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Merge branch 'main' of github.com:hao-ai-lab/FastVideo

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This commit is contained in:
“BrianChen1129”
2025-07-30 22:38:16 +00:00
parent f70cdc4c2e 3b1b54a74d
commit 5ae466be21
13 changed files with 38 additions and 109 deletions
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examples/inference/basic/basic.py
+4 -2
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@@ -11,11 +11,13 @@ def main():
generator = VideoGenerator.from_pretrained(
"Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
# FastVideo will automatically handle distributed setup
num_gpus=2,
num_gpus=1,
use_fsdp_inference=True,
dit_cpu_offload=False,
vae_cpu_offload=False,
text_encoder_cpu_offload=False,
text_encoder_cpu_offload=True,
# Set pin_cpu_memory to false if CPU RAM is limited and there're no frequent CPU-GPU transfer
pin_cpu_memory=False,
# image_encoder_cpu_offload=False,
)
examples/inference/basic/basic_dmd.py
+2 -1
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@@ -16,7 +16,8 @@ def main():
num_gpus=1,
use_fsdp_inference=True,
# Adjust these offload parameters if you have < 32GB of VRAM
text_encoder_cpu_offload=False,
text_encoder_cpu_offload=True,
pin_cpu_memory=False,
dit_cpu_offload=False,
vae_cpu_offload=False,
VSA_sparsity=0.8,
examples/inference/basic/basic_mps.py
+1 -1
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@@ -18,7 +18,7 @@ def main():
# Create sampling parameters with reduced number of frames
sampling_param = SamplingParam.from_pretrained("Wan-AI/Wan2.1-T2V-1.3B-Diffusers")
sampling_param.num_frames = 3 # Reduce from default 81 to 25 frames bc we have to use the SDPA attn backend for mps
sampling_param.num_frames = 25 # Reduce from default 81 to 25 frames bc we have to use the SDPA attn backend for mps
sampling_param.height = 256
sampling_param.width = 256
examples/inference/basic/default_args.py
-62
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@@ -1,62 +0,0 @@
from fastvideo import VideoGenerator
from fastvideo.configs.pipelines.base import PipelineConfig
def main():
# This is the config class for the model initialization
config = PipelineConfig.from_pretrained("FastVideo/FastHunyuan-Diffusers")
# can be used to dump the config to a yaml file
config.dump_to_yaml("config.yaml")
print(config)
# {
# 'vae_config': {
# 'scale_factor': 8,
# 'sp': True,
# 'tiling': True,
# 'precision': 'fp16'
# },
# 'text_encoder_config': {
# 'precision': 'fp16'
# },
# 'dit_config': {
# 'precision': 'fp16'
# },
# 'inference_args': {
# 'guidance_scale': 7.5,
# 'num_inference_steps': 5,
# 'seed': 1024,
# 'guidance_rescale': 0.0,
# 'flow_shift': 17,
# 'num_inference_steps': 5,
# }
# }
config.vae_config.scale_factor = 16
# FastVideo will automatically used the optimal default arguments for the model
# If a local path is provided, FastVideo will make a best effort attempt to
# identify the optimal arguments.
generator = VideoGenerator.from_pretrained(
"FastVideo/FastHunyuan-Diffusers",
num_gpus=4,
config=config,
# or
config_path="config.yaml",
)
sampling_param = SamplingParam.from_pretrained(
"FastVideo/FastHunyuan-Diffusers")
sampling_param.num_inference_steps = 5
# Generate videos with the same simple API, regardless of GPU count
prompt = "A beautiful woman in a red dress walking down a street"
video = generator.generate_video(prompt,
sampling_param=sampling_param,
num_inference_steps=6)
video2 = generator.generate_video(prompt2)
prompt2 = "A beautiful woman in a blue dress walking down a street"
if __name__ == "__main__":
main()
examples/inference/basic/test.py
-39
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@@ -1,39 +0,0 @@
from fastvideo import VideoGenerator, PipelineConfig, SamplingParam
# from fastvideo.configs.sample import SamplingParam
OUTPUT_PATH = "video_samples_fp16"
def main():
# FastVideo will automatically use the optimal default arguments for the
# model.
# If a local path is provided, FastVideo will make a best effort
# attempt to identify the optimal arguments.
model = "Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
pipeline_config = PipelineConfig.from_pretrained(model)
pipeline_config.text_encoder_precisions = ("bf16", )
generator = VideoGenerator.from_pretrained(
model,
# if num_gpus > 1, FastVideo will automatically handle distributed setup
pipeline_config=pipeline_config,
use_fsdp_inference=False, # Disable FSDP for MPS
dit_cpu_offload=True,
text_encoder_cpu_offload=True,
pin_cpu_memory=True,
disable_autocast=False,
num_gpus=1,
)
sampling_param = SamplingParam.from_pretrained(model)
sampling_param.num_frames = 30
# sampling_param.image_path = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/astronaut.jpg"
# Generate videos with the same simple API, regardless of GPU count
prompt = "Will Smith casually eats noodles, his relaxed demeanor contrasting with the energetic background of a bustling street food market. The scene captures a mix of humor and authenticity. Mid-shot framing, vibrant lighting."
