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Flux1-Schnell

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from functools import partial

import cv2
import random
from typing import Tuple, Optional

import gradio as gr
import numpy as np
import requests
import spaces
import torch
from PIL import Image, ImageFilter
from diffusers import FluxInpaintPipeline
from gradio_client import Client, handle_file

MARKDOWN = """
# FLUX Inpainting 
Model used is FLUX.1-schnell.
"""

MAX_SEED = np.iinfo(np.int32).max
IMAGE_SIZE = 1024
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
PIPE = FluxInpaintPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16).to(DEVICE)
CLIENT = Client("SkalskiP/florence-sam-masking")


EXAMPLES = [
    [
        {
            "background": Image.open(requests.get("https://media.roboflow.com/spaces/doge-2-image.png", stream=True).raw),
            "layers": [Image.open(requests.get("https://media.roboflow.com/spaces/doge-2-mask-2-removebg.png", stream=True).raw)],
            "composite": Image.open(requests.get("https://media.roboflow.com/spaces/doge-2-composite-2.png", stream=True).raw),
        },
        "little lion",
        "",
        5,
        5,
        42,
        False,
        0.85,
        20
    ],
    [
        {
            "background": Image.open(requests.get("https://media.roboflow.com/spaces/doge-5.jpeg", stream=True).raw),
            "layers": None,
            "composite": None
        },
        "big blue eyes",
        "eyes",
        10,
        5,
        42,
        False,
        0.9,
        20
    ]
]


def calculate_image_dimensions_for_flux(
    original_resolution_wh: Tuple[int, int],
    maximum_dimension: int = IMAGE_SIZE
) -> Tuple[int, int]:
    width, height = original_resolution_wh

    if width > height:
        scaling_factor = maximum_dimension / width
    else:
        scaling_factor = maximum_dimension / height

    new_width = int(width * scaling_factor)
    new_height = int(height * scaling_factor)

    new_width = new_width - (new_width % 32)
    new_height = new_height - (new_height % 32)

    return new_width, new_height


def is_mask_empty(image: Image.Image) -> bool:
    gray_img = image.convert("L")
    pixels = list(gray_img.getdata())
    return all(pixel == 0 for pixel in pixels)


def process_mask(
    mask: Image.Image,
    mask_inflation: Optional[int] = None,
    mask_blur: Optional[int] = None
) -> Image.Image:
    """
    Inflates and blurs the white regions of a mask.
    Args:
        mask (Image.Image): The input mask image.
        mask_inflation (Optional[int]): The number of pixels to inflate the mask by.
        mask_blur (Optional[int]): The radius of the Gaussian blur to apply.
    Returns:
        Image.Image: The processed mask with inflated and/or blurred regions.
    """
    if mask_inflation and mask_inflation > 0:
        mask_array = np.array(mask)
        kernel = np.ones((mask_inflation, mask_inflation), np.uint8)
        mask_array = cv2.dilate(mask_array, kernel, iterations=1)
        mask = Image.fromarray(mask_array)

    if mask_blur and mask_blur > 0:
        mask = mask.filter(ImageFilter.GaussianBlur(radius=mask_blur))

    return mask


def set_client_for_session(request: gr.Request):
    try:
        x_ip_token = request.headers['x-ip-token']
        return Client("SkalskiP/florence-sam-masking", headers={"X-IP-Token": x_ip_token})
    except:
        return CLIENT


@spaces.GPU(duration=50)
def run_flux(
    image: Image.Image,
    mask: Image.Image,
    prompt: str,
    seed_slicer: int,
    randomize_seed_checkbox: bool,
    strength_slider: float,
    num_inference_steps_slider: int,
    resolution_wh: Tuple[int, int],
) -> Image.Image:
    print("Running FLUX...")
    width, height = resolution_wh
    if randomize_seed_checkbox:
        seed_slicer = random.randint(0, MAX_SEED)
    generator = torch.Generator().manual_seed(seed_slicer)
    return PIPE(
        prompt=prompt,
        image=image,
        mask_image=mask,
        width=width,
        height=height,
        strength=strength_slider,
        generator=generator,
        num_inference_steps=num_inference_steps_slider
    ).images[0]


