app.py

import gradio as gr
import numpy as np
import random
import spaces 
from diffusers import ChromaPipeline, FlowMatchEulerDiscreteScheduler, EulerDiscreteScheduler, EulerAncestralDiscreteScheduler, DPMSolverMultistepScheduler, DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler
import torch

device = "cuda" if torch.cuda.is_available() else "cpu"
model_repo_id = "SG161222/SPARK.Chroma_preview"

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

# Load the base pipeline first
pipe = ChromaPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
pipe = pipe.to(device)

# Map scheduler names to their classes
SCHEDULERS = {
    "Flow Match Euler (Default)": FlowMatchEulerDiscreteScheduler,
    "Euler": EulerDiscreteScheduler,
    "Euler Ancestral": EulerAncestralDiscreteScheduler,
    "DPM Solver++": DPMSolverMultistepScheduler,
    "DDIM": DDIMScheduler,
    "PNDM": PNDMScheduler,
    "LMS": LMSDiscreteScheduler,
}

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

def set_scheduler(pipe, scheduler_name):
    """Set the scheduler for the pipeline."""
    scheduler_class = SCHEDULERS.get(scheduler_name)
    if scheduler_class:
        try:
            scheduler = scheduler_class.from_pipe(pipe.scheduler)
            pipe.scheduler = scheduler
        except Exception:
            # For schedulers that don't have from_pipe method, create fresh instance
            try:
                scheduler = scheduler_class()
                pipe.scheduler = scheduler
            except Exception:
                pass  # Keep current scheduler if it fails
    return pipe

@spaces.GPU() 
def infer(
    prompt, 
    negative_prompt="low quality, ugly, unfinished, out of focus, deformed, disfigured, blurry, smudged, restricted palette, flat colors", 
    seed=42, 
    randomize_seed=False, 
    width=1024, 
    height=1024, 
    guidance_scale=3.0, 
    num_inference_steps=40,
    scheduler_name="Flow Match Euler (Default)",
    progress=gr.Progress(track_tqdm=True)
):
    """Generate an image based on the prompt and settings."""
    
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
        
    # Set the scheduler
    global pipe
    pipe = set_scheduler(pipe, scheduler_name)
    
    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",
]

# Custom CSS for styling
css = """
#col-container {
    margin: 0 auto;
    max-width: 760px;
}
#run-button {
    align-self: stretch;
    font-weight: 600;
}
"""

# Create custom theme
custom_theme = gr.themes.Soft(
    primary_hue="indigo",
    secondary_hue="blue",
    neutral_hue="slate",
    font=gr.themes.GoogleFont("Inter"),
    text_size="lg",
    spacing_size="lg",
    radius_size="md"
).set(
    button_primary_background_fill="*primary_600",
    button_primary_background_fill_hover="*primary_700",
    block_title_text_weight="600",
)

with gr.Blocks() as demo:
    
    gr.Markdown(f"""
    # 🎨 SPARK.Chroma
    
    [SPARK.Chroma](https://huggingface.co/SG161222/SPARK.Chroma_preview) is an 8.9B parameter text-to-image fine-tuned model based on FLUX.1-schnell
    
    *Built with [anycoder](https://huggingface.co/spaces/akhaliq/anycoder)*
    """)
    
    with gr.Column(elem_id="col-container"):
        with gr.Row():
            prompt = gr.Text(
                label="Prompt",
                max_lines=1,
                placeholder="Enter your prompt",
                scale=2,
            )
            negative_prompt = gr.Text(
                label="Negative prompt",
                max_lines=1,
                placeholder="Enter a negative prompt",
                value="low quality, ugly, unfinished, out of focus, deformed, disfigured, blurry, smudged, restricted palette, flat colors",
                scale=1,
            )
        
        with gr.Row():
            run_button = gr.Button("Run", variant="primary", scale=1, elem_id="run-button")
        
        result = gr.Image(label="Result", show_label=False, type="pil")

        with gr.Accordion("⚙️ Advanced Settings", open=False):
            
            with gr.Row():
                scheduler_name = gr.Dropdown(
                    label="Sampler / Scheduler",
                    choices=list(SCHEDULERS.keys()),
                    value="Flow Match Euler (Default)",
                    info="Choose the sampling method. Flow Match Euler is recommended for FLUX models.",
                )
                
                guidance_scale = gr.Slider(
                    label="Guidance Scale",
                    minimum=1.0,
                    maximum=10.0,
                    step=0.1,
                    value=3.0,
                    info="Higher values make the model follow the prompt more closely",
                )
            
            with gr.Row():
                seed = gr.Slider(
                    label="Seed",
                    minimum=0,
                    maximum=MAX_SEED,
                    step=1,
                    value=433,
                )
                
                randomize_seed = gr.Checkbox(
                    label="Randomize seed", 
                    value=True,
                    info="Generate a random seed each time",
                )
            
            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,
                )
            
            with gr.Row():
                num_inference_steps = gr.Slider(
                    label="Inference Steps",
                    minimum=1,
                    maximum=100,
                    step=1,
                    value=40,
                    info="More steps can produce higher quality but takes longer",
                )
        
        gr.Markdown("### 💡 Example Prompts")
        gr.Examples(
            examples=examples,
            inputs=[prompt],
            outputs=[result, seed],
            fn=infer,
            cache_examples="lazy"
        )
    
    # Event listeners for running inference
    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, scheduler_name],
        outputs=[result, seed],
        api_visibility="public"
    )

# Launch with Gradio 6 syntax - theme and all app parameters go in launch()
demo.queue().launch(
    theme=custom_theme,
    css=css,
    footer_links=[
        {"label": "SPARK.Chroma Model", "url": "https://huggingface.co/SG161222/SPARK.Chroma_preview"},
        {"label": "Built with anycoder", "url": "https://huggingface.co/spaces/akhaliq/anycoder"}
    ]
)