[misc]: remove DreamX World, HunyuanGameCraft, GLM-Image, LongCat, TurboDiffusion, and Z-Image
These ports were marked pending removal in #1820. Each has a newer, maintained counterpart in the repo, and their model-specific hooks leaked into shared stages, request fields, CLI args, and loaders. Remove each model's code, configs, presets, examples, converters, tests, cookbook pages, CI planner rules, and registry entries, along with the shared-code hooks only they used: - camera kwargs (GameCraft camera_states, DreamX y_camera) in the shared denoising loop - LongCat refine branches in latent preparation, the decoding crop, the --bsa-* CLI args, its train model, and third_party/longcat_video - Z-Image cfg_normalization/cfg_truncation and GameCraft/DreamX camera/action fields in SamplingParam, ForwardBatch, and the schema - the GLM-Image vision-language loader and the TurboDiffusion rCM scheduler and SLA auto-select in the train entrypoint - the Wan definition groups that only preserved DreamX detector order Removed env vars and their legacy aliases move to DEPRECATED_VARIABLES per the env-var policy. Matrix Game 2.0 stays: the new trainer's world model scenarios depend on it. The SLA and BSA attention backends stay. The TurboDiffusion int8 kernels in fastvideo-kernel are left for a separate kernel PR. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
This commit is contained in:
SolitaryThinker
co-authored by
Claude Opus 5.5
Claude Opus 5.5
parent
02a027c49f
commit
d692d1cc3b
@@ -1,61 +0,0 @@
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import os
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from fastvideo import VideoGenerator
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OUTPUT_PATH = os.getenv("DREAMX_WORLD_OUTPUT_PATH", "video_samples_dreamx_world")
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def _env_int(name: str, default: int) -> int:
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return int(os.getenv(name, str(default)))
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def _env_float(name: str, default: float) -> float:
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return float(os.getenv(name, str(default)))
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def main():
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model_name = os.getenv("DREAMX_WORLD_MODEL_DIR", "FastVideo/DreamX-World-5B-Cam-Diffusers")
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generator = VideoGenerator.from_pretrained(
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model_name,
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num_gpus=1,
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use_fsdp_inference=False,
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dit_cpu_offload=False,
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vae_cpu_offload=True,
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text_encoder_cpu_offload=True,
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pin_cpu_memory=False,
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override_pipeline_cls_name="DreamXWorldPipeline",
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)
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prompt = os.getenv(
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"DREAMX_WORLD_PROMPT",
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"A cinematic first-person drive through a futuristic coastal city at "
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"sunrise, reflective glass towers, clean streets, soft volumetric light.",
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)
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image_path = os.getenv(
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"DREAMX_WORLD_IMAGE_PATH",
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"https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/wan_i2v_input.JPG",
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)
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kwargs = {
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"output_path": OUTPUT_PATH,
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"save_video": os.getenv("DREAMX_WORLD_SAVE_VIDEO", "1") != "0",
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"height": _env_int("DREAMX_WORLD_HEIGHT", 480),
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"width": _env_int("DREAMX_WORLD_WIDTH", 832),
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"num_frames": _env_int("DREAMX_WORLD_NUM_FRAMES", 161),
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"num_inference_steps": _env_int("DREAMX_WORLD_STEPS", 30),
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"guidance_scale": _env_float("DREAMX_WORLD_GUIDANCE", 5.0),
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"action_list": os.getenv("DREAMX_WORLD_ACTIONS", "w,d,w").split(","),
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"action_speed_list":
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[float(value) for value in os.getenv("DREAMX_WORLD_ACTION_SPEEDS", "4.0,2.0,4.0").split(",")],
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}
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if image_path:
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kwargs["image_path"] = image_path
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try:
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generator.generate_video(prompt, **kwargs)
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finally:
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generator.shutdown()
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if __name__ == "__main__":
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main()
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@@ -1,118 +0,0 @@
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# SPDX-License-Identifier: Apache-2.0
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"""
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Basic inference script for HunyuanGameCraft video generation.
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HunyuanGameCraft generates game-like videos with camera/action control.
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It takes an optional image input and generates video with camera motion
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based on simple action commands (forward, left, right, backward, rotations).
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Available actions:
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- forward (w): Move camera forward
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- backward (s): Move camera backward
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- left (a): Move camera left (strafe)
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- right (d): Move camera right (strafe)
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- left_rot: Rotate camera left (pan)
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- right_rot: Rotate camera right (pan)
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- up_rot: Rotate camera up (tilt)
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- down_rot: Rotate camera down (tilt)
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T2V vs I2V:
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- Default: I2V (uses a default reference image). Set GAMECRAFT_I2V_IMAGE to a
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URL or path to use a different image.
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- T2V only (no reference image): run with GAMECRAFT_I2V_IMAGE= (empty).
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"""
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import os
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import torch
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from fastvideo import VideoGenerator
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from fastvideo.models.camera import create_camera_trajectory
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# Model configuration (use GAMECRAFT_MODEL_PATH for local weights)
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MODEL_PATH = os.environ.get("GAMECRAFT_MODEL_PATH", "FastVideo/HunyuanGameCraft-Diffusers")
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# Default prompts for demo
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DEFAULT_PROMPTS = {
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"village":
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"A charming medieval village with cobblestone streets, thatched-roof houses, and vibrant flower gardens under a bright blue sky.",
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"temple":
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"A majestic ancient temple stands under a clear blue sky, its grandeur highlighted by towering Doric columns and intricate architectural details.",
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"forest":
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"A lush green forest with tall trees, dappled sunlight filtering through the leaves, and a winding dirt path.",
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"beach": "A tropical beach with crystal clear turquoise water, white sand, and palm trees swaying in the breeze.",
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}
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# I2V: default reference image (URL). Can override with a local path.
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DEFAULT_I2V_IMAGE_URL = ("https://huggingface.co/datasets/huggingface/documentation-images/"
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"resolve/main/diffusers/astronaut.jpg")
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DEFAULT_I2V_PROMPT = ("An astronaut hatching from an egg, on the surface of the moon, "
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"the darkness and depth of space realised in the background.")
