768 lines
29 KiB
Python
768 lines
29 KiB
Python
import math
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import torch
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from transformers import CLIPTextModel
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from diffusers import AutoencoderKL, UNet2DConditionModel
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# Tokenizer: checkpointから読み込むのではなくあらかじめ提供されているものを使う
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TOKENIZER_PATH = "openai/clip-vit-large-patch14"
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# StableDiffusionのモデルパラメータ
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NUM_TRAIN_TIMESTEPS = 1000
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BETA_START = 0.00085
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BETA_END = 0.0120
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UNET_PARAMS_MODEL_CHANNELS = 320
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UNET_PARAMS_CHANNEL_MULT = [1, 2, 4, 4]
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UNET_PARAMS_ATTENTION_RESOLUTIONS = [4, 2, 1]
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UNET_PARAMS_IMAGE_SIZE = 32 # unused
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UNET_PARAMS_IN_CHANNELS = 4
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UNET_PARAMS_OUT_CHANNELS = 4
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UNET_PARAMS_NUM_RES_BLOCKS = 2
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UNET_PARAMS_CONTEXT_DIM = 768
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UNET_PARAMS_NUM_HEADS = 8
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VAE_PARAMS_Z_CHANNELS = 4
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VAE_PARAMS_RESOLUTION = 256
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VAE_PARAMS_IN_CHANNELS = 3
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VAE_PARAMS_OUT_CH = 3
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VAE_PARAMS_CH = 128
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VAE_PARAMS_CH_MULT = [1, 2, 4, 4]
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VAE_PARAMS_NUM_RES_BLOCKS = 2
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# region conversion
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# checkpoint変換など ###############################
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# region StableDiffusion->Diffusersの変換コード
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# convert_original_stable_diffusion_to_diffusers をコピーしている（ASL 2.0）
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def shave_segments(path, n_shave_prefix_segments=1):
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"""
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Removes segments. Positive values shave the first segments, negative shave the last segments.
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"""
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if n_shave_prefix_segments >= 0:
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return ".".join(path.split(".")[n_shave_prefix_segments:])
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else:
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return ".".join(path.split(".")[:n_shave_prefix_segments])
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def renew_resnet_paths(old_list, n_shave_prefix_segments=0):
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"""
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Updates paths inside resnets to the new naming scheme (local renaming)
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"""
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mapping = []
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for old_item in old_list:
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new_item = old_item.replace("in_layers.0", "norm1")
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new_item = new_item.replace("in_layers.2", "conv1")
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new_item = new_item.replace("out_layers.0", "norm2")
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new_item = new_item.replace("out_layers.3", "conv2")
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new_item = new_item.replace("emb_layers.1", "time_emb_proj")
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new_item = new_item.replace("skip_connection", "conv_shortcut")
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new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
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mapping.append({"old": old_item, "new": new_item})
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return mapping
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def renew_vae_resnet_paths(old_list, n_shave_prefix_segments=0):
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"""
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Updates paths inside resnets to the new naming scheme (local renaming)
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"""
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mapping = []
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for old_item in old_list:
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new_item = old_item
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new_item = new_item.replace("nin_shortcut", "conv_shortcut")
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new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
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mapping.append({"old": old_item, "new": new_item})
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return mapping
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def renew_attention_paths(old_list, n_shave_prefix_segments=0):
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"""
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Updates paths inside attentions to the new naming scheme (local renaming)
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"""
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mapping = []
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for old_item in old_list:
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new_item = old_item
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# new_item = new_item.replace('norm.weight', 'group_norm.weight')
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# new_item = new_item.replace('norm.bias', 'group_norm.bias')
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# new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
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# new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')
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# new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
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mapping.append({"old": old_item, "new": new_item})
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return mapping
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def renew_vae_attention_paths(old_list, n_shave_prefix_segments=0):
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"""
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Updates paths inside attentions to the new naming scheme (local renaming)
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"""
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mapping = []
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for old_item in old_list:
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new_item = old_item
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new_item = new_item.replace("norm.weight", "group_norm.weight")
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new_item = new_item.replace("norm.bias", "group_norm.bias")
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new_item = new_item.replace("q.weight", "query.weight")
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new_item = new_item.replace("q.bias", "query.bias")
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new_item = new_item.replace("k.weight", "key.weight")
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new_item = new_item.replace("k.bias", "key.bias")
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new_item = new_item.replace("v.weight", "value.weight")
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new_item = new_item.replace("v.bias", "value.bias")
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new_item = new_item.replace("proj_out.weight", "proj_attn.weight")
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new_item = new_item.replace("proj_out.bias", "proj_attn.bias")
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new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
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mapping.append({"old": old_item, "new": new_item})
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return mapping
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def assign_to_checkpoint(
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paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None
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):
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"""
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This does the final conversion step: take locally converted weights and apply a global renaming
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to them. It splits attention layers, and takes into account additional replacements
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that may arise.
