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fix before hires

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This commit is contained in:
Vladimir Mandic
2023-10-23 10:19:40 -04:00
parent bbb37ccc50
commit d5c264ec75
4 changed files with 28 additions and 38 deletions
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modules/processing.py
+25 -35
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@@ -51,11 +51,7 @@ def setup_color_correction(image):
def apply_color_correction(correction, original_image):
shared.log.debug("Applying color correction.")
image = Image.fromarray(cv2.cvtColor(exposure.match_histograms(
cv2.cvtColor(np.asarray(original_image), cv2.COLOR_RGB2LAB),
correction,
channel_axis=2
), cv2.COLOR_LAB2RGB).astype("uint8"))
image = Image.fromarray(cv2.cvtColor(exposure.match_histograms(cv2.cvtColor(np.asarray(original_image), cv2.COLOR_RGB2LAB), correction, channel_axis=2), cv2.COLOR_LAB2RGB).astype("uint8"))
image = blendLayers(image, original_image, BlendType.LUMINOSITY)
return image
@@ -1050,21 +1046,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
def save_intermediate(image, index):
"""saves image before applying hires fix, if enabled in options; takes as an argument either an image or batch with latent space images"""
if not shared.opts.save or self.do_not_save_samples or not shared.opts.save_images_before_highres_fix:
return
if not isinstance(image, Image.Image):
image = modules.sd_samplers.sample_to_image(image, index, approximation=0)
orig1 = self.extra_generation_params
orig2 = self.restore_faces
self.extra_generation_params = {}
self.restore_faces = False
info = create_infotext(self, self.all_prompts, self.all_seeds, self.all_subseeds, [], iteration=self.iteration, position_in_batch=index)
self.extra_generation_params = orig1
self.restore_faces = orig2
images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], shared.opts.samples_format, info=info, suffix="-before-hires")
latent_scale_mode = shared.latent_upscale_modes.get(self.hr_upscaler, None) if self.hr_upscaler is not None else shared.latent_upscale_modes.get(shared.latent_upscale_default_mode, "None")
if latent_scale_mode is not None:
self.hr_force = False # no need to force anything
@@ -1087,13 +1068,26 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
if self.is_hr_pass:
target_width = self.hr_upscale_to_x
target_height = self.hr_upscale_to_y
for i in range(samples.shape[0]):
save_intermediate(samples, i)
if latent_scale_mode is None or self.hr_force: # non-latent upscaling
decoded_samples = None
if shared.opts.save and shared.opts.save_images_before_highres_fix and not self.do_not_save_samples:
decoded_samples = decode_first_stage(self.sd_model, samples.to(dtype=devices.dtype_vae), self.full_quality)
lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)
decoded_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)
for i, x_sample in enumerate(decoded_samples):
x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
x_sample = validate_sample(x_sample)
image = Image.fromarray(x_sample)
bak_extra_generation_params, bak_restore_faces = self.extra_generation_params, self.restore_faces
self.extra_generation_params = {}
self.restore_faces = False
info = create_infotext(self, self.all_prompts, self.all_seeds, self.all_subseeds, [], iteration=self.iteration, position_in_batch=i)
self.extra_generation_params, self.restore_faces = bak_extra_generation_params, bak_restore_faces
images.save_image(image, self.outpath_samples, "", seeds[i], prompts[i], shared.opts.samples_format, info=info, suffix="-before-hires")
if latent_scale_mode is None or self.hr_force: # non-latent upscaling
if decoded_samples is None:
decoded_samples = decode_first_stage(self.sd_model, samples.to(dtype=devices.dtype_vae), self.full_quality)
decoded_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)
batch_images = []
for _i, x_sample in enumerate(lowres_samples):
for _i, x_sample in enumerate(decoded_samples):
x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
x_sample = validate_sample(x_sample)
image = Image.fromarray(x_sample)
@@ -1101,18 +1095,14 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
image = np.array(image).astype(np.float32) / 255.0
image = np.moveaxis(image, 2, 0)
batch_images.append(image)
decoded_samples = torch.from_numpy(np.array(batch_images))
decoded_samples = decoded_samples.to(device=shared.device, dtype=devices.dtype_vae)
decoded_samples = 2. * decoded_samples - 1.
resized_samples = torch.from_numpy(np.array(batch_images))
resized_samples = resized_samples.to(device=shared.device, dtype=devices.dtype_vae)
resized_samples = 2.0 * resized_samples - 1.0
if shared.opts.sd_vae_sliced_encode and len(decoded_samples) > 1:
samples = torch.stack([
self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(torch.unsqueeze(decoded_sample, 0)))[0]
for decoded_sample
in decoded_samples
])
samples = torch.stack([self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(torch.unsqueeze(resized_sample, 0)))[0] for resized_sample in resized_samples])
else:
samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples))
image_conditioning = self.img2img_image_conditioning(decoded_samples, samples)
samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(resized_samples))
image_conditioning = self.img2img_image_conditioning(resized_samples, samples)
else:
samples = torch.nn.functional.interpolate(samples, size=(target_height // 8, target_width // 8), mode=latent_scale_mode["mode"], antialias=latent_scale_mode["antialias"])
if getattr(self, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) < 1.0: