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ComfyUI
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Add an option to the SaveLora node to store the bias diff.

This commit is contained in:
comfyanonymous 2024-09-07 02:56:24 -04:00
parent 9bfee68773
commit a09b29ca11
1 changed files with 9 additions and 5 deletions
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14
comfy_extras/nodes_lora_extract.py
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@ -38,7 +38,7 @@ def extract_lora(diff, rank):
Vh = Vh.reshape(rank, in_dim, kernel_size[0], kernel_size[1])
return (U, Vh)
def calc_lora_model(model_diff, rank, prefix_model, prefix_lora, output_sd):
def calc_lora_model(model_diff, rank, prefix_model, prefix_lora, output_sd, bias_diff=False):
comfy.model_management.load_models_gpu([model_diff], force_patch_weights=True)
sd = model_diff.model_state_dict(filter_prefix=prefix_model)
@ -53,6 +53,8 @@ def calc_lora_model(model_diff, rank, prefix_model, prefix_lora, output_sd):
output_sd["{}{}.lora_down.weight".format(prefix_lora, k[len(prefix_model):-7])] = out[1].contiguous().half().cpu()
except:
logging.warning("Could not generate lora weights for key {}, is the weight difference a zero?".format(k))
elif bias_diff and k.endswith(".bias"):
output_sd["{}{}.diff_b".format(prefix_lora, k[len(prefix_model):-5])] = sd[k].contiguous().half().cpu()
return output_sd
class LoraSave:
@ -62,7 +64,9 @@ class LoraSave:
@classmethod
def INPUT_TYPES(s):
return {"required": {"filename_prefix": ("STRING", {"default": "loras/ComfyUI_extracted_lora"}),
"rank": ("INT", {"default": 8, "min": 1, "max": 1024, "step": 1}),
"rank": ("INT", {"default": 8, "min": 1, "max": 4096, "step": 1}),
"lora_type": (["standard"],),
"bias_diff": ("BOOLEAN", {"default": True}),
},
"optional": {"model_diff": ("MODEL",),
"text_encoder_diff": ("CLIP",)},
@ -73,7 +77,7 @@ class LoraSave:
CATEGORY = "_for_testing"
def save(self, filename_prefix, rank, model_diff=None, text_encoder_diff=None):
def save(self, filename_prefix, rank, lora_type, bias_diff, model_diff=None, text_encoder_diff=None):
if model_diff is None and text_encoder_diff is None:
return {}
@ -81,9 +85,9 @@ class LoraSave:
output_sd = {}
if model_diff is not None:
output_sd = calc_lora_model(model_diff, rank, "diffusion_model.", "diffusion_model.", output_sd)
output_sd = calc_lora_model(model_diff, rank, "diffusion_model.", "diffusion_model.", output_sd, bias_diff=bias_diff)
if text_encoder_diff is not None:
output_sd = calc_lora_model(text_encoder_diff.patcher, rank, "", "text_encoders.", output_sd)
output_sd = calc_lora_model(text_encoder_diff.patcher, rank, "", "text_encoders.", output_sd, bias_diff=bias_diff)
output_checkpoint = f"{filename}_{counter:05}_.safetensors"
output_checkpoint = os.path.join(full_output_folder, output_checkpoint)