# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import annotations import io import os import pickle from functools import lru_cache import numpy as np from _io import BufferedReader MZ_ZIP_LOCAL_DIR_HEADER_SIZE = 30 import argparse import numpy as np import paddle import pickle from functools import lru_cache from paddlenlp.utils.downloader import get_path_from_url try: from omegaconf import OmegaConf except ImportError: raise ImportError( "OmegaConf is required to convert the LDM checkpoints. Please install it with `pip install OmegaConf`." ) from paddlenlp.transformers import CLIPTextModel, CLIPTokenizer from ppdiffusers import ( AutoencoderKL, DDIMScheduler, EulerAncestralDiscreteScheduler, LMSDiscreteScheduler, PNDMScheduler, StableDiffusionPipeline, UNet2DConditionModel, HeunDiscreteScheduler, EulerDiscreteScheduler, DPMSolverMultistepScheduler ) import io import os import pickle # Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import io import pickle from functools import lru_cache import numpy as np from zipfile import ZipFile from typing import Union def _maybe_decode_ascii(bytes_str: Union[bytes, str]) -> str: # When using encoding='bytes' in Py3, some **internal** keys stored as # strings in Py2 are loaded as bytes. This function decodes them with # ascii encoding, one that Py3 uses by default. # # NOTE: This should only be used on internal keys (e.g., `typename` and # `location` in `persistent_load` below! if isinstance(bytes_str, bytes): return bytes_str.decode('ascii') return bytes_str @lru_cache(maxsize=None) def _storage_type_to_dtype_to_map(): """convert storage type to numpy dtype""" return { "DoubleStorage": np.double, "FloatStorage": np.float32, "HalfStorage": np.half, "LongStorage": np.int64, "IntStorage": np.int32, "ShortStorage": np.int16, "CharStorage": np.int8, "ByteStorage": np.uint8, "BoolStorage": np.bool_, "ComplexDoubleStorage": np.cdouble, "ComplexFloatStorage": np.cfloat, } class StorageType: """Temp Class for Storage Type""" def __init__(self, name): self.dtype = _storage_type_to_dtype_to_map()[name] def __str__(self): return f"StorageType(dtype={self.dtype})" def _element_size(dtype: str) -> int: """ Returns the element size for a dtype, in bytes """ if dtype in [np.float16, np.float32, np.float64]: return np.finfo(dtype).bits >> 3 elif dtype == np.bool_: return 1 else: return np.iinfo(dtype).bits >> 3 class UnpicklerWrapperStage(pickle.Unpickler): def find_class(self, mod_name, name): if type(name) is str and "Storage" in name: try: return StorageType(name) except KeyError: pass # pure torch tensor builder if mod_name == "torch._utils": return _rebuild_tensor_stage # pytorch_lightning tensor builder if "pytorch_lightning" in mod_name: return dumpy return super().find_class(mod_name, name) def _rebuild_tensor_stage(storage, storage_offset, size, stride, requires_grad, backward_hooks): # if a tensor has shape [M, N] and stride is [1, N], it's column-wise / fortran-style # if a tensor has shape [M, N] and stride is [M, 1], it's row-wise / C-style # defautls to C-style if ( stride is not None and len(stride) > 1 and stride[0] == 1 and stride[1] > 1 ): order = "F" else: order = "C" return storage.reshape(size, order=order) def dumpy(*args, **kwarsg): return None def load_safe(path): from safetensors.numpy import load with open(path, "rb") as f: data = f.read() loaded = load(data) return loaded def load_torch(path: str, **pickle_load_args): """ load torch weight file with the following steps: 1. load the structure of pytorch weight file 2. read the tensor data and re-construct the state-dict Args: path: the path of pytorch weight file **pickle_load_args: args of pickle module Returns: """ pickle_load_args.update({"encoding": "utf-8"}) torch_zip = ZipFile(path, "r") loaded_storages = {} def load_tensor(dtype, numel, key, location): name = f'archive/data/{key}' typed_storage = np.frombuffer(torch_zip.open(name).read()[:numel], dtype=dtype) return typed_storage def persistent_load(saved_id): assert isinstance(saved_id, tuple) typename = _maybe_decode_ascii(saved_id[0]) data = saved_id[1:] assert typename == 'storage', \ f"Unknown typename for persistent_load, expected 'storage' but got '{typename}'" storage_type, key, location, numel = data dtype = storage_type.dtype if key in loaded_storages: typed_storage = loaded_storages[key] else: nbytes = numel * _element_size(dtype) typed_storage = load_tensor(dtype, nbytes, key, _maybe_decode_ascii(location)) loaded_storages[key] = typed_storage return typed_storage data_iostream = torch_zip.open("archive/data.pkl").read() unpickler_stage = UnpicklerWrapperStage(io.BytesIO(data_iostream), **pickle_load_args) unpickler_stage.persistent_load = persistent_load result = unpickler_stage.load() torch_zip.close() return result def shave_segments(path, n_shave_prefix_segments=1): """ Removes segments. Positive values shave the first segments, negative shave the last segments. """ if n_shave_prefix_segments >= 0: return ".".join(path.split(".")[n_shave_prefix_segments:]) else: return ".".join(path.split(".")[:n_shave_prefix_segments]) def renew_resnet_paths(old_list, n_shave_prefix_segments=0): """ Updates paths inside resnets to the new naming scheme (local renaming) """ mapping = [] for old_item in old_list: new_item = old_item.replace("in_layers.0", "norm1") new_item = new_item.replace("in_layers.2", "conv1") new_item = new_item.replace("out_layers.0", "norm2") new_item = new_item.replace("out_layers.3", "conv2") new_item = new_item.replace("emb_layers.1", "time_emb_proj") new_item = new_item.replace("skip_connection", "conv_shortcut") new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) mapping.append({"old": old_item, "new": new_item}) return mapping def renew_vae_resnet_paths(old_list, n_shave_prefix_segments=0): """ Updates paths inside resnets to the new naming scheme (local renaming) """ mapping = [] for old_item in old_list: new_item = old_item new_item = new_item.replace("nin_shortcut", "conv_shortcut") new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) mapping.append({"old": old_item, "new": new_item}) return mapping def renew_attention_paths(old_list, n_shave_prefix_segments=0): """ Updates paths inside attentions to the new naming scheme (local renaming) """ mapping = [] for old_item in old_list: new_item = old_item mapping.append({"old": old_item, "new": new_item}) return mapping def renew_vae_attention_paths(old_list, n_shave_prefix_segments=0): """ Updates paths inside attentions to the new naming scheme (local renaming) """ mapping = [] for old_item in old_list: new_item = old_item new_item = new_item.replace("norm.weight", "group_norm.weight") new_item = new_item.replace("norm.bias", "group_norm.bias") new_item = new_item.replace("q.weight", "query.weight") new_item = new_item.replace("q.bias", "query.bias") new_item = new_item.replace("k.weight", "key.weight") new_item = new_item.replace("k.bias", "key.bias") new_item = new_item.replace("v.weight", "value.weight") new_item = new_item.replace("v.bias", "value.bias") new_item = new_item.replace("proj_out.weight", "proj_attn.weight") new_item = new_item.replace("proj_out.bias", "proj_attn.bias") new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) mapping.append({"old": old_item, "new": new_item}) return mapping def assign_to_checkpoint( paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None ): """ This does the final conversion step: take locally converted weights and apply a global renaming to them. It splits attention layers, and takes into account additional replacements that may arise. Assigns the weights to the new checkpoint. """ assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys." # Splits the attention layers into three variables. if attention_paths_to_split is not None: for path, path_map in attention_paths_to_split.items(): old_tensor = old_checkpoint[path] channels = old_tensor.shape[0] // 3 target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1) num_heads = old_tensor.shape[0] // config["num_head_channels"] // 3 old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]) query, key, value = np.split(old_tensor, 3, axis=1) checkpoint[path_map["query"]] = query.reshape(target_shape) checkpoint[path_map["key"]] = key.reshape(target_shape) checkpoint[path_map["value"]] = value.reshape(target_shape) for path in paths: new_path = path["new"] # These have already been assigned if attention_paths_to_split is not None and new_path in attention_paths_to_split: continue # Global renaming happens here new_path = new_path.replace("middle_block.0", "mid_block.resnets.0") new_path = new_path.replace("middle_block.1", "mid_block.attentions.0") new_path = new_path.replace("middle_block.2", "mid_block.resnets.1") if additional_replacements is not None: for replacement in additional_replacements: new_path = new_path.replace(replacement["old"], replacement["new"]) # proj_attn.weight has to be converted from conv 1D to linear if "proj_attn.weight" in new_path: checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0] else: checkpoint[new_path] = old_checkpoint[path["old"]] def conv_attn_to_linear(checkpoint): keys = list(checkpoint.keys()) attn_keys = ["query.weight", "key.weight", "value.weight"] for key in keys: if ".".join(key.split(".")[-2:]) in attn_keys: if checkpoint[key].ndim > 2: checkpoint[key] = checkpoint[key][:, :, 0, 0] elif "proj_attn.weight" in key: if checkpoint[key].ndim > 2: checkpoint[key] = checkpoint[key][:, :, 0] def create_unet_diffusers_config(original_config, image_size: int): """ Creates a config for the diffusers based on the config of the LDM model. """ unet_params = original_config.model.params.unet_config.params vae_params = original_config.model.params.first_stage_config.params.ddconfig 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 vae_scale_factor = 2 ** (len(vae_params.ch_mult) - 1) head_dim = unet_params.num_heads if "num_heads" in unet_params else None use_linear_projection = ( unet_params.use_linear_in_transformer if "use_linear_in_transformer" in unet_params else False ) if use_linear_projection: # stable diffusion 2-base-512 and 2-768 if head_dim is None: head_dim = [5, 10, 20, 20] config = dict( sample_size=image_size // vae_scale_factor, 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=head_dim, use_linear_projection=use_linear_projection, ) return config def create_vae_diffusers_config(original_config, image_size: int): """ 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=image_size, 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 create_diffusers_schedular(original_config): schedular = DDIMScheduler( num_train_timesteps=original_config.model.params.timesteps, beta_start=original_config.model.params.linear_start, beta_end=original_config.model.params.linear_end, beta_schedule="scaled_linear", ) return schedular def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False): """ Takes a state dict and a config, and returns a converted checkpoint. """ if "state_dict" in checkpoint: checkpoint = checkpoint["state_dict"] if len(checkpoint["state_dict"]) > 25 else checkpoint # extract state_dict for UNet unet_state_dict = {} keys = list(checkpoint.keys()) unet_key = "model.diffusion_model." # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA if sum(k.startswith("model_ema") for k in keys) > 100: print(f"Checkpoint {path} has both EMA and non-EMA weights.") if extract_ema: print( "我们将提取EMA版的UNET权重。如果你想使用非EMA版权重进行微调的话，请确保将『是否提取ema权重』选项设置为 『否』！" ) for key in keys: if key.startswith("model.diffusion_model"): flat_ema_key = "model_ema." + "".join(key.split(".")[1:]) unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(flat_ema_key) else: print( "我们将提取非EMA版的UNET权重。如果你想使用EMA版权重的话，请确保将『是否提取ema权重』选项设置为 『是』" ) if extract_ema and len(unet_state_dict) == 0: print("由于我们在CKPT中未找到EMA权重，因此我们将不会『提取ema权重』！") # 如果没有找到ema的权重， if len(unet_state_dict) == 0: for key in keys: if "model_ema" in key: continue if key.startswith(unet_key): unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(key) if len(unet_state_dict) == 0: return None new_checkpoint = {} new_checkpoint["time_embedding.linear_1.weight"] = unet_state_dict["time_embed.0.weight"] new_checkpoint["time_embedding.linear_1.bias"] = unet_state_dict["time_embed.0.bias"] new_checkpoint["time_embedding.linear_2.weight"] = unet_state_dict["time_embed.2.weight"] new_checkpoint["time_embedding.linear_2.bias"] = unet_state_dict["time_embed.2.bias"] new_checkpoint["conv_in.weight"] = unet_state_dict["input_blocks.0.0.weight"] new_checkpoint["conv_in.bias"] = unet_state_dict["input_blocks.0.0.bias"] new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"] new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"] new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"] new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"] # Retrieves