0dce0df1ee
Yep. Fix gfpgan_model_arch requirement(s). Add Upscaler base class, move from images. Add a lot of methods to Upscaler. Re-work all the child upscalers to be proper classes. Add BSRGAN scaler. Add ldsr_model_arch class, removing the dependency for another repo that just uses regular latent-diffusion stuff. Add one universal method that will always find and load new upscaler models without having to add new "setup_model" calls. Still need to add command line params, but that could probably be automated. Add a "self.scale" property to all Upscalers so the scalers themselves can do "things" in response to the requested upscaling size. Ensure LDSR doesn't get stuck in a longer loop of "upscale/downscale/upscale" as we try to reach the target upscale size. Add typehints for IDE sanity. PEP-8 improvements. Moar.
196 lines
7.0 KiB
Python
196 lines
7.0 KiB
Python
import os
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import numpy as np
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from PIL import Image
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import torch
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import tqdm
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from modules import processing, shared, images, devices
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from modules.shared import opts
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import modules.gfpgan_model
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from modules.ui import plaintext_to_html
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import modules.codeformer_model
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import piexif
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import piexif.helper
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cached_images = {}
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def run_extras(extras_mode, image, image_folder, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility):
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devices.torch_gc()
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imageArr = []
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# Also keep track of original file names
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imageNameArr = []
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if extras_mode == 1:
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#convert file to pillow image
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for img in image_folder:
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image = Image.fromarray(np.array(Image.open(img)))
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imageArr.append(image)
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imageNameArr.append(os.path.splitext(img.orig_name)[0])
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else:
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imageArr.append(image)
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imageNameArr.append(None)
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outpath = opts.outdir_samples or opts.outdir_extras_samples
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outputs = []
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for image, image_name in zip(imageArr, imageNameArr):
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if image is None:
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return outputs, "Please select an input image.", ''
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existing_pnginfo = image.info or {}
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image = image.convert("RGB")
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info = ""
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if gfpgan_visibility > 0:
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restored_img = modules.gfpgan_model.gfpgan_fix_faces(np.array(image, dtype=np.uint8))
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res = Image.fromarray(restored_img)
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if gfpgan_visibility < 1.0:
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res = Image.blend(image, res, gfpgan_visibility)
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info += f"GFPGAN visibility:{round(gfpgan_visibility, 2)}\n"
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image = res
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if codeformer_visibility > 0:
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restored_img = modules.codeformer_model.codeformer.restore(np.array(image, dtype=np.uint8), w=codeformer_weight)
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res = Image.fromarray(restored_img)
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if codeformer_visibility < 1.0:
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res = Image.blend(image, res, codeformer_visibility)
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info += f"CodeFormer w: {round(codeformer_weight, 2)}, CodeFormer visibility:{round(codeformer_visibility, 2)}\n"
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image = res
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def upscale(image, scaler_index, resize):
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small = image.crop((image.width // 2, image.height // 2, image.width // 2 + 10, image.height // 2 + 10))
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pixels = tuple(np.array(small).flatten().tolist())
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key = (resize, scaler_index, image.width, image.height, gfpgan_visibility, codeformer_visibility, codeformer_weight) + pixels
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c = cached_images.get(key)
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if c is None:
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upscaler = shared.sd_upscalers[scaler_index]
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c = upscaler.scaler.upscale(image, resize, upscaler.data_path)
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cached_images[key] = c
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return c
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info += f"Upscale: {round(upscaling_resize, 3)}, model:{shared.sd_upscalers[extras_upscaler_1].name}\n"
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res = upscale(image, extras_upscaler_1, upscaling_resize)
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if extras_upscaler_2 != 0 and extras_upscaler_2_visibility > 0:
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res2 = upscale(image, extras_upscaler_2, upscaling_resize)
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info += f"Upscale: {round(upscaling_resize, 3)}, visibility: {round(extras_upscaler_2_visibility, 3)}, model:{shared.sd_upscalers[extras_upscaler_2].name}\n"
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res = Image.blend(res, res2, extras_upscaler_2_visibility)
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image = res
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while len(cached_images) > 2:
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del cached_images[next(iter(cached_images.keys()))]
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images.save_image(image, path=outpath, basename="", seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True,
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no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo,
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forced_filename=image_name if opts.use_original_name_batch else None)
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outputs.append(image)
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return outputs, plaintext_to_html(info), ''
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def run_pnginfo(image):
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if image is None:
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return '', '', ''
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items = image.info
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geninfo = ''
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if "exif" in image.info:
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exif = piexif.load(image.info["exif"])
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exif_comment = (exif or {}).get("Exif", {}).get(piexif.ExifIFD.UserComment, b'')
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try:
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exif_comment = piexif.helper.UserComment.load(exif_comment)
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except ValueError:
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exif_comment = exif_comment.decode('utf8', errors="ignore")
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items['exif comment'] = exif_comment
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geninfo = exif_comment
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for field in ['jfif', 'jfif_version', 'jfif_unit', 'jfif_density', 'dpi', 'exif',
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'loop', 'background', 'timestamp', 'duration']:
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items.pop(field, None)
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geninfo = items.get('parameters', geninfo)
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info = ''
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for key, text in items.items():
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info += f"""
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<div>
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<p><b>{plaintext_to_html(str(key))}</b></p>
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<p>{plaintext_to_html(str(text))}</p>
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</div>
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""".strip()+"\n"
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if len(info) == 0:
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message = "Nothing found in the image."
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info = f"<div><p>{message}<p></div>"
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return '', geninfo, info
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def run_modelmerger(modelname_0, modelname_1, interp_method, interp_amount):
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# Linear interpolation (https://en.wikipedia.org/wiki/Linear_interpolation)
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def weighted_sum(theta0, theta1, alpha):
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return ((1 - alpha) * theta0) + (alpha * theta1)
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# Smoothstep (https://en.wikipedia.org/wiki/Smoothstep)
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def sigmoid(theta0, theta1, alpha):
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alpha = alpha * alpha * (3 - (2 * alpha))
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return theta0 + ((theta1 - theta0) * alpha)
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if os.path.exists(modelname_0):
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model0_filename = modelname_0
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modelname_0 = os.path.splitext(os.path.basename(modelname_0))[0]
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else:
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model0_filename = 'models/' + modelname_0 + '.ckpt'
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if os.path.exists(modelname_1):
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model1_filename = modelname_1
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modelname_1 = os.path.splitext(os.path.basename(modelname_1))[0]
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else:
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model1_filename = 'models/' + modelname_1 + '.ckpt'
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print(f"Loading {model0_filename}...")
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model_0 = torch.load(model0_filename, map_location='cpu')
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print(f"Loading {model1_filename}...")
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model_1 = torch.load(model1_filename, map_location='cpu')
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theta_0 = model_0['state_dict']
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theta_1 = model_1['state_dict']
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theta_funcs = {
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"Weighted Sum": weighted_sum,
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"Sigmoid": sigmoid,
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}
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theta_func = theta_funcs[interp_method]
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print(f"Merging...")
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for key in tqdm.tqdm(theta_0.keys()):
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if 'model' in key and key in theta_1:
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theta_0[key] = theta_func(theta_0[key], theta_1[key], interp_amount)
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for key in theta_1.keys():
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if 'model' in key and key not in theta_0:
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theta_0[key] = theta_1[key]
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output_modelname = 'models/' + modelname_0 + '-' + modelname_1 + '-merged.ckpt'
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print(f"Saving to {output_modelname}...")
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torch.save(model_0, output_modelname)
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print(f"Checkpoint saved.")
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return "Checkpoint saved to " + output_modelname
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