941 lines
37 KiB
Python
941 lines
37 KiB
Python
import os
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import torch
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from transformers import CLIPTextModel, CLIPTextConfig
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from safetensors.torch import load_file
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import safetensors.torch
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from modules.sd_models import read_state_dict
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# DiffUsers版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 = 64 # fixed from old invalid value `32`
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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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# V2
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V2_UNET_PARAMS_ATTENTION_HEAD_DIM = [5, 10, 20, 20]
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V2_UNET_PARAMS_CONTEXT_DIM = 1024
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# Diffusersの設定を読み込むための参照モデル
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DIFFUSERS_REF_MODEL_ID_V1 = "runwayml/stable-diffusion-v1-5"
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DIFFUSERS_REF_MODEL_ID_V2 = "stabilityai/stable-diffusion-2-1"
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# region StableDiffusion->Diffusersの変換コード
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# convert_original_stable_diffusion_to_diffusers をコピーして修正している（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 linear_transformer_to_conv(checkpoint):
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keys = list(checkpoint.keys())
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tf_keys = ["proj_in.weight", "proj_out.weight"]
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for key in keys:
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if ".".join(key.split(".")[-2:]) in tf_keys:
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if checkpoint[key].ndim == 2:
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checkpoint[key] = checkpoint[key].unsqueeze(2).unsqueeze(2)
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def convert_ldm_unet_checkpoint(v2, 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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# オリジナル：
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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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# 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
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for l in output_block_list.values():
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l.sort()
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if ["conv.bias", "conv.weight"] in output_block_list.values():
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index = list(output_block_list.values()).index(["conv.bias", "conv.weight"])
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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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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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# 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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# SDのv2では1*1のconv2dがlinearに変わっているので、linear->convに変換する
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if v2:
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linear_transformer_to_conv(new_checkpoint)
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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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# if len(vae_state_dict) == 0:
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# # 渡されたcheckpointは.ckptから読み込んだcheckpointではなくvaeのstate_dict
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# vae_state_dict = checkpoint
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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"]
|
||
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"
|
||
)
|
||
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(v2):
|
||
"""
|
||
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,
|
||
)
|
||
|
||
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]
|
||
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
|
||
|
||
def is_safetensors(path):
|
||
return os.path.splitext(path)[1].lower() == '.safetensors'
|
||
|
||
def load_checkpoint_with_text_encoder_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.')
|
||
]
|
||
|
||
state_dict = read_state_dict(ckpt_path)
|
||
|
||
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 state_dict
|
||
|
||
def to_half(sd):
|
||
for key in sd.keys():
|
||
if 'model' in key and sd[key].dtype == torch.float:
|
||
sd[key] = sd[key].half()
|
||
return sd
|
||
|
||
def savemodel(state_dict,currentmodel,fname,savesets,model_a,metadata={}):
|
||
from modules import sd_models,shared
|
||
if "fp16" in savesets:
|
||
state_dict = to_half(state_dict)
|
||
pre = ".fp16"
|
||
else:pre = ""
|
||
ext = ".safetensors" if "safetensors" in savesets else ".ckpt"
|
||
|
||
# is it a inpainting or instruct-pix2pix2 model?
|
||
if "model.diffusion_model.input_blocks.0.0.weight" in state_dict.keys():
|
||
shape = state_dict["model.diffusion_model.input_blocks.0.0.weight"].shape
|
||
if shape[1] == 9:
|
||
pre += "-inpainting"
|
||
if shape[1] == 8:
|
||
pre += "-instruct-pix2pix"
|
||
|
||
checkpoint_info = sd_models.get_closet_checkpoint_match(model_a)
|
||
model_a_path= checkpoint_info.filename
|
||
modeldir = os.path.split(model_a_path)[0]
|
||
|
||
if not fname or fname == "":
|
||
fname = currentmodel.replace(" ","").replace(",","_").replace("(","_").replace(")","_")+pre+ext
|
||
if fname[0]=="_":fname = fname[1:]
|
||
else:
|
||
fname = fname if ext in fname else fname +pre+ext
|
||
|
||
fname = os.path.join(modeldir, fname)
|
||
|
||
if len(fname) > 255:
|
||
fname.replace(ext,"")
|
||
fname=fname[:240]+ext
|
||
|
||
# check if output file already exists
|
||
if os.path.isfile(fname) and not "overwrite" in savesets:
|
||
_err_msg = f"Output file ({fname}) existed and was not saved]"
|
||
print(_err_msg)
|
||
return _err_msg
|
||
|
||
print("Saving...")
