The space has been successfully deployed on A100 space on 2025-01-23; synchronize your space to fix the errors
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| import os | |
| import time | |
| from pathlib import Path | |
| from loguru import logger | |
| from datetime import datetime | |
| import gradio as gr | |
| import random | |
| import spaces | |
| import torch | |
| from hyvideo.utils.file_utils import save_videos_grid | |
| from hyvideo.utils.preprocess_text_encoder_tokenizer_utils import preprocess_text_encoder_tokenizer | |
| from hyvideo.config import parse_args | |
| from hyvideo.inference import HunyuanVideoSampler | |
| from hyvideo.constants import NEGATIVE_PROMPT | |
| from huggingface_hub import snapshot_download | |
| if torch.cuda.device_count() > 0: | |
| snapshot_download(repo_id="tencent/HunyuanVideo", repo_type="model", local_dir="ckpts", force_download=True) | |
| snapshot_download(repo_id="xtuner/llava-llama-3-8b-v1_1-transformers", repo_type="model", local_dir="ckpts/llava-llama-3-8b-v1_1-transformers", force_download=True) | |
| class Args: | |
| def __init__(self, input_dir, output_dir): | |
| self.input_dir = input_dir | |
| self.output_dir = output_dir | |
| # Create the object | |
| args = Args("ckpts/llava-llama-3-8b-v1_1-transformers", "ckpts/text_encoder") | |
| preprocess_text_encoder_tokenizer(args) | |
| snapshot_download(repo_id="openai/clip-vit-large-patch14", repo_type="model", local_dir="ckpts/text_encoder_2", force_download=True) | |
| def initialize_model(model_path): | |
| print("initialize_model: " + model_path) | |
| if torch.cuda.device_count() == 0: | |
| return None | |
| args = parse_args() | |
| models_root_path = Path(model_path) | |
| if not models_root_path.exists(): | |
| raise ValueError(f"`models_root` not exists: {models_root_path}") | |
| print(f"`models_root` exists: {models_root_path}") | |
| hunyuan_video_sampler = HunyuanVideoSampler.from_pretrained(models_root_path, args=args) | |
| print("Model initialized: " + model_path) | |
| return hunyuan_video_sampler | |
| model = initialize_model("ckpts") | |
| def generate_video( | |
| prompt, | |
| resolution, | |
| video_length, | |
| seed, | |
| num_inference_steps, | |
| guidance_scale, | |
| flow_shift, | |
| embedded_guidance_scale | |
| ): | |
| print("generate_video (prompt: " + prompt + ")") | |
| return generate_video_gpu( | |
| model, | |
| prompt, | |
| resolution, | |
| video_length, | |
| seed, | |
| num_inference_steps, | |
| guidance_scale, | |
| flow_shift, | |
| embedded_guidance_scale | |
| ) | |
| def generate_video_gpu( | |
| model, | |
| prompt, | |
| resolution, | |
| video_length, | |
| seed, | |
| num_inference_steps, | |
| guidance_scale, | |
| flow_shift, | |
| embedded_guidance_scale | |
| ): | |
| print("generate_video_gpu (prompt: " + prompt + ")") | |
| if torch.cuda.device_count() == 0: | |
| gr.Warning("Set this space to GPU config to make it work.") | |
| return None | |
| seed = None if seed == -1 else seed | |
| width, height = resolution.split("x") | |
| width, height = int(width), int(height) | |
| negative_prompt = "" # not applicable in the inference | |
| print("Predicting video...") | |
| outputs = model.predict( | |
| prompt=prompt, | |
| height=height, | |
| width=width, | |
| video_length=video_length, | |
| seed=seed, | |
| negative_prompt=negative_prompt, | |
| infer_steps=num_inference_steps, | |
| guidance_scale=guidance_scale, | |
| num_videos_per_prompt=1, | |
| flow_shift=flow_shift, | |
| batch_size=1, | |
| embedded_guidance_scale=embedded_guidance_scale | |
| ) | |
| print("Video predicted") | |
| samples = outputs["samples"] | |
| sample = samples[0].unsqueeze(0) | |