def process(
    client,
    input_image_editor: dict,
    inpainting_prompt_text: str,
    masking_prompt_text: str,
    mask_inflation_slider: int,
    mask_blur_slider: int,
    seed_slicer: int,
    randomize_seed_checkbox: bool,
    strength_slider: float,
    num_inference_steps_slider: int
):
    if not inpainting_prompt_text:
        gr.Info("Please enter inpainting text prompt.")
        return None, None

    image_path = input_image_editor['background']
    mask_path = input_image_editor['layers'][0]

    image = Image.open(image_path)
    mask = Image.open(mask_path)

    if not image:
        gr.Info("Please upload an image.")
        return None, None

    if is_mask_empty(mask) and not masking_prompt_text:
        gr.Info("Please draw a mask or enter a masking prompt.")
        return None, None

    if not is_mask_empty(mask) and masking_prompt_text:
        gr.Info("Both mask and masking prompt are provided. Please provide only one.")
        return None, None

    if is_mask_empty(mask):
        print("Generating mask...")
        mask = client.predict(
            image_input=handle_file(image_path),
            text_input=masking_prompt_text,
            api_name="/process_image")
        mask = Image.open(mask)
        print("Mask generated.")

    width, height = calculate_image_dimensions_for_flux(original_resolution_wh=image.size)
    image = image.resize((width, height), Image.LANCZOS)
    mask = mask.resize((width, height), Image.LANCZOS)
    mask = process_mask(mask, mask_inflation=mask_inflation_slider, mask_blur=mask_blur_slider)
    image = run_flux(
        image=image,
        mask=mask,
        prompt=inpainting_prompt_text,
        seed_slicer=seed_slicer,
        randomize_seed_checkbox=randomize_seed_checkbox,
        strength_slider=strength_slider,
        num_inference_steps_slider=num_inference_steps_slider,
        resolution_wh=(width, height)
    )
    return image, mask


process_example = partial(process, client=CLIENT)


with gr.Blocks() as demo:
    client_component = gr.State()
    gr.Markdown(MARKDOWN)
    with gr.Row():
        with gr.Column():
            input_image_editor_component = gr.ImageEditor(
                label='Image',
                type='filepath',
                sources=["upload", "webcam"],
                image_mode='RGB',
                layers=False,
                brush=gr.Brush(colors=["#FFFFFF"], color_mode="fixed"))

            with gr.Row():
                inpainting_prompt_text_component = gr.Text(
                    label="Inpainting prompt",
                    show_label=False,
                    max_lines=1,
                    placeholder="Enter text to generate inpainting",
                    container=False,
                )
                submit_button_component = gr.Button(
                    value='Submit', variant='primary', scale=0)

            with gr.Accordion("Advanced Settings", open=False):
                masking_prompt_text_component = gr.Text(
                    label="Masking prompt",
                    show_label=False,
                    max_lines=1,
                    placeholder="Enter text to generate masking",
                    container=False,
                )

                with gr.Row():
                    mask_inflation_slider_component = gr.Slider(
                        label="Mask inflation",
                        info="Adjusts the amount of mask edge expansion before "
                             "inpainting.",
                        minimum=0,
                        maximum=20,
                        step=1,
                        value=5,
                    )

                    mask_blur_slider_component = gr.Slider(
                        label="Mask blur",
                        info="Controls the intensity of the Gaussian blur applied to "
                             "the mask edges.",
                        minimum=0,
                        maximum=20,
                        step=1,
                        value=5,
                    )

                seed_slicer_component = gr.Slider(
                    label="Seed",
                    minimum=0,
                    maximum=MAX_SEED,
                    step=1,
                    value=42,
                )

                randomize_seed_checkbox_component = gr.Checkbox(
                    label="Randomize seed", value=True)

                with gr.Row():
                    strength_slider_component = gr.Slider(
                        label="Strength",
                        info="Indicates extent to transform the reference `image`. "
                             "Must be between 0 and 1. `image` is used as a starting "
                             "point and more noise is added the higher the `strength`.",
                        minimum=0,
                        maximum=1,
                        step=0.01,
                        value=0.85,
                    )

