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Merge branch 'comfyanonymous:master' into feature/preview-latent

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Dr.Lt.Data 2023-05-28 13:22:47 +09:00 committed by GitHub
commit 1daa559a39
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2 changed files with 11 additions and 9 deletions
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11
folder_paths.py
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@ -1,14 +1,7 @@
import os import os
supported_ckpt_extensions = set(['.ckpt', '.pth']) supported_ckpt_extensions = set(['.ckpt', '.pth', '.safetensors'])
supported_pt_extensions = set(['.ckpt', '.pt', '.bin', '.pth']) supported_pt_extensions = set(['.ckpt', '.pt', '.bin', '.pth', '.safetensors'])
try:
import safetensors.torch
supported_ckpt_extensions.add('.safetensors')
supported_pt_extensions.add('.safetensors')
except:
print("Could not import safetensors, safetensors support disabled.")
folder_names_and_paths = {} folder_names_and_paths = {}

9
nodes.py
View File

@ -556,6 +556,9 @@ class LoraLoader:
CATEGORY = "loaders" CATEGORY = "loaders"
def load_lora(self, model, clip, lora_name, strength_model, strength_clip): def load_lora(self, model, clip, lora_name, strength_model, strength_clip):
if strength_model == 0 and strength_clip == 0:
return (model, clip)
lora_path = folder_paths.get_full_path("loras", lora_name) lora_path = folder_paths.get_full_path("loras", lora_name)
model_lora, clip_lora = comfy.sd.load_lora_for_models(model, clip, lora_path, strength_model, strength_clip) model_lora, clip_lora = comfy.sd.load_lora_for_models(model, clip, lora_path, strength_model, strength_clip)
return (model_lora, clip_lora) return (model_lora, clip_lora)
@ -637,6 +640,9 @@ class ControlNetApply:
CATEGORY = "conditioning" CATEGORY = "conditioning"
def apply_controlnet(self, conditioning, control_net, image, strength): def apply_controlnet(self, conditioning, control_net, image, strength):
if strength == 0:
return (conditioning, )
c = [] c = []
control_hint = image.movedim(-1,1) control_hint = image.movedim(-1,1)
for t in conditioning: for t in conditioning:
@ -743,6 +749,9 @@ class unCLIPConditioning:
CATEGORY = "conditioning" CATEGORY = "conditioning"
def apply_adm(self, conditioning, clip_vision_output, strength, noise_augmentation): def apply_adm(self, conditioning, clip_vision_output, strength, noise_augmentation):
if strength == 0:
return (conditioning, )
c = [] c = []
for t in conditioning: for t in conditioning:
o = t[1].copy() o = t[1].copy()