app.py

import gradio as gr
import numpy as np

import spaces
import random
# from image_gen_aux import DepthPreprocessor
from PIL import Image
import torch
from torchvision import transforms

from diffusers import FluxFillPipeline, AutoencoderKL
from PIL import Image


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

pipe = FluxFillPipeline.from_pretrained("black-forest-labs/FLUX.1-Fill-dev", torch_dtype=torch.bfloat16).to("cuda")
pipe.load_lora_weights("alvdansen/flux-koda")
pipe.enable_lora()

# vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev", subfolder="vae")
# processor = DepthPreprocessor.from_pretrained("LiheYoung/depth-anything-large-hf")

# preprocess = transforms.Compose(
#     [
#         transforms.Resize(
#             (vae.config.sample_size, vae.config.sample_size),
#             interpolation=transforms.InterpolationMode.BILINEAR,
#         ),
#         transforms.ToTensor(),
#         transforms.Normalize([0.5], [0.5]),
#     ]
# )
#
# image_np = image[0].cpu().numpy()  # Move to CPU and convert to NumPy

# if image_np.shape[0] == 3:  # Check if channels are first
#     image_np = image_np.transpose(1, 2, 0)

# image_np = (image_np * 255).astype(np.uint8)

# image = Image.fromarray(image_np)

# 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(durations=300)
def infer(edit_images, prompt, prompt2, width, height, seed=42, randomize_seed=False, guidance_scale=3.5, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
    # pipe.enable_xformers_memory_efficient_attention()

    image = edit_images["background"]
    # width, height = calculate_optimal_dimensions(image)
    mask = edit_images["layers"][0]
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)

    # controlImage = processor(image)
    image = pipe(
        # mask_image_latent=vae.encode(controlImage),
        prompt=prompt,
        prompt_2=prompt2,
        image=image,
        mask_image=mask,
        height=height,
        width=width,
        guidance_scale=guidance_scale,
        num_inference_steps=num_inference_steps,
        generator=torch.Generator(device='cuda').manual_seed(seed),
        # lora_scale=0.75 // not supported in this version
    ).images[0]

    output_image_jpg = image.convert("RGB")
    output_image_jpg.save("output.jpg", "JPEG")

    return output_image_jpg, seed
    # return image, seed

examples = [
    "photography of a young woman,  accent lighting,  (front view:1.4),  "
    # "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"""# FLUX.1 [dev]
        """)
        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"]),
                    # height=600
                )
                prompt = gr.Text(
                    label="Prompt",
                    show_label=False,
                    max_lines=2,
                    placeholder="Enter your prompt",
                    container=False,
                )
                prompt2 = gr.Text(
                    label="Prompt2",
                    show_label=False,
                    max_lines=2,
                    placeholder="Enter your second prompt",
                    container=False,
                )
                run_button = gr.Button("Run")

            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,
                    visible=False
                )

                height = gr.Slider(
                    label="Height",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=1024,
                    visible=False
                )

            with gr.Row():

                guidance_scale = gr.Slider(
                    label="Guidance Scale",
                    minimum=1,
                    maximum=30,
                    step=0.5,
                    value=50,
                )

                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=50,
                    step=1,
                    value=28,
                )

            with gr.Row():

                width = gr.Slider(
                    label="width",
                    minimum=512,
                    maximum=3072,
                    step=1,
                    value=1024,
                )

                num_inference_steps = gr.Slider(
                    label="height",
                    minimum=512,
                    maximum=3072,
                    step=1,
                    value=1024,
                )

    gr.on(
        triggers=[run_button.click, prompt.submit],
        fn = infer,
        inputs = [edit_image, prompt, prompt2, width, height, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
        outputs = [result, seed]
    )

demo.launch()