lllyasviel/stable-diffusion-webui-forge
1
0
Fork 0
mirror of https://github.com/lllyasviel/stable-diffusion-webui-forge.git synced 2026-04-30 03:01:15 +00:00
Code Issues Packages Projects Releases Wiki Activity

Update controlnet.py

Browse Source
This commit is contained in:
lllyasviel
2024-01-29 17:08:58 -08:00
parent fe2c1b8279
commit a9827b4de0
1 changed files with 10 additions and 9 deletions
Download Patch File Download Diff File Expand all files Collapse all files

19
extensions-builtin/sd_forge_controlnet/scripts/controlnet.py
View File

@@ -456,8 +456,17 @@ class ControlNetForForgeOfficial(scripts.Script):
params.preprocessor = preprocessor params.preprocessor = preprocessor
model_filename = global_state.get_controlnet_filename(unit.model) model_filename = global_state.get_controlnet_filename(unit.model)
params.model = cached_controlnet_loader(model_filename) controlnet_model = cached_controlnet_loader(model_filename)
controlnet_model.strength = float(unit.weight)
controlnet_model.start_percent = float(unit.guidance_start)
controlnet_model.end_percent = float(unit.guidance_end)
controlnet_model.positive_advanced_weighting = None
controlnet_model.negative_advanced_weighting = None
controlnet_model.advanced_frame_weighting = None
controlnet_model.advanced_sigma_weighting = None
params.model = controlnet_model
logger.info(f"Current ControlNet: {model_filename}") logger.info(f"Current ControlNet: {model_filename}")
return return
@@ -475,14 +484,6 @@ class ControlNetForForgeOfficial(scripts.Script):
kwargs.update(dict(unit=unit, params=params)) kwargs.update(dict(unit=unit, params=params))
params.model.strength = float(unit.weight)
params.model.start_percent = float(unit.guidance_start)
params.model.end_percent = float(unit.guidance_end)
params.model.positive_advanced_weighting = None
params.model.negative_advanced_weighting = None
params.model.advanced_frame_weighting = None
params.model.advanced_sigma_weighting = None
params.preprocessor.process_before_every_sampling(process=p, cond=cond, **kwargs) params.preprocessor.process_before_every_sampling(process=p, cond=cond, **kwargs)
params.model.process_before_every_sampling(process=p, cond=cond, **kwargs) params.model.process_before_every_sampling(process=p, cond=cond, **kwargs)