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 ) @spaces.GPU(duration=120) 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("""

⚠️To use Hunyuan Video, duplicate this space and set a GPU with 80 GB VRAM. You can't use Hunyuan Video directly here because this space runs on a CPU, which is not enough for Hunyuan Video. Please provide feedback if you have issues.



The space has been successfully deployed on A100 space. Then we can discuss about the quality of the generation.

""") 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()