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OUTPUT_PATH = "video_samples_gamecraft"
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def main():
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# Initialize generator
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# FastVideo will automatically download weights from HuggingFace
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generator = VideoGenerator.from_pretrained(
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MODEL_PATH,
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num_gpus=1,
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use_fsdp_inference=True,
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dit_cpu_offload=True,
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vae_cpu_offload=True,
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text_encoder_cpu_offload=True,
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pin_cpu_memory=True,
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)
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# Video parameters
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height = 704
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width = 1280
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num_frames = 33
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action = "forward"
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action_speed = 0.2
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# Create camera trajectory (Plücker coordinates)
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camera_states = create_camera_trajectory(
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action=action,
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height=height,
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width=width,
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num_frames=num_frames,
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action_speed=action_speed,
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dtype=torch.bfloat16,
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)
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print(f"Camera states shape: {camera_states.shape}")
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# I2V vs T2V: unset GAMECRAFT_I2V_IMAGE -> I2V (default image). Set to "" -> T2V.
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env_image = os.environ.get("GAMECRAFT_I2V_IMAGE")
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if env_image is None:
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image_path = DEFAULT_I2V_IMAGE_URL # default: I2V
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elif env_image.strip() == "":
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image_path = None # T2V
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else:
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image_path = env_image.strip() # I2V with given URL/path
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is_i2v = image_path is not None
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prompt = DEFAULT_I2V_PROMPT if is_i2v else DEFAULT_PROMPTS["temple"]
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print(f"Mode: {'I2V' if is_i2v else 'T2V'}, prompt: {prompt[:60]}...")
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gen_kw = dict(
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prompt=prompt,
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negative_prompt="",
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camera_states=camera_states,
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height=height,
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width=width,
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num_frames=num_frames,
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num_inference_steps=50,
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guidance_scale=6.0,
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seed=42,
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fps=24,
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output_path=OUTPUT_PATH,
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save_video=True,
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)
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if is_i2v:
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gen_kw["image_path"] = image_path
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generator.generate_video(**gen_kw)
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if __name__ == "__main__":
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main()
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@@ -1,107 +0,0 @@
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# SPDX-License-Identifier: Apache-2.0
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"""Run GLM-Image text-to-image generation through FastVideo.
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User story:
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"I have the HF `zai-org/GLM-Image` checkpoint and want a minimal
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text-to-image generation command, saved as a PNG."
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"""
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import argparse
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from pathlib import Path
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from PIL import Image
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from fastvideo import VideoGenerator
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from fastvideo.api import (
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EngineConfig,
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GenerationRequest,
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GeneratorConfig,
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OutputConfig,
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ParallelismConfig,
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PipelineSelection,
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SamplingConfig,
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)
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def parse_args() -> argparse.Namespace:
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parser = argparse.ArgumentParser(description="Run GLM-Image text-to-image generation.")
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parser.add_argument(
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"--model-path",
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default="zai-org/GLM-Image",
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help="HF id or local diffusers-format GLM-Image weights directory.",
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)
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parser.add_argument(
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"--output",
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default="image_output/landscape.png",
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help="Output PNG path.",
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)
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parser.add_argument(
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"--prompt",
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default=("A beautiful landscape photography with rolling hills, "
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"a winding river, and a vibrant sunset in the background. "
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"Warm golden light, photorealistic style."),
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help="Text prompt.",
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)
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parser.add_argument("--height", type=int, default=1024)
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parser.add_argument("--width", type=int, default=1024)
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parser.add_argument("--steps", type=int, default=50)
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parser.add_argument("--guidance-scale", type=float, default=1.5)
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parser.add_argument("--seed", type=int, default=1024)
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parser.add_argument("--num-gpus", type=int, default=1)
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parser.add_argument("--tp-size", type=int, default=None)
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parser.add_argument("--sp-size", type=int, default=None)
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return parser.parse_args()
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def main() -> None:
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args = parse_args()
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output = Path(args.output)
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output.parent.mkdir(parents=True, exist_ok=True)
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tp_size = args.tp_size if args.tp_size is not None else (args.num_gpus if args.num_gpus > 1 else 1)
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sp_size = args.sp_size if args.sp_size is not None else (1 if args.num_gpus > 1 else args.num_gpus)
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# GLM-Image needs trust_remote_code for its AR encoder; offload and the
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# pipeline class come from the model's registered defaults — don't override.
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generator_config = GeneratorConfig(
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model_path=args.model_path,
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trust_remote_code=True,
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engine=EngineConfig(
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num_gpus=args.num_gpus,
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parallelism=ParallelismConfig(tp_size=tp_size, sp_size=sp_size),
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),
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pipeline=PipelineSelection(workload_type="t2i"),
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)
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generator = VideoGenerator.from_config(generator_config)
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try:
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request = GenerationRequest(
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prompt=args.prompt,
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sampling=SamplingConfig(
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height=args.height,
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width=args.width,
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num_frames=1,
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fps=1,
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num_inference_steps=args.steps,
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guidance_scale=args.guidance_scale,
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seed=args.seed,
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),
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output=OutputConfig(
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output_path=str(output.parent),
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save_video=False,
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return_frames=True,
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),
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)
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result = generator.generate(request)
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if isinstance(result, list):
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result = result[0]
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frames = result.frames
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if frames is not None and len(frames):
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Image.fromarray(frames[0]).save(output)
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print(f"Saved image to {output}")
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finally:
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generator.shutdown()
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if __name__ == "__main__":
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main()
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@@ -1,204 +0,0 @@
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"""
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LongCat Image-to-Video (I2V) Example Script
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This script demonstrates LongCat I2V inference using the FastVideo Python API.
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LongCat I2V takes an input image and generates a video from it.
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It runs both basic generation (50 steps) and distill+refine generation
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(16 steps distill + 50 steps refinement to 720p with BSA).
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Usage:
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python examples/inference/basic/basic_longcat_i2v.py
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Note:
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Refinement uses 768x768 dimensions where latent (48x48) is divisible by 8,
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compatible with BSA chunks [4, 4, 8].
