app.py

import gradio as gr
import torch
# import spaces
from diffusers import FluxInpaintPipeline
from PIL import Image, ImageFile

#ImageFile.LOAD_TRUNCATED_IMAGES = True

# Initialize the pipeline
pipe = FluxInpaintPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-dev", 
    torch_dtype=torch.bfloat16
)
pipe.to("cuda")
pipe.load_lora_weights(
    "ali-vilab/In-Context-LoRA", 
    weight_name="visual-identity-design.safetensors"
)

def square_center_crop(img, target_size=768):
    if img.mode in ('RGBA', 'P'):
        img = img.convert('RGB')

    width, height = img.size
    crop_size = min(width, height)

    left = (width - crop_size) // 2
    top = (height - crop_size) // 2
    right = left + crop_size
    bottom = top + crop_size

    img_cropped = img.crop((left, top, right, bottom))
    return img_cropped.resize((target_size, target_size), Image.Resampling.LANCZOS)

def duplicate_horizontally(img):
    width, height = img.size
    if width != height:
        raise ValueError(f"Input image must be square, got {width}x{height}")

    new_image = Image.new('RGB', (width * 2, height))
    new_image.paste(img, (0, 0))
    new_image.paste(img, (width, 0))
    return new_image


# @spaces.GPU
def generate(image, prompt_description, prompt_user, progress=gr.Progress(track_tqdm=True)):
    prompt_structure = "The two-panel image showcases the logo on the left and the application on the right, [LEFT] the left panel is showing "+prompt_description+" [RIGHT] this logo is applied to "
    prompt = prompt_structure + prompt_user 
    
    mask = Image.open("mask_square.png")
    cropped_image = square_center_crop(image)
    logo_dupli = duplicate_horizontally(cropped_image)

    out = pipe(
        prompt=prompt,
        image=logo_dupli,
        mask_image=mask,
        guidance_scale=3.5,
        height=768,
        width=1536,
        num_inference_steps=28,
        max_sequence_length=256,
        strength=1
    ).images[0]

    width, height = out.size
    half_width = width // 2
    image_2 = out.crop((half_width, 0, width, height))
    return image_2, out

with gr.Blocks() as demo:
    gr.Markdown("# Logo in Context")
    gr.Markdown("### [In-Context LoRA](https://huggingface.co/ali-vilab/In-Context-LoRA) + Image-to-Image + Inpainting, apply your logo to anything. diffusers implementation based on the [workflow by WizardWhitebeard/klinter](https://civitai.com/articles/8779)")

    with gr.Tab("Demo"):
        with gr.Row():
            with gr.Column():
                input_image = gr.Image(
                    label="Upload Logo Image",
                    type="pil",
                    height=384
                )
                prompt_description = gr.Textbox(
                    label="Describe your logo",
                    placeholder="A Hugging Face emoji logo",
                )
                prompt_input = gr.Textbox(
                    label="Where should the logo be applied?",
                    placeholder="e.g., a coffee cup on a wooden table"
                )
                generate_btn = gr.Button("Generate Application", variant="primary")
    
            with gr.Column():
                output_image = gr.Image(label="Generated Application")
                output_side = gr.Image(label="Side by side")
        
        gr.Examples(
            examples=[
                ["huggingface.png", "A Hugging Face emoji logo", "An embroidered hat"],
                ["awesome.png", "An awesome face logo", "A tattoo on a leg"],
                ["dvd_logo.png", "A DVD logo", "a coconut, engraved logo on a green coconut"]
            ],
            inputs=[input_image, prompt_description, prompt_input],
            outputs=[output_image, output_side],
            fn=generate,
            cache_examples="lazy"
        )
        
        with gr.Row():
            gr.Markdown("""
            ### Instructions:
            1. Upload a logo image (preferably square)
            2. Describe where you'd like to see the logo applied
            3. Click 'Generate Application' and wait for the result
    
            Note: The generation process might take a few moments.
            """)
    
    with gr.Tab("🧨 diffusers implementation"):
        gr.Markdown("The way this works is combining the [IC LoRA](https://github.com/ali-vilab/In-Context-LoRA) with image-to-image + inpainting. Where the image on the left (the logo) is uploaded by the user, and the image on the right is masked and applied on the product by the LoRA. Based on the [ComfyUI workflow by WizardWhitebeard/klinter](https://civitai.com/articles/8779). Below is a diffusers implementation of the idea")
        
        gr.Code(language="python", value="""# Support functions
def square_center_crop(img, target_size=768):
    if img.mode in ('RGBA', 'P'):
        img = img.convert('RGB')
    
    width, height = img.size
    crop_size = min(width, height)
    
    left = (width - crop_size) // 2
    top = (height - crop_size) // 2
    right = left + crop_size
    bottom = top + crop_size
    
    img_cropped = img.crop((left, top, right, bottom))
    return img_cropped.resize((target_size, target_size), Image.Resampling.LANCZOS)

def duplicate_horizontally(img):
    width, height = img.size
    if width != height:
        raise ValueError(f"Input image must be square, got {width}x{height}")
    
    new_image = Image.new('RGB', (width * 2, height))
    new_image.paste(img, (0, 0))
    new_image.paste(img, (width, 0))
    return new_image"""
        )
        
        gr.Code(language="python", value="""import torch
from diffusers import FluxInpaintPipeline
from PIL import Image

pipe = FluxInpaintPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16)
pipe.to("cuda")
pipe.load_lora_weights("ali-vilab/In-Context-LoRA", weight_name="visual-identity-design.safetensors")

mask = load_image("mask_square.png")
image = load_image("the_logo.png")
cropped_image = square_center_crop(image) #crop the image you upload to square
logo_dupli = duplicate_horizontally(cropped_image) #duplicate it so the right side can be masked

prompt_structure = "The two-panel image showcases the logo of a brand, [LEFT] the left panel is showing the logo [RIGHT] the right panel has this logo applied to "
prompt = prompt_structure + "an coconut, engraved logo on a green coconut"
out = pipe(
    prompt=prompt,
    image=logo_dupli,
    mask_image=mask,
    guidance_scale=6,
    height=768,
    width=1536,
    num_inference_steps=28,
    max_sequence_length=256,
    strength=1
).images[0]"""
        )
        
    # Set up the click event
    generate_btn.click(
        fn=generate,
        inputs=[input_image, prompt_description, prompt_input],
        outputs=[output_image, output_side]
    )

demo.launch()