| import torch |
| import comfy.model_management |
| import comfy.utils |
| import folder_paths |
| import os |
|
|
| 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 ControlLoraSave: |
| def __init__(self): |
| self.output_dir = folder_paths.get_output_directory() |
|
|
| @classmethod |
| def INPUT_TYPES(s): |
| return {"required": { "model": ("MODEL",), |
| "control_net": ("CONTROL_NET",), |
| "filename_prefix": ("STRING", {"default": "controlnet_loras/ComfyUI_control_lora"}), |
| "rank": ("INT", {"default": 64, "min": 0, "max": 1024, "step": 8}), |
| },} |
| RETURN_TYPES = () |
| FUNCTION = "save" |
| OUTPUT_NODE = True |
|
|
| CATEGORY = "stability/controlnet" |
|
|
| def save(self, model, control_net, filename_prefix, rank): |
| full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir) |
|
|
| output_sd = {} |
| prefix_key = "diffusion_model." |
| stored = set() |
|
|
| comfy.model_management.load_models_gpu([model]) |
| f = model.model_state_dict() |
| c = control_net.control_model.state_dict() |
|
|
| for k in f: |
| if k.startswith(prefix_key): |
| ck = k[len(prefix_key):] |
| if ck not in c: |
| ck = "control_model.{}".format(ck) |
| if ck in c: |
| model_weight = f[k] |
| if len(model_weight.shape) >= 2: |
| diff = c[ck].float().to(model_weight.device) - model_weight.float() |
| out = extract_lora(diff, rank) |
| name = ck |
| if name.endswith(".weight"): |
| name = name[:-len(".weight")] |
| out1_key = "{}.up".format(name) |
| out2_key = "{}.down".format(name) |
| output_sd[out1_key] = out[0].contiguous().half().cpu() |
| output_sd[out2_key] = out[1].contiguous().half().cpu() |
| else: |
| output_sd[ck] = c[ck] |
| print(ck, c[ck].shape) |
| stored.add(ck) |
|
|
| for k in c: |
| if k not in stored: |
| output_sd[k] = c[k].half() |
| output_sd["lora_controlnet"] = torch.tensor([]) |
|
|
| 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 = { |
| "ControlLoraSave": ControlLoraSave |
| } |
|
|
| NODE_DISPLAY_NAME_MAPPINGS = { |
| } |
|
|
