116 lines
4.3 KiB
Python
116 lines
4.3 KiB
Python
import torch
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import comfy.model_management
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import comfy.utils
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import folder_paths
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import os
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import logging
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from enum import Enum
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CLAMP_QUANTILE = 0.99
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def extract_lora(diff, rank):
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conv2d = (len(diff.shape) == 4)
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kernel_size = None if not conv2d else diff.size()[2:4]
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conv2d_3x3 = conv2d and kernel_size != (1, 1)
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out_dim, in_dim = diff.size()[0:2]
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rank = min(rank, in_dim, out_dim)
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if conv2d:
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if conv2d_3x3:
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diff = diff.flatten(start_dim=1)
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else:
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diff = diff.squeeze()
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U, S, Vh = torch.linalg.svd(diff.float())
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U = U[:, :rank]
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S = S[:rank]
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U = U @ torch.diag(S)
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Vh = Vh[:rank, :]
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dist = torch.cat([U.flatten(), Vh.flatten()])
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hi_val = torch.quantile(dist, CLAMP_QUANTILE)
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low_val = -hi_val
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U = U.clamp(low_val, hi_val)
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Vh = Vh.clamp(low_val, hi_val)
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if conv2d:
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U = U.reshape(out_dim, rank, 1, 1)
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Vh = Vh.reshape(rank, in_dim, kernel_size[0], kernel_size[1])
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return (U, Vh)
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class LORAType(Enum):
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STANDARD = 0
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FULL_DIFF = 1
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LORA_TYPES = {"standard": LORAType.STANDARD,
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"full_diff": LORAType.FULL_DIFF}
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def calc_lora_model(model_diff, rank, prefix_model, prefix_lora, output_sd, lora_type, bias_diff=False):
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comfy.model_management.load_models_gpu([model_diff], force_patch_weights=True)
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sd = model_diff.model_state_dict(filter_prefix=prefix_model)
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for k in sd:
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if k.endswith(".weight"):
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weight_diff = sd[k]
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if lora_type == LORAType.STANDARD:
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if weight_diff.ndim < 2:
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if bias_diff:
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output_sd["{}{}.diff".format(prefix_lora, k[len(prefix_model):-7])] = weight_diff.contiguous().half().cpu()
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continue
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try:
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out = extract_lora(weight_diff, rank)
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output_sd["{}{}.lora_up.weight".format(prefix_lora, k[len(prefix_model):-7])] = out[0].contiguous().half().cpu()
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output_sd["{}{}.lora_down.weight".format(prefix_lora, k[len(prefix_model):-7])] = out[1].contiguous().half().cpu()
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except:
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logging.warning("Could not generate lora weights for key {}, is the weight difference a zero?".format(k))
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elif lora_type == LORAType.FULL_DIFF:
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output_sd["{}{}.diff".format(prefix_lora, k[len(prefix_model):-7])] = weight_diff.contiguous().half().cpu()
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elif bias_diff and k.endswith(".bias"):
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output_sd["{}{}.diff_b".format(prefix_lora, k[len(prefix_model):-5])] = sd[k].contiguous().half().cpu()
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return output_sd
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class LoraSave:
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def __init__(self):
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self.output_dir = folder_paths.get_output_directory()
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {"filename_prefix": ("STRING", {"default": "loras/ComfyUI_extracted_lora"}),
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"rank": ("INT", {"default": 8, "min": 1, "max": 4096, "step": 1}),
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"lora_type": (tuple(LORA_TYPES.keys()),),
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"bias_diff": ("BOOLEAN", {"default": True}),
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},
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"optional": {"model_diff": ("MODEL",),
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"text_encoder_diff": ("CLIP",)},
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}
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RETURN_TYPES = ()
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FUNCTION = "save"
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OUTPUT_NODE = True
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CATEGORY = "_for_testing"
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def save(self, filename_prefix, rank, lora_type, bias_diff, model_diff=None, text_encoder_diff=None):
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if model_diff is None and text_encoder_diff is None:
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return {}
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lora_type = LORA_TYPES.get(lora_type)
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full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
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output_sd = {}
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if model_diff is not None:
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output_sd = calc_lora_model(model_diff, rank, "diffusion_model.", "diffusion_model.", output_sd, lora_type, bias_diff=bias_diff)
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if text_encoder_diff is not None:
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output_sd = calc_lora_model(text_encoder_diff.patcher, rank, "", "text_encoders.", output_sd, lora_type, bias_diff=bias_diff)
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output_checkpoint = f"{filename}_{counter:05}_.safetensors"
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output_checkpoint = os.path.join(full_output_folder, output_checkpoint)
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comfy.utils.save_torch_file(output_sd, output_checkpoint, metadata=None)
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return {}
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NODE_CLASS_MAPPINGS = {
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"LoraSave": LoraSave
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}
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