                    num_inference_steps_slider_component = gr.Slider(
                        label="Number of inference steps",
                        info="The number of denoising steps. More denoising steps "
                             "usually lead to a higher quality image at the",
                        minimum=1,
                        maximum=50,
                        step=1,
                        value=20,
                    )
        with gr.Column():
            output_image_component = gr.Image(
                type='pil', image_mode='RGB', label='Generated image', format="png")
            with gr.Accordion("Debug", open=False):
                output_mask_component = gr.Image(
                    type='pil', image_mode='RGB', label='Input mask', format="png")
    gr.Examples(
        fn=process_example,
        examples=EXAMPLES,
        inputs=[
            input_image_editor_component,
            inpainting_prompt_text_component,
            masking_prompt_text_component,
            mask_inflation_slider_component,
            mask_blur_slider_component,
            seed_slicer_component,
            randomize_seed_checkbox_component,
            strength_slider_component,
            num_inference_steps_slider_component
        ],
        outputs=[
            output_image_component,
            output_mask_component
        ],
        run_on_click=False
    )

    submit_button_component.click(
        fn=process,
        inputs=[
            client_component,
            input_image_editor_component,
            inpainting_prompt_text_component,
            masking_prompt_text_component,
            mask_inflation_slider_component,
            mask_blur_slider_component,
            seed_slicer_component,
            randomize_seed_checkbox_component,
            strength_slider_component,
            num_inference_steps_slider_component
        ],
        outputs=[
            output_image_component,
            output_mask_component
        ]
    )
    demo.load(set_client_for_session, None, client_component)

demo.launch(debug=False, show_error=True)



# from typing import Tuple

# import requests
# import random
# import numpy as np
# import gradio as gr
# import spaces
# import torch
# from PIL import Image
# from diffusers import FluxInpaintPipeline

# torch.cuda.empty_cache()


# MAX_SEED = np.iinfo(np.int32).max
# IMAGE_SIZE = 512  
# DEVICE = "cuda" if torch.cuda.is_available() else "cpu"


# def remove_background(image: Image.Image, threshold: int = 50) -> Image.Image:
#     image = image.convert("RGBA")
#     data = image.getdata()
#     new_data = []
#     for item in data:
#         avg = sum(item[:3]) / 3
#         if avg < threshold:
#             new_data.append((0, 0, 0, 0))
#         else:
#             new_data.append(item)

#     image.putdata(new_data)
#     return image


# # EXAMPLES = [
# #     [
# #         {
# #             "background": Image.open(requests.get("https://media.roboflow.com/spaces/doge-2-image.png", stream=True).raw),
# #             "layers": [remove_background(Image.open(requests.get("https://media.roboflow.com/spaces/doge-2-mask-2.png", stream=True).raw))],
# #             "composite": Image.open(requests.get("https://media.roboflow.com/spaces/doge-2-composite-2.png", stream=True).raw),
# #         },
# #         "little lion",
# #         42,
# #         False,
# #         0.85,
# #         30
# #     ],
# #     [
# #         {
# #             "background": Image.open(requests.get("https://media.roboflow.com/spaces/doge-2-image.png", stream=True).raw),
# #             "layers": [remove_background(Image.open(requests.get("https://media.roboflow.com/spaces/doge-2-mask-3.png", stream=True).raw))],
# #             "composite": Image.open(requests.get("https://media.roboflow.com/spaces/doge-2-composite-3.png", stream=True).raw),
# #         },
# #         "tribal tattoos",
# #         42,
# #         False,
# #         0.85,
# #         30
# #     ]
# # ]

# pipe = FluxInpaintPipeline.from_pretrained(
#     "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16).to(DEVICE)


# def resize_image_dimensions(
#     original_resolution_wh: Tuple[int, int],
#     maximum_dimension: int = IMAGE_SIZE
# ) -> Tuple[int, int]:
#     width, height = original_resolution_wh

#     # if width <= maximum_dimension and height <= maximum_dimension:
#     #     width = width - (width % 32)
#     #     height = height - (height % 32)
#     #     return width, height

#     if width > height:
#         scaling_factor = maximum_dimension / width
#     else:
#         scaling_factor = maximum_dimension / height

#     new_width = int(width * scaling_factor)
#     new_height = int(height * scaling_factor)

#     new_width = new_width - (new_width % 32)
#     new_height = new_height - (new_height % 32)

#     return new_width, new_height


# @spaces.GPU(duration=100)
# def process(
#     input_image_editor: dict,
#     input_text: str,
#     seed_slicer: int,
#     randomize_seed_checkbox: bool,
#     strength_slider: float,
#     num_inference_steps_slider: int,
#     progress=gr.Progress(track_tqdm=True)
# ):
#     if not input_text:
#         gr.Info("Please enter a text prompt.")
#         return None, None

