# mostly from https://github.com/kohya-ss/sd-scripts/blob/main/library/model_util.py # I am infinitely grateful to @kohya-ss for their amazing work in this field. # This version is updated to handle the latest version of the diffusers library. import json # v1: split from train_db_fixed.py. # v2: support safetensors import math import os import re import torch from transformers import CLIPTextModel, CLIPTokenizer, CLIPTextConfig, logging from diffusers import AutoencoderKL, DDIMScheduler, StableDiffusionPipeline, UNet2DConditionModel from safetensors.torch import load_file, save_file from collections import OrderedDict # DiffUsers版StableDiffusionのモデルパラメータ NUM_TRAIN_TIMESTEPS = 1000 BETA_START = 0.00085 BETA_END = 0.0120 UNET_PARAMS_MODEL_CHANNELS = 320 UNET_PARAMS_CHANNEL_MULT = [1, 2, 4, 4] UNET_PARAMS_ATTENTION_RESOLUTIONS = [4, 2, 1] UNET_PARAMS_IMAGE_SIZE = 64 # fixed from old invalid value `32` UNET_PARAMS_IN_CHANNELS = 4 UNET_PARAMS_OUT_CHANNELS = 4 UNET_PARAMS_NUM_RES_BLOCKS = 2 UNET_PARAMS_CONTEXT_DIM = 768 UNET_PARAMS_NUM_HEADS = 8 # UNET_PARAMS_USE_LINEAR_PROJECTION = False VAE_PARAMS_Z_CHANNELS = 4 VAE_PARAMS_RESOLUTION = 256 VAE_PARAMS_IN_CHANNELS = 3 VAE_PARAMS_OUT_CH = 3 VAE_PARAMS_CH = 128 VAE_PARAMS_CH_MULT = [1, 2, 4, 4] VAE_PARAMS_NUM_RES_BLOCKS = 2 # V2 V2_UNET_PARAMS_ATTENTION_HEAD_DIM = [5, 10, 20, 20] V2_UNET_PARAMS_CONTEXT_DIM = 1024 # V2_UNET_PARAMS_USE_LINEAR_PROJECTION = True # Diffusersの設定を読み込むための参照モデル DIFFUSERS_REF_MODEL_ID_V1 = "runwayml/stable-diffusion-v1-5" DIFFUSERS_REF_MODEL_ID_V2 = "stabilityai/stable-diffusion-2-1" # region StableDiffusion->Diffusersの変換コード # convert_original_stable_diffusion_to_diffusers をコピーして修正している（ASL 2.0） def shave_segments(path, n_shave_prefix_segments=1): """ Removes segments. Positive values shave the first segments, negative shave the last segments. """ if n_shave_prefix_segments >= 0: return ".".join(path.split(".")[n_shave_prefix_segments:]) else: return ".".join(path.split(".")[:n_shave_prefix_segments]) def renew_resnet_paths(old_list, n_shave_prefix_segments=0): """ Updates paths inside resnets to the new naming scheme (local renaming) """ mapping = [] for old_item in old_list: new_item = old_item.replace("in_layers.0", "norm1") new_item = new_item.replace("in_layers.2", "conv1") new_item = new_item.replace("out_layers.0", "norm2") new_item = new_item.replace("out_layers.3", "conv2") new_item = new_item.replace("emb_layers.1", "time_emb_proj") new_item = new_item.replace("skip_connection", "conv_shortcut") new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) mapping.append({"old": old_item, "new": new_item}) return mapping def renew_vae_resnet_paths(old_list, n_shave_prefix_segments=0): """ Updates paths inside resnets to the new naming scheme (local renaming) """ mapping = [] for old_item in old_list: new_item = old_item new_item = new_item.replace("nin_shortcut", "conv_shortcut") new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) mapping.append({"old": old_item, "new": new_item}) return mapping def renew_attention_paths(old_list, n_shave_prefix_segments=0): """ Updates paths inside attentions to the new naming scheme (local renaming) """ mapping = [] for old_item in old_list: new_item = old_item # new_item = new_item.replace('norm.weight', 'group_norm.weight') # new_item = new_item.replace('norm.bias', 'group_norm.bias') # new_item = new_item.replace('proj_out.weight', 'proj_attn.weight') # new_item = new_item.replace('proj_out.bias', 'proj_attn.bias') # new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) mapping.append({"old": old_item, "new": new_item}) return mapping def renew_vae_attention_paths(old_list, n_shave_prefix_segments=0): """ Updates paths inside attentions to the new naming scheme (local renaming) """ mapping = [] for old_item in old_list: new_item = old_item # updated for latest diffusers new_item = new_item.replace("norm.weight", "group_norm.weight") new_item = new_item.replace("norm.bias", "group_norm.bias") new_item = new_item.replace("q.weight", "to_q.weight") new_item = new_item.replace("q.bias", "to_q.bias") new_item = new_item.replace("k.weight", "to_k.weight") new_item = new_item.replace("k.bias", "to_k.bias") new_item = new_item.replace("v.weight", "to_v.weight") new_item = new_item.replace("v.bias", "to_v.bias") new_item = new_item.replace("proj_out.weight", "to_out.0.weight") new_item = new_item.replace("proj_out.bias", "to_out.0.bias") new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) mapping.append({"old": old_item, "new": new_item}) return mapping def assign_to_checkpoint( paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None ): """ This does the final conversion step: take locally converted weights and apply a global renaming to them. It splits attention layers, and takes into account additional replacements that may arise. Assigns the weights to the new checkpoint. """ assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys." # Splits the attention layers into three variables. if attention_paths_to_split is not None: for path, path_map in attention_paths_to_split.items(): old_tensor = old_checkpoint[path] channels = old_tensor.shape[0] // 3 target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1) num_heads = old_tensor.shape[0] // config["num_head_channels"] // 3 old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]) query, key, value = old_tensor.split(channels // num_heads, dim=1) checkpoint[path_map["query"]] = query.reshape(target_shape) checkpoint[path_map["key"]] = key.reshape(target_shape) checkpoint[path_map["value"]] = value.reshape(target_shape) for path in paths: new_path = path["new"] # These have already been assigned if attention_paths_to_split is not None and new_path in attention_paths_to_split: continue # Global renaming happens here new_path = new_path.replace("middle_block.0", "mid_block.resnets.0") new_path = new_path.replace("middle_block.1", "mid_block.attentions.0") new_path = new_path.replace("middle_block.2", "mid_block.resnets.1") if additional_replacements is not None: for replacement in additional_replacements: new_path = new_path.replace(replacement["old"], replacement["new"]) # proj_attn.weight has to be converted from conv 1D to linear is_attn_weight = "proj_attn.weight" in new_path or ("attentions" in new_path and "to_" in new_path) shape = old_checkpoint[path["old"]].shape if is_attn_weight and len(shape) == 3: checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0] elif is_attn_weight and len(shape) == 4: checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0, 0] else: checkpoint[new_path] = old_checkpoint[path["old"]] def conv_attn_to_linear(checkpoint): keys = list(checkpoint.keys()) attn_keys = ["query.weight", "key.weight", "value.weight"] for key in keys: if ".".join(key.split(".")