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| import gradio as gr | |
| from PIL import Image, ImageOps, ImageEnhance | |
| import torch | |
| from diffusers import DiffusionPipeline | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| if torch.cuda.is_available(): | |
| torch.cuda.max_memory_allocated(device=device) | |
| pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16", use_safetensors=True) | |
| pipe.enable_xformers_memory_efficient_attention() | |
| pipe = pipe.to(device) | |
| else: | |
| pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", use_safetensors=True) | |
| pipe = pipe.to(device) | |
| def edit_image(image, operation, *args): | |
| if operation == "rotate": | |
| angle = int(args[0]) | |
| image = image.rotate(angle, expand=True) | |
| elif operation == "crop": | |
| left, top, right, bottom = map(int, args) | |
| image = image.crop((left, top, right, bottom)) | |
| elif operation == "resize": | |
| width, height = map(int, args) | |
| image = image.resize((width, height)) | |
| elif operation == "flip": | |
| if args[0] == "horizontal": | |
| image = ImageOps.mirror(image) | |
| else: | |
| image = ImageOps.flip(image) | |
| elif operation == "color": | |
| factor = float(args[0]) | |
| image = ImageEnhance.Color(image).enhance(factor) | |
| return image | |
| def create_demo(): | |
| with gr.Blocks() as demo: | |
| with gr.Row(): | |
| gr.Markdown("# Image Editor") | |
| with gr.Row(): | |
| with gr.Column(): | |
| edit_operation = gr.Dropdown(choices=["rotate", "crop", "resize", "flip", "color"], label="Edit Operation") | |
| input_args = gr.State([]) | |
| angle = gr.Slider(0, 360, step=1, label="Angle", visible=False) | |
| left = gr.Slider(0, 500, step=1, label="Left", visible=False) | |
| top = gr.Slider(0, 500, step=1, label="Top", visible=False) | |
| right = gr.Slider(0, 500, step=1, label="Right", visible=False) | |
| bottom = gr.Slider(0, 500, step=1, label="Bottom", visible=False) | |
| width = gr.Slider(50, 1000, step=1, label="Width", visible=False) | |
| height = gr.Slider(50, 1000, step=1, label="Height", visible=False) | |
| flip_direction = gr.Dropdown(choices=["horizontal", "vertical"], label="Direction", visible=False) | |
| color_factor = gr.Slider(0.1, 2.0, step=0.1, label="Color Factor", visible=False) | |
| edit_button = gr.Button("Edit Image") | |
| uploaded_image = gr.Image(label="Upload Image", type="pil") | |
| edited_image = gr.Image(label="Edited Image", type="pil", interactive=True) | |
| def update_inputs(operation): | |
| if operation == "rotate": | |
| return [gr.update(visible=True), angle] | |
| elif operation == "crop": | |
| return [gr.update(visible=True), left, top, right, bottom] | |
| elif operation == "resize": | |
| return [gr.update(visible=True), width, height] | |
| elif operation == "flip": | |
| return [gr.update(visible=True), flip_direction] | |
| elif operation == "color": | |
| return [gr.update(visible=True), color_factor] | |
| else: | |
| return [] | |
| edit_operation.change(fn=update_inputs, inputs=[edit_operation], outputs=[angle, left, top, right, bottom, width, height, flip_direction, color_factor]) | |
| edit_button.click( | |
| fn=lambda img_data, operation, *args: edit_image(img_data, operation, *args), | |
| inputs=[uploaded_image, edit_operation, angle, left, top, right, bottom, width, height, flip_direction, color_factor], | |
| outputs=[edited_image] | |
| ) | |
| return demo | |
| demo = create_demo() | |
| demo.queue().launch() | |