mirror of
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2025-08-05 03:32:37 +00:00
Refactor upscale_2 helper out of ScuNET/SwinIR; make sure devices are right
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@@ -1,14 +1,10 @@
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import logging
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import sys
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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 modules import modelloader, devices, script_callbacks, shared
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from modules.shared import opts
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from modules import devices, modelloader, script_callbacks, shared, upscaler_utils
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from modules.upscaler import Upscaler, UpscalerData
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from modules.upscaler_utils import tiled_upscale_2
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SWINIR_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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@@ -36,9 +32,7 @@ class UpscalerSwinIR(Upscaler):
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self.scalers = scalers
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def do_upscale(self, img: Image.Image, model_file: str) -> Image.Image:
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current_config = (model_file, opts.SWIN_tile)
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device = self._get_device()
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current_config = (model_file, shared.opts.SWIN_tile)
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if self._cached_model_config == current_config:
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model = self._cached_model
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@@ -51,12 +45,13 @@ class UpscalerSwinIR(Upscaler):
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self._cached_model = model
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self._cached_model_config = current_config
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img = upscale(
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img = upscaler_utils.upscale_2(
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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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device=device,
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tile_size=shared.opts.SWIN_tile,
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tile_overlap=shared.opts.SWIN_tile_overlap,
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scale=4, # TODO: This was hard-coded before too...
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desc="SwinIR",
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)
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devices.torch_gc()
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return img
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@@ -77,7 +72,7 @@ class UpscalerSwinIR(Upscaler):
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dtype=devices.dtype,
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expected_architecture="SwinIR",
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)
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if getattr(opts, 'SWIN_torch_compile', False):
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if getattr(shared.opts, 'SWIN_torch_compile', False):
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try:
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model_descriptor.model.compile()
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except Exception:
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@@ -88,47 +83,6 @@ class UpscalerSwinIR(Upscaler):
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return devices.get_device_for('swinir')
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def upscale(
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img,
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model,
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*,
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tile: int,
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tile_overlap: int,
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window_size=8,
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scale=4,
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device,
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):
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img = np.array(img)
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img = img[:, :, ::-1]
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img = np.moveaxis(img, 2, 0) / 255
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img = torch.from_numpy(img).float()
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img = img.unsqueeze(0).to(device, dtype=devices.dtype)
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with torch.no_grad(), devices.autocast():
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_, _, h_old, w_old = img.size()
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h_pad = (h_old // window_size + 1) * window_size - h_old
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w_pad = (w_old // window_size + 1) * window_size - w_old
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img = torch.cat([img, torch.flip(img, [2])], 2)[:, :, : h_old + h_pad, :]
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img = torch.cat([img, torch.flip(img, [3])], 3)[:, :, :, : w_old + w_pad]
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output = tiled_upscale_2(
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img,
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model,
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tile_size=tile,
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tile_overlap=tile_overlap,
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scale=scale,
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device=device,
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desc="SwinIR tiles",
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)
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output = output[..., : h_old * scale, : w_old * scale]
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output = output.data.squeeze().float().cpu().clamp_(0, 1).numpy()
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if output.ndim == 3:
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output = np.transpose(
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output[[2, 1, 0], :, :], (1, 2, 0)
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) # CHW-RGB to HCW-BGR
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output = (output * 255.0).round().astype(np.uint8) # float32 to uint8
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return Image.fromarray(output, "RGB")
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def on_ui_settings():
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import gradio as gr
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