update template directions

Co-authored-by: Wei (Will) Feng <134637289+weifengpy@users.noreply.github.com>

.buildkite/pipeline.yml

@@ -16,9 +16,9 @@ steps:
           diff: 'git fetch origin "$BUILDKITE_PULL_REQUEST_BASE_BRANCH" && git diff --name-only origin/"$BUILDKITE_PULL_REQUEST_BASE_BRANCH"...HEAD'
           watch:
             - path: 
-                - "fastvideo/v1/models/encoders/**"
-                - "fastvideo/v1/models/loader/**"
-                - "fastvideo/v1/tests/encoders/**"
+                - "fastvideo/models/encoders/**"
+                - "fastvideo/models/loader/**"
+                - "fastvideo/tests/encoders/**"
                 - "pyproject.toml"
                 - "docker/Dockerfile.python3.12"
               config:
@@ -29,9 +29,9 @@ steps:
                 agents:
                   queue: "default"
             - path:
-                - "fastvideo/v1/models/vaes/**"
-                - "fastvideo/v1/models/loader/**"
-                - "fastvideo/v1/tests/vaes/**"
+                - "fastvideo/models/vaes/**"
+                - "fastvideo/models/loader/**"
+                - "fastvideo/tests/vaes/**"
                 - "pyproject.toml"
                 - "docker/Dockerfile.python3.12"
               config:
@@ -42,11 +42,11 @@ steps:
                 agents:
                   queue: "default"
             - path:
-                - "fastvideo/v1/models/dits/**"
-                - "fastvideo/v1/models/loader/**"
-                - "fastvideo/v1/tests/transformers/**"
-                - "fastvideo/v1/layers/**"
-                - "fastvideo/v1/attention/**"
+                - "fastvideo/models/dits/**"
+                - "fastvideo/models/loader/**"
+                - "fastvideo/tests/transformers/**"
+                - "fastvideo/layers/**"
+                - "fastvideo/attention/**"
                 - "pyproject.toml"
                 - "docker/Dockerfile.python3.12"
               config:
@@ -57,7 +57,7 @@ steps:
                 agents:
                   queue: "default"
             - path: 
-                - "fastvideo/v1/**/*.py"
+                - "fastvideo/**/*.py"
                 - "pyproject.toml"
                 - "docker/Dockerfile.python3.12"
               config:
@@ -68,11 +68,11 @@ steps:
                 agents:
                   queue: "default"
             - path:
-                - "fastvideo/v1/tests/lora/**"
-                - "fastvideo/v1/models/loader/**"
-                - "fastvideo/v1/tests/transformers/**"
-                - "fastvideo/v1/pipelines/**"
-                - "fastvideo/v1/layers/lora/**"
+                - "fastvideo/tests/lora/**"
+                - "fastvideo/models/loader/**"
+                - "fastvideo/tests/transformers/**"
+                - "fastvideo/pipelines/**"
+                - "fastvideo/layers/lora/**"
                 - "pyproject.toml"
                 - "docker/Dockerfile.python3.12"
               config:
@@ -83,7 +83,7 @@ steps:
                 agents:
                   queue: "default"
             - path:
-                - "fastvideo/v1/**"
+                - "fastvideo/**"
                 - "pyproject.toml"
                 - "docker/Dockerfile.python3.12"
               config:
@@ -94,7 +94,7 @@ steps:
                 agents:
                   queue: "default"
             - path:
-                - "fastvideo/v1/**"
+                - "fastvideo/**"
                 - "pyproject.toml"
                 - "docker/Dockerfile.python3.12"
               config:
@@ -105,7 +105,7 @@ steps:
                 agents:
                   queue: "default"
             - path:
-                - "fastvideo/v1/**"
+                - "fastvideo/**"
                 - "csrc/attn/vsa/**"
                 - "csrc/attn/tk/**"
                 - "csrc/attn/setup_vsa.py"
@@ -121,7 +121,7 @@ steps:
                 agents:
                   queue: "default"
             - path:
-                - "fastvideo/v1/**"
+                - "fastvideo/**"
                 - "csrc/attn/st_attn/**"
                 - "csrc/attn/setup_sta.py"
                 - "csrc/attn/config_sta.py"

.buildkite/scripts/pr_test.sh

@@ -50,7 +50,7 @@ else
     exit 1
 fi
 
-MODAL_TEST_FILE="fastvideo/v1/tests/modal/pr_test.py"
+MODAL_TEST_FILE="fastvideo/tests/modal/pr_test.py"
 
 if [ -z "${TEST_TYPE:-}" ]; then
     log "Error: TEST_TYPE environment variable is not set"

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

@@ -23,7 +23,7 @@ body:
   attributes:
     label: Environment
     description: |
-      Please share your environment with us. You can run the command **python fastvideo/utils/collect_env.py** and copy-paste its output below.
+      Please share your environment with us. You can run the command **python collect_env.py** and copy-paste its output below.
     placeholder: FastVideo version, platform, python version, cuda version...
   validations:
     required: true

.github/workflows/docs.yml

@@ -8,14 +8,14 @@ on:
       - main
     paths:
       - "docs/**/*.md"
-      - "fastvideo/v1/examples/**/*.py"
+      - "fastvideo/examples/**/*.py"
   pull_request:
     branches: 
       - main
     types: [opened, ready_for_review, synchronize, reopened]
     paths:
       - "docs/**/*.md"
-      - "fastvideo/v1/examples/**/*.py"
+      - "fastvideo/examples/**/*.py"
 
   # Allows you to run this workflow manually from the Actions tab
   workflow_dispatch:

.github/workflows/pr-test.yml

@@ -115,37 +115,37 @@ jobs:
               - 'csrc/attn/config_vsa.py'
               - 'csrc/attn/vsa.cpp'
             vsa-paths: &vsa-paths
-              - 'fastvideo/v1/**'
+              - 'fastvideo/**'
               - *common-paths
               - *vsa-kernel-paths
             
             # Actual tests
             encoder-test:
-              - 'fastvideo/v1/models/encoders/**'
-              - 'fastvideo/v1/models/loader/**'
-              - 'fastvideo/v1/tests/encoders/**'
+              - 'fastvideo/models/encoders/**'
+              - 'fastvideo/models/loader/**'
+              - 'fastvideo/tests/encoders/**'
               - *common-paths
             vae-test:
-              - 'fastvideo/v1/models/vaes/**'
-              - 'fastvideo/v1/models/loader/**'
-              - 'fastvideo/v1/tests/vaes/**'
+              - 'fastvideo/models/vaes/**'
+              - 'fastvideo/models/loader/**'
+              - 'fastvideo/tests/vaes/**'
               - *common-paths
             transformer-test:
-              - 'fastvideo/v1/models/dits/**'
-              - 'fastvideo/v1/models/loader/**'
-              - 'fastvideo/v1/tests/transformers/**'
-              - 'fastvideo/v1/layers/**'
-              - 'fastvideo/v1/attention/**'
+              - 'fastvideo/models/dits/**'
+              - 'fastvideo/models/loader/**'
+              - 'fastvideo/tests/transformers/**'
+              - 'fastvideo/layers/**'
+              - 'fastvideo/attention/**'
               - *common-paths
             training-test:
-              - 'fastvideo/v1/**'
+              - 'fastvideo/**'
               - *common-paths
             training-test-VSA:
-              - 'fastvideo/v1/**'
+              - 'fastvideo/**'
               - *common-paths
               - *vsa-kernel-paths
             inference-test-STA:
-              - 'fastvideo/v1/**'
+              - 'fastvideo/**'
               - *common-paths
               - *sta-kernel-paths
             precision-test-STA:
@@ -167,7 +167,7 @@ jobs:
       gpu_count: 1
       volume_size: 100
       image: "ghcr.io/${{ github.repository }}/fastvideo-dev:py3.12-latest"
-      test_command: "uv pip install -e .[test] && pytest ./fastvideo/v1/tests/encoders -s"
+      test_command: "uv pip install -e .[test] && pytest ./fastvideo/tests/encoders -s"
       timeout_minutes: 30
     secrets:
       RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
@@ -185,7 +185,7 @@ jobs:
       gpu_count: 1
       volume_size: 100
       image: "ghcr.io/${{ github.repository }}/fastvideo-dev:py3.12-latest"
-      test_command: "uv pip install -e .[test] && pytest ./fastvideo/v1/tests/vaes -s"
+      test_command: "uv pip install -e .[test] && pytest ./fastvideo/tests/vaes -s"
       timeout_minutes: 30
     secrets:
       RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
@@ -203,7 +203,7 @@ jobs:
       gpu_count: 1
       volume_size: 100
       image: "ghcr.io/${{ github.repository }}/fastvideo-dev:py3.12-latest"
-      test_command: "uv pip install -e .[test] && pytest ./fastvideo/v1/tests/transformers -s"
+      test_command: "uv pip install -e .[test] && pytest ./fastvideo/tests/transformers -s"
       timeout_minutes: 30
     secrets:
       RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
@@ -229,7 +229,7 @@ jobs:
       volume_size: 200
       disk_size: 200
       image: "ghcr.io/${{ github.repository }}/fastvideo-dev:${{ matrix.python-version.tag }}"
-      test_command: "uv pip install -e .[test] && pytest ./fastvideo/v1/tests/ssim -vs"
+      test_command: "uv pip install -e .[test] && pytest ./fastvideo/tests/ssim -vs"
       timeout_minutes: 60
     secrets:
       RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
@@ -248,7 +248,7 @@ jobs:
       volume_size: 100
       disk_size: 100
       image: "ghcr.io/${{ github.repository }}/fastvideo-dev:py3.12-latest"
-      test_command: "wandb login $WANDB_API_KEY && uv pip install -e .[test] && pytest ./fastvideo/v1/tests/training/Vanilla -srP"
+      test_command: "wandb login $WANDB_API_KEY && uv pip install -e .[test] && pytest ./fastvideo/tests/training/Vanilla -srP"
       timeout_minutes: 30
     secrets:
       RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
@@ -268,7 +268,7 @@ jobs:
       volume_size: 100
       disk_size: 100
       image: "ghcr.io/${{ github.repository }}/fastvideo-dev:py3.12-latest"
-      test_command: "wandb login $WANDB_API_KEY && uv pip install -e .[test] && pytest ./fastvideo/v1/tests/training/VSA -srP"
+      test_command: "wandb login $WANDB_API_KEY && uv pip install -e .[test] && pytest ./fastvideo/tests/training/VSA -srP"
       timeout_minutes: 30
     secrets:
       RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
@@ -288,7 +288,7 @@ jobs:
       volume_size: 100
       disk_size: 100
       image: "ghcr.io/${{ github.repository }}/fastvideo-dev:py3.12-latest"
-      test_command: "uv pip install -e .[test] && pytest ./fastvideo/v1/tests/inference/STA -srP"
+      test_command: "uv pip install -e .[test] && pytest ./fastvideo/tests/inference/STA -srP"
       timeout_minutes: 30
     secrets:
       RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
@@ -343,7 +343,7 @@ jobs:
       volume_size: 100
       disk_size: 100
       image: "ghcr.io/${{ github.repository }}/fastvideo-dev:py3.12-latest"
-      test_command: "wandb login $WANDB_API_KEY && uv pip install -e .[test] && pytest ./fastvideo/v1/tests/nightly/test_e2e_overfit_single_sample.py -vs"
+      test_command: "wandb login $WANDB_API_KEY && uv pip install -e .[test] && pytest ./fastvideo/tests/nightly/test_e2e_overfit_single_sample.py -vs"
       timeout_minutes: 30
     secrets:
       RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}

