mirror of
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2025-08-08 13:19:54 +00:00
Holy $hit.
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.
This commit is contained in:
d8ahazard
@@ -1,92 +1,91 @@
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import contextlib
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import os
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import sys
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import traceback
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import numpy as np
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import torch
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from PIL import Image
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from basicsr.utils.download_util import load_file_from_url
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import modules.images
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from modules import modelloader
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from modules.paths import models_path
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from modules.shared import cmd_opts, opts, device
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from modules.swinir_model_arch import SwinIR as net
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from modules.upscaler import Upscaler, UpscalerData
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model_dir = "SwinIR"
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model_url = "https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR-L_x4_GAN.pth"
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model_name = "SwinIR x4"
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model_path = os.path.join(models_path, model_dir)
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cmd_path = ""
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precision_scope = (
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torch.autocast if cmd_opts.precision == "autocast" else contextlib.nullcontext
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)
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def load_model(path, scale=4):
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global model_path
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global model_name
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if "http" in path:
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dl_name = "%s%s" % (model_name.replace(" ", "_"), ".pth")
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filename = load_file_from_url(url=path, model_dir=model_path, file_name=dl_name, progress=True)
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else:
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filename = path
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if filename is None or not os.path.exists(filename):
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return None
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model = net(
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upscale=scale,
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in_chans=3,
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img_size=64,
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window_size=8,
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img_range=1.0,
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depths=[6, 6, 6, 6, 6, 6, 6, 6, 6],
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embed_dim=240,
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num_heads=[8, 8, 8, 8, 8, 8, 8, 8, 8],
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mlp_ratio=2,
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upsampler="nearest+conv",
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resi_connection="3conv",
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)
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class UpscalerSwinIR(Upscaler):
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def __init__(self, dirname):
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self.name = "SwinIR"
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self.model_url = "https://github.com/JingyunLiang/SwinIR/releases/download/v0.0" \
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"/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR" \
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"-L_x4_GAN.pth "
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self.model_name = "SwinIR 4x"
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self.model_path = os.path.join(models_path, self.name)
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self.user_path = dirname
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super().__init__()
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scalers = []
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model_files = self.find_models(ext_filter=[".pt", ".pth"])
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for model in model_files:
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if "http" in model:
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name = self.model_name
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else:
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name = modelloader.friendly_name(model)
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model_data = UpscalerData(name, model, self)
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scalers.append(model_data)
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self.scalers = scalers
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pretrained_model = torch.load(filename)
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model.load_state_dict(pretrained_model["params_ema"], strict=True)
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if not cmd_opts.no_half:
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model = model.half()
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return model
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def do_upscale(self, img, model_file):
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model = self.load_model(model_file)
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if model is None:
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return img
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model = model.to(device)
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img = upscale(img, model)
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try:
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torch.cuda.empty_cache()
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except:
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pass
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return img
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def setup_model(dirname):
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global model_path
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global model_name
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global cmd_path
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if not os.path.exists(model_path):
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os.makedirs(model_path)
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cmd_path = dirname
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model_file = ""
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try:
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models = modelloader.load_models(model_path, ext_filter=[".pt", ".pth"], command_path=cmd_path)
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if len(models) != 0:
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model_file = models[0]
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name = modelloader.friendly_name(model_file)
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def load_model(self, path, scale=4):
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if "http" in path:
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dl_name = "%s%s" % (self.name.replace(" ", "_"), ".pth")
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filename = load_file_from_url(url=path, model_dir=self.model_path, file_name=dl_name, progress=True)
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else:
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# Add the "default" model if none are found.
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model_file = model_url
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name = model_name
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filename = path
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if filename is None or not os.path.exists(filename):
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return None
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model = net(
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upscale=scale,
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in_chans=3,
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img_size=64,
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window_size=8,
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img_range=1.0,
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depths=[6, 6, 6, 6, 6, 6, 6, 6, 6],
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embed_dim=240,
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num_heads=[8, 8, 8, 8, 8, 8, 8, 8, 8],
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mlp_ratio=2,
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upsampler="nearest+conv",
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resi_connection="3conv",
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)
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modules.shared.sd_upscalers.append(UpscalerSwin(model_file, name))
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except Exception:
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print(f"Error loading SwinIR model: {model_file}", file=sys.stderr)
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print(traceback.format_exc(), file=sys.stderr)
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pretrained_model = torch.load(filename)
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model.load_state_dict(pretrained_model["params_ema"], strict=True)
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if not cmd_opts.no_half:
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model = model.half()
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return model
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def upscale(
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img,
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model,
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tile=opts.SWIN_tile,
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tile_overlap=opts.SWIN_tile_overlap,
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window_size=8,
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scale=4,
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img,
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model,
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tile=opts.SWIN_tile,
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tile_overlap=opts.SWIN_tile_overlap,
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window_size=8,
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scale=4,
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):
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img = np.array(img)
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img = img[:, :, ::-1]
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@@ -125,34 +124,16 @@ def inference(img, model, tile, tile_overlap, window_size, scale):
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for h_idx in h_idx_list:
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for w_idx in w_idx_list:
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in_patch = img[..., h_idx : h_idx + tile, w_idx : w_idx + tile]
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in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile]
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out_patch = model(in_patch)
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out_patch_mask = torch.ones_like(out_patch)
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E[
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..., h_idx * sf : (h_idx + tile) * sf, w_idx * sf : (w_idx + tile) * sf
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..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
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].add_(out_patch)
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W[
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..., h_idx * sf : (h_idx + tile) * sf, w_idx * sf : (w_idx + tile) * sf
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..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
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].add_(out_patch_mask)
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output = E.div_(W)
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return output
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class UpscalerSwin(modules.images.Upscaler):
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def __init__(self, filename, title):
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self.name = title
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self.filename = filename
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def do_upscale(self, img):
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model = load_model(self.filename)
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if model is None:
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return img
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model = model.to(device)
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img = upscale(img, model)
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try:
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torch.cuda.empty_cache()
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except:
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pass
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return img
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