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
from llm_inference import LLMInferenceNode

# 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
)

# Initialize LLMInferenceNode
llm_node = LLMInferenceNode()

# 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, use_llm_generator, llm_provider, llm_model, prompt_type, 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)
        
        caption = florence_caption(image)
        print(f"Florence caption: {caption}")
        
        if use_llm_generator:
            prompt = generate_llm_prompt(caption, llm_provider, llm_model, prompt_type)
        else:
            prompt = caption
    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

def generate_llm_prompt(input_text, provider, model, prompt_type):
    try:
        dynamic_seed = random.randint(0, 1000000)
        result = llm_node.generate(
            input_text=input_text,
            long_talk=True,
            compress=False,
            compression_level="medium",
            poster=False,
            prompt_type=prompt_type,
            provider=provider,
            model=model
        )
        return result
    except Exception as e:
        print(f"An error occurred in generate_llm_prompt: {e}")
        return input_text  # Return original input if there's an error

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)
                use_llm_generator = gr.Checkbox(label="Use LLM Prompt Generator", value=False)
                llm_provider = gr.Dropdown(
                    choices=["Hugging Face", "SambaNova"],
                    label="LLM Provider",
                    value="Hugging Face",
                    visible=False
                )
                llm_model = gr.Dropdown(
                    label="LLM Model",
                    choices=["Qwen/Qwen2.5-72B-Instruct", "meta-llama/Meta-Llama-3.1-70B-Instruct", "mistralai/Mixtral-8x7B-Instruct-v0.1", "mistralai/Mistral-7B-Instruct-v0.3"],
                    value="Qwen/Qwen2.5-72B-Instruct",
                    visible=False
                )
                prompt_type = gr.Dropdown(
                    choices=["Random", "Long", "Short", "Medium", "OnlyObjects", "NoFigure", "Landscape", "Fantasy"],
                    label="Prompt Type",
                    value="Random",
                    visible=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")
    
    def update_llm_visibility(use_llm):
        return {
            llm_provider: gr.update(visible=use_llm),
            llm_model: gr.update(visible=use_llm),
            prompt_type: gr.update(visible=use_llm)
        }

    use_llm_generator.change(
        update_llm_visibility,
        inputs=[use_llm_generator],
        outputs=[llm_provider, llm_model, prompt_type]
    )

    generate_btn.click(
        fn=process_workflow,
        inputs=[
            input_image, text_prompt, use_enhancer, use_llm_generator, llm_provider, llm_model, prompt_type,
            seed, randomize_seed, width, height, guidance_scale, num_inference_steps, negative_prompt
        ],
        outputs=[output_image, final_prompt, used_seed]
    )

demo.launch(debug=True)