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"""
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import glob
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import os
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from fastvideo import VideoGenerator
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# Common prompts and settings matching the shell script examples
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PROMPT = ("A woman sits at a wooden table by the window in a cozy café. She reaches out "
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"with her right hand, picks up the white coffee cup from the saucer, and gently "
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"brings it to her lips to take a sip. After drinking, she places the cup back on "
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"the table and looks out the window, enjoying the peaceful atmosphere.")
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NEGATIVE_PROMPT = ("Bright tones, overexposed, static, blurred details, subtitles, style, works, "
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"paintings, images, static, overall gray, worst quality, low quality, JPEG compression "
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"residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, "
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"deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, "
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"three legs, many people in the background, walking backwards")
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# Input image path
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IMAGE_PATH = "assets/girl.png"
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SEED = 42
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def basic_generation():
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"""
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Run basic LongCat I2V generation (50 steps at 480p).
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This uses the full 50-step denoising process for highest quality.
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"""
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print("=" * 60)
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print("LongCat I2V: Basic Generation (50 steps, 480p)")
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print("=" * 60)
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generator = VideoGenerator.from_pretrained(
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"FastVideo/LongCat-Video-I2V-Diffusers",
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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=False,
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vae_cpu_offload=True,
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text_encoder_cpu_offload=True,
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pin_cpu_memory=False,
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enable_bsa=False,
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)
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output_path = "outputs_video/longcat_i2v_basic"
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generator.generate_video(
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prompt=PROMPT,
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negative_prompt=NEGATIVE_PROMPT,
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image_path=IMAGE_PATH,
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output_path=output_path,
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save_video=True,
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height=480,
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width=480, # Square
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num_frames=93,
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num_inference_steps=50,
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fps=15,
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guidance_scale=4.0,
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seed=SEED,
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)
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print(f"\nBasic generation complete! Video saved to: {output_path}")
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generator.shutdown()
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def distill_refine_generation():
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"""
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Run LongCat I2V with distill+refine pipeline (16 steps + refinement to 768p).
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This uses the distilled LoRA for fast 480p generation (16 steps),
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then refines to 768p using the refinement LoRA with BSA enabled.
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"""
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print("\n" + "=" * 60)
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print("LongCat I2V: Distill + Refine Pipeline")
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print("=" * 60)
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# Stage 1: Distilled generation (16 steps at 480p)
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print("\n[Stage 1] Distilled generation (16 steps, 480p)")
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print("-" * 40)
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generator = VideoGenerator.from_pretrained(
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"FastVideo/LongCat-Video-I2V-Diffusers",
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num_gpus=1,
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use_fsdp_inference=True,
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dit_cpu_offload=False,
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vae_cpu_offload=True,
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text_encoder_cpu_offload=True,
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pin_cpu_memory=False,
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enable_bsa=False,
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lora_path="FastVideo/LongCat-Video-T2V-Distilled-LoRA",
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lora_nickname="distilled",
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)
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distill_output_path = "outputs_video/longcat_i2v_distill"
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generator.generate_video(
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prompt=PROMPT,
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negative_prompt=NEGATIVE_PROMPT,
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image_path=IMAGE_PATH,
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output_path=distill_output_path,
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save_video=True,
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height=480,
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width=480, # Square
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num_frames=93,
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num_inference_steps=16,
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fps=15,
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guidance_scale=1.0,
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seed=SEED,
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)
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print(f"Distilled generation complete! Video saved to: {distill_output_path}")
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generator.shutdown()
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# Stage 2: Refinement (480p -> 768p)
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print("\n[Stage 2] Refinement (480p -> 768p with BSA)")
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print("-" * 40)
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# Find the actual saved video file from stage 1
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video_files = glob.glob(os.path.join(distill_output_path, "*.mp4"))
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if not video_files:
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raise FileNotFoundError(f"No video file found in {distill_output_path}")
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# Use the most recently created video file
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distill_video_path = max(video_files, key=os.path.getmtime)
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print(f"Using stage 1 video: {distill_video_path}")
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# Create a new generator with refinement LoRA and BSA enabled
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# Note: Refinement uses the T2V model (not I2V) since it's upscaling the generated video
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# For BSA [4, 4, 8]: latent must be divisible by 8
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# 768x768: latent 48x48, 48%8=0 ✓
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refine_generator = VideoGenerator.from_pretrained(
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"FastVideo/LongCat-Video-T2V-Diffusers",
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num_gpus=1,
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use_fsdp_inference=True,
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dit_cpu_offload=True,
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vae_cpu_offload=True,
|
||||
text_encoder_cpu_offload=True,
|
||||
pin_cpu_memory=False,
|
||||
enable_bsa=True,
|
||||
bsa_sparsity=0.875,
|
||||
bsa_chunk_q=[4, 4, 4],
|
||||
bsa_chunk_k=[4, 4, 4],
|
||||
lora_path="FastVideo/LongCat-Video-T2V-Refinement-LoRA",
|
||||
lora_nickname="refinement",
|
||||
)
|
||||
|
||||
refine_output_path = "outputs_video/longcat_i2v_refine_720p"
|
||||
|
||||
refine_generator.generate_video(
|
||||
prompt=PROMPT,
|
||||
negative_prompt=NEGATIVE_PROMPT,
|
||||
output_path=refine_output_path,
|
||||
save_video=True,
|
||||
refine_from=distill_video_path,
|
||||
t_thresh=0.5,
|
||||
spatial_refine_only=False,
|
||||
num_cond_frames=0,
|
||||
height=720,
|
||||
width=720,
|
||||
num_inference_steps=50,
|
||||
fps=30,
|
||||
guidance_scale=1.0,
|
||||
seed=SEED,
|
||||
)
|
||||
|
||||
print(f"Refinement complete! Video saved to: {refine_output_path}")
|
||||
refine_generator.shutdown()
|
||||
|
||||
|
||||
def main():
|
||||
"""Run both basic and distill+refine generation pipelines."""
|
||||
print("\n" + "=" * 60)
|
||||
print("LongCat Image-to-Video Example")
|
||||
print("=" * 60 + "\n")
|
||||
|
||||
# Run basic generation
|
||||
basic_generation()
|
||||
|
||||
# Run distill+refine pipeline
|
||||
distill_refine_generation()
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print("All generations complete!")
|
||||
print("=" * 60)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,192 +0,0 @@
|
||||
"""
|
||||
LongCat Text-to-Video (T2V) Example Script
|
||||
|
||||
This script demonstrates LongCat T2V inference using the FastVideo Python API.
|
||||
It runs both basic generation (50 steps) and distill+refine generation
|
||||
(16 steps distill + 50 steps refinement to 720p).
|
||||
|
||||
Usage:
|
||||
python examples/inference/basic/basic_longcat_t2v.py
|
||||
"""
|
||||
|
||||
import glob
|
||||
import os
|
||||
|
||||
from fastvideo import VideoGenerator
|
||||
|
||||
# Common prompts and settings matching the shell script examples
|
||||
PROMPT = ("In a realistic photography style, a white boy around seven or eight years old "
|
||||
"sits on a park bench, wearing a light blue T-shirt, denim shorts, and white sneakers. "
|
||||
"He holds an ice cream cone with vanilla and chocolate flavors, and beside him is a "
|
||||
"medium-sized golden Labrador. Smiling, the boy offers the ice cream to the dog, "
|
||||
"who eagerly licks it with its tongue. The sun is shining brightly, and the background "
|
||||
"features a green lawn and several tall trees, creating a warm and loving scene.")
|
||||
|
||||
NEGATIVE_PROMPT = ("Bright tones, overexposed, static, blurred details, subtitles, style, works, "
|
||||
"paintings, images, static, overall gray, worst quality, low quality, JPEG compression "
|
||||
"residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, "
|
||||
"deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, "
|
||||
"three legs, many people in the background, walking backwards")
|
||||
|
||||
SEED = 42
|
||||
|
||||
|
||||
def basic_generation():
|
||||
"""
|
||||
Run basic LongCat T2V generation (50 steps at 480p).
|
||||
|
||||
This uses the full 50-step denoising process for highest quality.
|
||||
"""
|
||||
print("=" * 60)
|
||||
print("LongCat T2V: Basic Generation (50 steps, 480p)")
|
||||
print("=" * 60)
|
||||
|
||||
generator = VideoGenerator.from_pretrained(
|
||||
"FastVideo/LongCat-Video-T2V-Diffusers",
|
||||
num_gpus=1,
|
||||
use_fsdp_inference=False, # set to True if GPU is out of memory
|
||||
dit_cpu_offload=False,
|
||||
vae_cpu_offload=True,
|
||||
text_encoder_cpu_offload=True,
|
||||
pin_cpu_memory=False,
|
||||
enable_bsa=False,
|
||||
)
|
||||
|
||||
output_path = "outputs_video/longcat_t2v_basic"
|
||||
|
||||
generator.generate_video(
|
||||
prompt=PROMPT,
|
||||
negative_prompt=NEGATIVE_PROMPT,
|
||||
output_path=output_path,
|
||||
save_video=True,
|
||||
height=480,
|
||||
width=832,
|
||||
num_frames=93,
|
||||
num_inference_steps=50,
|
||||
fps=15,
|
||||
guidance_scale=4.0,
|
||||
seed=SEED,
|
||||
)
|
||||
|
||||
print(f"\nBasic generation complete! Video saved to: {output_path}")
|
||||
generator.shutdown()
|
||||
|
||||
|
||||
def distill_refine_generation():
|
||||
"""
|
||||
Run LongCat T2V with distill+refine pipeline (16 steps + refinement to 720p).
|
||||
|
||||
This uses the distilled LoRA for fast 480p generation (16 steps),
|
||||
then refines to 720p using the refinement LoRA with BSA enabled.
|
||||
"""
|
||||
print("\n" + "=" * 60)
|
||||
print("LongCat T2V: Distill + Refine Pipeline")
|
||||
print("=" * 60)
|
||||
|
||||
# Stage 1: Distilled generation (16 steps at 480p)
|
||||
print("\n[Stage 1] Distilled generation (16 steps, 480p)")
|
||||
print("-" * 40)
|
||||
|
||||
generator = VideoGenerator.from_pretrained(
|
||||
"FastVideo/LongCat-Video-T2V-Diffusers",
|
||||
num_gpus=1,
|
||||
use_fsdp_inference=True,
|
||||
dit_cpu_offload=False,
|
||||
vae_cpu_offload=True,
|
||||
text_encoder_cpu_offload=True,
|
||||
pin_cpu_memory=False,
|
||||
enable_bsa=False,
|
||||
lora_path="FastVideo/LongCat-Video-T2V-Distilled-LoRA",
|
||||
lora_nickname="distilled",
|
||||
)
|
||||
|
||||
distill_output_path = "outputs_video/longcat_t2v_distill"
|
||||
|
||||
generator.generate_video(
|
||||
prompt=PROMPT,
|
||||
negative_prompt=NEGATIVE_PROMPT,
|
||||
output_path=distill_output_path,
|
||||
save_video=True,
|
||||
height=480,
|
||||
width=832,
|
||||
num_frames=93,
|
||||
num_inference_steps=16,
|
||||
fps=15,
|
||||
guidance_scale=1.0,
|
||||
seed=SEED,
|
||||
)
|
||||
|
||||
print(f"Distilled generation complete! Video saved to: {distill_output_path}")
|
||||
generator.shutdown()
|
||||
|
||||
# Stage 2: Refinement (480p -> 720p)
|
||||
print("\n[Stage 2] Refinement (480p -> 720p with BSA)")
|
||||
print("-" * 40)
|
||||
|
||||
# Find the actual saved video file from stage 1
|
||||
video_files = glob.glob(os.path.join(distill_output_path, "*.mp4"))
|
||||
if not video_files:
|
||||
raise FileNotFoundError(f"No video file found in {distill_output_path}")
|
||||
# Use the most recently created video file
|
||||
distill_video_path = max(video_files, key=os.path.getmtime)
|
||||
print(f"Using stage 1 video: {distill_video_path}")
|
||||
|
||||
# Create a new generator with refinement LoRA and BSA enabled
|
||||
refine_generator = VideoGenerator.from_pretrained(
|
||||
"FastVideo/LongCat-Video-T2V-Diffusers",
|
||||
num_gpus=1,
|
||||
use_fsdp_inference=True,
|
||||
dit_cpu_offload=True,
|
||||
vae_cpu_offload=True,
|
||||
text_encoder_cpu_offload=True,
|
||||
pin_cpu_memory=False,
|
||||
enable_bsa=True,
|
||||
bsa_sparsity=0.875,
|
||||
bsa_chunk_q=[4, 4, 8],
|
||||
bsa_chunk_k=[4, 4, 8],
|
||||
lora_path="FastVideo/LongCat-Video-T2V-Refinement-LoRA",
|
||||
lora_nickname="refinement",
|
||||
)
|
||||
|
||||
refine_output_path = "outputs_video/longcat_t2v_refine_720p"
|
||||
|
||||
refine_generator.generate_video(
|
||||
prompt=PROMPT,
|
||||
negative_prompt=NEGATIVE_PROMPT,
|
||||
output_path=refine_output_path,
|
||||
save_video=True,
|
||||
refine_from=distill_video_path,
|
||||
t_thresh=0.5,
|
||||
spatial_refine_only=False,
|
||||
num_cond_frames=0,
|
||||
height=720,
|
||||
width=1280,
|
||||
num_inference_steps=50,
|
||||
fps=30,
|
||||
guidance_scale=1.0,
|
||||
seed=SEED,
|
||||
)
|
||||
|
||||
print(f"Refinement complete! Video saved to: {refine_output_path}")
|
||||
refine_generator.shutdown()
|
||||
|
||||
|
||||
def main():
|
||||
"""Run both basic and distill+refine generation pipelines."""