the keys for the input blocks only num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "input_blocks" in layer}) input_blocks = { layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}" in key] for layer_id in range(num_input_blocks) } # Retrieves the keys for the middle blocks only num_middle_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "middle_block" in layer}) middle_blocks = { layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}" in key] for layer_id in range(num_middle_blocks) } # Retrieves the keys for the output blocks only num_output_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "output_blocks" in layer}) output_blocks = { layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}" in key] for layer_id in range(num_output_blocks) } for i in range(1, num_input_blocks): block_id = (i - 1) // (config["layers_per_block"] + 1) layer_in_block_id = (i - 1) % (config["layers_per_block"] + 1) resnets = [ key for key in input_blocks[i] if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key ] attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key] if f"input_blocks.{i}.0.op.weight" in unet_state_dict: new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = unet_state_dict.pop( f"input_blocks.{i}.0.op.weight" ) new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = unet_state_dict.pop( f"input_blocks.{i}.0.op.bias" ) paths = renew_resnet_paths(resnets) meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"} assign_to_checkpoint( paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config ) if len(attentions): paths = renew_attention_paths(attentions) meta_path = {"old": f"input_blocks.{i}.1", "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}"} assign_to_checkpoint( paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config ) resnet_0 = middle_blocks[0] attentions = middle_blocks[1] resnet_1 = middle_blocks[2] resnet_0_paths = renew_resnet_paths(resnet_0) assign_to_checkpoint(resnet_0_paths, new_checkpoint, unet_state_dict, config=config) resnet_1_paths = renew_resnet_paths(resnet_1) assign_to_checkpoint(resnet_1_paths, new_checkpoint, unet_state_dict, config=config) attentions_paths = renew_attention_paths(attentions) meta_path = {"old": "middle_block.1", "new": "mid_block.attentions.0"} assign_to_checkpoint( attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config ) for i in range(num_output_blocks): block_id = i // (config["layers_per_block"] + 1) layer_in_block_id = i % (config["layers_per_block"] + 1) output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]] output_block_list = {} for layer in output_block_layers: layer_id, layer_name = layer.split(".")[0], shave_segments(layer, 1) if layer_id in output_block_list: output_block_list[layer_id].append(layer_name) else: output_block_list[layer_id] = [layer_name] if len(output_block_list) > 1: resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key] attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key] resnet_0_paths = renew_resnet_paths(resnets) paths = renew_resnet_paths(resnets) meta_path = {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"} assign_to_checkpoint( paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config ) if ["conv.weight", "conv.bias"] in output_block_list.values(): index = list(output_block_list.values()).index(["conv.weight", "conv.bias"]) new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[ f"output_blocks.{i}.{index}.conv.weight" ] new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[ f"output_blocks.{i}.{index}.conv.bias" ] # Clear attentions as they have been attributed above. if len(attentions) == 2: attentions = [] elif ["conv.bias", "conv.weight"] in output_block_list.values(): index = list(output_block_list.values()).index(["conv.bias", "conv.weight"]) new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[ f"output_blocks.{i}.{index}.conv.weight" ] new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[ f"output_blocks.{i}.{index}.conv.bias" ] # Clear attentions as they have been attributed above. if len(attentions) == 2: attentions = [] if len(attentions): paths = renew_attention_paths(attentions) meta_path = { "old": f"output_blocks.