diff options
Diffstat (limited to 'modules/processing.py')
| -rw-r--r-- | modules/processing.py | 29 |
1 files changed, 9 insertions, 20 deletions
diff --git a/modules/processing.py b/modules/processing.py index 1db26c3e..685f9fcd 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -146,7 +146,8 @@ class Processed: 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]) 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.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]
@@ -332,16 +333,9 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration return f"{all_prompts[index]}{negative_prompt_text}\n{generation_params_text}".strip()
-def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0,
- aesthetic_imgs=None, aesthetic_slerp=False, aesthetic_imgs_text="",
- aesthetic_slerp_angle=0.15,
- aesthetic_text_negative=False) -> Processed:
+def process_images(p: StableDiffusionProcessing) -> Processed:
"""this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch"""
- aesthetic_lr = float(aesthetic_lr)
- aesthetic_weight = float(aesthetic_weight)
- aesthetic_steps = int(aesthetic_steps)
-
if type(p.prompt) == list:
assert (len(p.prompt) > 0)
else:
@@ -417,16 +411,10 @@ def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weigh # uc = p.sd_model.get_learned_conditioning(len(prompts) * [p.negative_prompt])
# c = p.sd_model.get_learned_conditioning(prompts)
with devices.autocast():
- if hasattr(shared.sd_model.cond_stage_model, "set_aesthetic_params"):
- shared.sd_model.cond_stage_model.set_aesthetic_params()
+ shared.aesthetic_clip.set_skip(True)
uc = prompt_parser.get_learned_conditioning(shared.sd_model, len(prompts) * [p.negative_prompt],
p.steps)
- if hasattr(shared.sd_model.cond_stage_model, "set_aesthetic_params"):
- shared.sd_model.cond_stage_model.set_aesthetic_params(aesthetic_lr, aesthetic_weight,
- aesthetic_steps, aesthetic_imgs,
- aesthetic_slerp, aesthetic_imgs_text,
- aesthetic_slerp_angle,
- aesthetic_text_negative)
+ shared.aesthetic_clip.set_skip(False)
c = prompt_parser.get_multicond_learned_conditioning(shared.sd_model, prompts, p.steps)
if len(model_hijack.comments) > 0:
@@ -582,7 +570,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): self.truncate_x = int(self.firstphase_width - firstphase_width_truncated) // opt_f
self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f
-
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)
@@ -600,10 +587,12 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): seed_resize_from_w=self.seed_resize_from_w, p=self)
samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning)
- 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[:, :, self.truncate_y // 2:samples.shape[2] - self.truncate_y // 2,
+ self.truncate_x // 2:samples.shape[3] - self.truncate_x // 2]
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")
+ 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)
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