|
||||
print("\n" + "=" * 60)
|
||||
print("LongCat Text-to-Video Example")
|
||||
print("=" * 60 + "\n")
|
||||
|
||||
# Run basic generation
|
||||
basic_generation()
|
||||
|
||||
# Run distill+refine pipeline
|
||||
distill_refine_generation()
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print("All generations complete!")
|
||||
print("=" * 60)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,218 +0,0 @@
|
||||
"""
|
||||
LongCat Video Continuation (VC) Example Script
|
||||
|
||||
This script demonstrates LongCat VC inference using the FastVideo Python API.
|
||||
LongCat VC takes an input video and generates a continuation of it.
|
||||
|
||||
It runs both basic generation (50 steps) and distill+refine generation
|
||||
(16 steps distill + 50 steps refinement to 720p).
|
||||
|
||||
Usage:
|
||||
python examples/inference/basic/basic_longcat_vc.py
|
||||
|
||||
Prerequisites:
|
||||
- Ensure the input video exists at assets/motorcycle.mp4
|
||||
(or provide your own video)
|
||||
"""
|
||||
|
||||
import glob
|
||||
import os
|
||||
|
||||
from fastvideo import VideoGenerator
|
||||
|
||||
# Common prompts and settings matching the shell script examples
|
||||
PROMPT = ("A person rides a motorcycle along a long, straight road that stretches between "
|
||||
"a body of water and a forested hillside. The rider steadily accelerates, keeping "
|
||||
"the motorcycle centered between the guardrails, while the scenery passes by on "
|
||||
"both sides. The video captures the journey from the rider's perspective, emphasizing "
|
||||
"the sense of motion and adventure.")
|
||||
|
||||
NEGATIVE_PROMPT = ("Bright tones, overexposed, static, blurred details, subtitles, style, works, "
|
||||
"paintings, images, static, overall gray, worst quality, low quality, JPEG compression "
|
||||
"residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, "
|
||||
"deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, "
|
||||
"three legs, many people in the background, walking backwards")
|
||||
|
||||
# Input video path
|
||||
VIDEO_PATH = "assets/motorcycle.mp4"
|
||||
|
||||
# Number of conditioning frames from the input video
|
||||
NUM_COND_FRAMES = 13
|
||||
|
||||
SEED = 42
|
||||
|
||||
|
||||
def basic_generation():
|
||||
"""
|
||||
Run basic LongCat VC generation (50 steps at 480p).
|
||||
|
||||
This uses the full 50-step denoising process for highest quality.
|
||||
"""
|
||||
print("=" * 60)
|
||||
print("LongCat VC: Basic Generation (50 steps, 480p)")
|
||||
print("=" * 60)
|
||||
|
||||
# Check if video exists
|
||||
if not os.path.exists(VIDEO_PATH):
|
||||
raise FileNotFoundError(f"Video not found at {VIDEO_PATH}. "
|
||||
"Please provide a valid video path.")
|
||||
|
||||
generator = VideoGenerator.from_pretrained(
|
||||
"FastVideo/LongCat-Video-VC-Diffusers",
|
||||
num_gpus=1,
|
||||
use_fsdp_inference=False, # set to True if GPU is out of memory
|
||||
dit_cpu_offload=False,
|
||||
vae_cpu_offload=True,
|
||||
text_encoder_cpu_offload=True,
|
||||
pin_cpu_memory=False,
|
||||
enable_bsa=False,
|
||||
)
|
||||
|
||||
output_path = "outputs_video/longcat_vc_basic"
|
||||
|
||||
generator.generate_video(
|
||||
prompt=PROMPT,
|
||||
negative_prompt=NEGATIVE_PROMPT,
|
||||
video_path=VIDEO_PATH,
|
||||
num_cond_frames=NUM_COND_FRAMES,
|
||||
output_path=output_path,
|
||||
save_video=True,
|
||||
height=480,
|
||||
width=832,
|
||||
num_frames=93,
|
||||
num_inference_steps=50,
|
||||
fps=15,
|
||||
guidance_scale=4.0,
|
||||
seed=SEED,
|
||||
)
|
||||
|
||||
print(f"\nBasic generation complete! Video saved to: {output_path}")
|
||||
generator.shutdown()
|
||||
|
||||
|
||||
def distill_refine_generation():
|
||||
"""
|
||||
Run LongCat VC with distill+refine pipeline (16 steps + refinement to 720p).
|
||||
|
||||
This uses the distilled LoRA for fast 480p generation (16 steps),
|
||||
then refines to 720p using the refinement LoRA with BSA enabled.