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Assigns the weights to the new checkpoint.
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"""
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assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys."
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# Splits the attention layers into three variables.
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if attention_paths_to_split is not None:
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for path, path_map in attention_paths_to_split.items():
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old_tensor = old_checkpoint[path]
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channels = old_tensor.shape[0] // 3
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target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1)
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num_heads = old_tensor.shape[0] // config["num_head_channels"] // 3
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old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:])
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query, key, value = old_tensor.split(channels // num_heads, dim=1)
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checkpoint[path_map["query"]] = query.reshape(target_shape)
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checkpoint[path_map["key"]] = key.reshape(target_shape)
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checkpoint[path_map["value"]] = value.reshape(target_shape)
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for path in paths:
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new_path = path["new"]
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# These have already been assigned
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if attention_paths_to_split is not None and new_path in attention_paths_to_split:
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continue
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# Global renaming happens here
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new_path = new_path.replace("middle_block.0", "mid_block.resnets.0")
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new_path = new_path.replace("middle_block.1", "mid_block.attentions.0")
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new_path = new_path.replace("middle_block.2", "mid_block.resnets.1")
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if additional_replacements is not None:
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for replacement in additional_replacements:
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new_path = new_path.replace(replacement["old"], replacement["new"])
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# proj_attn.weight has to be converted from conv 1D to linear
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if "proj_attn.weight" in new_path:
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checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0]
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else:
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checkpoint[new_path] = old_checkpoint[path["old"]]
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def conv_attn_to_linear(checkpoint):
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keys = list(checkpoint.keys())
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attn_keys = ["query.weight", "key.weight", "value.weight"]
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for key in keys:
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if ".".join(key.split(".")[-2:]) in attn_keys:
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if checkpoint[key].ndim > 2:
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checkpoint[key] = checkpoint[key][:, :, 0, 0]
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elif "proj_attn.weight" in key:
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if checkpoint[key].ndim > 2:
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checkpoint[key] = checkpoint[key][:, :, 0]
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def convert_ldm_unet_checkpoint(checkpoint, config):
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"""
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Takes a state dict and a config, and returns a converted checkpoint.
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"""
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# extract state_dict for UNet
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unet_state_dict = {}
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unet_key = "model.diffusion_model."
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keys = list(checkpoint.keys())
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for key in keys:
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if key.startswith(unet_key):
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unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(key)
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new_checkpoint = {}
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new_checkpoint["time_embedding.linear_1.weight"] = unet_state_dict["time_embed.0.weight"]
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new_checkpoint["time_embedding.linear_1.bias"] = unet_state_dict["time_embed.0.bias"]
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new_checkpoint["time_embedding.linear_2.weight"] = unet_state_dict["time_embed.2.weight"]
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new_checkpoint["time_embedding.linear_2.bias"] = unet_state_dict["time_embed.2.bias"]
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new_checkpoint["conv_in.weight"] = unet_state_dict["input_blocks.0.0.weight"]
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new_checkpoint["conv_in.bias"] = unet_state_dict["input_blocks.0.0.bias"]
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new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"]
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new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"]
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new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"]
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new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"]
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# Retrieves the keys for the input blocks only
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num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "input_blocks" in layer})
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input_blocks = {
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layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}" in key]
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for layer_id in range(num_input_blocks)
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}
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# Retrieves the keys for the middle blocks only
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num_middle_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "middle_block" in layer})
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middle_blocks = {
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layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}" in key]
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for layer_id in range(num_middle_blocks)
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}
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# Retrieves the keys for the output blocks only
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num_output_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "output_blocks" in layer})
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output_blocks = {
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layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}" in key]
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for layer_id in range(num_output_blocks)
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}
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for i in range(1, num_input_blocks):
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block_id = (i - 1) // (config["layers_per_block"] + 1)
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layer_in_block_id = (i - 1) % (config["layers_per_block"] + 1)
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resnets = [
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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
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]
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attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key]
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if f"input_blocks.{i}.0.op.weight" in unet_state_dict:
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new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = unet_state_dict.pop(
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f"input_blocks.{i}.0.op.weight"
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)
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new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = unet_state_dict.pop(
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f"input_blocks.{i}.0.op.bias"
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)
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paths = renew_resnet_paths(resnets)
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meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
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assign_to_checkpoint(
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paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
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)
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if len(attentions):
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paths = renew_attention_paths(attentions)
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meta_path = {"old": f"input_blocks.{i}.1", "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}"}
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assign_to_checkpoint(
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paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
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)
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resnet_0 = middle_blocks[0]
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attentions = middle_blocks[1]
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resnet_1 = middle_blocks[2]
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resnet_0_paths = renew_resnet_paths(resnet_0)
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assign_to_checkpoint(resnet_0_paths, new_checkpoint, unet_state_dict, config=config)
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resnet_1_paths = renew_resnet_paths(resnet_1)
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assign_to_checkpoint(resnet_1_paths, new_checkpoint, unet_state_dict, config=config)
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attentions_paths = renew_attention_paths(attentions)
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meta_path = {"old": "middle_block.1", "new": "mid_block.attentions.0"}
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assign_to_checkpoint(
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attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
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)
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for i in range(num_output_blocks):
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block_id = i // (config["layers_per_block"] + 1)
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layer_in_block_id = i % (config["layers_per_block"] + 1)
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output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]]
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output_block_list = {}
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for layer in output_block_layers:
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layer_id, layer_name = layer.split(".")[0], shave_segments(layer, 1)
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if layer_id in output_block_list:
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output_block_list[layer_id].append(layer_name)
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else:
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output_block_list[layer_id] = [layer_name]
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if len(output_block_list) > 1:
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resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key]
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attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key]
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resnet_0_paths = renew_resnet_paths(resnets)
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paths = renew_resnet_paths(resnets)
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meta_path = {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"}
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assign_to_checkpoint(
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paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
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)
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if ["conv.weight", "conv.bias"] in output_block_list.values():
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index = list(output_block_list.values()).index(["conv.weight", "conv.bias"])
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new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[
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f"output_blocks.{i}.{index}.conv.weight"
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]
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new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[
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f"output_blocks.{i}.{index}.conv.bias"
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]
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# Clear attentions as they have been attributed above.
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if len(attentions) == 2:
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attentions = []
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if len(attentions):
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paths = renew_attention_paths(attentions)
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meta_path = {
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"old": f"output_blocks.{i}.1",
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"new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}",
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}
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assign_to_checkpoint(
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paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
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)
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else:
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resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1)
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for path in resnet_0_paths:
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old_path = ".".join(["output_blocks", str(i), path["old"]])
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new_path = ".".join(["up_blocks", str(block_id), "resnets", str(layer_in_block_id), path["new"]])
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new_checkpoint[new_path] = unet_state_dict[old_path]
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return new_checkpoint
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def convert_ldm_vae_checkpoint(checkpoint, config):
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# extract state dict for VAE
