app.py

import spaces
from peft import PeftModel
import gradio as gr
from diffusers import StableDiffusionImg2ImgPipeline
from diffusers import AutoPipelineForImage2Image
import torch
from PIL import Image
from diffusers import StableDiffusionPipeline
# Load the model
model_id = "nitrosocke/Ghibli-Diffusion"
# model_id = "openfree/flux-chatgpt-ghibli-lora"
pipe = StableDiffusionImg2ImgPipeline.from_pretrained(model_id, torch_dtype=torch.float32)



# # 1. 选择一个基础模型，例如 SD 1.5
# base_model_id = "runwayml/stable-diffusion-v1-5"

# # 2. 加载基础模型
# pipe = StableDiffusionPipeline.from_pretrained(
#     base_model_id,
#     torch_dtype=torch.float32
# )

# # 3. 加载 LoRA 权重
# lora_model_id = "openfree/flux-chatgpt-ghibli-lora"
# pipe.load_lora_weights(lora_model_id)

# pipe = AutoPipelineForImage2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.bfloat16,token=True)
# pipe.load_lora_weights('openfree/flux-chatgpt-ghibli-lora', weight_name='flux-chatgpt-ghibli-lora.safetensors')
pipe.load_lora_weights("alvarobartt/ghibli-characters-flux-lora")


# Move pipeline to GPU if available
device = "cuda" if torch.cuda.is_available() else "cpu"
pipe = pipe.to(device)

# Define the inference function
@spaces.GPU
def ghibli_transform(input_image, prompt="GHBLI anime style photo", strength=0.75, guidance_scale=7.5, num_steps=50):
    if input_image is None:
        raise gr.Error("No image uploaded! Please upload an image before clicking Transform.")
    
    # Process the input image (keep it as PIL Image)
    try:
        init_image = input_image.convert("RGB").resize((768, 768))
    except Exception as e:
        raise gr.Error(f"Failed to process image: {str(e)}")
    
    # Generate the Ghibli-style image
    try:
        output = pipe(
            prompt=prompt,
            image=init_image,
            # strength=strength,
            # guidance_scale=guidance_scale,
            # num_inference_steps=num_steps  # Use the UI-provided value
            ######
            guidance_scale=10,
            prompt_strength= 0.75
            ######
        ).images[0]
    except Exception as e:
        raise gr.Error(f"Pipeline error: {str(e)}")
    
    return output

# Create the Gradio interface
with gr.Blocks(title="Ghibli Diffusion Image Transformer") as demo:
    gr.Markdown("# Ghibli Diffusion Image Transformer")
    gr.Markdown("Upload an image and transform it into Studio Ghibli style using nitrosocke/Ghibli-Diffusion!")
    
    with gr.Row():
        with gr.Column():
            input_img = gr.Image(label="Upload Image", type="pil")
            prompt = gr.Textbox(label="Prompt", value="ghibli style")
            strength = gr.Slider(0, 1, value=0.75, step=0.05, label="Strength (How much to transform)")
            guidance = gr.Slider(1, 20, value=7.5, step=0.5, label="Guidance Scale")
            num_steps = gr.Slider(10, 100, value=50, step=5, label="Inference Steps (Higher = Better Quality, Slower)")
            submit_btn = gr.Button("Transform")
        with gr.Column():
            output_img = gr.Image(label="Ghibli-Style Output")
    
    # Connect the button to the function
    submit_btn.click(
        fn=ghibli_transform,
        inputs=[input_img, prompt, strength, guidance, num_steps],
        outputs=output_img
    )

# Launch the Space with share=True for public link
demo.launch(share=True)