app.py

import gradio as gr
import numpy as np
import random
import spaces 
from diffusers import ChromaPipeline
import torch

device = "cuda" if torch.cuda.is_available() else "cpu"
model_repo_id = "lodestones/Chroma1-HD"

if torch.cuda.is_available():
    torch_dtype = torch.bfloat16
else:
    torch_dtype = torch.float32

pipe = ChromaPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
pipe = pipe.to(device)

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

@spaces.GPU() 
def infer(prompt, negative_prompt="low quality, ugly, unfinished, out of focus, deformed, disfigure, blurry, smudged, restricted palette, flat colors", seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=3.0, num_inference_steps=40, progress=gr.Progress(track_tqdm=True)):

    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
        
    generator = torch.Generator(device).manual_seed(seed)
    
    image = pipe(
        prompt=prompt, 
        negative_prompt=negative_prompt,
        guidance_scale=guidance_scale, 
        num_inference_steps=num_inference_steps, 
        width=width, 
        height=height,
        generator=generator,
        num_images_per_prompt=1
    ).images[0] 
    
    return image, seed

examples = [
    "A high-fashion close-up portrait of a blonde woman in clear sunglasses. The image uses a bold teal and red color split for dramatic lighting. The background is a simple teal-green. The photo is sharp and well-composed, and is designed for viewing with anaglyph 3D glasses for optimal effect. It looks professionally done.",
    "A dog eating pizza",
    "The spirit of a tamagotchi wandering in San Francisco",
]

css="""
#col-container {
    margin: 0 auto;
    max-width: 760px;
}
#button{
    align-self: stretch;
}
"""

with gr.Blocks(css=css) as demo:
    
    with gr.Column(elem_id="col-container"):
        gr.Markdown(f"""
        # Chroma1-HD
        [Chroma1-HD](https://huggingface.co/lodestones/Chroma1-HD) is an 8.9B parameter text-to-image foundational model based on FLUX.1-schnell
        """)
        
        with gr.Row():
            prompt = gr.Text(
                label="Prompt",
                max_lines=1,
                placeholder="Enter your prompt",
            )
            negative_prompt = gr.Text(
                label="Negative prompt",
                max_lines=1,
                placeholder="Enter a negative prompt",
                value="low quality, ugly, unfinished, out of focus, deformed, disfigure, blurry, smudged, restricted palette, flat colors"
            )
        
        with gr.Row():
            run_button = gr.Button("Run", scale=1, elem_id="button")
        
        result = gr.Image(label="Result", show_label=False)

        with gr.Accordion("Advanced Settings", open=False):
        
            
            guidance_scale = gr.Slider(
                label="Guidance Scale",
                minimum=1.0,
                maximum=10.0,
                step=0.1,
                value=3.0, 
            )
            
            seed = gr.Slider(
                label="Seed",
                minimum=0,
                maximum=MAX_SEED,
                step=1,
                value=433, 
            )
            
            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
            
            with gr.Row():
                
                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, 
                )
            
            num_inference_steps = gr.Slider(
                label="Number of inference steps",
                minimum=1,
                maximum=100,
                step=1,
                value=40, 
            )
        
        gr.Examples(
            examples=examples,
            inputs=[prompt],
            outputs=[result, seed],
            fn=infer,
            cache_examples="lazy"
        )
    
    gr.on(
        triggers=[run_button.click, prompt.submit, negative_prompt.submit],
        fn=infer,
        inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
        outputs=[result, seed]
    )

demo.queue().launch()