|
||
if ext == ".safetensors":
|
||
safetensors.torch.save_file(state_dict, fname, metadata=metadata)
|
||
else:
|
||
torch.save(state_dict, fname)
|
||
print("Done!")
|
||
return "Merged model saved in "+fname
|
||
|
||
def filenamecutter(name,model_a = False):
|
||
from modules import sd_models
|
||
if name =="" or name ==[]: return
|
||
checkpoint_info = sd_models.get_closet_checkpoint_match(name)
|
||
name= os.path.splitext(checkpoint_info.filename)[0]
|
||
|
||
if not model_a:
|
||
name = os.path.basename(name)
|
||
return name
|
||
|
||
# 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, dtype=None):
|
||
import diffusers
|
||
print("diffusers version : ",diffusers.__version__)
|
||
version = diffusers.__version__.split(".")[1]
|
||
if int(version) > 14:
|
||
return None,None,None, f"ERROR: version of diffusers is different, use version 0.10.2 - 0.14.0, your version is {diffusers.__version__}"
|
||
|
||
state_dict = load_checkpoint_with_text_encoder_conversion(ckpt_path)
|
||
if dtype is not None:
|
||
for k, v in state_dict.items():
|
||
if type(v) is torch.Tensor:
|
||
state_dict[k] = v.to(dtype)
|
||
|
||
# Convert the UNet2DConditionModel model.
|
||
unet_config = create_unet_diffusers_config(v2)
|
||
converted_unet_checkpoint = convert_ldm_unet_checkpoint(v2, state_dict, unet_config)
|
||
|
||
unet = diffusers.UNet2DConditionModel(**unet_config)
|
||
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 = diffusers.AutoencoderKL(**vae_config)
|
||
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)
|
||
text_model = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14")
|
||
info = text_model.load_state_dict(converted_text_encoder_checkpoint)
|
||
print("loading text encoder:", info)
|
||
|
||
return text_model, vae, unet, info
|
||
|
||
def usemodelgen(theta_0,model_a,model_name):
|
||
from modules import lowvram, devices, sd_hijack,shared, sd_vae
|
||
sd_hijack.model_hijack.undo_hijack(shared.sd_model)
|
||
|
||
model = shared.sd_model
|
||
model.load_state_dict(theta_0, strict=False)
|
||
del theta_0
|
||
if shared.cmd_opts.opt_channelslast:
|
||
model.to(memory_format=torch.channels_last)
|
||
|
||
if not shared.cmd_opts.no_half:
|
||
vae = model.first_stage_model
|
||
|
||
# with --no-half-vae, remove VAE from model when doing half() to prevent its weights from being converted to float16
|
||
if shared.cmd_opts.no_half_vae:
|
||
model.first_stage_model = None
|
||
|
||
model.half()
|
||
model.first_stage_model = vae
|
||
|
||
devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16
|
||
devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16
|
||
devices.dtype_unet = model.model.diffusion_model.dtype
|
||
|
||
if hasattr(shared.cmd_opts,"upcast_sampling"):
|
||
devices.unet_needs_upcast = shared.cmd_opts.upcast_sampling and devices.dtype == torch.float16 and devices.dtype_unet == torch.float16
|
||
else:
|
||
devices.unet_needs_upcast = devices.dtype == torch.float16 and devices.dtype_unet == torch.float16
|
||
|
||
model.first_stage_model.to(devices.dtype_vae)
|
||
sd_hijack.model_hijack.hijack(model)
|
||
|
||
model.logvar = shared.sd_model.logvar.to(devices.device)
|
||
|
||
if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
|
||
setup_for_low_vram_s(model, shared.cmd_opts.medvram)
|
||
else:
|
||
model.to(shared.device)
|
||
|
||
model.eval()
|
||
|
||
shared.sd_model = model
|
||
try:
|
||
sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings(force_reload=True)
|
||
except:
|
||
pass
|
||
#shared.sd_model.sd_checkpoint_info.model_name = model_name
|
||
|
||
def _setvae():
|
||
sd_vae.delete_base_vae()
|
||
sd_vae.clear_loaded_vae()
|
||
vae_file, vae_source = sd_vae.resolve_vae(model_a)
|
||
sd_vae.load_vae(shared.sd_model, vae_file, vae_source)
|
||
|
||
try:
|
||
_setvae()
|
||
except:
|
||
print("ERROR:setting VAE skipped")
|
||
|
||
|
||
import torch
|
||
from modules import devices
