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SaveLora node can now save "full diff" lora format.

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This isn't actually a lora format and is saving the full diff of the
weights in a format that can be used in the lora loader nodes.
This commit is contained in:
comfyanonymous 2024-09-07 03:21:02 -04:00
parent a09b29ca11
commit 8aabd7c8c0
1 changed files with 27 additions and 12 deletions
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23
comfy_extras/nodes_lora_extract.py
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@ -4,6 +4,7 @@ import comfy.utils
import folder_paths import folder_paths
import os import os
import logging import logging
from enum import Enum
CLAMP_QUANTILE = 0.99 CLAMP_QUANTILE = 0.99
@ -38,14 +39,24 @@ def extract_lora(diff, rank):
Vh = Vh.reshape(rank, in_dim, kernel_size[0], kernel_size[1]) Vh = Vh.reshape(rank, in_dim, kernel_size[0], kernel_size[1])
return (U, Vh) return (U, Vh)
def calc_lora_model(model_diff, rank, prefix_model, prefix_lora, output_sd, bias_diff=False): 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) comfy.model_management.load_models_gpu([model_diff], force_patch_weights=True)
sd = model_diff.model_state_dict(filter_prefix=prefix_model) sd = model_diff.model_state_dict(filter_prefix=prefix_model)
for k in sd: for k in sd:
if k.endswith(".weight"): if k.endswith(".weight"):
weight_diff = sd[k] weight_diff = sd[k]
if lora_type == LORAType.STANDARD:
if weight_diff.ndim < 2: 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 continue
try: try:
out = extract_lora(weight_diff, rank) out = extract_lora(weight_diff, rank)
@ -53,6 +64,9 @@ def calc_lora_model(model_diff, rank, prefix_model, prefix_lora, output_sd, bias
output_sd["{}{}.lora_down.weight".format(prefix_lora, k[len(prefix_model):-7])] = out[1].contiguous().half().cpu() output_sd["{}{}.lora_down.weight".format(prefix_lora, k[len(prefix_model):-7])] = out[1].contiguous().half().cpu()
except: except:
logging.warning("Could not generate lora weights for key {}, is the weight difference a zero?".format(k)) 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"): elif bias_diff and k.endswith(".bias"):
output_sd["{}{}.diff_b".format(prefix_lora, k[len(prefix_model):-5])] = sd[k].contiguous().half().cpu() output_sd["{}{}.diff_b".format(prefix_lora, k[len(prefix_model):-5])] = sd[k].contiguous().half().cpu()
return output_sd return output_sd
@ -65,7 +79,7 @@ class LoraSave:
def INPUT_TYPES(s): def INPUT_TYPES(s):
return {"required": {"filename_prefix": ("STRING", {"default": "loras/ComfyUI_extracted_lora"}), return {"required": {"filename_prefix": ("STRING", {"default": "loras/ComfyUI_extracted_lora"}),
"rank": ("INT", {"default": 8, "min": 1, "max": 4096, "step": 1}), "rank": ("INT", {"default": 8, "min": 1, "max": 4096, "step": 1}),
"lora_type": (["standard"],), "lora_type": (tuple(LORA_TYPES.keys()),),
"bias_diff": ("BOOLEAN", {"default": True}), "bias_diff": ("BOOLEAN", {"default": True}),
}, },
"optional": {"model_diff": ("MODEL",), "optional": {"model_diff": ("MODEL",),
@ -81,13 +95,14 @@ class LoraSave:
if model_diff is None and text_encoder_diff is None: if model_diff is None and text_encoder_diff is None:
return {} 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) full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
output_sd = {} output_sd = {}
if model_diff is not None: if model_diff is not None:
output_sd = calc_lora_model(model_diff, rank, "diffusion_model.", "diffusion_model.", output_sd, bias_diff=bias_diff) 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: if text_encoder_diff is not None:
output_sd = calc_lora_model(text_encoder_diff.patcher, rank, "", "text_encoders.", output_sd, bias_diff=bias_diff) 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 = f"{filename}_{counter:05}_.safetensors"
output_checkpoint = os.path.join(full_output_folder, output_checkpoint) output_checkpoint = os.path.join(full_output_folder, output_checkpoint)