent)
+        else:
+            x_start = self.encode_first_stage(video, **kwargs)
+        x_start = x_start.permute(0, 2, 1, 3, 4)  # [B, F, C, H, W]
+        t = torch.randint(low=0, high=self.num_timesteps, size=(len(video),), device=we.device_id)
+
+        if prompt and self.cond_stage_model:
+            with torch.autocast(device_type='cuda', enabled=True, dtype=torch.bfloat16):
+                cont = getattr(self.cond_stage_model, 'encode')(prompt, return_mask=False, use_mask=False)
+
+        if noise is None:
+            noise = torch.randn_like(x_start)
+
+        if image is not None:
+            if random.random() < self.noised_image_dropout:
+                image_latent = torch.zeros_like(noise)
+            else:
+                image_latent, _ = self.get_image_latent(image, video, noise)
+        else:
+            image_latent = None
+
+        height, width = image_size
+        image_rotary_emb = (
+            self._prepare_rotary_positional_embeddings(height, width, noise.size(1), we.device_id)
+            if self.use_rotary_positional_embeddings
+            else None
+        )
+
+        loss = self.diffusion.loss(x_0=x_start,
+                                   t=t,
+                                   model=self.model,
+                                   model_kwargs={"cond": cont,
+                                                 'image_latent': image_latent,
+                                                 'image_rotary_emb': image_rotary_emb},
+                                   noise=noise,
+                                   **kwargs)
+        loss = loss.mean()
+        ret = {'loss': loss, 'probe_data': {'prompt': prompt}}
+        return ret
+
+    @torch.no_grad()
+    @torch.autocast('cuda', dtype=torch.bfloat16)
+    def forward_test(self,
+                     video=None,
+                     image=None,
+                     prompt=None,
+                     n_prompt=None,
+                     sampler='ddim',
+                     sample_steps=50,
+                     seed=42,
+                     guide_scale=6.0,
+                     guide_rescale=0.0,
+                     num_frames=49,
+                     image_size=None,
+                     show_process=False,
+                     **kwargs):
+        if image_size is None:
+            image_size = [480, 720]
+        seed = seed if seed >= 0 else random.randint(0, 2**32 - 1)
+        generator = torch.Generator().manual_seed(seed)
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+        num_samples = len(prompt)
+        n_prompt = default(n_prompt, [self.default_n_prompt] * len(prompt))
+
+        if prompt and self.cond_stage_model:
+            with torch.autocast(device_type='cuda', enabled=True, dtype=torch.bfloat16):
+                cont = getattr(self.cond_stage_model, 'encode')(prompt, return_mask=False, use_mask=False)
+                null_cont = getattr(self.cond_stage_model, 'encode')(n_prompt, return_mask=False, use_mask=False)
+
+        height, width = image_size
+        noise = self.noise_sample(num_samples, num_frames, height, width, generator)
+        image_rotary_emb = (
+            self._prepare_rotary_positional_embeddings(height, width, noise.size(1), we.device_id)
+            if self.use_rotary_positional_embeddings
+            else None
+        )
+        image_latent, image = self.get_image_latent(image, video, noise) if image is not None else (None, None)
+
+        samples = self.diffusion.sample(noise=noise,
+                                        sampler=sampler,
+                                        model=self.model,
+                                        model_kwargs=[{
+                                            'cond': cont,
+                                            'image_latent': image_latent,
+                                            'image_rotary_emb': image_rotary_emb,
+                                        }, {
+                                            'cond': null_cont,
+                                            'image_latent': image_latent,
+                                            'image_rotary_emb': image_rotary_emb,
+                                        }],
+                                        steps=sample_steps,
+                                        show_progress=True,
+                                        use_dynamic_cfg=True,
+                                        guide_scale=guide_scale,
+                                        guide_rescale=guide_rescale,
+                                        return_intermediate=None,
+                                        **kwargs).float()
+
+        x_frames = self.decode_first_stage(samples).float()
+
+        outputs = []
+        for batch_idx in range(num_samples):
+            rec_video = torch.clamp(x_frames[batch_idx] / 2 + 0.5, min=0.0, max=1.0)
+            one_tup = {
+                'reconstruct_video': rec_video.squeeze(0).float(),
+                'instruction': prompt[batch_idx]
+            }
+            if image is not None:
+                ori_image = torch.clamp(image[batch_idx] / 2 + 0.5, min=0.0, max=1.0)
+                one_tup['edit_image'] = ori_image
+            if video is not None:
+                ori_video = torch.clamp(video[batch_idx] / 2 + 0.5, min=0.0, max=1.0)
+                one_tup['target_video'] = ori_video.squeeze(0)
+            outputs.append(one_tup)
+        return outputs
+
+
+    @staticmethod
+    def get_config_template():
+        return dict_to_yaml('MODEL',
+                            __class__.__name__,
+                            LatentDiffusionCogVideoX.para_dict,
+                            set_name=True)

scepter/modules/model/network/ldm/ldm_flux.py

@@ -4,15 +4,19 @@ import copy
 import math
 import numbers
 import random
+from contextlib import nullcontext
+
 import torch
 from scepter.modules.model.network.ldm import LatentDiffusion
 from scepter.modules.model.registry import MODELS, BACKBONES, LOSSES, TOKENIZERS, EMBEDDERS, DIFFUSIONS
-from scepter.modules.model.utils.basic_utils import disabled_train
+from scepter.modules.model.utils.basic_utils import disabled_train, check_list_of_list, to_device, \
+    pack_imagelist_into_tensor, unpack_tensor_into_imagelist, limit_batch_data
 from scepter.modules.utils.config import dict_to_yaml
 from scepter.modules.utils.distribute import we
 from scepter.modules.model.utils.basic_utils import count_params
 
 
+
 @MODELS.register_class()
 class LatentDiffusionFlux(LatentDiffusion):
     para_dict = LatentDiffusion.para_dict
@@ -137,7 +141,7 @@ class LatentDiffusionFlux(LatentDiffusion):
     def forward_test(self,
                      image=None,
                      prompt=None,
-                     sampler='flow_eluer',
+                     sampler='flow_euler',
                      sample_steps=20,
                      seed=2023,
                      guide_scale=4.5,
@@ -218,3 +222,164 @@ class LatentDiffusionFlux(LatentDiffusion):
     @torch.no_grad()
     def decode_first_stage(self, z):
         return self.first_stage_model.decode(z)
+
+@MODELS.register_class()
+class LatentDiffusionFluxMR(LatentDiffusionFlux):
+    para_dict = {
+    }
+    para_dict.update(LatentDiffusion.para_dict)
+    def forward_train(self,
+                      image=None,
+                      noise=None,
+                      prompt=[],
+                      **kwargs):
+        if check_list_of_list(prompt):
+            prompt = [pp[0] for pp in prompt]
+        assert self.cond_stage_model is not None
+        gc_seg = kwargs.pop("gc_seg", [])
+        gc_seg = int(gc_seg[0]) if len(gc_seg) > 0 else 0
+        context = getattr(self.cond_stage_model, 'encode')(prompt)
+
+        image = to_device(image)
+        x_start = self.encode_first_stage(image, **kwargs)
+        loss_mask, _ = pack_imagelist_into_tensor(tuple(torch.ones_like(ix, dtype=torch.bool, device=ix.device) for ix in x_start))
+        x_start, x_shapes = pack_imagelist_into_tensor(x_start)
+        context['x_shapes'] = x_shapes
+        guide_scale = self.guide_scale
+        if guide_scale is not None:
+            guide_scale = torch.full((x_start.shape[0],), guide_scale, device=x_start.device, dtype=x_start.dtype)
+        else:
+            guide_scale = None
+        loss = self.diffusion.loss(x_0=x_start,
+                                   model=self.model,
+                                   model_kwargs={"cond": context,
+                                                 "gc_seg": gc_seg,
+                                                 "guidance": guide_scale},
+                                   noise=None,
+                                   reduction='none',
+                                   **kwargs)
+        loss = loss[loss_mask].mean()
+        ret = {'loss': loss, 'probe_data': {'prompt': prompt}}
+        return ret
+
+    @torch.no_grad()
+    def forward_sample(self,
+                       noise = None,
+                       prompt=None,
+                       sampler='flow_euler',
+                       sample_steps=20,
+                       guide_scale=3.5,
+                       show_process=True,
+                       x = None,
+                       reverse_scale = 0.,
+                       **kwargs
+                       ):
+        noise, x_shapes = pack_imagelist_into_tensor(noise)
+        if x is not None:
+            x, _ = pack_imagelist_into_tensor(x)
+        context = getattr(self.cond_stage_model, 'encode')(prompt)
+        context["x_shapes"] = x_shapes
+        guide_scale = guide_scale or self.guide_scale
+        if guide_scale is not None:
+            guide_scale = torch.full((noise.shape[0],), guide_scale, device=noise.device, dtype=noise.dtype)
+        else:
+            guide_scale = None
+        # UNet use input n_prompt
+        model = self.model_ema if self.use_ema and self.eval_ema else self.model
+        embedding_context = model.no_sync if isinstance(model, torch.distributed.fsdp.FullyShardedDataParallel) \
+            else nullcontext
+        with embedding_context():
+            x_samples = self.diffusion.sample(
+                noise=noise,
+                sampler=sampler,
+                model=self.model,
+                model_kwargs={"cond": context, "guidance": guide_scale, "gc_seg": -1},
+                steps=sample_steps,
+                show_progress=True,
+                guide_scale=guide_scale,
+                return_intermediate=None,
+                reverse_scale = reverse_scale,
+                x = x,
+                **kwargs).float()
+        x_samples = unpack_tensor_into_imagelist(x_samples, x_shapes)
+        with torch.autocast(device_type="cuda", dtype=torch.bfloat16):
+            x_samples = self.decode_first_stage(x_samples)
+        return x_samples
+    @torch.no_grad()
+    def forward_test(self,
+                     image=None,
+                     prompt=[],
+                     sampler='flow_euler',
+                     sample_steps=20,
+                     seed=2023,
+                     guide_scale=3.5,
+                     guide_rescale=0.0,
+                     show_process=True,
+                     log_num = -1,
+                     **kwargs):
+
+        if check_list_of_list(prompt):
+            prompt = [pp[0] for pp in prompt]
+        assert self.cond_stage_model is not None
+        # gc_seg is unused
+        prompt, image = limit_batch_data([prompt, image], log_num)
+        seed = seed if seed >= 0 else random.randint(0, 2**32 - 1)
+
+        if 'index' in kwargs:
+            kwargs.pop('index')
+        if image is not None:
+            noise = [self.noise_sample(1, ix.shape[1], ix.shape[2], seed) for ix in image]
+        else:
+            image_size = None
+            if 'meta' in kwargs:
+                meta = kwargs.pop('meta')
+                if 'image_size' in meta:
+                    h = int(meta['image_size'][0][0])
+                    w = int(meta['image_size'][1][0])
+                    image_size = [h, w]
+            if 'image_size' in kwargs:
+                image_size = kwargs.pop('image_size')
+            if isinstance(image_size, numbers.Number):
+                image_size = [image_size, image_size]
+            if image_size is None:
+                image_size = [1024, 1024]
+            height, width = image_size
+            noise = [self.noise_sample(1, height, width, seed) for _ in prompt]
+
+        x_samples = self.forward_sample(
+            prompt=prompt,
+            sampler=sampler,
+            sample_steps=sample_steps,
+            guide_scale=guide_scale,
+            show_process=show_process,
+            noise=noise,
+        )
+
+
+        outputs = list()
+        for i in range(len(prompt)):
+            rec_img = torch.clamp((x_samples[i].float() + 1.0) / 2.0, min=0.0, max=1.0)
+            rec_img = rec_img.squeeze(0)
+            one_tup = {'prompt': prompt[i], 'n_prompt': '', 'image': rec_img}
+            outputs.append(one_tup)
+        return outputs
+    @staticmethod
+    def get_config_template():
+        return dict_to_yaml('MODEL',
+                            __class__.__name__,
+                            LatentDiffusionFlux.para_dict,
+                            set_name=True)
+    @torch.no_grad()
+    def encode_first_stage(self, x, **kwargs):
+        def run_one_image(u):
+            zu = self.first_stage_model.encode(u)
+            if isinstance(zu, (tuple, list)):
+                zu = zu[0]
+            return zu
+
+        z = [run_one_image(u.unsqueeze(0) if u.dim == 3 else u) for u in x]
+        return z
+
+    @torch.no_grad()
+    def decode_first_stage(self, z):
+        return [self.first_stage_model.decode(zu) for zu in z]

scepter/modules/model/utils/basic_utils.py

@@ -102,3 +102,25 @@ def to_device(inputs, strict=True):
 
 def check_list_of_list(ll):
     return isinstance(ll, list) and all(isinstance(i, list) for i in ll)
+
+
+def pack_imagelist_into_tensor(image_list):
+    image_tensor, shapes = [], []
+    for img in image_list:
+        _, c, h, w = img.size()
+        image_tensor.append(img.view(c, h * w).transpose(1, 0))  # h*w, c
+        shapes.append((h, w))
+
+    image_tensor = pad_sequence(image_tensor, batch_first=True).permute(0, 2, 1)  # b, c, l
+    return image_tensor, shapes
+
+def limit_batch_data(batch_data_list, log_num):
+    if log_num and log_num > 0:
+        batch_data_list_limited = []
+        for sub_data in batch_data_list:
+            if sub_data is not None:
+                sub_data = sub_data[:log_num]
+            batch_data_list_limited.append(sub_data)
+        return batch_data_list_limited
+    else:
+        return batch_data_list

scepter/modules/solver/__init__.py

@@ -1,7 +1,8 @@
 # -*- coding: utf-8 -*-
 # Copyright (c) Alibaba, Inc. and its affiliates.
 from scepter.modules.solver import hooks
-from scepter.modules.solver.ace_solver import ACESolver
 from scepter.modules.solver.base_solver import BaseSolver
 from scepter.modules.solver.diffusion_solver import LatentDiffusionSolver
 from scepter.modules.solver.train_val_solver import TrainValSolver
+from scepter.modules.solver.ace_solver import ACESolver
+from scepter.modules.solver.diffusion_video_solver import LatentDiffusionVideoSolver

scepter/modules/solver/diffusion_solver.py

@@ -10,24 +10,23 @@ from functools import partial
 import numpy as np
 import torch
 import torch.nn as nn
-from torch.distributed.fsdp import FullStateDictConfig
-from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
-from torch.distributed.fsdp import (MixedPrecision, ShardingStrategy,
-                                    StateDictType)
-from torch.distributed.fsdp.wrap import lambda_auto_wrap_policy
-from torch.nn.parallel import DistributedDataParallel
-from tqdm import tqdm
-
 from scepter.modules.data.dataset import DATASETS
 from scepter.modules.opt.lr_schedulers import LR_SCHEDULERS
 from scepter.modules.opt.optimizers import OPTIMIZERS
+from scepter.modules.solver import BaseSolver
 from scepter.modules.solver.registry import SOLVERS
 from scepter.modules.utils.config import Config, dict_to_yaml
 from scepter.modules.utils.data import transfer_data_to_cuda
 from scepter.modules.utils.distribute import we
 from scepter.modules.utils.probe import ProbeData
-
-from .base_solver import BaseSolver
+from torch.distributed.fsdp import FullStateDictConfig
+from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
+from torch.distributed.fsdp import (MixedPrecision, ShardingStrategy,
+                                    StateDictType)
+from torch.distributed.fsdp.wrap import (lambda_auto_wrap_policy,
+                                         size_based_auto_wrap_policy)
+from torch.nn.parallel import DistributedDataParallel
+from tqdm import tqdm
 
 sharding_strategy_map = {
     'full_shard': ShardingStrategy.FULL_SHARD,
@@ -38,17 +37,18 @@ sharding_strategy_map = {
 
 def shard_model(model,
                 device_id,
+                process_group=None,
                 param_dtype=torch.bfloat16,
                 reduce_dtype=torch.float32,
                 buffer_dtype=torch.float32,
                 fsdp_group=['blocks'],
                 sharding_strategy=ShardingStrategy.FULL_SHARD,
-                sync_module_states=False):
+                sync_module_states=False,
+                use_orig_params=False):
     wrap_modules = []
     for module_name in fsdp_group:
         if hasattr(model, module_name):
-            if isinstance(getattr(model, module_name),
-                          (list, tuple, nn.ModuleList)):
+            if isinstance(getattr(model, module_name), (list, tuple, nn.ModuleList)):
                 wrap_modules.extend([m for m in getattr(model, module_name)])
             else:
                 wrap_modules.extend([getattr(model, module_name)])
@@ -56,7 +56,7 @@ def shard_model(model,
             warnings.warn("Can't find module {} in model".format(module_name))
     return FSDP(
         module=model,
-        process_group=None,
+        process_group=process_group,
         sharding_strategy=sharding_strategy,
         auto_wrap_policy=partial(
             # size_based_auto_wrap_policy, min_num_params=int(1e6),
@@ -66,7 +66,8 @@ def shard_model(model,
                                        reduce_dtype=reduce_dtype,
                                        buffer_dtype=buffer_dtype),
         device_id=device_id,
-        sync_module_states=sync_module_states)
+        sync_module_states=sync_module_states,
+        use_orig_params=use_orig_params)
 
 
 def get_module(instance, sub_module):
@@ -195,7 +196,11 @@ class LatentDiffusionSolver(BaseSolver):
             self.logger.info('Use fsdp as the backend of ddp.')
         else:
             self.logger.info('Use default backend.')
+        self.use_scaler = cfg.get('USE_SCALER', True)
+        self.enable_gradscaler = cfg.get('ENABLE_GRADSCALER', False)
+        self.use_orig_params = cfg.get('USE_ORIG_PARAMS', False)
         self.model_shard = cfg.get('SHARDING_STRATEGY', 'full_shard')
+        self.sharding_size = cfg.get('SHARDING_SIZE', None)
         self.reduce_dtype = getattr(torch,
                                     cfg.get('FSDP_REDUCE_DTYPE', 'float32'))
         self.buffer_dtype = getattr(torch,
@@ -211,7 +216,7 @@ class LatentDiffusionSolver(BaseSolver):
         self.sample_args = cfg.get('SAMPLE_ARGS', None)
         self.tuner_cfg = cfg.get('TUNER', None)
         self.freeze_cfg = cfg.get('FREEZE', None)
-        self.log_train_num = cfg.get('LOG_TRAIN_NUM', -1)
+        self.log_train_num = cfg.get("LOG_TRAIN_NUM", -1)
 
     def set_up(self):
         self.construct_data()
@@ -220,6 +225,7 @@ class LatentDiffusionSolver(BaseSolver):
         self.model_to_device()
         self.init_lr()
         self.init_opti()
+        self.logger.info(self.model)
 
     def construct_hook(self):
         # initialize data
@@ -281,54 +287,79 @@ class LatentDiffusionSolver(BaseSolver):
 
     def init_opti(self):
         import torch.cuda.amp as amp
+        import torch.distributed as dist
 
         if we.is_distributed:
             if self.use_fairscale:
                 from fairscale.nn.data_parallel import ShardedDataParallel
                 from fairscale.optim.oss import OSS
                 if hasattr(self.model, 'ignored_parameters'):
-                    train_params, _ = self.model.parameters(
+                    train_params, ignored_params = self.model.parameters(
                     ), self.model.ignored_parameters()
                 else:
-                    train_params, _ = self.model.parameters(), None
+                    train_params, ignored_params = self.model.parameters(
+                    ), None
                 self.optimizer = OSS(params=train_params,
                                      optim=torch.optim.AdamW,
                                      lr=self.cfg.OPTIMIZER.LEARNING_RATE)
                 self.model = ShardedDataParallel(self.model, self.optimizer)
             elif self.use_fsdp:
                 shard_fn = partial
+                if self.model_shard == 'hybrid_shard' and self.sharding_size is not None and self.sharding_size > 1:
+                    if self.sharding_size > we.world_size:
+                        self.logger.info(f'Reset sharding_size ({self.sharding_size}) to world_size ({we.world_size})')
+                    sharding_size = min(self.sharding_size, we.world_size)
+                    assert we.world_size % sharding_size == 0
+                    # mesh to facilitate rank indexing
+                    mesh = torch.arange(we.world_size).view(-1, sharding_size)
+                    # sharding groups
+                    for ranks in mesh.tolist():
+                        group = dist.new_group(ranks=ranks)
+                        if we.rank in ranks:
+                            sharding_group = group
+                    # replication groups
+                    for ranks in mesh.t().tolist():
+                        group = dist.new_group(ranks=ranks)
+                        if we.rank in ranks:
+                            replication_group = group
+                    # fsdp group tuple
+                    fsdp_group = (sharding_group, replication_group)
+                    fsdp_rank0 = we.rank // sharding_size * sharding_size
+                else:
+                    fsdp_group = None
+                    fsdp_rank0 = 0
+
                 if self.shard_modules is not None:
                     for module in self.shard_modules:
                         if isinstance(module, str):
                             sub_module = get_module(self.model, module)
                             if sub_module is not None:
                                 sub_module = shard_model(
-                                    sub_module,
-                                    device_id=we.device_id,
-                                    param_dtype=self.dtype,
-                                    reduce_dtype=self.reduce_dtype,
-                                    buffer_dtype=self.buffer_dtype,
-                                    sharding_strategy=sharding_strategy_map[
-                                        self.model_shard],
-                                    sync_module_states=True)
+                                            sub_module,
+                                            process_group=fsdp_group,
+                                            device_id=we.device_id,
+                                            param_dtype=self.dtype,
+                                            reduce_dtype=self.reduce_dtype,
+                                            buffer_dtype=self.buffer_dtype,
+                                            sharding_strategy=sharding_strategy_map[self.model_shard],
+                                            sync_module_states=True,
+                                            use_orig_params=self.use_orig_params)
                                 set_module(self.model, module, sub_module)
                         elif isinstance(module, (dict, Config)):
-                            sub_module = get_module(self.model,
-                                                    module['MODULE'])
+                            sub_module = get_module(self.model, module["MODULE"])
                             if sub_module is not None:
                                 sub_module = shard_model(
                                     sub_module,
+                                    process_group=fsdp_group,
                                     device_id=we.device_id,
                                     param_dtype=self.dtype,
                                     reduce_dtype=self.reduce_dtype,
                                     buffer_dtype=self.buffer_dtype,
-                                    fsdp_group=module.get(
-                                        'FSDP_GROUP', ['blocks']),
-                                    sharding_strategy=sharding_strategy_map[
-                                        self.model_shard],
-                                    sync_module_states=True)
-                                set_module(self.model, module['MODULE'],
-                                           sub_module)
+                                    fsdp_group=module.get("FSDP_GROUP", ["blocks"]),
+                                    sharding_strategy=sharding_strategy_map[self.model_shard],
+                                    sync_module_states=module.get("SYNC_MODULE_STATES", True),
+                                    use_orig_params=self.use_orig_params)
+                                set_module(self.model, module["MODULE"], sub_module)
                 else:
                     self.logger.warning(
                         'FSDP_SHARD_MODULES is None, which means wraping the whold model as the '
@@ -380,22 +411,23 @@ class LatentDiffusionSolver(BaseSolver):
                                                     logger=self.logger,
                                                     optimizer=self.optimizer)
 
-        if self.cfg.DTYPE in ['float16']:
+        if self.use_scaler and self.cfg.DTYPE in ['float16', 'bfloat16']:
             if we.is_distributed:
                 if self.use_fairscale:
                     from fairscale.optim.grad_scaler import ShardedGradScaler
-                    self.scaler = ShardedGradScaler(enabled=True)
+                    self.scaler = ShardedGradScaler(enabled=self.enable_gradscaler)
                 elif self.use_fsdp:
                     from torch.distributed.fsdp.sharded_grad_scaler import ShardedGradScaler
-                    self.scaler = ShardedGradScaler(enabled=True,
+                    self.scaler = ShardedGradScaler(enabled=self.enable_gradscaler,
                                                     process_group=None)
                 else:
-                    self.scaler = amp.GradScaler()
-            else:
+                    self.scaler = amp.GradScaler(enabled=self.enable_gradscaler)
+            elif self.cfg.DTYPE in ['float16']:
                 self.scaler = amp.GradScaler()
+            else:
+                self.scaler = None
         else:
             self.scaler = None
-        self.logger.info(self.model)
 
     def load_checkpoint(self, checkpoint: dict):
         """
@@ -450,10 +482,10 @@ class LatentDiffusionSolver(BaseSolver):
                                 f'Load checkpoint for optimizer {module}.')
                 else:
                     self.optimizer.load_state_dict(checkpoint['optimizer'])
-                    self.logger.info('Load checkpoint for optimizer.')
+                    self.logger.info(f'Load checkpoint for optimizer.')
             if 'scaler' in checkpoint and self.scaler:
                 self.scaler.load_state_dict(checkpoint['scaler'])
-                self.logger.info('Load checkpoint for scaler.')
+                self.logger.info(f'Load checkpoint for scaler.')
         self.logger.info('Load checkpoint finished.')
 
     def save_checkpoint(self) -> dict:
@@ -522,7 +554,8 @@ class LatentDiffusionSolver(BaseSolver):
                         ckpt['model'][module] = current_module.state_dict()
             else:
                 ckpt['model'] = model.state_dict()
-        if self.optimizer and not self.use_fairscale:
+        if (self.optimizer and not self.use_fairscale
+                and self.save_modules and "optimizer" in self.save_modules):
             if self.use_fsdp and we.is_distributed:
                 ckpt['optimizer'] = OrderedDict()
                 for module in self.train_modules:
@@ -586,9 +619,6 @@ class LatentDiffusionSolver(BaseSolver):
                 'batch_size': len(batch_data['prompt'])
             })
             self.current_batch_data[self.mode] = batch_data
-            if self.sample_args:
-                self.current_batch_data[self.mode].update(
-                    self.sample_args.get_lowercase_dict())
             with torch.autocast(device_type='cuda',
                                 enabled=self.use_amp,
                                 dtype=self.dtype):
@@ -631,12 +661,12 @@ class LatentDiffusionSolver(BaseSolver):
             # the inference image use
             ret_images, ret_labels = [], []
             if 'hint' in result:
-                ret_images.append(
-                    (result['hint'][:result['image'].shape[0]].permute(
-                        1, 2, 0).cpu().numpy() * 255).astype(np.uint8))
-                ret_labels.append('Control Image')
-            ret_images.append((result['image'].permute(1, 2, 0).cpu().numpy() *
-                               255).astype(np.uint8))
+                ret_images.append((result['hint'][:result['image'].shape[0]].permute(1, 2, 0).cpu().numpy() *
+                                   255).astype(np.uint8))
+                ret_labels.append(f"Control Image")
+            ret_images.append(
+                (result['image'].permute(1, 2, 0).cpu().numpy() *
+                 255).astype(np.uint8))
             ret_labels.append(result['prompt'] +
                               " <font color='red'> |NegPrompt| </font> " +
                               result['n_prompt'])
@@ -678,15 +708,15 @@ class LatentDiffusionSolver(BaseSolver):
             # the inference image use
             ret_images, ret_labels = [], []
             if 'hint' in result:
-                ret_images.append(
-                    (result['hint'][:result['image'].shape[0]].permute(
-                        1, 2, 0).cpu().numpy() * 255).astype(np.uint8))
-                ret_labels.append('Control Image')
-            ret_images.append((result['image'].permute(1, 2, 0).cpu().numpy() *
-                               255).astype(np.uint8))
+                ret_images.append((result['hint'][:result['image'].shape[0]].permute(1, 2, 0).cpu().numpy() *
+                                 255).astype(np.uint8))
+                ret_labels.append(f"Control Image")
+            ret_images.append(
+                (result['image'].permute(1, 2, 0).cpu().numpy() *
+                 255).astype(np.uint8))
             ret_labels.append(result['prompt'] +
-                              " <font color='red'> |NegPrompt| </font> " +
-                              result['n_prompt'])
+                             " <font color='red'> |NegPrompt| </font> " +
+                             result['n_prompt'])
             log_data.append(ret_images)
             log_label.append(ret_labels)
             ori_label.append(result['prompt'])
@@ -715,11 +745,8 @@ class LatentDiffusionSolver(BaseSolver):
             swift_cfg_dict[f'{t_id}_{cfg_name}'] = init_config
         if len(swift_cfg_dict) > 0:
             from swift import Swift
-            model = Swift.prepare_model(self.model, config=swift_cfg_dict)
-
-            self.logger.info([(key, param.shape)
-                              for key, param in model.named_parameters()
-                              if param.requires_grad])
+            model = Swift.prepare_model(self.model, config=swift_cfg_dict, autocast_adapter_dtype=False)
+        self.logger.info([(key, param.shape) for key, param in model.named_parameters() if param.requires_grad])
         return model
 
     def freeze(self, freeze_cfg, model=None):
@@ -781,9 +808,7 @@ class LatentDiffusionSolver(BaseSolver):
                 for name, param in freeze_model.named_parameters():
                     if re.match(train_part, name):
                         param.requires_grad = True
-        self.logger.info([(key, param.shape)
-                          for key, param in freeze_model.named_parameters()
-                          if param.requires_grad])
+        self.logger.info([(key, param.shape) for key, param in freeze_model.named_parameters() if param.requires_grad])
         return model
 
     @torch.no_grad()
@@ -826,37 +851,32 @@ class LatentDiffusionSolver(BaseSolver):
     @property
     def probe_data(self):
         if not we.debug and self.mode == 'train':
-            batch_data = transfer_data_to_cuda(
-                self.current_batch_data[self.mode])
+            batch_data = self.current_batch_data[self.mode]
+            if self.sample_args:
+                batch_data.update(self.sample_args.get_lowercase_dict())
             self.eval_mode()
             with torch.autocast(device_type='cuda',
                                 enabled=self.use_amp,
                                 dtype=self.dtype):
                 batch_data['log_num'] = self.log_train_num
-                results = self.run_step_eval(batch_data)
-                images = batch_data['image'] if 'image' in batch_data else [
-                    None
-                ] * len(results)
+                results = self.run_step_eval(transfer_data_to_cuda(batch_data))
+                images = batch_data['image'] if 'image' in batch_data else [None] * len(results)
             self.train_mode()
             log_data, log_label = [], []
             for result, image in zip(results, images):
                 ret_images, ret_labels = [], []
                 if 'hint' in result:
-                    ret_images.append(
-                        (result['hint'][:result['image'].shape[0]].permute(
-                            1, 2, 0).cpu().numpy() * 255).astype(np.uint8))
+                    ret_images.append((result['hint'][:result['image'].shape[0]].permute(1, 2, 0).cpu().numpy() *
+                                     255).astype(np.uint8))
                 if image is not None:
                     image = torch.clamp((image + 1.0) / 2.0, min=0.0, max=1.0)
-                    ret_images.append((image.permute(1, 2, 0).cpu().numpy() *
-                                       255).astype(np.uint8))
-                    ret_labels.append('target image')
+                    ret_images.append((image.permute(1, 2, 0).cpu().numpy() * 255).astype(np.uint8))
+                    ret_labels.append(f'target image')
 
