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Commit 973dd66b authored by gushiqiao's avatar gushiqiao Committed by GitHub
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Merge pull request #85 from ModelTC/dev_ui 

Update gradio
parents 7260cb2e 61a41a95
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import os
import gradio as gr
import asyncio
import argparse
import json
import torch
import gc
from easydict import EasyDict
from loguru import logger
from lightx2v.infer import init_runner
from lightx2v.utils.envs import *
logger.add(
"inference_logs.log",
rotation="100 MB",
encoding="utf-8",
enqueue=True,
backtrace=True,
diagnose=True,
)
SUPPORTED_MODEL = "wan2.1"
TASK = "i2v"
def run_inference(
model_path,
prompt,
negative_prompt,
image_path,
save_video_path,
torch_compile,
infer_steps,
num_frames,
width,
height,
seed,
enable_teacache,
enable_cfg,
cfg_scale,
quant_option,
fps,
use_tiny_vae,
tiny_vae_path,
):
"""Wrapper for wan2.1 I2V inference logic with advanced options"""
if torch_compile:
os.environ["ENABLE_GRAPH_MODE"] = "true"
os.environ["DTYPE"] = "BF16"
os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:true"
config = {
"infer_steps": infer_steps,
"target_video_length": num_frames,
"target_height": height,
"target_width": width,
"attention_type": "sage_attn2",
"seed": seed,
"enable_cfg": enable_cfg,
"sample_guide_scale": cfg_scale,
"sample_shift": 5,
"cpu_offload": True,
"offload_granularity": "phase",
"t5_offload_granularity": "block",
"dit_quantized_ckpt": model_path,
"mm_config": {
"mm_type": ("W-fp8-channel-sym-A-fp8-channel-sym-dynamic-Vllm" if quant_option == "fp8" else "W-int8-channel-sym-A-int8-channel-sym-dynamic-Vllm"),
},
"fps": fps,
"feature_caching": "Tea" if enable_teacache else "NoCaching",
"coefficients": [
[
2.57151496e05,
-3.54229917e04,
1.40286849e03,
-1.35890334e01,
1.32517977e-01,
],
[
-3.02331670e02,
2.23948934e02,
-5.25463970e01,
5.87348440e00,
-2.01973289e-01,
],
],
"use_ret_steps": True,
"teacache_thresh": 0.26,
"t5_quantized": True,
"t5_quantized_ckpt": os.path.join(model_path, "models_t5_umt5-xxl-enc-fp8.pth"),
"t5_quant_scheme": "fp8",
"clip_quantized": True,
"clip_quantized_ckpt": os.path.join(model_path, "clip-fp8.pth"),
"clip_quant_scheme": "fp8",
"use_tiling_vae": True,
"tiny_vae": use_tiny_vae,
"tiny_vae_path": tiny_vae_path if use_tiny_vae else None,
"lazy_load": True,
"do_mm_calib": False,
"parallel_attn_type": None,
"parallel_vae": False,
"max_area": False,
"vae_stride": (4, 8, 8),
"patch_size": (1, 2, 2),
"teacache_thresh": 0.26,
"use_bfloat16": True,
"lora_path": None,
"strength_model": 1.0,
"use_prompt_enhancer": False,
"text_len": 512,
}
args = argparse.Namespace(
model_cls=SUPPORTED_MODEL,
task=TASK,
model_path=model_path,
prompt_enhancer=None,
prompt=prompt,
negative_prompt=negative_prompt,
image_path=image_path,
save_video_path=save_video_path,
)
config.update({k: v for k, v in vars(args).items()})
config = EasyDict(config)
config["mode"] = "infer"
if os.path.exists(os.path.join(model_path, "config.json")):
with open(os.path.join(model_path, "config.json"), "r") as f:
model_config = json.load(f)
config.update(model_config)
logger.info(f"Updated inference config:\n{json.dumps(config, indent=4, ensure_ascii=False)}")
runner = init_runner(config)
asyncio.run(runner.run_pipeline())
del runner
torch.cuda.empty_cache()
gc.collect()
return save_video_path
with gr.Blocks(
title="Wan2.1 I2V Video Generation",
css="""
.advanced-options { background: #f9f9ff; border-radius: 10px; padding: 15px; }
.output-video { max-height: 650px; }
.warning { color: #ff6b6b; font-weight: bold; }