.gitignore

@@ -57,7 +57,7 @@ docs/source/training/examples/
 *.pkl
 
 # Reference videos
-!fastvideo/v1/tests/ssim/reference_videos/**/*.mp4
+!fastvideo/tests/ssim/reference_videos/**/*.mp4
 
 # Static images
 !docs/source/_static/images/**/*.png

.pre-commit-config.yaml

@@ -3,7 +3,7 @@ default_stages:
   - manual # Run in CI
 exclude: |
   (?x)(
-    fastvideo/v1/third_party/.*|
+    fastvideo/third_party/.*|
     csrc/.*|
     assets/.*|
     tests/.*|
@@ -69,7 +69,7 @@ repos:
     entry: bash
     args:
       - -c
-      - 'git ls-files | grep -v "^fastvideo/v1/tests/ssim/" | grep -v "^fastvideo/v1/tests/inference/lora/L40S_reference_videos/" | grep " " && echo "Filenames should not contain spaces!" && exit 1 || exit 0'
+      - 'git ls-files | grep -v "^fastvideo/tests/ssim/" | grep -v "^fastvideo/tests/inference/lora/L40S_reference_videos/" | grep " " && echo "Filenames should not contain spaces!" && exit 1 || exit 0'
     language: system
     always_run: true
     pass_filenames: false

collect_env.py

@@ -16,7 +16,7 @@ import sys
 # Run it with `python collect_env.py` or `python -m torch.utils.collect_env`
 from collections import namedtuple
 
-from fastvideo.v1.envs import environment_variables
+from fastvideo.envs import environment_variables
 
 try:
     import torch

comfyui/README.md

@@ -74,8 +74,8 @@ After installation, the following nodes will be available in the ComfyUI interfa
 You may have noticed many arguments on the nodes have 'auto' as the default value. This is because FastVideo will automatically detect the best values for these parameters based on the model and the hardware. However, you can also manually configure these parameters to get the best performance for your specific use case. We plan on releasing more optimized workflow files for different models and hardware configurations in the future.
 
 You can see what some of the default configurations are by looking at the FastVideo repo:
-- [Wan2.1-I2V-14B-480P-Diffusers](https://github.com/hao-ai-lab/FastVideo/blob/main/fastvideo/v1/configs/wan_14B_i2v_480p_pipeline.json)
-- [FastHunyuan-diffusers](https://github.com/hao-ai-lab/FastVideo/blob/main/fastvideo/v1/configs/fasthunyuan_t2v.json)
+- [Wan2.1-I2V-14B-480P-Diffusers](https://github.com/hao-ai-lab/FastVideo/blob/main/fastvideo/configs/wan_14B_i2v_480p_pipeline.json)
+- [FastHunyuan-diffusers](https://github.com/hao-ai-lab/FastVideo/blob/main/fastvideo/configs/fasthunyuan_t2v.json)
 
 ### Node Configuration
 

docs/source/api/summary.md

@@ -9,11 +9,11 @@
 ## Initialization Configuration
 
 ```{autodoc2-summary}
-    fastvideo.v1.configs.pipelines.PipelineConfig
+    fastvideo.configs.pipelines.PipelineConfig
 ```
 
 ## Sampling Configuration
 
 ```{autodoc2-summary}
-    fastvideo.v1.configs.sample.SamplingParam
+    fastvideo.configs.sample.SamplingParam
 ```

docs/source/design/overview.md

@@ -1,24 +1,24 @@
 # 🔍 FastVideo Overview
 
-This document outlines FastVideo's architecture for developers interested in framework internals or contributions. It serves as an onboarding guide for new contributors by providing an overview of the most important directories and files within the `fastvideo/v1/` codebase.
+This document outlines FastVideo's architecture for developers interested in framework internals or contributions. It serves as an onboarding guide for new contributors by providing an overview of the most important directories and files within the `fastvideo/` codebase.
 
-## Table of Contents - V1 Directory Structure and Files
+## Table of Contents - Directory Structure and Files
 
-- [`fastvideo/v1/pipelines/`](#design-pipeline-system) - Core diffusion pipeline components
-- [`fastvideo/v1/models/`](#design-model-components) - Model implementations
+- [`fastvideo/pipelines/`](#design-pipeline-system) - Core diffusion pipeline components
+- [`fastvideo/models/`](#design-model-components) - Model implementations
   - [`dits/`](#design-transformer-models) - Transformer-based diffusion models
   - [`vaes/`](#design-vae-variational-auto-encoder) - Variational autoencoders
   - [`encoders/`](#design-text-and-image-encoders) - Text and image encoders
   - [`schedulers/`](#design-schedulers) - Diffusion schedulers
-- [`fastvideo/v1/attention/`](#design-optimized-attention) - Optimized attention implementations
-- [`fastvideo/v1/distributed/`](#design-distributed-processing) - Distributed computing utilities
-- [`fastvideo/v1/layers/`](#design-tensor-parallelism) - Custom neural network layers
-- [`fastvideo/v1/platforms/`](#design-platforms) - Hardware platform abstractions
-- [`fastvideo/v1/worker/`](#design-executor-and-worker-abstractions) - Multi-GPU process management
-- [`fastvideo/v1/fastvideo_args.py`](#design-fastvideo-args) - Argument handling
-- [`fastvideo/v1/forward_context.py`](#design-forwardcontext) - Forward pass context management
-- `fastvideo/v1/utils.py` - Utility functions
-- [`fastvideo/v1/logger.py`](#design-logger) - Logging infrastructure
+- [`fastvideo/attention/`](#design-optimized-attention) - Optimized attention implementations
+- [`fastvideo/distributed/`](#design-distributed-processing) - Distributed computing utilities
+- [`fastvideo/layers/`](#design-tensor-parallelism) - Custom neural network layers
+- [`fastvideo/platforms/`](#design-platforms) - Hardware platform abstractions
+- [`fastvideo/worker/`](#design-executor-and-worker-abstractions) - Multi-GPU process management
+- [`fastvideo/fastvideo_args.py`](#design-fastvideo-args) - Argument handling
+- [`fastvideo/forward_context.py`](#design-forwardcontext) - Forward pass context management
+- `fastvideo/utils.py` - Utility functions
+- [`fastvideo/logger.py`](#design-logger) - Logging infrastructure
 
 ## Core Architecture
 
@@ -32,7 +32,7 @@ FastVideo separates model components from execution logic with these principles:
 (design-fastvideo-args)=
 ## FastVideoArgs
 
-The `FastVideoArgs` class in `fastvideo/v1/fastvideo_args.py` serves as the central configuration system for FastVideo. It contains all parameters needed to control model loading, inference configuration, performance optimization settings, and more.
+The `FastVideoArgs` class in `fastvideo/fastvideo_args.py` serves as the central configuration system for FastVideo. It contains all parameters needed to control model loading, inference configuration, performance optimization settings, and more.
 
 Key features include:
 - **Command-line Interface**: Automatic conversion between CLI arguments and dataclass fields
@@ -111,7 +111,7 @@ def forward(self, batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> Forward
 (design-forwardbatch)=
 ### ForwardBatch
 
-Defined in `fastvideo/v1/pipelines/pipeline_batch_info.py`, `ForwardBatch` encapsulates the data payload passed between pipeline stages. It typically holds:
+Defined in `fastvideo/pipelines/pipeline_batch_info.py`, `ForwardBatch` encapsulates the data payload passed between pipeline stages. It typically holds:
 
 - **Input Data**: Prompts, images, generation parameters
 - **Intermediate State**: Embeddings, latents, timesteps, accumulated during stage execution
@@ -123,14 +123,14 @@ This structure facilitates clear state transitions between stages.
 (design-model-components)=
 ## Model Components
 
-The `fastvideo/v1/models/` directory contains implementations of the core neural network models used in video diffusion:
+The `fastvideo/models/` directory contains implementations of the core neural network models used in video diffusion:
 
 (design-transformer-models)=
 ### Transformer Models
 
 Transformer networks perform the actual denoising during diffusion:
 
-- **Location**: `fastvideo/v1/models/dits/`
+- **Location**: `fastvideo/models/dits/`
 - **Examples**:
   - `WanTransformer3DModel`
   - `HunyuanVideoTransformer3DModel`
@@ -157,7 +157,7 @@ def forward(
 
 VAEs handle conversion between pixel space and latent space:
 
-- **Location**: `fastvideo/v1/models/vaes/`
+- **Location**: `fastvideo/models/vaes/`
 - **Examples**:
   - `AutoencoderKLWan`
   - `AutoencoderKLHunyuanVideo`
@@ -175,7 +175,7 @@ FastVideo's VAE implementations include:
 
 Encoders process conditioning inputs into embeddings:
 
-- **Location**: `fastvideo/v1/models/encoders/`
+- **Location**: `fastvideo/models/encoders/`
 - **Text Encoders**:
   - `CLIPTextModel`
   - `LlamaModel`
@@ -193,7 +193,7 @@ FastVideo implements optimizations such as:
 
 Schedulers manage the diffusion sampling process:
 
-- **Location**: `fastvideo/v1/models/schedulers/`
+- **Location**: `fastvideo/models/schedulers/`
 - **Examples**:
   - `UniPCMultistepScheduler`
   - `FlowMatchEulerDiscreteScheduler`
@@ -219,7 +219,7 @@ def step(
 (design-optimized-attention)=
 ## Optimized Attention
 
-The `fastvideo/v1/attention/` directory contains optimized attention implementations crucial for efficient video diffusion:
+The `fastvideo/attention/` directory contains optimized attention implementations crucial for efficient video diffusion:
 
 ### Attention Backends
 Multiple implementations with automatic selection:
@@ -248,7 +248,7 @@ Supports various patterns with memory optimization techniques:
 (design-distributed-processing)=
 ## Distributed Processing
 
-The `fastvideo/v1/distributed/` directory contains implementations for distributed model execution:
+The `fastvideo/distributed/` directory contains implementations for distributed model execution:
 
 (design-tensor-parallelism)=
 ### Tensor Parallelism
@@ -260,7 +260,7 @@ Tensor parallelism splits model weights across devices:
 
 ```python
 # Tensor-parallel layers in a transformer block
-from fastvideo.v1.layers.linear import ColumnParallelLinear, RowParallelLinear
+from fastvideo.layers.linear import ColumnParallelLinear, RowParallelLinear
 
 # Split along output dimension
 self.qkv_proj = ColumnParallelLinear(
@@ -288,7 +288,7 @@ Sequence parallelism splits sequences across devices:
 
 ```python
 # Distributed attention for long sequences
-from fastvideo.v1.attention import DistributedAttention
+from fastvideo.attention import DistributedAttention
 
 self.attn = DistributedAttention(
     num_heads=num_heads,
@@ -312,7 +312,7 @@ Efficient communication primitives minimize distributed overhead:
 
 ### ForwardContext
 
-Defined in `fastvideo/v1/forward_context.py`, `ForwardContext` manages execution-specific state *within* a forward pass, particularly for low-level optimizations. It is accessed via `get_forward_context()`.
+Defined in `fastvideo/forward_context.py`, `ForwardContext` manages execution-specific state *within* a forward pass, particularly for low-level optimizations. It is accessed via `get_forward_context()`.
 
