diff options
Diffstat (limited to 'modules/processing.py')
| -rw-r--r-- | modules/processing.py | 91 |
1 files changed, 48 insertions, 43 deletions
diff --git a/modules/processing.py b/modules/processing.py index ab68d63a..1db26c3e 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -145,9 +145,8 @@ class Processed: self.sampler_noise_scheduler_override = p.sampler_noise_scheduler_override
self.prompt = self.prompt if type(self.prompt) != list else self.prompt[0]
self.negative_prompt = self.negative_prompt if type(self.negative_prompt) != list else self.negative_prompt[0]
- self.seed = int(self.seed if type(self.seed) != list else self.seed[0])
- self.subseed = int(
- self.subseed if type(self.subseed) != list else self.subseed[0]) if self.subseed is not None else -1
+ self.seed = int(self.seed if type(self.seed) != list else self.seed[0]) if self.seed is not None else -1
+ self.subseed = int(self.subseed if type(self.subseed) != list else self.subseed[0]) if self.subseed is not None else -1
self.all_prompts = all_prompts or [self.prompt]
self.all_seeds = all_seeds or [self.seed]
@@ -541,16 +540,15 @@ def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weigh class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
sampler = None
- firstphase_width = 0
- firstphase_height = 0
- firstphase_width_truncated = 0
- firstphase_height_truncated = 0
- def __init__(self, enable_hr=False, scale_latent=True, denoising_strength=0.75, **kwargs):
+ def __init__(self, enable_hr=False, denoising_strength=0.75, firstphase_width=0, firstphase_height=0, **kwargs):
super().__init__(**kwargs)
self.enable_hr = enable_hr
- self.scale_latent = scale_latent
self.denoising_strength = denoising_strength
+ self.firstphase_width = firstphase_width
+ self.firstphase_height = firstphase_height
+ self.truncate_x = 0
+ self.truncate_y = 0
def init(self, all_prompts, all_seeds, all_subseeds):
if self.enable_hr:
@@ -559,14 +557,31 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): else:
state.job_count = state.job_count * 2
- desired_pixel_count = 512 * 512
- actual_pixel_count = self.width * self.height
- scale = math.sqrt(desired_pixel_count / actual_pixel_count)
+ if self.firstphase_width == 0 or self.firstphase_height == 0:
+ desired_pixel_count = 512 * 512
+ actual_pixel_count = self.width * self.height
+ scale = math.sqrt(desired_pixel_count / actual_pixel_count)
+ self.firstphase_width = math.ceil(scale * self.width / 64) * 64
+ self.firstphase_height = math.ceil(scale * self.height / 64) * 64
+ firstphase_width_truncated = int(scale * self.width)
+ firstphase_height_truncated = int(scale * self.height)
+
+ else:
+
+ width_ratio = self.width / self.firstphase_width
+ height_ratio = self.height / self.firstphase_height
+
+ if width_ratio > height_ratio:
+ firstphase_width_truncated = self.firstphase_width
+ firstphase_height_truncated = self.firstphase_width * self.height / self.width
+ else:
+ firstphase_width_truncated = self.firstphase_height * self.width / self.height
+ firstphase_height_truncated = self.firstphase_height
+
+ self.extra_generation_params["First pass size"] = f"{self.firstphase_width}x{self.firstphase_height}"
+ self.truncate_x = int(self.firstphase_width - firstphase_width_truncated) // opt_f
+ self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f
- self.firstphase_width = math.ceil(scale * self.width / 64) * 64
- self.firstphase_height = math.ceil(scale * self.height / 64) * 64
- self.firstphase_width_truncated = int(scale * self.width)
- self.firstphase_height_truncated = int(scale * self.height)
def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
@@ -585,37 +600,27 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): seed_resize_from_w=self.seed_resize_from_w, p=self)
samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning)
- truncate_x = (self.firstphase_width - self.firstphase_width_truncated) // opt_f
- truncate_y = (self.firstphase_height - self.firstphase_height_truncated) // opt_f
+ samples = samples[:, :, self.truncate_y//2:samples.shape[2]-self.truncate_y//2, self.truncate_x//2:samples.shape[3]-self.truncate_x//2]
- samples = samples[:, :, truncate_y // 2:samples.shape[2] - truncate_y // 2,
- truncate_x // 2:samples.shape[3] - truncate_x // 2]
-
- if self.scale_latent:
- samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f),
- mode="bilinear")
+ if opts.use_scale_latent_for_hires_fix:
+ samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
else:
decoded_samples = decode_first_stage(self.sd_model, samples)
+ lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)
- 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)
-
- batch_images = []
- for i, x_sample in enumerate(lowres_samples):
- x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
- x_sample = x_sample.astype(np.uint8)
- image = Image.fromarray(x_sample)
- image = images.resize_image(0, image, self.width, self.height)
- 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(shared.device)
- decoded_samples = 2. * decoded_samples - 1.
+ batch_images = []
+ for i, x_sample in enumerate(lowres_samples):
+ x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
+ x_sample = x_sample.astype(np.uint8)
+ image = Image.fromarray(x_sample)
+ image = images.resize_image(0, image, self.width, self.height)
+ 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(shared.device)
+ decoded_samples = 2. * decoded_samples - 1.
samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples))
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