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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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15
modules/images.py
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@ -209,8 +209,16 @@ def draw_prompt_matrix(im, width, height, all_prompts):
def resize_image(resize_mode, im, width, height):
def resize(im, w, h):
if opts.upscaler_for_img2img is None or opts.upscaler_for_img2img == "None":
return im.resize((w, h), resample=LANCZOS)
upscaler = [x for x in shared.sd_upscalers if x.name == opts.upscaler_for_img2img][0]
return upscaler.upscale(im, w, h)
if resize_mode == 0:
res = im.resize((width, height), resample=LANCZOS)
res = resize(im, width, height)
elif resize_mode == 1:
ratio = width / height
src_ratio = im.width / im.height
@ -218,9 +226,10 @@ def resize_image(resize_mode, im, width, height):
src_w = width if ratio > src_ratio else im.width * height // im.height
src_h = height if ratio <= src_ratio else im.height * width // im.width
resized = im.resize((src_w, src_h), resample=LANCZOS)
resized = resize(im, src_w, src_h)
res = Image.new("RGB", (width, height))
res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2))
else:
ratio = width / height
src_ratio = im.width / im.height
@ -228,7 +237,7 @@ def resize_image(resize_mode, im, width, height):
src_w = width if ratio < src_ratio else im.width * height // im.height
src_h = height if ratio >= src_ratio else im.height * width // im.width
resized = im.resize((src_w, src_h), resample=LANCZOS)
resized = resize(im, src_w, src_h)
res = Image.new("RGB", (width, height))
res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2))

4
modules/processing.py
View file

@ -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)

2
modules/shared.py
View file

@ -168,7 +168,7 @@ options_templates.update(options_section(('upscaling', "Upscaling"), {
"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}),
"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}),
"upscaler_for_hires_fix": OptionInfo(None, "Upscaler for highres. fix", gr.Radio, lambda: {"choices": [x.name for x in sd_upscalers]}),
"upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Radio, lambda: {"choices": [x.name for x in sd_upscalers]}),
}))
options_templates.update(options_section(('face-restoration', "Face restoration"), {