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import torch
import comfy.model_management
import comfy.utils
import folder_paths
import os
import logging
from enum import Enum
CLAMP_QUANTILE = 0.99
def extract_lora(diff, rank):
conv2d = (len(diff.shape) == 4)
kernel_size = None if not conv2d else diff.size()[2:4]
conv2d_3x3 = conv2d and kernel_size != (1, 1)
out_dim, in_dim = diff.size()[0:2]
rank = min(rank, in_dim, out_dim)
if conv2d:
if conv2d_3x3:
diff = diff.flatten(start_dim=1)
else:
diff = diff.squeeze()
U, S, Vh = torch.linalg.svd(diff.float())
U = U[:, :rank]
S = S[:rank]
U = U @ torch.diag(S)
Vh = Vh[:rank, :]
dist = torch.cat([U.flatten(), Vh.flatten()])
hi_val = torch.quantile(dist, CLAMP_QUANTILE)
low_val = -hi_val
U = U.clamp(low_val, hi_val)
Vh = Vh.clamp(low_val, hi_val)
if conv2d:
U = U.reshape(out_dim, rank, 1, 1)
Vh = Vh.reshape(rank, in_dim, kernel_size[0], kernel_size[1])
return (U, Vh)
class LORAType(Enum):
STANDARD = 0
FULL_DIFF = 1
LORA_TYPES = {"standard": LORAType.STANDARD,
"full_diff": LORAType.FULL_DIFF}
def calc_lora_model(model_diff, rank, prefix_model, prefix_lora, output_sd, lora_type, 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)
for k in sd:
if k.endswith(".weight"):
weight_diff = sd[k]
if lora_type == LORAType.STANDARD:
if weight_diff.ndim < 2:
if bias_diff:
output_sd["{}{}.diff".format(prefix_lora, k[len(prefix_model):-7])] = weight_diff.contiguous().half().cpu()
continue
try:
out = extract_lora(weight_diff, rank)
output_sd["{}{}.lora_up.weight".format(prefix_lora, k[len(prefix_model):-7])] = out[0].contiguous().half().cpu()
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 lora_type == LORAType.FULL_DIFF:
output_sd["{}{}.diff".format(prefix_lora, k[len(prefix_model):-7])] = weight_diff.contiguous().half().cpu()
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:
def __init__(self):
self.output_dir = folder_paths.get_output_directory()
@classmethod
def INPUT_TYPES(s):
return {"required": {"filename_prefix": ("STRING", {"default": "loras/ComfyUI_extracted_lora"}),
"rank": ("INT", {"default": 8, "min": 1, "max": 4096, "step": 1}),
"lora_type": (tuple(LORA_TYPES.keys()),),
"bias_diff": ("BOOLEAN", {"default": True}),
},
"optional": {"model_diff": ("MODEL", {"tooltip": "The ModelSubtract output to be converted to a lora."}),
"text_encoder_diff": ("CLIP", {"tooltip": "The CLIPSubtract output to be converted to a lora."})},
}
RETURN_TYPES = ()
FUNCTION = "save"
OUTPUT_NODE = True
CATEGORY = "_for_testing"
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 {}
lora_type = LORA_TYPES.get(lora_type)
full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
output_sd = {}
if model_diff is not None:
output_sd = calc_lora_model(model_diff, rank, "diffusion_model.", "diffusion_model.", output_sd, lora_type, 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, lora_type, bias_diff=bias_diff)
output_checkpoint = f"{filename}_{counter:05}_.safetensors"
output_checkpoint = os.path.join(full_output_folder, output_checkpoint)
comfy.utils.save_torch_file(output_sd, output_checkpoint, metadata=None)
return {}
NODE_CLASS_MAPPINGS = {
"LoraSave": LoraSave
}
NODE_DISPLAY_NAME_MAPPINGS = {
"LoraSave": "Extract and Save Lora"
}
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