#     image = input_image_editor['background']
#     mask = input_image_editor['layers'][0]

#     if not image:
#         gr.Info("Please upload an image.")
#         return None, None

#     if not mask:
#         gr.Info("Please draw a mask on the image.")
#         return None, None

#     width, height = resize_image_dimensions(original_resolution_wh=image.size)
#     resized_image = image.resize((width, height), Image.LANCZOS)
#     resized_mask = mask.resize((width, height), Image.LANCZOS)

#     if randomize_seed_checkbox:
#         seed_slicer = random.randint(0, MAX_SEED)
#     generator = torch.Generator().manual_seed(seed_slicer)
#     with torch.no_grad(), torch.autocast("cuda"):
#         result = pipe(
#             prompt=input_text,
#             image=resized_image,
#             mask_image=resized_mask,
#             width=width,
#             height=height,
#             strength=strength_slider,
#             generator=generator,
#             num_inference_steps=num_inference_steps_slider
#         ).images[0]
#     torch.cuda.empty_cache()
#     return result, resized_mask


# with gr.Blocks() as demo:
#     gr.Markdown(MARKDOWN)
#     with gr.Row():
#         with gr.Column():
#             input_image_editor_component = gr.ImageEditor(
#                 label='Image',
#                 type='pil',
#                 sources=["upload", "webcam"],
#                 image_mode='RGB',
#                 layers=False,
#                 brush=gr.Brush(colors=["#FFFFFF"], color_mode="fixed"))

#             with gr.Row():
#                 input_text_component = gr.Text(
#                     label="Prompt",
#                     show_label=False,
#                     max_lines=1,
#                     placeholder="Enter your prompt",
#                     container=False,
#                 )
#                 submit_button_component = gr.Button(
#                     value='Submit', variant='primary', scale=0)

#             with gr.Accordion("Advanced Settings", open=False):
#                 seed_slicer_component = gr.Slider(
#                     label="Seed",
#                     minimum=0,
#                     maximum=MAX_SEED,
#                     step=1,
#                     value=42,
#                 )

#                 randomize_seed_checkbox_component = gr.Checkbox(
#                     label="Randomize seed", value=True)

#                 with gr.Row():
#                     strength_slider_component = gr.Slider(
#                         label="Strength",
#                         # info="Indicates extent to transform the reference `image`. "
#                         #      "Must be between 0 and 1. `image` is used as a starting "
#                         #      "point and more noise is added the higher the `strength`.",
#                         minimum=0,
#                         maximum=1,
#                         step=0.01,
#                         value=0.85,
#                     )

#                     num_inference_steps_slider_component = gr.Slider(
#                         label="Number of inference steps",
#                         # info="The number of denoising steps. More denoising steps "
#                         #      "usually lead to a higher quality image at the",
#                         minimum=1,
#                         maximum=20,
#                         step=1,
#                         value=20,
#                     )
#         with gr.Column():
#             output_image_component = gr.Image(
#                 type='pil', image_mode='RGB', label='Generated image', format="png")
#             with gr.Accordion("Debug", open=False):
#                 output_mask_component = gr.Image(
#                     type='pil', image_mode='RGB', label='Input mask', format="png")
#     # with gr.Row():
#     #     gr.Examples(
#     #         fn=process,
#     #         examples=EXAMPLES,
#     #         inputs=[
#     #             input_image_editor_component,
#     #             input_text_component,
#     #             seed_slicer_component,
#     #             randomize_seed_checkbox_component,
#     #             strength_slider_component,
#     #             num_inference_steps_slider_component
#     #         ],
#     #         outputs=[
#     #             output_image_component,
#     #             output_mask_component
#     #         ],
#     #         run_on_click=True,
#     #         cache_examples=True
#     #     )

#     submit_button_component.click(
#         fn=process,
#         inputs=[
#             input_image_editor_component,
#             input_text_component,
#             seed_slicer_component,
#             randomize_seed_checkbox_component,
#             strength_slider_component,
#             num_inference_steps_slider_component
#         ],
#         outputs=[
#             output_image_component,
#             output_mask_component
#         ]
#     )

# demo.launch(share=True)