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
from huggingface_hub import hf_hub_download

# Install flash-attn
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 if torch.cuda.is_available() else torch.float32

huggingface_token = os.getenv("HUGGINGFACE_TOKEN")

# SD3.5 model
pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-3.5-large", torch_dtype=dtype, use_safetensors=True, variant="fp16", 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="gokaygokay/Lamini-Prompt-Enchance-Long", device=device)

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024

hf_hub_download(
    repo_id="stabilityai/stable-diffusion-3.5-large-turbo",
    filename="LICENSE.md",
    local_dir = "./models",
    token = huggingface_token
)

# Florence caption function
@spaces.GPU
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=60)
def process_workflow(image, text_prompt, use_enhancer, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, negative_prompt="", 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)
        print(prompt)
    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,
        negative_prompt=negative_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">Stable Diffusion 3.5 with Florence-2 Captioner and Prompt Enhancer</h1>
<p><center>
<a href="https://huggingface.co/stabilityai/stable-diffusion-3.5-large" target="_blank">[Stable Diffusion 3.5 Model]</a>
<a href="https://huggingface.co/microsoft/Florence-2-base" target="_blank">[Florence-2 Model]</a>
<a href="https://huggingface.co/gokaygokay/Lamini-Prompt-Enchance-Long" target="_blank">[Prompt Enhancer Long]</a>
<p align="center">Create long prompts from images or enhance your short prompts with prompt enhancer</p>
</center></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)", height=512)
            
            with gr.Accordion("Advanced Settings", open=False):
                text_prompt = gr.Textbox(label="Text Prompt (optional, used if no image is uploaded)")
                negative_prompt = gr.Textbox(label="Negative Prompt")
                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=512, maximum=MAX_IMAGE_SIZE, step=32, value=1024)
                height = gr.Slider(label="Height", minimum=512, maximum=MAX_IMAGE_SIZE, step=32, value=1024)
                guidance_scale = gr.Slider(label="Guidance Scale", minimum=0.0, maximum=7.5, step=0.1, value=4.5)
                num_inference_steps = gr.Slider(label="Inference Steps", minimum=1, maximum=50, step=1, value=40)
            
            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, negative_prompt
        ],
        outputs=[output_image, final_prompt, used_seed]
    )

demo.launch(debug=True)