[-2:]) in attn_keys: if checkpoint[key].ndim > 2: checkpoint[key] = checkpoint[key][:, :, 0, 0] elif "proj_attn.weight" in key: if checkpoint[key].ndim > 2: checkpoint[key] = checkpoint[key][:, :, 0] def linear_transformer_to_conv(checkpoint): keys = list(checkpoint.keys()) tf_keys = ["proj_in.weight", "proj_out.weight"] for key in keys: if ".".join(key.split(".")[-2:]) in tf_keys: if checkpoint[key].ndim == 2: checkpoint[key] = checkpoint[key].unsqueeze(2).unsqueeze(2) def convert_ldm_unet_checkpoint(v2, checkpoint, config): mapping = {} """ Takes a state dict and a config, and returns a converted checkpoint. """ # extract state_dict for UNet unet_state_dict = {} unet_key = "model.diffusion_model." keys = list(checkpoint.keys()) for key in keys: if key.startswith(unet_key): unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(key) new_checkpoint = {} new_checkpoint["time_embedding.linear_1.weight"] = unet_state_dict["time_embed.0.weight"] new_checkpoint["time_embedding.linear_1.bias"] = unet_state_dict["time_embed.0.bias"] new_checkpoint["time_embedding.linear_2.weight"] = unet_state_dict["time_embed.2.weight"] new_checkpoint["time_embedding.linear_2.bias"] = unet_state_dict["time_embed.2.bias"] new_checkpoint["conv_in.weight"] = unet_state_dict["input_blocks.0.0.weight"] new_checkpoint["conv_in.bias"] = unet_state_dict["input_blocks.0.0.bias"] new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"] new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"] new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"] new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"] # Retrieves the keys for the input blocks only num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "input_blocks" in layer}) input_blocks = { layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}." in key] for layer_id in range(num_input_blocks) } # Retrieves the keys for the middle blocks only num_middle_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "middle_block" in layer}) middle_blocks = { layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}." in key] for layer_id in range(num_middle_blocks) } # Retrieves the keys for the output blocks only num_output_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "output_blocks" in layer}) output_blocks = { layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}." in key] for layer_id in range(num_output_blocks) } for i in range(1, num_input_blocks): block_id = (i - 1) // (config["layers_per_block"] + 1) layer_in_block_id = (i - 1) % (config["layers_per_block"] + 1) resnets = [key for key in input_blocks[i] if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key] attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key] if f"input_blocks.{i}.0.op.weight" in unet_state_dict: new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = unet_state_dict.pop( f"input_blocks.{i}.0.op.weight" ) mapping[f'input_blocks.{i}.0.op.weight'] = f"down_blocks.{block_id}.downsamplers.0.conv.weight" new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = unet_state_dict.pop( f"input_blocks.{i}.0.op.bias") mapping[f'input_blocks.{i}.0.op.bias'] = f"down_blocks.{block_id}.downsamplers.0.conv.bias" paths = renew_resnet_paths(resnets) meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"} assign_to_checkpoint(paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config) if len(attentions): paths = renew_attention_paths(attentions) meta_path = {"old": f"input_blocks.{i}.1", "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}"} assign_to_checkpoint(paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config) resnet_0 = middle_blocks[0] attentions = middle_blocks[1] resnet_1 = middle_blocks[2] resnet_0_paths = renew_resnet_paths(resnet_0) assign_to_checkpoint(resnet_0_paths, new_checkpoint, unet_state_dict, config=config) resnet_1_paths = renew_resnet_paths(resnet_1) assign_to_checkpoint(resnet_1_paths, new_checkpoint, unet_state_dict, config=config) attentions_paths = renew_attention_paths(attentions) meta_path = {"old": "middle_block.1", "new": "mid_block.attentions.0"} assign_to_checkpoint(attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config) for i in range(num_output_blocks): block_id = i // (config["layers_per_block"] + 1) layer_in_block_id = i % (config["layers_per_block"] + 1) output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]] output_block_list = {} for layer in output_block_layers: layer_id, layer_name = layer.split(".")[0], shave_segments(layer, 1) if layer_id in output_block_list: output_block_list[layer_id].append(layer_name) else: output_block_list[layer_id] = [layer_name] if len(output_block_list) > 1: resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key] attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key] resnet_0_paths = renew_resnet_paths(resnets) paths = renew_resnet_paths(resnets) meta_path = {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"} assign_to_checkpoint(paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config) # オリジナル： # if ["conv.weight", "conv.bias"] in output_block_list.values(): # index = list(output_block_list.values()).index(["conv.weight", "conv.bias"]) # biasとweightの順番に依存しないようにする：もっといいやり方がありそうだが for l in output_block_list.values(): l.sort() if ["conv.bias", "conv.weight"] in output_block_list.values(): index = list(output_block_list.values()).index(["conv.bias", "conv.weight"]) new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[ f"output_blocks.{i}.{index}.conv.bias" ] new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[ f"output_blocks.{i}.{index}.conv.weight" ] # Clear attentions as they have been attributed above. if len(attentions) == 2: attentions = [] if len(attentions): paths = renew_attention_paths(attentions) meta_path = { "old": f"output_blocks.