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Option to use advanced upscalers with normal img2img

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AUTOMATIC
2022-09-23 17:37:47 +03:00
parent 6fa20d51dc
commit 1a0353675d
3 changed files with 15 additions and 6 deletions
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4
modules/processing.py
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@@ -462,7 +462,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
else:
decoded_samples = self.sd_model.decode_first_stage(samples)
if opts.upscaler_for_hires_fix is None or opts.upscaler_for_hires_fix == "None":
if opts.upscaler_for_img2img is None or opts.upscaler_for_img2img == "None":
decoded_samples = torch.nn.functional.interpolate(decoded_samples, size=(self.height, self.width), mode="bilinear")
else:
lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)
@@ -472,7 +472,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
x_sample = x_sample.astype(np.uint8)
image = Image.fromarray(x_sample)
upscaler = [x for x in shared.sd_upscalers if x.name == opts.upscaler_for_hires_fix][0]
upscaler = [x for x in shared.sd_upscalers if x.name == opts.upscaler_for_img2img][0]
image = upscaler.upscale(image, self.width, self.height)
image = np.array(image).astype(np.float32) / 255.0
image = np.moveaxis(image, 2, 0)