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LoraSave node now supports generating text encoder loras.

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text_encoder_diff should be connected to a CLIPMergeSubtract node.

model_diff and text_encoder_diff are optional inputs so you can create
model only loras, text encoder only loras or a lora that contains both.
This commit is contained in:
comfyanonymous 2024-09-07 02:30:12 -04:00
parent ea77750759
commit 9bfee68773
1 changed files with 25 additions and 20 deletions
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45
comfy_extras/nodes_lora_extract.py
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@ -38,6 +38,23 @@ 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):
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 weight_diff.ndim < 2:
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))
return output_sd
class LoraSave: class LoraSave:
def __init__(self): def __init__(self):
self.output_dir = folder_paths.get_output_directory() self.output_dir = folder_paths.get_output_directory()
@ -47,7 +64,8 @@ class LoraSave:
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": 1024, "step": 1}), "rank": ("INT", {"default": 8, "min": 1, "max": 1024, "step": 1}),
}, },
"optional": {"model_diff": ("MODEL",),}, "optional": {"model_diff": ("MODEL",),
"text_encoder_diff": ("CLIP",)},
} }
RETURN_TYPES = () RETURN_TYPES = ()
FUNCTION = "save" FUNCTION = "save"
@ -55,30 +73,17 @@ class LoraSave:
CATEGORY = "_for_testing" CATEGORY = "_for_testing"
def save(self, filename_prefix, rank, model_diff=None): def save(self, filename_prefix, rank, model_diff=None, text_encoder_diff=None):
if model_diff is None: if model_diff is None and text_encoder_diff is None:
return {} return {}
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 = {}
prefix_key = "diffusion_model." if model_diff is not None:
stored = set() output_sd = calc_lora_model(model_diff, rank, "diffusion_model.", "diffusion_model.", output_sd)
if text_encoder_diff is not None:
comfy.model_management.load_models_gpu([model_diff], force_patch_weights=True) output_sd = calc_lora_model(text_encoder_diff.patcher, rank, "", "text_encoders.", output_sd)
sd = model_diff.model_state_dict(filter_prefix=prefix_key)
for k in sd:
if k.endswith(".weight"):
weight_diff = sd[k]
if weight_diff.ndim < 2:
continue
try:
out = extract_lora(weight_diff, rank)
output_sd["{}.lora_up.weight".format(k[:-7])] = out[0].contiguous().half().cpu()
output_sd["{}.lora_down.weight".format(k[:-7])] = out[1].contiguous().half().cpu()
except:
logging.warning("Could not generate lora weights for key {}, is the weight difference a zero?".format(k))
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)