app.py

import spaces
import gradio as gr
import torch
from PIL import Image
from transformers import AutoProcessor
from longcat_image.models import LongCatImageTransformer2DModel
from longcat_image.pipelines import LongCatImageEditPipeline, LongCatImagePipeline
import numpy as np
import random

import os
import requests
import tempfile
import shutil
from urllib.parse import urlparse


MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 2048


# --- Model Loading ---
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

# Text-to-Image Model
t2i_model_id = 'meituan-longcat/LongCat-Image'
print(f"🔄 Loading Text-to-Image model from {t2i_model_id}...")
t2i_text_processor = AutoProcessor.from_pretrained(
    t2i_model_id,
    subfolder='tokenizer'
)

t2i_transformer = LongCatImageTransformer2DModel.from_pretrained(
    t2i_model_id,
    subfolder='transformer',
    torch_dtype=torch.bfloat16,
    use_safetensors=True
).to(device)

pipe = LongCatImagePipeline.from_pretrained(
    t2i_model_id,
    transformer=t2i_transformer,
    text_processor=t2i_text_processor,
)
pipe.to(device, torch.bfloat16)

print(f"✅ Text-to-Image model loaded successfully")

# Image Edit Model
edit_model_id = 'meituan-longcat/LongCat-Image-Edit'
print(f"🔄 Loading Image Edit model from {edit_model_id}...")
edit_text_processor = AutoProcessor.from_pretrained(
    edit_model_id,
    subfolder='tokenizer'
)

edit_transformer = LongCatImageTransformer2DModel.from_pretrained(
    edit_model_id,
    subfolder='transformer',
    torch_dtype=torch.bfloat16,
    use_safetensors=True
).to(device)

edit_pipe = LongCatImageEditPipeline.from_pretrained(
    edit_model_id,
    transformer=edit_transformer,
    text_processor=edit_text_processor,
)
edit_pipe.to(device, torch.bfloat16)

print(f"✅ Image Edit model loaded successfully on {device}")
def load_lora_auto(pipe, lora_input):
    lora_input = lora_input.strip()
    if not lora_input:
        return

    # If it's just an ID like "author/model"
    if "/" in lora_input and not lora_input.startswith("http"):
        pipe.load_lora_weights(lora_input)
        return

    if lora_input.startswith("http"):
        url = lora_input

        # Repo page (no blob/resolve)
        if "huggingface.co" in url and "/blob/" not in url and "/resolve/" not in url:
            repo_id = urlparse(url).path.strip("/")
            pipe.load_lora_weights(repo_id)
            return

        # Blob link → convert to resolve link
        if "/blob/" in url:
            url = url.replace("/blob/", "/resolve/")

        # Download direct file
        tmp_dir = tempfile.mkdtemp()
        local_path = os.path.join(tmp_dir, os.path.basename(urlparse(url).path))

        try:
            print(f"Downloading LoRA from {url}...")
            resp = requests.get(url, stream=True)
            resp.raise_for_status()
            with open(local_path, "wb") as f:
                for chunk in resp.iter_content(chunk_size=8192):
                    f.write(chunk)
            print(f"Saved LoRA to {local_path}")
            pipe.load_lora_weights(local_path)
        finally:
            shutil.rmtree(tmp_dir, ignore_errors=True)

@spaces.GPU(duration=120)
def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=4, num_inference_steps=28, lora_id=None, lora_scale=0.95, progress=gr.Progress(track_tqdm=True)):
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    generator = torch.Generator().manual_seed(seed)

    
    if lora_id and lora_id.strip() != "":
        pipe.unload_lora_weights()
        load_lora_auto(pipe, lora_id)
    
    try:
        image = pipe(
        prompt=prompt,
        negative_prompt="",
        width=width,
        height=height,
        num_inference_steps=num_inference_steps,
        generator=generator,
        guidance_scale=guidance_scale
    ).images[0]
        print("Image Generation Completed for: ", prompt, lora_id)
        return image, seed
    finally:
        # Unload LoRA weights if they were loaded
        if lora_id:
            pipe.unload_lora_weights()

