Merge pull request #91 from hntee/th_gradio

gradio server update

README.md

@@ -254,6 +254,14 @@ python3 sample_video.py \
     --save-path ./results
 ```
 
+### Run a Gradio Server
+```bash
+python3 gradio_server.py --flow-reverse
+
+# set SERVER_NAME and SERVER_PORT manually
+# SERVER_NAME=0.0.0.0 SERVER_PORT=8081 python3 gradio_server.py --flow-reverse
+```
+
 ### More Configurations
 
 We list some more useful configurations for easy usage:

README_zh.md

@@ -243,6 +243,14 @@ python3 sample_video.py \
     --save-path ./results
 ```
 
+### 运行gradio服务
+```bash
+python3 gradio_server.py --flow-reverse
+
+# set SERVER_NAME and SERVER_PORT manually
+# SERVER_NAME=0.0.0.0 SERVER_PORT=8081 python3 gradio_server.py --flow-reverse
+```
+
 ### 更多配置
 
 下面列出了更多关键配置项：

gradio_server.py

@@ -0,0 +1,141 @@
+import os
+import time
+from pathlib import Path
+from loguru import logger
+from datetime import datetime
+import gradio as gr
+import random
+
+from hyvideo.utils.file_utils import save_videos_grid
+from hyvideo.config import parse_args
+from hyvideo.inference import HunyuanVideoSampler
+from hyvideo.constants import NEGATIVE_PROMPT
+
+def initialize_model(model_path):
+    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}")
+
+    hunyuan_video_sampler = HunyuanVideoSampler.from_pretrained(models_root_path, args=args)
+    return hunyuan_video_sampler
+
+def generate_video(
+    model,
+    prompt,
+    resolution,
+    video_length,
+    seed,
+    num_inference_steps,
+    guidance_scale,
+    flow_shift,
+    embedded_guidance_scale
+):
+    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
+
+    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
+    )
+
+    samples = outputs['samples']
+    sample = samples[0].unsqueeze(0)
+
+    save_path = os.path.join(os.getcwd(), "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}')
+
+    return video_path
+
+def create_demo(model_path, save_path):
+    model = initialize_model(model_path)
+
+    with gr.Blocks() as demo:
+        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="1280x720",
+                        label="Resolution"
+                    )
+                    video_length = gr.Dropdown(
+                        label="Video Length",
+                        choices=[
+                            ("2s(65f)", 65),
+                            ("5s(129f)", 129),
+                        ],
+                        value=129,
+                    )
+                num_inference_steps = gr.Slider(1, 100, value=50, step=1, label="Number of Inference Steps")
+                show_advanced = gr.Checkbox(label="Show Advanced Options", value=False)
+                with gr.Row(visible=False) as advanced_row:
+                    with gr.Column():
+                        seed = gr.Number(value=-1, label="Seed (-1 for random)")
+                        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")
+                show_advanced.change(fn=lambda x: gr.Row(visible=x), inputs=[show_advanced], outputs=[advanced_row])
+                generate_btn = gr.Button("Generate")
+
+            with gr.Column():
+                output = gr.Video(label="Generated Video")
+
+        generate_btn.click(
+            fn=lambda *inputs: generate_video(model, *inputs),
+            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"
+    server_name = os.getenv("SERVER_NAME", "0.0.0.0")
+    server_port = int(os.getenv("SERVER_PORT", "8081"))
+    args = parse_args()
+    print(args)
+    demo = create_demo(args.model_base, args.save_path)
+    demo.launch(server_name=server_name, server_port=server_port)

requirements.txt

@@ -12,3 +12,5 @@ loguru==0.7.2
 imageio==2.34.0
 imageio-ffmpeg==0.5.1
 safetensors==0.4.3
+gradio==4.43.0
+urllib3==1.26.6