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https://github.com/openvinotoolkit/stable-diffusion-webui.git
synced 2024-12-14 22:53:25 +03:00
Option to use advanced upscalers with normal img2img
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@ -209,8 +209,16 @@ def draw_prompt_matrix(im, width, height, all_prompts):
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def resize_image(resize_mode, im, width, height):
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def resize(im, w, h):
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if opts.upscaler_for_img2img is None or opts.upscaler_for_img2img == "None":
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return im.resize((w, h), resample=LANCZOS)
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upscaler = [x for x in shared.sd_upscalers if x.name == opts.upscaler_for_img2img][0]
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return upscaler.upscale(im, w, h)
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if resize_mode == 0:
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res = im.resize((width, height), resample=LANCZOS)
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res = resize(im, width, height)
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elif resize_mode == 1:
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ratio = width / height
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src_ratio = im.width / im.height
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@ -218,9 +226,10 @@ def resize_image(resize_mode, im, width, height):
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src_w = width if ratio > src_ratio else im.width * height // im.height
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src_h = height if ratio <= src_ratio else im.height * width // im.width
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resized = im.resize((src_w, src_h), resample=LANCZOS)
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resized = resize(im, src_w, src_h)
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res = Image.new("RGB", (width, height))
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res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2))
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else:
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ratio = width / height
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src_ratio = im.width / im.height
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@ -228,7 +237,7 @@ def resize_image(resize_mode, im, width, height):
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src_w = width if ratio < src_ratio else im.width * height // im.height
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src_h = height if ratio >= src_ratio else im.height * width // im.width
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resized = im.resize((src_w, src_h), resample=LANCZOS)
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resized = resize(im, src_w, src_h)
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res = Image.new("RGB", (width, height))
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res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2))
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@ -462,7 +462,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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else:
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decoded_samples = self.sd_model.decode_first_stage(samples)
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if opts.upscaler_for_hires_fix is None or opts.upscaler_for_hires_fix == "None":
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if opts.upscaler_for_img2img is None or opts.upscaler_for_img2img == "None":
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decoded_samples = torch.nn.functional.interpolate(decoded_samples, size=(self.height, self.width), mode="bilinear")
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else:
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lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)
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@ -472,7 +472,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
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x_sample = x_sample.astype(np.uint8)
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image = Image.fromarray(x_sample)
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upscaler = [x for x in shared.sd_upscalers if x.name == opts.upscaler_for_hires_fix][0]
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upscaler = [x for x in shared.sd_upscalers if x.name == opts.upscaler_for_img2img][0]
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image = upscaler.upscale(image, self.width, self.height)
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image = np.array(image).astype(np.float32) / 255.0
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image = np.moveaxis(image, 2, 0)
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@ -168,7 +168,7 @@ options_templates.update(options_section(('upscaling', "Upscaling"), {
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"ldsr_pre_down": OptionInfo(1, "LDSR Pre-process downssample scale. 1 = no down-sampling, 4 = 1/4 scale.", gr.Slider, {"minimum": 1, "maximum": 4, "step": 1}),
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"ldsr_post_down": OptionInfo(1, "LDSR Post-process down-sample scale. 1 = no down-sampling, 4 = 1/4 scale.", gr.Slider, {"minimum": 1, "maximum": 4, "step": 1}),
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"upscaler_for_hires_fix": OptionInfo(None, "Upscaler for highres. fix", gr.Radio, lambda: {"choices": [x.name for x in sd_upscalers]}),
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"upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Radio, lambda: {"choices": [x.name for x in sd_upscalers]}),
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}))
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options_templates.update(options_section(('face-restoration', "Face restoration"), {
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