Delete app.py

app.py

@@ -1,128 +0,0 @@
-from PIL import Image
-from diffusers import StableDiffusionUpscalePipeline
-import torch
-from split_image import split
-import os
-import random
-
-
-def split_image(im, rows, cols, should_square, should_quiet=False):
-    im_width, im_height = im.size
-    row_width = int(im_width / cols)
-    row_height = int(im_height / rows)
-    name = "image"
-    ext = ".png"
-    name = os.path.basename(name)
-    images = []
-    if should_square:
-        min_dimension = min(im_width, im_height)
-        max_dimension = max(im_width, im_height)
-        if not should_quiet:
-            print("Resizing image to a square...")
-            print("Determining background color...")
-        bg_color = split.determine_bg_color(im)
-        if not should_quiet:
-            print("Background color is... " + str(bg_color))
-        im_r = Image.new("RGBA" if ext == "png" else "RGB",
-                         (max_dimension, max_dimension), bg_color)
-        offset = int((max_dimension - min_dimension) / 2)
-        if im_width > im_height:
-            im_r.paste(im, (0, offset))
-        else:
-            im_r.paste(im, (offset, 0))
-        im = im_r
-        row_width = int(max_dimension / cols)
-        row_height = int(max_dimension / rows)
-    n = 0
-    for i in range(0, rows):
-        for j in range(0, cols):
-            box = (j * row_width, i * row_height, j * row_width +
-                   row_width, i * row_height + row_height)
-            outp = im.crop(box)
-            outp_path = name + "_" + str(n) + ext
-            if not should_quiet:
-                print("Exporting image tile: " + outp_path)
-            images.append(outp)
-            n += 1
-    return [img for img in images]
-
-def upscale_image(img, rows, cols,seed,prompt,negative_prompt,xformers,cpu_offload,attention_slicing,enable_custom_sliders=False,guidance=7,iterations=50):
-    model_id = "stabilityai/stable-diffusion-x4-upscaler"
-    try:
-        pipeline = StableDiffusionUpscalePipeline.from_pretrained(model_id, torch_dtype=torch.float16)
-    except:
-        pipeline = StableDiffusionUpscalePipeline.from_pretrained(model_id, torch_dtype=torch.float16, local_files_only=True)
-    pipeline = pipeline.to("cuda")
-    if xformers:
-        pipeline.enable_xformers_memory_efficient_attention()
-    else:
-        pipeline.disable_xformers_memory_efficient_attention()
-    if cpu_offload:
-        try:
-            pipeline.enable_sequential_cpu_offload()
-        except:
-            pass
-    if attention_slicing:
-        pipeline.enable_attention_slicing()
-    else:
-        pipeline.disable_attention_slicing()
-    img = Image.fromarray(img)
-    # load model and scheduler
-    if seed==-1:
-        generator = torch.manual_seed(random.randint(0, 9999999))
-    else:
-        generator = torch.manual_seed(seed)
-    
-    original_width, original_height = img.size
-    max_dimension = max(original_width, original_height)
-    tiles = split_image(img, rows, cols, True, False)
-    ups_tiles = []
-    i = 0
-    for x in tiles:
-        i=i+1
-        if enable_custom_sliders:
-            ups_tile = pipeline(prompt=prompt,negative_prompt=negative_prompt,guidance_scale=guidance, num_inference_steps=iterations, image=x.convert("RGB"),generator=generator).images[0]
-        else:
-            ups_tile = pipeline(prompt=prompt,negative_prompt=negative_prompt, image=x.convert("RGB"),generator=generator).images[0]
-        ups_tiles.append(ups_tile)
-        
-    # Determine the size of the merged upscaled image
-    total_width = 0
-    total_height = 0
-    side = 0
-    for ups_tile in ups_tiles:
-        side = ups_tile.width
-        break
-    for x in tiles:
-        tsize = x.width
-        break
-
-    ups_times = abs(side/tsize)
-    new_size = (max_dimension * ups_times, max_dimension * ups_times)
-    total_width = cols*side
-    total_height = rows*side
-
-    # Create a blank image with the calculated size
-    merged_image = Image.new("RGB", (total_width, total_height))
-
-    # Paste each upscaled tile into the blank image
-    current_width = 0
-    current_height = 0
-    maximum_width = cols*side
-    for ups_tile in ups_tiles:
-        merged_image.paste(ups_tile, (current_width, current_height))
-        current_width += ups_tile.width
-        if current_width>=maximum_width:
-            current_width = 0
-            current_height = current_height+side
-
-    # Using the center of the image as pivot, crop the image to the original dimension times four
-    crop_left = (new_size[0] - original_width * ups_times) // 2
-    crop_upper = (new_size[1] - original_height * ups_times) // 2
-    crop_right = crop_left + original_width * ups_times
-    crop_lower = crop_upper + original_height * ups_times
-    final_img = merged_image.crop((crop_left, crop_upper, crop_right, crop_lower))
-
-    # The resulting image should be identical to the original image in proportions / aspect ratio, with no loss of elements.
-    # Save the merged image
-    return final_img