 - **Attention Metadata**: Configuration for optimized attention kernels (`attn_metadata`)
 - **Profiling Data**: Potential hooks for performance metrics collection
@@ -333,7 +333,7 @@ with set_forward_context(current_timestep, attn_metadata, fastvideo_args):
 (design-executor-and-worker-abstractions)=
 ## Executor and Worker System
 
-The `fastvideo/v1/worker/` directory contains the distributed execution framework:
+The `fastvideo/worker/` directory contains the distributed execution framework:
 
 ### Executor Abstraction
 
@@ -360,7 +360,7 @@ This design allows FastVideo to efficiently utilize multiple GPUs while providin
 (design-platforms)=
 ## Platforms
 
-The `fastvideo/v1/platforms/` directory provides hardware platform abstractions that enable FastVideo to run efficiently on different hardware configurations:
+The `fastvideo/platforms/` directory provides hardware platform abstractions that enable FastVideo to run efficiently on different hardware configurations:
 
 ### Platform Abstraction
 
@@ -377,7 +377,7 @@ The primary components include:
 Usage example:
 
 ```python
-from fastvideo.v1.platforms import current_platform, _Backend
+from fastvideo.platforms import current_platform, _Backend
 
 # Check hardware capabilities
 if current_platform.supports_backend(_Backend.FLASH_ATTN):
@@ -398,9 +398,9 @@ See [PR](https://github.com/hao-ai-lab/FastVideo/pull/356)
 
 If you're a new contributor, here are some common areas to explore:
 
-1. **Adding a new model**: Implement new model types in the appropriate subdirectory of `fastvideo/v1/models/`
+1. **Adding a new model**: Implement new model types in the appropriate subdirectory of `fastvideo/models/`
 2. **Optimizing performance**: Look at attention implementations or memory management
-3. **Adding a new pipeline**: Create a new pipeline subclass in `fastvideo/v1/pipelines/`
+3. **Adding a new pipeline**: Create a new pipeline subclass in `fastvideo/pipelines/`
 4. **Hardware support**: Extend the `platforms` module for new hardware targets
 
 When adding code, follow these practices:

docs/source/getting_started/v1_api.md

@@ -10,20 +10,20 @@ This class will be the primary Python API for generating videos and images.
     fastvideo.VideoGenerator
 ```
 
-`````{py:class} VideoGenerator(fastvideo_args: fastvideo.v1.fastvideo_args.FastVideoArgs, executor_class: type[fastvideo.v1.worker.executor.Executor], log_stats: bool)
-:canonical: fastvideo.v1.entrypoints.video_generator.VideoGenerator
+`````{py:class} VideoGenerator(fastvideo_args: fastvideo.fastvideo_args.FastVideoArgs, executor_class: type[fastvideo.worker.executor.Executor], log_stats: bool)
+:canonical: fastvideo.entrypoints.video_generator.VideoGenerator
 
-```{autodoc2-docstring} fastvideo.v1.entrypoints.video_generator.VideoGenerator
+```{autodoc2-docstring} fastvideo.entrypoints.video_generator.VideoGenerator
 :parser: docs.source.autodoc2_docstring_parser
 ```
 
 `VideoGenerator.from_pretrained()` should be the primary way of creating a new video generator.
 
-````{py:method} from_pretrained(model_path: str, device: typing.Optional[str] = None, torch_dtype: typing.Optional[torch.dtype] = None, pipeline_config: typing.Optional[typing.Union[str | fastvideo.v1.configs.pipelines.PipelineConfig]] = None, **kwargs) -> fastvideo.v1.entrypoints.video_generator.VideoGenerator
-:canonical: fastvideo.v1.entrypoints.video_generator.VideoGenerator.from_pretrained
+````{py:method} from_pretrained(model_path: str, device: typing.Optional[str] = None, torch_dtype: typing.Optional[torch.dtype] = None, pipeline_config: typing.Optional[typing.Union[str | fastvideo.configs.pipelines.PipelineConfig]] = None, **kwargs) -> fastvideo.entrypoints.video_generator.VideoGenerator
+:canonical: fastvideo.entrypoints.video_generator.VideoGenerator.from_pretrained
 :classmethod:
 
-```{autodoc2-docstring} fastvideo.v1.entrypoints.video_generator.VideoGenerator.from_pretrained
+```{autodoc2-docstring} fastvideo.entrypoints.video_generator.VideoGenerator.from_pretrained
 :parser: docs.source.autodoc2_docstring_parser
 ```
 
@@ -38,25 +38,25 @@ The follow two classes `PipelineConfig` and `SamplingParam` are used to configur
 ```
 
 `````{py:class} PipelineConfig
-:canonical: fastvideo.v1.configs.pipelines.base.PipelineConfig
+:canonical: fastvideo.configs.pipelines.base.PipelineConfig
 
-```{autodoc2-docstring} fastvideo.v1.configs.pipelines.base.PipelineConfig
+```{autodoc2-docstring} fastvideo.configs.pipelines.base.PipelineConfig
 :parser: docs.source.autodoc2_docstring_parser
 ```
 
-````{py:method} from_pretrained(model_path: str) -> fastvideo.v1.configs.pipelines.base.PipelineConfig
-:canonical: fastvideo.v1.configs.pipelines.base.PipelineConfig.from_pretrained
+````{py:method} from_pretrained(model_path: str) -> fastvideo.configs.pipelines.base.PipelineConfig
+:canonical: fastvideo.configs.pipelines.base.PipelineConfig.from_pretrained
 :classmethod:
 
-```{autodoc2-docstring} fastvideo.v1.configs.pipelines.base.PipelineConfig.from_pretrained
+```{autodoc2-docstring} fastvideo.configs.pipelines.base.PipelineConfig.from_pretrained
 :parser: docs.source.autodoc2_docstring_parser
 ```
 
 
 ````{py:method} dump_to_json(file_path: str)
-:canonical: fastvideo.v1.configs.pipelines.base.PipelineConfig.dump_to_json
+:canonical: fastvideo.configs.pipelines.base.PipelineConfig.dump_to_json
 
-```{autodoc2-docstring} fastvideo.v1.configs.pipelines.base.PipelineConfig.dump_to_json
+```{autodoc2-docstring} fastvideo.configs.pipelines.base.PipelineConfig.dump_to_json
 :parser: docs.source.autodoc2_docstring_parser
 ```
 
@@ -68,16 +68,16 @@ The follow two classes `PipelineConfig` and `SamplingParam` are used to configur
 ```
 
 `````{py:class} SamplingParam
-:canonical: fastvideo.v1.configs.sample.base.SamplingParam
+:canonical: fastvideo.configs.sample.base.SamplingParam
 
-```{autodoc2-docstring} fastvideo.v1.configs.sample.base.SamplingParam
+```{autodoc2-docstring} fastvideo.configs.sample.base.SamplingParam
 :parser: docs.source.autodoc2_docstring_parser
 ```
 
-````{py:method} from_pretrained(model_path: str) -> fastvideo.v1.configs.sample.base.SamplingParam
-:canonical: fastvideo.v1.configs.sample.base.SamplingParam.from_pretrained
+````{py:method} from_pretrained(model_path: str) -> fastvideo.configs.sample.base.SamplingParam
+:canonical: fastvideo.configs.sample.base.SamplingParam.from_pretrained
 :classmethod:
 
-```{autodoc2-docstring} fastvideo.v1.configs.sample.base.SamplingParam.from_pretrained
+```{autodoc2-docstring} fastvideo.configs.sample.base.SamplingParam.from_pretrained
 :parser: docs.source.autodoc2_docstring_parser
 ```

docs/source/inference/add_pipeline.md

@@ -46,25 +46,25 @@ FastVideo uses the Hugging Face Diffusers format for model organization:
 ### Implementing Modules
 
 Place new modules in the appropriate directories:
-- Encoders: `fastvideo/v1/models/encoders/`
-- VAEs: `fastvideo/v1/models/vaes/`
-- Transformer models: `fastvideo/v1/models/dits/`
-- Schedulers: `fastvideo/v1/models/schedulers/`
+- Encoders: `fastvideo/models/encoders/`
+- VAEs: `fastvideo/models/vaes/`
+- Transformer models: `fastvideo/models/dits/`
+- Schedulers: `fastvideo/models/schedulers/`
 
 ### Adapting Model Layers
 
 #### Layer Replacements
 Replace standard PyTorch layers with FastVideo optimized versions:
-- nn.LayerNorm → fastvideo.v1.layers.layernorm.RMSNorm
-- Embedding layers → fastvideo.v1.layers.vocab_parallel_embedding modules
-- Activation functions → versions from fastvideo.v1.layers.activation
+- nn.LayerNorm → fastvideo.layers.layernorm.RMSNorm
+- Embedding layers → fastvideo.layers.vocab_parallel_embedding modules
+- Activation functions → versions from fastvideo.layers.activation
 
 #### Distributed Linear Layers
 Use appropriate parallel layers for distribution:
 
 ```python
 # Output dimension parallelism
-from fastvideo.v1.layers.linear import ColumnParallelLinear
+from fastvideo.layers.linear import ColumnParallelLinear
 self.q_proj = ColumnParallelLinear(
     input_size=hidden_size,
     output_size=head_size * num_heads,
@@ -73,7 +73,7 @@ self.q_proj = ColumnParallelLinear(
 )
 
 # Fused QKV projection
-from fastvideo.v1.layers.linear import QKVParallelLinear
+from fastvideo.layers.linear import QKVParallelLinear
 self.qkv_proj = QKVParallelLinear(
     hidden_size=hidden_size,
     head_size=attention_head_dim,
@@ -82,7 +82,7 @@ self.qkv_proj = QKVParallelLinear(
 )
 
 # Input dimension parallelism
-from fastvideo.v1.layers.linear import RowParallelLinear
+from fastvideo.layers.linear import RowParallelLinear
 self.out_proj = RowParallelLinear(
     input_size=head_size * num_heads,
     output_size=hidden_size,
@@ -96,8 +96,8 @@ Replace standard attention with FastVideo's optimized attention:
 
 ```python
 # Local attention patterns
-from fastvideo.v1.attention import LocalAttention
-from fastvideo.v1.attention.backends.abstract import _Backend
+from fastvideo.attention import LocalAttention
+from fastvideo.attention.backends.abstract import _Backend
 self.attn = LocalAttention(
     num_heads=num_heads,
     head_size=head_dim,
@@ -108,7 +108,7 @@ self.attn = LocalAttention(
 )
 
 # Distributed attention for long sequences
-from fastvideo.v1.attention import DistributedAttention
+from fastvideo.attention import DistributedAttention
 self.attn = DistributedAttention(
     num_heads=num_heads,
     head_size=head_dim,
@@ -130,7 +130,7 @@ self.attn = DistributedAttention(
 Register implemented modules in the model registry:
 
 ```python
-# In fastvideo/v1/models/registry.py
+# In fastvideo/models/registry.py
 _TEXT_TO_VIDEO_DIT_MODELS = {
     "YourTransformerModel": ("dits", "yourmodule", "YourTransformerClass"),
 }
@@ -145,7 +145,7 @@ _VAE_MODELS = {
 Create a new directory for your pipeline:
 
 ```
-fastvideo/v1/pipelines/
+fastvideo/pipelines/
 ├── your_pipeline/
 │   ├── __init__.py
 │   └── your_pipeline.py
@@ -167,13 +167,13 @@ Pipelines are composed of stages, each handling a specific part of the diffusion
 ### Creating Your Pipeline
 
 ```python
-from fastvideo.v1.pipelines.composed_pipeline_base import ComposedPipelineBase
-from fastvideo.v1.pipelines.stages import (
+from fastvideo.pipelines.composed_pipeline_base import ComposedPipelineBase
+from fastvideo.pipelines.stages import (
     InputValidationStage, CLIPTextEncodingStage, TimestepPreparationStage,
     LatentPreparationStage, DenoisingStage, DecodingStage
 )
-from fastvideo.v1.fastvideo_args import FastVideoArgs
-from fastvideo.v1.pipelines.pipeline_batch_info import ForwardBatch
+from fastvideo.fastvideo_args import FastVideoArgs
+from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
 import torch
 
 class MyCustomPipeline(ComposedPipelineBase):
@@ -246,7 +246,7 @@ EntryClass = MyCustomPipeline
 If existing stages don't meet your needs, create custom ones:
 