video = generator.generate_video(prompt, output_path=OUTPUT_PATH, save_video=True, sampling_param=sampling_param)
# video = generator.generate_video(prompt, sampling_param=sampling_param, output_path="wan_t2v_videos/")
# Generate another video with a different prompt, without reloading the
# model!
if __name__ == "__main__":
main()
examples/inference/lora/wan_lora_inference.py
+4
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@@ -7,6 +7,10 @@ def main():
generator = VideoGenerator.from_pretrained(
"Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
num_gpus=1,
dit_cpu_offload=False,
vae_cpu_offload=False,
text_encoder_cpu_offload=True,
pin_cpu_memory=False,
lora_path="benjamin-paine/steamboat-willie-1.3b",
lora_nickname="steamboat"
)
examples/inference/lora/wan_lora_inference_from_ckpt.py
+4
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@@ -10,6 +10,10 @@ def main():
generator = VideoGenerator.from_pretrained(
"Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
num_gpus=1,
dit_cpu_offload=False,
vae_cpu_offload=False,
text_encoder_cpu_offload=True,
pin_cpu_memory=False,
lora_path="checkpoints/wan_t2v_finetune_lora/checkpoint-1250/transformer",
lora_nickname="crush_smol"
)
examples/inference/optimizations/attention_example.py
+4
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@@ -11,6 +11,10 @@ def main():
gen = VideoGenerator.from_pretrained(
model_path="Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
num_gpus=1,
dit_cpu_offload=False,
vae_cpu_offload=False,
text_encoder_cpu_offload=True,
pin_cpu_memory=False,
)
load_time = time.perf_counter() - start_time
print(f"Model loading time: {load_time:.2f} seconds")
examples/inference/optimizations/teacache_example.py
+3
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@@ -10,6 +10,9 @@ def main():
model_path="Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
num_gpus=1,
dit_cpu_offload=False,
vae_cpu_offload=False,
text_encoder_cpu_offload=True,
pin_cpu_memory=False,
)
load_time = time.perf_counter() - start_time
print(f"Model loading time: {load_time:.2f} seconds")
fastvideo/entrypoints/video_generator.py
+4 -4
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@@ -288,10 +288,6 @@ class VideoGenerator:
""" # type: ignore[attr-defined]
logger.info(debug_str)
# Use prompt[:100] for video name
if sampling_param.output_video_name is None:
sampling_param.output_video_name = prompt[:100]
# Prepare batch
batch = ForwardBatch(
**shallow_asdict(sampling_param),
@@ -301,6 +297,10 @@ class VideoGenerator:
extra={},
)
# Use prompt[:100] for video name
if batch.output_video_name is None:
batch.output_video_name = prompt[:100]
# Run inference
start_time = time.perf_counter()
output_batch = self.executor.execute_forward(batch, fastvideo_args)
scripts/inference/v1_inference_wan.sh
+4
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@@ -10,6 +10,10 @@ fastvideo generate \
--sp-size $num_gpus \
--tp-size 1 \
--num-gpus $num_gpus \
--dit-cpu-offload False \
--vae-cpu-offload False \
--text-encoder-cpu-offload True \
--pin-cpu-memory False \
--height 480 \
--width 832 \
--num-frames 77 \
scripts/inference/v1_inference_wan_VSA.sh
+4
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@@ -10,6 +10,10 @@ fastvideo generate \
--sp-size $num_gpus \
--tp-size 1 \
--num-gpus $num_gpus \
--dit-cpu-offload False \
--vae-cpu-offload False \
--text-encoder-cpu-offload True \
--pin-cpu-memory False \
--height 448 \
--width 832 \
--num-frames 77 \
scripts/inference/v1_inference_wan_dmd.sh
+4
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@@ -10,6 +10,10 @@ fastvideo generate \
--sp-size $num_gpus \
--tp-size 1 \
--num-gpus $num_gpus \
--dit-cpu-offload False \
--vae-cpu-offload False \
--text-encoder-cpu-offload True \
--pin-cpu-memory False \
--height 480 \
--width 848 \
--num-frames 81 \