-                ret_images.append(
-                    (result['image'].permute(1, 2, 0).cpu().numpy() *
-                     255).astype(np.uint8))
-                ret_labels.append(result['prompt'] +
-                                  " <font color='red'> |NegPrompt| </font> " +
-                                  result['n_prompt'])
+                ret_images.append((result['image'].permute(1, 2, 0).cpu().numpy() * 255).astype(np.uint8))
+                ret_labels.append(result['prompt']
+                                  + " <font color='red'> |NegPrompt| </font> "
+                                  + result['n_prompt'])
                 log_data.append(ret_images)
                 log_label.append(ret_labels)
             self.register_probe({
@@ -947,4 +967,4 @@ class LatentDiffusionSolver(BaseSolver):
             logger.info(
                 f'Load ema frozen params {ema_param_numel} / {all_param_numel} = '
                 f'{ema_param_numel / all_param_numel:.2%}, '
-                f'frozen part: {ema_param_dict}.')
+                f'frozen part: {ema_param_dict}.')

scepter/modules/solver/diffusion_video_solver.py

@@ -0,0 +1,190 @@
+# -*- coding: utf-8 -*-
+# Copyright (c) Alibaba, Inc. and its affiliates.
+
+import torch
+import numpy as np
+from tqdm import tqdm
+
+from scepter.modules.utils.config import dict_to_yaml
+from scepter.modules.solver import LatentDiffusionSolver
+from scepter.modules.solver.registry import SOLVERS
+from scepter.modules.utils.data import transfer_data_to_cuda
+from scepter.modules.utils.distribute import we
+from scepter.modules.utils.probe import ProbeData
+
+@SOLVERS.register_class()
+class LatentDiffusionVideoSolver(LatentDiffusionSolver):
+    para_dict = LatentDiffusionSolver.para_dict
+    def __init__(self, cfg, logger=None):
+        super().__init__(cfg, logger=logger)
+        self.fps = cfg.get("FPS", 8)
+
+    def save_results(self, results):
+        log_data, log_label = [], []
+        for result in results:
+            ret_videos, ret_labels = [], []
+            if 'edit_video' in result:
+                ret_videos.append((result['edit_video'].permute(1, 2, 3, 0).cpu().numpy() *
+                             255).astype(np.uint8))
+                ret_labels.append("left: edit video")
+            if 'edit_image' in result:
+                ret_videos.append((result['edit_image'].permute(1, 2, 3, 0).cpu().numpy() *
+                                   255).astype(np.uint8))
+                ret_labels.append("left: edit image")
+            if 'target_video' in result:
+                if len(ret_videos) > 0:
+                    ret_labels.append("middle: target video")
+                else:
+                    ret_labels.append("left: target video")
+                ret_videos.append((result['target_video'].permute(1, 2, 3, 0).cpu().numpy() *
+                                   255).astype(np.uint8))
+
+            ret_videos.append((result['reconstruct_video'].permute(1, 2, 3, 0).cpu().numpy() *
+                             255).astype(np.uint8))
+            ret_labels.append("right: generation video" + " Prompt: " + result['instruction'])
+
+            log_data.append(ret_videos)
+            log_label.append(ret_labels)
+        return log_data, log_label
+
+    def run_train(self):
+        self.train_mode()
+        self.before_all_iter(self.hooks_dict[self._mode])
+        data_iter = iter(self.datas[self._mode].dataloader)
+        self.print_memory_status()
+        for step in range(self.max_steps):
+            if 'eval' in self._mode_set and (self.eval_interval > 0 and
+                                             step % self.eval_interval == 0):
+                self.run_eval()
+                self.train_mode()
+            batch_data = next(data_iter)
+            self.before_iter(self.hooks_dict[self._mode])
+            if 'meta' in batch_data and isinstance(batch_data['meta'], dict):
+                self.register_probe({
+                    'data_key':
+                    ProbeData(batch_data['meta'].get('data_key', []),
+                              view_distribute=True)
+                })
+            self.register_probe({
+                'prompt': batch_data['prompt'],
+                'batch_size': len(batch_data['prompt'])
+            })
+            self.current_batch_data[self.mode] = batch_data
+            if self.sample_args:
+                self.current_batch_data[self.mode].update(
+                    self.sample_args.get_lowercase_dict())
+            batch_data = transfer_data_to_cuda(batch_data)
+            with torch.autocast(device_type='cuda',
+                                enabled=self.use_amp,
+                                dtype=self.dtype):
+                results = self.run_step_train(
+                    batch_data,
+                    step,
+                    step=self.total_iter,
+                    rank=we.rank)
+                self._iter_outputs[self._mode] = self._reduce_scalar(results)
+            self.after_iter(self.hooks_dict[self._mode])
+            if we.debug:
+                self.print_trainable_params_status(prefix='model.')
+            if 'eval' in self._mode_set and (self.eval_interval > 0
+                                             and step == self.max_steps - 1):
+                self.run_eval()
+                self.train_mode()
+        self.after_all_iter(self.hooks_dict[self._mode])
+
+    @torch.no_grad()
+    def run_eval(self):
+        self.eval_mode()
+        self.before_all_iter(self.hooks_dict[self._mode])
+        all_results = []
+        for batch_idx, batch_data in tqdm(
+                enumerate(self.datas[self._mode].dataloader)):
+            self.before_iter(self.hooks_dict[self._mode])
+            if self.sample_args:
+                batch_data.update(self.sample_args.get_lowercase_dict())
+            with torch.autocast(device_type='cuda',
+                                enabled=self.use_amp,
+                                dtype=self.dtype):
+                results = self.run_step_eval(transfer_data_to_cuda(batch_data),
+                                             batch_idx,
+                                             step=self.total_iter,
+                                             rank=we.rank)
+                all_results.extend(results)
+            self.after_iter(self.hooks_dict[self._mode])
+        log_data, log_label = self.save_results(all_results)
+        self.register_probe({'eval_label': log_label})
+        self.register_probe({
+            'eval_video':
+                ProbeData(log_data,
+                          is_image=False,
+                          is_video=True,
+                          fps=self.fps,
+                          build_html=True,
+                          build_label=log_label)
+        })
+        self.after_all_iter(self.hooks_dict[self._mode])
+
+    @torch.no_grad()
+    def run_test(self):
+        self.test_mode()
+        self.before_all_iter(self.hooks_dict[self._mode])
+        all_results = []
+        for batch_idx, batch_data in tqdm(
+                enumerate(self.datas[self._mode].dataloader)):
+            self.before_iter(self.hooks_dict[self._mode])
+            if self.sample_args:
+                batch_data.update(self.sample_args.get_lowercase_dict())
+            with torch.autocast(device_type='cuda',
+                                enabled=self.use_amp,
+                                dtype=self.dtype):
+                results = self.run_step_eval(transfer_data_to_cuda(batch_data),
+                                             batch_idx,
+                                             step=self.total_iter,
+                                             rank=we.rank)
+                all_results.extend(results)
+            self.after_iter(self.hooks_dict[self._mode])
+        log_data, log_label = self.save_results(all_results)
+        self.register_probe({'test_label': log_label})
+        self.register_probe({
+            'test_video':
+                ProbeData(log_data,
+                          is_image=False,
+                          is_video=True,
+                          fps=self.fps,
+                          build_html=True,
+                          build_label=log_label)
+        })
+        self.after_all_iter(self.hooks_dict[self._mode])
+
+    @property
+    def probe_data(self):
+        if not we.debug and self.mode == 'train':
+            batch_data = self.current_batch_data[self.mode]
+            if self.sample_args is not None:
+                batch_data.update(self.sample_args.get_lowercase_dict())
+            self.eval_mode()
+            with torch.autocast(device_type='cuda',
+                                enabled=self.use_amp,
+                                dtype=self.dtype):
+                batch_data['log_train_num'] = self.log_train_num
+                all_results = self.run_step_eval(transfer_data_to_cuda(batch_data))
+            self.train_mode()
+            log_data, log_label = self.save_results(all_results)
+            self.register_probe({
+                'train_video':
+                    ProbeData(log_data,
+                              is_image=False,
+                              is_video=True,
+                              fps=self.fps,
+                              build_html=True,
+                              build_label=log_label)
+            })
+            self.register_probe({'train_label': log_label})
+        return super(LatentDiffusionSolver, self).probe_data
+
+    @staticmethod
+    def get_config_template():
+        return dict_to_yaml('SOLVER',
+                            __class__.__name__,
+                            LatentDiffusionVideoSolver.para_dict,
+                            set_name=True)

scepter/modules/solver/hooks/backward.py

@@ -127,25 +127,25 @@ class BackwardHook(Hook):
             if solver.scaler is not None:
                 solver.scaler.scale(solver.loss /
                                     self.accumulate_step).backward()
-                if self.gradient_clip > 0:
-                    solver.scaler.unscale_(solver.optimizer)
-                    self.grad_clip(solver.train_parameters())
                 self.current_step += 1
                 # Suppose profiler run after backward, so we need to set backward_prev_step
                 # as the previous one step before the backward step
                 if self.current_step % self.accumulate_step == 0:
+                    if self.gradient_clip > 0:
+                        solver.scaler.unscale_(solver.optimizer)
+                        self.grad_clip(solver.train_parameters())
                     self.profile(solver)
                     solver.scaler.step(solver.optimizer)
                     solver.scaler.update()
                     solver.optimizer.zero_grad()
             else:
                 (solver.loss / self.accumulate_step).backward()
-                if self.gradient_clip > 0:
-                    self.grad_clip(solver.train_parameters())
                 self.current_step += 1
                 # Suppose profiler run after backward, so we need to set backward_prev_step
                 # as the previous one step before the backward step
                 if self.current_step % self.accumulate_step == 0:
+                    if self.gradient_clip > 0:
+                        self.grad_clip(solver.train_parameters())
                     self.profile(solver)
                     solver.optimizer.step()
                     solver.optimizer.zero_grad()

scepter/modules/solver/hooks/checkpoint.py

@@ -128,13 +128,24 @@ class CheckpointHook(Hook):
                     solver.work_dir,
                     'checkpoints/{}-{}'.format(self.save_name_prefix,
                                                solver.total_iter + 1))
-                if we.rank == 0:
-                    local_folder, _ = FS.map_to_local(save_path)
-                    if hasattr(solver.model, 'module'):
-                        solver.model.module.save_pretrained(local_folder)
-                    else:
-                        solver.model.save_pretrained(local_folder)
-                    FS.put_dir_from_local_dir(local_folder, save_path)
+                solver_model = solver.model.module if hasattr(solver.model, 'module') else solver.model
+                if isinstance(solver_model.base_model.model, torch.distributed.fsdp.FullyShardedDataParallel):
+                    full_state_dict_config = torch.distributed.fsdp.FullStateDictConfig(offload_to_cpu=True, rank0_only=True)
+                    with torch.distributed.fsdp.FullyShardedDataParallel.state_dict_type(solver_model.base_model, torch.distributed.fsdp.StateDictType.FULL_STATE_DICT, full_state_dict_config):
+                        state_dict = solver_model.base_model.state_dict()
+                    if we.rank == 0:
+                        state_dict_new = {}
+                        local_folder, _ = FS.map_to_local(save_path)
+                        for adapter_name in solver_model.adapters.keys():
+                            state_dict_adapter = solver_model.adapters[adapter_name].state_dict_callback(state_dict, adapter_name, replace_key=False)
+                            state_dict_new.update(state_dict_adapter)
+                        solver_model.save_pretrained(local_folder, state_dict=state_dict_new)
+                        FS.put_dir_from_local_dir(local_folder, save_path)
+                else:
+                    if we.rank == 0:
+                        local_folder, _ = FS.map_to_local(save_path)
+                        solver_model.save_pretrained(local_folder)
+                        FS.put_dir_from_local_dir(local_folder, save_path)
             else:
                 if hasattr(solver, 'save_pretrained'):
                     save_path = osp.join(

scepter/modules/solver/hooks/log.py

@@ -117,6 +117,7 @@ class LogHook(Hook):
         super(LogHook, self).__init__(cfg, logger=logger)
         self.priority = cfg.get('PRIORITY', _DEFAULT_LOG_PRIORITY)
         self.log_interval = cfg.get('LOG_INTERVAL', 10)
+        self.interval = cfg.get('INTERVAL', self.log_interval)
         self.show_gpu_mem = cfg.get('SHOW_GPU_MEM', False)
         self.log_agg_dict = defaultdict(LogAgg)
 
@@ -147,18 +148,18 @@ class LogHook(Hook):
         outputs['time'] = iter_time
         outputs['data_time'] = self.data_time
         if solver.mode in self.batch_size:
-            outputs['throughput'] = int(self.batch_size[solver.mode] * we.world_size / iter_time * 86400)
+            outputs['throughput'] = int(self.batch_size[solver.mode] * we.data_group_world_size / iter_time * 86400)
         log_agg.update(outputs, 1)
-        log_agg = log_agg.aggregate(self.log_interval)
+        log_agg = log_agg.aggregate(self.interval)
         if 'throughput' in log_agg:
             log_agg['throughput'] = f"{int(log_agg['throughput'][-1])}/day"
         if solver.mode in self.batch_size:
-            log_agg['all_throughput'] = (solver.iter + 1) * we.world_size * self.batch_size[solver.mode]
+            log_agg['all_throughput'] = (solver.iter + 1) * we.data_group_world_size * self.batch_size[solver.mode]
 
         if self.show_gpu_mem:
             log_agg['nvidia-smi'] = str(print_memory_status()) +"MiB"
 
-        if (solver.iter + 1) % self.log_interval == 0:
+        if (solver.iter + 1) % self.interval == 0:
             _print_iter_log(solver,
                             log_agg,
                             start_time=self.start_time,
@@ -206,7 +207,7 @@ class LogHook(Hook):
             solver.logger.info(f'Current Epoch {mode} Summary:')
             log_agg = self.log_agg_dict[mode]
             _print_iter_log(solver,
-                            log_agg.aggregate(self.log_interval),
+                            log_agg.aggregate(self.interval),
                             start_time=self.start_time,
                             mode=mode)
             if not mode == 'train':
@@ -242,6 +243,7 @@ class TensorboardLogHook(Hook):
         self.priority = cfg.get('PRIORITY', _DEFAULT_LOG_PRIORITY)
         self.log_dir = cfg.get('LOG_DIR', None)
         self.log_interval = cfg.get('LOG_INTERVAL', 1000)
+        self.interval = cfg.get('INTERVAL', self.log_interval)
         self._local_log_dir = None
         self.writer: Optional[SummaryWriter] = None
 
@@ -286,7 +288,7 @@ class TensorboardLogHook(Hook):
             self.writer.add_scalar(f'{mode}/iter/{key}',
                                    value,
                                    global_step=solver.total_iter)
-        if solver.total_iter % self.log_interval:
+        if solver.total_iter % self.interval:
             self.writer.flush()
             # Put to remote file systems every epoch
             FS.put_dir_from_local_dir(self._local_log_dir, self.log_dir)

scepter/modules/utils/distribute.py

@@ -721,6 +721,7 @@ class Workenv(object):
             torch.backends.cudnn.benchmark = config.ENV.get(
                 'CUDNN_BENCHMARK', False)
             fn(config)
+            return
         else:
             import torch.multiprocessing as mp
             if 'MASTER_ADDR' not in os.environ:
@@ -741,10 +742,13 @@ class Workenv(object):
             if self.is_distributed:
                 self.backend = config.ENV.get('BACKEND', 'nccl')
                 self.sync_bn = config.ENV.get('SYNC_BN', False)
-            mp.spawn(mp_worker,
-                     nprocs=ngpus_per_node,
-                     args=(ngpus_per_node, config, fn, pmi_rank, world_size,
-                           self))
+            spawn_join = config.ENV.get('SPAWN_JOIN', True)
+            context = mp.spawn(mp_worker,
+                               nprocs=ngpus_per_node,
+                               join=spawn_join,
+                               args=(ngpus_per_node, config, fn, pmi_rank, world_size,
+                                     self))
+            return context
 
     def get_env(self):
         ret_dict = {}

scepter/modules/utils/visualization.py

@@ -1,6 +1,10 @@
 # -*- coding: utf-8 -*-
 # Copyright (c) Alibaba, Inc. and its affiliates.
+import copy
 from enum import Enum
+import os
+
+from scepter.modules.utils.file_system import FS
 
 
 class Media(Enum):
@@ -9,18 +13,21 @@ class Media(Enum):
     VIDEO = 3
     AUDIO = 4
     IMAGE_PAIR = 5
+    VIDEO_PAIR = 6
 
 
 class HtmlVisualization(object):
-    def __init__(self,
-                 allow_annotation=False,
-                 slice_size=1000,
-                 align='center',
-                 width_scale='60%',
-                 title='Visualization',
-                 height=600,
-                 width=None,
-                 text_cols=40):
+    def __init__(
+            self,
+            allow_annotation=False,
+            slice_size=1000,
+            align='center',
+            width_scale='60%',
+            title='Visualization',
+            height=600,
+            width=None,
+            text_cols=40
+    ):
         self.content_list = []
         self.rows_meta = []
         self.allow_annotation = allow_annotation
@@ -30,9 +37,9 @@ class HtmlVisualization(object):
         self.title = title
         self.html_start = '<html>'
         self.html_head = f'<head><meta charset="utf-8"><title>{title}</title></head>'
-        self.height = height if height is not None else '600'
-        self.width = width if width is not None else 'auto'
-        self.text_cols = text_cols if text_cols is not None else 'auto'
+        self.height = height if height is not None else "600"
+        self.width = width if width is not None else "auto"
+        self.text_cols = text_cols if text_cols is not None else "auto"
         self.html_style = ('''
             <style> \n
                 .container {
@@ -52,11 +59,21 @@ class HtmlVisualization(object):
                     height: 100%; \n
                     transition: 0.4s ease; \n
                 }\n
-                .image img {
+                .image img { \n
                     width: 100%; \n
                     height: 100%; \n
                     object-fit: contain; \n
                 } \n
+
+                .video { \n
+                    display:flex; \n
+                    position:absolute; \n
+                    width:100%; \n
+                    height:100%; \n
+                    transition:0.4s ease; \n
+                    object-fit:contain; \n
+                } \n
+
                 .slider {
                     position: absolute; \n
                     cursor: ew-resize; \n
@@ -64,13 +81,6 @@ class HtmlVisualization(object):
                     background-color: rgba(255, 255, 255, 0.5); \n
                     z-index: 10; \n
                 } \n
-                video { \n
-                    width: auto;
-                    height: 100%;
-                    margin: 0px; \n
-                    border: 0px solid #ccc; \n
-                    padding: 0px; \n
-                } \n
                 textarea { \n
                     margin: 0px; \n
                                 border: 0px; \n
@@ -78,10 +88,12 @@ class HtmlVisualization(object):
                     resize: none; \n
                     border: 1px solid #ccc; \n
                 } \n
+                .large-checkbox {transform: scale(2.5); margin-left: 20px; margin-bottom: 20px; vertical-align: middle;} \n
             </style> \n
             \n
-        '''.replace('{width_scale}', self.width_scale).replace(
-            '{align}', self.align).replace('{pair_height}', f'{self.height}'))
+        '''.replace('{width_scale}',
+                    self.width_scale).replace('{align}', self.align)
+                           .replace('{pair_height}', f'{self.height}'))
 
         self.html_body_script = '''
                 <script>\n
@@ -89,7 +101,7 @@ class HtmlVisualization(object):
                     containers.forEach(container => {\n
                         let isDragging = true;\n
                         const slider = container.querySelector('.slider')\n
-                        const image2 = container.querySelector('#image2')\n
+                        const media2 = container.querySelector('#media2')\n
 
                         container.addEventListener('mousedown', () => {\n
                             isDragging = true;\n
@@ -113,7 +125,7 @@ class HtmlVisualization(object):
 
                             percentage = Math.max(0, Math.min(100, percentage));\n
 
-                            image2.style.clipPath = `inset(0 ${100 - percentage}% 0 0)`;\n
+                            media2.style.clipPath = `inset(0 ${100 - percentage}% 0 0)`;\n
 
                             slider.style.left = `${percentage}%`;\n
 
@@ -124,36 +136,6 @@ class HtmlVisualization(object):
                         slider.style.left = '50%';\n
                     });\n
                 </script>\n
-                <script> \n
-                    const videos = document.querySelectorAll('video'); \n
-                    \n
-                    const observer = new IntersectionObserver((entries) => { \n
-                        entries.forEach(entry => { \n
-                            if (entry.isIntersecting) { \n
-                                const video = entry.target; \n
-                                video.src = video.dataset.src; \n
-                                video.load(); \n
-                                observer.unobserve(video); \n
-                            } \n
-                        }); \n
-                    }); \n
-                    \n
-                    videos.forEach(video => { \n
-                        observer.observe(video); \n
-                    }); \n
-                    \n
-                    function adjustHeight() { \n
-                        const textareas = document.querySelectorAll('textarea'); \n
-                        textareas.forEach(textarea => { \n
-                        const td = textarea.parentNode; \n
-                        const tdHeight = td.clientHeight; \n
-                        textarea.style.height = tdHeight + 'px'; \n
-                        }); \n
-                    } \n
-                    window.onload = adjustHeight; \n
-                    window.onresize = adjustHeight; \n
-                </script> \n
-
         '''
 
         self.html_body = '<body>{BODY}\n' + self.html_body_script + '</body>\n'
@@ -183,26 +165,27 @@ class HtmlVisualization(object):
 
                 '''
         self.label_button = (
-            '<table><tr><td>' +
-            "<button style='height: 50px;' type=\"button\" onclick=\"saveSamples()\">Save Samples</button>"
-            + '</td></tr></table>')
+                '<table><tr><td>' +
+                "<button style='height: 50px;' type=\"button\" onclick=\"saveSamples()\">Save Samples</button>"
+                + '</td></tr></table>')
 
     def format_col(self,
                    content='',
                    label='',
                    type=Media.TEXT,
                    show_label=True,
-                   cols_span=1):
+                   cols_span=1
+                   ):
         if type == Media.TEXT:
             ret_str = '<textarea'  # noqa: E501
             if self.height is not None:
-                rows = f"rows={self.height//30}"
+                rows = f"rows={self.height // 30}"
                 ret_str += f" {rows}"
             if self.width is not None:
                 cols = f"cols={self.text_cols * cols_span}"
                 ret_str += f" {cols}"
             ret_str += f'>"{content}"</textarea>'
-            sec_ret_str = f'<font size="3"><strong>{label}<strong></font>' if show_label else ''
+            sec_ret_str = f'<font size="3"><strong>{label}<strong></font>' if show_label else ""
         elif type == Media.IMAGE:
             ret_str = f'<img src="{content}"'
             if self.height is not None:
@@ -212,7 +195,7 @@ class HtmlVisualization(object):
                 width = f'width="{self.width}"'
                 ret_str += f" {width}"
             ret_str += '  >'
-            sec_ret_str = f'<font size="3"><strong>{label}<strong></font>' if show_label else ''
+            sec_ret_str = f'<font size="3"><strong>{label}<strong></font>' if show_label else ""
         elif type == Media.VIDEO:
             ret_str = '<video'  # noqa
             if self.height is not None:
@@ -221,61 +204,87 @@ class HtmlVisualization(object):
             if self.width is not None:
                 width = f'width="{self.width}"'
                 ret_str += f" {width}"
-            ret_str += ' preload="none" controls>'
+            ret_str += ' preload="none" autoplay muted loop>'
             ret_str += f'<source src="{content}" type="video/mp4"></video>'
-            sec_ret_str = f'<font size="3"><strong>{label}<strong></font>' if show_label else ''
+            sec_ret_str = f'<font size="3"><strong>{label}<strong></font>' if show_label else ""
         elif type == Media.AUDIO:
             ret_str = f'<audio src="{content}" controls>'
-            sec_ret_str = f'<font size="3"><strong>{label}<strong></font>' if show_label else ''
+            sec_ret_str = f'<font size="3"><strong>{label}<strong></font>' if show_label else ""
         elif type == Media.IMAGE_PAIR:
             assert isinstance(content, (list, tuple)) and len(content) == 2
-            ret_str = '\n'
-            ret_str += '       <div class="container"'
-            ret_str += (
-                f'> \n'
-                f'      <div class="image" id="image1">'  # noqa
-                f'          <img src="{content[1]}" alt="before">\n'  # noqa
-                f'      </div>\n'  # noqa
-                f'      <div class="image" id="image2" style="clip-path: inset(0 50% 0 0);">\n'  # noqa
-                f'            <img src="{content[0]}" alt="after">\n'  # noqa
-                f'      </div>\n'  # noqa
-                f'      <div class="slider" id="slider"></div>\n'  # noqa
-                f'')
-            sec_ret_str = f'<font size="3"><strong>{label}<strong></font>' if show_label else ''
+            ret_str = f'\n'
+            ret_str += f'       <div class="container"'
+            ret_str += (f'> \n'
+                        f'      <div class="image" id="media1">'
+                        f'          <img src="{content[1]}" alt="before">\n'
+                        f'      </div>\n'
+                        f'      <div class="image" id="media2" style="clip-path: inset(0 50% 0 0);">\n'
+                        f'            <img src="{content[0]}" alt="after">\n'
+                        f'      </div>\n'
+                        f'      <div class="slider" id="slider"></div>\n'
+                        f'')
+            sec_ret_str = f'<font size="3"><strong>{label}<strong></font>' if show_label else ""
+        elif type == Media.VIDEO_PAIR:
+            assert isinstance(content, (list, tuple)) and len(content) == 2
+            ret_str = f'\n'
+            ret_str += f'       <div class="container"'
+            ret_str += (f'> \n'
+                        f'      <video autoplay muted loop class="video" id="media1"><source src="{content[1]}" type="video/mp4"></video>\n'
+                        f'      <video autoplay muted loop class="video" id="media2" style="clip-path: inset(0 50% 0 0);"><source src="{content[0]}" type="video/mp4"></video>\n'
+                        f'      <div class="slider" id="slider"></div>\n'
+                        f'</div>')
+            sec_ret_str = f'<font size="3"><strong>{label}<strong></font>' if show_label else ""
         else:
             raise NotImplementedError
+
+        if self.allow_annotation:
+            ret_str = f'<label for="sample#sample_id#">{ret_str}</label>'
+
         if cols_span > 1:
             ret_str = f'<th colspan="{cols_span}">{ret_str}</th>\n'
-            sec_ret_str = f'<th colspan="{cols_span}">{sec_ret_str}</th>\n' if not sec_ret_str == '' else sec_ret_str
+            sec_ret_str = f'<th colspan="{cols_span}">{sec_ret_str}</th>\n' if not sec_ret_str == "" else sec_ret_str
         else:
             ret_str = f'<td>{ret_str}</td>\n'
-            sec_ret_str = f'<td align="center">{sec_ret_str}</td>\n' if not sec_ret_str == '' else sec_ret_str
+            sec_ret_str = f'<td align="center">{sec_ret_str}</td>\n' if not sec_ret_str == "" else sec_ret_str
         return [ret_str, sec_ret_str]
 
     def format_row(self):
-        sample_id = 0
-        all_sample_html = []
-        for one_content, one_row_meta in zip(self.content_list,
-                                             self.rows_meta):
-            one_row_str = '<tr>'
-            one_row_str += '\n'.join([v[0] for v in one_content])
-            if self.allow_annotation:
-                row_meta = '#;#'.join(one_row_meta)
-                one_row_str += (
-                    f'<td><input type="checkbox" class="large-checkbox" '
-                    f'id="sample{sample_id}" name="sample[]" value="{row_meta}"></td>\n'
-                )
-            one_row_str += '</tr><tr>'
-            one_row_str += '\n'.join([v[1] for v in one_content])  # noqa
-            if self.allow_annotation:  # noqa
-                one_row_str += f'<td></td>\n'  # noqa
-            one_row_str += '</tr>'
-            if self.allow_annotation:
-                one_row_str = f'<label for="sample{sample_id}">{one_row_str}</label>'
-            all_sample_html.append(one_row_str)
-            sample_id += 1
 