|
||||
"""
|
||||
print("\n" + "=" * 60)
|
||||
print("LongCat VC: Distill + Refine Pipeline")
|
||||
print("=" * 60)
|
||||
|
||||
# Check if video exists
|
||||
if not os.path.exists(VIDEO_PATH):
|
||||
raise FileNotFoundError(f"Video not found at {VIDEO_PATH}. "
|
||||
"Please provide a valid video path.")
|
||||
|
||||
# Stage 1: Distilled generation (16 steps at 480p)
|
||||
print("\n[Stage 1] Distilled generation (16 steps, 480p)")
|
||||
print("-" * 40)
|
||||
|
||||
generator = VideoGenerator.from_pretrained(
|
||||
"FastVideo/LongCat-Video-VC-Diffusers",
|
||||
num_gpus=1,
|
||||
use_fsdp_inference=True,
|
||||
dit_cpu_offload=False,
|
||||
vae_cpu_offload=True,
|
||||
text_encoder_cpu_offload=True,
|
||||
pin_cpu_memory=False,
|
||||
enable_bsa=False,
|
||||
lora_path="FastVideo/LongCat-Video-T2V-Distilled-LoRA",
|
||||
lora_nickname="distilled",
|
||||
)
|
||||
|
||||
distill_output_path = "outputs_video/longcat_vc_distill"
|
||||
|
||||
generator.generate_video(
|
||||
prompt=PROMPT,
|
||||
negative_prompt=NEGATIVE_PROMPT,
|
||||
video_path=VIDEO_PATH,
|
||||
num_cond_frames=NUM_COND_FRAMES,
|
||||
output_path=distill_output_path,
|
||||
save_video=True,
|
||||
height=480,
|
||||
width=832,
|
||||
num_frames=93,
|
||||
num_inference_steps=16,
|
||||
fps=15,
|
||||
guidance_scale=1.0,
|
||||
seed=SEED,
|
||||
)
|
||||
|
||||
print(f"Distilled generation complete! Video saved to: {distill_output_path}")
|
||||
generator.shutdown()
|
||||
|
||||
# Stage 2: Refinement (480p -> 720p)
|
||||
print("\n[Stage 2] Refinement (480p -> 720p with BSA)")
|
||||
print("-" * 40)
|
||||
|
||||
# Find the actual saved video file from stage 1
|
||||
video_files = glob.glob(os.path.join(distill_output_path, "*.mp4"))
|
||||
if not video_files:
|
||||
raise FileNotFoundError(f"No video file found in {distill_output_path}")
|
||||
# Use the most recently created video file
|
||||
distill_video_path = max(video_files, key=os.path.getmtime)
|
||||
print(f"Using stage 1 video: {distill_video_path}")
|
||||
|
||||
# Create a new generator with refinement LoRA and BSA enabled
|
||||
# Note: Refinement uses the T2V model (not VC) since it's upscaling the generated video
|
||||
refine_generator = VideoGenerator.from_pretrained(
|
||||
"FastVideo/LongCat-Video-T2V-Diffusers",
|
||||
num_gpus=1,
|
||||
use_fsdp_inference=True,
|
||||
dit_cpu_offload=True,
|
||||
vae_cpu_offload=True,
|
||||
text_encoder_cpu_offload=True,
|
||||
pin_cpu_memory=False,
|
||||
enable_bsa=True,
|
||||
bsa_sparsity=0.875,
|
||||
bsa_chunk_q=[4, 4, 8],
|
||||
bsa_chunk_k=[4, 4, 8],
|
||||
lora_path="FastVideo/LongCat-Video-T2V-Refinement-LoRA",
|
||||
lora_nickname="refinement",
|
||||
)
|
||||
|
||||
refine_output_path = "outputs_video/longcat_vc_refine_720p"
|
||||
|
||||
refine_generator.generate_video(
|
||||
prompt=PROMPT,
|
||||
negative_prompt=NEGATIVE_PROMPT,
|
||||
output_path=refine_output_path,
|
||||
save_video=True,
|
||||
refine_from=distill_video_path,
|
||||
t_thresh=0.5,
|
||||
spatial_refine_only=False,
|
||||
num_cond_frames=0, # For refinement, no conditioning frames
|
||||
height=720,
|
||||
width=1280,
|
||||
num_inference_steps=50,
|
||||
fps=30,
|
||||
guidance_scale=1.0,
|
||||
seed=SEED,
|
||||
)
|
||||
|
||||
print(f"Refinement complete! Video saved to: {refine_output_path}")
|
||||
refine_generator.shutdown()
|
||||
|
||||
|
||||
def main():
|
||||
"""Run both basic and distill+refine generation pipelines."""
|
||||
print("\n" + "=" * 60)
|
||||
print("LongCat Video Continuation Example")
|
||||
print("=" * 60 + "\n")
|
||||
|
||||
# Run basic generation
|
||||
basic_generation()
|
||||
|
||||
# Run distill+refine pipeline
|
||||
distill_refine_generation()
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print("All generations complete!")
|
||||
print("=" * 60)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,51 +0,0 @@
|
||||
import os
|
||||
|
||||
# Set SLA attention backend BEFORE fastvideo imports
|
||||
os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "SLA_ATTN"
|
||||
|
||||
from fastvideo import VideoGenerator
|
||||
|
||||
OUTPUT_PATH = "video_samples_turbodiffusion"
|
||||
|
||||
|
||||
def main() -> None:
|
||||
# TurboDiffusion: 1-4 step video generation using RCM scheduler + SLA attention
|
||||
# FastVideo will automatically use TurboDiffusionPipeline when specified
|
||||
generator = VideoGenerator.from_pretrained(
|
||||
"loayrashid/TurboWan2.1-T2V-1.3B-Diffusers",
|
||||
# FastVideo will automatically handle distributed setup
|
||||
num_gpus=1,
|
||||
use_fsdp_inference=False, # set to True if GPU is out of memory
|
||||
|
||||
# set to false if using RTX 4090
|
||||
# pin_cpu_memory=False,
|
||||
)
|
||||
|
||||
# Generate videos with the same simple API, regardless of GPU count
|
||||
# TurboDiffusion defaults: guidance_scale=1.0 and num_inference_steps=4 (from config)
|
||||
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.")
