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
@spaces.GPU
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=75)
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
    
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>
"""

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;
}
/* Updated styles for sliders */
.custom-slider input[type="range"] {
    -webkit-appearance: none;
    width: 100%;
    height: 10px;
    border-radius: 5px;
    background: #d3d3d3;
    outline: none;
    opacity: 0.7;
    transition: opacity .2s;
}
.custom-slider input[type="range"]:hover {
    opacity: 1;
}
.custom-slider input[type="range"]::-webkit-slider-thumb {
    -webkit-appearance: none;
    appearance: none;
    width: 20px;
    height: 20px;
    border-radius: 50%;
    background: #2980b9;
    cursor: pointer;
}
.custom-slider input[type="range"]::-moz-range-thumb {
    width: 20px;
    height: 20px;
    border-radius: 50%;
    background: #2980b9;
    cursor: pointer;
}
"""

with gr.Blocks(theme='bethecloud/storj_theme', css=custom_css) 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("Image Settings", open=False):
                width = gr.Slider(label="Width", minimum=512, maximum=MAX_IMAGE_SIZE, step=32, value=1024, elem_classes="custom-slider")
                height = gr.Slider(label="Height", minimum=512, maximum=MAX_IMAGE_SIZE, step=32, value=1024, elem_classes="custom-slider")
                guidance_scale = gr.Slider(label="Guidance Scale", minimum=0.0, maximum=7.5, step=0.1, value=4.5, elem_classes="custom-slider")
                num_inference_steps = gr.Slider(label="Inference Steps", minimum=1, maximum=50, step=1, value=40, elem_classes="custom-slider")
                seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, elem_classes="custom-slider")
                randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
                negative_prompt = gr.Textbox(label="Negative Prompt")

        with gr.Column(scale=1):
            with gr.Group(elem_classes="input-group"):
                text_prompt = gr.Textbox(label="Text Prompt (optional, used if no image is uploaded)")
                use_enhancer = gr.Checkbox(label="Use Prompt Enhancer", value=False)
                use_llm_generator = gr.Checkbox(label="Use LLM Prompt Generator", value=False)
                with gr.Accordion("LLM Settings", open=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="Short",
                        visible=False
                    )
                generate_prompt_btn = gr.Button("Generate Prompt", elem_classes="submit-btn")
                final_prompt = gr.Textbox(label="Final Prompt", interactive=False)
            
            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)
                used_seed = gr.Number(label="Seed Used")

    def update_model_choices(provider):
        provider_models = {
            "Hugging Face": [
                "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"
            ],
            "SambaNova": [
                "Meta-Llama-3.1-70B-Instruct",
                "Meta-Llama-3.1-405B-Instruct",
                "Meta-Llama-3.1-8B-Instruct"
            ],
        }
        models = provider_models.get(provider, [])
        return gr.Dropdown(choices=models, value=models[0] if models else "")

    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]
    )

    llm_provider.change(
        update_model_choices,
        inputs=[llm_provider],
        outputs=[llm_model]
    )

    def generate_prompt(image, text_prompt, use_enhancer, use_llm_generator, llm_provider, llm_model, prompt_type):
        if image is not None:
            caption = florence_caption(image)
            initial_prompt = caption
        else:
            initial_prompt = text_prompt
        
        if use_llm_generator:
            prompt = generate_llm_prompt(initial_prompt, llm_provider, llm_model, prompt_type)
        else:
            prompt = initial_prompt
        
        if use_enhancer:
            prompt = enhance_prompt(prompt)
        
        return prompt

    generate_prompt_btn.click(
        fn=generate_prompt,
        inputs=[
            input_image, text_prompt, use_enhancer, use_llm_generator, llm_provider, llm_model, prompt_type
        ],
        outputs=[final_prompt]
    )

    generate_btn.click(
        fn=process_workflow,
        inputs=[
            input_image, final_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)