app.py

import spaces
import torch
from diffusers import StableDiffusion3Pipeline
import gradio as gr
import os
import random
import transformers
import numpy as np
from transformers import T5Tokenizer, T5ForConditionalGeneration

HF_TOKEN = os.getenv("HF_TOKEN")

if torch.cuda.is_available():
    device = "cuda"
    print("Using GPU")
else:
    device = "cpu"
    print("Using CPU")


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

CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES", "0") == "1"


# Initialize the pipeline and download the sd3 medium model
pipe = StableDiffusion3Pipeline.from_pretrained("stabilityai/stable-diffusion-3-medium-diffusers", torch_dtype=torch.float16)
pipe.to(device)

# superprompt-v1
tokenizer = T5Tokenizer.from_pretrained("roborovski/superprompt-v1")
model = T5ForConditionalGeneration.from_pretrained("roborovski/superprompt-v1", device_map="auto", torch_dtype="auto")
model.to(device)

# toggle visibility the enhanced prompt output
def update_visibility(enhance_prompt):
    return gr.update(visible=enhance_prompt)


# Define the image generation function
@spaces.GPU(duration=80)
def generate_image(prompt, enhance_prompt, negative_prompt, num_inference_steps, height, width, guidance_scale, seed, num_images_per_prompt, progress=gr.Progress(track_tqdm=True)):
    if seed == 0:
        seed = random.randint(1, 2**32-1)
        
    if enhance_prompt:
        transformers.set_seed(seed)
        
        input_text = f"Expand the following prompt to add more detail: {prompt}"
        input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(device)
        
        outputs = model.generate(
        input_ids,
        max_new_tokens=512,
        repetition_penalty=1.2,
        do_sample=True,
        temperature=0.7,
        top_p=1,
        top_k=50
        )
        prompt = tokenizer.decode(outputs[0], skip_special_tokens=True)
        
    generator = torch.Generator().manual_seed(seed)
    
    output = pipe(
        prompt=prompt,
        negative_prompt=negative_prompt,
        num_inference_steps=num_inference_steps,
        height=height,
        width=width,
        guidance_scale=guidance_scale,
        generator=generator,
        num_images_per_prompt=num_images_per_prompt
    ).images
    
    return output, prompt



# Create the Gradio interface

examples = [
    ["A white car racing fast to the moon.", True],
    ["A woman in a red dress singing on top of a building.", True],
    ["An astrounat on mars in a futuristic cyborg suit.", True],
]

css = '''
.gradio-container{max-width: 1000px !important}
h1{text-align:center}
'''
with gr.Blocks(css=css) as demo:
    with gr.Row():
        with gr.Column():
            gr.HTML(
            """
            <h1 style='text-align: center'>
            Stable Diffusion 3 Medium Superprompt
            </h1>
            """
        )
            gr.HTML(
                """
               Made by <a href='https://linktr.ee/Nick088' target='_blank'>Nick088</a>
               <br> <a href="https://discord.gg/AQsmBmgEPy"> <img src="https://img.shields.io/discord/1198701940511617164?color=%23738ADB&label=Discord&style=for-the-badge" alt="Discord"> </a>
                """
        )
    with gr.Group():
        with gr.Column():
            prompt = gr.Textbox(label="Prompt", info="Describe the image you want", placeholder="A cat...")
            enhance_prompt = gr.Checkbox(label="Prompt Enhancement with SuperPrompt-v1", value=True)
            run_button = gr.Button("Run")
        result = gr.Gallery(label="Generated AI Images", elem_id="gallery")
        better_prompt = gr.Textbox(label="Enhanced Prompt", info="The output of your enhanced prompt used for the Image Generation", visible=True)
        enhance_prompt.change(fn=update_visibility, inputs=enhance_prompt, outputs=better_prompt)
    with gr.Accordion("Advanced options", open=False):
        with gr.Row():
            negative_prompt = gr.Textbox(label="Negative Prompt", info="Describe what you don't want in the image", value="deformed, distorted, disfigured, poorly drawn, bad anatomy, incorrect anatomy, extra limb, missing limb, floating limbs, mutated hands and fingers, disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation", placeholder="Ugly, bad anatomy...")
        with gr.Row():
            num_inference_steps = gr.Slider(label="Number of Inference Steps", info="The number of denoising steps of the image. More denoising steps usually lead to a higher quality image at the cost of slower inference", minimum=1, maximum=50, value=25, step=1)
            guidance_scale = gr.Slider(label="Guidance Scale", info="Controls how much the image generation process follows the text prompt. Higher values make the image stick more closely to the input text.", minimum=0.0, maximum=10.0, value=7.5, step=0.1)
        with gr.Row():
            width = gr.Slider(label="Width", info="Width of the Image", minimum=256, maximum=1344, step=32, value=1024)
            height = gr.Slider(label="Height", info="Height of the Image", minimum=256, maximum=1344, step=32, value=1024)
        with gr.Row():
            seed = gr.Slider(value=42, minimum=0, maximum=MAX_SEED, step=1, label="Seed", info="A starting point to initiate the generation process, put 0 for a random one")
            num_images_per_prompt = gr.Slider(label="Images Per Prompt", info="Number of Images to generate with the settings",minimum=1, maximum=4, step=1, value=2)

    gr.Examples(
        examples=examples,
        fn=generate_image,
        inputs=[prompt, enhance_prompt, negative_prompt, num_inference_steps, guidance_scale, height, width, seed, num_images_per_prompt],
        outputs=[result, better_prompt],
        cache_examples=CACHE_EXAMPLES
    )

    gr.on(
        triggers=[
            prompt.submit,
            run_button.click,
        ],
        fn=generate_image,
        inputs=[
            prompt,
            enhance_prompt,
            negative_prompt,
            num_inference_steps,
            width,
            height,
            guidance_scale,
            seed,
            num_images_per_prompt,
        ],
        outputs=[result, better_prompt],
    )

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