app.py

import gradio as gr
import numpy as np
import random

import spaces
import torch

from diffusers import FluxPriorReduxPipeline, FluxPipeline
from diffusers.utils import load_image

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

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-dev" , 
    torch_dtype=torch.bfloat16
).to("cuda")
pipe_prior_redux = FluxPriorReduxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-Redux-dev",
    text_encoder=pipe.text_encoder,
    tokenizer=pipe.tokenizer,
    text_encoder_2=pipe.text_encoder_2,
    tokenizer_2=pipe.tokenizer_2,
    torch_dtype=torch.bfloat16
).to("cuda")

@spaces.GPU
def infer(control_image, prompt,  image_2, prompt_2, reference_scale= 0.03 , 
          seed=42, randomize_seed=False, width=1024, height=1024, 
          guidance_scale=3.5, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    if image_2 is not None:
        pipe_prior_output = pipe_prior_redux([control_image, image_2], prompt=[prompt, prompt_2])
    else:
        pipe_prior_output = pipe_prior_redux(control_image, prompt=prompt)
    cond_size = 729
    hidden_size = 4096
    max_sequence_length = 512
    full_attention_size = max_sequence_length + hidden_size + cond_size
    attention_mask = torch.zeros(
        (full_attention_size, full_attention_size), device="cuda", dtype=torch.bfloat16
    )
    bias = torch.log(
        torch.tensor(reference_scale, dtype=torch.bfloat16, device="cuda").clamp(min=1e-5, max=1)
    )
    attention_mask[:, max_sequence_length : max_sequence_length + cond_size] = bias
    joint_attention_kwargs=dict(attention_mask=attention_mask)
    images = pipe(
        guidance_scale=guidance_scale,
        num_inference_steps=num_inference_steps,
        generator=torch.Generator("cpu").manual_seed(seed),
        joint_attention_kwargs=joint_attention_kwargs,
        **pipe_prior_output,
    ).images[0]
    return images, seed

css="""
#col-container {
    margin: 0 auto;
    max-width: 960px;
}
"""

with gr.Blocks(css=css) as demo:
    
    with gr.Column(elem_id="col-container"):
        gr.Markdown(f"""# FLUX.1 Redux [dev]
An adapter for FLUX [dev] to create image variations
[[non-commercial license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md)] [[blog](https://blackforestlabs.ai/announcing-black-forest-labs/)] [[model](https://huggingface.co/black-forest-labs/FLUX.1-dev)]
        """)
        with gr.Row():
            with gr.Column():
                    input_image = gr.Image(label="Image to create variations", type="pil")
                    prompt = gr.Text(
                        label="Prompt",
                        show_label=False,
                        max_lines=1,
                        placeholder="Enter your prompt",
                        container=False,
                    )
                    reference_scale = gr.Slider(
                        label="Masking Scale",
                        minimum=0.01,
                        maximum=0.08,
                        step=0.001,
                        value=0.03,
                    )
                    run_button = gr.Button("Run")
            with gr.Column():
                    image_2 = gr.Image(label="2nd image to create interpolated variations", type="pil")
                    prompt_2 = gr.Text(
                        label="2nd Prompt",
                        show_label=False,
                        max_lines=1,
                        placeholder="Enter your prompt",
                        container=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=0,
            )
            
            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,
                )
            
            with gr.Row():

                guidance_scale = gr.Slider(
                    label="Guidance Scale",
                    minimum=1,
                    maximum=15,
                    step=0.1,
                    value=3.5,
                )
  
                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=50,
                    step=1,
                    value=28,
                )

    gr.on(
        triggers=[run_button.click],
        fn = infer,
        inputs = [input_image, prompt, image_2, prompt_2, reference_scale, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
        outputs = [result, seed]
    )

demo.launch()