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vae_state_dict = {}
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vae_key = "first_stage_model."
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keys = list(checkpoint.keys())
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for key in keys:
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if key.startswith(vae_key):
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vae_state_dict[key.replace(vae_key, "")] = checkpoint.get(key)
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new_checkpoint = {}
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new_checkpoint["encoder.conv_in.weight"] = vae_state_dict["encoder.conv_in.weight"]
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new_checkpoint["encoder.conv_in.bias"] = vae_state_dict["encoder.conv_in.bias"]
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new_checkpoint["encoder.conv_out.weight"] = vae_state_dict["encoder.conv_out.weight"]
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new_checkpoint["encoder.conv_out.bias"] = vae_state_dict["encoder.conv_out.bias"]
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new_checkpoint["encoder.conv_norm_out.weight"] = vae_state_dict["encoder.norm_out.weight"]
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new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict["encoder.norm_out.bias"]
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new_checkpoint["decoder.conv_in.weight"] = vae_state_dict["decoder.conv_in.weight"]
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new_checkpoint["decoder.conv_in.bias"] = vae_state_dict["decoder.conv_in.bias"]
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new_checkpoint["decoder.conv_out.weight"] = vae_state_dict["decoder.conv_out.weight"]
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new_checkpoint["decoder.conv_out.bias"] = vae_state_dict["decoder.conv_out.bias"]
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new_checkpoint["decoder.conv_norm_out.weight"] = vae_state_dict["decoder.norm_out.weight"]
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new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict["decoder.norm_out.bias"]
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new_checkpoint["quant_conv.weight"] = vae_state_dict["quant_conv.weight"]
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new_checkpoint["quant_conv.bias"] = vae_state_dict["quant_conv.bias"]
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new_checkpoint["post_quant_conv.weight"] = vae_state_dict["post_quant_conv.weight"]
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new_checkpoint["post_quant_conv.bias"] = vae_state_dict["post_quant_conv.bias"]
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# Retrieves the keys for the encoder down blocks only
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num_down_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "encoder.down" in layer})
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down_blocks = {
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layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks)
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}
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# Retrieves the keys for the decoder up blocks only
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num_up_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "decoder.up" in layer})
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up_blocks = {
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layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks)
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}
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for i in range(num_down_blocks):
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resnets = [key for key in down_blocks[i] if f"down.{i}" in key and f"down.{i}.downsample" not in key]
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if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict:
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new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = vae_state_dict.pop(
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f"encoder.down.{i}.downsample.conv.weight"
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)
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new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = vae_state_dict.pop(
|
||
f"encoder.down.{i}.downsample.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"
|
||
]
|
||
new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"] = vae_state_dict[
|
||
f"decoder.up.{block_id}.upsample.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():
|
||
"""
|
||
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,
|
||
attention_head_dim=UNET_PARAMS_NUM_HEADS,
|
||
)
|
||
|
||
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(checkpoint):
|
||
text_model = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14")
|
||
|
||
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]
|
||
|
||
text_model.load_state_dict(text_model_dict)
|
||
|
||
return text_model
|
||
|
||
# endregion
|
||
|
||
|
||
# region Diffusers->StableDiffusion の変換コード
|
||
# convert_diffusers_to_original_stable_diffusion をコピーしている（ASL 2.0）
|
||
|
||
def convert_unet_state_dict(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()}
|
||
return new_state_dict
|
||
|
||
# endregion
|
||
|
||
|
||
def load_checkpoint_with_conversion(ckpt_path):
|
||
# 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.')
|
||
]
|
||
|
||
checkpoint = torch.load(ckpt_path, map_location="cpu")
|
||
state_dict = checkpoint["state_dict"]
|
||
|
||
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
|
||
|
||
|
||
def load_models_from_stable_diffusion_checkpoint(ckpt_path):
|
||
checkpoint = load_checkpoint_with_conversion(ckpt_path)
|
||
state_dict = checkpoint["state_dict"]
|
||
|
||
# Convert the UNet2DConditionModel model.
|
||
unet_config = create_unet_diffusers_config()
|
||
converted_unet_checkpoint = convert_ldm_unet_checkpoint(state_dict, unet_config)
|
||
|
||
unet = UNet2DConditionModel(**unet_config)
|
||
unet.load_state_dict(converted_unet_checkpoint)
|
||
|
||
# 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)
|
||
vae.load_state_dict(converted_vae_checkpoint)
|
||
|
||
# convert text_model
|
||
text_model = convert_ldm_clip_checkpoint(state_dict)
|
||
|
||
return text_model, vae, unet
|
||
|
||
|
||
def save_stable_diffusion_checkpoint(output_file, text_encoder, unet, ckpt_path, epochs, steps, save_dtype=None):
|
||
# VAEがメモリ上にないので、もう一度VAEを含めて読み込む
|
||
checkpoint = load_checkpoint_with_conversion(ckpt_path)
|
||
state_dict = checkpoint["state_dict"]
|
||
|
||
# Convert the UNet model
|
||
unet_state_dict = convert_unet_state_dict(unet.state_dict())
|
||
for k, v in unet_state_dict.items():
|
||
key = "model.diffusion_model." + k
|
||
assert 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 text encoder model
|
||
text_enc_dict = text_encoder.state_dict() # 変換不要
|
||
for k, v in text_enc_dict.items():
|
||
key = "cond_stage_model.transformer." + k
|
||
assert 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
|
||
|
||
# Put together new checkpoint
|
||
new_ckpt = {'state_dict': state_dict}
|
||
|
||
if 'epoch' in checkpoint:
|
||
epochs += checkpoint['epoch']
|
||
if 'global_step' in checkpoint:
|
||
steps += checkpoint['global_step']
|
||
|
||
new_ckpt['epoch'] = epochs
|
||
new_ckpt['global_step'] = steps
|
||
|
||
torch.save(new_ckpt, output_file)
|
||
# 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()
|
||
|
||
aspect_ratios = [w / h for w, h in resos]
|
||
return resos, aspect_ratios
|
||
|
||
|
||
if __name__ == '__main__':
|
||
resos, aspect_ratios = make_bucket_resolutions((512, 768))
|
||
print(len(resos))
|
||
print(resos)
|
||
print(aspect_ratios)
|
||
|
||
ars = set()
|
||
for ar in aspect_ratios:
|
||
if ar in ars:
|
||
print("error! duplicate ar:", ar)
|
||
ars.add(ar)
|