{i}.1", "new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}", } assign_to_checkpoint( paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config ) else: resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1) for path in resnet_0_paths: old_path = ".".join(["output_blocks", str(i), path["old"]]) new_path = ".".join(["up_blocks", str(block_id), "resnets", str(layer_in_block_id), path["new"]]) new_checkpoint[new_path] = unet_state_dict[old_path] return new_checkpoint def convert_ldm_vae_checkpoint(checkpoint, config, only_vae=False): if "state_dict" in checkpoint: checkpoint = checkpoint["state_dict"] if len(checkpoint["state_dict"]) > 25 else checkpoint # extract state dict for VAE vae_state_dict = {} vae_key = "first_stage_model." keys = list(checkpoint.keys()) for key in keys: if key.startswith(vae_key): vae_state_dict[key.replace(vae_key, "")] = checkpoint.get(key) if only_vae: vae_state_dict = checkpoint if len(vae_state_dict) == 0: return None new_checkpoint = {} new_checkpoint["encoder.conv_in.weight"] = vae_state_dict["encoder.conv_in.weight"] new_checkpoint["encoder.conv_in.bias"] = vae_state_dict["encoder.conv_in.bias"] new_checkpoint["encoder.conv_out.weight"] = vae_state_dict["encoder.conv_out.weight"] new_checkpoint["encoder.conv_out.bias"] = vae_state_dict["encoder.conv_out.bias"] new_checkpoint["encoder.conv_norm_out.weight"] = vae_state_dict["encoder.norm_out.weight"] new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict["encoder.norm_out.bias"] new_checkpoint["decoder.conv_in.weight"] = vae_state_dict["decoder.conv_in.weight"] new_checkpoint["decoder.conv_in.bias"] = vae_state_dict["decoder.conv_in.bias"] new_checkpoint["decoder.conv_out.weight"] = vae_state_dict["decoder.conv_out.weight"] new_checkpoint["decoder.conv_out.bias"] = vae_state_dict["decoder.conv_out.bias"] new_checkpoint["decoder.conv_norm_out.weight"] = vae_state_dict["decoder.norm_out.weight"] new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict["decoder.norm_out.bias"] new_checkpoint["quant_conv.weight"] = vae_state_dict["quant_conv.weight"] new_checkpoint["quant_conv.bias"] = vae_state_dict["quant_conv.bias"] new_checkpoint["post_quant_conv.weight"] = vae_state_dict["post_quant_conv.weight"] new_checkpoint["post_quant_conv.bias"] = vae_state_dict["post_quant_conv.bias"] # Retrieves the keys for the encoder down blocks only num_down_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "encoder.down" in layer}) down_blocks = { layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks) } # Retrieves the keys for the decoder up blocks only num_up_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "decoder.up" in layer}) up_blocks = { layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks) } for i in range(num_down_blocks): resnets = [key for key in down_blocks[i] if f"down.{i}" in key and f"down.{i}.downsample" not in key] if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict: new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = vae_state_dict.pop( f"encoder.down.{i}.downsample.conv.weight" ) 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 convert_diffusers_vae_unet_to_ppdiffusers(vae_or_unet, diffusers_vae_unet_checkpoint, dtype="float32"): need_transpose = [] for k, v in vae_or_unet.named_sublayers(include_self=True): if isinstance(v, paddle.nn.Linear): need_transpose.append(k + ".weight") new_vae_or_unet = {} for k, v in diffusers_vae_unet_checkpoint.items(): if k not in need_transpose: new_vae_or_unet[k] = v.astype(dtype) else: new_vae_or_unet[k] = v.T.astype(dtype) return new_vae_or_unet def check_keys(model, state_dict): cls_name = model.__class__.__name__ missing_keys = [] mismatched_keys = [] for k, v in model.state_dict().items(): if k not in state_dict.keys(): missing_keys.append(k) else: if list(v.shape) != list(state_dict[k].shape): mismatched_keys.append(k) if len(missing_keys): missing_keys_str = ", ".join(missing_keys) print(f"{cls_name} Found missing_keys {missing_keys_str}!") if len(mismatched_keys): mismatched_keys_str = ", ".join(mismatched_keys) print(f"{cls_name} Found mismatched_keys {mismatched_keys_str}!") def convert_hf_clip_to_ppnlp_clip(checkpoint, dtype="float32"): if "state_dict" in checkpoint: checkpoint = checkpoint["state_dict"] if len(checkpoint["state_dict"]) > 25 else checkpoint