-        return '<table>' + '\n'.join(all_sample_html) + '</table>'
+        all_sample_html = []
+        current_content_list = copy.deepcopy(self.content_list)
+        current_rows_meta = copy.deepcopy(self.rows_meta)
+
+        while len(current_content_list) > 0:
+            sample_id = 0
+            batch_content_list = current_content_list[:self.slice_size]
+            current_content_list = current_content_list[self.slice_size:]
+            batch_rows_meta = current_rows_meta[:self.slice_size]
+            current_rows_meta = current_rows_meta[self.slice_size:]
+            current_sample_html = []
+            for one_content, one_row_meta in zip(batch_content_list,
+                                                 batch_rows_meta):
+                one_row_str = '<tr>'
+                if not self.allow_annotation:
+                    one_row_str += '\n'.join([v[0] for v in one_content])
+                else:
+                    one_row_str += '\n'.join([v[0].replace('#sample_id#', f'{sample_id}')  for v in one_content])
+                    row_meta = '#;#'.join(one_row_meta)
+                    one_row_str += (
+                        f'<td><input type="checkbox" class="large-checkbox" '
+                        f'id="sample{sample_id}" name="sample[]" value="{row_meta}"></td>\n'
+                    )
+                one_row_str += '</tr><tr>'
+                one_row_str += '\n'.join([v[1] for v in one_content])  # noqa
+                if self.allow_annotation:  # noqa
+                    one_row_str += f'<td></td>\n'  # noqa
+                one_row_str += '</tr>'
+                # if self.allow_annotation:
+                #     one_row_str = f'<label for="sample{sample_id}">{one_row_str}</label>'
+                current_sample_html.append(one_row_str)
+                sample_id += 1
+            all_sample_html.append("<table>" + '\n'.join(current_sample_html) + "</table>")
+
+        return all_sample_html
 
     def add_record(self,
                    content,
@@ -295,11 +304,8 @@ class HtmlVisualization(object):
         if col_id > len(self.content_list[row_id]):
             raise RuntimeError(
                 'col_id should be next number of the last col_id.')
-        format_col = self.format_col(content,
-                                     f"{row_id}-{col_id}: {label}",
-                                     type,
-                                     show_label=show_label,
-                                     cols_span=cols_span)
+        format_col = self.format_col(content, f"{row_id}-{col_id}: {label}",
+                                     type, show_label=show_label, cols_span=cols_span)
 
         annotation_meta = annotation_meta if annotation_meta else ''
         if col_id == len(self.content_list[row_id]):
@@ -311,14 +317,30 @@ class HtmlVisualization(object):
 
     def save_html(self, path):
         html_body = self.format_row()
-        ret_html_list = [
-            self.html_start, self.html_head, self.html_style,
-            self.html_body.replace('{BODY}', html_body)
-        ]
-        if self.allow_annotation:
-            ret_html_list.append(self.label_button)
-            ret_html_list.append(self.html_script)
-        ret_html_list.append(self.html_end)
-        ret_html = '\n'.join(ret_html_list)
-        with open(path, 'w') as f:
-            f.write(ret_html)
+        if isinstance(html_body, list) and len(html_body) > 1:
+            try:
+                os.makedirs(path, exist_ok=True)
+            except:
+                print("Create folder path failed.")
+            for html_id, one_html in enumerate(html_body):
+                ret_html_list = [
+                    self.html_start, self.html_head, self.html_style,
+                    self.html_body.replace('{BODY}', one_html)
+                ]
+                if self.allow_annotation:
+                    ret_html_list.append(self.label_button)
+                    ret_html_list.append(self.html_script)
+                ret_html_list.append(self.html_end)
+                ret_html = '\n'.join(ret_html_list)
+                FS.put_object(ret_html.encode(), os.path.join(path, f"{html_id}.html"))
+        else:
+            ret_html_list = [
+                self.html_start, self.html_head, self.html_style,
+                self.html_body.replace('{BODY}', html_body[0])
+            ]
+            if self.allow_annotation:
+                ret_html_list.append(self.label_button)
+                ret_html_list.append(self.html_script)
+            ret_html_list.append(self.html_end)
+            ret_html = '\n'.join(ret_html_list)
+            FS.put_object(ret_html.encode(), path)

scepter/studio/chatbot/chatbot.py

@@ -12,15 +12,13 @@ import re
 import string
 import sys
 import threading
+import warnings
 
 import cv2
 import gradio as gr
 import numpy as np
 import torch
 import transformers
-from diffusers import CogVideoXImageToVideoPipeline
-from diffusers.utils import export_to_video
-from gradio_imageslider import ImageSlider
 from PIL import Image
 from transformers import AutoModel, AutoTokenizer
 
@@ -29,6 +27,8 @@ from scepter.modules.utils.config import Config
 from scepter.modules.utils.directory import get_md5
 from scepter.modules.utils.file_system import FS
 from scepter.studio.utils.env import init_env
+import scepter
+from importlib.metadata import version
 
 from .example import get_examples
 from .utils import load_image
@@ -51,32 +51,42 @@ class ChatBotUI(object):
                  is_debug=False,
                  language='en',
                  root_work_dir='./'):
-
-        cfg = Config(cfg_file=cfg_general_file)
+        try:
+            from diffusers import CogVideoXImageToVideoPipeline
+            from diffusers.utils import export_to_video
+        except Exception as e:
+            print(f"Import diffusers failed, please install or upgrade diffusers. Error information: {e}")
+        if isinstance(cfg_general_file, str):
+            cfg = Config(cfg_file=cfg_general_file)
+        else:
+            cfg = cfg_general_file
         cfg.WORK_DIR = os.path.join(root_work_dir, cfg.WORK_DIR)
         if not FS.exists(cfg.WORK_DIR):
             FS.make_dir(cfg.WORK_DIR)
         cfg = init_env(cfg)
         self.cache_dir = cfg.WORK_DIR
-        self.chatbot_examples = get_examples(self.cache_dir)
+        self.chatbot_examples = get_examples(self.cache_dir) if not cfg.get('SKIP_EXAMPLES', False) else []
         self.model_cfg_dir = cfg.MODEL.EDIT_MODEL.MODEL_CFG_DIR
-        self.model_yamls = glob.glob(os.path.join(self.model_cfg_dir,
+        self.model_yamls = glob.glob(os.path.join(
+                            os.path.dirname(scepter.dirname), self.model_cfg_dir,
                                                   '*.yaml'))
         self.model_choices = dict()
+        self.default_model_name = ''
         for i in self.model_yamls:
-            model_name = '.'.join(i.split('/')[-1].split('.')[:-1])
-            self.model_choices[model_name] = i
-        print('Models: ', self.model_choices)
-
-        self.model_name = cfg.MODEL.EDIT_MODEL.DEFAULT
-        assert self.model_name in self.model_choices
-        model_cfg = Config(load=True,
-                           cfg_file=self.model_choices[self.model_name])
+            model_cfg = Config(load=True, cfg_file=i)
+            model_name = model_cfg.NAME
+            if model_cfg.IS_DEFAULT: self.default_model_name = model_name
+            self.model_choices[model_name] = model_cfg
+        print('Models: ', self.model_choices.keys())
+        assert len(self.model_choices) > 0
+        if self.default_model_name == "": self.default_model_name = list(self.model_choices.keys())[0]
+        self.model_name = self.default_model_name
         self.pipe = ACEInference()
-        self.pipe.init_from_cfg(model_cfg)
+        self.pipe.init_from_cfg(self.model_choices[self.default_model_name])
         self.max_msgs = 20
-
         self.enable_i2v = cfg.get('ENABLE_I2V', False)
+        self.gradio_version = version('gradio')
+
         if self.enable_i2v:
             self.i2v_model_dir = cfg.MODEL.I2V.MODEL_DIR
             self.i2v_model_name = cfg.MODEL.I2V.MODEL_NAME
@@ -167,6 +177,7 @@ class ChatBotUI(object):
             ]
 
     def create_ui(self):
+
         css = '.chatbot.prose.md {opacity: 1.0 !important} #chatbot {opacity: 1.0 !important}'
         with gr.Blocks(css=css,
                        title='Chatbot',
@@ -177,7 +188,8 @@ class ChatBotUI(object):
             self.history_result = gr.State(value={})
             self.retry_msg = gr.State(value='')
             with gr.Group():
-                with gr.Row(equal_height=True):
+                self.ui_mode = gr.State(value='legacy')
+                with gr.Row(equal_height=True, visible=False) as self.chat_group:
                     with gr.Column(visible=True) as self.chat_page:
                         self.chatbot = gr.Chatbot(
                             height=600,
@@ -192,7 +204,7 @@ class ChatBotUI(object):
                                                        size='sm')
 
                     with gr.Column(visible=False) as self.editor_page:
-                        with gr.Tabs():
+                        with gr.Tabs(visible=False) as self.upload_tabs:
                             with gr.Tab(id='ImageUploader',
                                         label='Image Uploader',
                                         visible=True) as self.upload_tab:
@@ -201,7 +213,7 @@ class ChatBotUI(object):
                                     interactive=True,
                                     type='pil',
                                     image_mode='RGB',
-                                    sources='upload',
+                                    sources=['upload'],
                                     elem_id='image_uploader',
                                     format='png')
                                 with gr.Row():
@@ -209,10 +221,9 @@ class ChatBotUI(object):
                                         value='Submit',
                                         elem_id='upload_submit')
                                     self.ext_btn_1 = gr.Button(value='Exit')
-
+                        with gr.Tabs(visible=False) as self.edit_tabs:
                             with gr.Tab(id='ImageEditor',
-                                        label='Image Editor',
-                                        visible=False) as self.edit_tab:
+                                        label='Image Editor') as self.edit_tab:
                                 self.mask_type = gr.Dropdown(
                                     label='Mask Type',
                                     choices=[
@@ -275,13 +286,23 @@ class ChatBotUI(object):
                                     self.ext_btn_2 = gr.Button(value='Exit')
 
                             with gr.Tab(id='ImageViewer',
-                                        label='Image Viewer',
-                                        visible=False) as self.image_view_tab:
-                                self.image_viewer = ImageSlider(
-                                    label='Image',
-                                    type='pil',
-                                    show_download_button=True,
-                                    elem_id='image_viewer')
+                                        label='Image Viewer') as self.image_view_tab:
+                                if self.gradio_version >= '5.0.0':
+                                    self.image_viewer = gr.Image(
+                                        label='Image',
+                                        type='pil',
+                                        show_download_button=True,
+                                        elem_id='image_viewer')
+                                else:
+                                    try:
+                                        from gradio_imageslider import ImageSlider
+                                    except Exception as e:
+                                        print(f"Import gradio_imageslider failed, please install.")
+                                    self.image_viewer = ImageSlider(
+                                        label='Image',
+                                        type='pil',
+                                        show_download_button=True,
+                                        elem_id='image_viewer')
 
                                 self.ext_btn_3 = gr.Button(value='Exit')
 
@@ -300,11 +321,30 @@ class ChatBotUI(object):
 
                                 self.ext_btn_4 = gr.Button(value='Exit')
 
+                with gr.Row(equal_height=True, visible=True) as self.legacy_group:
+                    with gr.Column():
+                        self.legacy_image_uploader = gr.Image(
+                            height=550,
+                            interactive=True,
+                            type='pil',
+                            image_mode='RGB',
+                            elem_id='legacy_image_uploader',
+                            format='png')
+                    with gr.Column():
+                        self.legacy_image_viewer = gr.Image(
+                            label='Image',
+                            height=550,
+                            type='pil',
+                            interactive=False,
+                            show_download_button=True,
+                            elem_id='image_viewer')
+
+
                 with gr.Accordion(label='Setting', open=False):
                     with gr.Row():
                         self.model_name_dd = gr.Dropdown(
                             choices=self.model_choices,
-                            value=self.model_name,
+                            value=self.default_model_name,
                             label='Model Version')
 
                     with gr.Row():
@@ -315,39 +355,63 @@ class ChatBotUI(object):
                             label='Negative Prompt',
                             container=False)
 
+                    with gr.Row():
+                        # REFINER_PROMPT
+                        self.refiner_prompt = gr.Textbox(
+                            value=self.pipe.input.get("refiner_prompt", ""),
+                            visible=self.pipe.input.get("refiner_prompt", None) is not None,
+                            placeholder=
+                            'Prompt used for refiner',
+                            label='Refiner Prompt',
+                            container=False)
+
+
                     with gr.Row():
                         with gr.Column(scale=8, min_width=500):
                             with gr.Row():
                                 self.step = gr.Slider(minimum=1,
                                                       maximum=1000,
-                                                      value=20,
+                                                      value=self.pipe.input.get("sample_steps", 20),
+                                                      visible=self.pipe.input.get("sample_steps", None) is not None,
                                                       label='Sample Step')
                                 self.cfg_scale = gr.Slider(
                                     minimum=1.0,
                                     maximum=20.0,
-                                    value=4.5,
+                                    value=self.pipe.input.get("guide_scale", 4.5),
+                                    visible=self.pipe.input.get("guide_scale", None) is not None,
                                     label='Guidance Scale')
                                 self.rescale = gr.Slider(minimum=0.0,
                                                          maximum=1.0,
-                                                         value=0.5,
+                                                         value=self.pipe.input.get("guide_rescale", 0.5),
+                                                         visible=self.pipe.input.get("guide_rescale", None) is not None,
                                                          label='Rescale')
+                                self.refiner_scale = gr.Slider(minimum=-0.1,
+                                                         maximum=1.0,
+                                                         value=self.pipe.input.get("refiner_scale", -1),
+                                                         visible=self.pipe.input.get("refiner_scale", None) is not None,
+                                                         label='Refiner Scale')
                                 self.seed = gr.Slider(minimum=-1,
                                                       maximum=10000000,
                                                       value=-1,
                                                       label='Seed')
                                 self.output_height = gr.Slider(
                                     minimum=256,
-                                    maximum=1024,
-                                    value=512,
+                                    maximum=1440,
+                                    value=self.pipe.input.get("output_height", 1024),
+                                    visible=self.pipe.input.get("output_height", None) is not None,
                                     label='Output Height')
                                 self.output_width = gr.Slider(
                                     minimum=256,
-                                    maximum=1024,
-                                    value=512,
+                                    maximum=1440,
+                                    value=self.pipe.input.get("output_width", 1024),
+                                    visible=self.pipe.input.get("output_width", None) is not None,
                                     label='Output Width')
                         with gr.Column(scale=1, min_width=50):
                             self.use_history = gr.Checkbox(value=False,
                                                            label='Use History')
+                            self.use_ace = gr.Checkbox(value=self.pipe.input.get("use_ace", True),
+                                                       visible=self.pipe.input.get("use_ace", None) is not None,
+                                                       label='Use ACE')
                             self.video_auto = gr.Checkbox(
                                 value=False,
                                 label='Auto Gen Video',
@@ -384,7 +448,7 @@ class ChatBotUI(object):
                                                     visible=True)
 
                 with gr.Row():
-                    inst = """
+                    self.chatbot_inst = """
                        **Instruction**:
 
                        1. Click 'Upload' button to upload one or more images as input images.
@@ -398,12 +462,25 @@ class ChatBotUI(object):
                        8. If you find our work valuable, we invite you to refer to the [ACE Page](https://ali-vilab.github.io/ace-page/) for comprehensive information.
 
                     """
-                    gr.Markdown(value=inst)
+
+                    self.legacy_inst = """
+                       **Instruction**:
+
+                       1. You can edit the image by uploading it; if no image is uploaded, an image will be generated from text..
+                       2. Enter '@' in the text box will exhibit all images in the gallery.
+                       3. Select the image you wish to edit from the gallery, and its Image ID will be displayed in the text box.
+                       4. **Important** To render text on an image, please ensure to include a space between each letter. For instance, "add text 'g i r l' on the mask area of @xxxxx".
+                       5. To perform multi-step editing, partial editing, inpainting, outpainting, and other operations, please click the Chatbot Checkbox to enable the conversational editing mode and follow the relevant instructions..
+                       6. If you find our work valuable, we invite you to refer to the [ACE Page](https://ali-vilab.github.io/ace-page/) for comprehensive information.
+
+                    """
+
+                    self.instruction = gr.Markdown(value=self.legacy_inst)
 
                 with gr.Row(variant='panel',
                             equal_height=True,
                             show_progress=False):
-                    with gr.Column(scale=1, min_width=100):
+                    with gr.Column(scale=1, min_width=100, visible=False) as self.upload_panel:
                         self.upload_btn = gr.Button(value=upload_sty +
                                                     ' Upload',
                                                     variant='secondary')
@@ -413,12 +490,16 @@ class ChatBotUI(object):
                             label='Instruction',
                             container=False)
                     with gr.Column(scale=1, min_width=100):
-                        self.chat_btn = gr.Button(value=chat_sty + ' Chat',
+                        self.chat_btn = gr.Button(value='Generate',
                                                   variant='primary')
                     with gr.Column(scale=1, min_width=100):
                         self.retry_btn = gr.Button(value=refresh_sty +
                                                    ' Retry',
                                                    variant='secondary')
+                    with gr.Column(scale=1, min_width=100):
+                        self.mode_checkbox = gr.Checkbox(
+                            value=False,
+                            label='ChatBot')
                     with gr.Column(scale=(1 if self.enable_i2v else 0),
                                    min_width=0):
                         self.video_gen_btn = gr.Button(value=video_sty +
@@ -453,19 +534,78 @@ class ChatBotUI(object):
                 lock.acquire()
                 del self.pipe
                 torch.cuda.empty_cache()
-                model_cfg = Config(load=True,
-                                   cfg_file=self.model_choices[model_name])
+                torch.cuda.ipc_collect()
                 self.pipe = ACEInference()
-                self.pipe.init_from_cfg(model_cfg)
+                self.pipe.init_from_cfg(self.model_choices[model_name])
                 self.model_name = model_name
                 lock.release()
 
-            return model_name, gr.update(), gr.update()
+            return (model_name, gr.update(), gr.update(),
+                    gr.Slider(
+                              value=self.pipe.input.get("sample_steps", 20),
+                              visible=self.pipe.input.get("sample_steps", None) is not None),
+                    gr.Slider(
+                        value=self.pipe.input.get("guide_scale", 4.5),
+                        visible=self.pipe.input.get("guide_scale", None) is not None),
+                    gr.Slider(
+                              value=self.pipe.input.get("guide_rescale", 0.5),
+                              visible=self.pipe.input.get("guide_rescale", None) is not None),
+                    gr.Slider(
+                        value=self.pipe.input.get("output_height", 1024),
+                        visible=self.pipe.input.get("output_height", None) is not None),
+                    gr.Slider(
+                        value=self.pipe.input.get("output_width", 1024),
+                        visible=self.pipe.input.get("output_width", None) is not None),
+                    gr.Textbox(
+                        value=self.pipe.input.get("refiner_prompt", ""),
+                        visible=self.pipe.input.get("refiner_prompt", None) is not None),
+                    gr.Slider(
+                              value=self.pipe.input.get("refiner_scale", -1),
+                              visible=self.pipe.input.get("refiner_scale", None) is not None
+                        ),
+                    gr.Checkbox(
+                        value=self.pipe.input.get("use_ace", True),
+                        visible=self.pipe.input.get("use_ace", None) is not None
+                    )
+                    )
 
         self.model_name_dd.change(
             change_model,
             inputs=[self.model_name_dd],
-            outputs=[self.model_name_dd, self.chatbot, self.text])
+            outputs=[
+                self.model_name_dd, self.chatbot, self.text,
+                self.step,
+                self.cfg_scale, self.rescale, self.output_height,
+                self.output_width, self.refiner_prompt, self.refiner_scale,
+                self.use_ace])
+
+
+        def mode_change(mode_check):
+            if mode_check:
+                # ChatBot
+                return (
+                    gr.Row(visible=False),
+                    gr.Row(visible=True),
+                    gr.Button(value='Generate'),
+                    gr.State(value='chatbot'),
+                    gr.Column(visible=True),
+                    gr.Markdown(value=self.chatbot_inst)
+                )
+            else:
+                # Legacy
+                return (
+                    gr.Row(visible=True),
+                    gr.Row(visible=False),
+                    gr.Button(value=chat_sty + ' Chat'),
+                    gr.State(value='legacy'),
+                    gr.Column(visible=False),
+                    gr.Markdown(value=self.legacy_inst)
+                )
+        self.mode_checkbox.change(mode_change, inputs=[self.mode_checkbox],
+                                  outputs=[self.legacy_group, self.chat_group,
+                                           self.chat_btn, self.ui_mode,
+                                           self.upload_panel, self.instruction])
+
 
         ########################################
         def generate_gallery(text, images):
@@ -522,6 +662,9 @@ class ChatBotUI(object):
                            fps,
                            seed,
                            progress=gr.Progress(track_tqdm=True)):
+
+            from diffusers.utils import export_to_video
+
             generator = torch.Generator(device='cuda').manual_seed(seed)
             img_ids = re.findall('@(.*?)[ ,;.?$]', message)
             if len(img_ids) == 0:
@@ -592,7 +735,11 @@ class ChatBotUI(object):
             outputs=[self.history, self.chatbot, self.text, self.gallery])
 
         ########################################
-        def run_chat(message,
+        def run_chat(
+                     message,
+                     legacy_image,
+                     ui_mode,
+                     use_ace,
                      extend_prompt,
                      history,
                      images,
@@ -601,6 +748,8 @@ class ChatBotUI(object):
                      negative_prompt,
                      cfg_scale,
                      rescale,
+                     refiner_prompt,
+                     refiner_scale,
                      step,
                      seed,
                      output_h,
@@ -612,12 +761,25 @@ class ChatBotUI(object):
                      video_fps,
                      video_seed,
                      progress=gr.Progress(track_tqdm=True)):
+            legacy_img_ids = []
+            if ui_mode == 'legacy':
+                if legacy_image is not None:
+                    history, images, img_id = self.add_uploaded_image_to_history(
+                        legacy_image, history, images)
+                    legacy_img_ids.append(img_id)
             retry_msg = message
             gen_id = get_md5(message)[:12]
             save_path = os.path.join(self.cache_dir, f'{gen_id}.png')
 
             img_ids = re.findall('@(.*?)[ ,;.?$]', message)
             history_io = None
+
+            if len(img_ids) < 1:
+                img_ids = legacy_img_ids
+                for img_id in img_ids:
+                    if f'@{img_id}' not in message:
+                        message = f'@{img_id} ' + message
+
             new_message = message
 
             if len(img_ids) > 0:
@@ -676,6 +838,9 @@ class ChatBotUI(object):
                 guide_scale=cfg_scale,
                 guide_rescale=rescale,
                 seed=seed,
+                refiner_prompt=refiner_prompt,
+                refiner_scale=refiner_scale,
+                use_ace=use_ace
             )
 
             img = imgs[0]
@@ -784,21 +949,25 @@ class ChatBotUI(object):
             while len(history) >= self.max_msgs:
                 history.pop(0)
 
-            return history, images, history_result, self.get_history(
-                history), gr.update(value=''), gr.update(
-                    visible=False), retry_msg
+            return (history, images, gr.Image(value=save_path),
+                    history_result, self.get_history(
+                history), gr.update(), gr.update(
+                    visible=False), retry_msg)
 
         chat_inputs = [
+            self.legacy_image_uploader, self.ui_mode, self.use_ace,
             self.extend_prompt, self.history, self.images, self.use_history,
             self.history_result, self.negative_prompt, self.cfg_scale,
-            self.rescale, self.step, self.seed, self.output_height,
+            self.rescale, self.refiner_prompt, self.refiner_scale,
+            self.step, self.seed, self.output_height,
             self.output_width, self.video_auto, self.video_step,
             self.video_frames, self.video_cfg_scale, self.video_fps,
             self.video_seed
         ]
 
         chat_outputs = [
-            self.history, self.images, self.history_result, self.chatbot,
+            self.history, self.images, self.legacy_image_viewer,
+            self.history_result, self.chatbot,
             self.text, self.gallery, self.retry_msg
         ]
 
@@ -832,9 +1001,13 @@ class ChatBotUI(object):
                     w = int(w / ratio)
                 img = img.resize((w, h))
                 edit_image.append(img)
+                if img_mask is not None:
+                    img_mask = img_mask if np.sum(np.array(img_mask)) > 0 else None
                 edit_image_mask.append(
                     img_mask if img_mask is not None else None)
                 edit_task.append(task)
+                if ref1 is not None:
+                    ref1 = ref1 if np.sum(np.array(ref1)) > 0 else None
                 if ref1 is not None:
                     edit_image.append(ref1)
                     edit_image_mask.append(None)
@@ -859,6 +1032,8 @@ class ChatBotUI(object):
                 prompt=[prompt] * img_num,
                 negative_prompt=[''] * img_num,
                 seed=seed,
+                refiner_prompt=self.pipe.input.get("refiner_prompt", ""),
+                refiner_scale=self.pipe.input.get("refiner_scale", 0.0),
             )
 
             img = imgs[0]
@@ -868,8 +1043,13 @@ class ChatBotUI(object):
             img_str = f'<img src="data:image/png;base64,{img_b64}" style="pointer-events: none;">'
             history = [(prompt,
                         f'{pre_info} The generated image is:\n {img_str}')]
+
+            img_id = get_md5(img_b64)[:12]
+            save_path = os.path.join(self.cache_dir, f'{img_id}.png')
+            img.convert('RGB').save(save_path)
+
             return self.get_history(history), gr.update(value=''), gr.update(
-                visible=False), gr.update(value=-1)
+                visible=False), gr.Image(value=save_path), gr.update(value=-1)
 
         with self.eg:
             self.example_task = gr.Text(label='Task Name',
@@ -895,8 +1075,9 @@ class ChatBotUI(object):
                     self.example_task, self.example_image, self.example_mask,
                     self.example_ref_im1, self.text, self.seed
                 ],
-                outputs=[self.chatbot, self.text, self.gallery, self.seed],
+                outputs=[self.chatbot, self.text, self.gallery, self.legacy_image_viewer, self.seed],
                 examples_per_page=4,
+                cache_examples=False,
                 run_on_click=True)
 
         ########################################
@@ -904,14 +1085,16 @@ class ChatBotUI(object):
             return (gr.update(visible=True,
                               scale=1), gr.update(visible=True, scale=1),
                     gr.update(visible=True), gr.update(visible=False),
-                    gr.update(visible=False), gr.update(visible=False))
+                    gr.update(visible=False), gr.update(visible=False),
+                    gr.update(visible=True))
 
         self.upload_btn.click(upload_image,
                               inputs=[],
                               outputs=[
                                   self.chat_page, self.editor_page,
                                   self.upload_tab, self.edit_tab,
-                                  self.image_view_tab, self.video_view_tab
+                                  self.image_view_tab, self.video_view_tab,
+                                  self.upload_tabs
                               ])
 
         ########################################
@@ -926,13 +1109,19 @@ class ChatBotUI(object):
                 ]
                 if len(imgs) > 0:
                     if len(imgs) == 2:
-                        view_img = copy.deepcopy(imgs)
+                        if self.gradio_version >= '5.0.0':
+                            view_img = copy.deepcopy(imgs[-1])
+                        else:
+                            view_img = copy.deepcopy(imgs)
                         edit_img = copy.deepcopy(imgs[-1])
                     else:
-                        view_img = [
-                            copy.deepcopy(imgs[-1]),
-                            copy.deepcopy(imgs[-1])
-                        ]
+                        if self.gradio_version >= '5.0.0':
+                            view_img = copy.deepcopy(imgs[-1])
+                        else:
+                            view_img = [
+                                copy.deepcopy(imgs[-1]),
+                                copy.deepcopy(imgs[-1])
+                            ]
                         edit_img = copy.deepcopy(imgs[-1])
 
                     return (gr.update(visible=True,
@@ -941,11 +1130,12 @@ class ChatBotUI(object):
                             gr.update(visible=False), gr.update(visible=True),
                             gr.update(visible=True), gr.update(visible=False),
                             gr.update(value=edit_img),
-                            gr.update(value=view_img), gr.update(value=None))
+                            gr.update(value=view_img), gr.update(value=None),
+                            gr.update(visible=True))
                 else:
                     return (gr.update(), gr.update(), gr.update(), gr.update(),
                             gr.update(), gr.update(), gr.update(), gr.update(),
-                            gr.update())
+                            gr.update(), gr.update())
             elif isinstance(evt.value, dict) and evt.value.get(
                     'component', '') == 'video':
                 value = evt.value['value']['video']['path']
@@ -953,11 +1143,12 @@ class ChatBotUI(object):
                                   scale=1), gr.update(visible=True, scale=1),
                         gr.update(visible=False), gr.update(visible=False),
                         gr.update(visible=False), gr.update(visible=True),
-                        gr.update(), gr.update(), gr.update(value=value))
+                        gr.update(), gr.update(), gr.update(value=value),
+                        gr.update())
             else:
                 return (gr.update(), gr.update(), gr.update(), gr.update(),
                         gr.update(), gr.update(), gr.update(), gr.update(),
-                        gr.update())
+                        gr.update(), gr.update())
 
         self.chatbot.select(edit_image,
                             outputs=[
@@ -965,16 +1156,17 @@ class ChatBotUI(object):
                                 self.upload_tab, self.edit_tab,
                                 self.image_view_tab, self.video_view_tab,
                                 self.image_editor, self.image_viewer,
-                                self.video_viewer
+                                self.video_viewer, self.edit_tabs
                             ])
 
-        self.image_viewer.change(lambda x: x,
-                                 inputs=self.image_viewer,
-                                 outputs=self.image_viewer)
+        if self.gradio_version < '5.0.0':
+            self.image_viewer.change(lambda x: x,
+                                     inputs=self.image_viewer,
+                                     outputs=self.image_viewer)
 
         ########################################
         def submit_upload_image(image, history, images):
-            history, images = self.add_uploaded_image_to_history(
+            history, images, _ = self.add_uploaded_image_to_history(
                 image, history, images)
             return gr.update(visible=False), gr.update(
                 visible=True), gr.update(
@@ -1207,13 +1399,13 @@ class ChatBotUI(object):
         history.append(
             (None,
              f'This is uploaded image:\n {img_str} image ID is: {img_id}'))
-        return history, images
+        return history, images, img_id
 
 
 def run_gr(cfg):
     with gr.Blocks() as demo:
         chatbot = ChatBotUI(cfg)
-        chatbot.create_bot_ui()
+        chatbot.create_ui()
         chatbot.set_callbacks()
         demo.launch(server_name='0.0.0.0',
                     server_port=cfg.args.server_port,
@@ -1225,6 +1417,7 @@ if __name__ == '__main__':
     parser.add_argument('--server_port',
                         dest='server_port',
                         help='',
+                        type=int,
                         default=2345)
     parser.add_argument('--root_path', dest='root_path', help='', default='')
     cfg = Config(load=True, parser_ins=parser)

scepter/studio/chatbot/example.py

@@ -3,6 +3,7 @@
 import os
 
 from scepter.modules.utils.file_system import FS
+from PIL import Image
 
 
 def download_image(image, local_path=None):
@@ -10,44 +11,56 @@ def download_image(image, local_path=None):
         local_path = FS.get_from(image, local_path=local_path)
     return local_path
 