{i}.1", "new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}", } assign_to_checkpoint(paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config) else: resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1) for path in resnet_0_paths: old_path = ".".join(["output_blocks", str(i), path["old"]]) new_path = ".".join(["up_blocks", str(block_id), "resnets", str(layer_in_block_id), path["new"]]) new_checkpoint[new_path] = unet_state_dict[old_path] # SDのv2では1*1のconv2dがlinearに変わっている # 誤って Diffusers 側を conv2d のままにしてしまったので、変換必要 if v2 and not config.get('use_linear_projection', False): linear_transformer_to_conv(new_checkpoint) # print("mapping: ", json.dumps(mapping, indent=4)) return new_checkpoint # ldm key: diffusers key vae_ldm_to_diffusers_dict = { "decoder.conv_in.bias": "decoder.conv_in.bias", "decoder.conv_in.weight": "decoder.conv_in.weight", "decoder.conv_out.bias": "decoder.conv_out.bias", "decoder.conv_out.weight": "decoder.conv_out.weight", "decoder.mid.attn_1.k.bias": "decoder.mid_block.attentions.0.to_k.bias", "decoder.mid.attn_1.k.weight": "decoder.mid_block.attentions.0.to_k.weight", "decoder.mid.attn_1.norm.bias": "decoder.mid_block.attentions.0.group_norm.bias", "decoder.mid.attn_1.norm.weight": "decoder.mid_block.attentions.0.group_norm.weight", "decoder.mid.attn_1.proj_out.bias": "decoder.mid_block.attentions.0.to_out.0.bias", "decoder.mid.attn_1.proj_out.weight": "decoder.mid_block.attentions.0.to_out.0.weight", "decoder.mid.attn_1.q.bias": "decoder.mid_block.attentions.0.to_q.bias", "decoder.mid.attn_1.q.weight": "decoder.mid_block.attentions.0.to_q.weight", "decoder.mid.attn_1.v.bias": "decoder.mid_block.attentions.0.to_v.bias", "decoder.mid.attn_1.v.weight": "decoder.mid_block.attentions.0.to_v.weight", "decoder.mid.block_1.conv1.bias": "decoder.mid_block.resnets.0.conv1.bias", "decoder.mid.block_1.conv1.weight": "decoder.mid_block.resnets.0.conv1.weight", "decoder.mid.block_1.conv2.bias": "decoder.mid_block.resnets.0.conv2.bias", "decoder.mid.block_1.conv2.weight": "decoder.mid_block.resnets.0.conv2.weight", "decoder.mid.block_1.norm1.bias": "decoder.mid_block.resnets.0.norm1.bias", "decoder.mid.block_1.norm1.weight": "decoder.mid_block.resnets.0.norm1.weight", "decoder.mid.block_1.norm2.bias": "decoder.mid_block.resnets.0.norm2.bias", "decoder.mid.block_1.norm2.weight": "decoder.mid_block.resnets.0.norm2.weight", "decoder.mid.block_2.conv1.bias": "decoder.mid_block.resnets.1.conv1.bias", "decoder.mid.block_2.conv1.weight": "decoder.mid_block.resnets.1.conv1.weight", "decoder.mid.block_2.conv2.bias": "decoder.mid_block.resnets.1.conv2.bias", "decoder.mid.block_2.conv2.weight": "decoder.mid_block.resnets.1.conv2.weight", "decoder.mid.block_2.norm1.bias": "decoder.mid_block.resnets.1.norm1.bias", "decoder.mid.block_2.norm1.weight": "decoder.mid_block.resnets.1.norm1.weight", "decoder.mid.block_2.norm2.bias": "decoder.mid_block.resnets.1.norm2.bias", "decoder.mid.block_2.norm2.weight": "decoder.mid_block.resnets.1.norm2.weight", "decoder.norm_out.bias": "decoder.conv_norm_out.bias", "decoder.norm_out.weight": "decoder.conv_norm_out.weight", "decoder.up.0.block.0.conv1.bias": "decoder.up_blocks.3.resnets.0.conv1.bias", "decoder.up.0.block.0.conv1.weight": "decoder.up_blocks.3.resnets.0.conv1.weight", "decoder.up.0.block.0.conv2.bias": "decoder.up_blocks.3.resnets.0.conv2.bias", "decoder.up.0.block.0.conv2.weight": "decoder.up_blocks.3.resnets.0.conv2.weight", "decoder.up.0.block.0.nin_shortcut.bias": "decoder.up_blocks.3.resnets.0.conv_shortcut.bias", "decoder.up.0.block.0.nin_shortcut.weight": "decoder.up_blocks.3.resnets.0.conv_shortcut.weight", "decoder.up.0.block.0.norm1.bias": "decoder.up_blocks.3.resnets.0.norm1.bias", "decoder.up.0.block.0.norm1.weight": "decoder.up_blocks.3.resnets.0.norm1.weight", "decoder.up.0.block.0.norm2.bias": "decoder.up_blocks.3.resnets.0.norm2.bias", "decoder.up.0.block.0.norm2.weight": "decoder.up_blocks.3.resnets.0.norm2.weight", "decoder.up.0.block.1.conv1.bias": "decoder.up_blocks.3.resnets.1.conv1.bias", "decoder.up.0.block.1.conv1.weight": "decoder.up_blocks.3.resnets.1.conv1.weight", "decoder.up.0.block.1.conv2.bias": "decoder.up_blocks.3.resnets.1.conv2.bias", "decoder.up.0.block.1.conv2.weight": "decoder.up_blocks.3.resnets.1.conv2.weight", "decoder.up.0.block.1.norm1.bias": "decoder.up_blocks.3.resnets.1.norm1.bias", "decoder.up.0.block.1.norm1.weight": "decoder.up_blocks.3.resnets.1.norm1.weight", "decoder.up.0.block.1.norm2.bias": "decoder.up_blocks.3.resnets.1.norm2.bias", "decoder.up.0.block.1.norm2.weight": "decoder.up_blocks.3.resnets.1.norm2.weight", "decoder.up.0.block.2.conv1.bias": "decoder.up_blocks.3.resnets.2.conv1.bias", "decoder.up.0.block.2.conv1.weight": "decoder.up_blocks.3.resnets.2.conv1.weight", "decoder.up.0.block.2.conv2.bias": "decoder.up_blocks.3.resnets.2.conv2.bias", "decoder.up.0.block.2.conv2.weight": "decoder.up_blocks.3.resnets.2.conv2.weight", "decoder.up.0.block.2.norm1.bias": "decoder.up_blocks.3.resnets.2.norm1.bias", "decoder.up.0.block.2.norm1.weight": "decoder.up_blocks.3.resnets.2.norm1.weight", "decoder.up.0.block.2.norm2.bias": "decoder.up_blocks.3.resnets.2.norm2.bias", "decoder.up.0.block.2.norm2.weight": "decoder.up_blocks.3.resnets.2.norm2.weight", "decoder.up.1.block.0.conv1.bias": "decoder.up_blocks.2.resnets.0.conv1.bias", "decoder.up.1.block.0.conv1.weight": "decoder.up_blocks.2.resnets.0.conv1.weight", "decoder.up.1.block.0.conv2.bias": "decoder.up_blocks.2.resnets.0.conv2.bias", "decoder.up.1.block.0.conv2.weight": "decoder.up_blocks.2.resnets.0.conv2.weight", "decoder.up.1.block.0.nin_shortcut.bias": "decoder.up_blocks.2.resnets.0.conv_shortcut.bias", "decoder.up.1.block.0.nin_shortcut.weight": "decoder.up_blocks.2.resnets.0.conv_shortcut.weight", "decoder.up.1.block.0.norm1.bias": "decoder.up_blocks.2.resnets.0.norm1.bias", "decoder.up.1.block.0.norm1.weight": "decoder.up_blocks.2.resnets.0.norm1.weight", "decoder.up.1.block.0.norm2.bias": "decoder.up_blocks.2.resnets.0.norm2.bias", "decoder.up.1.block.0.norm2.weight": "decoder.up_blocks.2.resnets.0.norm2.weight", "decoder.up.1.block.1.conv1.bias": "decoder.up_blocks.2.resnets.1.conv1.bias", "decoder.up.1.block.1.conv1.weight": "decoder.up_blocks.2.resnets.1.conv1.weight", "decoder.up.1.block.1.conv2.bias": "decoder.up_blocks.2.resnets.1.conv2.bias", "decoder.up.1.block.1.conv2.weight": "decoder.up_blocks.2.resnets.1.conv2.weight", "decoder.up.1.block.1.norm1.bias": "decoder.up_blocks.2.resnets.1.norm1.bias", "decoder.up.1.block.1.norm1.weight": "decoder.up_blocks.2.resnets.1.norm1.weight", "decoder.up.1.block.1.norm2.bias": "decoder.up_blocks.2.resnets.1.norm2.bias", "decoder.up.1.block.1.norm2.weight": "decoder.up_blocks.2.resnets.1.norm2.weight", "decoder.up.1.block.2.conv1.bias": "decoder.up_blocks.2.resnets.2.conv1.bias", "decoder.up.1.block.2.conv1.weight": "decoder.up_blocks.2.resnets.2.conv1.weight", "decoder.up.1.block.2.conv2.bias": "decoder.up_blocks.2.resnets.2.conv2.bias", "decoder.up.1.block.2.conv2.weight": "decoder.up_blocks.2.resnets.2.conv2.weight", "decoder.up.1.block.2.norm1.bias": "decoder.up_blocks.2.resnets.2.norm1.bias", "decoder.up.1.block.2.norm1.weight": "decoder.up_blocks.2.resnets.2.norm1.weight", "decoder.up.1.block.2.norm2.bias": "decoder.up_blocks.2.resnets.2.norm2.bias", "decoder.up.1.block.2.norm2.weight": "decoder.up_blocks.2.resnets.2.norm2.weight", "decoder.up.1.upsample.conv.bias": "decoder.up_blocks.2.upsamplers.0.conv.bias", "decoder.up.1.upsample.conv.weight": "decoder.up_blocks.2.upsamplers.0.conv.weight", "decoder.up.2.block.0.conv1.bias": "decoder.up_blocks.1.resnets.0.conv1.bias", "decoder.up.2.block.0.conv1.weight": "decoder.up_blocks.1.resnets.0.conv1.weight", "decoder.up.2.block.0.conv2.bias": "decoder.up_blocks.1.resnets.0.conv2.bias", "decoder.up.2.block.0.conv2.weight": "decoder.up_blocks.1.resnets.0.conv2.weight", "decoder.up.2.block.0.norm1.bias": "decoder.up_blocks.1.resnets.0.norm1.bias", 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"encoder.mid_block.resnets.0.norm2.weight", "encoder.mid.block_2.conv1.bias": "encoder.mid_block.resnets.1.conv1.bias", "encoder.mid.block_2.conv1.weight": "encoder.mid_block.resnets.1.conv1.weight", "encoder.mid.block_2.conv2.bias": "encoder.mid_block.resnets.1.conv2.bias", "encoder.mid.block_2.conv2.weight": "encoder.mid_block.resnets.1.conv2.weight", "encoder.mid.block_2.norm1.bias": "encoder.mid_block.resnets.1.norm1.bias", "encoder.mid.block_2.norm1.weight": "encoder.mid_block.resnets.1.norm1.weight", "encoder.mid.block_2.norm2.bias": "encoder.mid_block.resnets.1.norm2.bias", "encoder.mid.block_2.norm2.weight": "encoder.mid_block.resnets.1.norm2.weight", "encoder.norm_out.bias": "encoder.conv_norm_out.bias", "encoder.norm_out.weight": "encoder.conv_norm_out.weight", "post_quant_conv.bias": "post_quant_conv.bias", "post_quant_conv.weight": "post_quant_conv.weight", "quant_conv.bias": "quant_conv.bias", "quant_conv.weight": "quant_conv.weight" } def get_diffusers_vae_key_from_ldm_key(target_ldm_key, i=None): for ldm_key, diffusers_key in vae_ldm_to_diffusers_dict.items(): if i is not None: ldm_key = ldm_key.replace("{i}", str(i)) diffusers_key = diffusers_key.replace("{i}", str(i)) if ldm_key == target_ldm_key: return diffusers_key if ldm_key in vae_ldm_to_diffusers_dict: return vae_ldm_to_diffusers_dict[ldm_key] else: return None # def get_ldm_vae_key_from_diffusers_key(target_diffusers_key): # for ldm_key, diffusers_key in vae_ldm_to_diffusers_dict.items(): # if diffusers_key == target_diffusers_key: # return ldm_key # return None def get_ldm_vae_key_from_diffusers_key(target_diffusers_key): for ldm_key, diffusers_key in vae_ldm_to_diffusers_dict.items(): if "{" in diffusers_key: # if we have a placeholder # escape special characters in the key, and replace the placeholder with a regex group pattern = re.escape(diffusers_key).replace("\\{i\\}", "(\\d+)") match = re.match(pattern, target_diffusers_key) if match: # if we found a match return ldm_key.format(i=match.group(1)) elif diffusers_key == target_diffusers_key: return ldm_key return None vae_keys_squished_on_diffusers = [ "decoder.mid_block.attentions.0.to_k.weight", "decoder.mid_block.attentions.0.to_out.0.weight", "decoder.mid_block.attentions.0.to_q.weight", "decoder.mid_block.attentions.0.to_v.weight", "encoder.mid_block.attentions.0.to_k.weight", "encoder.mid_block.attentions.0.to_out.0.weight", "encoder.mid_block.attentions.0.to_q.weight", "encoder.mid_block.attentions.0.to_v.weight" ] def convert_diffusers_back_to_ldm(diffusers_vae): new_state_dict = OrderedDict() diffusers_state_dict = diffusers_vae.state_dict() for key, value in diffusers_state_dict.items(): val_to_save = value if key in vae_keys_squished_on_diffusers: val_to_save = value.clone() # (512, 512) diffusers and (512, 512, 1, 1) ldm val_to_save = val_to_save.unsqueeze(-1).unsqueeze(-1) ldm_key = get_ldm_vae_key_from_diffusers_key(key) if ldm_key is not None: new_state_dict[ldm_key] = val_to_save else: # for now add current key new_state_dict[key] = val_to_save return new_state_dict def convert_ldm_vae_checkpoint(checkpoint, config): mapping = {} # extract state dict for VAE vae_state_dict = {} vae_key = "first_stage_model." keys = list(checkpoint.keys()) for key in keys: if key.startswith(vae_key): vae_state_dict[key.replace(vae_key, "")] = checkpoint.get(key) # if len(vae_state_dict) == 0: # # 渡されたcheckpointは.ckptから読み込んだcheckpointではなくvaeのstate_dict # vae_state_dict = checkpoint new_checkpoint = {} # for key in list(vae_state_dict.keys()): # diffusers_key = get_diffusers_vae_key_from_ldm_key(key) # if diffusers_key is not None: # new_checkpoint[diffusers_key] = vae_state_dict[key] new_checkpoint["encoder.conv_in.weight"] = vae_state_dict["encoder.conv_in.weight"] new_checkpoint["encoder.conv_in.bias"] = vae_state_dict["encoder.conv_in.bias"] new_checkpoint["encoder.conv_out.weight"] = vae_state_dict["encoder.conv_out.weight"] new_checkpoint["encoder.conv_out.bias"] = vae_state_dict["encoder.conv_out.bias"] new_checkpoint["encoder.conv_norm_out.weight"] = vae_state_dict["encoder.norm_out.weight"] new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict["encoder.norm_out.bias"] new_checkpoint["decoder.conv_in.weight"] = vae_state_dict["decoder.conv_in.weight"] new_checkpoint["decoder.conv_in.bias"] = vae_state_dict["decoder.conv_in.bias"] new_checkpoint["decoder.conv_out.weight"] = vae_state_dict["decoder.conv_out.weight"] new_checkpoint["decoder.conv_out.bias"] = vae_state_dict["decoder.conv_out.bias"] new_checkpoint["decoder.conv_norm_out.weight"] = vae_state_dict["decoder.norm_out.weight"] new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict["decoder.norm_out.bias"] new_checkpoint["quant_conv.weight"] = vae_state_dict["quant_conv.weight"] new_checkpoint["quant_conv.bias"] = vae_state_dict["quant_conv.bias"] new_checkpoint["post_quant_conv.weight"] = vae_state_dict["post_quant_conv.weight"] new_checkpoint["post_quant_conv.bias"] = vae_state_dict["post_quant_conv.bias"] # Retrieves the keys for the encoder down blocks only num_down_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "encoder.down" in layer}) down_blocks = {layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks)} # Retrieves the keys for the decoder up blocks only num_up_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "decoder.up" in layer}) up_blocks = {layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks)} for i in range(num_down_blocks): resnets = [key for key in down_blocks[i] if f"down.{i}" in key and f"down.{i}.downsample" not in key] if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict: new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = vae_state_dict.pop( f"encoder.down.{i}.downsample.conv.weight" ) mapping[f"encoder.down.{i}.downsample.conv.weight"] = f"encoder.down_blocks.{i}.downsamplers.0.conv.weight" new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = vae_state_dict.pop( f"encoder.down.{i}.downsample.conv.bias" ) mapping[f"encoder.down.{i}.downsample.conv.bias"] = f"encoder.down_blocks.{i}.downsamplers.0.conv.bias" paths = renew_vae_resnet_paths(resnets) meta_path = {"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"} assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) mid_resnets = [key for key in vae_state_dict if "encoder.mid.block" in key] num_mid_res_blocks = 2 for i in range(1, num_mid_res_blocks + 1): resnets = [key for key in mid_resnets if f"encoder.mid.block_{i}" in key] paths = renew_vae_resnet_paths(resnets) meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"} assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) mid_attentions = [key for key in vae_state_dict if "encoder.mid.attn" in key] paths = renew_vae_attention_paths(mid_attentions) meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"} assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) conv_attn_to_linear(new_checkpoint) for i in range(num_up_blocks): block_id = num_up_blocks - 1 - i resnets = [key for key in up_blocks[block_id] if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key] if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict: new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"] = vae_state_dict[ f"decoder.up.{block_id}.upsample.conv.weight" ] mapping[f"decoder.up.{block_id}.upsample.conv.weight"] = f"decoder.up_blocks.{i}.upsamplers.0.conv.weight" new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"] = vae_state_dict[ f"decoder.up.{block_id}.upsample.conv.bias" ] mapping[f"decoder.up.{block_id}.upsample.conv.bias"] = f"decoder.up_blocks.{i}.upsamplers.0.conv.bias" paths = renew_vae_resnet_paths(resnets) meta_path = {"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"} assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) mid_resnets = [key for key in vae_state_dict if "decoder.mid.block" in key] num_mid_res_blocks = 2 for i in range(1, num_mid_res_blocks + 1): resnets = [key for key in mid_resnets if f"decoder.mid.block_{i}" in key] paths = renew_vae_resnet_paths(resnets) meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"} assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) mid_attentions = [key for key in vae_state_dict if "decoder.mid.attn" in key] paths = renew_vae_attention_paths(mid_attentions) meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"} assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) conv_attn_to_linear(new_checkpoint) return new_checkpoint def create_unet_diffusers_config(v2, use_linear_projection_in_v2=False): """ Creates a config for the diffusers based on the config of the LDM model. """ # unet_params = original_config.model.params.unet_config.params block_out_channels = [UNET_PARAMS_MODEL_CHANNELS * mult for mult in UNET_PARAMS_CHANNEL_MULT] down_block_types = [] resolution = 1 for i in range(len(block_out_channels)): block_type = "CrossAttnDownBlock2D" if resolution in UNET_PARAMS_ATTENTION_RESOLUTIONS else "DownBlock2D" down_block_types.append(block_type) if i != len(block_out_channels) - 1: resolution *= 2 up_block_types = [] for i in range(len(block_out_channels)): block_type = "CrossAttnUpBlock2D" if resolution in UNET_PARAMS_ATTENTION_RESOLUTIONS else "UpBlock2D" up_block_types.append(block_type) resolution //= 2 config = dict( sample_size=UNET_PARAMS_IMAGE_SIZE, in_channels=UNET_PARAMS_IN_CHANNELS, out_channels=UNET_PARAMS_OUT_CHANNELS, down_block_types=tuple(down_block_types), up_block_types=tuple(up_block_types), block_out_channels=tuple(block_out_channels), layers_per_block=UNET_PARAMS_NUM_RES_BLOCKS, cross_attention_dim=UNET_PARAMS_CONTEXT_DIM if not v2 else V2_UNET_PARAMS_CONTEXT_DIM, attention_head_dim=UNET_PARAMS_NUM_HEADS if not v2 else V2_UNET_PARAMS_ATTENTION_HEAD_DIM, # use_linear_projection=UNET_PARAMS_USE_LINEAR_PROJECTION if not v2 else V2_UNET_PARAMS_USE_LINEAR_PROJECTION, ) if v2 and use_linear_projection_in_v2: config["use_linear_projection"] = True return config def create_vae_diffusers_config(): """ Creates a config for the diffusers based on the config of the LDM model. """ # vae_params = original_config.model.params.first_stage_config.params.ddconfig # _ = original_config.model.params.first_stage_config.params.embed_dim block_out_channels = [VAE_PARAMS_CH * mult for mult in VAE_PARAMS_CH_MULT] down_block_types = ["DownEncoderBlock2D"] * len(block_out_channels) up_block_types = ["UpDecoderBlock2D"] * len(block_out_channels) config = dict( sample_size=VAE_PARAMS_RESOLUTION, in_channels=VAE_PARAMS_IN_CHANNELS, out_channels=VAE_PARAMS_OUT_CH, down_block_types=tuple(down_block_types), up_block_types=tuple(up_block_types), block_out_channels=tuple(block_out_channels), latent_channels=VAE_PARAMS_Z_CHANNELS, layers_per_block=VAE_PARAMS_NUM_RES_BLOCKS, ) return config def convert_ldm_clip_checkpoint_v1(checkpoint): keys = list(checkpoint.keys()) text_model_dict = {} for key in keys: if key.startswith("cond_stage_model.transformer"): text_model_dict[key[len("cond_stage_model.transformer."):]] = checkpoint[key] # support checkpoint without position_ids (invalid checkpoint) if "text_model.embeddings.position_ids" not in text_model_dict: text_model_dict["text_model.embeddings.position_ids"] = torch.arange(77).unsqueeze(0) # 77 is the max length of the text return text_model_dict def convert_ldm_clip_checkpoint_v2(checkpoint, max_length): # 嫌になるくらい違うぞ！ def convert_key(key): if not key.startswith("cond_stage_model"): return None # common conversion key = key.replace("cond_stage_model.model.transformer.", "text_model.encoder.") key = key.replace("cond_stage_model.model.", "text_model.") if "resblocks" in key: # resblocks conversion key = key.replace(".resblocks.", ".layers.") if ".ln_" in key: key = key.replace(".ln_", ".layer_norm") elif ".mlp." in key: key = key.replace(".c_fc.", ".fc1.") key = key.replace(".c_proj.", ".fc2.") elif ".attn.out_proj" in key: key = key.replace(".attn.out_proj.", ".self_attn.out_proj.") elif ".attn.in_proj" in key: key = None # 特殊なので後で処理する else: raise ValueError(f"unexpected key in SD: {key}") elif ".positional_embedding" in key: key = key.replace(".positional_embedding", ".embeddings.position_embedding.weight") elif ".text_projection" in key: key = None # 使われない??? elif ".logit_scale" in key: key = None # 使われない??? elif ".token_embedding" in key: key = key.replace(".token_embedding.weight", ".embeddings.token_embedding.weight") elif ".ln_final" in key: key = key.replace(".ln_final", ".final_layer_norm") return key keys = list(checkpoint.keys()) new_sd = {} for key in keys: # remove resblocks 23 if ".resblocks.23." in key: continue new_key = convert_key(key) if new_key is None: continue new_sd[new_key] = checkpoint[key] # attnの変換 for key in keys: if ".resblocks.23." in key: continue if ".resblocks" in key and ".attn.in_proj_" in key: # 三つに分割 values = torch.chunk(checkpoint[key], 3) key_suffix = ".weight" if "weight" in key else ".bias" key_pfx = key.replace("cond_stage_model.model.transformer.resblocks.", "text_model.encoder.layers.") key_pfx = key_pfx.replace("_weight", "") key_pfx = key_pfx.replace("_bias", "") key_pfx = key_pfx.replace(".attn.in_proj", ".self_attn.") new_sd[key_pfx + "q_proj" + key_suffix] = values[0] new_sd[key_pfx + "k_proj" + key_suffix] = values[1] new_sd[key_pfx + "v_proj" + key_suffix] = values[2] # rename or add position_ids ANOTHER_POSITION_IDS_KEY = "text_model.encoder.text_model.embeddings.position_ids" if ANOTHER_POSITION_IDS_KEY in new_sd: # waifu diffusion v1.4 position_ids = new_sd[ANOTHER_POSITION_IDS_KEY] del new_sd[ANOTHER_POSITION_IDS_KEY] else: position_ids = torch.Tensor([list(range(max_length))]).to(torch.int64) new_sd["text_model.embeddings.position_ids"] = position_ids return new_sd # endregion # region Diffusers->StableDiffusion の変換コード # convert_diffusers_to_original_stable_diffusion をコピーして修正している（ASL 2.0） def conv_transformer_to_linear(checkpoint): keys = list(checkpoint.keys()) tf_keys = ["proj_in.weight", "proj_out.weight"] for key in keys: if ".".join(key.split(".")[-2:]) in tf_keys: if checkpoint[key].ndim > 2: checkpoint[key] = checkpoint[key][:, :, 0, 0] def convert_unet_state_dict_to_sd(v2, unet_state_dict): unet_conversion_map = [ # (stable-diffusion, HF Diffusers) ("time_embed.0.weight", "time_embedding.linear_1.weight"), ("time_embed.0.bias", "time_embedding.linear_1.bias"), ("time_embed.2.weight", "time_embedding.linear_2.weight"), ("time_embed.2.bias", "time_embedding.linear_2.bias"), ("input_blocks.0.0.weight", "conv_in.weight"), ("input_blocks.0.0.bias", "conv_in.bias"), ("out.0.weight", "conv_norm_out.weight"), ("out.0.bias", "conv_norm_out.bias"), ("out.2.weight", "conv_out.weight"), ("out.2.bias", "conv_out.bias"), ] unet_conversion_map_resnet = [ # (stable-diffusion, HF Diffusers) ("in_layers.0", "norm1"), ("in_layers.2", "conv1"), ("out_layers.0", "norm2"), ("out_layers.3", "conv2"), ("emb_layers.1", "time_emb_proj"), ("skip_connection", "conv_shortcut"), ] unet_conversion_map_layer = [] for i in range(4): # loop over downblocks/upblocks for j in range(2): # loop over resnets/attentions for downblocks hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}." sd_down_res_prefix = f"input_blocks.{3 * i + j + 1}.0." unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix)) if i < 3: # no attention layers in down_blocks.3 hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}." sd_down_atn_prefix = f"input_blocks.{3 * i + j + 1}.1." unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix)) for j in range(3): # loop over resnets/attentions for upblocks hf_up_res_prefix = f"up_blocks.{i}.resnets.{j}." sd_up_res_prefix = f"output_blocks.{3 * i + j}.0." unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix)) if i > 0: # no attention layers in up_blocks.0 hf_up_atn_prefix = f"up_blocks.{i}.attentions.{j}." sd_up_atn_prefix = f"output_blocks.{3 * i + j}.1." unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix)) if i < 3: # no downsample in down_blocks.3 hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv." sd_downsample_prefix = f"input_blocks.{3 * (i + 1)}.0.op." unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix)) # no upsample in up_blocks.3 hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0." sd_upsample_prefix = f"output_blocks.{3 * i + 2}.{1 if i == 0 else 2}." unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix)) hf_mid_atn_prefix = "mid_block.attentions.0." sd_mid_atn_prefix = "middle_block.1." unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix)) for j in range(2): hf_mid_res_prefix = f"mid_block.resnets.{j}." sd_mid_res_prefix = f"middle_block.{2 * j}." unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix)) # buyer beware: this is a *brittle* function, # and correct output requires that all of these pieces interact in # the exact order in which I have arranged them. mapping = {k: k for k in unet_state_dict.keys()} for sd_name, hf_name in unet_conversion_map: mapping[hf_name] = sd_name for k, v in mapping.items(): if "resnets" in k: for sd_part, hf_part in unet_conversion_map_resnet: v = v.replace(hf_part, sd_part) mapping[k] = v for k, v in mapping.items(): for sd_part, hf_part in unet_conversion_map_layer: v = v.replace(hf_part, sd_part) mapping[k] = v new_state_dict = {v: unet_state_dict[k] for k, v in mapping.items()} if v2: conv_transformer_to_linear(new_state_dict) return new_state_dict # ================# # VAE Conversion # # ================# def reshape_weight_for_sd(w): # convert HF linear weights to SD conv2d weights return w.reshape(*w.shape, 1, 1) def convert_vae_state_dict(vae_state_dict): vae_conversion_map = [ # (stable-diffusion, HF Diffusers) ("nin_shortcut", "conv_shortcut"), ("norm_out", "conv_norm_out"), ("mid.attn_1.", "mid_block.attentions.0."), ] for i in range(4): # down_blocks have two resnets for j in range(2): hf_down_prefix = f"encoder.down_blocks.