 ```python
-from fastvideo.v1.pipelines.stages.base import PipelineStage
+from fastvideo.pipelines.stages.base import PipelineStage
 
 class MyCustomStage(PipelineStage):
     """Custom processing stage for the pipeline."""
@@ -305,7 +305,7 @@ EntryClass = [MyCustomPipeline, MyOtherPipeline]
 ```
 
 The registry will automatically:
-1. Scan all packages under `fastvideo/v1/pipelines/`
+1. Scan all packages under `fastvideo/pipelines/`
 2. Look for `EntryClass` variables
 3. Register pipelines using their class names as identifiers
 

examples/inference/basic/README.md

@@ -18,6 +18,16 @@ git clone https://github.com/hao-ai-lab/FastVideo.git && cd FastVideo
 python examples/inference/basic/basic.py
 ```
 
+For an example on Apple silicon: 
+```
+python examples/inference/basic/basic_mps.py
+```
+
+For an example running DMD+VSA inference:
+```
+python examples/inference/basic/basic_dmd.py
+```
+
 ## Basic Walkthrough
 
 All you need to generate videos using multi-gpus from state-of-the-art diffusion pipelines is the following few lines!

examples/inference/basic/basic.py

@@ -1,6 +1,6 @@
 from fastvideo import VideoGenerator
 
-# from fastvideo.v1.configs.sample import SamplingParam
+# from fastvideo.configs.sample import SamplingParam
 
 OUTPUT_PATH = "video_samples"
 def main():

examples/inference/basic/basic_dmd.py

@@ -0,0 +1,46 @@
+import os
+import time
+from fastvideo import VideoGenerator
+
+from fastvideo.configs.sample import SamplingParam
+
+OUTPUT_PATH = "video_samples_dmd2"
+def main():
+    os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "VIDEO_SPARSE_ATTN"
+
+    load_start_time = time.perf_counter()
+    model_name = "FastVideo/FastWan2.1-T2V-1.3B-Diffusers"
+    generator = VideoGenerator.from_pretrained(
+        model_name,
+        # FastVideo will automatically handle distributed setup
+        num_gpus=1,
+        use_fsdp_inference=True,
+        # Adjust these offload parameters if you have < 32GB of VRAM
+        text_encoder_offload=False,
+        use_cpu_offload=False,
+        VSA_sparsity=0.8,
+    )
+    load_end_time = time.perf_counter()
+    load_time = load_end_time - load_start_time
+
+
+    sampling_param = SamplingParam.from_pretrained(model_name)
+
+    prompt = (
+        "A curious raccoon peers through a vibrant field of yellow sunflowers, its eyes "
+        "wide with interest. The playful yet serene atmosphere is complemented by soft "
+        "natural light filtering through the petals. Mid-shot, warm and cheerful tones."
+    )
+    start_time = time.perf_counter()
+    video = generator.generate_video(prompt, output_path=OUTPUT_PATH, save_video=True, sampling_param=sampling_param)
+    end_time = time.perf_counter()
+    gen_time = end_time - start_time
+    e2e_gen_time = end_time - start_time
+
+    print(f"Time taken to load model: {load_time} seconds")
+    print(f"Time taken to generate video: {gen_time} seconds")
+    print(f"Time taken for e2e generation: {e2e_gen_time} seconds")
+
+
+if __name__ == "__main__":
+    main()

examples/inference/basic/basic_mps.py

@@ -1,5 +1,5 @@
 from fastvideo import VideoGenerator, PipelineConfig
-from fastvideo.v1.configs.sample import SamplingParam
+from fastvideo.configs.sample import SamplingParam
 
 def main():
     config = PipelineConfig.from_pretrained("Wan-AI/Wan2.1-T2V-1.3B-Diffusers")

examples/inference/basic/default_args.py

@@ -1,5 +1,5 @@
 from fastvideo import VideoGenerator
-from fastvideo.v1.configs.pipelines.base import PipelineConfig
+from fastvideo.configs.pipelines.base import PipelineConfig
 
 def main():
 

examples/inference/basic/test.py

@@ -1,6 +1,6 @@
 from fastvideo import VideoGenerator, PipelineConfig, SamplingParam
 
-# from fastvideo.v1.configs.sample import SamplingParam
+# from fastvideo.configs.sample import SamplingParam
 
 OUTPUT_PATH = "video_samples_fp16"
 def main():

examples/inference/gradio/gradio_demo.py

@@ -6,7 +6,7 @@ import gradio as gr
 import torch
 
 from fastvideo import VideoGenerator
-from fastvideo.v1.configs.sample.base import SamplingParam
+from fastvideo.configs.sample.base import SamplingParam
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(description="FastVideo Gradio Demo")

examples/inference/lora/wan_lora_inference.py

@@ -1,5 +1,5 @@
 from fastvideo import VideoGenerator
-from fastvideo.v1.configs.sample import SamplingParam
+from fastvideo.configs.sample import SamplingParam
 
 OUTPUT_PATH = "./lora_out"
 def main():
@@ -30,17 +30,8 @@ def main():
         negative_prompt=negative_prompt,
         **kwargs
     )
-    del generator
     
-    # Until FSDP resharding bug is fixed, multi-lora requires reloading the model or disabling FSDP
-    # see https://github.com/pytorch/pytorch/issues/157209
-    generator = VideoGenerator.from_pretrained(
-        "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
-        num_gpus=1,
-        lora_path="motimalu/wan-flat-color-1.3b-v2",
-        lora_nickname="flat_color"
-    )
-    # generator.set_lora_adapter(lora_nickname="flat_color", lora_path="motimalu/wan-flat-color-1.3b-v2")
+    generator.set_lora_adapter(lora_nickname="flat_color", lora_path="motimalu/wan-flat-color-1.3b-v2")
     prompt = "flat color, no lineart, blending, negative space, artist:[john kafka|ponsuke kaikai|hara id 21|yoneyama mai|fuzichoco],  1girl, sakura miko, pink hair, cowboy shot, white shirt, floral print, off shoulder, outdoors, cherry blossom, tree shade, wariza, looking up, falling petals, half-closed eyes, white sky, clouds,  live2d animation, upper body, high quality cinematic video of a woman sitting under a sakura tree. Dreamy and lonely, the camera close-ups on the face of the woman as she turns towards the viewer. The Camera is steady, This is a cowboy shot. The animation is smooth and fluid."
     negative_prompt = "bad quality video,色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走"
     video = generator.generate_video(

examples/inference/lora/wan_lora_inference_from_ckpt.py

@@ -2,7 +2,7 @@
 Inference using a LoRA checkpoint from FastVideo trainer.
 """
 from fastvideo import VideoGenerator
-from fastvideo.v1.configs.sample import SamplingParam
+from fastvideo.configs.sample import SamplingParam
 
 OUTPUT_PATH = "./lora_out"
 def main():

examples/training/finetune/Wan2.1-Fun-1.3B-InP/crush_smol/finetune_i2v.sh

@@ -84,7 +84,7 @@ miscellaneous_args=(
 torchrun \
   --nnodes 1 \
   --nproc_per_node $NUM_GPUS \
-    fastvideo/v1/training/wan_i2v_training_pipeline.py \
+    fastvideo/training/wan_i2v_training_pipeline.py \
     "${parallel_args[@]}" \
     "${model_args[@]}" \
     "${dataset_args[@]}" \

examples/training/finetune/Wan2.1-Fun-1.3B-InP/crush_smol/finetune_i2v.slurm

@@ -120,7 +120,7 @@ srun torchrun \
 --node_rank $SLURM_PROCID \
 --rdzv_backend=c10d \
 --rdzv_endpoint="$MASTER_ADDR:$MASTER_PORT" \
-    fastvideo/v1/training/wan_i2v_training_pipeline.py \
+    fastvideo/training/wan_i2v_training_pipeline.py \
     "${parallel_args[@]}" \
     "${model_args[@]}" \
     "${dataset_args[@]}" \

examples/training/finetune/Wan2.1-Fun-1.3B-InP/crush_smol/preprocess_wan_data_i2v.sh

@@ -7,7 +7,7 @@ DATA_MERGE_PATH="data/crush-smol/merge.txt"
 OUTPUT_DIR="data/crush-smol_processed_i2v_1_3b_inp/"
 
 torchrun --nproc_per_node=$GPU_NUM \
-    fastvideo/v1/pipelines/preprocess/v1_preprocess.py \
+    fastvideo/pipelines/preprocess/v1_preprocess.py \
     --model_path $MODEL_PATH \
     --data_merge_path $DATA_MERGE_PATH \
     --preprocess_video_batch_size 8 \

examples/training/finetune/wan_i2v_14b_480p/crush_smol/finetune_i2v.sh

@@ -84,7 +84,7 @@ miscellaneous_args=(
 torchrun \
   --nnodes 1 \
   --nproc_per_node $NUM_GPUS \
-    fastvideo/v1/training/wan_i2v_training_pipeline.py \
+    fastvideo/training/wan_i2v_training_pipeline.py \
     "${parallel_args[@]}" \
     "${model_args[@]}" \
     "${dataset_args[@]}" \

examples/training/finetune/wan_i2v_14b_480p/crush_smol/finetune_i2v.slurm

@@ -120,7 +120,7 @@ srun torchrun \
 --node_rank $SLURM_PROCID \
 --rdzv_backend=c10d \
 --rdzv_endpoint="$MASTER_ADDR:$MASTER_PORT" \
-    fastvideo/v1/training/wan_i2v_training_pipeline.py \
+    fastvideo/training/wan_i2v_training_pipeline.py \
     "${parallel_args[@]}" \
     "${model_args[@]}" \
     "${dataset_args[@]}" \

examples/training/finetune/wan_i2v_14b_480p/crush_smol/finetune_i2v_lora.sh

@@ -83,7 +83,7 @@ miscellaneous_args=(
 torchrun \
   --nnodes 1 \
   --nproc_per_node $NUM_GPUS \
-    fastvideo/v1/training/wan_i2v_training_pipeline.py \
+    fastvideo/training/wan_i2v_training_pipeline.py \
     "${parallel_args[@]}" \
     "${model_args[@]}" \
     "${dataset_args[@]}" \

examples/training/finetune/wan_i2v_14b_480p/crush_smol/preprocess_wan_data_i2v.sh

@@ -7,7 +7,7 @@ DATA_MERGE_PATH="data/crush-smol/merge.txt"
 OUTPUT_DIR="data/crush-smol_processed_i2v/"
 
 torchrun --nproc_per_node=$GPU_NUM \
-    fastvideo/v1/pipelines/preprocess/v1_preprocess.py \
+    fastvideo/pipelines/preprocess/v1_preprocess.py \
     --model_path $MODEL_PATH \
     --data_merge_path $DATA_MERGE_PATH \
     --preprocess_video_batch_size 8 \

examples/training/finetune/wan_t2v_1_3b/crush_smol/finetune_t2v.sh

@@ -83,7 +83,7 @@ miscellaneous_args=(
 torchrun \
   --nnodes 1 \
   --nproc_per_node $NUM_GPUS \
-    fastvideo/v1/training/wan_training_pipeline.py \
+    fastvideo/training/wan_training_pipeline.py \
     "${parallel_args[@]}" \
     "${model_args[@]}" \
     "${dataset_args[@]}" \

examples/training/finetune/wan_t2v_1_3b/crush_smol/finetune_t2v.slurm

@@ -117,7 +117,7 @@ srun torchrun \
 --node_rank $SLURM_PROCID \
 --rdzv_backend=c10d \
 --rdzv_endpoint="$MASTER_ADDR:$MASTER_PORT" \
-    fastvideo/v1/training/wan_training_pipeline.py \
+    fastvideo/training/wan_training_pipeline.py \
     "${parallel_args[@]}" \
     "${model_args[@]}" \
     "${dataset_args[@]}" \

examples/training/finetune/wan_t2v_1_3b/crush_smol/finetune_t2v_lora.sh

@@ -83,7 +83,7 @@ torchrun \
   --nnodes 1 \
   --nproc_per_node $NUM_GPUS \
   --master_port 29501 \
-    fastvideo/v1/training/wan_training_pipeline.py \
+    fastvideo/training/wan_training_pipeline.py \
     "${parallel_args[@]}" \
     "${model_args[@]}" \
     "${dataset_args[@]}" \

examples/training/finetune/wan_t2v_1_3b/crush_smol/preprocess_wan_data_t2v.sh

@@ -7,7 +7,7 @@ DATA_MERGE_PATH="data/crush-smol/merge.txt"
 OUTPUT_DIR="data/crush-smol_processed_t2v/"
 
 torchrun --nproc_per_node=$GPU_NUM \
-    fastvideo/v1/pipelines/preprocess/v1_preprocess.py \
+    fastvideo/pipelines/preprocess/v1_preprocess.py \
     --model_path $MODEL_PATH \
     --data_merge_path $DATA_MERGE_PATH \
     --preprocess_video_batch_size 8 \

fastvideo/STA_configuration.py

@@ -6,7 +6,7 @@ from typing import Any
 
 import numpy as np
 
-from fastvideo.v1.utils import dict_to_3d_list
+from fastvideo.utils import dict_to_3d_list
 
 
 def configure_sta(mode: str = 'STA_searching',

fastvideo/__init__.py

@@ -1,6 +1,6 @@
-from fastvideo.v1.configs.pipelines import PipelineConfig
-from fastvideo.v1.configs.sample import SamplingParam
-from fastvideo.v1.entrypoints.video_generator import VideoGenerator
+from fastvideo.configs.pipelines import PipelineConfig
+from fastvideo.configs.sample import SamplingParam
+from fastvideo.entrypoints.video_generator import VideoGenerator
 from fastvideo.version import __version__
 
