app.py

import spaces
import gradio as gr
from sd3_pipeline import StableDiffusion3Pipeline
import torch
import random
import numpy as np
import os
import gc
from diffusers import AutoencoderKLWan
from wan_pipeline import WanPipeline
from diffusers.schedulers.scheduling_unipc_multistep import UniPCMultistepScheduler
from PIL import Image
from diffusers.utils import export_to_video
from huggingface_hub import login

# Authenticate with HF
login(token=os.getenv('HF_TOKEN'))

def set_seed(seed):
    random.seed(seed)
    os.environ['PYTHONHASHSEED'] = str(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)

model_paths = {
    "sd3": "stabilityai/stable-diffusion-3-medium-diffusers",
    "sd3.5": "stabilityai/stable-diffusion-3.5-large",
    # "wan-t2v": "Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
}

current_model = None
OUTPUT_DIR = "generated_videos"
os.makedirs(OUTPUT_DIR, exist_ok=True)

def load_model(model_name):
    global current_model
    if current_model is not None:
        del current_model
        torch.cuda.empty_cache()
        gc.collect()

    if "wan-t2v" in model_name:
        vae = AutoencoderKLWan.from_pretrained(model_paths[model_name], subfolder="vae", torch_dtype=torch.bfloat16)
        scheduler = UniPCMultistepScheduler(prediction_type='flow_prediction', use_flow_sigmas=True, num_train_timesteps=1000, flow_shift=8.0)
        current_model = WanPipeline.from_pretrained(model_paths[model_name], vae=vae, torch_dtype=torch.float16).to("cuda")
        current_model.scheduler = scheduler
    else:
        current_model = StableDiffusion3Pipeline.from_pretrained(model_paths[model_name], torch_dtype=torch.bfloat16).to("cuda")

    return current_model.to("cuda")

@spaces.GPU(duration=120)
def generate_content(prompt, model_name, guidance_scale=7.5, num_inference_steps=50,
                     use_cfg_zero_star=True, use_zero_init=True, zero_steps=0,
                     seed=None, compare_mode=False):
    
    model = load_model(model_name)
    if seed is None:
        seed = random.randint(0, 2**32 - 1)
    set_seed(seed)

    is_video_model = "wan-t2v" in model_name
    print('prompt: ',prompt)
    if is_video_model:
        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"
        video1_frames = model(
            prompt=prompt,
            negative_prompt=negative_prompt,
            height=480,
            width=832,
            num_frames=81,
            num_inference_steps=num_inference_steps,
            guidance_scale=guidance_scale,
            use_cfg_zero_star=True,
            use_zero_init=True,
            zero_steps=zero_steps
        ).frames[0]
        video1_path = os.path.join(OUTPUT_DIR, f"{seed}_CFG-Zero-Star.mp4")
        export_to_video(video1_frames, video1_path, fps=16)

        return None, None, video1_path, seed

    if compare_mode:
        set_seed(seed)
        image1 = model(
            prompt,
            guidance_scale=guidance_scale,
            num_inference_steps=num_inference_steps,
            use_cfg_zero_star=True,
            use_zero_init=use_zero_init,
            zero_steps=zero_steps
        ).images[0]

        set_seed(seed)
        image2 = model(
            prompt,
            guidance_scale=guidance_scale,
            num_inference_steps=num_inference_steps,
            use_cfg_zero_star=False,
            use_zero_init=use_zero_init,
            zero_steps=zero_steps
        ).images[0]

        return image1, image2, seed
    else:
        image = model(
            prompt,
            guidance_scale=guidance_scale,
            num_inference_steps=num_inference_steps,
            use_cfg_zero_star=use_cfg_zero_star,
            use_zero_init=use_zero_init,
            zero_steps=zero_steps
        ).images[0]

        if use_cfg_zero_star:
            return image, None, seed
        else:
            return None, image, seed

# Gradio UI with left-right layout
with gr.Blocks() as demo:
    gr.HTML("""
        <div style="text-align: center; font-size: 32px; font-weight: bold; margin-bottom: 20px;">
            CFG-Zero*: Improved Classifier-Free Guidance for Flow Matching Models
        </div>
        <div style="text-align: center;">
            <a href="https://github.com/WeichenFan/CFG-Zero-star">Code</a> |
            <a href="https://arxiv.org/abs/2503.18886">Paper</a>
        </div>
    """)

    with gr.Row():
        with gr.Column(scale=1):
            prompt = gr.Textbox(value="A spooky haunted mansion on a hill silhouetted by a full moon.", label="Enter your prompt")
            model_choice = gr.Dropdown(choices=list(model_paths.keys()), label="Choose Model")
            guidance_scale = gr.Slider(1, 20, value=4.0, step=0.5, label="Guidance Scale")
            inference_steps = gr.Slider(10, 100, value=50, step=5, label="Inference Steps")
            use_opt_scale = gr.Checkbox(value=True, label="Use Optimized-Scale")
            use_zero_init = gr.Checkbox(value=True, label="Use Zero Init")
            zero_steps = gr.Slider(0, 20, value=1, step=1, label="Zero out steps")
            seed = gr.Number(value=42, label="Seed (Leave blank for random)")
            compare_mode = gr.Checkbox(value=True, label="Compare Mode")
            generate_btn = gr.Button("Generate")

        with gr.Column(scale=2):
            out1 = gr.Image(type="pil", label="CFG-Zero* Image")
            out2 = gr.Image(type="pil", label="CFG Image")
            #video = gr.Video(label="Video")
            used_seed = gr.Textbox(label="Used Seed")

    def update_params(model_name):
        print('model_name: ',model_name)
        if model_name == "wan-t2v":
            return (
                gr.update(value=5),
                gr.update(value=50),
                gr.update(value=True),
                gr.update(value=True),
                gr.update(value=1)
            )
        else:
            return (
                gr.update(value=4.0),
                gr.update(value=50),
                gr.update(value=True),
                gr.update(value=True),
                gr.update(value=1)
            )

    model_choice.change(
        fn=update_params,
        inputs=[model_choice],
        outputs=[guidance_scale, inference_steps, use_opt_scale, use_zero_init, zero_steps]
    )

    generate_btn.click(
        fn=generate_content,
        inputs=[
            prompt, model_choice, guidance_scale, inference_steps,
            use_opt_scale, use_zero_init, zero_steps, seed, compare_mode
        ],
        outputs=[out1, out2, used_seed]
    )

demo.launch(ssr_mode=False)