{i}.resnets.{j}." sd_down_prefix = f"encoder.down.{i}.block.{j}." vae_conversion_map.append((sd_down_prefix, hf_down_prefix)) if i < 3: hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0." sd_downsample_prefix = f"down.{i}.downsample." vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix)) hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0." sd_upsample_prefix = f"up.{3 - i}.upsample." vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix)) # up_blocks have three resnets # also, up blocks in hf are numbered in reverse from sd for j in range(3): hf_up_prefix = f"decoder.up_blocks.{i}.resnets.{j}." sd_up_prefix = f"decoder.up.{3 - i}.block.{j}." vae_conversion_map.append((sd_up_prefix, hf_up_prefix)) # this part accounts for mid blocks in both the encoder and the decoder for i in range(2): hf_mid_res_prefix = f"mid_block.resnets.{i}." sd_mid_res_prefix = f"mid.block_{i + 1}." vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix)) vae_conversion_map_attn = [ # (stable-diffusion, HF Diffusers) ("norm.", "group_norm."), ("q.", "query."), ("k.", "key."), ("v.", "value."), ("proj_out.", "proj_attn."), ] mapping = {k: k for k in vae_state_dict.keys()} for k, v in mapping.items(): for sd_part, hf_part in vae_conversion_map: v = v.replace(hf_part, sd_part) mapping[k] = v for k, v in mapping.items(): if "attentions" in k: for sd_part, hf_part in vae_conversion_map_attn: v = v.replace(hf_part, sd_part) mapping[k] = v new_state_dict = {v: vae_state_dict[k] for k, v in mapping.items()} weights_to_convert = ["q", "k", "v", "proj_out"] for k, v in new_state_dict.items(): for weight_name in weights_to_convert: if f"mid.attn_1.{weight_name}.weight" in k: # print(f"Reshaping {k} for SD format") new_state_dict[k] = reshape_weight_for_sd(v) return new_state_dict # endregion # region 自作のモデル読み書きなど def is_safetensors(path): return os.path.splitext(path)[1].lower() == ".safetensors" def load_checkpoint_with_text_encoder_conversion(ckpt_path, device="cpu"): # text encoderの格納形式が違うモデルに対応する ('text_model'がない) TEXT_ENCODER_KEY_REPLACEMENTS = [ ("cond_stage_model.transformer.embeddings.", "cond_stage_model.transformer.text_model.embeddings."), ("cond_stage_model.transformer.encoder.", "cond_stage_model.transformer.text_model.encoder."), ("cond_stage_model.transformer.final_layer_norm.", "cond_stage_model.transformer.text_model.final_layer_norm."), ] if is_safetensors(ckpt_path): checkpoint = None state_dict = load_file(ckpt_path) # , device) # may causes error else: checkpoint = torch.load(ckpt_path, map_location=device) if "state_dict" in checkpoint: state_dict = checkpoint["state_dict"] else: state_dict = checkpoint checkpoint = None key_reps = [] for rep_from, rep_to in TEXT_ENCODER_KEY_REPLACEMENTS: for key in state_dict.keys(): if key.startswith(rep_from): new_key = rep_to + key[len(rep_from):] key_reps.append((key, new_key)) for key, new_key in key_reps: state_dict[new_key] = state_dict[key] del state_dict[key] return checkpoint, state_dict # TODO dtype指定の動作が怪しいので確認する text_encoderを指定形式で作れるか未確認 def load_models_from_stable_diffusion_checkpoint(v2, ckpt_path, device="cpu", dtype=None, unet_use_linear_projection_in_v2=False): _, state_dict = load_checkpoint_with_text_encoder_conversion(ckpt_path, device) # Convert the UNet2DConditionModel model. unet_config = create_unet_diffusers_config(v2, unet_use_linear_projection_in_v2) converted_unet_checkpoint = convert_ldm_unet_checkpoint(v2, state_dict, unet_config) unet = UNet2DConditionModel(**unet_config).to(device) info = unet.load_state_dict(converted_unet_checkpoint) print("loading u-net:", info) # Convert the VAE model. vae_config = create_vae_diffusers_config() converted_vae_checkpoint = convert_ldm_vae_checkpoint(state_dict, vae_config) vae = AutoencoderKL(**vae_config).to(device) info = vae.load_state_dict(converted_vae_checkpoint) print("loading vae:", info) # convert text_model if v2: converted_text_encoder_checkpoint = convert_ldm_clip_checkpoint_v2(state_dict, 77) cfg = CLIPTextConfig( vocab_size=49408, hidden_size=1024, intermediate_size=4096, num_hidden_layers=23, num_attention_heads=16, max_position_embeddings=77, hidden_act="gelu", layer_norm_eps=1e-05, dropout=0.0, attention_dropout=0.0, initializer_range=0.02, initializer_factor=1.0, pad_token_id=1, bos_token_id=0, eos_token_id=2, model_type="clip_text_model", projection_dim=512, torch_dtype="float32", transformers_version="4.25.0.dev0", ) text_model = CLIPTextModel._from_config(cfg) info = text_model.load_state_dict(converted_text_encoder_checkpoint) else: converted_text_encoder_checkpoint = convert_ldm_clip_checkpoint_v1(state_dict) logging.set_verbosity_error() # don't show annoying warning text_model = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14").to(device) logging.set_verbosity_warning() # latest transformers doesnt have position ids. Do we remove it? if "text_model.embeddings.position_ids" not in text_model.state_dict(): del converted_text_encoder_checkpoint["text_model.embeddings.position_ids"] info = text_model.load_state_dict(converted_text_encoder_checkpoint) print("loading text encoder:", info) return text_model, vae, unet def convert_text_encoder_state_dict_to_sd_v2(checkpoint, make_dummy_weights=False): def convert_key(key): # position_idsの除去 if ".position_ids" in key: return None # common key = key.replace("text_model.encoder.", "transformer.") key = key.replace("text_model.", "") if "layers" in key: # resblocks conversion key = key.replace(".layers.", ".resblocks.") if ".layer_norm" in key: key = key.replace(".layer_norm", ".ln_") elif ".mlp." in key: key = key.replace(".fc1.", ".c_fc.") key = key.replace(".fc2.", ".c_proj.") elif ".self_attn.out_proj" in key: key = key.replace(".self_attn.out_proj.", ".attn.out_proj.") elif ".self_attn." in key: key = None # 特殊なので後で処理する else: raise ValueError(f"unexpected key in DiffUsers model: {key}") elif ".position_embedding" in key: key = key.replace("embeddings.position_embedding.weight", "positional_embedding") elif ".token_embedding" in key: key = key.replace("embeddings.token_embedding.weight", "token_embedding.weight") elif "final_layer_norm" in key: key = key.replace("final_layer_norm", "ln_final") return