-__all__ = ["VideoGenerator", "PipelineConfig", "SamplingParam", "__version__"]
+__all__ = ["VideoGenerator", "PipelineConfig", "SamplingParam", "__version__"]

fastvideo/attention/__init__.py

@@ -0,0 +1,19 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from fastvideo.attention.backends.abstract import (AttentionBackend,
+                                                   AttentionMetadata,
+                                                   AttentionMetadataBuilder)
+from fastvideo.attention.layer import (DistributedAttention,
+                                       DistributedAttention_VSA, LocalAttention)
+from fastvideo.attention.selector import get_attn_backend
+
+__all__ = [
+    "DistributedAttention",
+    "LocalAttention",
+    "DistributedAttention_VSA",
+    "AttentionBackend",
+    "AttentionMetadata",
+    "AttentionMetadataBuilder",
+    # "AttentionState",
+    "get_attn_backend",
+]

fastvideo/attention/backends/abstract.py

@@ -6,8 +6,8 @@ from dataclasses import dataclass, fields
 from typing import TYPE_CHECKING, Any, Generic, Protocol, TypeVar
 
 if TYPE_CHECKING:
-    from fastvideo.v1.fastvideo_args import FastVideoArgs
-    from fastvideo.v1.pipelines.pipeline_batch_info import ForwardBatch
+    from fastvideo.fastvideo_args import FastVideoArgs
+    from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
 
 import torch
 

fastvideo/attention/backends/flash_attn.py

@@ -12,11 +12,11 @@ try:
 except ImportError:
     flash_attn_func = flash_attn_2_func
 
-from fastvideo.v1.attention.backends.abstract import (AttentionBackend,
-                                                      AttentionImpl,
-                                                      AttentionMetadata,
-                                                      AttentionMetadataBuilder)
-from fastvideo.v1.logger import init_logger
+from fastvideo.attention.backends.abstract import (AttentionBackend,
+                                                   AttentionImpl,
+                                                   AttentionMetadata,
+                                                   AttentionMetadataBuilder)
+from fastvideo.logger import init_logger
 
 logger = init_logger(__name__)
 

fastvideo/attention/backends/sage_attn.py

@@ -3,11 +3,9 @@
 import torch
 from sageattention import sageattn
 
-from fastvideo.v1.attention.backends.abstract import (
-    AttentionBackend)  # FlashAttentionMetadata,
-from fastvideo.v1.attention.backends.abstract import (AttentionImpl,
-                                                      AttentionMetadata)
-from fastvideo.v1.logger import init_logger
+from fastvideo.attention.backends.abstract import (  # FlashAttentionMetadata,
+    AttentionBackend, AttentionImpl, AttentionMetadata)
+from fastvideo.logger import init_logger
 
 logger = init_logger(__name__)
 

fastvideo/attention/backends/sdpa.py

@@ -2,11 +2,9 @@
 
 import torch
 
-from fastvideo.v1.attention.backends.abstract import (
-    AttentionBackend)  # FlashAttentionMetadata,
-from fastvideo.v1.attention.backends.abstract import (AttentionImpl,
-                                                      AttentionMetadata)
-from fastvideo.v1.logger import init_logger
+from fastvideo.attention.backends.abstract import (  # FlashAttentionMetadata,
+    AttentionBackend, AttentionImpl, AttentionMetadata)
+from fastvideo.logger import init_logger
 
 logger = init_logger(__name__)
 

fastvideo/attention/backends/sliding_tile_attn.py

@@ -7,17 +7,17 @@ import torch
 from einops import rearrange
 from st_attn import sliding_tile_attention
 
-import fastvideo.v1.envs as envs
-from fastvideo.v1.attention.backends.abstract import (AttentionBackend,
-                                                      AttentionImpl,
-                                                      AttentionMetadata,
-                                                      AttentionMetadataBuilder)
-from fastvideo.v1.distributed import get_sp_group
-from fastvideo.v1.fastvideo_args import FastVideoArgs
-from fastvideo.v1.forward_context import ForwardContext, get_forward_context
-from fastvideo.v1.logger import init_logger
-from fastvideo.v1.pipelines.pipeline_batch_info import ForwardBatch
-from fastvideo.v1.utils import dict_to_3d_list
+import fastvideo.envs as envs
+from fastvideo.attention.backends.abstract import (AttentionBackend,
+                                                   AttentionImpl,
+                                                   AttentionMetadata,
+                                                   AttentionMetadataBuilder)
+from fastvideo.distributed import get_sp_group
+from fastvideo.fastvideo_args import FastVideoArgs
+from fastvideo.forward_context import ForwardContext, get_forward_context
+from fastvideo.logger import init_logger
+from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
+from fastvideo.utils import dict_to_3d_list
 
 logger = init_logger(__name__)
 

fastvideo/attention/backends/video_sparse_attn.py

@@ -10,14 +10,14 @@ try:
 except ImportError:
     video_sparse_attn = None
 
-from fastvideo.v1.attention.backends.abstract import (AttentionBackend,
-                                                      AttentionImpl,
-                                                      AttentionMetadata,
-                                                      AttentionMetadataBuilder)
-from fastvideo.v1.distributed import get_sp_group
-from fastvideo.v1.fastvideo_args import FastVideoArgs
-from fastvideo.v1.logger import init_logger
-from fastvideo.v1.pipelines.pipeline_batch_info import ForwardBatch
+from fastvideo.attention.backends.abstract import (AttentionBackend,
+                                                   AttentionImpl,
+                                                   AttentionMetadata,
+                                                   AttentionMetadataBuilder)
+from fastvideo.distributed import get_sp_group
+from fastvideo.fastvideo_args import FastVideoArgs
+from fastvideo.logger import init_logger
+from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
 
 logger = init_logger(__name__)
 

fastvideo/attention/layer.py

@@ -3,15 +3,14 @@
 import torch
 import torch.nn as nn
 
-from fastvideo.v1.attention.selector import (backend_name_to_enum,
-                                             get_attn_backend)
-from fastvideo.v1.distributed.communication_op import (
+from fastvideo.attention.selector import backend_name_to_enum, get_attn_backend
+from fastvideo.distributed.communication_op import (
     sequence_model_parallel_all_gather, sequence_model_parallel_all_to_all_4D)
-from fastvideo.v1.distributed.parallel_state import (get_sp_parallel_rank,
-                                                     get_sp_world_size)
-from fastvideo.v1.forward_context import ForwardContext, get_forward_context
-from fastvideo.v1.platforms import AttentionBackendEnum
-from fastvideo.v1.utils import get_compute_dtype
+from fastvideo.distributed.parallel_state import (get_sp_parallel_rank,
+                                                  get_sp_world_size)
+from fastvideo.forward_context import ForwardContext, get_forward_context
+from fastvideo.platforms import AttentionBackendEnum
+from fastvideo.utils import get_compute_dtype
 
 
 class DistributedAttention(nn.Module):

fastvideo/attention/selector.py

@@ -9,11 +9,11 @@ from typing import cast
 
 import torch
 
-import fastvideo.v1.envs as envs
-from fastvideo.v1.attention.backends.abstract import AttentionBackend
-from fastvideo.v1.logger import init_logger
-from fastvideo.v1.platforms import AttentionBackendEnum, current_platform
-from fastvideo.v1.utils import STR_BACKEND_ENV_VAR, resolve_obj_by_qualname
+import fastvideo.envs as envs
+from fastvideo.attention.backends.abstract import AttentionBackend
+from fastvideo.logger import init_logger
+from fastvideo.platforms import AttentionBackendEnum, current_platform
+from fastvideo.utils import STR_BACKEND_ENV_VAR, resolve_obj_by_qualname
 
 logger = init_logger(__name__)
 

fastvideo/configs/models/__init__.py

@@ -0,0 +1,6 @@
+from fastvideo.configs.models.base import ModelConfig
+from fastvideo.configs.models.dits.base import DiTConfig
+from fastvideo.configs.models.encoders.base import EncoderConfig
+from fastvideo.configs.models.vaes.base import VAEConfig
+
+__all__ = ["ModelConfig", "VAEConfig", "DiTConfig", "EncoderConfig"]

fastvideo/configs/models/base.py

@@ -2,7 +2,7 @@
 from dataclasses import dataclass, field, fields
 from typing import Any
 
-from fastvideo.v1.logger import init_logger
+from fastvideo.logger import init_logger
 
 logger = init_logger(__name__)
 

fastvideo/configs/models/dits/__init__.py

@@ -0,0 +1,5 @@
+from fastvideo.configs.models.dits.hunyuanvideo import HunyuanVideoConfig
+from fastvideo.configs.models.dits.stepvideo import StepVideoConfig
+from fastvideo.configs.models.dits.wanvideo import WanVideoConfig
+
+__all__ = ["HunyuanVideoConfig", "WanVideoConfig", "StepVideoConfig"]

fastvideo/configs/models/dits/base.py

@@ -2,9 +2,9 @@
 from dataclasses import dataclass, field
 from typing import Any
 
-from fastvideo.v1.configs.models.base import ArchConfig, ModelConfig
-from fastvideo.v1.layers.quantization import QuantizationConfig
-from fastvideo.v1.platforms import AttentionBackendEnum
+from fastvideo.configs.models.base import ArchConfig, ModelConfig
+from fastvideo.layers.quantization import QuantizationConfig
+from fastvideo.platforms import AttentionBackendEnum
 
 
 @dataclass

fastvideo/configs/models/dits/hunyuanvideo.py

@@ -3,7 +3,7 @@ from dataclasses import dataclass, field
 
 import torch
 
-from fastvideo.v1.configs.models.dits.base import DiTArchConfig, DiTConfig
+from fastvideo.configs.models.dits.base import DiTArchConfig, DiTConfig
 
 
 def is_double_block(n: str, m) -> bool:

fastvideo/configs/models/dits/stepvideo.py

@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 from dataclasses import dataclass, field
 
-from fastvideo.v1.configs.models.dits.base import DiTArchConfig, DiTConfig
+from fastvideo.configs.models.dits.base import DiTArchConfig, DiTConfig
 