clip = {} for key in checkpoint.keys(): if key.startswith("cond_stage_model.transformer"): newkey = key[len("cond_stage_model.transformer.") :] if not newkey.startswith("text_model."): newkey = "text_model." + newkey clip[newkey] = checkpoint[key] if len(clip) == 0: return None, None new_model_state = {} transformers2ppnlp = { ".encoder.": ".transformer.", ".layer_norm": ".norm", ".mlp.": ".", ".fc1.": ".linear1.", ".fc2.": ".linear2.", ".final_layer_norm.": ".ln_final.", ".embeddings.": ".", ".position_embedding.": ".positional_embedding.", ".patch_embedding.": ".conv1.", "visual_projection.weight": "vision_projection", "text_projection.weight": "text_projection", ".pre_layrnorm.": ".ln_pre.", ".post_layernorm.": ".ln_post.", ".vision_model.": ".", } ignore_value = ["position_ids"] donot_transpose = ["embeddings", "norm", "concept_embeds", "special_care_embeds"] for name, value in clip.items(): # step1: ignore position_ids if any(i in name for i in ignore_value): continue # step2: transpose nn.Linear weight if value.ndim == 2 and not any(i in name for i in donot_transpose): value = value.T # step3: hf_name -> ppnlp_name mapping for hf_name, ppnlp_name in transformers2ppnlp.items(): name = name.replace(hf_name, ppnlp_name) # step4: 0d tensor -> 1d tensor if name == "logit_scale": value = value.reshape((1,)) new_model_state[name] = value.astype(dtype) new_config = { "max_text_length": new_model_state["text_model.positional_embedding.weight"].shape[0], "vocab_size": new_model_state["text_model.token_embedding.weight"].shape[0], "text_embed_dim": new_model_state["text_model.token_embedding.weight"].shape[1], "text_heads": 12, "text_layers": 12, "text_hidden_act": "quick_gelu", "projection_dim": 768, "initializer_range": 0.02, "initializer_factor": 1.0, } return new_model_state, new_config def parse_args(): parser = argparse.ArgumentParser() parser.add_argument( "--checkpoint_path", default=None, type=str, help="Path to the checkpoint to convert." ) parser.add_argument( "--vae_checkpoint_path", default=None, type=str, help="Path to the vae checkpoint to convert." ) # !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml parser.add_argument( "--original_config_file", default=None, type=str, help="The YAML config file corresponding to the original architecture.", ) parser.add_argument( "--num_in_channels", default=None, type=int, help="The number of input channels. If `None` number of input channels will be automatically inferred.", ) parser.add_argument( "--scheduler_type", default="pndm", type=str, help="Type of scheduler to use. Should be one of ['pndm', 'lms', 'ddim', 'euler', 'euler-ancestral', 'dpm']", ) parser.add_argument( "--extract_ema", action="store_true", help=( "Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights" " or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield" " higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning." ), ) parser.add_argument("--dump_path", default=None, type=str, help="Path to the output model.") args = parser.parse_known_args()[0] return args def main(args): #主函数 if args.checkpoint_path.strip() == "": print("ckpt or safetensors 模型文件位置不能为空！") return if not os.path.exists(args.checkpoint_path): print(f"{args.checkpoint_path} 文件不存在，请检查是否存在！") return if args.vae_checkpoint_path is not None and args.vae_checkpoint_path.strip() == "": args.vae_checkpoint_path = None if args.vae_checkpoint_path is not None: if not os.path.exists(args.vae_checkpoint_path): print(f"{args.vae_checkpoint_path} vae 文件不存在，我们将尝试使用ckpt文件的vae权重！") args.vae_checkpoint_path = None print("正在开始转换，请耐心等待！！！") image_size = 512 if "safetensors" in args.checkpoint_path: checkpoint = load_safe(args.checkpoint_path) else: checkpoint = load_torch(args.checkpoint_path) checkpoint = checkpoint.get("state_dict", checkpoint) if args.original_config_file is None: get_path_from_url("https://paddlenlp.bj.bcebos.com/models/community/CompVis/stable-diffusion-v1-4/v1-inference.yaml", root_dir="./") args.original_config_file = "./v1-inference.yaml" original_config = OmegaConf.load(args.original_config_file) if args.num_in_channels is not None: original_config["model"]["params"]["unet_config"]["params"]["in_channels"] = args.num_in_channels num_train_timesteps = original_config.model.params.timesteps