|
||
|
||
module_in_gpu = None
|
||
cpu = torch.device("cpu")
|
||
|
||
|
||
def send_everything_to_cpu():
|
||
global module_in_gpu
|
||
|
||
if module_in_gpu is not None:
|
||
module_in_gpu.to(cpu)
|
||
|
||
module_in_gpu = None
|
||
|
||
def setup_for_low_vram_s(sd_model, use_medvram):
|
||
parents = {}
|
||
|
||
def send_me_to_gpu(module, _):
|
||
"""send this module to GPU; send whatever tracked module was previous in GPU to CPU;
|
||
we add this as forward_pre_hook to a lot of modules and this way all but one of them will
|
||
be in CPU
|
||
"""
|
||
global module_in_gpu
|
||
|
||
module = parents.get(module, module)
|
||
|
||
if module_in_gpu == module:
|
||
return
|
||
|
||
if module_in_gpu is not None:
|
||
module_in_gpu.to(cpu)
|
||
|
||
module.to(devices.device)
|
||
module_in_gpu = module
|
||
|
||
# see below for register_forward_pre_hook;
|
||
# first_stage_model does not use forward(), it uses encode/decode, so register_forward_pre_hook is
|
||
# useless here, and we just replace those methods
|
||
|
||
first_stage_model = sd_model.first_stage_model
|
||
first_stage_model_encode = sd_model.first_stage_model.encode
|
||
first_stage_model_decode = sd_model.first_stage_model.decode
|
||
|
||
def first_stage_model_encode_wrap(x):
|
||
send_me_to_gpu(first_stage_model, None)
|
||
return first_stage_model_encode(x)
|
||
|
||
def first_stage_model_decode_wrap(z):
|
||
send_me_to_gpu(first_stage_model, None)
|
||
return first_stage_model_decode(z)
|
||
|
||
# for SD1, cond_stage_model is CLIP and its NN is in the tranformer frield, but for SD2, it's open clip, and it's in model field
|
||
if hasattr(sd_model.cond_stage_model, 'model'):
|
||
sd_model.cond_stage_model.transformer = sd_model.cond_stage_model.model
|
||
|
||
# remove four big modules, cond, first_stage, depth (if applicable), and unet from the model and then
|
||
# send the model to GPU. Then put modules back. the modules will be in CPU.
|
||
stored = sd_model.first_stage_model, getattr(sd_model, 'depth_model', None), sd_model.model
|
||
sd_model.first_stage_model, sd_model.depth_model, sd_model.model = None, None, None
|
||
sd_model.to(devices.device)
|
||
sd_model.first_stage_model, sd_model.depth_model, sd_model.model = stored
|
||
|
||
# register hooks for those the first three models
|
||
sd_model.first_stage_model.register_forward_pre_hook(send_me_to_gpu)
|
||
sd_model.first_stage_model.encode = first_stage_model_encode_wrap
|
||
sd_model.first_stage_model.decode = first_stage_model_decode_wrap
|
||
if sd_model.depth_model:
|
||
sd_model.depth_model.register_forward_pre_hook(send_me_to_gpu)
|
||
|
||
if hasattr(sd_model.cond_stage_model, 'model'):
|
||
sd_model.cond_stage_model.model = sd_model.cond_stage_model.transformer
|
||
del sd_model.cond_stage_model.transformer
|
||
|
||
if use_medvram:
|
||
sd_model.model.register_forward_pre_hook(send_me_to_gpu)
|
||
else:
|
||
diff_model = sd_model.model.diffusion_model
|
||
|
||
# the third remaining model is still too big for 4 GB, so we also do the same for its submodules
|
||
# so that only one of them is in GPU at a time
|
||
stored = diff_model.input_blocks, diff_model.middle_block, diff_model.output_blocks, diff_model.time_embed
|
||
diff_model.input_blocks, diff_model.middle_block, diff_model.output_blocks, diff_model.time_embed = None, None, None, None
|
||
sd_model.model.to(devices.device)
|
||
diff_model.input_blocks, diff_model.middle_block, diff_model.output_blocks, diff_model.time_embed = stored
|
||
|
||
# install hooks for bits of third model
|
||
diff_model.time_embed.register_forward_pre_hook(send_me_to_gpu)
|
||
for block in diff_model.input_blocks:
|
||
block.register_forward_pre_hook(send_me_to_gpu)
|
||
diff_model.middle_block.register_forward_pre_hook(send_me_to_gpu)
|
||
for block in diff_model.output_blocks:
|
||
block.register_forward_pre_hook(send_me_to_gpu)
|