Public/automatic
1
0
Fork 0
mirror of https://github.com/vladmandic/automatic synced 2026-08-26 15:16:01 +02:00
Code Issues Packages Projects Releases Wiki Activity

Type standardization in processing_class

Browse Source
This commit is contained in:
awsr
2026-01-21 16:35:19 -08:00
parent 6344db1b09
commit 2f8976e28d
6 changed files with 19 additions and 20 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/face/faceid.py
+1 -1
View File
@@ -205,7 +205,7 @@ def face_id(
ip_model_dict["faceid_embeds"] = face_embeds # overwrite placeholder
faceid_model.set_scale(scale)
if p.all_prompts is None or len(p.all_prompts) == 0:
if not p.all_prompts:
processing.process_init(p)
p.init(p.all_prompts, p.all_seeds, p.all_subseeds)
for n in range(p.n_iter):
modules/face/instantid.py
+3 -3
View File
@@ -63,7 +63,7 @@ def instant_id(p: processing.StableDiffusionProcessing, app, source_images, stre
sd_models.move_model(shared.sd_model, devices.device) # move pipeline to device
# pipeline specific args
if p.all_prompts is None or len(p.all_prompts) == 0:
if not p.all_prompts:
processing.process_init(p)
p.init(p.all_prompts, p.all_seeds, p.all_subseeds)
orig_prompt_attention = shared.opts.prompt_attention
@@ -73,8 +73,8 @@ def instant_id(p: processing.StableDiffusionProcessing, app, source_images, stre
p.task_args['controlnet_conditioning_scale'] = float(conditioning)
p.task_args['ip_adapter_scale'] = float(strength)
shared.log.debug(f"InstantID args: {p.task_args}")
p.task_args['prompt'] = p.all_prompts[0] if p.all_prompts is not None else p.prompt
p.task_args['negative_prompt'] = p.all_negative_prompts[0] if p.all_negative_prompts is not None else p.negative_prompt
p.task_args['prompt'] = p.all_prompts[0] if p.all_prompts else p.prompt
p.task_args['negative_prompt'] = p.all_negative_prompts[0] if p.all_negative_prompts else p.negative_prompt
p.task_args['image_embeds'] = face_embeds[0] # overwrite placeholder
# run processing
modules/face/photomaker.py
+2 -2
View File
@@ -34,7 +34,7 @@ def photo_maker(p: processing.StableDiffusionProcessing, app, model: str, input_
return None
# validate prompt
if p.all_prompts is None or len(p.all_prompts) == 0:
if not p.all_prompts:
processing.process_init(p)
p.init(p.all_prompts, p.all_seeds, p.all_subseeds)
trigger_ids = shared.sd_model.tokenizer.encode(trigger) + shared.sd_model.tokenizer_2.encode(trigger)
@@ -61,7 +61,7 @@ def photo_maker(p: processing.StableDiffusionProcessing, app, model: str, input_
shared.opts.data['prompt_attention'] = 'fixed' # otherwise need to deal with class_tokens_mask
p.task_args['input_id_images'] = input_images
p.task_args['start_merge_step'] = int(start * p.steps)
p.task_args['prompt'] = p.all_prompts[0] if p.all_prompts is not None else p.prompt
p.task_args['prompt'] = p.all_prompts[0] if p.all_prompts else p.prompt
is_v2 = 'v2' in model
if is_v2:
modules/processing.py
+5 -5
View File
@@ -243,13 +243,13 @@ def process_init(p: StableDiffusionProcessing):
seed = get_fixed_seed(p.seed)
subseed = get_fixed_seed(p.subseed)
reset_prompts = False
if p.all_prompts is None:
if not p.all_prompts:
p.all_prompts = p.prompt if isinstance(p.prompt, list) else p.batch_size * p.n_iter * [p.prompt]
reset_prompts = True
if p.all_negative_prompts is None:
if not p.all_negative_prompts:
p.all_negative_prompts = p.negative_prompt if isinstance(p.negative_prompt, list) else p.batch_size * p.n_iter * [p.negative_prompt]
reset_prompts = True
if p.all_seeds is None:
if not p.all_seeds:
reset_prompts = True
if type(seed) == list:
p.all_seeds = [int(s) for s in seed]
@@ -262,7 +262,7 @@ def process_init(p: StableDiffusionProcessing):
for i in range(len(p.all_prompts)):
seed = get_fixed_seed(p.seed)
p.all_seeds.append(int(seed) + (i if p.subseed_strength == 0 else 0))
if p.all_subseeds is None:
if not p.all_subseeds:
if type(subseed) == list:
p.all_subseeds = [int(s) for s in subseed]
else:
@@ -433,7 +433,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
p.subseeds = p.all_subseeds[n * p.batch_size:(n+1) * p.batch_size]
if p.scripts is not None and isinstance(p.scripts, scripts_manager.ScriptRunner):
p.scripts.before_process_batch(p, batch_number=n, prompts=p.prompts, seeds=p.seeds, subseeds=p.subseeds)
if len(p.prompts) == 0:
if not p.prompts:
break
p.prompts, p.network_data = extra_networks.parse_prompts(p.prompts)
if p.scripts is not None and isinstance(p.scripts, scripts_manager.ScriptRunner):
modules/processing_class.py
+6 -7
View File
@@ -308,15 +308,14 @@ class StableDiffusionProcessing:
shared.log.error(f'Override: {override_settings} {e}')
self.override_settings = {}
# null items initialized later
self.prompts = None
self.negative_prompts = None
self.all_prompts = None
self.all_negative_prompts = None
self.prompts = []
self.negative_prompts = []
self.all_prompts = []
self.all_negative_prompts = []
self.seeds = []
self.subseeds = []
self.all_seeds = None
self.all_subseeds = None
self.all_seeds = []
self.all_subseeds = []
# a1111 compatibility items
self.seed_enable_extras: bool = True
modules/processing_diffusers.py
+2 -2
View File
@@ -563,9 +563,9 @@ def process_diffusers(p: processing.StableDiffusionProcessing):
shared.sd_model = sd_models.set_diffuser_pipe(shared.sd_model, sd_models.DiffusersTaskType.INPAINTING) # force pipeline
if len(getattr(p, 'init_images', [])) == 0:
p.init_images = [TF.to_pil_image(torch.rand((3, getattr(p, 'height', 512), getattr(p, 'width', 512))))]
if p.prompts is None or len(p.prompts) == 0:
if not p.prompts:
p.prompts = p.all_prompts[p.iteration * p.batch_size:(p.iteration+1) * p.batch_size]
if p.negative_prompts is None or len(p.negative_prompts) == 0:
if not p.negative_prompts:
p.negative_prompts = p.all_negative_prompts[p.iteration * p.batch_size:(p.iteration+1) * p.batch_size]
sd_models_compile.openvino_recompile_model(p, hires=False, refiner=False) # recompile if a parameter changes