app.py

import gradio as gr
import spaces
import torch
from gradio_client import Client, handle_file
from colorama import Fore, Style
from diffusers import AutoPipelineForImage2Image
from PIL import Image
from live_preview_helpers import flux_pipe_call_that_returns_an_iterable_of_images

joy_client = Client("fancyfeast/joy-caption-alpha-two")
qwen_client = Client("Qwen/Qwen2.5-72B-Instruct")

pipeline = AutoPipelineForImage2Image.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16)
pipeline.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipeline)

lora_ids = {
    "Realism": "XLabs-AI/flux-RealismLora",
    "Cartoonism": "aleksa-codes/flux-ghibsky-illustration",
}

def load_lora(lora_name):
    print(f"Loading LoRA model: {lora_name}")
    global pipeline
    pipeline.unload_lora_weights()
    pipeline.load_lora_weights(lora_ids[lora_name])
    pipeline.enable_model_cpu_offload()
    print(f"{Fore.GREEN}LoRA model loaded{Style.RESET_ALL}")

def describe_image(image_path):
    print(f"Describing image: {image_path}")
    image_description = joy_client.predict(
            input_image=handle_file(image_path),
            caption_type="Descriptive",
            caption_length="long",
            extra_options=[],
            name_input="",
            custom_prompt="",
            api_name="/stream_chat"
    )[-1]
    print(f"{Fore.GREEN}{image_description}{Style.RESET_ALL}")
    return image_description

def refine_prompt(image_description):
    print(f"Improving prompt: {image_description}")
    qwen_prompt = f"""This is the description of the image: {image_description}

And those some good AI Art Prompts:
- a cat on a windowsill gazing out at a starry night sky and distant city lights
- a fisherman casting a line into a peaceful village lake surrounded by quaint cottages
- cozy mountain cabin covered in snow, with smoke curling from the chimney and a warm, inviting light spilling through the windows
- Mykonos
- an orange Lamborghini driving down a hill road at night with a beautiful ocean view in the background, side view, no text
- a small Yorkie on a windowsill during a snowy winter night, with a warm, cozy glow from inside and soft snowflakes drifting outside
- serene Japanese garden with a koi pond and a traditional tea house, nestled under a canopy of cherry blossoms in full bloom
- the most beautiful place in the universe

Based on what i gave you, Write a great short AI Art Prompt for me that is based on the image description above (Don't write anything else, just the prompt)
"""
    refined_prompt = qwen_client.predict(
            query=qwen_prompt,
            history=[],
            system="You are Qwen, created by Alibaba Cloud. You are a helpful assistant.",
            api_name="/model_chat"
    )[1][0][-1]
    print(f"{Fore.GREEN}{refined_prompt}{Style.RESET_ALL}")
    return refined_prompt

@spaces.GPU
def img2img_infer(image_path, image_description):
    pil_image = Image.open(image_path)
    width, height = pil_image.size
    for enhanced_image in pipeline.flux_pipe_call_that_returns_an_iterable_of_images(
        prompt=f'GHIBSKY style, {image_description}',
        guidance_scale=3.5,
        num_inference_steps=28,
        width=1024,
        height=1024,
        generator=torch.Generator("cpu").manual_seed(0),
        output_type="pil",
    ):
        yield enhanced_image.resize((width, height))


with gr.Blocks(title="Magnific") as demo:
    gr.HTML("<center><h1>Magnific</h1></center>")
    gr.Markdown("This space is an attempt at replicating the functionality of the [Magnific](https://magnific.ai/) service.")

    with gr.Row():
        with gr.Column():
            image_path = gr.Image(label="Image", type="filepath")
            lora_dropdown = gr.Dropdown(label="LoRA Model", choices=list(lora_ids.keys()), value=None)
            describe_btn = gr.Button(value="Describe Image", variant="primary")
            with gr.Row(equal_height=True):
                image_description = gr.Textbox(label="Image Description", scale=4)
                refine_prompt_btn = gr.Button(value="Refine", variant="primary", scale=1)
            submit_btn = gr.Button(value="Submit", variant="primary")
        enhanced_image = gr.Image(label="Enhanced Image", type="pil")

    lora_dropdown.change(load_lora, inputs=lora_dropdown)
    refine_prompt_btn.click(refine_prompt, inputs=image_description, outputs=image_description)
    describe_btn.click(describe_image, inputs=image_path, outputs=image_description)
    submit_btn.click(img2img_infer, inputs=[image_path, image_description], outputs=enhanced_image)

demo.queue().launch(share=False)