app.py

import spaces
import gradio as gr
import torch
from PIL import Image
from diffusers.utils import load_image
from pipeline import FluxConditionalPipeline
from transformer import FluxTransformer2DConditionalModel
from garment_pipeline import generate_with_garment
from recaption import enhance_prompt, enhance_garment_prompt
import os

pipe = None
CHECKPOINT = "primecai/dsd_model"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
dtype = torch.bfloat16 if torch.cuda.is_available() else torch.float32

transformer = FluxTransformer2DConditionalModel.from_pretrained(
    CHECKPOINT,
    subfolder="transformer",
    torch_dtype=dtype,
    low_cpu_mem_usage=False,
    ignore_mismatched_sizes=True,
    use_auth_token=os.getenv("HF_TOKEN"),
)

pipe = FluxConditionalPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-dev",
    transformer=transformer,
    torch_dtype=dtype,
    use_auth_token=os.getenv("HF_TOKEN"),
)

pipe.load_lora_weights(
    CHECKPOINT,
    weight_name="pytorch_lora_weights.safetensors",
    use_auth_token=os.getenv("HF_TOKEN"),
)

pipe.to(device, dtype=dtype)

@spaces.GPU
def generate_image(
    image: Image.Image,
    text: str,
    gemini_prompt: bool = True,
    guidance: float = 3.5,
    i_guidance: float = 1.0,
    t_guidance: float = 1.0
):
    w, h, min_size = image.size[0], image.size[1], min(image.size)
    image = image.crop(
        ((w - min_size) // 2, (h - min_size) // 2, (w + min_size) // 2, (h + min_size) // 2)
    ).resize((512, 512))
    
    control_image = load_image(image)
    result_image = pipe(
        prompt=text.strip(),
        negative_prompt="",
        num_inference_steps=28,
        height=512,
        width=1024,
        guidance_scale=guidance,
        image=control_image,
        guidance_scale_real_i=i_guidance,
        guidance_scale_real_t=t_guidance,
        gemini_prompt=gemini_prompt,
    ).images[0]
    
    return result_image

@spaces.GPU
def generate_with_garment_interface(
    garment_image: Image.Image,
    text: str,
    gemini_prompt: bool = True,
    guidance: float = 3.5,
    i_guidance: float = 1.5,  # Default higher to maintain garment fidelity
    t_guidance: float = 1.0
):
    """Interface function for generating images with a garment"""
    # Use garment-specific prompt enhancement if enabled
    if gemini_prompt:
        text = enhance_garment_prompt(garment_image, text)
    
    # Call the garment-specific generation function
    result_image = generate_with_garment(
        pipe=pipe,
        garment_image=garment_image,
        text=text,
        gemini_prompt=False,  # Already enhanced above if needed
        guidance=guidance,
        i_guidance=i_guidance,
        t_guidance=t_guidance,
        device=device
    )
    
    return result_image

def get_samples():
    sample_list = [
        {
            "image": "assets/hf-logo.png",
            "text": "This item, holding a sign that reads 'DSD!', is placed on a shiny glass table.",
        },
        {
            "image": "assets/seededit_example.png",
            "text": "an adorable small creature with big round orange eyes, fluffy brown fur, wearing a blue scarf with a golden charm, sitting atop a towering stack of colorful books in the middle of a vibrant futuristic city street with towering buildings and glowing neon signs, soft daylight illuminating the scene, detailed and whimsical 3D style.",
        },
        {
            "image": "assets/wanrong_character.png",
            "text": "A chibi-style girl with pink hair, green eyes, wearing a black and gold ornate dress, dancing gracefully in a flower garden, anime art style with clean and detailed lines.",
        },
        {
            "image": "assets/ben_character_squared.png",
            "text": "A confident green-eye young woman with platinum blonde hair in a high ponytail, wearing an oversized orange jacket and black pants, is striking a dynamic pose, anime-style with sharp details and vibrant colors.",
        },
        {
            "image": "assets/action_hero_figure.jpeg",
            "text": "A cartoonish muscular action hero figure with long blue hair and red headband sits on a crowded sidewalk on a Christmas evening, covered in snow and wearing a Christmas hat, holding a sign that reads 'DSD!', dramatic cinematic lighting, close-up view, 3D-rendered in a stylized, vibrant art style.",
        },
        {
            "image": "assets/anime_soldier.jpeg",
            "text": "An adorable cartoon goat soldier sits under a beach umbrella with 'DSD!' written on it, bright teal background with soft lighting, 3D-rendered in a playful and vibrant art style.",
        },
        {
            "image": "assets/goat_logo.jpeg",
            "text": "A shirt with this logo on it.",
        },
        {
            "image": "assets/cartoon_cat.png",
            "text": "A cheerful cartoon orange cat sits under a beach umbrella with 'DSD!' written on it under a sunny sky, simplistic and humorous comic art style.",
        },
    ]
    return [
        [
            Image.open(sample["image"]),
            sample["text"],
        ]
        for sample in sample_list
    ]