+def blank_image():
+    return Image.new('RGBA', (128, 128), (0, 0, 0, 0))
+
+
 
 def get_examples(cache_dir):
     print('Downloading Examples ...')
+    bl_img = blank_image()
     examples = [
         [
             'Facial Editing',
             download_image(
                 'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/e33edc106953.png?raw=true',
-                os.path.join(cache_dir, 'examples/e33edc106953.png')), None,
-            None, '{image} let the man smile', 6666
+                os.path.join(cache_dir, 'examples/e33edc106953.png')), bl_img,
+                bl_img, '{image} let the man smile', 6666
         ],
         [
             'Facial Editing',
             download_image(
                 'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/5d2bcc91a3e9.png?raw=true',
-                os.path.join(cache_dir, 'examples/5d2bcc91a3e9.png')), None,
-            None, 'let the man in {image} wear sunglasses', 9999
+                os.path.join(cache_dir, 'examples/5d2bcc91a3e9.png')), bl_img,
+                bl_img, 'let the man in {image} wear sunglasses', 9999
+        ],
+        [
+            'Facial Editing',
+            download_image(
+                'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/5d2bcc91a3e9.png?raw=true',
+                os.path.join(cache_dir, 'examples/5d2bcc91a3e9.png')), bl_img,
+            bl_img, 'let the man in {image} wear sunglasses', 9999
         ],
         [
             'Facial Editing',
             download_image(
                 'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/3a52eac708bd.png?raw=true',
-                os.path.join(cache_dir, 'examples/3a52eac708bd.png')), None,
-            None, '{image} red hair', 9999
+                os.path.join(cache_dir, 'examples/3a52eac708bd.png')), bl_img,
+            bl_img, '{image} red hair', 9999
         ],
         [
             'Facial Editing',
             download_image(
                 'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/3f4dc464a0ea.png?raw=true',
-                os.path.join(cache_dir, 'examples/3f4dc464a0ea.png')), None,
-            None, '{image} let the man serious', 99999
+                os.path.join(cache_dir, 'examples/3f4dc464a0ea.png')), bl_img,
+            bl_img, '{image} let the man serious', 99999
         ],
         [
             'Controllable Generation',
             download_image(
                 'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/131ca90fd2a9.png?raw=true',
                 os.path.join(cache_dir,
-                             'examples/131ca90fd2a9.png')), None, None,
+                             'examples/131ca90fd2a9.png')), bl_img, bl_img,
             '"A person sits contemplatively on the ground, surrounded by falling autumn leaves. Dressed in a green sweater and dark blue pants, they rest their chin on their hand, exuding a relaxed demeanor. Their stylish checkered slip-on shoes add a touch of flair, while a black purse lies in their lap. The backdrop of muted brown enhances the warm, cozy atmosphere of the scene." , generate the image that corresponds to the given scribble {image}.',
             613725
         ],
@@ -59,7 +72,7 @@ def get_examples(cache_dir):
             download_image(
                 'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/33e9f27c2c48_mask.png?raw=true',
                 os.path.join(cache_dir,
-                             'examples/33e9f27c2c48_mask.png')), None,
+                             'examples/33e9f27c2c48_mask.png')), bl_img,
             'Put the text "C A T" at the position marked by mask in the {image}',
             6666
         ],
@@ -67,7 +80,7 @@ def get_examples(cache_dir):
             'Style Transfer',
             download_image(
                 'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/9e73e7eeef55.png?raw=true',
-                os.path.join(cache_dir, 'examples/9e73e7eeef55.png')), None,
+                os.path.join(cache_dir, 'examples/9e73e7eeef55.png')), bl_img,
             download_image(
                 'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/2e02975293d6.png?raw=true',
                 os.path.join(cache_dir, 'examples/2e02975293d6.png')),
@@ -81,7 +94,7 @@ def get_examples(cache_dir):
             download_image(
                 'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/f2b22c08be3f_mask.png?raw=true',
                 os.path.join(cache_dir,
-                             'examples/f2b22c08be3f_mask.png')), None,
+                             'examples/f2b22c08be3f_mask.png')), bl_img,
             'Could the {image} be widened within the space designated by mask, while retaining the original?',
             6666
         ],
@@ -89,57 +102,57 @@ def get_examples(cache_dir):
             'Image Segmentation',
             download_image(
                 'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/db3ebaa81899.png?raw=true',
-                os.path.join(cache_dir, 'examples/db3ebaa81899.png')), None,
-            None, '{image} Segmentation', 6666
+                os.path.join(cache_dir, 'examples/db3ebaa81899.png')), bl_img,
+            bl_img, '{image} Segmentation', 6666
         ],
         [
             'Depth Estimation',
             download_image(
                 'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/f1927c4692ba.png?raw=true',
-                os.path.join(cache_dir, 'examples/f1927c4692ba.png')), None,
-            None, '{image} Depth Estimation', 6666
+                os.path.join(cache_dir, 'examples/f1927c4692ba.png')), bl_img,
+            bl_img, '{image} Depth Estimation', 6666
         ],
         [
             'Pose Estimation',
             download_image(
                 'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/014e5bf3b4d1.png?raw=true',
-                os.path.join(cache_dir, 'examples/014e5bf3b4d1.png')), None,
-            None, '{image} distinguish the poses of the figures', 999999
+                os.path.join(cache_dir, 'examples/014e5bf3b4d1.png')), bl_img,
+            bl_img, '{image} distinguish the poses of the figures', 999999
         ],
         [
             'Scribble Extraction',
             download_image(
                 'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/5f59a202f8ac.png?raw=true',
-                os.path.join(cache_dir, 'examples/5f59a202f8ac.png')), None,
-            None, 'Generate a scribble of {image}, please.', 6666
+                os.path.join(cache_dir, 'examples/5f59a202f8ac.png')), bl_img,
+            bl_img, 'Generate a scribble of {image}, please.', 6666
         ],
         [
             'Mosaic',
             download_image(
                 'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/3a2f52361eea.png?raw=true',
-                os.path.join(cache_dir, 'examples/3a2f52361eea.png')), None,
-            None, 'Adapt {image} into a mosaic representation.', 6666
+                os.path.join(cache_dir, 'examples/3a2f52361eea.png')), bl_img,
+            bl_img, 'Adapt {image} into a mosaic representation.', 6666
         ],
         [
             'Edge map Extraction',
             download_image(
                 'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/b9d1e519d6e5.png?raw=true',
-                os.path.join(cache_dir, 'examples/b9d1e519d6e5.png')), None,
-            None, 'Get the edge-enhanced result for {image}.', 6666
+                os.path.join(cache_dir, 'examples/b9d1e519d6e5.png')), bl_img,
+            bl_img, 'Get the edge-enhanced result for {image}.', 6666
         ],
         [
             'Grayscale',
             download_image(
                 'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/c4ebbe2ba29b.png?raw=true',
-                os.path.join(cache_dir, 'examples/c4ebbe2ba29b.png')), None,
-            None, 'transform {image} into a black and white one', 6666
+                os.path.join(cache_dir, 'examples/c4ebbe2ba29b.png')), bl_img,
+            bl_img, 'transform {image} into a black and white one', 6666
         ],
         [
             'Contour Extraction',
             download_image(
                 'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/19652d0f6c4b.png?raw=true',
                 os.path.join(cache_dir,
-                             'examples/19652d0f6c4b.png')), None, None,
+                             'examples/19652d0f6c4b.png')), bl_img, bl_img,
             'Would you be able to make a contour picture from {image} for me?',
             6666
         ],
@@ -148,7 +161,7 @@ def get_examples(cache_dir):
             download_image(
                 'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/249cda2844b7.png?raw=true',
                 os.path.join(cache_dir,
-                             'examples/249cda2844b7.png')), None, None,
+                             'examples/249cda2844b7.png')), bl_img, bl_img,
             'Following the segmentation outcome in mask of {image}, develop a real-life image using the explanatory note in "a mighty cat lying on the bed”.',
             6666
         ],
@@ -157,7 +170,7 @@ def get_examples(cache_dir):
             download_image(
                 'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/411f6c4b8e6c.png?raw=true',
                 os.path.join(cache_dir,
-                             'examples/411f6c4b8e6c.png')), None, None,
+                             'examples/411f6c4b8e6c.png')), bl_img, bl_img,
             'use the depth map {image} and the text caption "a cut white cat" to create a corresponding graphic image',
             999999
         ],
@@ -166,7 +179,7 @@ def get_examples(cache_dir):
             download_image(
                 'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/a35c96ed137a.png?raw=true',
                 os.path.join(cache_dir,
-                             'examples/a35c96ed137a.png')), None, None,
+                             'examples/a35c96ed137a.png')), bl_img, bl_img,
             'help translate this posture schema {image} into a colored image based on the context I provided "A beautiful woman Climbing the climbing wall, wearing a harness and climbing gear, skillfully maneuvering up the wall with her back to the camera, with a safety rope."',
             3599999
         ],
@@ -175,7 +188,7 @@ def get_examples(cache_dir):
             download_image(
                 'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/dcb2fc86f1ce.png?raw=true',
                 os.path.join(cache_dir,
-                             'examples/dcb2fc86f1ce.png')), None, None,
+                             'examples/dcb2fc86f1ce.png')), bl_img, bl_img,
             'Transform and generate an image using mosaic {image} and "Monarch butterflies gracefully perch on vibrant purple flowers, showcasing their striking orange and black wings in a lush garden setting." description',
             6666
         ],
@@ -184,7 +197,7 @@ def get_examples(cache_dir):
             download_image(
                 'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/4cd4ee494962.png?raw=true',
                 os.path.join(cache_dir,
-                             'examples/4cd4ee494962.png')), None, None,
+                             'examples/4cd4ee494962.png')), bl_img, bl_img,
             'make this {image} colorful as per the "beautiful sunflowers"',
             6666
         ],
@@ -193,7 +206,7 @@ def get_examples(cache_dir):
             download_image(
                 'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/a47e3a9cd166.png?raw=true',
                 os.path.join(cache_dir,
-                             'examples/a47e3a9cd166.png')), None, None,
+                             'examples/a47e3a9cd166.png')), bl_img, bl_img,
             'Take the edge conscious {image} and the written guideline "A whimsical animated character is depicted holding a delectable cake adorned with blue and white frosting and a drizzle of chocolate. The character wears a yellow headband with a bow, matching a cozy yellow sweater. Her dark hair is styled in a braid, tied with a yellow ribbon. With a golden fork in hand, she stands ready to enjoy a slice, exuding an air of joyful anticipation. The scene is creatively rendered with a charming and playful aesthetic." and produce a realistic image.',
             613725
         ],
@@ -202,7 +215,7 @@ def get_examples(cache_dir):
             download_image(
                 'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/d890ed8a3ac2.png?raw=true',
                 os.path.join(cache_dir,
-                             'examples/d890ed8a3ac2.png')), None, None,
+                             'examples/d890ed8a3ac2.png')), bl_img, bl_img,
             'creating a vivid image based on {image} and description "This image features a delicious rectangular tart with a flaky, golden-brown crust. The tart is topped with evenly sliced tomatoes, layered over a creamy cheese filling. Aromatic herbs are sprinkled on top, adding a touch of green and enhancing the visual appeal. The background includes a soft, textured fabric and scattered white flowers, creating an elegant and inviting presentation. Bright red tomatoes in the upper right corner hint at the fresh ingredients used in the dish."',
             6666
         ],
@@ -211,7 +224,7 @@ def get_examples(cache_dir):
             download_image(
                 'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/0844a686a179.png?raw=true',
                 os.path.join(cache_dir,
-                             'examples/0844a686a179.png')), None, None,
+                             'examples/0844a686a179.png')), bl_img, bl_img,
             'Eliminate noise interference in {image} and maximize the crispness to obtain superior high-definition quality',
             6666
         ],
@@ -223,7 +236,7 @@ def get_examples(cache_dir):
             download_image(
                 'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/fa91b6b7e59b_mask.png?raw=true',
                 os.path.join(cache_dir,
-                             'examples/fa91b6b7e59b_mask.png')), None,
+                             'examples/fa91b6b7e59b_mask.png')), bl_img,
             'Ensure to overhaul the parts of the {image} indicated by the mask.',
             6666
         ],
@@ -235,7 +248,7 @@ def get_examples(cache_dir):
             download_image(
                 'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/632899695b26_mask.png?raw=true',
                 os.path.join(cache_dir,
-                             'examples/632899695b26_mask.png')), None,
+                             'examples/632899695b26_mask.png')), bl_img,
             'Refashion the mask portion of {image} in accordance with "A yellow egg with a smiling face painted on it"',
             6666
         ],
@@ -244,7 +257,7 @@ def get_examples(cache_dir):
             download_image(
                 'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/354d17594afe.png?raw=true',
                 os.path.join(cache_dir,
-                             'examples/354d17594afe.png')), None, None,
+                             'examples/354d17594afe.png')), bl_img, bl_img,
             '{image} change the dog\'s posture to walking in the water, and change the background to green plants and a pond.',
             6666
         ],
@@ -253,7 +266,7 @@ def get_examples(cache_dir):
             download_image(
                 'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/38946455752b.png?raw=true',
                 os.path.join(cache_dir,
-                             'examples/38946455752b.png')), None, None,
+                             'examples/38946455752b.png')), bl_img, bl_img,
             '{image} change the color of the dress from white to red and the model\'s hair color red brown to blonde.Other parts remain unchanged',
             6669
         ],
@@ -262,7 +275,7 @@ def get_examples(cache_dir):
             download_image(
                 'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/3ba5202f0cd8.png?raw=true',
                 os.path.join(cache_dir,
-                             'examples/3ba5202f0cd8.png')), None, None,
+                             'examples/3ba5202f0cd8.png')), bl_img, bl_img,
             'Keep the same facial feature in @3ba5202f0cd8, change the woman\'s clothing from a Blue denim jacket to a white turtleneck sweater and adjust her posture so that she is supporting her chin with both hands. Other aspects, such as background, hairstyle, facial expression, etc, remain unchanged.',
             99999
         ],
@@ -270,22 +283,22 @@ def get_examples(cache_dir):
             'Facial Editing',
             download_image(
                 'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/369365b94725.png?raw=true',
-                os.path.join(cache_dir, 'examples/369365b94725.png')), None,
-            None, '{image} Make her looking at the camera', 6666
+                os.path.join(cache_dir, 'examples/369365b94725.png')), bl_img,
+            bl_img, '{image} Make her looking at the camera', 6666
         ],
         [
             'Facial Editing',
             download_image(
                 'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/92751f2e4a0e.png?raw=true',
-                os.path.join(cache_dir, 'examples/92751f2e4a0e.png')), None,
-            None, '{image} Remove the smile from his face', 9899999
+                os.path.join(cache_dir, 'examples/92751f2e4a0e.png')), bl_img,
+            bl_img, '{image} Remove the smile from his face', 9899999
         ],
         [
             'Remove Text',
             download_image(
                 'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/8530a6711b2e.png?raw=true',
-                os.path.join(cache_dir, 'examples/8530a6711b2e.png')), None,
-            None, 'Aim to remove any textual element in {image}', 6666
+                os.path.join(cache_dir, 'examples/8530a6711b2e.png')), bl_img,
+            bl_img, 'Aim to remove any textual element in {image}', 6666
         ],
         [
             'Remove Text',
@@ -295,7 +308,7 @@ def get_examples(cache_dir):
             download_image(
                 'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/c4d7fb28f8f6_mask.png?raw=true',
                 os.path.join(cache_dir,
-                             'examples/c4d7fb28f8f6_mask.png')), None,
+                             'examples/c4d7fb28f8f6_mask.png')), bl_img,
             'Rub out any text found in the mask sector of the {image}.', 6666
         ],
         [
@@ -303,7 +316,7 @@ def get_examples(cache_dir):
             download_image(
                 'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/e2f318fa5e5b.png?raw=true',
                 os.path.join(cache_dir,
-                             'examples/e2f318fa5e5b.png')), None, None,
+                             'examples/e2f318fa5e5b.png')), bl_img, bl_img,
             'Remove the unicorn in this {image}, ensuring a smooth edit.',
             99999
         ],
@@ -315,7 +328,7 @@ def get_examples(cache_dir):
             download_image(
                 'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/1ae96d8aca00_mask.png?raw=true',
                 os.path.join(cache_dir, 'examples/1ae96d8aca00_mask.png')),
-            None, 'Discard the contents of the mask area from {image}.', 99999
+            bl_img, 'Discard the contents of the mask area from {image}.', 99999
         ],
         [
             'Add Object',
@@ -325,22 +338,22 @@ def get_examples(cache_dir):
             download_image(
                 'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/80289f48e511_mask.png?raw=true',
                 os.path.join(cache_dir,
-                             'examples/80289f48e511_mask.png')), None,
+                             'examples/80289f48e511_mask.png')), bl_img,
             'add a Hot Air Balloon into the {image}, per the mask', 613725
         ],
         [
             'Style Transfer',
             download_image(
                 'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/d725cb2009e8.png?raw=true',
-                os.path.join(cache_dir, 'examples/d725cb2009e8.png')), None,
-            None, 'Change the style of {image} to colored pencil style', 99999
+                os.path.join(cache_dir, 'examples/d725cb2009e8.png')), bl_img,
+            bl_img, 'Change the style of {image} to colored pencil style', 99999
         ],
         [
             'Style Transfer',
             download_image(
                 'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/e0f48b3fd010.png?raw=true',
-                os.path.join(cache_dir, 'examples/e0f48b3fd010.png')), None,
-            None, 'make {image} to Walt Disney Animation style', 99999
+                os.path.join(cache_dir, 'examples/e0f48b3fd010.png')), bl_img,
+            bl_img, 'make {image} to Walt Disney Animation style', 99999
         ],
         [
             'Try On',
@@ -359,8 +372,8 @@ def get_examples(cache_dir):
             'Workflow',
             download_image(
                 'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/cb85353c004b.png?raw=true',
-                os.path.join(cache_dir, 'examples/cb85353c004b.png')), None,
-            None, '<workflow> ice cream {image}', 99999
+                os.path.join(cache_dir, 'examples/cb85353c004b.png')), bl_img,
+            bl_img, '<workflow> ice cream {image}', 99999
         ],
     ]
     print('Finish. Start building UI ...')

scepter/studio/inference/inference_manager/infer_runer.py

@@ -6,6 +6,7 @@ from scepter.modules.inference.largen_inference import LargenInference
 from scepter.modules.inference.pixart_inference import PixArtInference
 from scepter.modules.inference.sd3_inference import SD3Inference
 from scepter.modules.inference.stylebooth_inference import StyleboothInference
+from scepter.modules.inference.cogvideox_inference import CogVideoXInference
 from scepter.modules.utils.logger import get_logger
 
 
@@ -111,6 +112,8 @@ class PipelineManager():
             PipelineBuilder = SD3Inference
         elif pipeline_name.startswith('FLUX'):
             PipelineBuilder = FluxInference
+        elif pipeline_name.startswith('COGVIDEO'):
+            PipelineBuilder = CogVideoXInference
         else:
             PipelineBuilder = DiffusionInference
         new_inference = PipelineBuilder(logger=self.logger)

scepter/studio/inference/inference_ui/component_names.py

@@ -98,6 +98,8 @@ class DiffusionUIName():
             self.sample = 'Sampler'
             self.sample_steps = 'Sample Steps'
             self.image_number = 'Images Number'
+            self.num_frames = 'Number of Frames'
+            self.fps = 'Number of FPS'
             self.resolutions_height = 'Output Height'
             self.resolutions_width = 'Output Width'
             self.negative_prompt = 'Negative Prompt'
@@ -121,6 +123,8 @@ class DiffusionUIName():
             self.sample = '采样器'
             self.sample_steps = '采样步数'
             self.image_number = '图片数量'
+            self.num_frames = '视频帧数量'
+            self.fps = '视频帧率'
             self.resolutions_height = '输出高度'
             self.resolutions_width = '输出宽度'
             self.negative_prompt = '负向提示'

scepter/studio/inference/inference_ui/control_ui.py

@@ -100,6 +100,7 @@ class ControlUI(UIBase):
                             label=self.component_names.control_model,
                             choices=self.controller_choices,
                             value=self.controller_default,
+                            allow_custom_value=True,
                             interactive=True)
                 with gr.Column(scale=1, min_width=0):
                     self.cond_button = gr.Button('Extract')

scepter/studio/inference/inference_ui/diffusion_ui.py

@@ -26,7 +26,7 @@ class DiffusionUI(UIBase):
         self.default_resolutions = pipe_manager.pipeline_level_modules[
             now_pipeline].paras.RESOLUTIONS
         self.default_input = pipe_manager.pipeline_level_modules[
-            now_pipeline].input
+            now_pipeline].input_cfg
 
         self.diffusion_paras = self.load_all_paras()
         # deal with resolution
@@ -66,7 +66,6 @@ class DiffusionUI(UIBase):
                     if value is not None and cur_default.get(
                             key.lower()) not in value:
                         value.append(cur_default.get(key.lower()))
-
         return diffusion_paras
 
     def load_all_paras(self):
@@ -115,22 +114,15 @@ class DiffusionUI(UIBase):
                     label=self.component_names.resolutions_height,
                     choices=[key for key in self.cur_h_level_dict.keys()],
                     value=default_res[0],
+                    allow_custom_value=True,
                     interactive=True)
             with gr.Column(scale=1):
                 self.output_width = gr.Dropdown(
                     label=self.component_names.resolutions_width,
                     choices=self.cur_h_level_dict[default_res[0]],
                     value=default_res[1],
+                    allow_custom_value=True,
                     interactive=True)
-        with gr.Row(equal_height=True):
-            self.image_number = gr.Slider(
-                label=self.component_names.image_number,
-                minimum=self.cur_paras.SAMPLES.get('MIN', 1),
-                maximum=self.cur_paras.SAMPLES.get('MAX', 4),
-                step=1,
-                value=self.cur_paras.SAMPLES.get('DEFAULT', 1),
-                visible=self.cur_paras.SAMPLES.get('VISIBLE', True),
-                interactive=True)
         with gr.Row(equal_height=True):
             self.sample_steps = gr.Slider(
                 label=self.component_names.sample_steps,
@@ -139,7 +131,6 @@ class DiffusionUI(UIBase):
                 step=1,
                 value=self.cur_paras.SAMPLE_STEPS.get('DEFAULT', 30),
                 interactive=True)
-
             self.guide_scale = gr.Slider(
                 label=self.component_names.guide_scale,
                 minimum=self.cur_paras.GUIDE_SCALE.get('MIN', 1),
@@ -156,6 +147,32 @@ class DiffusionUI(UIBase):
                 value=self.cur_paras.GUIDE_RESCALE.get('DEFAULT', 0.5),
                 visible=self.cur_paras.GUIDE_RESCALE.get('VISIBLE', True),
                 interactive=True)
+        with gr.Row(equal_height=True):
+            self.fps = gr.Slider(
+                label=self.component_names.fps,
+                minimum=self.cur_paras.FPS.get('MIN', 1),
+                maximum=self.cur_paras.FPS.get('MAX', 50),
+                step=1,
+                value=self.cur_paras.FPS.get('DEFAULT', 8),
+                visible=self.cur_paras.FPS.get('VISIBLE', True),
+                interactive=True)
+            self.num_frames = gr.Slider(
+                label=self.component_names.num_frames,
+                minimum=self.cur_paras.NUM_FRAMES.get('MIN', 1),
+                maximum=self.cur_paras.NUM_FRAMES.get('MAX', 100),
+                step=1,
+                value=self.cur_paras.NUM_FRAMES.get('DEFAULT', 49),
+                visible=self.cur_paras.NUM_FRAMES.get('VISIBLE', True),
+                interactive=True)
+        with gr.Row(equal_height=True):
+            self.image_number = gr.Slider(
+                label=self.component_names.image_number,
+                minimum=self.cur_paras.SAMPLES.get('MIN', 1),
+                maximum=self.cur_paras.SAMPLES.get('MAX', 4),
+                step=1,
+                value=self.cur_paras.SAMPLES.get('DEFAULT', 1),
+                visible=self.cur_paras.SAMPLES.get('VISIBLE', True),
+                interactive=True)
         with gr.Row(equal_height=True):
             with gr.Column(scale=1):
                 self.seed_random = gr.Checkbox(
@@ -177,6 +194,8 @@ class DiffusionUI(UIBase):
             'output_height': self.output_height,
             'output_width': self.output_width,
             'image_number': self.image_number,
+            'num_frames': self.num_frames,
+            'fps': self.fps,
             'sample_steps': self.sample_steps,
             'guide_scale': self.guide_scale,
             'guide_rescale': self.guide_rescale,

scepter/studio/inference/inference_ui/gallery_ui.py

@@ -1,6 +1,7 @@
 # -*- coding: utf-8 -*-
 # Copyright (c) Alibaba, Inc. and its affiliates.
 import os
+import imageio
 from collections import OrderedDict
 
 import gradio as gr
@@ -183,8 +184,10 @@ class GalleryUI(UIBase):
                 'guide_scale':
                 args.pop('guide_scale'),
                 'guide_rescale':
-                args.pop('guide_rescale')
+                args.pop('guide_rescale'),
             }
+            if 'num_frames' in args:
+                pipeline_input.update({'num_frames': args['num_frames']})
             args.update({'input': pipeline_input})
 
         def appedix_init(args):
@@ -217,7 +220,7 @@ class GalleryUI(UIBase):
         load_init(args)
         results = current_pipeline(**args)
 
-        images = []
+        images, videos = [], []
         before_images = []
         if 'images' in results:
             images_tensor = results['images'] * 255
@@ -226,6 +229,11 @@ class GalleryUI(UIBase):
                     1, 2, 0).cpu().numpy().astype(np.uint8))
                 for idx in range(images_tensor.shape[0])
             ]
+        if 'videos' in results:
+            videos = [
+                (video.permute(1, 2, 3, 0).cpu().numpy() * 255).astype(np.uint8)
+                for video in results['videos']
+            ]
         if 'before_refine_images' in results and results[
                 'before_refine_images'] is not None:
             before_refine_images_tensor = results['before_refine_images'] * 255
@@ -236,7 +244,7 @@ class GalleryUI(UIBase):
             ]
         if 'seed' in results:
             print(results['seed'])
-        print(images, before_images)
+        # print(images, before_images)
 
         if args['largen_state']:
             largen_history.extend(images)
@@ -250,12 +258,27 @@ class GalleryUI(UIBase):
                                           f'cur_gallery_{i}.jpg')
                 img.save(save_image)
                 save_list.append(save_image)
-            images = save_list
+            ret_data = save_list
+        else:
+            ret_data = images
+
+        if len(videos) > 0:
+            save_list = []
+            fps = args.get('fps', 8)
+            for i, video in enumerate(videos):
+                save_video_path = os.path.join(self.local_work_dir,
+                                          f'cur_gallery_{i}.mp4')
+                writer = imageio.get_writer(save_video_path, fps=fps)
+                for frame in video:
+                    writer.append_data(np.array(frame))
+                writer.close()
+                save_list.append(save_video_path)
+            ret_data = save_list
 
         return (
             gr.Column(visible=len(before_images) > 0),
             before_images,
-            images,
+            ret_data,
             largen_history,
             gr.update(value=largen_history),
         )

scepter/studio/inference/inference_ui/model_manage_ui.py

@@ -188,10 +188,11 @@ class ModelManageUI(UIBase):
             diffusion_ui.cur_h_level_dict = h_level_dict
 
             default_input = self.pipe_manager.pipeline_level_modules[
-                now_pipeline].input
+                now_pipeline].input_cfg
             cur_paras = diffusion_ui.get_default(diffusion_ui.diffusion_paras,
                                                  default_input)
             diffusion_ui.cur_paras = cur_paras
+
             return (
                 diffusion_model,
                 gr.Dropdown(value=all_module_name['first_stage_model']),
@@ -224,7 +225,11 @@ class ModelManageUI(UIBase):
                 gr.Slider(value=cur_paras.GUIDE_SCALE.get('DEFAULT', 7.5),
                           visible=cur_paras.GUIDE_SCALE.get('VISIBLE', True)),
                 gr.Slider(value=cur_paras.GUIDE_RESCALE.get('DEFAULT', 0.5),
-                          visible=cur_paras.GUIDE_RESCALE.get('VISIBLE', True)))
+                          visible=cur_paras.GUIDE_RESCALE.get('VISIBLE', True)),
+                gr.Slider(value=cur_paras.NUM_FRAMES.get('DEFAULT', 49),
+                          visible=cur_paras.NUM_FRAMES.get('VISIBLE', False)),
+                gr.Slider(value=cur_paras.FPS.get('DEFAULT', 8),
+                          visible=cur_paras.FPS.get('VISIBLE', False)))
 
         self.diffusion_model.change(
             diffusion_model_change,
@@ -240,6 +245,7 @@ class ModelManageUI(UIBase):
                 diffusion_ui.prompt_prefix, diffusion_ui.output_height,
                 diffusion_ui.sampler, diffusion_ui.discretization,
                 diffusion_ui.sample_steps, diffusion_ui.guide_scale,
-                diffusion_ui.guide_rescale
+                diffusion_ui.guide_rescale, diffusion_ui.num_frames,
+                diffusion_ui.fps
             ],
             queue=True)

scepter/studio/preprocess/caption_editor_ui/component_names.py

@@ -22,7 +22,8 @@ class CreateDatasetUIName():
             self.dataset_type = 'Dataset Type'
             self.dataset_type_name = {
                 'scepter_txt2img': 'Text2Image Generation',
-                'scepter_img2img': 'Image Edit Generation'
+                'scepter_img2img': 'Image Edit Generation',
+                'scepter_txt2vid': 'Text2Video Generation'
             }
             self.user_data_name = (
                 f'Current Dataset Name. Changes of dataset name take '
@@ -34,30 +35,35 @@ class CreateDatasetUIName():
                 'scepter_txt2img':
                 f'{self.default_dataset_repo}repo?Revision=master&FilePath=datasets/3D_example_csv.zip',
                 'scepter_img2img':
-                f'{self.default_dataset_repo}repo?Revision=master&FilePath=datasets/hed_pair.zip'
+                f'{self.default_dataset_repo}repo?Revision=master&FilePath=datasets/hed_pair.zip',
+                'scepter_txt2vid':
+                f'{self.default_dataset_repo}repo?Revision=master&FilePath=datasets/video_example.zip'
             }
             self.default_dataset_zip_str = ' and '.join(
                 [f'[{k}]({v})' for k, v in self.default_dataset_zip.items()])
 
