diff options
Diffstat (limited to 'modules/processing.py')
| -rwxr-xr-x | modules/processing.py | 42 |
1 files changed, 25 insertions, 17 deletions
diff --git a/modules/processing.py b/modules/processing.py index 1d098302..d4926524 100755 --- a/modules/processing.py +++ b/modules/processing.py @@ -81,6 +81,12 @@ def apply_overlay(image, paste_loc, index, overlays): return image
+def create_binary_mask(image):
+ if image.mode == 'RGBA' and image.getextrema()[-1] != (255, 255):
+ image = image.split()[-1].convert("L").point(lambda x: 255 if x > 128 else 0)
+ else:
+ image = image.convert('L')
+ return image
def txt2img_image_conditioning(sd_model, x, width, height):
if sd_model.model.conditioning_key in {'hybrid', 'concat'}: # Inpainting models
@@ -380,14 +386,14 @@ class StableDiffusionProcessing: return self.token_merging_ratio or opts.token_merging_ratio
def setup_prompts(self):
- if type(self.prompt) == list:
+ if isinstance(self.prompt,list):
self.all_prompts = self.prompt
- elif type(self.negative_prompt) == list:
+ elif isinstance(self.negative_prompt, list):
self.all_prompts = [self.prompt] * len(self.negative_prompt)
else:
self.all_prompts = self.batch_size * self.n_iter * [self.prompt]
- if type(self.negative_prompt) == list:
+ if isinstance(self.negative_prompt, list):
self.all_negative_prompts = self.negative_prompt
else:
self.all_negative_prompts = [self.negative_prompt] * len(self.all_prompts)
@@ -506,10 +512,10 @@ class Processed: self.s_noise = p.s_noise
self.s_min_uncond = p.s_min_uncond
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]) 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.prompt = self.prompt if not isinstance(self.prompt, list) else self.prompt[0]
+ self.negative_prompt = self.negative_prompt if not isinstance(self.negative_prompt, list) else self.negative_prompt[0]
+ self.seed = int(self.seed if not isinstance(self.seed, list) else self.seed[0]) if self.seed is not None else -1
+ self.subseed = int(self.subseed if not isinstance(self.subseed, list) else self.subseed[0]) if self.subseed is not None else -1
self.is_using_inpainting_conditioning = p.is_using_inpainting_conditioning
self.all_prompts = all_prompts or p.all_prompts or [self.prompt]
@@ -696,11 +702,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed: stored_opts = {k: opts.data[k] for k in p.override_settings.keys()}
try:
- # after running refiner, the refiner model is not unloaded - webui swaps back to main model here
- if shared.sd_model.sd_checkpoint_info.title != opts.sd_model_checkpoint:
- sd_models.reload_model_weights()
-
# if no checkpoint override or the override checkpoint can't be found, remove override entry and load opts checkpoint
+ # and if after running refiner, the refiner model is not unloaded - webui swaps back to main model here, if model over is present it will be reloaded afterwards
if sd_models.checkpoint_aliases.get(p.override_settings.get('sd_model_checkpoint')) is None:
p.override_settings.pop('sd_model_checkpoint', None)
sd_models.reload_model_weights()
@@ -735,7 +738,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed: def process_images_inner(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"""
- if type(p.prompt) == list:
+ if isinstance(p.prompt, list):
assert(len(p.prompt) > 0)
else:
assert p.prompt is not None
@@ -766,12 +769,12 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: p.setup_prompts()
- if type(seed) == list:
+ if isinstance(seed, list):
p.all_seeds = seed
else:
p.all_seeds = [int(seed) + (x if p.subseed_strength == 0 else 0) for x in range(len(p.all_prompts))]
- if type(subseed) == list:
+ if isinstance(subseed, list):
p.all_subseeds = subseed
else:
p.all_subseeds = [int(subseed) + x for x in range(len(p.all_prompts))]
@@ -1149,6 +1152,9 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): devices.torch_gc()
def sample_hr_pass(self, samples, decoded_samples, seeds, subseeds, subseed_strength, prompts):
+ if shared.state.interrupted:
+ return samples
+
self.is_hr_pass = True
target_width = self.hr_upscale_to_x
@@ -1262,12 +1268,12 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): if self.hr_negative_prompt == '':
self.hr_negative_prompt = self.negative_prompt
- if type(self.hr_prompt) == list:
+ if isinstance(self.hr_prompt, list):
self.all_hr_prompts = self.hr_prompt
else:
self.all_hr_prompts = self.batch_size * self.n_iter * [self.hr_prompt]
- if type(self.hr_negative_prompt) == list:
+ if isinstance(self.hr_negative_prompt, list):
self.all_hr_negative_prompts = self.hr_negative_prompt
else:
self.all_hr_negative_prompts = self.batch_size * self.n_iter * [self.hr_negative_prompt]
@@ -1385,7 +1391,9 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): image_mask = self.image_mask
if image_mask is not None:
- image_mask = image_mask.convert('L')
+ # image_mask is passed in as RGBA by Gradio to support alpha masks,
+ # but we still want to support binary masks.
+ image_mask = create_binary_mask(image_mask)
if self.inpainting_mask_invert:
image_mask = ImageOps.invert(image_mask)
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