SolitaryThinker
bbbb7ab021
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.
74 lines
3.0 KiB
Python
74 lines
3.0 KiB
Python
import os
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import time
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from fastvideo import VideoGenerator
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from fastvideo.api import (EngineConfig, GenerationRequest, GeneratorConfig,
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OffloadConfig, OutputConfig, PipelineSelection,
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SamplingConfig)
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OUTPUT_PATH = "video_samples_dmd2"
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def main():
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os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "VIDEO_SPARSE_ATTN"
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load_start_time = time.perf_counter()
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model_name = "FastVideo/FastWan2.1-T2V-1.3B-Diffusers"
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generator = VideoGenerator.from_config(
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GeneratorConfig(
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model_path=model_name,
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engine=EngineConfig(
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# FastVideo will automatically handle distributed setup
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num_gpus=1,
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use_fsdp_inference=False, # set to True if GPU is out of memory
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# Adjust these offload parameters if you have < 32GB of VRAM
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offload=OffloadConfig(
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text_encoder=True,
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pin_cpu_memory=True, # set to false if low CPU RAM or hit obscure "CUDA error: Invalid argument"
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dit=False,
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vae=False,
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),
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),
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pipeline=PipelineSelection(experimental={"VSA_sparsity": 0.8}),
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))
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load_end_time = time.perf_counter()
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load_time = load_end_time - load_start_time
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prompt = (
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"A neon-lit alley in futuristic Tokyo during a heavy rainstorm at night. The puddles reflect glowing signs in kanji, advertising ramen, karaoke, and VR arcades. A woman in a translucent raincoat walks briskly with an LED umbrella. Steam rises from a street food cart, and a cat darts across the screen. Raindrops are visible on the camera lens, creating a cinematic bokeh effect."
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)
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start_time = time.perf_counter()
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video = generator.generate(
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GenerationRequest(
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prompt=prompt,
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sampling=SamplingConfig(num_frames=81),
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output=OutputConfig(output_path=OUTPUT_PATH, save_video=True),
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))
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end_time = time.perf_counter()
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gen_time = end_time - start_time
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# Generate another video with a different prompt, without reloading the
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# model!
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prompt2 = (
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"A majestic lion strides across the golden savanna, its powerful frame "
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"glistening under the warm afternoon sun. The tall grass ripples gently in "
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"the breeze, enhancing the lion's commanding presence. The tone is vibrant, "
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"embodying the raw energy of the wild. Low angle, steady tracking shot, "
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"cinematic.")
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start_time = time.perf_counter()
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video2 = generator.generate(
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GenerationRequest(
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prompt=prompt2,
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sampling=SamplingConfig(num_frames=81),
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output=OutputConfig(output_path=OUTPUT_PATH, save_video=True),
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))
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end_time = time.perf_counter()
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gen_time2 = end_time - start_time
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print(f"Time taken to load model: {load_time} seconds")
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print(f"Time taken to generate video: {gen_time} seconds")
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print(f"Time taken to generate video2: {gen_time2} seconds")
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if __name__ == "__main__":
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main()
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