Kaiqin Kongandmergify[bot]
773d44b875
Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com>
99 lines
3.5 KiB
Python
99 lines
3.5 KiB
Python
from fastvideo.entrypoints.streaming_generator import StreamingVideoGenerator
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from fastvideo.models.dits.matrixgame2.utils import get_current_action_async, expand_action_to_frames
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import torch
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import asyncio
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# Available variants: "base_distilled_model", "gta_distilled_model", "templerun_distilled_model"
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# Each variant has different keyboard_dim:
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# - base_distilled_model: keyboard_dim=4
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# - gta_distilled_model: keyboard_dim=2
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# - templerun_distilled_model: keyboard_dim=7 (keyboard only, no mouse)
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MODEL_VARIANT = "base_distilled_model"
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# Variant-specific settings
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VARIANT_CONFIG = {
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"base_distilled_model": {
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"model_path": "FastVideo/Matrix-Game-2.0-Base-Distilled-Diffusers",
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"keyboard_dim": 4,
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"mode": "universal",
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"image_url": "https://raw.githubusercontent.com/SkyworkAI/Matrix-Game/main/Matrix-Game-2/demo_images/universal/0000.png",
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},
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"gta_distilled_model": {
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"model_path": "FastVideo/Matrix-Game-2.0-GTA-Distilled-Diffusers",
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"keyboard_dim": 2,
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"mode": "gta_drive",
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"image_url": "https://raw.githubusercontent.com/SkyworkAI/Matrix-Game/main/Matrix-Game-2/demo_images/gta_drive/0000.png",
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},
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"templerun_distilled_model": {
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"model_path": "FastVideo/Matrix-Game-2.0-TempleRun-Distilled-Diffusers",
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"keyboard_dim": 7,
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"mode": "templerun",
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"image_url": "https://raw.githubusercontent.com/SkyworkAI/Matrix-Game/main/Matrix-Game-2/demo_images/temple_run/0000.png",
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},
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}
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OUTPUT_PATH = "video_samples_matrixgame2"
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async def main():
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# FastVideo will automatically use the optimal default arguments for the
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# model.
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# If a local path is provided, FastVideo will make a best effort
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# attempt to identify the optimal arguments.
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config = VARIANT_CONFIG[MODEL_VARIANT]
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generator = StreamingVideoGenerator.from_pretrained(
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config["model_path"],
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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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dit_cpu_offload=True, # DiT need to be offloaded for MoE
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vae_cpu_offload=False,
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text_encoder_cpu_offload=True,
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# Set pin_cpu_memory to false if CPU RAM is limited and there're no frequent CPU-GPU transfer
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pin_cpu_memory=True,
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# image_encoder_cpu_offload=False,
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)
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max_blocks = 50
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num_frames = 597
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actions = {
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"keyboard": torch.zeros((num_frames, config["keyboard_dim"])),
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"mouse": torch.zeros((num_frames, 2))
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}
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grid_sizes = torch.tensor([150, 44, 80])
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mode = config["mode"]
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generator.reset(
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prompt="",
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image_path=config["image_url"],
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mouse_cond=actions["mouse"].unsqueeze(0),
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keyboard_cond=actions["keyboard"].unsqueeze(0),
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grid_sizes=grid_sizes,
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num_frames=num_frames,
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height=352,
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width=640,
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num_inference_steps=50,
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output_path=OUTPUT_PATH,
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save_video=True,
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)
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print("Initialization complete.")
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for block_id in range(max_blocks):
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print(f"\n=== Block {block_id + 1}/{max_blocks} ===")
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action = await get_current_action_async(mode)
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keyboard_cond, mouse_cond = expand_action_to_frames(action, 12)
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await generator.step_async(keyboard_cond, mouse_cond)
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if (await asyncio.to_thread(input, "\nContinue? (y/n): ")).lower() == 'n':
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break
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# Save final video
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generator.finalize()
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generator.shutdown()
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if __name__ == "__main__":
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asyncio.run(main())
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