             self.default_dataset_name = {
                 'scepter_txt2img': '3D_example',
-                'scepter_img2img': 'hed_example'
+                'scepter_img2img': 'hed_example',
+                'scepter_txt2vid': 'video_example'
             }
-
             self.btn_create_datasets_from_file = 'Create Dataset From File'
             self.user_direction = (
                 '### User Guide: \n' +
                 f'* {self.btn_create_datasets} button is used to create a new dataset '
                 ". Please make sure to modify the dataset's name and version. After creation, "
-                'you can upload images one by one. \n'
+                'you can upload images or videos one by one. \n'
                 f'* The "{self.btn_create_datasets_from_file}" button supports creating a new dataset from '
                 'a file, currently supporting zip files. For zip files, the format should be consistent'
-                " with the one used during training, ensuring it contains an 'images/' folder and a '"
-                "train.csv' (which will use the image paths in this file); "
-                'The first line is Target:FILE, Prompt, followed by the format of each line: image path, description.'
+                " with the one used during training, ensuring it contains an 'images/' or 'videos/' folder and a '"
+                "train.csv' (which will use the image or video paths in this file); "
+                'The first line is Target:FILE, Prompt, followed by the format of each line: image path or video path, '
+                'description.'
                 'we also surpport the zip of '
-                'one level subfolder of images whose format are in jpg, jpeg, png, webp. '
+                'one level subfolder of images or videos whose format are in jpg, jpeg, png, mp4, webp. '
                 f'See the ZIP examples: {self.default_dataset_zip_str}. \n'  # noqa
+                'Addition, txt2vid data also supports batch upload of txt file list, followed by the format of '
+                'each line: video path#;#video description '
                 f'* If you have refreshed the page, please click the {self.refresh_list_button} '
                 'button to ensure all previously created datasets are visible in the dropdown menu.\n'
                 '* For processing and training with large-scale data(for example more than 10K samples), '
@@ -97,7 +103,8 @@ class CreateDatasetUIName():
             self.dataset_type = '数据集类型'
             self.dataset_type_name = {
                 'scepter_txt2img': '文生图数据',
-                'scepter_img2img': '图像编辑（图生图）数据'
+                'scepter_img2img': '图像编辑（图生图）数据',
+                'scepter_txt2vid': '文生视频数据'
             }
 
             self.user_data_name = f'当前数据集名称，修改后点{self.modify_data_button}生效'
@@ -108,26 +115,30 @@ class CreateDatasetUIName():
                 'scepter_txt2img':
                 f'{self.default_dataset_repo}repo?Revision=master&FilePath=datasets/3D_example_csv.zip',
                 'scepter_img2img':
-                f'{self.default_dataset_repo}repo?Revision=master&FilePath=datasets/hed_pair.zip'
+                f'{self.default_dataset_repo}repo?Revision=master&FilePath=datasets/hed_pair.zip',
+                'scepter_txt2vid':
+                f'{self.default_dataset_repo}repo?Revision=master&FilePath=datasets/video_example.zip'
             }
             self.default_dataset_zip_str = ' 和 '.join(
                 [f'[{k}]({v})' for k, v in self.default_dataset_zip.items()])
 
             self.default_dataset_name = {
                 'scepter_txt2img': '3D_example',
-                'scepter_img2img': 'hed_example'
+                'scepter_img2img': 'hed_example',
+                'scepter_txt2vid': 'video_example'
             }
             self.btn_create_datasets_from_file = '从文件新建'
             self.user_direction = (
                 '### 使用说明 \n' +
                 f'* {self.btn_create_datasets} 按钮用于从零新建数据集，请注意修改数据集的name和version，'
-                '新建完成后可以逐个上传图片。\n' +
+                '新建完成后可以逐个上传图片或视频。\n' +
                 f'* {self.btn_create_datasets_from_file} 按钮支持从文件中来新建数据集，目前支持zip文件，'
-                '需要保证在文件夹外进行打包，并包含images/文件夹和train.csv(会使用该文件中的图片路径)，首行为Target:FILE,Prompt，'
-                '其次每行格式为：图片路径,描述；'
-                f'同时我们也支持图像文件的zip包，格式在jpg、jpeg、png或webp。数据ZIP样例路径：{self.default_dataset_zip_str}. \n'
+                '需要保证在文件夹外进行打包，并包含 images/ 或 videos/ 文件夹和train.csv(会使用该文件中的图片或视频路径)，首行为Target:FILE,Prompt，'
+                '其次每行格式为：图片 或 视频 路径,描述；'
+                f'同时我们也支持图像或视频文件的zip包，格式在jpg、jpeg、png、mp4或webp。数据ZIP样例路径：{self.default_dataset_zip_str}； \n'
+                '另外，文生视频数据还支持txt文件列表批量上传，文件每行格式为：视频路径#;#视频描述；\n '
                 +
-                f'* 如果刷新了页面，请点击{self.refresh_list_button} 按钮以确保所有以往创建的数据集在下拉框中可见。\n'
+                f'如果刷新了页面，请点击 {self.refresh_list_button} 按钮以确保所有以往创建的数据集在下拉框中可见\n'
                 '* 对于大规模数据的处理和训练（数据规模大于1万），建议使用命令行形式\n'
                 '* <span style="color: blue;">请注意观察系统日志的输出以帮助改进操作。</span> \n')
             # Error or Warning
@@ -154,11 +165,13 @@ class DatasetGalleryUIName():
             self.illegal_blank_dataset = 'Illgal or blank dataset is not allowed editing.'
             self.delete_blank_dataset = 'Blank dataset is not allowed deleting.'
             self.upload_image = 'Upload Target Image'
+            self.upload_video = 'Upload Video'
             self.upload_src_image = 'Upload Source Image'
             self.upload_src_mask = 'Mask Image'
             self.upload_image_btn = '\U00002714'  # ✔️
             self.cancel_upload_btn = '\U00002716'  # ✖️
             self.image_caption = 'Image Caption'
+            self.video_caption = 'Video Caption'
 
             self.btn_modify = '\U0001F4DD'  # 📝
             self.btn_delete = '\U0001f5d1'  # 🗑️
@@ -196,10 +209,12 @@ class DatasetGalleryUIName():
                 f'click{self.btn_reset_edit} to reset edited data，'
                 f'click{self.btn_cancel_edit} to out of editing mode.')
             self.preprocess_choices = [
-                'Image Preprocess', 'Caption Preprocess'
+                'Image Preprocess', 'Caption Preprocess', 'Caption translation'
             ]
+            self.preprocess_choices_video = ['Video caption generation', 'Caption translation']
 
             self.preview_target_image = 'Preview Target Image'
+            self.preview_target_video = 'Preview Target Video'
             self.preview_src_image = 'Preview Source Image'
             self.preview_src_mask_image = 'Preview Source Image Mask'
             self.preview_caption = 'Preview Caption'
@@ -211,7 +226,7 @@ class DatasetGalleryUIName():
             self.caption_preprocess_btn = 'apply'
             self.caption_preview_btn = 'preview'
             self.caption_update_mode = 'Caption Update Mode'
-            self.caption_update_choices = ['Append', 'Replace']
+            self.caption_update_choices = ['Replace', 'Append']
 
             self.used_device = 'Used Device'
             self.used_memory = 'Used Memory'
@@ -234,11 +249,13 @@ class DatasetGalleryUIName():
             self.illegal_blank_dataset = '不合法或空白数据集不允许编辑。'
             self.delete_blank_dataset = '空白数据集不允许删除。'
             self.upload_image = '上传目标图片'
+            self.upload_video = '上传视频'
             self.upload_src_image = '上传待编辑图片'
             self.upload_src_mask = '蒙版区域'
             self.upload_image_btn = '\U00002714'  # ✔️
             self.cancel_upload_btn = '\U00002716'  # ✖️
             self.image_caption = '图片描述'
+            self.video_caption = '视频描述'
 
             self.btn_modify = '\U0001F4DD'  # 📝
             self.dataset_images = f'图片数据，点击{self.btn_modify}进入编辑模式'
@@ -256,8 +273,8 @@ class DatasetGalleryUIName():
             self.edit_caption = '编辑描述'
             self.batch_caption_generate = '处理范围'
 
-            self.ori_dataset = '原始数据 高({}) * 宽({}) 图像格式({})'
-            self.edit_dataset = '可编辑数据 高({}) * 宽({}) 图像格式({})'
+            self.ori_dataset = '原始数据 高({}) * 宽({}) 格式({})'
+            self.edit_dataset = '可编辑数据 高({}) * 宽({}) 格式({})'
             self.upload_image_info = '图像信息 高({}) * 宽({})'
             self.upload_src_image_info = '源图像信息 高({}) * 宽({})'
 
@@ -276,8 +293,10 @@ class DatasetGalleryUIName():
                                       f'点击{self.btn_cancel_edit}取消编辑，'
                                       f'点击{self.btn_reset_edit}重置数据，'
                                       f'修改编辑范围可以批量编辑不同范围的数据。')
-            self.preprocess_choices = ['图像预处理', '描述生成']
+            self.preprocess_choices = ['图像预处理', '描述生成', '描述翻译']
+            self.preprocess_choices_video = ['视频描述生成', '描述翻译']
             self.preview_target_image = '预览图片'
+            self.preview_target_video = '预览视频'
             self.preview_src_image = '预览原图'
             self.preview_src_mask_image = '预览蒙版'
             self.preview_caption = '预览描述'
@@ -288,7 +307,7 @@ class DatasetGalleryUIName():
             self.caption_preprocess_btn = '应用'
             self.caption_preview_btn = '预览'
             self.caption_update_mode = '描述更新方式'
-            self.caption_update_choices = ['追加', '替换']
+            self.caption_update_choices = ['替换', '追加']
             self.used_device = '使用设备'
             self.used_memory = '使用内存'
             self.caption_language = '描述语言'
@@ -386,3 +405,33 @@ class Image2ImageDataCardName():
             self.illegal_data_err7 = '上传图像失败{}'
             self.delete_err1 = '删除失败，数据已经为空了'
             self.export_zip_err1 = '压缩文件失败!'
+
+
+class Text2VideoDataCardName():
+    def __init__(self, language='en'):
+        if language == 'en':
+            self.illegal_data_err1 = (
+                'The list supports only "," or "#;#" as delimiters. '
+                'The two columns represent video path and description, '
+                'respectively.')
+            self.illegal_data_err2 = 'Illegal file format'
+            self.illegal_data_err3 = 'File decompression failed, failed to upload to storage!'
+            self.illegal_data_err4 = 'Illegal width({}),height({})'
+            self.illegal_data_err5 = (
+                'The path should not contain "{}". '
+                'It should be an OSS path (oss://) or the prefix '
+                'can be omitted (xxx/xxx)."')
+            self.illegal_data_err6 = 'Video download failed {}'
+            self.illegal_data_err7 = 'Video upload failed {}'
+            self.delete_err1 = 'Deletion failed, the data is already empty.'
+            self.export_zip_err1 = 'Failed to compress the file!'
+        elif language == 'zh':
+            self.illegal_data_err1 = '列表只支持,或#;#作为分割符，两列分别为视频路径/描述'
+            self.illegal_data_err2 = '非法的文件格式'
+            self.illegal_data_err3 = '文件解压失败，上传存储器失败！'
+            self.illegal_data_err4 = '不合法的width({}),height({})'
+            self.illegal_data_err5 = '路径不支持{}，应该为oss路径（oss://）或者省略前缀（xxx/xxx）'
+            self.illegal_data_err6 = '下载视频失败{}'
+            self.illegal_data_err7 = '上传视频失败{}'
+            self.delete_err1 = '删除失败，数据已经为空了'
+            self.export_zip_err1 = '压缩文件失败!'

scepter/studio/preprocess/caption_editor_ui/create_dataset_ui.py

@@ -15,6 +15,8 @@ from scepter.studio.preprocess.utils.img2img_data_card import \
     Image2ImageDataCard
 from scepter.studio.preprocess.utils.txt2img_data_card import \
     Text2ImageDataCard
+from scepter.studio.preprocess.utils.txt2vid_data_card import \
+    Text2VideoDataCard
 from scepter.studio.utils.uibase import UIBase
 from tqdm import tqdm
 
@@ -44,7 +46,9 @@ class CreateDatasetUI(UIBase):
             'scepter_txt2img':
             Text2ImageDataCard,
             'scepter_img2img':
-            Image2ImageDataCard
+            Image2ImageDataCard,
+            'scepter_txt2vid':
+            Text2VideoDataCard
         })
         self.components_name = CreateDatasetUIName(language)
         self.default_dataset_type = list(self.dataset_type_dict.keys())[0]
@@ -466,8 +470,7 @@ class CreateDatasetUI(UIBase):
         ], [
             self.panel_state, self.dataset_name, self.user_dataset_name,
             self.sys_log
-        ],
-                                       queue=False)
+        ], queue=False)
 
         def show_edit_panel(panel_state, data_name):
             if panel_state:
@@ -599,10 +602,9 @@ class CreateDatasetUI(UIBase):
                     trans_dataset_type, [])
             else:
                 dataset_list = []
-            return gr.Dropdown(
-                value=dataset_list[-1] if len(dataset_list) > 0 else '',
-                choices=dataset_list), self.components_name.system_log.format(
-                    '')
+            return (gr.Dropdown(
+                value=dataset_list[-1] if len(dataset_list) > 0 else '', choices=dataset_list),
+                self.components_name.system_log.format(''))
 
         manager.user_name.change(dataset_type_change,
                                  inputs=[self.dataset_type, manager.user_name],

scepter/studio/preprocess/processors/caption_processors.py

@@ -15,8 +15,11 @@ from scepter.modules.utils.file_system import FS
 import numpy as np
 from scepter.studio.preprocess.processors.base_processor import \
     BaseCaptionProcessor
+import io
+from decord import cpu, VideoReader, bridge
 
-__all__ = ['BlipImageBase', 'QWVL', 'QWVLQuantize', 'InternVL15']
+__all__ = ['BlipImageBase', 'QWVL', 'QWVLQuantize', 'InternVL15', 'CogVLM2Llama3Caption',
+           'OpusMtZhEn', 'OpusMtEnZh']
 
 def get_region(image, mask, mask_id):
     locs = np.where(np.array(mask) == mask_id)
@@ -467,3 +470,306 @@ class InternVL15(QWVL):
         response = self.model_info['model'].chat(self.model_info['tokenizer'], image, prompt, generation_config=generation_config)
         response = response.replace("\n", "").strip()
         return response
+
+
+class CogVLM2Llama3Caption(BaseCaptionProcessor):
+    def __init__(self, cfg, language='en'):
+        super().__init__(cfg, language=language)
+        self.model_path = cfg.MODEL_PATH
+        self.model_info = {
+            'device': 'offline',
+            'model': None,
+            'tokenizer': None
+        }
+        self.prompt = cfg.PROMPT
+        self.temperature = cfg.TEMPERATURE
+        self.max_new_tokens = cfg.MAX_NEW_TOKENS
+        self.pad_token_id = cfg.PAD_TOKEN_ID
+        self.top_k = cfg.TOP_K
+        self.top_p = cfg.TOP_P
+        self.TORCH_TYPE = torch.bfloat16 if (torch.cuda.is_available() and
+                                             torch.cuda.get_device_capability()
+                                             [0] >= 8) else torch.float16
+
+    def load_model(self):
+        is_flg, msg = super().load_model()
+        if not is_flg:
+            return is_flg, msg
+        if self.model_info['device'] == 'offline':
+            model = None
+            try:
+                from transformers import AutoModelForCausalLM, AutoTokenizer
+                local_model_dir = FS.get_dir_to_local_dir(self.model_path)
+                tokenizer = AutoTokenizer.from_pretrained(
+                    local_model_dir,
+                    trust_remote_code=True,
+                )
+                model = AutoModelForCausalLM.from_pretrained(
+                    local_model_dir,
+                    device_map='auto',
+                    torch_dtype=self.TORCH_TYPE,
+                    trust_remote_code=True
+                ).eval().to(we.device_id)
+            except Exception as e:
+                if model is not None:
+                    del model
+                return False, f"Load model error '{e}'"
+            self.model_info['device'] = model.device
+            self.model_info['model'] = model
+            self.model_info['tokenizer'] = tokenizer
+        elif self.model_info['device'] == 'cpu':
+            try:
+                self.model_info['model'].to(we.device_id)
+                self.model_info['device'] = we.device_id
+                torch.cuda.empty_cache()
+                torch.cuda.ipc_collect()
+            except Exception as e:
+                del self.model_info['model']
+                self.model_info['model'] = None
+                self.model_info['device'] = 'offline'
+                torch.cuda.empty_cache()
+                torch.cuda.ipc_collect()
+                return False, f"Load model error '{e}'"
+        return True, ''
+
+    def unload_model(self):
+        super().unload_model()
+        if self.delete_instance:
+            self.model_info['device'] = 'offline'
+            if self.model_info['model'] is not None:
+                self.model_info['model'] = self.model_info['model'].to('cpu')
+                del self.model_info['model']
+            self.model_info['model'] = None
+        elif (isinstance(self.model_info['device'], numbers.Number)
+              or str(self.model_info['device']).startswith('cuda')):
+            self.model_info['device'] = 'cpu'
+            self.model_info['model'] = self.model_info['model'].to('cpu')
+        torch.cuda.empty_cache()
+        torch.cuda.ipc_collect()
+        return True, ''
+
+    def load_video(self, video_data, strategy='chat'):
+        bridge.set_bridge('torch')
+        mp4_stream = video_data
+        num_frames = 24
+        decord_vr = VideoReader(io.BytesIO(mp4_stream), ctx=cpu(0))
+
+        frame_id_list = None
+        total_frames = len(decord_vr)
+        if strategy == 'base':
+            clip_end_sec = 60
+            clip_start_sec = 0
+            start_frame = int(clip_start_sec * decord_vr.get_avg_fps())
+            end_frame = min(total_frames,
+                            int(clip_end_sec * decord_vr.get_avg_fps())) if clip_end_sec is not None else total_frames
+            frame_id_list = np.linspace(start_frame, end_frame - 1, num_frames, dtype=int)
+        elif strategy == 'chat':
+            timestamps = decord_vr.get_frame_timestamp(np.arange(total_frames))
+            timestamps = [i[0] for i in timestamps]
+            max_second = round(max(timestamps)) + 1
+            frame_id_list = []
+            for second in range(max_second):
+                closest_num = min(timestamps, key=lambda x: abs(x - second))
+                index = timestamps.index(closest_num)
+                frame_id_list.append(index)
+                if len(frame_id_list) >= num_frames:
+                    break
+        video_data = decord_vr.get_batch(frame_id_list)
+        video_data = video_data.permute(3, 0, 1, 2)
+        return video_data
+
+    def get_caption(self, prompt, video_data, temperature):
+        strategy = 'chat'
+        video = self.load_video(video_data, strategy=strategy)
+
+        history = []
+        query = prompt
+        model = self.model_info['model']
+        tokenizer = self.model_info['tokenizer']
+        inputs = model.build_conversation_input_ids(
+            tokenizer=tokenizer,
+            query=query,
+            images=[video],
+            history=history,
+            template_version=strategy
+        )
+        inputs = {
+            'input_ids': inputs['input_ids'].unsqueeze(0).to('cuda'),
+            'token_type_ids': inputs['token_type_ids'].unsqueeze(0).to(we.device_id),
+            'attention_mask': inputs['attention_mask'].unsqueeze(0).to(we.device_id),
+            'images': [[inputs['images'][0].to(we.device_id).to(self.TORCH_TYPE)]],
+        }
+        gen_kwargs = {
+            "max_new_tokens": self.max_new_tokens,
+            "pad_token_id": self.pad_token_id,
+            "top_k": self.top_k,
+            "do_sample": True,
+            "top_p": self.top_p,
+            "temperature": temperature,
+        }
+        with torch.no_grad():
+            outputs = model.generate(**inputs, **gen_kwargs)
+            outputs = outputs[:, inputs['input_ids'].shape[1]:]
+            response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+            return response
+
+    def __call__(self, *args, **kwargs):
+        video_path = kwargs.pop('video_path', None)
+        with open(video_path, 'rb') as f:
+            video_data = f.read()
+        response = (self.get_caption(self.prompt, video_data, self.temperature))
+        return response
+
+
+class OpusMtZhEn(BaseCaptionProcessor):
+    def __init__(self, cfg, language='en'):
+        super().__init__(cfg, language=language)
+        self.model_path = cfg.MODEL_PATH
+        self.model_info = {
+            'device': 'offline',
+            'model': None,
+            'tokenizer': None
+        }
+
+    def load_model(self):
+        is_flg, msg = super().load_model()
+        if not is_flg:
+            return is_flg, msg
+        if self.model_info['device'] == 'offline':
+            model = None
+            try:
+                from transformers import MarianMTModel, AutoTokenizer
+                local_model_dir = FS.get_dir_to_local_dir(self.model_path)
+                tokenizer = AutoTokenizer.from_pretrained(
+                    local_model_dir
+                )
+                model = MarianMTModel.from_pretrained(
+                    local_model_dir
+                ).to(we.device_id)
+            except Exception as e:
+                if model is not None:
+                    del model
+                return False, f"Load model error '{e}'"
+            self.model_info['device'] = model.device
+            self.model_info['model'] = model
+            self.model_info['tokenizer'] = tokenizer
+        elif self.model_info['device'] == 'cpu':
+            try:
+                self.model_info['model'].to(we.device_id)
+                self.model_info['device'] = we.device_id
+                torch.cuda.empty_cache()
+                torch.cuda.ipc_collect()
+            except Exception as e:
+                del self.model_info['model']
+                self.model_info['model'] = None
+                self.model_info['device'] = 'offline'
+                torch.cuda.empty_cache()
+                torch.cuda.ipc_collect()
+                return False, f"Load model error '{e}'"
+        return True, ''
+
+    def unload_model(self):
+        super().unload_model()
+        if self.delete_instance:
+            self.model_info['device'] = 'offline'
+            if self.model_info['model'] is not None:
+                self.model_info['model'] = self.model_info['model'].to('cpu')
+                del self.model_info['model']
+            self.model_info['model'] = None
+        elif (isinstance(self.model_info['device'], numbers.Number)
+              or str(self.model_info['device']).startswith('cuda')):
+            self.model_info['device'] = 'cpu'
+            self.model_info['model'] = self.model_info['model'].to('cpu')
+        torch.cuda.empty_cache()
+        torch.cuda.ipc_collect()
+        return True, ''
+
+    def get_caption(self, data):
+        model = self.model_info['model']
+        tokenizer = self.model_info['tokenizer']
+        batch = tokenizer(data, return_tensors="pt").to(we.device_id)
+        generated_ids = model.generate(**batch)
+        translated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+        return translated_text
+
+    def __call__(self, *args, **kwargs):
+        caption = kwargs.pop('caption', None)
+        response = (self.get_caption(caption))
+        return response
+
+
+class OpusMtEnZh(BaseCaptionProcessor):
+    def __init__(self, cfg, language='en'):
+        super().__init__(cfg, language=language)
+        self.model_path = cfg.MODEL_PATH
+        self.model_info = {
+            'device': 'offline',
+            'model': None,
+            'tokenizer': None
+        }
+
+    def load_model(self):
+        is_flg, msg = super().load_model()
+        if not is_flg:
+            return is_flg, msg
+        if self.model_info['device'] == 'offline':
+            model = None
+            try:
+                from transformers import MarianMTModel, AutoTokenizer
+                local_model_dir = FS.get_dir_to_local_dir(self.model_path)
+                tokenizer = AutoTokenizer.from_pretrained(
+                    local_model_dir
+                )
+                model = MarianMTModel.from_pretrained(
+                    local_model_dir
+                ).to(we.device_id)
+            except Exception as e:
+                if model is not None:
+                    del model
+                return False, f"Load model error '{e}'"
+            self.model_info['device'] = model.device
+            self.model_info['model'] = model
+            self.model_info['tokenizer'] = tokenizer
+        elif self.model_info['device'] == 'cpu':
+            try:
+                self.model_info['model'].to(we.device_id)
+                self.model_info['device'] = we.device_id
+                torch.cuda.empty_cache()
+                torch.cuda.ipc_collect()
+            except Exception as e:
+                del self.model_info['model']
+                self.model_info['model'] = None
+                self.model_info['device'] = 'offline'
+                torch.cuda.empty_cache()
+                torch.cuda.ipc_collect()
+                return False, f"Load model error '{e}'"
+        return True, ''
+
+    def unload_model(self):
+        super().unload_model()
+        if self.delete_instance:
+            self.model_info['device'] = 'offline'
+            if self.model_info['model'] is not None:
+                self.model_info['model'] = self.model_info['model'].to('cpu')
+                del self.model_info['model']
+            self.model_info['model'] = None
+        elif (isinstance(self.model_info['device'], numbers.Number)
+              or str(self.model_info['device']).startswith('cuda')):
+            self.model_info['device'] = 'cpu'
+            self.model_info['model'] = self.model_info['model'].to('cpu')
+        torch.cuda.empty_cache()
+        torch.cuda.ipc_collect()
+        return True, ''
+
+    def get_caption(self, data):
+        model = self.model_info['model']
+        tokenizer = self.model_info['tokenizer']
+        batch = tokenizer(data, return_tensors="pt").to(we.device_id)
+        generated_ids = model.generate(**batch)
+        translated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+        return translated_text
+
+    def __call__(self, *args, **kwargs):
+        caption = kwargs.pop('caption', None)
+        response = (self.get_caption(caption))
+        return response