|
||||
video = generator.generate_video(
|
||||
prompt,
|
||||
output_path=OUTPUT_PATH,
|
||||
save_video=True,
|
||||
seed=42,
|
||||
)
|
||||
|
||||
# Generate another video with a different prompt, without reloading the model!
|
||||
prompt2 = ("A majestic lion strides across the golden savanna, its powerful frame "
|
||||
"glistening under the warm afternoon sun. The tall grass ripples gently in "
|
||||
"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.")
|
||||
video2 = generator.generate_video(
|
||||
prompt2,
|
||||
output_path=OUTPUT_PATH,
|
||||
save_video=True,
|
||||
seed=42,
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,45 +0,0 @@
|
||||
import os
|
||||
|
||||
# Set SLA attention backend BEFORE fastvideo imports
|
||||
os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "SLA_ATTN"
|
||||
|
||||
from fastvideo import VideoGenerator
|
||||
|
||||
OUTPUT_PATH = "video_samples_turbodiffusion_14B"
|
||||
|
||||
|
||||
def main() -> None:
|
||||
# TurboDiffusion 14B: 1-4 step video generation using RCM scheduler + SLA attention
|
||||
# FastVideo will automatically use TurboDiffusionPipeline when specified
|
||||
generator = VideoGenerator.from_pretrained(
|
||||
"loayrashid/TurboWan2.1-T2V-14B-Diffusers",
|
||||
# 14B model needs more GPUs
|
||||
num_gpus=2,
|
||||
)
|
||||
|
||||
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.")
|
||||
video = generator.generate_video(
|
||||
prompt,
|
||||
output_path=OUTPUT_PATH,
|
||||
save_video=True,
|
||||
seed=42,
|
||||
)
|
||||
|
||||
# Generate another video with a different prompt, without reloading the model!
|
||||
prompt2 = ("A majestic lion strides across the golden savanna, its powerful frame "
|
||||
"glistening under the warm afternoon sun. The tall grass ripples gently in "
|
||||
"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.")
|
||||
video2 = generator.generate_video(
|
||||
prompt2,
|
||||
output_path=OUTPUT_PATH,
|
||||
save_video=True,
|
||||
seed=42,
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,38 +0,0 @@
|
||||
import os
|
||||
|
||||
# Set SLA attention backend BEFORE fastvideo imports
|
||||
os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "SLA_ATTN"
|
||||
|
||||
from fastvideo import VideoGenerator
|
||||
|
||||
# Use local model path
|
||||
MODEL_PATH = "loayrashid/TurboWan2.2-I2V-A14B-Diffusers"
|
||||
OUTPUT_PATH = "video_samples_turbodiffusion_i2v"
|
||||
|
||||
|
||||
def main() -> None:
|
||||
# TurboDiffusion I2V: 1-4 step image-to-video generation
|
||||
generator = VideoGenerator.from_pretrained(
|
||||
MODEL_PATH,
|
||||
num_gpus=2,
|
||||
)
|
||||
|
||||
# Example prompt and image for I2V
|
||||
prompt = (
|
||||
"Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside."
|
||||
)
|
||||
|
||||
# Use an example image path
|
||||
image_path = "https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/wan_i2v_input.JPG"
|
||||
|
||||
video = generator.generate_video(
|
||||
prompt,
|
||||
image_path=image_path,
|
||||
output_path=OUTPUT_PATH,
|
||||
save_video=True,
|
||||
seed=42,
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,94 +0,0 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
"""Run Z-Image-Turbo text-to-image generation through FastVideo.
|
||||
|
||||
User story:
|
||||
"I want the official Z-Image-Turbo defaults and a deterministic PNG from
|
||||
a local or Hugging Face checkpoint."
|
||||
"""
|
||||
|
||||
import argparse
|
||||
from pathlib import Path
|
||||
|
||||
from fastvideo import VideoGenerator
|
||||
from fastvideo.api import (
|
||||
EngineConfig,
|
||||
GenerationRequest,
|
||||
GeneratorConfig,
|
||||
OutputConfig,
|
||||
ParallelismConfig,
|
||||
PipelineSelection,
|
||||
SamplingConfig,
|
||||
)
|
||||
|
||||
DEFAULT_PROMPT = (
|
||||
"Young Chinese woman in red Hanfu, intricate embroidery. Impeccable makeup, red floral forehead pattern. "
|
||||
"Elaborate high bun, golden phoenix headdress, red flowers, beads. Holds round folding fan with lady, trees, bird. "
|
||||
"Neon lightning-bolt lamp (⚡️), bright yellow glow, above extended left palm. Soft-lit outdoor night background, "
|
||||
"silhouetted tiered pagoda (西安大雁塔), blurred colorful distant lights.")
|
||||
DEFAULT_REVISION = "f332072aa78be7aecdf3ee76d5c247082da564a6"
|
||||
|
||||
|
||||
def parse_args() -> argparse.Namespace:
|
||||
parser = argparse.ArgumentParser(description="Run Z-Image-Turbo text-to-image generation.")
|
||||
parser.add_argument("--model-path", default="Tongyi-MAI/Z-Image-Turbo")
|
||||
parser.add_argument("--revision", default=DEFAULT_REVISION)
|
||||
parser.add_argument("--output", default="outputs/zimage/zimage_turbo.png")
|
||||
parser.add_argument("--prompt", default=DEFAULT_PROMPT)
|
||||
parser.add_argument("--negative-prompt", default="")
|
||||
parser.add_argument("--height", type=int, default=1024)
|
||||
parser.add_argument("--width", type=int, default=1024)
|
||||
parser.add_argument("--steps", type=int, default=8)
|
||||
parser.add_argument("--guidance-scale", type=float, default=0.0)
|
||||
parser.add_argument("--max-sequence-length", type=int, default=512)
|
||||
parser.add_argument("--cfg-normalization", action=argparse.BooleanOptionalAction, default=False)
|
||||
parser.add_argument("--cfg-truncation", type=float, default=1.0)
|
||||
parser.add_argument("--seed", type=int, default=42)
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
def main() -> None:
|
||||
args = parse_args()
|
||||
output = Path(args.output)
