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https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
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commit
3f7f61e541
@ -1,6 +1,7 @@
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import logging
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import sys
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import torch
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from PIL import Image
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from modules import devices, modelloader, script_callbacks, shared, upscaler_utils
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@ -50,7 +51,7 @@ class UpscalerSwinIR(Upscaler):
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model,
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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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scale=model.scale,
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desc="SwinIR",
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)
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devices.torch_gc()
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@ -69,7 +70,7 @@ class UpscalerSwinIR(Upscaler):
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model_descriptor = modelloader.load_spandrel_model(
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filename,
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device=self._get_device(),
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dtype=devices.dtype,
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prefer_half=(devices.dtype == torch.float16),
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expected_architecture="SwinIR",
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)
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if getattr(shared.opts, 'SWIN_torch_compile', False):
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@ -94,6 +94,7 @@ def tiled_upscale_2(
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tile_size: int,
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tile_overlap: int,
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scale: int,
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device: torch.device,
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desc="Tiled upscale",
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):
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# Alternative implementation of `upscale_with_model` originally used by
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@ -101,9 +102,6 @@ def tiled_upscale_2(
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# weighting is done in PyTorch space, as opposed to `images.Grid` doing it in
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# Pillow space without weighting.
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# Grab the device the model is on, and use it.
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device = torch_utils.get_param(model).device
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b, c, h, w = img.size()
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tile_size = min(tile_size, h, w)
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@ -175,7 +173,8 @@ def upscale_2(
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"""
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Convenience wrapper around `tiled_upscale_2` that handles PIL images.
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"""
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tensor = pil_image_to_torch_bgr(img).float().unsqueeze(0) # add batch dimension
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param = torch_utils.get_param(model)
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tensor = pil_image_to_torch_bgr(img).to(dtype=param.dtype).unsqueeze(0) # add batch dimension
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with torch.no_grad():
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output = tiled_upscale_2(
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@ -185,5 +184,6 @@ def upscale_2(
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tile_overlap=tile_overlap,
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scale=scale,
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desc=desc,
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device=param.device,
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)
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return torch_bgr_to_pil_image(output)
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