app.py

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

device = "cuda" if torch.cuda.is_available() else "cpu"

# Load the model in FP16
pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.float16)

# Move the pipeline to GPU if available
pipe = pipe.to(device)

# Convert text encoders to full precision
pipe.text_encoder = pipe.text_encoder.to(torch.float32)
if hasattr(pipe, 'text_encoder_2'):
    pipe.text_encoder_2 = pipe.text_encoder_2.to(torch.float32)

# Enable memory efficient attention if available and on CUDA
if device == "cuda" and hasattr(pipe, 'enable_xformers_memory_efficient_attention'):
    try:
        pipe.enable_xformers_memory_efficient_attention()
        print("xformers memory efficient attention enabled")
    except Exception as e:
        print(f"Could not enable memory efficient attention: {e}")

# Compile the UNet for potential speedups if on CUDA
if device == "cuda":
    try:
        pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
        print("UNet compiled for potential speedups")
    except Exception as e:
        print(f"Could not compile UNet: {e}")

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 2048

@spaces.GPU()
def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, num_inference_steps=4, progress=gr.Progress(track_tqdm=True)):
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    generator = torch.Generator(device=device).manual_seed(seed)
    
    # Use full precision for text encoding
    with torch.no_grad():
        text_inputs = pipe.tokenizer(prompt, return_tensors="pt").to(device)
        text_embeddings = pipe.text_encoder(text_inputs.input_ids)[0]
    
    # Use mixed precision for the rest of the pipeline
    with torch.inference_mode(), torch.autocast(device_type='cuda', dtype=torch.float16):
        image = pipe(
            prompt_embeds=text_embeddings,
            width=width,
            height=height,
            num_inference_steps=num_inference_steps,
            generator=generator,
            guidance_scale=0.0
        ).images[0]
    
    return image, seed
 
examples = [
    "a tiny astronaut hatching from an egg on the moon",
    "a cat holding a sign that says hello world",
    "an anime illustration of a wiener schnitzel",
]

css = """
#col-container {
    margin: 0 auto;
    max-width: 720px;
}
.container {
    margin: 0 auto;
    padding: 20px;
    border-radius: 10px;
    background-color: #f0f0f0;
}
.title {
    text-align: center;
    color: #2c3e50;
    margin-bottom: 20px;
}
.subtitle {
    text-align: center;
    color: #34495e;
    margin-bottom: 30px;
}
.speed-info {
    background-color: #e74c3c;
    color: white;
    padding: 10px;
    border-radius: 5px;
    text-align: center;
    margin-bottom: 20px;
}
.prompt-container {
    display: flex;
    gap: 10px;
    margin-bottom: 20px;
}
.advanced-settings {
    background-color: #ecf0f1;
    padding: 15px;
    border-radius: 5px;
    margin-top: 20px;
}
"""

with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="col-container"):
        gr.HTML(
            """
            <div class="container">
                <h1 class="title">FLUX.1 [schnell] - Mixed Precision Edition</h1>
                <h3 class="subtitle">12B param rectified flow transformer optimized for maximum inference speed</h3>
                <div class="speed-info">
                    <strong>Mixed Precision Pipeline:</strong> FP32 Text Encoders + FP16 Core for optimal speed and quality
                </div>
            </div>
            """
        )
        
        with gr.Column(elem_id="prompt-container"):
            prompt = gr.Text(
                label="Enter your prompt",
                placeholder="A futuristic cityscape with flying cars",
                lines=2
            )
            run_button = gr.Button("Generate Image", variant="primary")
        
        result = gr.Image(label="Generated Image")
        
        with gr.Accordion("Advanced Settings", open=False):
            with gr.Column(elem_id="advanced-settings"):
                seed = gr.Slider(
                    label="Seed",
                    minimum=0,
                    maximum=MAX_SEED,
                    step=1,
                    value=0,
                    info="Set to 0 for random seed"
                )
                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=50,
                    step=1,
                    value=4,
                    info="Lower values = faster generation, higher values = potentially better quality"
                )
        
        gr.Markdown(
            """
            ### About FLUX.1 [schnell]
            - Distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/)
            - Optimized for 4-step generation
            - Mixed precision pipeline for maximum speed
            
            [[Blog]](https://blackforestlabs.ai/announcing-black-forest-labs/) | [[Model]](https://huggingface.co/black-forest-labs/FLUX.1-schnell)
            """
        )
        
        gr.Examples(
            examples=examples,
            fn=infer,
            inputs=[prompt],
            outputs=[result, seed],
            cache_examples="lazy"
        )
    
    gr.on(
        triggers=[run_button.click, prompt.submit],
        fn=infer,
        inputs=[prompt, seed, randomize_seed, width, height, num_inference_steps],
        outputs=[result, seed]
    )

demo.launch()