scepter/studio/preprocess/utils/txt2vid_data_card.py

@@ -0,0 +1,347 @@
+# -*- coding: utf-8 -*-
+# Copyright (c) Alibaba, Inc. and its affiliates.
+import csv
+import os
+import shutil
+import time
+import decord
+from tqdm import tqdm
+import gradio as gr
+
+from scepter.modules.utils.directory import get_md5
+from scepter.modules.utils.file_system import FS
+from scepter.studio.preprocess.caption_editor_ui.component_names import \
+    Text2VideoDataCardName
+from scepter.studio.preprocess.utils.data_card import (BaseDataCard, find_prefix)
+
+
+class Text2VideoDataCard(BaseDataCard):
+    def __init__(self,
+                 dataset_folder,
+                 dataset_name=None,
+                 src_file=None,
+                 surfix=None,
+                 user_name='admin',
+                 language='en'
+                 ):
+        super().__init__(dataset_folder,
+                         dataset_name=dataset_name,
+                         user_name=user_name)
+        self.meta['task_type'] = 'txt2vid'
+        self.components_name = Text2VideoDataCardName(language)
+        if self.new_dataset:
+            if surfix == '.zip':
+                file_list = self.load_from_zip(src_file, dataset_folder,
+                                               self.local_dataset_folder)
+            elif surfix in ['.txt', '.csv']:
+                file_list = self.load_from_list(src_file, dataset_folder,
+                                                self.local_dataset_folder)
+            elif surfix is None:
+                file_list = []
+            else:
+                raise gr.Error(
+                    f'{self.components_name.illegal_data_err2} {surfix}')
+
+            is_flag = FS.put_dir_from_local_dir(self.local_dataset_folder,
+                                                dataset_folder,
+                                                multi_thread=True)
+            if not is_flag:
+                raise gr.Error(f'{self.components_name.illegal_data_err3}')
+
+            self.meta['cursor'] = 0 if len(file_list) > 0 else -1
+            self.meta['file_list'] = file_list
+            self.update_dataset()
+
+    def apply_changes(self):
+        edit_index_list = self.edit_list
+        for index in edit_index_list:
+            one_data = self.data[index]
+            self.data[index]['caption'] = one_data['edit_caption']
+
+        self.update_dataset()
+        return True, ''
+
+    def load_from_list(self, save_file, dataset_folder, local_dataset_folder):
+        file_list = []
+        videos_folder = os.path.join(local_dataset_folder, 'videos')
+        os.makedirs(videos_folder, exist_ok=True)
+        with FS.get_from(save_file) as local_path:
+            with open(local_path, 'r') as f:
+                for line in tqdm(f):
+                    line = line.strip()
+                    if line == '':
+                        continue
+                    try:
+                        src_video_path, caption = line.split(
+                            '#;#', 1)
+                    except Exception:
+                        try:
+                            src_video_path, caption = line.split(
+                                ',', 1)
+                        except Exception:
+                            raise gr.Error(
+                                self.components_name.illegal_data_err1)
+                    relative_path = os.path.join(
+                        'videos', f'{get_md5(src_video_path)[:18]}_{int(time.time())}.mp4')
+                    video_path = os.path.join(dataset_folder, relative_path)
+                    FS.get_from(src_video_path, local_path=video_path)
+                    is_legal, new_path, prefix = find_prefix(src_video_path)
+                    w, h, fps, duration = self.get_video_meta(video_path)
+
+                    file_list.append({
+                        'video_path':
+                        video_path,
+                        'relative_path':
+                        relative_path,
+                        'width':
+                        w,
+                        'height':
+                        h,
+                        'fps':
+                        fps,
+                        'duration':
+                        duration,
+                        'caption':
+                        caption,
+                        'edit_caption':
+                        caption,
+                        'prefix':
+                        prefix
+                    })
+        return file_list
+
+    def load_from_zip(self, save_file, data_folder, local_dataset_folder):
+        with FS.get_from(save_file) as local_path:
+            res = os.popen(
+                f"unzip -o '{local_path}' -d '{local_dataset_folder}'")
+            res = res.readlines()
+        if not os.path.exists(local_dataset_folder):
+            raise gr.Error(f'Unzip {save_file} failed {str(res)}')
+        file_folder = None
+        train_list = None
+        hit_dir = None
+        raw_list = {}
+        mac_osx = os.path.join(local_dataset_folder, '__MACOSX')
+        if os.path.exists(mac_osx):
+            res = os.popen(f"rm -rf '{mac_osx}'")
+            res = res.readlines()
+        for one_dir in FS.walk_dir(local_dataset_folder, recurse=False):
+            if one_dir.endswith('__MACOSX'):
+                res = os.popen(f"rm -rf '{one_dir}'")
+                res = res.readlines()
+                continue
+            if FS.isdir(one_dir):
+                if one_dir.endswith('videos') or one_dir.endswith('videos/'):
+                    file_folder = one_dir
+                    hit_dir = one_dir
+                else:
+                    sub_dir = FS.walk_dir(one_dir)
+                    for one_s_dir in sub_dir:
+                        if FS.isdir(one_s_dir) and one_s_dir.split(
+                                one_dir)[1].replace('/', '') == 'videos':
+                            file_folder = one_s_dir
+                            hit_dir = one_dir
+                        if FS.isfile(one_s_dir) and one_s_dir.split(
+                                one_dir)[1].replace('/', '') == 'train.csv':
+                            train_list = one_s_dir
+                        if file_folder is not None and train_list is not None:
+                            break
+            elif one_dir.endswith('train.csv'):
+                train_list = one_dir
+            else:
+                continue
+            if file_folder is not None and train_list is not None:
+                break
+        if file_folder is None and len(raw_list) < 1:
+            raise gr.Error(
+                "video doesn't exist, or nothing exists in your zip")
+
+        if train_list is None:
+            raise gr.Error("pair list doesn't exist")
+        new_file_folder = f'{local_dataset_folder}/videos'
+        os.makedirs(new_file_folder, exist_ok=True)
+
+        if file_folder is not None:
+            _ = FS.get_dir_to_local_dir(file_folder, new_file_folder)
+
+        if not os.path.exists(new_file_folder):
+            raise gr.Error(f'{str(res)}')
+        new_train_list = f'{local_dataset_folder}/train.csv'
+        res = os.popen(f"mv '{train_list}' '{new_train_list}'")
+        res = res.readlines()
+        if not os.path.exists(new_train_list):
+            raise gr.Error(f'{str(res)}')
+        if not file_folder == hit_dir:
+            try:
+                res = os.popen(f"rm -rf '{hit_dir}/videos/*'")
+                _ = res.readlines()
+                res = os.popen(f"rm -rf '{hit_dir}'")
+                _ = res.readlines()
+
+            except Exception:
+                pass
+        file_list = self.load_train_file(new_train_list)
+        # remove unused data
+        for one_dir in FS.walk_dir(local_dataset_folder):
+            if 'videos' in one_dir or one_dir.endswith(
+                    'file.csv') or one_dir.endswith('train.csv'):
+                continue
+            os.system(f'rm -rf {one_dir}')
+        return file_list
+
+    def load_train_file(self, file_path):
+        base_folder = os.path.dirname(file_path)
+        file_list = []
+        video_set = set()
+
+        with open(file_path, 'r') as f:
+            reader = csv.reader(f)
+            for row in reader:
+                if len(row) == 2:
+                   src_video_path, prompt = row[0], row[1]
+                else:
+                    return gr.Error(self.components_name.illegal_data_err2)
+                if src_video_path == 'Target:FILE':
+                    continue
+
+                local_video_path = os.path.join(base_folder, src_video_path)
+                w, h, fps, duration = self.get_video_meta(local_video_path)
+                if src_video_path in video_set:
+                    src_video_path, surfix = os.path.splitext(src_video_path)
+                    src_video_path = f'{src_video_path}_{int(time.time() * 100)}{surfix}'
+                    new_local_video_path = os.path.join(
+                        base_folder, src_video_path)
+                    self.copy_video(src_video_path, new_local_video_path)
+                video_set.add(src_video_path)
+
+                file_list.append({
+                    'video_path':
+                        local_video_path,
+                    'relative_path':
+                        src_video_path,
+                    'width':
+                        w,
+                    'height':
+                        h,
+                    'fps':
+                        fps,
+                    "duration":
+                        duration,
+                    'caption':
+                        prompt,
+                    'edit_caption':
+                        prompt,
+                    'prefix':
+                    ''
+                })
+        return file_list
+
+    def write_train_file(self):
+        file_list = self.meta['file_list']
+        with open(self.local_train_file, 'w') as f:
+            writer = csv.writer(f)
+            writer.writerow(['Target:FILE', 'Prompt'])
+            for one_file in file_list:
+                relative_file = one_file['relative_path']
+                if relative_file.startswith('/'):
+                    relative_file = relative_file[1:]
+                writer.writerow([relative_file, one_file['caption'].
+                                strip().replace("\n", "")])
+        FS.put_object_from_local_file(self.local_train_file, self.train_file)
+
+    def write_data_file(self):
+        file_list = self.meta['file_list']
+        with open(self.local_save_file_list, 'w') as f:
+            for one_file in file_list:
+                f.write('{}#;#{}#;#{}#;#{}\n'.format(one_file['relative_path'],
+                                                     one_file['width'],
+                                                     one_file['height'],
+                                                     one_file['caption'].strip().replace("\n", ""))
+                        )
+        FS.put_object_from_local_file(self.local_save_file_list,
+                                      self.save_file_list)
+
+    def add_record(self, video, caption, **kwargs):
+        local_work_dir = self.meta['local_work_dir']
+        work_dir = self.meta['work_dir']
+
+        save_folder = os.path.join(local_work_dir, 'videos')
+        os.makedirs(save_folder, exist_ok=True)
+        w, h, fps, duration = self.get_video_meta(video)
+
+        relative_path = os.path.join(
+            'videos', f'{get_md5(video)[:18]}_{int(time.time())}.mp4')
+        video_path = os.path.join(work_dir, relative_path)
+        local_video_path = os.path.join(local_work_dir, relative_path)
+        self.copy_video(video, local_video_path)
+
+        self.data.append({
+            'video_path': video_path,
+            'relative_path': relative_path,
+            'width': w,
+            'height': h,
+            'fps': fps,
+            'duration': duration,
+            'caption': caption,
+            'edit_caption': caption,
+            'prefix': ''
+        })
+
+        self.set_cursor(len(self.meta['file_list']) - 1)
+        self.update_dataset()
+        return True
+
+    def copy_video(self, source_path, target_path):
+        if not os.path.isfile(source_path):
+            raise gr.Error('Video path not exist.')
+        try:
+            shutil.copy2(source_path, target_path)
+        except Exception as e:
+            raise gr.Error(str(e))
+
+    def get_video_meta(self, video):
+        video_reader = decord.VideoReader(video)
+        w = video_reader[0].shape[1]
+        h = video_reader[0].shape[0]
+        fps = video_reader.get_avg_fps()
+        video_length = len(video_reader)
+        duration = video_length / fps
+
+        return w, h, fps, duration
+
+    def delete_record(self):
+        if len(self) < 1:
+            raise gr.Error(self.components_name.delete_err1)
+        current_file = self.data.pop(self.cursor)
+        self.set_cursor(self.cursor - 1)
+        local_file = os.path.join(self.meta['local_work_dir'],
+                                  current_file['relative_path'])
+        try:
+            os.remove(local_file)
+        except Exception:
+            print(f'remove file {local_file} error')
+
+        if self.cursor >= len(self.meta['file_list']):
+            self.set_cursor(0)
+        if self.cursor < 0:
+            self.set_cursor(len(self) - 1)
+        if len(self.meta['file_list']) == 0:
+            self.set_cursor(-1)
+        self.update_dataset()
+
+    def export_zip(self, export_folder):
+        self.update_dataset()
+        zip_path = os.path.join(export_folder, f'{self.dataset_name}.zip')
+        local_zip, _ = FS.map_to_local(zip_path)
+        os.makedirs(os.path.dirname(local_zip), exist_ok=True)
+        res = os.popen(
+            f"cd '{self.local_work_dir}' && mkdir -p '{self.dataset_name}' "
+            f"&& cp -rf videos '{self.dataset_name}/videos' "
+            f"&& cp -rf train.csv '{self.dataset_name}/train.csv' "
+            f"&& zip -r '{os.path.abspath(local_zip)}' '{self.dataset_name}'/* "
+            f"&& rm -rf '{self.dataset_name}'")
+        print(res.readlines())
+        FS.put_object_from_local_file(local_zip, zip_path)
+        if not FS.exists(zip_path):
+            raise gr.Error(self.components_name.export_zip_err1)
+        return local_zip

scepter/studio/self_train/scripts/trainer.py

@@ -44,18 +44,22 @@ def kill_job(pid):
 
 
 class Trainer():
-    def __init__(self, run_script, status_message):
+    def __init__(self, run_script, status_message, visible_gpus):
         self.run_script = run_script
         self.status_message = status_message
+        self.visible_gpus = visible_gpus
         self.proc = None
 
     def __call__(self, task_name):
         torch.cuda.empty_cache()
         error_folder = './error_logs'
         os.makedirs(error_folder, exist_ok=True)
+
         self.status_message.error_log = f'{error_folder}/{int(time.time())}.log'
         cmd = f'PYTHONPATH=. python {self.run_script} ' \
               f'--cfg={task_name}/train.yaml 2> {self.status_message.error_log}'
+        if self.visible_gpus is not None and len(self.visible_gpus) > 0:
+            cmd = f'CUDA_VISIBLE_DEVICES={",".join([str(i) for i in self.visible_gpus])} ' + cmd
         # cmd = [f"python {self.run_script}"]
         print(cmd)
         try:
@@ -87,6 +91,7 @@ class TrainManager():
         self.runing_tasks = {}
         self.run_script = run_script
         self.work_dir = work_dir
+        self.visible_gpus = list(range(torch.cuda.device_count()))
 
         def task_dispatch():
             while True:
@@ -143,7 +148,7 @@ class TrainManager():
                     task_name = self.task_queue.pop(0)
                     print(f'start task {task_name}')
                     status_message = TaskStatus()
-                    train_ins = Trainer(self.run_script, status_message)
+                    train_ins = Trainer(self.run_script, status_message, self.visible_gpus)
                     train_thread = threading.Thread(target=train_ins,
                                                     args=(os.path.join(
                                                         self.work_dir,
@@ -174,11 +179,18 @@ class TrainManager():
         self.task_manage = threading.Thread(target=task_dispatch, daemon=True)
         self.task_manage.start()
 
+    def set_gpus(self, gpus=None):
+        if gpus is None:
+            self.visible_gpus = list(range(torch.cuda.device_count()))
+        else:
+            self.visible_gpus = list(gpus)
+
     def check_memory(self):
         # Check Cuda Memory
+        visible_gpus = self.visible_gpus
         mem_msg = ''
         if torch.cuda.is_available():
-            for device_id in range(torch.cuda.device_count()):
+            for device_id in visible_gpus:
                 free_mem, total_mem = torch.cuda.mem_get_info(device_id)
                 free_mem = free_mem / (1024**3)
                 total_mem = total_mem / (1024**3)

scepter/studio/self_train/self_train_ui/component_names.py

@@ -74,12 +74,13 @@ class TrainerUIName():
         self.task_choices = ['Text2Image', 'Image Editing']
         self.data_task_map = {
             'scepter_txt2img': None,
-            'scepter_img2img': 'edit'
+            'scepter_img2img': 'edit',
+            'scepter_txt2vid': 'dit'
         }
         if language == 'en':
             self.user_direction = '''
                 ### User Guide
-                - Data: Data preparation is done through the Data Manager. (zip example: [3D](https://www.modelscope.cn/api/v1/models/iic/scepter/repo?Revision=master&FilePath=datasets/3D_example_csv.zip))
+                - Data: Data preparation is done through the Data Manager. (zip example: [3D](https://www.modelscope.cn/api/v1/models/iic/scepter/repo?Revision=master&FilePath=datasets/3D_example_csv.zip), txt example(Support only video data): [txt](https://modelscope.cn/models/iic/scepter/resolve/master/datasets/video_example.txt))
                 - Parameters: You can try modifying the related parameters.
                 - Training: Click on [Start Training].
                 - Testing: After completing the training, click [Go to inference].
@@ -93,14 +94,20 @@ class TrainerUIName():
             self.data_source_choices = [
                 'Dataset zip', 'MaaS Dataset', 'Dataset Management'
             ]
+            self.illegal_data_err = (
+                'The list supports only "," or "#;#" as delimiters. '
+                'The two columns represent video path and description, '
+                'respectively.')
             self.data_source_value = 'Dataset zip'
             self.data_source_name = 'Data Source'
             self.data_type_map = {
                 'scepter_txt2img': 'Text2Image Generation',
-                'scepter_img2img': 'Image Edit Generation'
+                'scepter_img2img': 'Image Edit Generation',
+                'scepter_txt2vid': 'Text2Video Generation'
             }
             self.data_type_choices = list(self.data_type_map.keys())
             self.data_type_value = 'scepter_txt2img'
+            self.data_type_value_video = 'scepter_txt2vid'
             self.data_type_name = 'Data Type'
             self.ori_data_name = 'Data Name'
             # Supports MaaS dataset/local/HTTP Zip package
@@ -120,8 +127,8 @@ class TrainerUIName():
             self.base_model = 'Base Model'
             self.tuner_name = 'Tuner Method'
             self.base_model_revision = 'Model Version Number'
-            self.resolution_height = 'Train Image Height'
-            self.resolution_width = 'Train Image Width'
+            self.resolution_height = 'Train Image or Video Height'
+            self.resolution_width = 'Train Image or Video Width'
             self.resolution_height_max = 'Resolution Height Max'
             self.resolution_width_max = 'Resolution Width Max'
             self.train_epoch = 'Total Training Epochs'
@@ -142,6 +149,8 @@ class TrainerUIName():
             self.bucket_resolution_steps = 'Bucket Resolution Steps'
             self.bucket_no_upscale = 'Bucket No Upscale'
             self.bucket_no_upscale_ins = 'Disable Automatic Image Upscaling'
+            self.accumulate_step = 'Accumulate Step'
+            self.gpus = 'Select GPUs'
             # Error or Warning
             self.training_err1 = 'CUDA is unavailable.'
             self.training_err2 = 'Currently insufficient VRAM, training failed!'
@@ -153,7 +162,7 @@ class TrainerUIName():
         elif language == 'zh':
             self.user_direction = '''
                 ### 使用说明
-                - 数据: 通过数据管理器进行数据的准备（ZIP样例：[3D](https://www.modelscope.cn/api/v1/models/iic/scepter/repo?Revision=master&FilePath=datasets/3D_example_csv.zip)）
+                - 数据: 通过数据管理器进行数据的准备（ZIP样例：[3D](https://www.modelscope.cn/api/v1/models/iic/scepter/repo?Revision=master&FilePath=datasets/3D_example_csv.zip)，txt样例（仅支持视频数据）：[txt](https://modelscope.cn/models/iic/scepter/resolve/master/datasets/video_example.txt)）
                 - 参数: 可尝试进行相关参数的修改
                 - 训练: 点击【开始训练】
                 - 测试: 完成训练后点击【使用模型】
@@ -161,14 +170,17 @@ class TrainerUIName():
                 - 对于大规模数据的处理和训练，建议使用命令行形式
                 '''  # noqa
             self.data_source_choices = ['数据集zip', 'MaaS数据集', '数据管理器']
+            self.illegal_data_err = '列表只支持,或#;#作为分割符，两列分别为视频路径/描述'
             self.data_source_value = '数据集zip'
             self.data_source_name = '数据集来源'
             self.data_type_map = {
                 'scepter_txt2img': '文生图数据',
-                'scepter_img2img': '图像编辑（图生图）数据'
+                'scepter_img2img': '图像编辑（图生图）数据',
+                'scepter_txt2vid': '文生视频数据'
             }
             self.data_type_choices = list(self.data_type_map.keys())
             self.data_type_value = 'scepter_txt2img'
+            self.data_type_value_video = 'scepter_txt2vid'
             self.data_type_name = '数据类型'
             self.ori_data_name = '数据集名称'
             self.ms_data_name_place_hold = '请使用数据管理器导入'  # '支持MaaS数据集/本地/Http Zip包'
@@ -187,8 +199,8 @@ class TrainerUIName():
             self.base_model = '基础模型'
             self.tuner_name = '微调方法'
             self.base_model_revision = '模型版本号'
-            self.resolution_height = '训练图片高度'
-            self.resolution_width = '训练图片宽度'
+            self.resolution_height = '训练图片或视频高度'
+            self.resolution_width = '训练图片或视频宽度'
             self.resolution_height_max = '最大训练高度'
             self.resolution_width_max = '最大训练宽度'
             self.train_epoch = '总训练轮数'
@@ -208,6 +220,8 @@ class TrainerUIName():
             self.bucket_resolution_steps = '分桶分辨率步长'
             self.bucket_no_upscale = '分桶分辨率不做放大'
             self.bucket_no_upscale_ins = '禁止图片分辨率上采样'
+            self.accumulate_step = '梯度累积数量'
+            self.gpus = '选择GPU'
             # Error or Warning
             self.training_err1 = 'CUDA不可用.'
             self.training_err2 = '目前显存不足，训练失败！'

scepter/studio/self_train/self_train_ui/model_ui.py

@@ -59,13 +59,16 @@ class ModelUI(UIBase):
                     status_file = os.path.join(self.work_dir, one_dir,
                                                'status.json')
                     if FS.exists(status_file):
-                        status = json.load(open(status_file, 'r'))
+                        try:
+                            status = json.load(open(status_file, 'r'))
+                        except:
+                            continue
                         status['model_name'] = one_dir
                         have_model_list.append(status)
         have_model_list.sort(key=lambda x: x['start_time'])
         self.user_level_model_list[user_name] = [
             v['model_name'] for v in have_model_list
-        ][:100]
+        ]
 
     def get_ckpt_list(self, output_model):
         all_ckpt_list = []
@@ -177,11 +180,12 @@ class ModelUI(UIBase):
 
         def model_name_change(model_name):
             if model_name is None:
-                return '', gr.Column(), '', []
+                return '', gr.Column(), None, []
             message = trainer_ui.trainer_ins.get_log(model_name)
             status = trainer_ui.trainer_ins.get_status(model_name)
             ckpt_list = self.get_ckpt_list(model_name)
-            ckpt_value = ckpt_list[-1] if len(ckpt_list) > 0 else ''
+            ckpt_value = ckpt_list[-1] if len(ckpt_list) > 0 else None
+            ckpt_list = ckpt_list if isinstance(ckpt_list, list) and len(ckpt_list) > 0 else None
             if ckpt_value is not None and len(ckpt_value) > 0:
                 gallery_value = self.get_gallery_list(model_name, ckpt_value)
             else:
@@ -264,13 +268,14 @@ class ModelUI(UIBase):
             message = trainer_ui.trainer_ins.get_log(model_name)
             status = trainer_ui.trainer_ins.get_status(model_name)
             ckpt_list = self.get_ckpt_list(model_name)
-            ckpt_value = ckpt_list[-1] if len(ckpt_list) > 0 else ''
+            ckpt_value = ckpt_list[-1] if len(ckpt_list) > 0 else None
+            ckpt_list = ckpt_list if isinstance(ckpt_list, list) and len(ckpt_list) > 0 else None
             ret_gallery = ckpt_name_change(model_name, ckpt_value)
+            model_list = self.user_level_model_list.get(login_user_name, [])
             self.load_history(login_user_name)
             return (message, gr.Column(visible=status in ('running',
                                                           'success')),
-                    gr.Dropdown(choices=self.user_level_model_list.get(
-                        login_user_name, []),
+                    gr.Dropdown(choices=model_list,
                                 value=model_name),
                     gr.Dropdown(choices=ckpt_list,
                                 value=ckpt_value), ret_gallery)
@@ -330,7 +335,8 @@ class ModelUI(UIBase):
             message = trainer_ui.trainer_ins.get_log(model_name)
             status = trainer_ui.trainer_ins.get_status(model_name)
             ckpt_list = self.get_ckpt_list(model_name)
-            ckpt_value = ckpt_list[-1] if len(ckpt_list) > 0 else ''
+            ckpt_value = ckpt_list[-1] if len(ckpt_list) > 0 else None
+            ckpt_list = ckpt_list if isinstance(ckpt_list, list) and len(ckpt_list) > 0 else None
             ret_gallery = ckpt_name_change(model_name, ckpt_value)
             self.load_history(login_user_name)
             return (message, gr.Column(visible=status in ('running',
@@ -553,7 +559,7 @@ class ModelUI(UIBase):
             if len(model_list) > 0:
                 model_name = model_list[-1]
             else:
-                model_name = ''
+                model_name = None
             return gr.Dropdown(choices=model_list, value=model_name)
 
         manager.user_name.change(model_name_change,

scepter/studio/self_train/self_train_ui/trainer_ui.py

@@ -5,12 +5,17 @@ import datetime
 import json
 import os
 import random
+import time
 from collections import OrderedDict
 
+from tqdm import tqdm
+
+import decord
 import gradio as gr
 import scepter
 import torch
 import yaml
+from scepter.modules.utils.directory import get_md5
 from scepter.modules.utils.file_system import FS
 from scepter.studio.self_train.scripts.trainer import TrainManager
 from scepter.studio.self_train.self_train_ui.component_names import \
@@ -84,6 +89,7 @@ class TrainerUI(UIBase):
         self.current_train_model = None
         self.trainer_ins = TrainManager(self.run_script, self.work_dir_pre)
         self.component_names = TrainerUIName(language=language)
+        self.save_file_local_path = cfg.SAVE_FILE_LOCAL_PATH
 
         self.h_level_dict = {}
         for hw_tuple in self.train_para_data.RESOLUTIONS.get('VALUES', []):
@@ -107,7 +113,7 @@ class TrainerUI(UIBase):
                             choices=self.component_names.data_source_choices,
                             value=self.component_names.data_source_value,
                             label=self.component_names.data_source_name,
-                            interactive=False)
+                            interactive=True)
                         self.data_type = gr.Dropdown(
                             choices=[
                                 self.component_names.data_type_map[key] for key
@@ -116,20 +122,20 @@ class TrainerUI(UIBase):
                             value=self.component_names.data_type_map[
                                 self.component_names.data_type_value],
                             label=self.component_names.data_type_name,
-                            interactive=False)
+                            interactive=True)
                         self.ori_data_name = gr.Textbox(
                             label=self.component_names.ori_data_name,
                             max_lines=1,
                             placeholder=self.component_names.ori_data_name,
-                            interactive=False)
+                            interactive=True)
                         self.ms_data_name = gr.Textbox(
                             label=' or '.join(
                                 self.component_names.data_source_choices),
                             max_lines=1,
                             placeholder=self.component_names.
                             ms_data_name_place_hold,
-                            visible=False,
-                            interactive=False)
+                            visible=True,
+                            interactive=True)
                         with gr.Group(visible=False) as self.ms_data_box:
                             with gr.Row():
                                 self.ms_data_space = gr.Textbox(
@@ -199,8 +205,7 @@ class TrainerUI(UIBase):
                                     visible=lora_visible) as self.lora_param:
                                 self.lora_alpha = gr.Number(
                                     label='LoRA Alpha',
-                                    value=self.para_data.get(
-                                        'lora_alpha', 4),
+                                    value=self.para_data.get('lora_alpha', 4),
                                     interactive=True)
                                 self.lora_rank = gr.Number(
                                     label='LoRA Rank',
@@ -310,6 +315,19 @@ class TrainerUI(UIBase):
                                 precision=0,
                                 interactive=True)
 
+                        with gr.Row():
+                            self.accumulate_step = gr.Number(
+                                label=self.component_names.accumulate_step,
+                                value=self.para_data.get('ACCUMULATE_STEP', 1),
+                                precision=0,
+                                interactive=True)
+                            self.gpus = gr.Dropdown(
+                                choices=list(range(torch.cuda.device_count())),
+                                value=list(range(torch.cuda.device_count())),
+                                label=self.component_names.gpus,
+                                multiselect=True,
+                                interactive=True)
+
                         with gr.Row():
                             self.prompt_prefix = gr.Text(
                                 label=self.component_names.prompt_prefix,
@@ -355,6 +373,15 @@ class TrainerUI(UIBase):
                                 self.component_names.data_type_value], 'damo',
                             'style_custom_dataset', 'style_custom_dataset',
                             '3D'
+                        ],
+                        [
+                            self.component_names.data_source_choices[2],
+                            self.component_names.data_type_map[
+                                self.component_names.data_type_value_video],
+                            '',
+                            'https://modelscope.cn/models/iic/scepter/resolve/master/datasets/video_example.txt',   # noqa
+                            'video_example_txt',
+                            ''
                         ]
                     ],
                     inputs=[
@@ -439,6 +466,7 @@ class TrainerUI(UIBase):
             eval_prompts = [] if is_edit else self.train_para_data.get(
                 'EVAL_PROMPTS', [])
             eval_prompts = ret_data.get('EVAL_PROMPTS', eval_prompts)
+
             return ret_data.get('EPOCHS', 10), \
                 ret_data.get('LEARNING_RATE', 0.0001), \
                 ret_data.get('SAVE_INTERVAL', 10), \
@@ -613,7 +641,8 @@ class TrainerUI(UIBase):
                       lora_alpha, lora_rank, text_lora_alpha, text_lora_rank,
                       sce_ratio, enable_resolution_bucket,
                       min_bucket_resolution, max_bucket_resolution,
-                      bucket_resolution_steps, bucket_no_upscale, user_name):
+                      bucket_resolution_steps, bucket_no_upscale,
+                      accumulate_step, gpus, user_name):
             # Check Cuda
             if not torch.cuda.is_available() and not self.is_debug:
                 raise gr.Error(self.component_names.training_err1)
@@ -621,7 +650,7 @@ class TrainerUI(UIBase):
             if work_name == 'custom' or work_name is None or work_name == '':
                 raise gr.Error(self.component_names.training_err4)
             work_dir = os.path.join(self.work_dir_pre, work_name)
-            login_user_name = user_name
+
             self.current_train_model = work_name
             if os.path.exists(work_dir) or os.path.exists(
                     f'.flag/{work_name}.tmp'):
@@ -655,7 +684,7 @@ class TrainerUI(UIBase):
             if ms_data_name is None:
                 raise gr.Error(self.component_names.training_err3)
 
-            def prepare_train_data(data_cfg):
+            def prepare_train_image_data(data_cfg):
                 data_cfg['BATCH_SIZE'] = int(train_batch_size)
                 data_cfg['PROMPT_PREFIX'] = prompt_prefix
                 data_cfg['REPLACE_KEYWORDS'] = replace_keywords
@@ -737,8 +766,12 @@ class TrainerUI(UIBase):
                         if os.path.exists(local_data_dir) and os.path.exists(
                                 local_file_list):
                             data_cfg.update({
-                                'NAME': 'ImageTextPairDataset' if data_cfg['NAME'] == 'ImageTextPairMSDataset' else data_cfg['NAME'],
-                                'ENABLE_RESOLUTION_BUCKET': enable_resolution_bucket,
+                                'NAME':
+                                'ImageTextPairDataset'
+                                if data_cfg['NAME'] == 'ImageTextPairMSDataset'
+                                else data_cfg['NAME'],
+                                'ENABLE_RESOLUTION_BUCKET':
+                                enable_resolution_bucket,
                                 'SAMPLER': {
                                     'NAME':
                                     'ResolutionBatchSampler',
@@ -761,7 +794,8 @@ class TrainerUI(UIBase):
                                     'BUCKET_NO_UPSCALE':
                                     bucket_no_upscale
                                 },
-                                'DATA_NUM': data_num
+                                'DATA_NUM':
+                                data_num
                             })
                             if 'TRANSFORMS' in data_cfg:
                                 for trans in data_cfg['TRANSFORMS']:
@@ -776,6 +810,69 @@ class TrainerUI(UIBase):
 
                 return data_cfg
 
+            def prepare_train_video_data(data_cfg):
+                if ms_data_name.startswith('http') and (
+                        '.txt' in ms_data_name or '.csv' in ms_data_name):
+                    data_name = get_data_from_list()
+                else:
+                    data_name = os.path.join(ms_data_name, 'file.txt')
+
+                data_cfg['BATCH_SIZE'] = int(train_batch_size)
+                data_cfg['PROMPT_PREFIX'] = prompt_prefix
+                if data_cfg['NAME'] in ['VideoGenDataset']:
+                    data_cfg['SAMPLER']['SUB_SAMPLERS'][0][
+                        'PATH_PREFIX'] = os.path.dirname(data_name)
+                    data_cfg['SAMPLER']['SUB_SAMPLERS'][0][
+                        'INDEX_FILE'] = data_name
+                elif data_cfg['NAME'] in ['VideoGenDatasetOTF']:
+                    data_cfg['PATH_PREFIX'] = os.path.dirname(data_name)
+                    data_cfg['DATA_FILE'] = data_name
+                else:
+                    raise Exception('Unsupported data type {}'.format(
+                        data_cfg['NAME']))
+                return data_cfg
+
+            def get_data_from_list():
+                file_list = []
+                file = FS.get_from(ms_data_name)
+                with FS.get_from(file) as local_path:
+                    with open(local_path, 'r') as f:
+                        for line in tqdm(f):
+                            line = line.strip()
+                            if line == '':
+                                continue
+                            try:
+                                src_video_path, caption = line.split('#;#', 1)
+                            except Exception:
+                                try:
+                                    src_video_path, caption = line.split(
+                                        ',', 1)
+                                except Exception:
+                                    raise gr.Error(
+                                        self.component_names.illegal_data_err)
+                            relative_path = os.path.join(
+                                'videos',
+                                f'{get_md5(src_video_path)[:18]}_{int(time.time())}.mp4'
+                            )
+                            video_path = os.path.join(
+                                self.save_file_local_path, ori_data_name,
+                                relative_path)
+                            local_path = FS.get_from(src_video_path,
+                                                     local_path=video_path)
+                            video_reader = decord.VideoReader(local_path)
+                            w = video_reader[0].shape[1]
+                            h = video_reader[0].shape[0]
+                            file_list.append('{}#;#{}#;#{}#;#{}\n'.format(
+                                relative_path, w, h, caption))
+                local_save_file_list = os.path.join(self.save_file_local_path,
+                                                    ori_data_name, 'file.txt')
+                directory = os.path.dirname(local_save_file_list)
+                os.makedirs(directory, exist_ok=True)
+                FS.delete_object(file)
+                with open(local_save_file_list, 'w') as f:
+                    f.writelines(file_list)
+                return local_save_file_list
+
             def prepare_eval_data(data_cfg):
                 data_cfg['PROMPT_PREFIX'] = prompt_prefix
                 data_cfg['IMAGE_SIZE'] = [
@@ -850,8 +947,14 @@ class TrainerUI(UIBase):
                         ]
                     cfg['SOLVER']['TUNER'] = tuner_cfg_list
 