demo = gr.Blocks()

with demo:
    gr.HTML(
        """
        <div style="text-align: center;">
            <h2>Diffusion Self-Distillation (beta)</h2>
            <a href="https://primecai.github.io/dsd/" target="_blank"><img src="https://img.shields.io/badge/Project-Website-blue" style="display:inline-block;"></a>
            <a href="https://github.com/primecai/diffusion-self-distillation" target="_blank"><img src="https://img.shields.io/github/stars/primecai/diffusion-self-distillation?label=GitHub%20%E2%98%85&logo=github&color=green" style="display:inline-block;"></a>
            <a href="https://huggingface.co./papers/2411.18616" target="_blank"><img src="https://img.shields.io/badge/🤗%20Hugging%20Face-Space-yellow" style="display:inline-block;"></a>
            <a href="https://huggingface.co./datasets/primecai/dsd_data" target="_blank"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace%20-Data-yellow" style="display:inline-block;"></a>
            <a href="https://huggingface.co./datasets/primecai/dsd_model" target="_blank"><img src="https://img.shields.io/badge/🤗%20Hugging%20Face%20-Model-green" style="display:inline-block;"></a>
            <a href="https://x.com/prime_cai?lang=en" target="_blank"><img src="https://img.shields.io/twitter/follow/prime_cai?style=social" style="display:inline-block;"></a>
        </div>
        """
    )
    
    with gr.Tabs():
        with gr.TabItem("Standard Generation"):
            iface = gr.Interface(
                fn=generate_image,
                inputs=[
                    gr.Image(type="pil", width=512),
                    gr.Textbox(lines=2, label="text"),
                    gr.Checkbox(label="Gemini prompt", value=True),
                    gr.Slider(minimum=1.0, maximum=6.0, step=0.5, value=3.5, label="guidance scale"),
                    gr.Slider(minimum=1.0, maximum=2.0, step=0.05, value=1.5, label="real guidance scale for image"),
                    gr.Slider(minimum=1.0, maximum=2.0, step=0.05, value=1.0, label="real guidance scale for prompt"),
                ],
                outputs=gr.Image(type="pil"),
                live=False,
            )
            gr.Examples(
                examples=get_samples(),
                inputs=iface.input_components,
                outputs=iface.output_components,
                run_on_click=False  # Prevents auto-submission
            )
            
        with gr.TabItem("Garment Generation"):
            garment_iface = gr.Interface(
                fn=generate_with_garment_interface,
                inputs=[
                    gr.Image(type="pil", width=512, label="Garment Image"),
                    gr.Textbox(lines=2, label="Model and Background Description"),
                    gr.Checkbox(label="Enhance Prompt", value=True),
                    gr.Slider(minimum=1.0, maximum=6.0, step=0.5, value=3.5, label="guidance scale"),
                    gr.Slider(minimum=1.0, maximum=2.0, step=0.05, value=1.5, label="garment fidelity"),
                    gr.Slider(minimum=1.0, maximum=2.0, step=0.05, value=1.0, label="prompt adherence"),
                ],
                outputs=gr.Image(type="pil"),
                live=False,
                description="Generate an image of a model wearing the provided garment in a new setting",
            )
    
    gr.HTML(
        """
        <div style="text-align: center;">
            * We borrowed some prompts from the awesome <a href="https://arxiv.org/abs/2411.15098" target="_blank">OminiControl</a>.
        </div>
        """
    )

if __name__ == "__main__":
    demo.launch(debug=False, share=True)