Davids048andClaude Opus 5.5
ecb15bf9e0
Every key that repository YAML configs, Python examples, the Dreamverse backend, and tests passed through pipeline.experimental has a typed GeneratorConfig field, so they use those fields instead: engine.attention, engine.compile.regional, pipeline.flow_shift, pipeline.embedded_cfg_scale, pipeline.dmd_denoising_steps, pipeline.components.lora_nickname, pipeline.minimax_h3, and pipeline.longcat. No repository config outside the tests uses pipeline.experimental any more. The flat FastVideoArgs keywords that each migrated YAML file and each basic_fasth3.py option set produces match the previous ones. The golden snapshots of the two Hunyuan scripts now record embedded_cfg_scale and flow_shift as floats (6.0 instead of 6), because the typed fields are floats. The guidance tensor is built as float32 either way. The golden case untyped_experimental_keys is renamed attention_precision_and_flow_shift with identical content, and keys_without_typed_fields covers keywords that still go to pipeline.experimental. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_012M91wnVFmPEvJ9h39r5BH7
89 lines
2.9 KiB
Python
89 lines
2.9 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 (
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AttentionConfig,
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EngineConfig,
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GenerationRequest,
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GeneratorConfig,
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OffloadConfig,
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OutputConfig,
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)
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OUTPUT_PATH = "video_samples_dmd2_typed"
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def main():
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os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "VIDEO_SPARSE_ATTN"
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model_name = "FastVideo/FastWan2.1-T2V-1.3B-Diffusers"
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generator_config = GeneratorConfig(
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model_path=model_name,
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engine=EngineConfig(
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num_gpus=1,
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use_fsdp_inference=False,
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offload=OffloadConfig(
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text_encoder=True,
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pin_cpu_memory=True,
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dit=False,
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vae=False,
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),
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attention=AttentionConfig(vsa_sparsity=0.8),
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),
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)
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load_start_time = time.perf_counter()
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generator = VideoGenerator.from_config(generator_config)
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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 = ("A neon-lit alley in futuristic Tokyo during a heavy rainstorm at night. "
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"The puddles reflect glowing signs in kanji, advertising ramen, karaoke, "
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"and VR arcades. A woman in a translucent raincoat walks briskly with an "
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"LED umbrella. Steam rises from a street food cart, and a cat darts "
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"across the screen. Raindrops are visible on the camera lens, creating "
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"a cinematic bokeh effect.")
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request = GenerationRequest(
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prompt=prompt,
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output=OutputConfig(
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output_path=OUTPUT_PATH,
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save_video=True,
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return_frames=False,
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),
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)
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start_time = time.perf_counter()
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result = generator.generate(request)
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end_time = time.perf_counter()
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gen_time = end_time - start_time
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prompt2 = ("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 "
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"in the breeze, enhancing the lion's commanding presence. The tone is "
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"vibrant, embodying the raw energy of the wild. Low angle, steady "
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"tracking shot, cinematic.")
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request2 = GenerationRequest(
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prompt=prompt2,
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output=OutputConfig(
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output_path=OUTPUT_PATH,
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save_video=True,
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return_frames=False,
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),
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)
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start_time = time.perf_counter()
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result2 = generator.generate(request2)
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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"First output written to: {result.video_path}")
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print(f"Time taken to generate video2: {gen_time2} seconds")
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print(f"Second output written to: {result2.video_path}")
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if __name__ == "__main__":
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main()
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