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[refactor]: migrate examples/scripts to typed VideoGenerator API; rename api/compat.py to api/translation.py

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Move all examples/inference and scripts off the legacy
VideoGenerator.from_pretrained(model, **kwargs) +
generate_video(prompt, **kwargs) surface onto the typed
from_config(GeneratorConfig(...)) + generate(GenerationRequest(...))
convention, including the Kandinsky-5 and DreamX-World examples added
on main after the original migration. git-mv api/compat.py to
api/translation.py ('compat' implied a temporary shim; the forward
translation is its honest permanent role) and repoint all importers,
tests, and doc links.
This commit is contained in:
SolitaryThinker
2026-07-11 03:26:24 -07:00
parent 19a51a1fe6
commit bbbb7ab021
87 changed files with 2493 additions and 1461 deletions
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examples/inference/basic/basic_wan2_2.py
+35 -20
View File
@@ -1,6 +1,8 @@
from fastvideo import VideoGenerator
# from fastvideo.api.sampling_param import SamplingParam
from fastvideo.api import (
EngineConfig, GenerationRequest, GeneratorConfig, OffloadConfig,
OutputConfig, SamplingConfig,
)
OUTPUT_PATH = "video_samples_wan2_2_14B_t2v"
def main():
@@ -8,30 +10,37 @@ def main():
# model.
# If a local path is provided, FastVideo will make a best effort
# attempt to identify the optimal arguments.
generator = VideoGenerator.from_pretrained(
"Wan-AI/Wan2.2-T2V-A14B-Diffusers",
# FastVideo will automatically handle distributed setup
num_gpus=2,
use_fsdp_inference=False, # set to True if GPU is out of memory
dit_cpu_offload=True, # DiT need to be offloaded for MoE
vae_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=True,
# image_encoder_cpu_offload=False,
generator = VideoGenerator.from_config(
GeneratorConfig(
model_path="Wan-AI/Wan2.2-T2V-A14B-Diffusers",
engine=EngineConfig(
# FastVideo will automatically handle distributed setup
num_gpus=2,
use_fsdp_inference=False, # set to True if GPU is out of memory
offload=OffloadConfig(
dit=True, # DiT need to be offloaded for MoE
vae=False,
text_encoder=True,
# Set pin_cpu_memory to false if CPU RAM is limited and there're no frequent CPU-GPU transfer
pin_cpu_memory=True,
),
),
)
)
# sampling_param = SamplingParam.from_pretrained("Wan-AI/Wan2.1-T2V-1.3B-Diffusers")
# sampling_param.num_frames = 45
# 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 = (
"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."
)
_ = generator.generate_video(prompt, output_path=OUTPUT_PATH, save_video=True, height=720, width=1280, num_frames=81)
# video = generator.generate_video(prompt, sampling_param=sampling_param, output_path="wan_t2v_videos/")
_ = generator.generate(
GenerationRequest(
prompt=prompt,
sampling=SamplingConfig(height=720, width=1280, num_frames=81),
output=OutputConfig(output_path=OUTPUT_PATH, save_video=True),
)
)
# Generate another video with a different prompt, without reloading the
# model!
@@ -41,8 +50,14 @@ def main():
"the breeze, enhancing the lion's commanding presence. The tone is vibrant, "
"embodying the raw energy of the wild. Low angle, steady tracking shot, "
"cinematic.")
_ = generator.generate_video(prompt2, output_path=OUTPUT_PATH, save_video=True, height=720, width=1280, num_frames=81)
_ = generator.generate(
GenerationRequest(
prompt=prompt2,
sampling=SamplingConfig(height=720, width=1280, num_frames=81),
output=OutputConfig(output_path=OUTPUT_PATH, save_video=True),
)
)
if __name__ == "__main__":
main()
main()