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| 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 | |
| MARKDOWN = """ | |
| # FLUX.1 Inpainting 🔥 | |
| Shoutout to [Black Forest Labs](https://huggingface.co/black-forest-labs) team for | |
| creating this amazing model, and a big thanks to [Gothos](https://github.com/Gothos) | |
| for taking it to the next level by enabling inpainting with the FLUX. | |
| """ | |
| MAX_SEED = np.iinfo(np.int32).max | |
| IMAGE_SIZE = 1024 | |
| 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, | |
| None | |
| ], | |
| [ | |
| { | |
| "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, | |
| None | |
| ] | |
| ] | |
| 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 | |
| def process( | |
| input_image_editor: dict, | |
| input_text: str, | |
| seed_slicer: int, | |
| randomize_seed_checkbox: bool, | |
| strength_slider: float, | |
| num_inference_steps_slider: int, | |
| uploaded_mask: Image.Image, | |
| 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'] | |
| if uploaded_mask is None: | |
| mask = input_image_editor['layers'][0] | |
| else: | |
| mask = uploaded_mask | |
| if not image: | |
| gr.Info("Please upload an image.") | |
| return None, None | |
| if not mask: | |
| gr.Info("Please draw a mask on the image or upload a mask.") | |
| 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) | |
| 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] | |
| print('INFERENCE DONE') | |
| 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("Upload a mask", open = False): | |
| uploaded_mask_component = gr.Image(label = "Already made mask (white pixels will be preserved, black pixels will be redrawn)", sources = ["upload"], type = "pil") | |
| 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=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") | |
| 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, | |
| uploaded_mask_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, | |
| uploaded_mask_component | |
| ], | |
| outputs=[ | |
| output_image_component, | |
| output_mask_component | |
| ] | |
| ) | |
| demo.launch(debug=False, show_error=True) | |