 
 @dataclass

fastvideo/configs/models/dits/wanvideo.py

@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 from dataclasses import dataclass, field
 
-from fastvideo.v1.configs.models.dits.base import DiTArchConfig, DiTConfig
+from fastvideo.configs.models.dits.base import DiTArchConfig, DiTConfig
 
 
 def is_blocks(n: str, m) -> bool:

fastvideo/configs/models/encoders/__init__.py

@@ -0,0 +1,14 @@
+from fastvideo.configs.models.encoders.base import (BaseEncoderOutput,
+                                                    EncoderConfig,
+                                                    ImageEncoderConfig,
+                                                    TextEncoderConfig)
+from fastvideo.configs.models.encoders.clip import (CLIPTextConfig,
+                                                    CLIPVisionConfig)
+from fastvideo.configs.models.encoders.llama import LlamaConfig
+from fastvideo.configs.models.encoders.t5 import T5Config
+
+__all__ = [
+    "EncoderConfig", "TextEncoderConfig", "ImageEncoderConfig",
+    "BaseEncoderOutput", "CLIPTextConfig", "CLIPVisionConfig", "LlamaConfig",
+    "T5Config"
+]

fastvideo/configs/models/encoders/base.py

@@ -4,9 +4,9 @@ from typing import Any
 
 import torch
 
-from fastvideo.v1.configs.models.base import ArchConfig, ModelConfig
-from fastvideo.v1.layers.quantization import QuantizationConfig
-from fastvideo.v1.platforms import AttentionBackendEnum
+from fastvideo.configs.models.base import ArchConfig, ModelConfig
+from fastvideo.layers.quantization import QuantizationConfig
+from fastvideo.platforms import AttentionBackendEnum
 
 
 @dataclass

fastvideo/configs/models/encoders/clip.py

@@ -1,10 +1,10 @@
 # SPDX-License-Identifier: Apache-2.0
 from dataclasses import dataclass, field
 
-from fastvideo.v1.configs.models.encoders.base import (ImageEncoderArchConfig,
-                                                       ImageEncoderConfig,
-                                                       TextEncoderArchConfig,
-                                                       TextEncoderConfig)
+from fastvideo.configs.models.encoders.base import (ImageEncoderArchConfig,
+                                                    ImageEncoderConfig,
+                                                    TextEncoderArchConfig,
+                                                    TextEncoderConfig)
 
 
 def _is_transformer_layer(n: str, m) -> bool:

fastvideo/configs/models/encoders/llama.py

@@ -1,8 +1,8 @@
 # SPDX-License-Identifier: Apache-2.0
 from dataclasses import dataclass, field
 
-from fastvideo.v1.configs.models.encoders.base import (TextEncoderArchConfig,
-                                                       TextEncoderConfig)
+from fastvideo.configs.models.encoders.base import (TextEncoderArchConfig,
+                                                    TextEncoderConfig)
 
 
 def _is_transformer_layer(n: str, m) -> bool:

fastvideo/configs/models/encoders/t5.py

@@ -1,8 +1,8 @@
 # SPDX-License-Identifier: Apache-2.0
 from dataclasses import dataclass, field
 
-from fastvideo.v1.configs.models.encoders.base import (TextEncoderArchConfig,
-                                                       TextEncoderConfig)
+from fastvideo.configs.models.encoders.base import (TextEncoderArchConfig,
+                                                    TextEncoderConfig)
 
 
 def _is_transformer_layer(n: str, m) -> bool:

fastvideo/configs/models/vaes/__init__.py

@@ -0,0 +1,9 @@
+from fastvideo.configs.models.vaes.hunyuanvae import HunyuanVAEConfig
+from fastvideo.configs.models.vaes.stepvideovae import StepVideoVAEConfig
+from fastvideo.configs.models.vaes.wanvae import WanVAEConfig
+
+__all__ = [
+    "HunyuanVAEConfig",
+    "WanVAEConfig",
+    "StepVideoVAEConfig",
+]

fastvideo/configs/models/vaes/base.py

@@ -6,8 +6,8 @@ from typing import Any
 
 import torch
 
-from fastvideo.v1.configs.models.base import ArchConfig, ModelConfig
-from fastvideo.v1.utils import StoreBoolean
+from fastvideo.configs.models.base import ArchConfig, ModelConfig
+from fastvideo.utils import StoreBoolean
 
 
 @dataclass

fastvideo/configs/models/vaes/hunyuanvae.py

@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 from dataclasses import dataclass, field
 
-from fastvideo.v1.configs.models.vaes.base import VAEArchConfig, VAEConfig
+from fastvideo.configs.models.vaes.base import VAEArchConfig, VAEConfig
 
 
 @dataclass

fastvideo/configs/models/vaes/stepvideovae.py

@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 from dataclasses import dataclass, field
 
-from fastvideo.v1.configs.models.vaes.base import VAEArchConfig, VAEConfig
+from fastvideo.configs.models.vaes.base import VAEArchConfig, VAEConfig
 
 
 @dataclass

fastvideo/configs/models/vaes/wanvae.py

@@ -3,7 +3,7 @@ from dataclasses import dataclass, field
 
 import torch
 
-from fastvideo.v1.configs.models.vaes.base import VAEArchConfig, VAEConfig
+from fastvideo.configs.models.vaes.base import VAEArchConfig, VAEConfig
 
 
 @dataclass

fastvideo/configs/pipelines/__init__.py

@@ -0,0 +1,15 @@
+from fastvideo.configs.pipelines.base import (PipelineConfig,
+                                              SlidingTileAttnConfig)
+from fastvideo.configs.pipelines.hunyuan import FastHunyuanConfig, HunyuanConfig
+from fastvideo.configs.pipelines.registry import (
+    get_pipeline_config_cls_from_name)
+from fastvideo.configs.pipelines.stepvideo import StepVideoT2VConfig
+from fastvideo.configs.pipelines.wan import (WanI2V480PConfig, WanI2V720PConfig,
+                                             WanT2V480PConfig, WanT2V720PConfig)
+
+__all__ = [
+    "HunyuanConfig", "FastHunyuanConfig", "PipelineConfig",
+    "SlidingTileAttnConfig", "WanT2V480PConfig", "WanI2V480PConfig",
+    "WanT2V720PConfig", "WanI2V720PConfig", "StepVideoT2VConfig",
+    "get_pipeline_config_cls_from_name"
+]

fastvideo/configs/pipelines/base.py

@@ -7,13 +7,12 @@ from typing import Any, cast
 
 import torch
 
-from fastvideo.v1.configs.models import (DiTConfig, EncoderConfig, ModelConfig,
-                                         VAEConfig)
-from fastvideo.v1.configs.models.encoders import BaseEncoderOutput
-from fastvideo.v1.configs.utils import update_config_from_args
-from fastvideo.v1.logger import init_logger
-from fastvideo.v1.utils import (FlexibleArgumentParser, StoreBoolean,
-                                shallow_asdict)
+from fastvideo.configs.models import (DiTConfig, EncoderConfig, ModelConfig,
+                                      VAEConfig)
+from fastvideo.configs.models.encoders import BaseEncoderOutput
+from fastvideo.configs.utils import update_config_from_args
+from fastvideo.logger import init_logger
+from fastvideo.utils import FlexibleArgumentParser, StoreBoolean, shallow_asdict
 
 logger = init_logger(__name__)
 
@@ -83,6 +82,9 @@ class PipelineConfig:
     STA_mode: STA_Mode = STA_Mode.STA_INFERENCE
     skip_time_steps: int = 15
 
+    # DMD parameters
+    dmd_denoising_steps: list[int] | None = field(default=None)
+
     # Compilation
     # enable_torch_compile: bool = False
 
@@ -200,12 +202,21 @@ class PipelineConfig:
             "Bool for applying scheduler scale in set_timesteps, used in stepvideo",
         )
 
+        # DMD parameters
+        parser.add_argument(
+            f"--{prefix_with_dot}dmd-denoising-steps",
+            type=parse_int_list,
+            default=PipelineConfig.dmd_denoising_steps,
+            help=
+            "Comma-separated list of denoising steps (e.g., '1000,757,522')",
+        )
+
         # Add VAE configuration arguments
-        from fastvideo.v1.configs.models.vaes.base import VAEConfig
+        from fastvideo.configs.models.vaes.base import VAEConfig
         VAEConfig.add_cli_args(parser, prefix=f"{prefix_with_dot}vae-config")
 
         # Add DiT configuration arguments
-        from fastvideo.v1.configs.models.dits.base import DiTConfig
+        from fastvideo.configs.models.dits.base import DiTConfig
         DiTConfig.add_cli_args(parser, prefix=f"{prefix_with_dot}dit-config")
 
         return parser
@@ -229,7 +240,7 @@ class PipelineConfig:
         """
         use the pipeline class setting from model_path to match the pipeline config
         """
-        from fastvideo.v1.configs.pipelines.registry import (
+        from fastvideo.configs.pipelines.registry import (
             get_pipeline_config_cls_from_name)
         pipeline_config_cls = get_pipeline_config_cls_from_name(model_path)
 
@@ -244,7 +255,7 @@ class PipelineConfig:
         kwargs: dictionary of kwargs
         config_cli_prefix: prefix of CLI arguments for this PipelineConfig instance
         """
-        from fastvideo.v1.configs.pipelines.registry import (
+        from fastvideo.configs.pipelines.registry import (
             get_pipeline_config_cls_from_name)
 
         prefix_with_dot = f"{config_cli_prefix}." if (config_cli_prefix.strip()
@@ -381,3 +392,10 @@ class SlidingTileAttnConfig(PipelineConfig):
     # Additional configuration specific to sliding tile attention
     pad_to_square: bool = False
     use_overlap_optimization: bool = True
+
+
+def parse_int_list(value: str) -> list[int]:
+    """Parse a comma-separated string of integers into a list."""
+    if not value:
+        return []
+    return [int(x.strip()) for x in value.split(",")]

fastvideo/configs/pipelines/hunyuan.py

@@ -5,12 +5,12 @@ from typing import TypedDict
 
 import torch
 
-from fastvideo.v1.configs.models import DiTConfig, EncoderConfig, VAEConfig
-from fastvideo.v1.configs.models.dits import HunyuanVideoConfig
-from fastvideo.v1.configs.models.encoders import (BaseEncoderOutput,
-                                                  CLIPTextConfig, LlamaConfig)
-from fastvideo.v1.configs.models.vaes import HunyuanVAEConfig
-from fastvideo.v1.configs.pipelines.base import PipelineConfig
+from fastvideo.configs.models import DiTConfig, EncoderConfig, VAEConfig
+from fastvideo.configs.models.dits import HunyuanVideoConfig
+from fastvideo.configs.models.encoders import (BaseEncoderOutput,
+                                               CLIPTextConfig, LlamaConfig)
+from fastvideo.configs.models.vaes import HunyuanVAEConfig
+from fastvideo.configs.pipelines.base import PipelineConfig
 
 PROMPT_TEMPLATE_ENCODE_VIDEO = (
     "<|start_header_id|>system<|end_header_id|>\n\nDescribe the video by detailing the following aspects: "

fastvideo/configs/pipelines/registry.py

@@ -4,17 +4,15 @@
 import os
 from collections.abc import Callable
 
-from fastvideo.v1.configs.pipelines.base import PipelineConfig
-from fastvideo.v1.configs.pipelines.hunyuan import (FastHunyuanConfig,
-                                                    HunyuanConfig)
-from fastvideo.v1.configs.pipelines.stepvideo import StepVideoT2VConfig
-from fastvideo.v1.configs.pipelines.wan import (WanI2V480PConfig,
-                                                WanI2V720PConfig,
-                                                WanT2V480PConfig,
-                                                WanT2V720PConfig)
-from fastvideo.v1.logger import init_logger
-from fastvideo.v1.utils import (maybe_download_model_index,
-                                verify_model_config_and_directory)
+from fastvideo.configs.pipelines.base import PipelineConfig
+from fastvideo.configs.pipelines.hunyuan import FastHunyuanConfig, HunyuanConfig
+from fastvideo.configs.pipelines.stepvideo import StepVideoT2VConfig
+from fastvideo.configs.pipelines.wan import (FastWanT2V480PConfig,
+                                             WanI2V480PConfig, WanI2V720PConfig,
+                                             WanT2V480PConfig, WanT2V720PConfig)
+from fastvideo.logger import init_logger
+from fastvideo.utils import (maybe_download_model_index,
+                             verify_model_config_and_directory)
 
 logger = init_logger(__name__)
 