-                cfg['SOLVER']['TRAIN_DATA'] = prepare_train_data(
-                    cfg['SOLVER']['TRAIN_DATA'])
+                if cfg['SOLVER']['TRAIN_DATA']['NAME'] in [
+                        'VideoGenDataset', 'VideoGenDatasetOTF'
+                ]:
+                    cfg['SOLVER']['TRAIN_DATA'] = prepare_train_video_data(
+                        cfg['SOLVER']['TRAIN_DATA'])
+                else:
+                    cfg['SOLVER']['TRAIN_DATA'] = prepare_train_image_data(
+                        cfg['SOLVER']['TRAIN_DATA'])
                 if eval_prompts is not None and len(eval_prompts) > 0:
                     cfg['SOLVER']['EVAL_DATA'] = prepare_eval_data(
                         cfg['SOLVER']['EVAL_DATA'])
@@ -868,6 +971,8 @@ class TrainerUI(UIBase):
                         hook['INTERVAL'] = save_interval
                         hook['PUSH_TO_HUB'] = push_to_hub
                         hook['HUB_MODEL_ID'] = hub_model_id
+                    if hook['NAME'] == 'BackwardHook':
+                        hook['ACCUMULATE_STEP'] = int(accumulate_step)
                 if 'EVAL_HOOKS' in cfg['SOLVER']:
                     for hook in cfg['SOLVER']['EVAL_HOOKS']:
                         if hook['NAME'] == 'ProbeDataHook':
@@ -881,6 +986,7 @@ class TrainerUI(UIBase):
                               default_flow_style=False)
                 return cfg_file
 
+            self.trainer_ins.set_gpus(gpus)
             before_kill_inference = self.trainer_ins.check_memory()
             if hasattr(manager, 'inference'):
                 for k, v in manager.inference.pipe_manager.pipeline_level_modules.items(
@@ -892,7 +998,6 @@ class TrainerUI(UIBase):
                     'dynamic_unload')):
                 manager.preprocess.dataset_gallery.processors_manager.dynamic_unload(
                 )
-
             after_kill_inference = self.trainer_ins.check_memory()
             message = f'GPU info: {before_kill_inference}. \n\n'
             message += f'After unloading inference models, the GPU info: {after_kill_inference}. \n\n'
@@ -923,7 +1028,8 @@ class TrainerUI(UIBase):
                 self.text_lora_alpha, self.text_lora_rank, self.sce_ratio,
                 self.enable_resolution_bucket, self.min_bucket_resolution,
                 self.max_bucket_resolution, self.bucket_resolution_steps,
-                self.bucket_no_upscale, manager.user_name
+                self.bucket_no_upscale, self.accumulate_step, self.gpus,
+                manager.user_name
             ],
             outputs=[inference_ui.output_model_name],
             queue=True)

scepter/tools/webui.py

@@ -65,6 +65,13 @@ if __name__ == '__main__':
                         choices=['en', 'zh'],
                         default='en',
                         help='Now we only support english(en) and chinese(zh)')
+    parser.add_argument('--tab',
+                        dest='tab',
+                        choices=['all', 'chatbot'],
+                        default='all',
+                        help='The tabs will be launched, '
+                             'set [all] to use all tools and set [chatbot] to use chatbot only.')
+
     args = parser.parse_args()
     if not os.path.exists(args.config):
         print(
@@ -88,7 +95,7 @@ if __name__ == '__main__':
         if not FS.exists(info['CONFIG']):
             raise f"{info['CONFIG']} doesn't exist."
         interface = None
-        if ifid == 'home':
+        if ifid == 'home' and args.tab in ["all", ifid]:
             from scepter.studio.home.home import HomeUI
 
             interface = HomeUI(info['CONFIG'],
@@ -96,7 +103,7 @@ if __name__ == '__main__':
                                language=args.language,
                                root_work_dir=config.WORK_DIR)
             print('init home page success!')
-        if ifid == 'preprocess':
+        if ifid == 'preprocess' and args.tab in ["all", ifid]:
             from scepter.studio.preprocess.preprocess import PreprocessUI
 
             interface = PreprocessUI(info['CONFIG'],
@@ -104,7 +111,7 @@ if __name__ == '__main__':
                                      language=args.language,
                                      root_work_dir=config.WORK_DIR)
             print('init preprocess success!')
-        if ifid == 'self_train':
+        if ifid == 'self_train'  and args.tab in ["all", ifid]:
             from scepter.studio.self_train.self_train import SelfTrainUI
 
             interface = SelfTrainUI(info['CONFIG'],
@@ -112,14 +119,14 @@ if __name__ == '__main__':
                                     language=args.language,
                                     root_work_dir=config.WORK_DIR)
             print('init self-train success!')
-        if ifid == 'tuner_manager':
+        if ifid == 'tuner_manager'  and args.tab in ["all", ifid]:
             from scepter.studio.tuner_manager.tuner_manager import TunerManagerUI
             interface = TunerManagerUI(info['CONFIG'],
                                        is_debug=args.debug,
                                        language=args.language,
                                        root_work_dir=config.WORK_DIR)
             print('init tuner-manager success!')
-        if ifid == 'inference':
+        if ifid == 'inference'  and args.tab in ["all", ifid]:
             from scepter.studio.inference.inference import InferenceUI
 
             interface = InferenceUI(info['CONFIG'],
@@ -127,7 +134,7 @@ if __name__ == '__main__':
                                     language=args.language,
                                     root_work_dir=config.WORK_DIR)
             print('init inference success!')
-        if ifid == 'ChatBot':
+        if ifid == 'chatbot' and args.tab in ["all", ifid]:
             from scepter.studio.chatbot.chatbot import ChatBotUI
 
             interface = ChatBotUI(info['CONFIG'],
@@ -177,10 +184,13 @@ if __name__ == '__main__':
         if len(auth_info) > 0:
             demo.load(init_value, outputs=[tab_manager.user_name])
 
+    allowed_paths = [config['WORK_DIR']]
+    allowed_paths.extend(list(set([fs_cfg['TEMP_DIR'] for fs_cfg in config['FILE_SYSTEM']])) if 'FILE_SYSTEM' in config else [])
     demo.queue(status_update_rate=1).launch(
         server_name=args.host if args.host else config['HOST'],
         server_port=int(args.port) if args.port else config['PORT'],
         root_path=config['ROOT'],
         show_error=True,
         debug=True,
-        auth=check_auth if len(auth_info) > 0 else None)
+        auth=check_auth if len(auth_info) > 0 else None,
+        allowed_paths=allowed_paths)

scepter/version.py

@@ -1,7 +1,7 @@
 # -*- coding: utf-8 -*-
 # Copyright (c) Alibaba, Inc. and its affiliates.
 
-__version__ = '1.2.0'
+__version__ = '1.3.0'
 
 version_info = tuple(int(x) for x in __version__.split('.')[0:3])
 

scepter/workflow/config/ace_0.6b_1024_pro.yaml

@@ -0,0 +1,296 @@
+NAME: ACE_0.6B_1024
+IS_DEFAULT: False
+DEFAULT_PARAS:
+  PARAS:
+  #
+  INPUT:
+    INPUT_IMAGE:
+    INPUT_MASK:
+    TASK:
+    PROMPT: ""
+    NEGATIVE_PROMPT: ""
+    OUTPUT_HEIGHT: 1024
+    OUTPUT_WIDTH: 1024
+    SAMPLER: ddim
+    SAMPLE_STEPS: 20
+    GUIDE_SCALE: 4.5
+    GUIDE_RESCALE: 0.5
+    SEED: -1
+    TAR_INDEX: 0
+  OUTPUT:
+    LATENT:
+    IMAGES:
+    SEED:
+  MODULES_PARAS:
+    FIRST_STAGE_MODEL:
+      FUNCTION:
+        - NAME: encode
+          DTYPE: float16
+          INPUT: ["IMAGE"]
+        - NAME: decode
+          DTYPE: float16
+          INPUT: ["LATENT"]
+    #
+    DIFFUSION_MODEL:
+      FUNCTION:
+        - NAME: forward
+          DTYPE: float16
+          INPUT: ["SAMPLE_STEPS", "SAMPLE", "GUIDE_SCALE"]
+    #
+    COND_STAGE_MODEL:
+      FUNCTION:
+        - NAME: encode_list_of_list
+          DTYPE: bfloat16
+          INPUT: ["PROMPT"]
+#
+TEXT_IDENTIFIER: [ '{image}', '{image1}', '{image2}', '{image3}', '{image4}', '{image5}', '{image6}', '{image7}', '{image8}', '{image9}' ]
+USE_TEXT_POS_EMBEDDINGS: True
+#
+MODEL:
+  NAME: LatentDiffusionACE
+  PRETRAINED_MODEL:
+  IGNORE_KEYS: [ ]
+  SCALE_FACTOR: 0.18215
+  SIZE_FACTOR: 8
+  DECODER_BIAS: 0.5
+  DEFAULT_N_PROMPT: ""
+  TEXT_IDENTIFIER: [ '{image}', '{image1}', '{image2}', '{image3}', '{image4}', '{image5}', '{image6}', '{image7}', '{image8}', '{image9}' ]
+  USE_TEXT_POS_EMBEDDINGS: True
+  #
+  DIFFUSION:
+    NAME: BaseDiffusion
+    PREDICTION_TYPE: eps
+    MIN_SNR_GAMMA:
+    NOISE_SCHEDULER:
+      NAME: LinearScheduler
+      NUM_TIMESTEPS: 1000
+      BETA_MIN: 0.0001
+      BETA_MAX: 0.02
+  #
+  DIFFUSION_MODEL:
+    NAME: ACE
+    PRETRAINED_MODEL: ms://iic/ACE-0.6B-1024px@models/dit/ace_0.6b_1024px.pth
+    IGNORE_KEYS: [ ]
+    PATCH_SIZE: 2
+    IN_CHANNELS: 4
+    HIDDEN_SIZE: 1152
+    DEPTH: 28
+    NUM_HEADS: 16
+    MLP_RATIO: 4.0
+    PRED_SIGMA: True
+    DROP_PATH: 0.0
+    WINDOW_DIZE: 0
+    Y_CHANNELS: 4096
+    MAX_SEQ_LEN: 1024
+    QK_NORM: True
+    USE_GRAD_CHECKPOINT: True
+    ATTENTION_BACKEND: flash_attn
+  #
+  FIRST_STAGE_MODEL:
+    NAME: AutoencoderKL
+    EMBED_DIM: 4
+    PRETRAINED_MODEL: ms://iic/ACE-0.6B-1024px@models/vae/vae.bin
+    IGNORE_KEYS: []
+    #
+    ENCODER:
+      NAME: Encoder
+      CH: 128
+      OUT_CH: 3
+      NUM_RES_BLOCKS: 2
+      IN_CHANNELS: 3
+      ATTN_RESOLUTIONS: [ ]
+      CH_MULT: [ 1, 2, 4, 4 ]
+      Z_CHANNELS: 4
+      DOUBLE_Z: True
+      DROPOUT: 0.0
+      RESAMP_WITH_CONV: True
+    #
+    DECODER:
+      NAME: Decoder
+      CH: 128
+      OUT_CH: 3
+      NUM_RES_BLOCKS: 2
+      IN_CHANNELS: 3
+      ATTN_RESOLUTIONS: [ ]
+      CH_MULT: [ 1, 2, 4, 4 ]
+      Z_CHANNELS: 4
+      DROPOUT: 0.0
+      RESAMP_WITH_CONV: True
+      GIVE_PRE_END: False
+      TANH_OUT: False
+  #
+  COND_STAGE_MODEL:
+    NAME: T5EmbedderHF
+    PRETRAINED_MODEL: ms://iic/ACE-0.6B-1024px@models/text_encoder/t5-v1_1-xxl/
+    TOKENIZER_PATH: ms://iic/ACE-0.6B-1024px@models/tokenizer/t5-v1_1-xxl
+    LENGTH: 120
+    T5_DTYPE: bfloat16
+    ADDED_IDENTIFIER: [ '{image}', '{caption}', '{mask}', '{ref_image}', '{image1}', '{image2}', '{image3}', '{image4}', '{image5}', '{image6}', '{image7}', '{image8}', '{image9}' ]
+    CLEAN: whitespace
+    USE_GRAD: False
+#
+MODEL_LOCAL:
+  NAME: LatentDiffusionACE
+  PRETRAINED_MODEL:
+  IGNORE_KEYS: [ ]
+  SCALE_FACTOR: 0.18215
+  SIZE_FACTOR: 8
+  DECODER_BIAS: 0.5
+  DEFAULT_N_PROMPT: ""
+  TEXT_IDENTIFIER: [ '{image}', '{image1}', '{image2}', '{image3}', '{image4}', '{image5}', '{image6}', '{image7}', '{image8}', '{image9}' ]
+  USE_TEXT_POS_EMBEDDINGS: True
+  #
+  DIFFUSION:
+    NAME: BaseDiffusion
+    PREDICTION_TYPE: eps
+    MIN_SNR_GAMMA:
+    NOISE_SCHEDULER:
+      NAME: LinearScheduler
+      NUM_TIMESTEPS: 1000
+      BETA_MIN: 0.0001
+      BETA_MAX: 0.02
+  #
+  DIFFUSION_MODEL:
+    NAME: ACE
+    PRETRAINED_MODEL: models/scepter/ACE-0.6B-1024px/models/dit/ace_0.6b_1024px.pth
+    IGNORE_KEYS: [ ]
+    PATCH_SIZE: 2
+    IN_CHANNELS: 4
+    HIDDEN_SIZE: 1152
+    DEPTH: 28
+    NUM_HEADS: 16
+    MLP_RATIO: 4.0
+    PRED_SIGMA: True
+    DROP_PATH: 0.0
+    WINDOW_DIZE: 0
+    Y_CHANNELS: 4096
+    MAX_SEQ_LEN: 1024
+    QK_NORM: True
+    USE_GRAD_CHECKPOINT: True
+    ATTENTION_BACKEND: flash_attn
+  #
+  FIRST_STAGE_MODEL:
+    NAME: AutoencoderKL
+    EMBED_DIM: 4
+    PRETRAINED_MODEL: models/scepter/ACE-0.6B-1024px/models/vae/vae.bin
+    IGNORE_KEYS: []
+    #
+    ENCODER:
+      NAME: Encoder
+      CH: 128
+      OUT_CH: 3
+      NUM_RES_BLOCKS: 2
+      IN_CHANNELS: 3
+      ATTN_RESOLUTIONS: [ ]
+      CH_MULT: [ 1, 2, 4, 4 ]
+      Z_CHANNELS: 4
+      DOUBLE_Z: True
+      DROPOUT: 0.0
+      RESAMP_WITH_CONV: True
+    #
+    DECODER:
+      NAME: Decoder
+      CH: 128
+      OUT_CH: 3
+      NUM_RES_BLOCKS: 2
+      IN_CHANNELS: 3
+      ATTN_RESOLUTIONS: [ ]
+      CH_MULT: [ 1, 2, 4, 4 ]
+      Z_CHANNELS: 4
+      DROPOUT: 0.0
+      RESAMP_WITH_CONV: True
+      GIVE_PRE_END: False
+      TANH_OUT: False
+  #
+  COND_STAGE_MODEL:
+    NAME: T5EmbedderHF
+    PRETRAINED_MODEL: models/scepter/ACE-0.6B-1024px/models/text_encoder/t5-v1_1-xxl/
+    TOKENIZER_PATH: models/scepter/ACE-0.6B-1024px/models/tokenizer/t5-v1_1-xxl
+    LENGTH: 120
+    T5_DTYPE: bfloat16
+    ADDED_IDENTIFIER: [ '{image}', '{caption}', '{mask}', '{ref_image}', '{image1}', '{image2}', '{image3}', '{image4}', '{image5}', '{image6}', '{image7}', '{image8}', '{image9}' ]
+    CLEAN: whitespace
+    USE_GRAD: False
+#
+MODEL_HF:
+  NAME: LatentDiffusionACE
+  PRETRAINED_MODEL:
+  IGNORE_KEYS: [ ]
+  SCALE_FACTOR: 0.18215
+  SIZE_FACTOR: 8
+  DECODER_BIAS: 0.5
+  DEFAULT_N_PROMPT: ""
+  TEXT_IDENTIFIER: [ '{image}', '{image1}', '{image2}', '{image3}', '{image4}', '{image5}', '{image6}', '{image7}', '{image8}', '{image9}' ]
+  USE_TEXT_POS_EMBEDDINGS: True
+  #
+  DIFFUSION:
+    NAME: BaseDiffusion
+    PREDICTION_TYPE: eps
+    MIN_SNR_GAMMA:
+    NOISE_SCHEDULER:
+      NAME: LinearScheduler
+      NUM_TIMESTEPS: 1000
+      BETA_MIN: 0.0001
+      BETA_MAX: 0.02
+  #
+  DIFFUSION_MODEL:
+    NAME: ACE
+    PRETRAINED_MODEL: hf://scepter-studio/ACE-0.6B-1024px@models/dit/ace_0.6b_1024px.pth
+    IGNORE_KEYS: [ ]
+    PATCH_SIZE: 2
+    IN_CHANNELS: 4
+    HIDDEN_SIZE: 1152
+    DEPTH: 28
+    NUM_HEADS: 16
+    MLP_RATIO: 4.0
+    PRED_SIGMA: True
+    DROP_PATH: 0.0
+    WINDOW_DIZE: 0
+    Y_CHANNELS: 4096
+    MAX_SEQ_LEN: 1024
+    QK_NORM: True
+    USE_GRAD_CHECKPOINT: True
+    ATTENTION_BACKEND: flash_attn
+  #
+  FIRST_STAGE_MODEL:
+    NAME: AutoencoderKL
+    EMBED_DIM: 4
+    PRETRAINED_MODEL: hf://scepter-studio/ACE-0.6B-1024px@models/vae/vae.bin
+    IGNORE_KEYS: []
+    #
+    ENCODER:
+      NAME: Encoder
+      CH: 128
+      OUT_CH: 3
+      NUM_RES_BLOCKS: 2
+      IN_CHANNELS: 3
+      ATTN_RESOLUTIONS: [ ]
+      CH_MULT: [ 1, 2, 4, 4 ]
+      Z_CHANNELS: 4
+      DOUBLE_Z: True
+      DROPOUT: 0.0
+      RESAMP_WITH_CONV: True
+    #
+    DECODER:
+      NAME: Decoder
+      CH: 128
+      OUT_CH: 3
+      NUM_RES_BLOCKS: 2
+      IN_CHANNELS: 3
+      ATTN_RESOLUTIONS: [ ]
+      CH_MULT: [ 1, 2, 4, 4 ]
+      Z_CHANNELS: 4
+      DROPOUT: 0.0
+      RESAMP_WITH_CONV: True
+      GIVE_PRE_END: False
+      TANH_OUT: False
+  #
+  COND_STAGE_MODEL:
+    NAME: T5EmbedderHF
+    PRETRAINED_MODEL: hf://scepter-studio/ACE-0.6B-1024px@models/text_encoder/t5-v1_1-xxl/
+    TOKENIZER_PATH: hf://scepter-studio/ACE-0.6B-1024px@models/tokenizer/t5-v1_1-xxl
+    LENGTH: 120
+    T5_DTYPE: bfloat16
+    ADDED_IDENTIFIER: [ '{image}', '{caption}', '{mask}', '{ref_image}', '{image1}', '{image2}', '{image3}', '{image4}', '{image5}', '{image6}', '{image7}', '{image8}', '{image9}' ]
+    CLEAN: whitespace
+    USE_GRAD: False

