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Merge pull request #106 from LucianoCirino/Transformer/Diffuser-LoRA-Compatability

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Transformer/Diffuser LoRA Compatability
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TSC
2023-08-21 08:36:26 -05:00
committed by GitHub
parent 60d00ff1b3 5ea2ba3623
commit 32e960c6b3
1 changed files with 4 additions and 6 deletions
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10
tsc_utils.py
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@@ -24,11 +24,9 @@ sys.path.append(comfy_dir)
# Import functions from ComfyUI # Import functions from ComfyUI
import comfy.sd import comfy.sd
from comfy.cli_args import args import comfy.utils
import latent_preview import latent_preview
from comfy.cli_args import args
# Load my custom ComfyUI functions
from tsc_sd import *
# Cache for Efficiency Node models # Cache for Efficiency Node models
loaded_objects = { loaded_objects = {
@@ -180,7 +178,7 @@ def load_checkpoint(ckpt_name, id, output_vae=True, cache=None, cache_overwrite=
ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name) ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
with suppress_output(): with suppress_output():
out = load_checkpoint_guess_config(ckpt_path, output_vae, output_clip=True, out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae, output_clip=True,
embedding_directory=folder_paths.get_folder_paths("embeddings")) embedding_directory=folder_paths.get_folder_paths("embeddings"))
model = out[0] model = out[0]
clip = out[1] clip = out[1]
@@ -307,7 +305,7 @@ def load_lora(lora_params, ckpt_name, id, cache=None, ckpt_cache=None, cache_ove
lora_name, strength_model, strength_clip = lora_params[0] lora_name, strength_model, strength_clip = lora_params[0]
lora_path = folder_paths.get_full_path("loras", lora_name) lora_path = folder_paths.get_full_path("loras", lora_name)
lora_model, lora_clip = load_lora_for_models_tsc(ckpt, clip, lora_path, strength_model, strength_clip) lora_model, lora_clip = comfy.sd.load_lora_for_models(ckpt, clip, comfy.utils.load_torch_file(lora_path), strength_model, strength_clip)
# Call the function again with the new lora_model and lora_clip and the remaining tuples # Call the function again with the new lora_model and lora_clip and the remaining tuples
return recursive_load_lora(lora_params[1:], lora_model, lora_clip, id, ckpt_cache, cache_overwrite, folder_paths) return recursive_load_lora(lora_params[1:], lora_model, lora_clip, id, ckpt_cache, cache_overwrite, folder_paths)