@@ -27,6 +25,7 @@ PIPE_NAME_TO_CONFIG: dict[str, type[PipelineConfig]] = {
     "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers": WanI2V480PConfig,
     "Wan-AI/Wan2.1-I2V-14B-720P-Diffusers": WanI2V720PConfig,
     "Wan-AI/Wan2.1-T2V-14B-Diffusers": WanT2V720PConfig,
+    "FastVideo/FastWan2.1-T2V-1.3B-Diffusers": FastWanT2V480PConfig,
     "FastVideo/stepvideo-t2v-diffusers": StepVideoT2VConfig,
     # Add other specific weight variants
 }
@@ -36,6 +35,7 @@ PIPELINE_DETECTOR: dict[str, Callable[[str], bool]] = {
     "hunyuan": lambda id: "hunyuan" in id.lower(),
     "wanpipeline": lambda id: "wanpipeline" in id.lower(),
     "wanimagetovideo": lambda id: "wanimagetovideo" in id.lower(),
+    "wandmdpipeline": lambda id: "wandmdpipeline" in id.lower(),
     "stepvideo": lambda id: "stepvideo" in id.lower(),
     # Add other pipeline architecture detectors
 }
@@ -47,6 +47,7 @@ PIPELINE_FALLBACK_CONFIG: dict[str, type[PipelineConfig]] = {
     "wanpipeline":
     WanT2V480PConfig,  # Base Wan config as fallback for any Wan variant
     "wanimagetovideo": WanI2V480PConfig,
+    "wandmdpipeline": FastWanT2V480PConfig,
     "stepvideo": StepVideoT2VConfig
     # Other fallbacks by architecture
 }

fastvideo/configs/pipelines/stepvideo.py

@@ -1,10 +1,10 @@
 # SPDX-License-Identifier: Apache-2.0
 from dataclasses import dataclass, field
 
-from fastvideo.v1.configs.models import DiTConfig, VAEConfig
-from fastvideo.v1.configs.models.dits import StepVideoConfig
-from fastvideo.v1.configs.models.vaes import StepVideoVAEConfig
-from fastvideo.v1.configs.pipelines.base import PipelineConfig
+from fastvideo.configs.models import DiTConfig, VAEConfig
+from fastvideo.configs.models.dits import StepVideoConfig
+from fastvideo.configs.models.vaes import StepVideoVAEConfig
+from fastvideo.configs.pipelines.base import PipelineConfig
 
 
 @dataclass

fastvideo/configs/pipelines/wan.py

@@ -4,12 +4,12 @@ from dataclasses import dataclass, field
 
 import torch
 
-from fastvideo.v1.configs.models import DiTConfig, EncoderConfig, VAEConfig
-from fastvideo.v1.configs.models.dits import WanVideoConfig
-from fastvideo.v1.configs.models.encoders import (BaseEncoderOutput,
-                                                  CLIPVisionConfig, T5Config)
-from fastvideo.v1.configs.models.vaes import WanVAEConfig
-from fastvideo.v1.configs.pipelines.base import PipelineConfig
+from fastvideo.configs.models import DiTConfig, EncoderConfig, VAEConfig
+from fastvideo.configs.models.dits import WanVideoConfig
+from fastvideo.configs.models.encoders import (BaseEncoderOutput,
+                                               CLIPVisionConfig, T5Config)
+from fastvideo.configs.models.vaes import WanVAEConfig
+from fastvideo.configs.pipelines.base import PipelineConfig
 
 
 def t5_postprocess_text(outputs: BaseEncoderOutput) -> torch.tensor:
@@ -95,3 +95,19 @@ class WanI2V720PConfig(WanI2V480PConfig):
 
     # Denoising stage
     flow_shift: int = 5
+
+
+@dataclass
+class FastWanT2V480PConfig(WanT2V480PConfig):
+    """Base configuration for FastWan T2V 1.3B 480P pipeline architecture with DMD"""
+
+    # WanConfig-specific parameters with defaults
+
+    # Denoising stage
+    flow_shift: int = 8
+    dmd_denoising_steps: list[int] | None = field(
+        default_factory=lambda: [1000, 757, 522])
+
+    def __post_init__(self) -> None:
+        self.vae_config.load_encoder = True
+        self.vae_config.load_decoder = True

fastvideo/configs/sample/__init__.py

@@ -0,0 +1,3 @@
+from fastvideo.configs.sample.base import SamplingParam
+
+__all__ = ["SamplingParam"]

fastvideo/configs/sample/base.py

@@ -2,7 +2,7 @@
 from dataclasses import dataclass
 from typing import Any
 
-from fastvideo.v1.logger import init_logger
+from fastvideo.logger import init_logger
 
 logger = init_logger(__name__)
 
@@ -66,7 +66,7 @@ class SamplingParam:
 
     @classmethod
     def from_pretrained(cls, model_path: str) -> "SamplingParam":
-        from fastvideo.v1.configs.sample.registry import (
+        from fastvideo.configs.sample.registry import (
             get_sampling_param_cls_for_name)
         sampling_cls = get_sampling_param_cls_for_name(model_path)
         if sampling_cls is not None:

fastvideo/configs/sample/hunyuan.py

@@ -1,8 +1,8 @@
 # SPDX-License-Identifier: Apache-2.0
 from dataclasses import dataclass, field
 
-from fastvideo.v1.configs.sample.base import SamplingParam
-from fastvideo.v1.configs.sample.teacache import TeaCacheParams
+from fastvideo.configs.sample.base import SamplingParam
+from fastvideo.configs.sample.teacache import TeaCacheParams
 
 
 @dataclass

fastvideo/configs/sample/registry.py

@@ -3,16 +3,17 @@ import os
 from collections.abc import Callable
 from typing import Any
 
-from fastvideo.v1.configs.sample.hunyuan import (FastHunyuanSamplingParam,
-                                                 HunyuanSamplingParam)
-from fastvideo.v1.configs.sample.stepvideo import StepVideoT2VSamplingParam
-from fastvideo.v1.configs.sample.wan import (WanI2V_14B_480P_SamplingParam,
-                                             WanI2V_14B_720P_SamplingParam,
-                                             WanT2V_1_3B_SamplingParam,
-                                             WanT2V_14B_SamplingParam)
-from fastvideo.v1.logger import init_logger
-from fastvideo.v1.utils import (maybe_download_model_index,
-                                verify_model_config_and_directory)
+from fastvideo.configs.sample.hunyuan import (FastHunyuanSamplingParam,
+                                              HunyuanSamplingParam)
+from fastvideo.configs.sample.stepvideo import StepVideoT2VSamplingParam
+from fastvideo.configs.sample.wan import (FastWanT2V480PConfig,
+                                          WanI2V_14B_480P_SamplingParam,
+                                          WanI2V_14B_720P_SamplingParam,
+                                          WanT2V_1_3B_SamplingParam,
+                                          WanT2V_14B_SamplingParam)
+from fastvideo.logger import init_logger
+from fastvideo.utils import (maybe_download_model_index,
+                             verify_model_config_and_directory)
 
 logger = init_logger(__name__)
 # Registry maps specific model weights to their config classes
@@ -24,6 +25,7 @@ SAMPLING_PARAM_REGISTRY: dict[str, Any] = {
     "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers": WanI2V_14B_480P_SamplingParam,
     "Wan-AI/Wan2.1-I2V-14B-720P-Diffusers": WanI2V_14B_720P_SamplingParam,
     "FastVideo/stepvideo-t2v-diffusers": StepVideoT2VSamplingParam,
+    "FastVideo/FastWan2.1-T2V-1.3B-Diffusers": FastWanT2V480PConfig,
     # Add other specific weight variants
 }
 

fastvideo/configs/sample/stepvideo.py

@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 from dataclasses import dataclass
 
-from fastvideo.v1.configs.sample.base import SamplingParam
+from fastvideo.configs.sample.base import SamplingParam
 
 
 @dataclass

fastvideo/configs/sample/teacache.py

@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 from dataclasses import dataclass, field
 
-from fastvideo.v1.configs.sample.base import CacheParams
+from fastvideo.configs.sample.base import CacheParams
 
 
 @dataclass

fastvideo/configs/sample/wan.py

@@ -1,8 +1,8 @@
 # SPDX-License-Identifier: Apache-2.0
 from dataclasses import dataclass, field
 
-from fastvideo.v1.configs.sample.base import SamplingParam
-from fastvideo.v1.configs.sample.teacache import WanTeaCacheParams
+from fastvideo.configs.sample.base import SamplingParam
+from fastvideo.configs.sample.teacache import WanTeaCacheParams
 
 
 @dataclass
@@ -94,3 +94,14 @@ class WanI2V_14B_720P_SamplingParam(WanT2V_14B_SamplingParam):
                 -5784.54975374, 5449.50911966, -1811.16591783, 256.27178429,
                 -13.02252404
             ]))
+
+
+@dataclass
+class FastWanT2V480PConfig(WanT2V_1_3B_SamplingParam):
+    # DMD parameters
+    # dmd_denoising_steps: list[int] | None = field(default_factory=lambda: [1000, 757, 522])
+    num_inference_steps: int = 3
+    num_frames: int = 61
+    height: int = 448
+    width: int = 832
+    fps: int = 16

fastvideo/dataset/__init__.py

@@ -2,13 +2,12 @@
 from torchvision import transforms
 from torchvision.transforms import Lambda
 
-from fastvideo.v1.dataset.parquet_dataset_map_style import (
+from fastvideo.dataset.parquet_dataset_map_style import (
     build_parquet_map_style_dataloader)
-from fastvideo.v1.dataset.preprocessing_datasets import (
-    VideoCaptionMergedDataset)
-from fastvideo.v1.dataset.transform import (CenterCropResizeVideo, Normalize255,
-                                            TemporalRandomCrop)
-from fastvideo.v1.dataset.validation_dataset import ValidationDataset
+from fastvideo.dataset.preprocessing_datasets import VideoCaptionMergedDataset
+from fastvideo.dataset.transform import (CenterCropResizeVideo, Normalize255,
+                                         TemporalRandomCrop)
+from fastvideo.dataset.validation_dataset import ValidationDataset
 
 
 def getdataset(args) -> VideoCaptionMergedDataset:

fastvideo/dataset/benchmarks/benchmark_parquet_dataset_iterable_style.py

@@ -7,13 +7,13 @@ import time
 import torch.distributed as dist
 import torch.distributed.checkpoint as dist_cp
 
-from fastvideo.v1.dataset.parquet_dataset_iterable_style import (
+from fastvideo.dataset.parquet_dataset_iterable_style import (
     build_parquet_iterable_style_dataloader)
-from fastvideo.v1.distributed import get_world_rank
-from fastvideo.v1.distributed.parallel_state import (
+from fastvideo.distributed import get_world_rank
+from fastvideo.distributed.parallel_state import (
     cleanup_dist_env_and_memory, get_local_torch_device,
     maybe_init_distributed_environment_and_model_parallel)
-from fastvideo.v1.logger import init_logger
+from fastvideo.logger import init_logger
 
 logger = init_logger(__name__)
 
