app.py

import spaces
import gradio as gr
import torch
from PIL import Image
from transformers import AutoProcessor, AutoModelForCausalLM, pipeline
from diffusers import DiffusionPipeline
import random
import numpy as np
import os
import subprocess
subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)

# Initialize models
device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.bfloat16

huggingface_token = os.getenv("HUGGINGFACE_TOKEN")

# FLUX.1-dev model
pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=dtype, token = huggingface_token).to(device)

# Initialize Florence model
florence_model = AutoModelForCausalLM.from_pretrained('microsoft/Florence-2-base', trust_remote_code=True).to(device).eval()
florence_processor = AutoProcessor.from_pretrained('microsoft/Florence-2-base', trust_remote_code=True)

# Prompt Enhancer
enhancer_long = pipeline("summarization", model="mobenta/M_Prompter", device=device)

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

# Florence caption function
def florence_caption(image):
    # Convert image to PIL if it's not already
    if not isinstance(image, Image.Image):
        image = Image.fromarray(image)
    
    inputs = florence_processor(text="<MORE_DETAILED_CAPTION>", images=image, return_tensors="pt").to(device)
    generated_ids = florence_model.generate(
        input_ids=inputs["input_ids"],
        pixel_values=inputs["pixel_values"],
        max_new_tokens=1024,
        early_stopping=False,
        do_sample=False,
        num_beams=3,
    )
    generated_text = florence_processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
    parsed_answer = florence_processor.post_process_generation(
        generated_text,
        task="<MORE_DETAILED_CAPTION>",
        image_size=(image.width, image.height)
    )
    return parsed_answer["<MORE_DETAILED_CAPTION>"]

# Prompt Enhancer function
def enhance_prompt(input_prompt):
    result = enhancer_long("Enhance the description: " + input_prompt)
    enhanced_text = result[0]['summary_text']
    return enhanced_text

@spaces.GPU(duration=190)
def process_workflow(image, text_prompt, use_enhancer, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, progress=gr.Progress(track_tqdm=True)):
    if image is not None:
        # Convert image to PIL if it's not already
        if not isinstance(image, Image.Image):
            image = Image.fromarray(image)
        
        prompt = florence_caption(image)
    else:
        prompt = text_prompt
    
    if use_enhancer:
        prompt = enhance_prompt(prompt)
    
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    
    generator = torch.Generator(device=device).manual_seed(seed)
    
    image = pipe(
        prompt=prompt,
        generator=generator,
        num_inference_steps=num_inference_steps,
        width=width,
        height=height,
        guidance_scale=guidance_scale
    ).images[0]
    
    return image, prompt, seed

custom_css = """
.input-group, .output-group {
    border: 1px solid #e0e0e0;
    border-radius: 10px;
    padding: 20px;
    margin-bottom: 20px;
    background-color: #f9f9f9;
}
.submit-btn {
    background-color: #2980b9 !important;
    color: white !important;
}
.submit-btn:hover {
    background-color: #3498db !important;
}
"""

title = """<h1 align="center">Mobenta: Creative AI Image Generation</h1>
<p align="center">
 your creative partner for generating stunning AI-powered images. Whether you start with a photo or a text prompt, it enhances your ideas with advanced captioning and prompt refinement. Dive into the world of creativity and explore endless possibilities!
</p>
"""


with gr.Blocks(css=custom_css, theme=gr.themes.Soft(primary_hue="blue", secondary_hue="gray")) as demo:
    gr.HTML(title)
    
    with gr.Row():
        with gr.Column(scale=1):
            with gr.Group(elem_classes="input-group"):
                input_image = gr.Image(label="Input Image (Florence-2 Captioner)")
            
            with gr.Accordion("Advanced Settings", open=False):
                text_prompt = gr.Textbox(label="Text Prompt (optional, used if no image is uploaded)")
                use_enhancer = gr.Checkbox(label="Use Prompt Enhancer", value=False)
                seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
                randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
                width = gr.Slider(label="Width", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1024)
                height = gr.Slider(label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1024)
                guidance_scale = gr.Slider(label="Guidance Scale", minimum=1, maximum=15, step=0.1, value=3.5)
                num_inference_steps = gr.Slider(label="Inference Steps", minimum=1, maximum=50, step=1, value=28)
            
            generate_btn = gr.Button("Generate Image", elem_classes="submit-btn")
        
        with gr.Column(scale=1):
            with gr.Group(elem_classes="output-group"):
                output_image = gr.Image(label="Result", elem_id="gallery", show_label=False)
                final_prompt = gr.Textbox(label="Final Prompt Used")
                used_seed = gr.Number(label="Seed Used")
    
    generate_btn.click(
        fn=process_workflow,
        inputs=[
            input_image, text_prompt, use_enhancer, seed, randomize_seed,
            width, height, guidance_scale, num_inference_steps
        ],
        outputs=[output_image, final_prompt, used_seed]
    )

demo.launch(debug=True)