app.py

import gradio as gr
import numpy as np

import spaces
import torch
import spaces
import random 

from diffusers import AutoPipelineForText2Image
from PIL import Image


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

pipe = AutoPipelineForText2Image.from_pretrained(
    "ostris/Flex.2-preview",
    custom_pipeline="pipeline.py",
    torch_dtype=torch.bfloat16,
).to("cuda")

# def calculate_optimal_dimensions(image: Image.Image):
#     # Extract the original dimensions
#     original_width, original_height = image.size
    
#     # Set constants
#     MIN_ASPECT_RATIO = 9 / 16
#     MAX_ASPECT_RATIO = 16 / 9
#     FIXED_DIMENSION = 1024

#     # Calculate the aspect ratio of the original image
#     original_aspect_ratio = original_width / original_height

#     # Determine which dimension to fix
#     if original_aspect_ratio > 1:  # Wider than tall
#         width = FIXED_DIMENSION
#         height = round(FIXED_DIMENSION / original_aspect_ratio)
#     else:  # Taller than wide
#         height = FIXED_DIMENSION
#         width = round(FIXED_DIMENSION * original_aspect_ratio)

#     # Ensure dimensions are multiples of 8
#     width = (width // 8) * 8
#     height = (height // 8) * 8

#     # Enforce aspect ratio limits
#     calculated_aspect_ratio = width / height
#     if calculated_aspect_ratio > MAX_ASPECT_RATIO:
#         width = (height * MAX_ASPECT_RATIO // 8) * 8
#     elif calculated_aspect_ratio < MIN_ASPECT_RATIO:
#         height = (width / MIN_ASPECT_RATIO // 8) * 8

#     # Ensure width and height remain above the minimum dimensions
#     width = max(width, 576) if width == FIXED_DIMENSION else width
#     height = max(height, 576) if height == FIXED_DIMENSION else height

#     return width, height

@spaces.GPU
def infer(edit_images, prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=3.5,control_strength=0.5, control_stop=0.33, num_inference_steps=50, progress=gr.Progress(track_tqdm=True)):
    image = edit_images["background"].convert("RGB")
    # width, height = calculate_optimal_dimensions(image)
    mask = edit_images["layers"][0].convert("RGB")
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    out_image = pipe(
        prompt=prompt,
        inpaint_image=image,
        inpaint_mask=mask,
        height=height,
        width=width,
        guidance_scale=guidance_scale,
        control_strength=control_strength, 
        control_stop=control_stop,
        num_inference_steps=num_inference_steps,
        generator=torch.Generator("cpu").manual_seed(seed)
    ).images[0]
    return (image, out_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: 1000px;
}
"""

with gr.Blocks(css=css) as demo:
    
    with gr.Column(elem_id="col-container"):
        gr.Markdown(f"""# Flex.2 Preview - Inpaint
Inpainting demo for Flex.2 Preview - Open Source 8B parameter Text to Image Diffusion Model with universal control and built-in inpainting support
trained and devloped by [ostris](https://huggingface.co/ostris)
[[apache-2.0 license](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md)] [[model](https://huggingface.co/ostris/Flex.2-preview)]
        """)
        with gr.Row():
            with gr.Column():
                edit_image = gr.ImageEditor(
                    label='Upload and draw mask for inpainting',
                    type='pil',
                    sources=["upload", "webcam"],
                    image_mode='RGB',
                    layers=False,
                    brush=gr.Brush(colors=["#FFFFFF"], color_mode="fixed"),
                    height=600
                )
                prompt = gr.Text(
                    label="Prompt",
                    show_label=False,
                    max_lines=1,
                    placeholder="Enter your prompt",
                    container=False,
                )
                run_button = gr.Button("Run")
                
            result = gr.ImageSlider(label="Generated Image", type="pil", image_mode='RGB')
        
        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():
                
                height = gr.Slider(64, 2048, value=512, step=64, label="Height")
                width = gr.Slider(64, 2048, value=512, step=64, label="Width")
            
            with gr.Row():

                guidance_scale = gr.Slider(0.0, 20.0, value=3.5, step=0.1, label="Guidance Scale")
                control_strength = gr.Slider(0.0, 1.0, value=0.5, step=0.05, label="Control Strength")
                control_stop = gr.Slider(0.0, 1.0, value=0.33, step=0.05, label="Control Stop")
                num_inference_steps = gr.Slider(1, 100, value=50, step=1, label="Inference Steps")

    run_button.click(
        fn = infer,
        inputs = [edit_image, prompt, seed, randomize_seed, width, height, guidance_scale, control_strength, control_stop,  num_inference_steps],
        outputs = [result, seed]
    )
    


demo.launch()