@@ -52,9 +52,9 @@ def main() -> None:
                         help='Path to save/load checkpoint')
     '''
     example launch command:
-    torchrun --nproc_per_node=1 --master_port=12358 fastvideo/v1/dataset/benchmarks/benchmark_parquet_dataset_iterable_style.py --path data/crush-smol/latents/combined_parquet_dataset --batch_size 2 --num_data_workers 2 --num_epoch 2 --num_batches_per_epoch 2 --verify_resume
-    torchrun --nproc_per_node=8 --master_port=12358 fastvideo/v1/dataset/benchmarks/benchmark_parquet_dataset_iterable_style.py --path data/crush-smol/latents/combined_parquet_dataset --batch_size 2 --num_data_workers 1 --num_epoch 2 --num_batches_per_epoch 5 --verify_resume
-    torchrun --nproc_per_node=8 --master_port=12358 fastvideo/v1/dataset/benchmarks/benchmark_parquet_dataset_iterable_style.py --path /mnt/sharefs/users/hao.zhang/Vchitect-2M/Wan-Syn/latents/ --batch_size 2 --num_data_workers 4 --num_epoch 2 --num_batches_per_epoch 100 
+    torchrun --nproc_per_node=1 --master_port=12358 fastvideo/dataset/benchmarks/benchmark_parquet_dataset_iterable_style.py --path data/crush-smol/latents/combined_parquet_dataset --batch_size 2 --num_data_workers 2 --num_epoch 2 --num_batches_per_epoch 2 --verify_resume
+    torchrun --nproc_per_node=8 --master_port=12358 fastvideo/dataset/benchmarks/benchmark_parquet_dataset_iterable_style.py --path data/crush-smol/latents/combined_parquet_dataset --batch_size 2 --num_data_workers 1 --num_epoch 2 --num_batches_per_epoch 5 --verify_resume
+    torchrun --nproc_per_node=8 --master_port=12358 fastvideo/dataset/benchmarks/benchmark_parquet_dataset_iterable_style.py --path /mnt/sharefs/users/hao.zhang/Vchitect-2M/Wan-Syn/latents/ --batch_size 2 --num_data_workers 4 --num_epoch 2 --num_batches_per_epoch 100 
     '''
     args = parser.parse_args()
     world_size = int(os.environ.get("WORLD_SIZE", 1))

fastvideo/dataset/benchmarks/benchmark_parquet_dataset_map_style.py

@@ -8,14 +8,14 @@ import torch
 import torch.distributed as dist
 import torch.distributed.checkpoint as dist_cp
 
-from fastvideo.v1.dataset.dataloader.schema import pyarrow_schema_t2v
-from fastvideo.v1.dataset.parquet_dataset_map_style import (
+from fastvideo.dataset.dataloader.schema import pyarrow_schema_t2v
+from fastvideo.dataset.parquet_dataset_map_style import (
     build_parquet_map_style_dataloader)
-from fastvideo.v1.distributed import get_world_rank
-from fastvideo.v1.distributed.parallel_state import (
+from fastvideo.distributed import get_world_rank
+from fastvideo.distributed.parallel_state import (
     cleanup_dist_env_and_memory, get_local_torch_device,
     maybe_init_distributed_environment_and_model_parallel)
-from fastvideo.v1.logger import init_logger
+from fastvideo.logger import init_logger
 
 logger = init_logger(__name__)
 
@@ -55,9 +55,9 @@ def main() -> None:
                         help='Path to save/load checkpoint')
     '''
     example launch command:
-    torchrun --nproc_per_node=1 --master_port=12358 fastvideo/v1/dataset/benchmarks/benchmark_parquet_dataset_map_style.py --path data/crush-smol/latents/combined_parquet_dataset --batch_size 4 --num_data_workers 1 --num_epoch 2 --num_batches_per_epoch 3 --verify_resume
-    torchrun --nproc_per_node=8 --master_port=12358 fastvideo/v1/dataset/benchmarks/benchmark_parquet_dataset_map_style.py --path data/crush-smol/latents/combined_parquet_dataset --batch_size 2 --num_data_workers 1 --num_epoch 2 --num_batches_per_epoch 5 --verify_resume
-    torchrun --nproc_per_node=8 --master_port=12358 fastvideo/v1/dataset/benchmarks/benchmark_parquet_dataset_map_style.py --path /mnt/sharefs/users/hao.zhang/Vchitect-2M/Wan-Syn/latents/ --batch_size 2 --num_data_workers 4 --num_epoch 2 --num_batches_per_epoch 100 
+    torchrun --nproc_per_node=1 --master_port=12358 fastvideo/dataset/benchmarks/benchmark_parquet_dataset_map_style.py --path data/crush-smol/latents/combined_parquet_dataset --batch_size 4 --num_data_workers 1 --num_epoch 2 --num_batches_per_epoch 3 --verify_resume
+    torchrun --nproc_per_node=8 --master_port=12358 fastvideo/dataset/benchmarks/benchmark_parquet_dataset_map_style.py --path data/crush-smol/latents/combined_parquet_dataset --batch_size 2 --num_data_workers 1 --num_epoch 2 --num_batches_per_epoch 5 --verify_resume
+    torchrun --nproc_per_node=8 --master_port=12358 fastvideo/dataset/benchmarks/benchmark_parquet_dataset_map_style.py --path /mnt/sharefs/users/hao.zhang/Vchitect-2M/Wan-Syn/latents/ --batch_size 2 --num_data_workers 4 --num_epoch 2 --num_batches_per_epoch 100 
     '''
     args = parser.parse_args()
     world_size = int(os.environ.get("WORLD_SIZE", 1))

fastvideo/dataset/parquet_dataset_iterable_style.py

@@ -9,10 +9,10 @@ import tqdm
 from torch.utils.data import IterableDataset, get_worker_info
 from torchdata.stateful_dataloader import StatefulDataLoader
 
-from fastvideo.v1.dataset.utils import collate_latents_embs_masks
-from fastvideo.v1.distributed import (get_sp_world_size, get_world_group,
-                                      get_world_rank, get_world_size)
-from fastvideo.v1.logger import init_logger
+from fastvideo.dataset.utils import collate_latents_embs_masks
+from fastvideo.distributed import (get_sp_world_size, get_world_group,
+                                   get_world_rank, get_world_size)
+from fastvideo.logger import init_logger
 
 logger = init_logger(__name__)
 

fastvideo/dataset/parquet_dataset_map_style.py

@@ -13,10 +13,10 @@ import tqdm
 from torch.utils.data import Dataset, Sampler
 from torchdata.stateful_dataloader import StatefulDataLoader
 
-from fastvideo.v1.dataset.utils import collate_rows_from_parquet_schema
-from fastvideo.v1.distributed import (get_sp_world_size, get_world_group,
-                                      get_world_rank, get_world_size)
-from fastvideo.v1.logger import init_logger
+from fastvideo.dataset.utils import collate_rows_from_parquet_schema
+from fastvideo.distributed import (get_sp_world_size, get_world_group,
+                                   get_world_rank, get_world_size)
+from fastvideo.logger import init_logger
 
 logger = init_logger(__name__)
 

fastvideo/dataset/preprocessing_datasets.py

@@ -16,7 +16,7 @@ from einops import rearrange
 from PIL import Image
 from transformers import AutoTokenizer
 
-from fastvideo.v1.logger import init_logger
+from fastvideo.logger import init_logger
 
 logger = init_logger(__name__)
 

fastvideo/dataset/validation_dataset.py

@@ -6,10 +6,10 @@ import pathlib
 import datasets
 from torch.utils.data import IterableDataset
 
-from fastvideo.v1.distributed import (get_sp_world_size, get_world_rank,
-                                      get_world_size)
-from fastvideo.v1.logger import init_logger
-from fastvideo.v1.models.vision_utils import load_image, load_video
+from fastvideo.distributed import (get_sp_world_size, get_world_rank,
+                                   get_world_size)
+from fastvideo.logger import init_logger
+from fastvideo.models.vision_utils import load_image, load_video
 
 logger = init_logger(__name__)
 

fastvideo/distributed/__init__.py

@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 
-from fastvideo.v1.distributed.communication_op import *
-from fastvideo.v1.distributed.parallel_state import (
+from fastvideo.distributed.communication_op import *
+from fastvideo.distributed.parallel_state import (
     cleanup_dist_env_and_memory, get_dp_group, get_dp_rank, get_dp_world_size,
     get_local_torch_device, get_sp_group, get_sp_parallel_rank,
     get_sp_world_size, get_tp_group, get_tp_rank, get_tp_world_size,
@@ -9,7 +9,7 @@ from fastvideo.v1.distributed.parallel_state import (
     init_distributed_environment, initialize_model_parallel,
     maybe_init_distributed_environment_and_model_parallel,
     model_parallel_is_initialized)
-from fastvideo.v1.distributed.utils import *
+from fastvideo.distributed.utils import *
 
 __all__ = [
     # Initialization

fastvideo/distributed/communication_op.py

@@ -4,7 +4,7 @@
 import torch
 import torch.distributed
 
-from fastvideo.v1.distributed.parallel_state import get_sp_group, get_tp_group
+from fastvideo.distributed.parallel_state import get_sp_group, get_tp_group
 
 
 def tensor_model_parallel_all_reduce(input_: torch.Tensor) -> torch.Tensor:

fastvideo/distributed/device_communicators/cpu_communicator.py

@@ -6,8 +6,8 @@ import os
 import torch
 from torch.distributed import ProcessGroup
 
-from fastvideo.v1.platforms import current_platform
-from fastvideo.v1.platforms.interface import CpuArchEnum
+from fastvideo.platforms import current_platform
+from fastvideo.platforms.interface import CpuArchEnum
 
 from .base_device_communicator import DeviceCommunicatorBase
 

fastvideo/distributed/device_communicators/cuda_communicator.py

@@ -4,7 +4,7 @@
 import torch
 from torch.distributed import ProcessGroup
 
-from fastvideo.v1.distributed.device_communicators.base_device_communicator import (
+from fastvideo.distributed.device_communicators.base_device_communicator import (
     DeviceCommunicatorBase)
 
 
@@ -17,7 +17,7 @@ class CudaCommunicator(DeviceCommunicatorBase):
                  unique_name: str = ""):
         super().__init__(cpu_group, device, device_group, unique_name)
 
-        from fastvideo.v1.distributed.device_communicators.pynccl import (
+        from fastvideo.distributed.device_communicators.pynccl import (
             PyNcclCommunicator)
 
         self.pynccl_comm: PyNcclCommunicator | None = None

fastvideo/distributed/device_communicators/pynccl.py

@@ -6,12 +6,12 @@ import torch
 import torch.distributed as dist
 from torch.distributed import ProcessGroup, ReduceOp
 
-from fastvideo.v1.distributed.device_communicators.pynccl_wrapper import (
+from fastvideo.distributed.device_communicators.pynccl_wrapper import (
     NCCLLibrary, buffer_type, cudaStream_t, ncclComm_t, ncclDataTypeEnum,
     ncclRedOpTypeEnum, ncclUniqueId)
-from fastvideo.v1.distributed.utils import StatelessProcessGroup
-from fastvideo.v1.logger import init_logger
-from fastvideo.v1.utils import current_stream
+from fastvideo.distributed.utils import StatelessProcessGroup
+from fastvideo.logger import init_logger
+from fastvideo.utils import current_stream
 
 logger = init_logger(__name__)
 

fastvideo/distributed/device_communicators/pynccl_wrapper.py

@@ -32,8 +32,8 @@ from typing import Any
 import torch
 from torch.distributed import ReduceOp
 
-from fastvideo.v1.logger import init_logger
-from fastvideo.v1.utils import find_nccl_library
+from fastvideo.logger import init_logger
+from fastvideo.utils import find_nccl_library
 
 logger = init_logger(__name__)