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cleanup

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Vladimir Mandic
2023-09-01 13:47:33 -04:00
parent 1b2890d02c
commit f32fcb5d16
3 changed files with 13 additions and 12 deletions
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modules/processing.py
+11 -10
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@@ -27,10 +27,6 @@ import modules.sd_models as sd_models
import modules.sd_vae as sd_vae
opt_C = 4
opt_f = 8
def setup_color_correction(image):
shared.log.debug("Calibrating color correction.")
correction_target = cv2.cvtColor(np.asarray(image.copy()), cv2.COLOR_RGB2LAB)
@@ -907,6 +903,10 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
else:
target_w = self.hr_resize_x
target_h = self.hr_resize_y
"""
self.hr_upscale_to_x = self.hr_resize_x
self.hr_upscale_to_y = self.hr_resize_y
"""
src_ratio = self.width / self.height
dst_ratio = self.hr_resize_x / self.hr_resize_y
if src_ratio < dst_ratio:
@@ -915,8 +915,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
else:
self.hr_upscale_to_x = self.hr_resize_y * self.width // self.height
self.hr_upscale_to_y = self.hr_resize_y
self.truncate_x = (self.hr_upscale_to_x - target_w) // opt_f
self.truncate_y = (self.hr_upscale_to_y - target_h) // opt_f
self.truncate_x = (self.hr_upscale_to_x - target_w) // 8
self.truncate_y = (self.hr_upscale_to_y - target_h) // 8
# special case: the user has chosen to do nothing
if self.hr_upscale_to_x == self.width and self.hr_upscale_to_y == self.height:
self.extra_generation_params.pop("Hires upscale", None)
@@ -932,6 +932,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
if self.hr_upscaler is not None:
self.extra_generation_params["Hires upscaler"] = self.hr_upscaler
self.extra_generation_params["Secondary sampler"] = self.latent_sampler
shared.log.debug(f'Init hires: upscaler={self.hr_upscaler} sampler={self.latent_sampler} width={self.hr_upscale_to_x} height={self.hr_upscale_to_y}')
def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
@@ -960,7 +961,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
if len([x for x in shared.sd_upscalers if x.name == self.hr_upscaler]) == 0:
shared.log.warning("Could not find upscaler to use with hrfix")
self.enable_hr = False
x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
x = create_random_tensors([4, self.height // 8, self.width // 8], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
if not self.enable_hr or shared.state.interrupted or shared.state.skipped:
return samples
@@ -973,7 +974,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
if latent_scale_mode is not None:
for i in range(samples.shape[0]):
save_intermediate(samples, i)
samples = torch.nn.functional.interpolate(samples, size=(target_height // opt_f, target_width // opt_f), mode=latent_scale_mode["mode"], antialias=latent_scale_mode["antialias"])
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:
image_conditioning = self.img2img_image_conditioning(decode_first_stage(self.sd_model, samples.to(dtype=devices.dtype_vae)), samples)
else:
@@ -1143,7 +1144,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
image = image.to(device=shared.device, dtype=devices.dtype_vae)
self.init_latent = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image))
if self.resize_mode == 4:
self.init_latent = torch.nn.functional.interpolate(self.init_latent, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
self.init_latent = torch.nn.functional.interpolate(self.init_latent, size=(self.height // 8, self.width // 8), mode="bilinear")
if image_mask is not None:
init_mask = latent_mask
latmask = init_mask.convert('RGB').resize((self.init_latent.shape[3], self.init_latent.shape[2]))
@@ -1167,7 +1168,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
sd_models.set_diffuser_pipe(self.sd_model, sd_models.DiffusersTaskType.INPAINTING)
self.sd_model.dtype = self.sd_model.unet.dtype
x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
x = create_random_tensors([4, self.height // 8, self.width // 8], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
if self.initial_noise_multiplier != 1.0:
self.extra_generation_params["Noise multiplier"] = self.initial_noise_multiplier
x *= self.initial_noise_multiplier