nodes/hao-ai-lab-FastVideo
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

checkpoint

Browse Source
This commit is contained in:
SolitaryThinker
2025-08-27 02:12:43 -07:00
parent 9f24aef7cf
commit e2b6f49879
12 changed files with 46 additions and 81 deletions
Download Patch File Download Diff File Expand all files Collapse all files
examples/inference/basic/basic_causal.py → examples/inference/basic/basic_self_forcing_causal.py
View File
fastvideo/configs/pipelines/registry.py
+2
View File
@@ -9,6 +9,7 @@ from fastvideo.configs.pipelines.hunyuan import FastHunyuanConfig, HunyuanConfig
from fastvideo.configs.pipelines.stepvideo import StepVideoT2VConfig
from fastvideo.configs.pipelines.wan import (FastWan2_1_T2V_480P_Config,
FastWan2_2_TI2V_5B_Config,
SelfForcingWanT2V480PConfig,
WanI2V480PConfig, WanI2V720PConfig,
WanT2V480PConfig, WanT2V720PConfig)
from fastvideo.logger import init_logger
@@ -34,6 +35,7 @@ PIPE_NAME_TO_CONFIG: dict[str, type[PipelineConfig]] = {
"Wan-AI/Wan2.2-TI2V-5B-Diffusers": WanT2V720PConfig,
"Wan-AI/Wan2.2-T2V-A14B-Diffusers": WanT2V480PConfig,
"Wan-AI/Wan2.2-I2V-A14B-Diffusers": WanI2V480PConfig,
"wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers": SelfForcingWanT2V480PConfig,
# Add other specific weight variants
}
fastvideo/configs/pipelines/wan.py
+8
View File
@@ -138,3 +138,11 @@ class Wan2_2_T2V_A14B_Config(WanT2V480PConfig):
@dataclass
class Wan2_2_I2V_A14B_Config(WanT2V480PConfig):
pass
# =============================================
# ============= Causal Self-Forcing =============
# =============================================
@dataclass
class SelfForcingWanT2V480PConfig(WanT2V480PConfig):
is_causal: bool = True
fastvideo/configs/sample/registry.py
+15 -3
View File
@@ -17,6 +17,7 @@ from fastvideo.configs.sample.wan import (
WanI2V_14B_720P_SamplingParam,
WanT2V_1_3B_SamplingParam,
WanT2V_14B_SamplingParam,
SelfForcingWanT2V480PConfig,
)
# isort: on
from fastvideo.logger import init_logger
@@ -28,16 +29,27 @@ logger = init_logger(__name__)
SAMPLING_PARAM_REGISTRY: dict[str, Any] = {
"FastVideo/FastHunyuan-diffusers": FastHunyuanSamplingParam,
"hunyuanvideo-community/HunyuanVideo": HunyuanSamplingParam,
"FastVideo/stepvideo-t2v-diffusers": StepVideoT2VSamplingParam,
# Wan2.1
"Wan-AI/Wan2.1-T2V-1.3B-Diffusers": WanT2V_1_3B_SamplingParam,
"Wan-AI/Wan2.1-T2V-14B-Diffusers": WanT2V_14B_SamplingParam,
"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,
# Wan2.2
"Wan-AI/Wan2.2-TI2V-5B-Diffusers": Wan2_2_TI2V_5B_SamplingParam,
"FastVideo/FastWan2.2-TI2V-5B-Diffusers": Wan2_2_TI2V_5B_SamplingParam,
"Wan-AI/Wan2.2-T2V-A14B-Diffusers": Wan2_2_T2V_A14B_SamplingParam,
"Wan-AI/Wan2.2-I2V-A14B-Diffusers": Wan2_2_I2V_A14B_SamplingParam,
# FastWan2.1
"FastVideo/FastWan2.1-T2V-1.3B-Diffusers": FastWanT2V480PConfig,
# FastWan2.2
"FastVideo/FastWan2.2-TI2V-5B-Diffusers": Wan2_2_TI2V_5B_SamplingParam,
# Causal Self-Forcing Wan2.1
"wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers": SelfForcingWanT2V480PConfig,
# Add other specific weight variants
}
fastvideo/configs/sample/wan.py
+9
View File
@@ -141,3 +141,12 @@ class Wan2_2_I2V_A14B_SamplingParam(Wan2_2_Base_SamplingParam):
guidance_scale_2: float = 3.5
num_inference_steps: int = 40
fps: int = 16
# =============================================
# ============= Causal Self-Forcing =============
# =============================================
@dataclass
class SelfForcingWanT2V480PConfig(WanT2V_1_3B_SamplingParam):
num_frames_per_block: int = 3
sliding_window_num_frames: int = 21
fastvideo/models/loader/fsdp_load.py
+1
View File
@@ -248,6 +248,7 @@ def load_model_from_full_model_state_dict(
sharded_sd = {}
custom_param_sd, reverse_param_names_mapping = hf_to_custom_state_dict(
full_sd_iterator, param_names_mapping) # type: ignore
print(custom_param_sd.keys())
for target_param_name, full_tensor in custom_param_sd.items():
meta_sharded_param = meta_sd.get(target_param_name)
if meta_sharded_param is None:
fastvideo/models/registry.py
+2
View File
@@ -25,12 +25,14 @@ _TEXT_TO_VIDEO_DIT_MODELS = {
"HunyuanVideoTransformer3DModel":
("dits", "hunyuanvideo", "HunyuanVideoTransformer3DModel"),
"WanTransformer3DModel": ("dits", "wanvideo", "WanTransformer3DModel"),
"CausalWanTransformer3DModel": ("dits", "causal_wanvideo", "CausalWanTransformer3DModel"),
"StepVideoModel": ("dits", "stepvideo", "StepVideoModel")
}
_IMAGE_TO_VIDEO_DIT_MODELS = {
# "HunyuanVideoTransformer3DModel": ("dits", "hunyuanvideo", "HunyuanVideoDiT"),
"WanTransformer3DModel": ("dits", "wanvideo", "WanTransformer3DModel"),
"CausalWanTransformer3DModel": ("dits", "causal_wanvideo", "CausalWanTransformer3DModel"),
}
_TEXT_ENCODER_MODELS = {
fastvideo/pipelines/basic/wan/wan_causal_pipeline.py
-71
View File
@@ -1,71 +0,0 @@
# SPDX-License-Identifier: Apache-2.0
"""
Wan causal pipeline implementation.
This module contains an implementation of the Wan causal pipeline
using the modular pipeline architecture.
"""
from fastvideo.fastvideo_args import FastVideoArgs
from fastvideo.logger import init_logger
from fastvideo.models.schedulers.scheduling_flow_match_euler_discrete import (
FlowMatchEulerDiscreteScheduler)
from fastvideo.pipelines import ComposedPipelineBase, LoRAPipeline
# isort: off
from fastvideo.pipelines.stages import (ConditioningStage, DecodingStage,
CausalDenoisingStage,
InputValidationStage,
LatentPreparationStage,
TextEncodingStage,
TimestepPreparationStage)
# isort: on
logger = init_logger(__name__)
class WanCausalPipeline(LoRAPipeline, ComposedPipelineBase):
_required_config_modules = [
"text_encoder", "tokenizer", "vae", "transformer", "scheduler"
]
def initialize_pipeline(self, fastvideo_args: FastVideoArgs):
self.modules["scheduler"] = FlowMatchEulerDiscreteScheduler(
shift=fastvideo_args.pipeline_config.flow_shift)
def create_pipeline_stages(self, fastvideo_args: FastVideoArgs) -> None:
"""Set up pipeline stages with proper dependency injection."""