| save_path = "./gradio_outputs" | |
| os.makedirs(save_path, exist_ok=True) | |
| time_flag = datetime.fromtimestamp(time.time()).strftime("%Y-%m-%d-%H:%M:%S") | |
| video_path = f"{save_path}/{time_flag}_seed{outputs['seeds'][0]}_{outputs['prompts'][0][:100].replace('/','')}.mp4" | |
| save_videos_grid(sample, video_path, fps=24) | |
| logger.info(f"Sample saved to: {video_path}") | |
| print("Return the video") | |
| return video_path | |
| def create_demo(model_path): | |
| with gr.Blocks() as demo: | |
| if torch.cuda.device_count() == 0: | |
| with gr.Row(): | |
| gr.HTML(""" | |
| <p style="background-color: red;"><big><big><big><b>⚠️To use <i>Hunyuan Video</i>, <a href="https://huggingface.co/spaces/Fabrice-TIERCELIN/HunyuanVideo?duplicate=true">duplicate this space</a> and set a GPU with 80 GB VRAM.</b> | |
| You can't use <i>Hunyuan Video</i> directly here because this space runs on a CPU, which is not enough for <i>Hunyuan Video</i>. Please provide <a href="https://huggingface.co/spaces/Fabrice-TIERCELIN/HunyuanVideo/discussions/new">feedback</a> if you have issues. | |
| </big></big></big></p><br/> | |
| <p style="background-color: light-green;"><big>The space has been successfully deployed on A100 space on 2025-01-23. Synchronize your space to fix the errors.</big></p> | |
| """) | |
| gr.Markdown("# Hunyuan Video Generation") | |
| with gr.Row(): | |
| with gr.Column(): | |
| prompt = gr.Textbox(label="Prompt", value="A cat walks on the grass, realistic style.") | |
| with gr.Row(): | |
| resolution = gr.Dropdown( | |
| choices=[ | |
| # 720p | |
| ("1280x720 (16:9, 720p)", "1280x720"), | |
| ("720x1280 (9:16, 720p)", "720x1280"), | |
| ("1104x832 (4:3, 720p)", "1104x832"), | |
| ("832x1104 (3:4, 720p)", "832x1104"), | |
| ("960x960 (1:1, 720p)", "960x960"), | |
| # 540p | |
| ("960x544 (16:9, 540p)", "960x544"), | |
| ("544x960 (9:16, 540p)", "544x960"), | |
| ("832x624 (4:3, 540p)", "832x624"), | |
| ("624x832 (3:4, 540p)", "624x832"), | |
| ("720x720 (1:1, 540p)", "720x720"), | |
| ], | |
| value="832x624", | |
| label="Resolution" | |
| ) | |
| video_length = gr.Dropdown( | |
| label="Video Length", | |
| choices=[ | |
| ("2s(65f)", 65), | |
| ("5s(129f)", 129), | |
| ], | |
| value=65, | |
| ) | |
| num_inference_steps = gr.Slider(1, 100, value=5, step=1, label="Number of Inference Steps") | |
| with gr.Accordion("Advanced Options", open=False): | |
| with gr.Column(): | |
| seed = gr.Slider(label="Seed (-1 for random)", value=-1, minimum=-1, maximum=2**63 - 1, step=1) | |
| guidance_scale = gr.Slider(1.0, 20.0, value=1.0, step=0.5, label="Guidance Scale") | |
| flow_shift = gr.Slider(0.0, 10.0, value=7.0, step=0.1, label="Flow Shift") | |
| embedded_guidance_scale = gr.Slider(1.0, 20.0, value=6.0, step=0.5, label="Embedded Guidance Scale") | |
| generate_btn = gr.Button(value = "🚀 Generate Video", variant = "primary") | |
| with gr.Row(): | |
| output = gr.Video(label = "Generated Video", autoplay = True) | |
| gr.Markdown(""" | |
| ## **Alternatives** | |
| If you can't use _Hunyuan Video_, you can use _[CogVideoX](https://huggingface.co/spaces/THUDM/CogVideoX-5B-Space)_ or _[LTX Video Playground](https://huggingface.co/spaces/Lightricks/LTX-Video-Playground)_ instead. | |
| """) | |
| generate_btn.click( | |
| fn=generate_video, | |
| inputs=[ | |
| prompt, | |
| resolution, | |
| video_length, | |
| seed, | |
| num_inference_steps, | |
| guidance_scale, | |
| flow_shift, | |
| embedded_guidance_scale | |
| ], | |
| outputs=output | |
| ) | |
| return demo | |
| if __name__ == "__main__": | |
| os.environ["GRADIO_ANALYTICS_ENABLED"] = "False" | |
| demo = create_demo("ckpts") | |
| demo.queue(10).launch() |