scepter/workflow/config/ace_0.6b_1024_refiner_pro.yaml

@@ -0,0 +1,729 @@
+NAME: ACE_0.6B_1024_REFINER
+IS_DEFAULT: False
+DEFAULT_PARAS:
+  PARAS:
+  #
+  INPUT:
+    INPUT_IMAGE:
+    INPUT_MASK:
+    TASK:
+    PROMPT: ""
+    NEGATIVE_PROMPT: ""
+    OUTPUT_HEIGHT: 1024
+    OUTPUT_WIDTH: 1024
+    SAMPLER: ddim
+    SAMPLE_STEPS: 50
+    GUIDE_SCALE: 4.5
+    GUIDE_RESCALE: 0.5
+    SEED: -1
+    TAR_INDEX: 0
+  OUTPUT:
+    LATENT:
+    IMAGES:
+    SEED:
+  MODULES_PARAS:
+    FIRST_STAGE_MODEL:
+      FUNCTION:
+        - NAME: encode
+          DTYPE: float16
+          INPUT: ["IMAGE"]
+        - NAME: decode
+          DTYPE: float16
+          INPUT: ["LATENT"]
+    #
+    DIFFUSION_MODEL:
+      FUNCTION:
+        - NAME: forward
+          DTYPE: float16
+          INPUT: ["SAMPLE_STEPS", "SAMPLE", "GUIDE_SCALE"]
+    #
+    COND_STAGE_MODEL:
+      FUNCTION:
+        - NAME: encode_list_of_list
+          DTYPE: bfloat16
+          INPUT: ["PROMPT"]
+#
+MODEL:
+  NAME: LatentDiffusionACE
+  PRETRAINED_MODEL:
+  IGNORE_KEYS: [ ]
+  SCALE_FACTOR: 0.18215
+  SIZE_FACTOR: 8
+  DECODER_BIAS: 0.5
+  DEFAULT_N_PROMPT: ""
+  TEXT_IDENTIFIER: [ '{image}', '{image1}', '{image2}', '{image3}', '{image4}', '{image5}', '{image6}', '{image7}', '{image8}', '{image9}' ]
+  USE_TEXT_POS_EMBEDDINGS: True
+  #
+  DIFFUSION:
+    NAME: BaseDiffusion
+    PREDICTION_TYPE: eps
+    MIN_SNR_GAMMA:
+    NOISE_SCHEDULER:
+      NAME: LinearScheduler
+      NUM_TIMESTEPS: 1000
+      BETA_MIN: 0.0001
+      BETA_MAX: 0.02
+  #
+  DIFFUSION_MODEL:
+    NAME: ACE
+    PRETRAINED_MODEL: ms://iic/ACE-0.6B-1024px@models/dit/ace_0.6b_1024px.pth
+    IGNORE_KEYS: [ ]
+    PATCH_SIZE: 2
+    IN_CHANNELS: 4
+    HIDDEN_SIZE: 1152
+    DEPTH: 28
+    NUM_HEADS: 16
+    MLP_RATIO: 4.0
+    PRED_SIGMA: True
+    DROP_PATH: 0.0
+    WINDOW_DIZE: 0
+    Y_CHANNELS: 4096
+    MAX_SEQ_LEN: 1024
+    QK_NORM: True
+    USE_GRAD_CHECKPOINT: True
+    ATTENTION_BACKEND: flash_attn
+  #
+  FIRST_STAGE_MODEL:
+    NAME: AutoencoderKL
+    EMBED_DIM: 4
+    PRETRAINED_MODEL: ms://iic/ACE-0.6B-1024px@models/vae/vae.bin
+    IGNORE_KEYS: []
+    #
+    ENCODER:
+      NAME: Encoder
+      CH: 128
+      OUT_CH: 3
+      NUM_RES_BLOCKS: 2
+      IN_CHANNELS: 3
+      ATTN_RESOLUTIONS: [ ]
+      CH_MULT: [ 1, 2, 4, 4 ]
+      Z_CHANNELS: 4
+      DOUBLE_Z: True
+      DROPOUT: 0.0
+      RESAMP_WITH_CONV: True
+    #
+    DECODER:
+      NAME: Decoder
+      CH: 128
+      OUT_CH: 3
+      NUM_RES_BLOCKS: 2
+      IN_CHANNELS: 3
+      ATTN_RESOLUTIONS: [ ]
+      CH_MULT: [ 1, 2, 4, 4 ]
+      Z_CHANNELS: 4
+      DROPOUT: 0.0
+      RESAMP_WITH_CONV: True
+      GIVE_PRE_END: False
+      TANH_OUT: False
+  #
+  COND_STAGE_MODEL:
+    NAME: T5EmbedderHF
+    PRETRAINED_MODEL: ms://iic/ACE-0.6B-1024px@models/text_encoder/t5-v1_1-xxl/
+    TOKENIZER_PATH: ms://iic/ACE-0.6B-1024px@models/tokenizer/t5-v1_1-xxl
+    LENGTH: 120
+    T5_DTYPE: bfloat16
+    ADDED_IDENTIFIER: [ '{image}', '{caption}', '{mask}', '{ref_image}', '{image1}', '{image2}', '{image3}', '{image4}', '{image5}', '{image6}', '{image7}', '{image8}', '{image9}' ]
+    CLEAN: whitespace
+    USE_GRAD: False
+#
+MODEL_LOCAL:
+  NAME: LatentDiffusionACE
+  PRETRAINED_MODEL:
+  IGNORE_KEYS: [ ]
+  SCALE_FACTOR: 0.18215
+  SIZE_FACTOR: 8
+  DECODER_BIAS: 0.5
+  DEFAULT_N_PROMPT: ""
+  TEXT_IDENTIFIER: [ '{image}', '{image1}', '{image2}', '{image3}', '{image4}', '{image5}', '{image6}', '{image7}', '{image8}', '{image9}' ]
+  USE_TEXT_POS_EMBEDDINGS: True
+  #
+  DIFFUSION:
+    NAME: BaseDiffusion
+    PREDICTION_TYPE: eps
+    MIN_SNR_GAMMA:
+    NOISE_SCHEDULER:
+      NAME: LinearScheduler
+      NUM_TIMESTEPS: 1000
+      BETA_MIN: 0.0001
+      BETA_MAX: 0.02
+  #
+  DIFFUSION_MODEL:
+    NAME: ACE
+    PRETRAINED_MODEL: models/scepter/ACE-0.6B-1024px/models/dit/ace_0.6b_1024px.pth
+    IGNORE_KEYS: [ ]
+    PATCH_SIZE: 2
+    IN_CHANNELS: 4
+    HIDDEN_SIZE: 1152
+    DEPTH: 28
+    NUM_HEADS: 16
+    MLP_RATIO: 4.0
+    PRED_SIGMA: True
+    DROP_PATH: 0.0
+    WINDOW_DIZE: 0
+    Y_CHANNELS: 4096
+    MAX_SEQ_LEN: 1024
+    QK_NORM: True
+    USE_GRAD_CHECKPOINT: True
+    ATTENTION_BACKEND: flash_attn
+  #
+  FIRST_STAGE_MODEL:
+    NAME: AutoencoderKL
+    EMBED_DIM: 4
+    PRETRAINED_MODEL: models/scepter/ACE-0.6B-1024px/models/vae/vae.bin
+    IGNORE_KEYS: []
+    #
+    ENCODER:
+      NAME: Encoder
+      CH: 128
+      OUT_CH: 3
+      NUM_RES_BLOCKS: 2
+      IN_CHANNELS: 3
+      ATTN_RESOLUTIONS: [ ]
+      CH_MULT: [ 1, 2, 4, 4 ]
+      Z_CHANNELS: 4
+      DOUBLE_Z: True
+      DROPOUT: 0.0
+      RESAMP_WITH_CONV: True
+    #
+    DECODER:
+      NAME: Decoder
+      CH: 128
+      OUT_CH: 3
+      NUM_RES_BLOCKS: 2
+      IN_CHANNELS: 3
+      ATTN_RESOLUTIONS: [ ]
+      CH_MULT: [ 1, 2, 4, 4 ]
+      Z_CHANNELS: 4
+      DROPOUT: 0.0
+      RESAMP_WITH_CONV: True
+      GIVE_PRE_END: False
+      TANH_OUT: False
+  #
+  COND_STAGE_MODEL:
+    NAME: T5EmbedderHF
+    PRETRAINED_MODEL: models/scepter/ACE-0.6B-1024px/models/text_encoder/t5-v1_1-xxl/
+    TOKENIZER_PATH: models/scepter/ACE-0.6B-1024px/models/tokenizer/t5-v1_1-xxl
+    LENGTH: 120
+    T5_DTYPE: bfloat16
+    ADDED_IDENTIFIER: [ '{image}', '{caption}', '{mask}', '{ref_image}', '{image1}', '{image2}', '{image3}', '{image4}', '{image5}', '{image6}', '{image7}', '{image8}', '{image9}' ]
+    CLEAN: whitespace
+    USE_GRAD: False
+#
+MODEL_HF:
+  NAME: LatentDiffusionACE
+  PRETRAINED_MODEL:
+  IGNORE_KEYS: [ ]
+  SCALE_FACTOR: 0.18215
+  SIZE_FACTOR: 8
+  DECODER_BIAS: 0.5
+  DEFAULT_N_PROMPT: ""
+  TEXT_IDENTIFIER: [ '{image}', '{image1}', '{image2}', '{image3}', '{image4}', '{image5}', '{image6}', '{image7}', '{image8}', '{image9}' ]
+  USE_TEXT_POS_EMBEDDINGS: True
+  #
+  DIFFUSION:
+    NAME: BaseDiffusion
+    PREDICTION_TYPE: eps
+    MIN_SNR_GAMMA:
+    NOISE_SCHEDULER:
+      NAME: LinearScheduler
+      NUM_TIMESTEPS: 1000
+      BETA_MIN: 0.0001
+      BETA_MAX: 0.02
+  #
+  DIFFUSION_MODEL:
+    NAME: ACE
+    PRETRAINED_MODEL: hf://scepter-studio/ACE-0.6B-1024px@models/dit/ace_0.6b_1024px.pth
+    IGNORE_KEYS: [ ]
+    PATCH_SIZE: 2
+    IN_CHANNELS: 4
+    HIDDEN_SIZE: 1152
+    DEPTH: 28
+    NUM_HEADS: 16
+    MLP_RATIO: 4.0
+    PRED_SIGMA: True
+    DROP_PATH: 0.0
+    WINDOW_DIZE: 0
+    Y_CHANNELS: 4096
+    MAX_SEQ_LEN: 1024
+    QK_NORM: True
+    USE_GRAD_CHECKPOINT: True
+    ATTENTION_BACKEND: flash_attn
+  #
+  FIRST_STAGE_MODEL:
+    NAME: AutoencoderKL
+    EMBED_DIM: 4
+    PRETRAINED_MODEL: hf://scepter-studio/ACE-0.6B-1024px@models/vae/vae.bin
+    IGNORE_KEYS: []
+    #
+    ENCODER:
+      NAME: Encoder
+      CH: 128
+      OUT_CH: 3
+      NUM_RES_BLOCKS: 2
+      IN_CHANNELS: 3
+      ATTN_RESOLUTIONS: [ ]
+      CH_MULT: [ 1, 2, 4, 4 ]
+      Z_CHANNELS: 4
+      DOUBLE_Z: True
+      DROPOUT: 0.0
+      RESAMP_WITH_CONV: True
+    #
+    DECODER:
+      NAME: Decoder
+      CH: 128
+      OUT_CH: 3
+      NUM_RES_BLOCKS: 2
+      IN_CHANNELS: 3
+      ATTN_RESOLUTIONS: [ ]
+      CH_MULT: [ 1, 2, 4, 4 ]
+      Z_CHANNELS: 4
+      DROPOUT: 0.0
+      RESAMP_WITH_CONV: True
+      GIVE_PRE_END: False
+      TANH_OUT: False
+  #
+  COND_STAGE_MODEL:
+    NAME: T5EmbedderHF
+    PRETRAINED_MODEL: hf://scepter-studio/ACE-0.6B-1024px@models/text_encoder/t5-v1_1-xxl/
+    TOKENIZER_PATH: hf://scepter-studio/ACE-0.6B-1024px@models/tokenizer/t5-v1_1-xxl
+    LENGTH: 120
+    T5_DTYPE: bfloat16
+    ADDED_IDENTIFIER: [ '{image}', '{caption}', '{mask}', '{ref_image}', '{image1}', '{image2}', '{image3}', '{image4}', '{image5}', '{image6}', '{image7}', '{image8}', '{image9}' ]
+    CLEAN: whitespace
+    USE_GRAD: False
+
+REFINER_SCALE: 0.4
+#REFINER_PROMPT: "High Resolution, Sharpness, Clarity, Detail Enhancement, Noise Reduction, HD, 4k, Image Restoration, HDR"
+REFINER_PROMPT: ""
+ACE_PROMPT: [
+  "A cute cartoon rabbit holding a whiteboard that says 'ACE Refiner', standing in a sunny meadow filled with flowers, with a big smile and bright colors.",
+  "A beautiful young woman with long flowing hair, wearing a summer dress, holding a whiteboard that reads 'ACE Refiner' while sitting on a park bench surrounded by cherry blossoms.",
+  "An adorable cartoon cat wearing oversized glasses, holding a whiteboard that says 'ACE Refiner', perched on a stack of colorful books in a cozy library setting.",
+  "A charming girl with pigtails, wearing a cute school uniform, enthusiastically holding a whiteboard that has 'ACE Refiner' written on it, in a bright and cheerful classroom full of educational posters.",
+  "A friendly cartoon dog with floppy ears, sitting in front of a doghouse, proudly holding a whiteboard that says 'ACE Refiner', with a playful expression and a blue sky in the background.",
+  "A cute anime girl with big expressive eyes, dressed in a colorful outfit, holding a whiteboard that reads 'ACE Refiner' in a fantastical landscape filled with mythical creatures.",
+  "A vibrant cartoon fox holding a whiteboard that says 'ACE Refiner', standing on a rock by a sparkling stream, surrounded by lush greenery and butterflies.",
+  "A stylish young woman in a business outfit, smiling as she holds a whiteboard written with 'ACE Refiner', in a modern office filled with plants and natural light.",
+  "A cute cartoon unicorn holding a sparkling whiteboard that says 'ACE Refiner', frolicking in a magical forest, with rainbows and stars in the background.",
+  "A happy family, consisting of a cute little girl and her playful puppy, holding a whiteboard that says 'ACE Refiner', together in their backyard on a sunny day."
+]
+
+REFINER_MODEL:
+  NAME: ""
+  IS_DEFAULT: False
+  DEFAULT_PARAS:
+    PARAS:
+      RESOLUTIONS: [ [ 1024, 1024 ] ]
+    INPUT:
+      INPUT_IMAGE:
+      INPUT_MASK:
+      TASK:
+      PROMPT: ""
+      NEGATIVE_PROMPT: ""
+      OUTPUT_HEIGHT: 1024
+      OUTPUT_WIDTH: 1024
+      SAMPLER: flow_euler
+      SAMPLE_STEPS: 50
+      GUIDE_SCALE: 3.5
+      GUIDE_RESCALE: 0.5
+    OUTPUT:
+      LATENT:
+      IMAGES:
+      SEED:
+    MODULES_PARAS:
+      FIRST_STAGE_MODEL:
+        FUNCTION:
+          - NAME: encode
+            DTYPE: bfloat16
+            INPUT: [ "IMAGE" ]
+          - NAME: decode
+            DTYPE: bfloat16
+            INPUT: [ "LATENT" ]
+        PARAS:
+          SCALE_FACTOR: 1.5305
+          SHIFT_FACTOR: 0.0609
+          SIZE_FACTOR: 8
+      DIFFUSION_MODEL:
+        FUNCTION:
+          - NAME: forward
+            DTYPE: bfloat16
+            INPUT: [ "SAMPLE_STEPS", "SAMPLE", "GUIDE_SCALE" ]
+      COND_STAGE_MODEL:
+        FUNCTION:
+          - NAME: encode
+            DTYPE: bfloat16
+            INPUT: [ "PROMPT" ]
+
+  MODEL:
+    DIFFUSION:
+      NAME: DiffusionFluxRF
+      PREDICTION_TYPE: raw
+      NOISE_SCHEDULER:
+        NAME: FlowMatchSigmaScheduler
+        WEIGHTING_SCHEME: logit_normal
+        SHIFT: 3.0
+        LOGIT_MEAN: 0.0
+        LOGIT_STD: 1.0
+        MODE_SCALE: 1.29
+    DIFFUSION_MODEL:
+      NAME: FluxMR
+      PRETRAINED_MODEL: ms://AI-ModelScope/FLUX.1-dev@flux1-dev.safetensors
+      IN_CHANNELS: 64
+      OUT_CHANNELS: 64
+      HIDDEN_SIZE: 3072
+      NUM_HEADS: 24
+      AXES_DIM: [ 16, 56, 56 ]
+      THETA: 10000
+      VEC_IN_DIM: 768
+      GUIDANCE_EMBED: True
+      CONTEXT_IN_DIM: 4096
+      MLP_RATIO: 4.0
+      QKV_BIAS: True
+      DEPTH: 19
+      DEPTH_SINGLE_BLOCKS: 38
+      USE_GRAD_CHECKPOINT: True
+      ATTN_BACKEND: flash_attn
+    #
+    FIRST_STAGE_MODEL:
+      NAME: AutoencoderKLFlux
+      EMBED_DIM: 16
+      PRETRAINED_MODEL:  ms://AI-ModelScope/FLUX.1-dev@ae.safetensors
+      IGNORE_KEYS: [ ]
+      BATCH_SIZE: 8
+      USE_CONV: False
+      SCALE_FACTOR: 0.3611
+      SHIFT_FACTOR: 0.1159
+      #
+      ENCODER:
+        NAME: Encoder
+        USE_CHECKPOINT: False
+        CH: 128
+        OUT_CH: 3
+        NUM_RES_BLOCKS: 2
+        IN_CHANNELS: 3
+        ATTN_RESOLUTIONS: [ ]
+        CH_MULT: [ 1, 2, 4, 4 ]
+        Z_CHANNELS: 16
+        DOUBLE_Z: True
+        DROPOUT: 0.0
+        RESAMP_WITH_CONV: True
+      #
+      DECODER:
+        NAME: Decoder
+        USE_CHECKPOINT: False
+        CH: 128
+        OUT_CH: 3
+        NUM_RES_BLOCKS: 2
+        IN_CHANNELS: 3
+        ATTN_RESOLUTIONS: [ ]
+        CH_MULT: [ 1, 2, 4, 4 ]
+        Z_CHANNELS: 16
+        DROPOUT: 0.0
+        RESAMP_WITH_CONV: True
+        GIVE_PRE_END: False
+        TANH_OUT: False
+    #
+    COND_STAGE_MODEL:
+      NAME: T5PlusClipFluxEmbedder
+      T5_MODEL:
+        NAME: HFEmbedder
+        HF_MODEL_CLS: T5EncoderModel
+        MODEL_PATH: ms://AI-ModelScope/FLUX.1-dev@text_encoder_2/
+        HF_TOKENIZER_CLS: T5Tokenizer
+        TOKENIZER_PATH: ms://AI-ModelScope/FLUX.1-dev@tokenizer_2/
+        MAX_LENGTH: 512
+        OUTPUT_KEY: last_hidden_state
+        D_TYPE: bfloat16
+        BATCH_INFER: False
+        CLEAN: whitespace
+      CLIP_MODEL:
+        NAME: HFEmbedder
+        HF_MODEL_CLS: CLIPTextModel
+        MODEL_PATH: ms://AI-ModelScope/FLUX.1-dev@text_encoder/
+        HF_TOKENIZER_CLS: CLIPTokenizer
+        TOKENIZER_PATH: ms://AI-ModelScope/FLUX.1-dev@tokenizer/
+        MAX_LENGTH: 77
+        OUTPUT_KEY: pooler_output
+        D_TYPE: bfloat16
+        BATCH_INFER: True
+        CLEAN: whitespace
+
+REFINER_MODEL_LOCAL:
+  NAME: ""
+  IS_DEFAULT: False
+  DEFAULT_PARAS:
+    PARAS:
+      RESOLUTIONS: [ [ 1024, 1024 ] ]
+    INPUT:
+      INPUT_IMAGE:
+      INPUT_MASK:
+      TASK:
+      PROMPT: ""
+      NEGATIVE_PROMPT: ""
+      OUTPUT_HEIGHT: 1024
+      OUTPUT_WIDTH: 1024
+      SAMPLER: flow_euler
+      SAMPLE_STEPS: 50
+      GUIDE_SCALE: 3.5
+      GUIDE_RESCALE: 0.5
+    OUTPUT:
+      LATENT:
+      IMAGES:
+      SEED:
+    MODULES_PARAS:
+      FIRST_STAGE_MODEL:
+        FUNCTION:
+          - NAME: encode
+            DTYPE: bfloat16
+            INPUT: [ "IMAGE" ]
+          - NAME: decode
+            DTYPE: bfloat16
+            INPUT: [ "LATENT" ]
+        PARAS:
+          SCALE_FACTOR: 1.5305
+          SHIFT_FACTOR: 0.0609
+          SIZE_FACTOR: 8
+      DIFFUSION_MODEL:
+        FUNCTION:
+          - NAME: forward
+            DTYPE: bfloat16
+            INPUT: [ "SAMPLE_STEPS", "SAMPLE", "GUIDE_SCALE" ]
+      COND_STAGE_MODEL:
+        FUNCTION:
+          - NAME: encode
+            DTYPE: bfloat16
+            INPUT: [ "PROMPT" ]
+
+  MODEL:
+    DIFFUSION:
+      NAME: DiffusionFluxRF
+      PREDICTION_TYPE: raw
+      NOISE_SCHEDULER:
+        NAME: FlowMatchSigmaScheduler
+        WEIGHTING_SCHEME: logit_normal
+        SHIFT: 3.0
+        LOGIT_MEAN: 0.0
+        LOGIT_STD: 1.0
+        MODE_SCALE: 1.29
+    DIFFUSION_MODEL:
+      NAME: FluxMR
+      PRETRAINED_MODEL: models/scepter/FLUX.1-dev/flux1-dev.safetensors
+      IN_CHANNELS: 64
+      OUT_CHANNELS: 64
+      HIDDEN_SIZE: 3072
+      NUM_HEADS: 24
+      AXES_DIM: [ 16, 56, 56 ]
+      THETA: 10000
+      VEC_IN_DIM: 768
+      GUIDANCE_EMBED: True
+      CONTEXT_IN_DIM: 4096
+      MLP_RATIO: 4.0
+      QKV_BIAS: True
+      DEPTH: 19
+      DEPTH_SINGLE_BLOCKS: 38
+      USE_GRAD_CHECKPOINT: True
+      ATTN_BACKEND: flash_attn
+    #
+    FIRST_STAGE_MODEL:
+      NAME: AutoencoderKLFlux
+      EMBED_DIM: 16
+      PRETRAINED_MODEL: models/scepter/FLUX.1-dev/ae.safetensors
+      IGNORE_KEYS: [ ]
+      BATCH_SIZE: 8
+      USE_CONV: False
+      SCALE_FACTOR: 0.3611
+      SHIFT_FACTOR: 0.1159
+      #
+      ENCODER:
+        NAME: Encoder
+        USE_CHECKPOINT: False
+        CH: 128
+        OUT_CH: 3
+        NUM_RES_BLOCKS: 2
+        IN_CHANNELS: 3
+        ATTN_RESOLUTIONS: [ ]
+        CH_MULT: [ 1, 2, 4, 4 ]
+        Z_CHANNELS: 16
+        DOUBLE_Z: True
+        DROPOUT: 0.0
+        RESAMP_WITH_CONV: True
+      #
+      DECODER:
+        NAME: Decoder
+        USE_CHECKPOINT: False
+        CH: 128
+        OUT_CH: 3
+        NUM_RES_BLOCKS: 2
+        IN_CHANNELS: 3
+        ATTN_RESOLUTIONS: [ ]
+        CH_MULT: [ 1, 2, 4, 4 ]
+        Z_CHANNELS: 16
+        DROPOUT: 0.0
+        RESAMP_WITH_CONV: True
+        GIVE_PRE_END: False
+        TANH_OUT: False
+    #
+    COND_STAGE_MODEL:
+      NAME: T5PlusClipFluxEmbedder
+      T5_MODEL:
+        NAME: HFEmbedder
+        HF_MODEL_CLS: T5EncoderModel
+        MODEL_PATH: models/scepter/FLUX.1-dev/text_encoder_2/
+        HF_TOKENIZER_CLS: T5Tokenizer
+        TOKENIZER_PATH: models/scepter/FLUX.1-dev/FLUX.1-dev/tokenizer_2/
+        MAX_LENGTH: 512
+        OUTPUT_KEY: last_hidden_state
+        D_TYPE: bfloat16
+        BATCH_INFER: False
+        CLEAN: whitespace
+      CLIP_MODEL:
+        NAME: HFEmbedder
+        HF_MODEL_CLS: CLIPTextModel
+        MODEL_PATH: models/scepter/FLUX.1-dev/FLUX.1-dev/text_encoder/
+        HF_TOKENIZER_CLS: CLIPTokenizer
+        TOKENIZER_PATH: models/scepter/FLUX.1-dev/FLUX.1-dev/tokenizer/
+        MAX_LENGTH: 77
+        OUTPUT_KEY: pooler_output
+        D_TYPE: bfloat16
+        BATCH_INFER: True
+        CLEAN: whitespace
+
+REFINER_MODEL_HF:
+  NAME: ""
+  IS_DEFAULT: False
+  DEFAULT_PARAS:
+    PARAS:
+      RESOLUTIONS: [ [ 1024, 1024 ] ]
+    INPUT:
+      INPUT_IMAGE:
+      INPUT_MASK:
+      TASK:
+      PROMPT: ""
+      NEGATIVE_PROMPT: ""
+      OUTPUT_HEIGHT: 1024
+      OUTPUT_WIDTH: 1024
+      SAMPLER: flow_euler
+      SAMPLE_STEPS: 50
+      GUIDE_SCALE: 3.5
+      GUIDE_RESCALE: 0.5
+    OUTPUT:
+      LATENT:
+      IMAGES:
+      SEED:
+    MODULES_PARAS:
+      FIRST_STAGE_MODEL:
+        FUNCTION:
+          - NAME: encode
+            DTYPE: bfloat16
+            INPUT: [ "IMAGE" ]
+          - NAME: decode
+            DTYPE: bfloat16
+            INPUT: [ "LATENT" ]
+        PARAS:
+          SCALE_FACTOR: 1.5305
+          SHIFT_FACTOR: 0.0609
+          SIZE_FACTOR: 8
+      DIFFUSION_MODEL:
+        FUNCTION:
+          - NAME: forward
+            DTYPE: bfloat16
+            INPUT: [ "SAMPLE_STEPS", "SAMPLE", "GUIDE_SCALE" ]
+      COND_STAGE_MODEL:
+        FUNCTION:
+          - NAME: encode
+            DTYPE: bfloat16
+            INPUT: [ "PROMPT" ]
+
+  MODEL:
+    DIFFUSION:
+      NAME: DiffusionFluxRF
+      PREDICTION_TYPE: raw
+      NOISE_SCHEDULER:
+        NAME: FlowMatchSigmaScheduler
+        WEIGHTING_SCHEME: logit_normal
+        SHIFT: 3.0
+        LOGIT_MEAN: 0.0
+        LOGIT_STD: 1.0
+        MODE_SCALE: 1.29
+    DIFFUSION_MODEL:
+      NAME: FluxMR
+      PRETRAINED_MODEL: hf://black-forest-labs/FLUX.1-dev@flux1-dev.safetensors
+      IN_CHANNELS: 64
+      OUT_CHANNELS: 64
+      HIDDEN_SIZE: 3072
+      NUM_HEADS: 24
+      AXES_DIM: [ 16, 56, 56 ]
+      THETA: 10000
+      VEC_IN_DIM: 768
+      GUIDANCE_EMBED: True
+      CONTEXT_IN_DIM: 4096
+      MLP_RATIO: 4.0
+      QKV_BIAS: True
+      DEPTH: 19
+      DEPTH_SINGLE_BLOCKS: 38
+      USE_GRAD_CHECKPOINT: True
+      ATTN_BACKEND: flash_attn
+    #
+    FIRST_STAGE_MODEL:
+      NAME: AutoencoderKLFlux
+      EMBED_DIM: 16
+      PRETRAINED_MODEL: hf://black-forest-labs/FLUX.1-dev@ae.safetensors
+      IGNORE_KEYS: [ ]
+      BATCH_SIZE: 8
+      USE_CONV: False
+      SCALE_FACTOR: 0.3611
+      SHIFT_FACTOR: 0.1159
+      #
+      ENCODER:
+        NAME: Encoder
+        USE_CHECKPOINT: False
+        CH: 128
+        OUT_CH: 3
+        NUM_RES_BLOCKS: 2
+        IN_CHANNELS: 3
+        ATTN_RESOLUTIONS: [ ]
+        CH_MULT: [ 1, 2, 4, 4 ]
+        Z_CHANNELS: 16
+        DOUBLE_Z: True
+        DROPOUT: 0.0
+        RESAMP_WITH_CONV: True
+      #
+      DECODER:
+        NAME: Decoder
+        USE_CHECKPOINT: False
+        CH: 128
+        OUT_CH: 3
+        NUM_RES_BLOCKS: 2
+        IN_CHANNELS: 3
+        ATTN_RESOLUTIONS: [ ]
+        CH_MULT: [ 1, 2, 4, 4 ]
+        Z_CHANNELS: 16
+        DROPOUT: 0.0
+        RESAMP_WITH_CONV: True
+        GIVE_PRE_END: False
+        TANH_OUT: False
+    #
+    COND_STAGE_MODEL:
+      NAME: T5PlusClipFluxEmbedder
+      T5_MODEL:
+        NAME: HFEmbedder
+        HF_MODEL_CLS: T5EncoderModel
+        MODEL_PATH: hf://black-forest-labs/FLUX.1-dev@text_encoder_2/
+        HF_TOKENIZER_CLS: T5Tokenizer
+        TOKENIZER_PATH: hf://black-forest-labs/FLUX.1-dev@tokenizer_2/
+        MAX_LENGTH: 512
+        OUTPUT_KEY: last_hidden_state
+        D_TYPE: bfloat16
+        BATCH_INFER: False
+        CLEAN: whitespace
+      CLIP_MODEL:
+        NAME: HFEmbedder
+        HF_MODEL_CLS: CLIPTextModel
+        MODEL_PATH: hf://black-forest-labs/FLUX.1-dev@text_encoder/
+        HF_TOKENIZER_CLS: CLIPTokenizer
+        TOKENIZER_PATH: hf://black-forest-labs/FLUX.1-dev@tokenizer/
+        MAX_LENGTH: 77
+        OUTPUT_KEY: pooler_output
+        D_TYPE: bfloat16
+        BATCH_INFER: True
+        CLEAN: whitespace

scepter/workflow/config/ace_0.6b_512_pro.yaml

@@ -39,7 +39,7 @@ DEFAULT_PARAS:
     #
     COND_STAGE_MODEL:
       FUNCTION:
-        - NAME: encode_list
+        - NAME: encode_list_of_list
           DTYPE: bfloat16
           INPUT: ["PROMPT"]
 #

scepter/workflow/config/flux1.0_dev_pro.yaml

@@ -13,8 +13,8 @@ DEFAULT_PARAS:
       VISIBLE: False
     PROMPT_PREFIX: ""
     SAMPLE:
-      VALUES: ["flow_eluer"]
-      DEFAULT: "flow_eluer"
+      VALUES: ["flow_euler"]
+      DEFAULT: "flow_euler"
     SAMPLE_STEPS: 50
     GUIDE_SCALE: 3.5
     GUIDE_RESCALE:

scepter/workflow/config/flux1.0_schnell_pro.yaml

@@ -13,8 +13,8 @@ DEFAULT_PARAS:
       VISIBLE: False
     PROMPT_PREFIX: ""
     SAMPLE:
-      VALUES: ["flow_eluer"]
-      DEFAULT: "flow_eluer"
+      VALUES: ["flow_euler"]
+      DEFAULT: "flow_euler"
     SAMPLE_STEPS: 4
     GUIDE_SCALE: 3.5
     GUIDE_RESCALE:

scepter/workflow/config/scepter_workflow.yaml

@@ -59,6 +59,18 @@ BASE_MODELS:
       FIRST_STAGE_MODEL: ACE_0.6B_512_AutoencoderKL
       COND_STAGE_MODEL: ACE_0.6B_512_T5EmbedderHF
       CONFIG: config/ace_0.6b_512_pro.yaml
+    -
+      NAME: ACE_0.6B_1024
+      DIFFUSION_MODEL: ACE_0.6B_1024_ACE
+      FIRST_STAGE_MODEL: ACE_0.6B_1024_AutoencoderKL
+      COND_STAGE_MODEL: ACE_0.6B_1024_T5EmbedderHF
+      CONFIG: config/ace_0.6b_1024_pro.yaml
+    -
+      NAME: ACE_0.6B_1024_REFINER
+      DIFFUSION_MODEL: ACE_0.6B_1024_REFINER_ACE
+      FIRST_STAGE_MODEL: ACE_0.6B_1024_REFINER_AutoencoderKL
+      COND_STAGE_MODEL: ACE_0.6B_1024_REFINER_T5EmbedderHF
+      CONFIG: config/ace_0.6b_1024_refiner_pro.yaml
 
 MODEL_SOURCE:
   - "ModelScope"
@@ -83,7 +95,7 @@ BASE_PARAMETERS:
       - "dpmpp_2m_karras"
       - "dpmpp_sde_karras"
       - "dpmpp_2m_sde_karras"
-      - "flow_eluer"
+      - "flow_euler"
 
     DISCRETIZATION:
       - "trailing"

scepter/workflow/model_node.py

@@ -39,8 +39,8 @@ class ModelNode:
                 'mantras': ('CONDITIONING', ),
                 'tuners': ('CONDITIONING', ),
                 'controls': ('CONDITIONING', ),
-                'image': ('IMAGE',),
-                'mask': ('MASK',)
+                'image': ('IMAGE', ),
+                'mask': ('MASK', )
             }
         }
 
@@ -64,15 +64,17 @@ class ModelNode:
             image = [TT.ToPILImage()(image.squeeze(0).permute(2, 0, 1))]
         if mask is not None:
             mask = [TT.ToPILImage()(mask.squeeze(0))]
-        data = self.format_parameters(model, model_source, prompt, negative_prompt,
-                                      parameters, mantras, tuners, controls, image, mask)
+        data = self.format_parameters(model, model_source, prompt,
+                                      negative_prompt, parameters, mantras,
+                                      tuners, controls, image, mask)
         cfg = self.model_file.get(model)['config']
         cfg = self.source_mapping(cfg, model_source)
         self.init_infer(model, cfg)
 
         if model.startswith('ACE'):
             output = self.diff_infer(**data[0], **data[1])
-            output_image = torch.stack([ TT.ToTensor()(img) for img in output]).permute(0, 2, 3, 1).unsqueeze(0)
+            output_image = torch.stack([TT.ToTensor()(img) for img in output
+                                        ]).permute(0, 2, 3, 1).unsqueeze(0)
         else:
             output = self.diff_infer(data[0], **data[1])
             x = output['images'].permute(0, 2, 3, 1)
@@ -94,10 +96,14 @@ class ModelNode:
             elif source == 'Local':
                 cfg_new = copy.deepcopy(cfg)
                 cfg_new.MODEL = cfg_new.MODEL_LOCAL
+                if hasattr(cfg_new, 'REFINER_MODEL_LOCAL'):
+                    cfg_new.REFINER_MODEL = cfg_new.REFINER_MODEL_LOCAL
                 return cfg_new
             elif source == 'HuggingFace':
                 cfg_new = copy.deepcopy(cfg)
                 cfg_new.MODEL = cfg_new.MODEL_HF
+                if hasattr(cfg_new, 'REFINER_MODEL_HF'):
+                    cfg_new.REFINER_MODEL = cfg_new.REFINER_MODEL_HF
                 return cfg_new
             else:
                 raise NotImplementedError(f'Unknown model source: {source}')
@@ -143,17 +149,8 @@ class ModelNode:
             self.pipeline[model_name] = diff_infer
         self.diff_infer = diff_infer
 
-    def format_parameters(self,
-                          model,
-                          model_source,
-                          prompt,
-                          negative_prompt,
-                          parameters,
-                          mantras,
-                          tuners,
-                          controls,
-                          image,
-                          mask):
+    def format_parameters(self, model, model_source, prompt, negative_prompt,
+                          parameters, mantras, tuners, controls, image, mask):
         input_data = {'prompt': prompt, 'negative_prompt': negative_prompt}
         input_params = {
             'diffusion_model': self.model_file.get(model)['diffusion_model'],
@@ -163,13 +160,13 @@ class ModelNode:
         }
 
         if image is not None:
-            input_data.update({"image": image})
+            input_data.update({'image': image})
 
         if mask is not None:
-            input_data.update({"mask": mask})
+            input_data.update({'mask': mask})
 
         if parameters:
-            seed = parameters.pop('seed', -1)
+            seed = parameters.get('random_seed', -1)
             input_params.update({'seed': seed})
             input_data.update(parameters)
 

scepter/workflow/parameter_node.py

@@ -59,6 +59,6 @@ class ParameterNode:
             'guide_rescale': guide_rescale,
             'discretization': discretization,
             'target_size_as_tuple': [output_height, output_width],
-            'seed': random_seed
+            'random_seed': random_seed
         }
         return (out, )

tests/modules/test_diffusion_inference.py

@@ -4,6 +4,7 @@
 import os
 import unittest
 
+import imageio
 import numpy as np
 import torchvision.transforms as TT
 import torchvision.transforms.functional as TF
@@ -13,13 +14,15 @@ from torchvision.utils import save_image
 from scepter.modules.annotator.registry import ANNOTATORS
 from scepter.modules.inference.ace_inference import ACEInference
 from scepter.modules.inference.diffusion_inference import DiffusionInference
-from scepter.modules.inference.flux_inference import FluxInference
 from scepter.modules.inference.sd3_inference import SD3Inference
+from scepter.modules.inference.flux_inference import FluxInference
 from scepter.modules.inference.stylebooth_inference import StyleboothInference
+from scepter.modules.inference.cogvideox_inference import CogVideoXInference
 from scepter.modules.utils.config import Config
 from scepter.modules.utils.distribute import we
 from scepter.modules.utils.file_system import FS
 from scepter.modules.utils.logger import get_logger
+from torchvision.utils import save_image
 
 
 class DiffusionInferenceTest(unittest.TestCase):
@@ -235,10 +238,12 @@ class DiffusionInferenceTest(unittest.TestCase):
         cfg = Config(cfg_file=config_file)
         diff_infer = SD3Inference(logger=self.logger)
         diff_infer.init_from_cfg(cfg)
-        output = diff_infer({
-            'prompt': 'a cat holds a blackboard that writes "hello world"',
+        input_params = {
             'seed': 2024
-        })
+        }
+        output = diff_infer({
+            'prompt': 'a cat holds a blackboard that writes "hello world"'
+        }, **input_params)
         save_path = os.path.join(self.tmp_dir, 'sd3_cat.png')
         save_image(output['images'], save_path)
         print(save_path)
@@ -249,12 +254,17 @@ class DiffusionInferenceTest(unittest.TestCase):
         cfg = Config(cfg_file=config_file)
         diff_infer = FluxInference(logger=self.logger)
         diff_infer.init_from_cfg(cfg)
-        output = diff_infer({'prompt': '1 girl', 'seed': 2024})
+        input_params = {
+            'seed': 2024
+        }
+        output = diff_infer({
+            'prompt': '1 girl'
+        }, **input_params)
         save_path = os.path.join(self.tmp_dir, 'flux_dev_1girl.png')
         save_image(output['images'], save_path)
         print(save_path)
 
-    # @unittest.skip('')
+    @unittest.skip('')
     def test_ace(self):
         config_file = 'scepter/methods/studio/chatbot/models/ace_0.6b_512.yaml'
         cfg = Config(cfg_file=config_file)
@@ -266,5 +276,26 @@ class DiffusionInferenceTest(unittest.TestCase):
         print(save_path)
 
 
+    # @unittest.skip('')
+    def test_cogvideox_2b(self):
+        config_file = 'scepter/methods/studio/inference/dit/cogvideox_2b_pro.yaml'
+        cfg = Config(cfg_file=config_file)
+        diff_infer = CogVideoXInference(logger=self.logger)
+        diff_infer.init_from_cfg(cfg)
+        input_params = {
+            'seed': 42
+        }
+        output = diff_infer({
+            'prompt': 'A girl riding a bike.'
+        }, **input_params)
+        frames = (output['videos'][0].permute(1, 2, 3, 0).cpu().numpy() * 255).astype(np.uint8)
+        save_path = os.path.join(self.tmp_dir, 'cogvideox_2b_girlbike.mp4')
+        writer = imageio.get_writer(save_path, fps=8)
+        for frame in frames:
+            writer.append_data(np.array(frame))
+        writer.close()
+        print(save_path)
+
+
 if __name__ == '__main__':
     unittest.main()