app.py

import random

import gradio as gr
import numpy as np
import spaces
import torch
from diffusers import  DiffusionPipeline, FlowMatchEulerDiscreteScheduler
from transformers import CLIPTextModel, CLIPTokenizer,T5EncoderModel, T5TokenizerFast
from gradio_imagefeed import ImageFeed


dtype = torch.bfloat16
device = "cuda" if torch.cuda.is_available() else "cpu"
pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to(device)

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 2048
LICENSE=f"""# Better UI for FLUX.1 [dev] [[non-commercial license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md)]"""
CSS = "#col-container { margin: 0 auto; max-width: 900px; }"
EXAMPLES = ["a tiny elephant hatching from a turtle egg in the palm of a human hand, highly detailed textures, close-up"]


@spaces.GPU(duration=45)
def infer(prompt, seed=99999, randomize_seed=True, width=896, height=1152, guidance_scale=5.0, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    
    generator = torch.Generator().manual_seed(seed)
    
    image = pipe(
        prompt = prompt, 
        width = width,
        height = height,
        num_inference_steps = num_inference_steps, 
        generator = generator,
        guidance_scale=guidance_scale).images[0]
    
    yield image, seed


with gr.Blocks(css=CSS) as demo:
    with gr.Column(elem_id="col-container"):
        gr.Markdown(LICENSE)
        
        with gr.Row():
            prompt = gr.Text(
                label="Prompt",
                show_label=False,
                max_lines=5,
                placeholder="Prompt",
                container=False)
            
            run_button = gr.Button("Run", scale=0)

        result = ImageFeed(label="Result", show_label=False)
        # result = gr.Image(label="Result", show_label=False)
        
        with gr.Accordion("Advanced Settings", open=False):
            
            seed = gr.Slider(
                label="Seed",
                minimum=0,
                maximum=MAX_SEED,
                step=1,
                value=random.randint(0, MAX_SEED))
            
            randomize_seed = gr.Checkbox(label="Randomize", value=True)
            
            with gr.Row():
                width = gr.Slider(
                    label="Width",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=896)
                
                height = gr.Slider(
                    label="Height",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=1152)
            
            with gr.Row():
                guidance_scale = gr.Slider(
                    label="Guidance Scale",
                    minimum=1,
                    maximum=15,
                    step=0.1,
                    value=3)
  
                num_inference_steps = gr.Slider(
                    label="Inference Steps",
                    minimum=1,
                    maximum=50,
                    step=1,
                    value=28)
        
        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, guidance_scale, num_inference_steps],
        outputs = [result, seed]
    )

demo.launch()