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2.1.782
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lllyasviel
2023-11-11 01:43:01 -08:00
parent a9bb1079cf
commit 4fe08161a5
48 changed files with 1041 additions and 2639 deletions
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modules/async_worker.py
+12 -10
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@@ -20,7 +20,7 @@ def worker():
import modules.default_pipeline as pipeline
import modules.core as core
import modules.flags as flags
import modules.path
import modules.config
import modules.patch
import fcbh.model_management
import fooocus_extras.preprocessors as preprocessors
@@ -143,7 +143,7 @@ def worker():
cn_tasks[cn_type].append([cn_img, cn_stop, cn_weight])
outpaint_selections = [o.lower() for o in outpaint_selections]
loras_raw = copy.deepcopy(loras)
base_model_additional_loras = []
raw_style_selections = copy.deepcopy(style_selections)
uov_method = uov_method.lower()
@@ -221,7 +221,7 @@ def worker():
else:
steps = 36
progressbar(1, 'Downloading upscale models ...')
modules.path.downloading_upscale_model()
modules.config.downloading_upscale_model()
if (current_tab == 'inpaint' or (current_tab == 'ip' and advanced_parameters.mixing_image_prompt_and_inpaint))\
and isinstance(inpaint_input_image, dict):
inpaint_image = inpaint_input_image['image']
@@ -230,8 +230,8 @@ def worker():
if isinstance(inpaint_image, np.ndarray) and isinstance(inpaint_mask, np.ndarray) \
and (np.any(inpaint_mask > 127) or len(outpaint_selections) > 0):
progressbar(1, 'Downloading inpainter ...')
inpaint_head_model_path, inpaint_patch_model_path = modules.path.downloading_inpaint_models(advanced_parameters.inpaint_engine)
loras += [(inpaint_patch_model_path, 1.0)]
inpaint_head_model_path, inpaint_patch_model_path = modules.config.downloading_inpaint_models(advanced_parameters.inpaint_engine)
base_model_additional_loras += [(inpaint_patch_model_path, 1.0)]
print(f'[Inpaint] Current inpaint model is {inpaint_patch_model_path}')
goals.append('inpaint')
if current_tab == 'ip' or \
@@ -240,11 +240,11 @@ def worker():
goals.append('cn')
progressbar(1, 'Downloading control models ...')
if len(cn_tasks[flags.cn_canny]) > 0:
controlnet_canny_path = modules.path.downloading_controlnet_canny()
controlnet_canny_path = modules.config.downloading_controlnet_canny()
if len(cn_tasks[flags.cn_cpds]) > 0:
controlnet_cpds_path = modules.path.downloading_controlnet_cpds()
controlnet_cpds_path = modules.config.downloading_controlnet_cpds()
if len(cn_tasks[flags.cn_ip]) > 0:
clip_vision_path, ip_negative_path, ip_adapter_path = modules.path.downloading_ip_adapters()
clip_vision_path, ip_negative_path, ip_adapter_path = modules.config.downloading_ip_adapters()
progressbar(1, 'Loading control models ...')
# Load or unload CNs
@@ -286,7 +286,8 @@ def worker():
extra_negative_prompts = negative_prompts[1:] if len(negative_prompts) > 1 else []
progressbar(3, 'Loading models ...')
pipeline.refresh_everything(refiner_model_name=refiner_model_name, base_model_name=base_model_name, loras=loras)
pipeline.refresh_everything(refiner_model_name=refiner_model_name, base_model_name=base_model_name,
loras=loras, base_model_additional_loras=base_model_additional_loras)
progressbar(3, 'Processing prompts ...')
tasks = []
@@ -618,11 +619,12 @@ def worker():
('ADM Guidance', str((modules.patch.positive_adm_scale, modules.patch.negative_adm_scale))),
('Base Model', base_model_name),
('Refiner Model', refiner_model_name),
('Refiner Switch', refiner_switch),
('Sampler', sampler_name),
('Scheduler', scheduler_name),
('Seed', task['task_seed'])
]
for n, w in loras_raw:
for n, w in loras:
if n != 'None':
d.append((f'LoRA [{n}] weight', w))
log(x, d, single_line_number=3)