|
||||
output.parent.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
generator = VideoGenerator.from_config(
|
||||
GeneratorConfig(
|
||||
model_path=args.model_path,
|
||||
revision=args.revision,
|
||||
engine=EngineConfig(
|
||||
num_gpus=1,
|
||||
parallelism=ParallelismConfig(tp_size=1, sp_size=1),
|
||||
use_fsdp_inference=False,
|
||||
),
|
||||
# The model registry selects the native zimage_turbo preset.
|
||||
pipeline=PipelineSelection(workload_type="t2i"),
|
||||
))
|
||||
try:
|
||||
generator.generate(
|
||||
GenerationRequest(
|
||||
prompt=args.prompt,
|
||||
negative_prompt=args.negative_prompt,
|
||||
sampling=SamplingConfig(
|
||||
height=args.height,
|
||||
width=args.width,
|
||||
num_frames=1,
|
||||
fps=1,
|
||||
num_inference_steps=args.steps,
|
||||
guidance_scale=args.guidance_scale,
|
||||
max_sequence_length=args.max_sequence_length,
|
||||
cfg_normalization=args.cfg_normalization,
|
||||
cfg_truncation=args.cfg_truncation,
|
||||
seed=args.seed,
|
||||
),
|
||||
output=OutputConfig(
|
||||
output_path=str(output),
|
||||
save_video=True,
|
||||
return_frames=False,
|
||||
),
|
||||
))
|
||||
finally:
|
||||
generator.shutdown()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,120 +0,0 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
"""Run GLM-Image image-to-image (edit) generation through FastVideo.
|
||||
|
||||
User story:
|
||||
"I have the HF `zai-org/GLM-Image` checkpoint and a condition image, and
|
||||
want a minimal edit command (text + image -> edited image), saved as a PNG."
|
||||
|
||||
GLM-Image is a single unified pipeline: passing a condition image switches it
|
||||
from text-to-image to the edit path (the condition enters the DiT via a KV-cache
|
||||
write pass), so the generator config is identical to `basic_glm_image.py` — the
|
||||
`inputs.pil_image` on the request is what selects the edit mode.
|
||||
"""
|
||||
import argparse
|
||||
from pathlib import Path
|
||||
|
||||
from PIL import Image
|
||||
|
||||
from fastvideo import VideoGenerator
|
||||
from fastvideo.api import (
|
||||
EngineConfig,
|
||||
GenerationRequest,
|
||||
GeneratorConfig,
|
||||
InputConfig,
|
||||
OutputConfig,
|
||||
ParallelismConfig,
|
||||
PipelineSelection,
|
||||
SamplingConfig,
|
||||
)
|
||||
|
||||
|
||||
def parse_args() -> argparse.Namespace:
|
||||
parser = argparse.ArgumentParser(description="Run GLM-Image image-to-image (edit) generation.")
|
||||
parser.add_argument(
|
||||
"--model-path",
|
||||
default="zai-org/GLM-Image",
|
||||
help="HF id or local diffusers-format GLM-Image weights directory.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--image",
|
||||
default="assets/images/couple.jpg",
|
||||
help="Condition image to edit.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--output",
|
||||
default="image_output/edited.png",
|
||||
help="Output PNG path.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--prompt",
|
||||
default="Change the background to a snowy mountain landscape at golden hour.",
|
||||
help="Edit instruction.",
|
||||
)
|
||||
parser.add_argument("--height", type=int, default=1024)
|
||||
parser.add_argument("--width", type=int, default=1024)
|
||||
parser.add_argument("--steps", type=int, default=50)
|
||||
parser.add_argument("--guidance-scale", type=float, default=1.5)
|
||||
parser.add_argument("--seed", type=int, default=1024)
|
||||
parser.add_argument("--num-gpus", type=int, default=1)
|
||||
parser.add_argument("--tp-size", type=int, default=None)
|
||||
parser.add_argument("--sp-size", type=int, default=None)
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
def main() -> None:
|
||||
args = parse_args()
|
||||
|
||||
output = Path(args.output)
|
||||
output.parent.mkdir(parents=True, exist_ok=True)
|
||||
condition = Image.open(args.image).convert("RGB")
|
||||
tp_size = args.tp_size if args.tp_size is not None else (args.num_gpus if args.num_gpus > 1 else 1)
|
||||
sp_size = args.sp_size if args.sp_size is not None else (1 if args.num_gpus > 1 else args.num_gpus)
|
||||
|
||||
# GLM-Image needs trust_remote_code for its AR encoder; offload and the
|
||||
# pipeline class come from the model's registered defaults — don't override.
|
||||
# The pipeline is registered as t2i; passing inputs.pil_image below switches
|
||||
# it to the edit path.
|
||||
generator_config = GeneratorConfig(
|
||||
model_path=args.model_path,
|
||||
trust_remote_code=True,
|
||||
engine=EngineConfig(
|
||||
num_gpus=args.num_gpus,
|
||||
parallelism=ParallelismConfig(tp_size=tp_size, sp_size=sp_size),
|
||||
),
|
||||
pipeline=PipelineSelection(workload_type="t2i"),
|
||||
)
|
||||
|
||||
generator = VideoGenerator.from_config(generator_config)
|
||||
try:
|
||||
request = GenerationRequest(
|
||||
prompt=args.prompt,
|
||||
inputs=InputConfig(pil_image=condition),
|
||||
sampling=SamplingConfig(
|
||||
height=args.height,
|
||||
width=args.width,
|
||||
num_frames=1,
|
||||
fps=1,
|
||||
num_inference_steps=args.steps,
|
||||
guidance_scale=args.guidance_scale,
|
||||
seed=args.seed,
|
||||
),
|
||||
output=OutputConfig(
|
||||
output_path=str(output.parent),
|
||||
save_video=False,
|
||||
return_frames=True,
|
||||
),
|
||||
)
|
||||
result = generator.generate(request)
|
||||
if isinstance(result, list):
|
||||
result = result[0]
|
||||
|
||||
frames = result.frames
|
||||
if frames is not None and len(frames):
|
||||
Image.fromarray(frames[0]).save(output)
|
||||
print(f"Saved image to {output}")
|
||||
finally:
|
||||
generator.shutdown()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user