self.add_stage(stage_name="input_validation_stage",
stage=InputValidationStage())
self.add_stage(stage_name="prompt_encoding_stage",
stage=TextEncodingStage(
text_encoders=[self.get_module("text_encoder")],
tokenizers=[self.get_module("tokenizer")],
))
self.add_stage(stage_name="conditioning_stage",
stage=ConditioningStage())
self.add_stage(stage_name="timestep_preparation_stage",
stage=TimestepPreparationStage(
scheduler=self.get_module("scheduler")))
self.add_stage(stage_name="latent_preparation_stage",
stage=LatentPreparationStage(
scheduler=self.get_module("scheduler"),
transformer=self.get_module("transformer", None)))
self.add_stage(stage_name="denoising_stage",
stage=CausalDenoisingStage(
transformer=self.get_module("transformer"),
scheduler=self.get_module("scheduler")))
self.add_stage(stage_name="decoding_stage",
stage=DecodingStage(vae=self.get_module("vae")))
EntryClass = WanCausalPipeline
fastvideo/pipelines/pipeline_registry.py
+1
View File
@@ -21,6 +21,7 @@ _PIPELINE_NAME_TO_ARCHITECTURE_NAME: dict[str, str] = {
"WanPipeline": "wan",
"WanDMDPipeline": "wan",
"WanImageToVideoPipeline": "wan",
"WanCausalDMDPipeline": "wan",
"StepVideoPipeline": "stepvideo",
"HunyuanVideoPipeline": "hunyuan",
}
fastvideo/pipelines/stages/__init__.py
+1 -3
View File
@@ -7,12 +7,11 @@ complete diffusion pipelines.
"""
from fastvideo.pipelines.stages.base import PipelineStage
from fastvideo.pipelines.stages.causal_denoising import CausalDMDDenosingStage
from fastvideo.pipelines.stages.conditioning import ConditioningStage
from fastvideo.pipelines.stages.decoding import DecodingStage
from fastvideo.pipelines.stages.denoising import (DenoisingStage,
DmdDenoisingStage)
from fastvideo.pipelines.stages.causal_denoising import (
CausalDMDDenosingStage, CausalDenoisingStage)
from fastvideo.pipelines.stages.encoding import EncodingStage
from fastvideo.pipelines.stages.image_encoding import (ImageEncodingStage,
ImageVAEEncodingStage)
@@ -32,7 +31,6 @@ __all__ = [
"ConditioningStage",
"DenoisingStage",
"DmdDenoisingStage",
"CausalDenoisingStage",
"CausalDMDDenosingStage",
"EncodingStage",
"DecodingStage",
scripts/checkpoint_conversion/wan_to_diffusers.py
+5 -2
View File
@@ -1,4 +1,5 @@
from huggingface_hub import save_torch_state_dict, load_state_dict_from_file
# from safetensors import safetensors
from safetensors.torch import save_file
import torch
import re
@@ -67,7 +68,8 @@ _param_names_mapping: dict = {
r"blocks.\1.norm2.\2",
}
state_dict = load_state_dict_from_file("FastVideo/diffusion_pytorch_model.safetensors")
state_dict = load_state_dict_from_file("checkpoints/self_forcing_dmd.pt")
# state_dict = state_dict["generator_ema"]
new_state_dict = OrderedDict()
for k, v in state_dict.items():
new_key = k
@@ -84,5 +86,6 @@ del state_dict
save_torch_state_dict(
new_state_dict,
"Wan2.1-Fun-1.3B-InP-Diffusers/transformer"
"new_diffusers/",
max_shard_size="10GB"
)
scripts/huggingface/upload_hf.py
+2 -2
View File
@@ -3,7 +3,7 @@ from huggingface_hub import HfApi
api = HfApi()
api.upload_folder(
folder_path="Wan2.2-TI2V-5B-Diffusers",
repo_id="FastVideo/FastWan2.2-TI2V-5B-Diffusers",
folder_path="wow",
repo_id="wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers",
repo_type="model",
)