Delete app_upscale.py

app_upscale.py

@@ -1,95 +0,0 @@
-import requests
-from PIL import Image
-from io import BytesIO
-from diffusers import StableDiffusionUpscalePipeline
-import torch
-import gradio as gr
-import time
-import spaces
-
-from segment_utils import(
-    segment_image,
-    restore_result,
-)
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-print(f'{device} is available')
-
-model_id = "stabilityai/stable-diffusion-x4-upscaler"
-upscale_pipe = StableDiffusionUpscalePipeline.from_pretrained(model_id, torch_dtype=torch.float16)
-upscale_pipe = upscale_pipe.to(device)
-
-DEFAULT_SRC_PROMPT = "a person with pefect face"
-DEFAULT_CATEGORY = "face"
-
-
-def create_demo() -> gr.Blocks:
-
-    @spaces.GPU(duration=30)
-    def upscale_image(
-        input_image: Image,
-        prompt: str,
-        num_inference_steps: int = 10,
-    ):
-        time_cost_str = ''
-        run_task_time = 0
-        run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str)
-        upscaled_image = upscale_pipe(
-            prompt=prompt, 
-            image=input_image,
-            num_inference_steps=num_inference_steps,
-        ).images[0]
-        run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str)
-        
-        return upscaled_image, time_cost_str
-
-    def get_time_cost(run_task_time, time_cost_str):
-        now_time = int(time.time()*1000)
-        if run_task_time == 0:
-            time_cost_str = 'start'
-        else:
-            if time_cost_str != '': 
-                time_cost_str += f'-->'
-            time_cost_str += f'{now_time - run_task_time}'
-        run_task_time = now_time
-        return run_task_time, time_cost_str
-
-    with gr.Blocks() as demo:
-        croper = gr.State()
-        with gr.Row():
-            with gr.Column():
-                input_image_prompt = gr.Textbox(lines=1, label="Input Image Prompt", value=DEFAULT_SRC_PROMPT)
-            with gr.Column():
-                num_inference_steps = gr.Number(label="Num Inference Steps", value=5)
-                generate_size = gr.Number(label="Generate Size", value=512)
-                g_btn = gr.Button("Upscale Image")
-                
-        with gr.Row():
-            with gr.Column():
-                input_image = gr.Image(label="Input Image", type="pil")
-            with gr.Column():
-                restored_image = gr.Image(label="Restored Image", format="png", type="pil", interactive=False)
-                origin_area_image = gr.Image(label="Origin Area Image", format="png", type="pil", interactive=False, visible=False)
-                upscaled_image = gr.Image(label="Upscaled Image", format="png", type="pil", interactive=False)
-                download_path = gr.File(label="Download the output image", interactive=False)
-                generated_cost = gr.Textbox(label="Time cost by step (ms):", visible=True, interactive=False)
-                category = gr.Textbox(label="Category", value=DEFAULT_CATEGORY, visible=False)
-                mask_expansion = gr.Number(label="Mask Expansion", value=20, visible=False)
-                mask_dilation = gr.Slider(minimum=0, maximum=10, value=2, step=1, label="Mask Dilation", visible=False)
-
-        g_btn.click(
-            fn=segment_image,
-            inputs=[input_image, category, generate_size, mask_expansion, mask_dilation],
-            outputs=[origin_area_image, croper],
-        ).success(
-            fn=upscale_image,
-            inputs=[origin_area_image, input_image_prompt, num_inference_steps],
-            outputs=[upscaled_image, generated_cost],
-        ).success(
-            fn=restore_result,
-            inputs=[croper, category, upscaled_image],
-            outputs=[restored_image, download_path],
-        )
-
-    return demo