""",
) as demo:
gr.Markdown("# 🎬 Wan2.1 Image-to-Video (I2V) Generator")
with gr.Row():
with gr.Column(scale=4):
with gr.Group():
gr.Markdown("## 📥 Input Parameters")
with gr.Row():
image_path = gr.Image(
label="Input Image",
type="filepath",
height=300,
interactive=True,
)
model_path = gr.Textbox(
label="Model Path",
placeholder="/your/path/to/wan2.1_quant_model",
info="Local model folder path (in8/fp8 quantization supported)",
)
with gr.Row():
with gr.Column():
prompt = gr.Textbox(
label="Prompt",
lines=3,
placeholder="Describe the video content...",
max_lines=5,
)
with gr.Column():
negative_prompt = gr.Textbox(
label="Negative Prompt",
lines=3,
placeholder="Unwanted content...",
max_lines=5,
value="镜头晃动，色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走",
)
with gr.Column():
tiny_vae_path = gr.Textbox(
label="Tiny vae path",
lines=3,
placeholder="/your/path/to/tiny_vae.pth",
max_lines=5,
)
save_video_path = gr.Textbox(
label="Output Video Path",
value="./save_results/wan2.1_i2v_output.mp4",
info="Must include .mp4 suffix",
)
with gr.Accordion("⚙️ Advanced Options", open=False):
with gr.Group(elem_classes="advanced-options"):
gr.Markdown("### Performance Settings")
with gr.Row():
torch_compile = gr.Checkbox(
label="Enable Torch Compile",
value=False,
info="Use torch.compile for faster inference",
)
quant_option = gr.Radio(
label="Quantization Method",
choices=["fp8", "int8"],
value="fp8",
info="Select quantization method for model",
)
infer_steps = gr.Slider(
label="Inference Steps",
minimum=1,
maximum=100,
step=1,
value=20,
info="Infer steps for video generation",
)
enable_teacache = gr.Checkbox(
label="Enable Teacache",
value=False,
info="Teacache for caching features during inference",
)
enable_cfg = gr.Checkbox(
label="Enable CFG",
value=False,
info="Classifier-Free Guidance for prompt strength control",
)
use_tiny_vae = gr.Checkbox(
label="Use Tiny VAE",
value=False,
info="Tiny VAE for faster inference",
)
cfg_scale = gr.Slider(
label="CFG scale",
minimum=1,
maximum=100,
step=1,
value=5,
info="CFG scale for controlling the strength of the prompt",
)
seed = gr.Slider(
label="Seed",
minimum=-10000000,
maximum=10000000,
step=1,
value=42,
info="Random seed for reproducibility",
)
gr.Markdown("### Video Parameters")
with gr.Row():
fps = gr.Slider(
label="FPS (Frames Per Second)",
minimum=8,
maximum=30,
step=1,
value=16,
info="Higher FPS = smoother video",
)
num_frames = gr.Slider(
label="Number of Frames",
minimum=16,
maximum=120,
step=1,
value=81,
info="More frames = longer video",
)
with gr.Row():
width = gr.Number(
label="Width",
value=832,
precision=0,
minimum=320,
maximum=1920,
info="Output video width",
)
height = gr.Number(
label="Height",
value=480,
precision=0,
minimum=240,
maximum=1080,
info="Output video height",
)
gr.Markdown(
"""
<div class="warning">
⚠️ Note: Changing resolution may affect video quality and performance
</div>
"""
)
infer_btn = gr.Button("Generate Video", variant="primary", size="lg")
with gr.Column(scale=6):
gr.Markdown("## 📤 Generated Video")
output_video = gr.Video(
label="Result",
height=624,
width=360,
autoplay=True,
elem_classes=["output-video"],
)
infer_btn.click(
fn=run_inference,
inputs=[
model_path,
prompt,
negative_prompt,
image_path,
save_video_path,
torch_compile,
infer_steps,
num_frames,
width,
height,
seed,
enable_teacache,
enable_cfg,
cfg_scale,
quant_option,
fps,
use_tiny_vae,
tiny_vae_path,
],
outputs=output_video,
)
if __name__ == "__main__":
demo.launch(share=True, server_port=7862, server_name="0.0.0.0")
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