key keys = list(checkpoint.keys()) new_sd = {} for key in keys: new_key = convert_key(key) if new_key is None: continue new_sd[new_key] = checkpoint[key] # attnの変換 for key in keys: if "layers" in key and "q_proj" in key: # 三つを結合 key_q = key key_k = key.replace("q_proj", "k_proj") key_v = key.replace("q_proj", "v_proj") value_q = checkpoint[key_q] value_k = checkpoint[key_k] value_v = checkpoint[key_v] value = torch.cat([value_q, value_k, value_v]) new_key = key.replace("text_model.encoder.layers.", "transformer.resblocks.") new_key = new_key.replace(".self_attn.q_proj.", ".attn.in_proj_") new_sd[new_key] = value # 最後の層などを捏造するか if make_dummy_weights: print("make dummy weights for resblock.23, text_projection and logit scale.") keys = list(new_sd.keys()) for key in keys: if key.startswith("transformer.resblocks.22."): new_sd[key.replace(".22.", ".23.")] = new_sd[key].clone() # copyしないとsafetensorsの保存で落ちる # Diffusersに含まれない重みを作っておく new_sd["text_projection"] = torch.ones((1024, 1024), dtype=new_sd[keys[0]].dtype, device=new_sd[keys[0]].device) new_sd["logit_scale"] = torch.tensor(1) return new_sd def save_stable_diffusion_checkpoint(v2, output_file, text_encoder, unet, ckpt_path, epochs, steps, save_dtype=None, vae=None): if ckpt_path is not None: # epoch/stepを参照する。またVAEがメモリ上にないときなど、もう一度VAEを含めて読み込む checkpoint, state_dict = load_checkpoint_with_text_encoder_conversion(ckpt_path) if checkpoint is None: # safetensors または state_dictのckpt checkpoint = {} strict = False else: strict = True if "state_dict" in state_dict: del state_dict["state_dict"] else: # 新しく作る assert vae is not None, "VAE is required to save a checkpoint without a given checkpoint" checkpoint = {} state_dict = {} strict = False def update_sd(prefix, sd): for k, v in sd.items(): key = prefix + k assert not strict or key in state_dict, f"Illegal key in save SD: {key}" if save_dtype is not None: v = v.detach().clone().to("cpu").to(save_dtype) state_dict[key] = v # Convert the UNet model unet_state_dict = convert_unet_state_dict_to_sd(v2, unet.state_dict()) update_sd("model.diffusion_model.", unet_state_dict) # Convert the text encoder model if v2: make_dummy = ckpt_path is None # 参照元のcheckpointがない場合は最後の層を前の層から複製して作るなどダミーの重みを入れる text_enc_dict = convert_text_encoder_state_dict_to_sd_v2(text_encoder.state_dict(), make_dummy) update_sd("cond_stage_model.model.", text_enc_dict) else: text_enc_dict = text_encoder.state_dict() update_sd("cond_stage_model.transformer.", text_enc_dict) # Convert the VAE if vae is not None: vae_dict = convert_vae_state_dict(vae.state_dict()) update_sd("first_stage_model.", vae_dict) # Put together new checkpoint key_count = len(state_dict.keys()) new_ckpt = {"state_dict": state_dict} # epoch and global_step are sometimes not int try: if "epoch" in checkpoint: epochs += checkpoint["epoch"] if "global_step" in checkpoint: steps += checkpoint["global_step"] except: pass new_ckpt["epoch"] = epochs new_ckpt["global_step"] = steps if is_safetensors(output_file): # TODO Tensor以外のdictの値を削除したほうがいいか save_file(state_dict, output_file) else: torch.save(new_ckpt, output_file) return key_count def save_diffusers_checkpoint(v2, output_dir, text_encoder, unet, pretrained_model_name_or_path, vae=None, use_safetensors=False): if pretrained_model_name_or_path is None: # load default settings for v1/v2 if v2: pretrained_model_name_or_path = DIFFUSERS_REF_MODEL_ID_V2 else: pretrained_model_name_or_path = DIFFUSERS_REF_MODEL_ID_V1 scheduler = DDIMScheduler.from_pretrained(pretrained_model_name_or_path, subfolder="scheduler") tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_name_or_path, subfolder="tokenizer") if vae is None: vae = AutoencoderKL.from_pretrained(pretrained_model_name_or_path, subfolder="vae") pipeline = StableDiffusionPipeline( unet=unet, text_encoder=text_encoder, vae=vae, scheduler=scheduler, tokenizer=tokenizer, safety_checker=None, feature_extractor=None, requires_safety_checker=None, ) pipeline.save_pretrained(output_dir, safe_serialization=use_safetensors) VAE_PREFIX = "first_stage_model." def load_vae(vae_id, dtype): print(f"load VAE: {vae_id}") if os.path.isdir(vae_id) or not os.path.isfile(vae_id): # Diffusers local/remote try: vae = AutoencoderKL.from_pretrained(vae_id, subfolder=None, torch_dtype=dtype) except EnvironmentError as e: print(f"exception occurs in loading vae: {e}") print("retry with subfolder='vae'") vae = AutoencoderKL.from_pretrained(vae_id, subfolder="vae", torch_dtype=dtype) return vae # local vae_config = create_vae_diffusers_config() if vae_id.endswith(".bin"): # SD 1.5 VAE on Huggingface converted_vae_checkpoint = torch.load(vae_id, map_location="cpu") else: # StableDiffusion vae_model = load_file(vae_id, "cpu") if is_safetensors(vae_id) else torch.load(vae_id, map_location="cpu") vae_sd = vae_model["state_dict"] if "state_dict" in vae_model else vae_model # vae only or full model full_model = False for vae_key in vae_sd: if vae_key.startswith(VAE_PREFIX): full_model = True break if not full_model: sd = {} for key, value in vae_sd.items(): sd[VAE_PREFIX + key] = value vae_sd = sd del sd # Convert the VAE model. converted_vae_checkpoint = convert_ldm_vae_checkpoint(vae_sd, vae_config) vae = AutoencoderKL(**vae_config) vae.load_state_dict(converted_vae_checkpoint) return vae # endregion def make_bucket_resolutions(max_reso, min_size=256, max_size=1024, divisible=64): max_width, max_height = max_reso max_area = (max_width // divisible) * (max_height // divisible) resos = set() size = int(math.sqrt(max_area)) * divisible resos.add((size, size)) size = min_size while size <= max_size: width = size height = min(max_size, (max_area // (width // divisible)) * divisible) resos.add((width, height)) resos.add((height, width)) # # make additional resos # if width >= height and width - divisible >= min_size: # resos.add((width - divisible, height)) # resos.add((height, width - divisible)) # if height >= width and height - divisible >= min_size: # resos.add((width, height - divisible)) # resos.add((height - divisible, width)) size += divisible resos = list(resos) resos.sort() return resos if __name__ == "__main__": resos = make_bucket_resolutions((512, 768)) print(len(resos)) print(resos) aspect_ratios = [w / h for w, h in resos] print(aspect_ratios) ars = set() for ar in aspect_ratios: if ar in ars: print("error! duplicate ar:", ar) ars.add(ar)