@spaces.GPU(duration=120)
def edit_image(
    input_image: Image.Image,
    prompt: str,
    seed: int,
    progress=gr.Progress()
):
    """Edit image based on text prompt"""
    if input_image is None:
        raise gr.Error("Please upload an image first")
    if not prompt or prompt.strip() == "":
        raise gr.Error("Please enter an edit instruction")
    try:
        progress(0.1, desc="Preparing image...")
        if input_image.mode != 'RGB':
            input_image = input_image.convert('RGB')
        progress(0.2, desc="Generating edited image...")
        generator = torch.Generator("cuda" if torch.cuda.is_available() else "cpu").manual_seed(seed)
        with torch.inference_mode():
            output = edit_pipe(
                input_image,
                prompt,
                negative_prompt="",
                guidance_scale=4.5,
                num_inference_steps=50,
                num_images_per_prompt=1,
                generator=generator
            )
        progress(1.0, desc="Done!")
        return output.images[0]
    except Exception as e:
        raise gr.Error(f"Error during image editing: {str(e)}")


examples = [
    "a tiny astronaut hatching from an egg on the moon",
    "a cat holding a sign that says hello world",
    "an anime illustration of a wiener schnitzel",
]
    
css = """
#col-container {
   margin: 0 auto;
   max-width: 960px;
}
.generate-btn {
   background: linear-gradient(90deg, #4B79A1 0%, #283E51 100%) !important;
   border: none !important;
   color: white !important;
}
.generate-btn:hover {
   transform: translateY(-2px);
   box-shadow: 0 5px 15px rgba(0,0,0,0.2);
}
"""

with gr.Blocks(css=css) as app:
    gr.HTML("<center><h1>LongCat-Image 6B</h1></center>")
    with gr.Column(elem_id="col-container"):
        with gr.Row():
            with gr.Column():
                with gr.Row():
                    text_prompt = gr.Textbox(label="Prompt", placeholder="Enter a prompt here", lines=3, elem_id="prompt-text-input")
                # with gr.Row():
                #     custom_lora = gr.Textbox(label="Custom LoRA (optional)", info="URL or the path to the LoRA weights", placeholder="kudzueye/boreal-qwen-image")
                with gr.Row():
                    with gr.Accordion("Advanced Settings", open=False):
            
                        with gr.Row():
                            custom_lora = gr.Textbox(label="Custom LoRA (optional)", info="URL or the path to the LoRA weights", placeholder="kudzueye/boreal-qwen-image")
                            lora_scale = gr.Slider(
                                label="LoRA Scale",
                                minimum=0,
                                maximum=2,
                                step=0.01,
                                value=1,
                            )
                        with gr.Row():
                            width = gr.Slider(label="Width", value=1024, minimum=64, maximum=2048, step=16)
                            height = gr.Slider(label="Height", value=1024, minimum=64, maximum=2048, step=16)
                        seed = gr.Slider(label="Seed", value=-1, minimum=-1, maximum=4294967296, step=1)
                        randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
                        with gr.Row():
                            steps = gr.Slider(label="Inference steps steps", value=28, minimum=1, maximum=100, step=1)
                            cfg = gr.Slider(label="Guidance Scale", value=4.5, minimum=1, maximum=20, step=0.5)
                        # method = gr.Radio(label="Sampling method", value="DPM++ 2M Karras", choices=["DPM++ 2M Karras", "DPM++ SDE Karras", "Euler", "Euler a", "Heun", "DDIM"])

                with gr.Row():
                    # text_button = gr.Button("Run", variant='primary', elem_id="gen-button")
                    text_button = gr.Button("✨ Generate Image", variant='primary', elem_classes=["generate-btn"])
            with gr.Column():
                with gr.Row():
                    image_output = gr.Image(type="pil", label="Image Output", elem_id="gallery")
        
        # gr.Markdown(article_text)
        with gr.Column():
            gr.Examples(
                examples = examples,
                inputs = [text_prompt],
            )
    gr.on(
        triggers=[text_button.click, text_prompt.submit],
        fn = infer,
        inputs=[text_prompt, seed, randomize_seed, width, height, cfg, steps, custom_lora, lora_scale], 
        outputs=[image_output, seed]
    )
        
        # text_button.click(query, inputs=[custom_lora, text_prompt, steps, cfg, randomize_seed, seed, width, height], outputs=[image_output,seed_output, seed])
        # text_button.click(infer, inputs=[text_prompt, seed, randomize_seed, width, height, cfg, steps, custom_lora, lora_scale], outputs=[image_output,seed_output, seed])

app.launch(share=True, mcp_server=True)