beta_start = original_config.model.params.linear_start beta_end = original_config.model.params.linear_end scheduler = DDIMScheduler( beta_end=beta_end, beta_schedule="scaled_linear", beta_start=beta_start, num_train_timesteps=num_train_timesteps, steps_offset=1, clip_sample=False, set_alpha_to_one=False, ) # make sure scheduler works correctly with DDIM scheduler.register_to_config(clip_sample=False) if args.scheduler_type == "pndm": config = dict(scheduler.config) config["skip_prk_steps"] = True scheduler = PNDMScheduler.from_config(config) elif args.scheduler_type == "lms": scheduler = LMSDiscreteScheduler.from_config(scheduler.config) elif args.scheduler_type == "heun": scheduler = HeunDiscreteScheduler.from_config(scheduler.config) elif args.scheduler_type == "euler": scheduler = EulerDiscreteScheduler.from_config(scheduler.config) elif args.scheduler_type == "euler-ancestral": scheduler = EulerAncestralDiscreteScheduler.from_config(scheduler.config) elif args.scheduler_type == "dpm": scheduler = DPMSolverMultistepScheduler.from_config(scheduler.config) elif args.scheduler_type == "ddim": scheduler = scheduler else: raise ValueError(f"Scheduler of type {args.scheduler_type} doesn't exist!") print("1. 开始转换Unet！") # 1. Convert the UNet2DConditionModel model. diffusers_unet_config = create_unet_diffusers_config(original_config, image_size=image_size) diffusers_unet_checkpoint = convert_ldm_unet_checkpoint( checkpoint, diffusers_unet_config, path=args.checkpoint_path, extract_ema=args.extract_ema ) if diffusers_unet_checkpoint is not None: unet = UNet2DConditionModel.from_config(diffusers_unet_config) ppdiffusers_unet_checkpoint = convert_diffusers_vae_unet_to_ppdiffusers(unet, diffusers_unet_checkpoint) check_keys(unet, ppdiffusers_unet_checkpoint) unet.load_dict(ppdiffusers_unet_checkpoint) print(">>> Unet转换成功！") else: unet = None print("在CKPT中，未发现Unet权重，请确认是否存在！") print("2. 开始转换Vae！") # 2. Convert the VAE model. vae_config = create_vae_diffusers_config(original_config, image_size=image_size) if args.vae_checkpoint_path is not None: if "safetensors" in args.vae_checkpoint_path: vae_checkpoint = load_safe(args.vae_checkpoint_path) else: vae_checkpoint = load_torch(args.vae_checkpoint_path) print(f"发现 {args.vae_checkpoint_path}，我们将转换该文件的vae权重！") only_vae = True else: vae_checkpoint = checkpoint only_vae = False diffusers_vae_checkpoint = convert_ldm_vae_checkpoint(vae_checkpoint, vae_config, only_vae=only_vae) if diffusers_vae_checkpoint is not None: vae = AutoencoderKL.from_config(vae_config) ppdiffusers_vae_checkpoint = convert_diffusers_vae_unet_to_ppdiffusers(vae, diffusers_vae_checkpoint) check_keys(vae, ppdiffusers_vae_checkpoint) vae.load_dict(ppdiffusers_vae_checkpoint) print(">>> VAE转换成功！") else: vae = None print("在CKPT中，未发现Vae权重，请确认是否存在！") print("3. 开始转换text_encoder！") # 3. Convert the text_encoder model. text_model_state_dict, text_config = convert_hf_clip_to_ppnlp_clip(checkpoint, dtype="float32") if text_model_state_dict is not None: text_model = CLIPTextModel(**text_config) text_model.eval() check_keys(text_model, text_model_state_dict) text_model.load_dict(text_model_state_dict) print(">>> text_encoder转换成功！") else: text_model = None print("在CKPT中，未发现TextModel权重，请确认是否存在！") print("4. 开始转换CLIPTokenizer！") # 4. Convert the tokenizer. tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14", pad_token="!", model_max_length=77) print(">>> CLIPTokenizer 转换成功！") if text_model is not None and vae is not None and unet is not None: pipe = StableDiffusionPipeline( vae=vae, text_encoder=text_model, tokenizer=tokenizer, unet=unet, scheduler=scheduler, safety_checker=None, feature_extractor=None, requires_safety_checker=False, ) pipe.save_pretrained(args.dump_path) print(">>> 所有权重转换完成啦，请前往"+str(args.dump_path)+"查看转换好的模型！") else: if vae is not None: vae.save_pretrained(os.path.join(args.dump_path, "vae")) if text_model is not None: text_model.save_pretrained(os.path.join(args.dump_path, "text_encoder")) if unet is not None: unet.save_pretrained(os.path.join(args.dump_path, "unet")) scheduler.save_pretrained(os.path.join(args.dump_path, "scheduler")) tokenizer.save_pretrained(os.path.join(args.dump_path, "tokenizer")) print(">>> 部分权重转换完成啦，请前往"+str(args.dump_path)+"查看转换好的部分模型！")