Delete app.py

app.py

@@ -1,188 +0,0 @@
-import gradio as gr
-import spaces
-import torch
-from diffusers import AutoencoderKL, TCDScheduler
-from diffusers.models.model_loading_utils import load_state_dict
-from gradio_imageslider import ImageSlider
-from huggingface_hub import hf_hub_download
-from PIL import ImageDraw, ImageFont, Image
-from controlnet_union import ControlNetModel_Union
-from pipeline_fill_sd_xl import StableDiffusionXLFillPipeline
-
-MODELS = {
-    "RealVisXL V5.0 Lightning": "SG161222/RealVisXL_V5.0_Lightning",
-}
-
-config_file = hf_hub_download(
-    "xinsir/controlnet-union-sdxl-1.0",
-    filename="config_promax.json",
-)
-
-config = ControlNetModel_Union.load_config(config_file)
-controlnet_model = ControlNetModel_Union.from_config(config)
-model_file = hf_hub_download(
-    "xinsir/controlnet-union-sdxl-1.0",
-    filename="diffusion_pytorch_model_promax.safetensors",
-)
-state_dict = load_state_dict(model_file)
-model, _, _, _, _ = ControlNetModel_Union._load_pretrained_model(
-    controlnet_model, state_dict, model_file, "xinsir/controlnet-union-sdxl-1.0"
-)
-model.to(device="cuda", dtype=torch.float16)
-
-vae = AutoencoderKL.from_pretrained(
-    "madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16
-).to("cuda")
-
-pipe = StableDiffusionXLFillPipeline.from_pretrained(
-    "SG161222/RealVisXL_V5.0_Lightning",
-    torch_dtype=torch.float16,
-    vae=vae,
-    controlnet=model,
-    variant="fp16",
-).to("cuda")
-
-pipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config)
-
-
-def add_watermark(image, text="ProFaker", font_path="BRLNSDB.TTF", font_size=25):
-    # Load the Berlin Sans Demi font with the specified size
-    font = ImageFont.truetype(font_path, font_size)
-
-    # Position the watermark in the bottom right corner, adjusting for text size
-    text_bbox = font.getbbox(text)
-    text_width, text_height = text_bbox[2], text_bbox[3]
-    watermark_position = (image.width - text_width - 100, image.height - text_height - 150)
-
-    # Draw the watermark text with a translucent white color
-    draw = ImageDraw.Draw(image)
-    draw.text(watermark_position, text, font=font, fill=(255, 255, 255, 150))  # RGBA for transparency
-
-    return image
-
-
-@spaces.GPU
-def fill_image(prompt, image, model_selection, paste_back):
-    (
-        prompt_embeds,
-        negative_prompt_embeds,
-        pooled_prompt_embeds,
-        negative_pooled_prompt_embeds,
-    ) = pipe.encode_prompt(prompt, "cuda", True)
-
-    source = image["background"]
-    mask = image["layers"][0]
-
-    alpha_channel = mask.split()[3]
-    binary_mask = alpha_channel.point(lambda p: p > 0 and 255)
-    cnet_image = source.copy()
-    cnet_image.paste(0, (0, 0), binary_mask)
-
-    for image in pipe(
-        prompt_embeds=prompt_embeds,
-        negative_prompt_embeds=negative_prompt_embeds,
-        pooled_prompt_embeds=pooled_prompt_embeds,
-        negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
-        image=cnet_image,
-    ):
-        yield image, cnet_image
-
-    print(f"{model_selection=}")
-    print(f"{paste_back=}")
-
-    if paste_back:
-        image = image.convert("RGBA")
-        cnet_image.paste(image, (0, 0), binary_mask)
-    else:
-        cnet_image = image
-
-    cnet_image = add_watermark(cnet_image)
-    yield source, cnet_image
-
-
-def clear_result():
-    return gr.update(value=None)
-
-
-title = """<h1 align="center">ProFaker's Editing</h1>"""
-
-with gr.Blocks() as demo:
-    gr.HTML(title)
-    with gr.Row():
-        with gr.Column():
-            prompt = gr.Textbox(
-                label="Prompt",
-                info="Describe what to inpaint the mask with",
-                lines=3,
-            )
-        with gr.Column():
-            model_selection = gr.Dropdown(
-                choices=list(MODELS.keys()),
-                value="RealVisXL V5.0 Lightning",
-                label="Model",
-            )
-
-            with gr.Row():
-                with gr.Column():
-                    run_button = gr.Button("Generate")
-
-                with gr.Column():
-                    paste_back = gr.Checkbox(True, label="Paste back original")
-
-    with gr.Row():
-        input_image = gr.ImageMask(
-            type="pil", label="Input Image", crop_size=(1024, 1024), layers=False
-        )
-
-        result = ImageSlider(
-            interactive=False,
-            label="Generated Image",
-        )
-
-    use_as_input_button = gr.Button("Use as Input Image", visible=False)
-
-    def use_output_as_input(output_image):
-        return gr.update(value=output_image[1])
-
-    use_as_input_button.click(
-        fn=use_output_as_input, inputs=[result], outputs=[input_image]
-    )
-
-    run_button.click(
-        fn=clear_result,
-        inputs=None,
-        outputs=result,
-    ).then(
-        fn=lambda: gr.update(visible=False),
-        inputs=None,
-        outputs=use_as_input_button,
-    ).then(
-        fn=fill_image,
-        inputs=[prompt, input_image, model_selection, paste_back],
-        outputs=result,
-    ).then(
-        fn=lambda: gr.update(visible=True),
-        inputs=None,
-        outputs=use_as_input_button,
-    )
-
-    prompt.submit(
-        fn=clear_result,
-        inputs=None,
-        outputs=result,
-    ).then(
-        fn=lambda: gr.update(visible=False),
-        inputs=None,
-        outputs=use_as_input_button,
-    ).then(
-        fn=fill_image,
-        inputs=[prompt, input_image, model_selection, paste_back],
-        outputs=result,
-    ).then(
-        fn=lambda: gr.update(visible=True),
-        inputs=None,
-        outputs=use_as_input_button,
-    )
-
-
-demo.queue(max_size=12).launch(share=False)