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| # coding=utf-8 | |
| # Copyright 2022 The HuggingFace Inc. team. | |
| # | |
| # 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. | |
| """ Conversion script for the LDM checkpoints. """ | |
| import argparse | |
| import os | |
| import torch | |
| 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 diffusers import ( | |
| AutoencoderKL, | |
| DDIMScheduler, | |
| DPMSolverMultistepScheduler, | |
| EulerAncestralDiscreteScheduler, | |
| EulerDiscreteScheduler, | |
| HeunDiscreteScheduler, | |
| LDMTextToImagePipeline, | |
| LMSDiscreteScheduler, | |
| PNDMScheduler, | |
| StableDiffusionPipeline, | |
| UNet2DConditionModel, | |
| ) | |
| from diffusers.pipelines.latent_diffusion.pipeline_latent_diffusion import LDMBertConfig, LDMBertModel | |
| from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder, PaintByExamplePipeline | |
| from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker | |
| from transformers import AutoFeatureExtractor, BertTokenizerFast, CLIPTextModel, CLIPTokenizer, CLIPVisionConfig | |
| def shave_segments(path, n_shave_prefix_segments=1): | |
| """ | |
| Removes segments. Positive values shave the first segments, negative shave the last segments. | |
| """ | |
| if n_shave_prefix_segments >= 0: | |
| return ".".join(path.split(".")[n_shave_prefix_segments:]) | |
| else: | |
| return ".".join(path.split(".")[:n_shave_prefix_segments]) | |
| def renew_resnet_paths(old_list, n_shave_prefix_segments=0): | |
| """ | |
| Updates paths inside resnets to the new naming scheme (local renaming) | |
| """ | |
| mapping = [] | |
| for old_item in old_list: | |
| new_item = old_item.replace("in_layers.0", "norm1") | |
| new_item = new_item.replace("in_layers.2", "conv1") | |
| new_item = new_item.replace("out_layers.0", "norm2") | |
| new_item = new_item.replace("out_layers.3", "conv2") | |
| new_item = new_item.replace("emb_layers.1", "time_emb_proj") | |
| new_item = new_item.replace("skip_connection", "conv_shortcut") | |
| new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) | |
| mapping.append({"old": old_item, "new": new_item}) | |
| return mapping | |
| def renew_vae_resnet_paths(old_list, n_shave_prefix_segments=0): | |
| """ | |
| Updates paths inside resnets to the new naming scheme (local renaming) | |
| """ | |
| mapping = [] | |
| for old_item in old_list: | |
| new_item = old_item | |
| new_item = new_item.replace("nin_shortcut", "conv_shortcut") | |
| new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) | |
| mapping.append({"old": old_item, "new": new_item}) | |
| return mapping | |
| def renew_attention_paths(old_list, n_shave_prefix_segments=0): | |
| """ | |
| Updates paths inside attentions to the new naming scheme (local renaming) | |
| """ | |
| mapping = [] | |
| for old_item in old_list: | |
| new_item = old_item | |
| # new_item = new_item.replace('norm.weight', 'group_norm.weight') | |
| # new_item = new_item.replace('norm.bias', 'group_norm.bias') | |
| # new_item = new_item.replace('proj_out.weight', 'proj_attn.weight') | |
| # new_item = new_item.replace('proj_out.bias', 'proj_attn.bias') | |
| # new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) | |
| mapping.append({"old": old_item, "new": new_item}) | |
| return mapping | |
| def renew_vae_attention_paths(old_list, n_shave_prefix_segments=0): | |
| """ | |
| Updates paths inside attentions to the new naming scheme (local renaming) | |
| """ | |
| mapping = [] | |
| for old_item in old_list: | |
| new_item = old_item | |
| 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 = old_tensor.split(channels // num_heads, dim=1) | |
| checkpoint[path_map["query"]] = query.reshape(target_shape) | |
| checkpoint[path_map["key"]] = key.reshape(target_shape) | |
| checkpoint[path_map["value"]] = value.reshape(target_shape) | |
| for path in paths: | |
| new_path = path["new"] | |
| # These have already been assigned | |
| if attention_paths_to_split is not None and new_path in attention_paths_to_split: | |
| continue | |
| # Global renaming happens here | |
| new_path = new_path.replace("middle_block.0", "mid_block.resnets.0") | |
| new_path = new_path.replace("middle_block.1", "mid_block.attentions.0") | |
| new_path = new_path.replace("middle_block.2", "mid_block.resnets.1") | |
| if additional_replacements is not None: | |
| for replacement in additional_replacements: | |
| new_path = new_path.replace(replacement["old"], replacement["new"]) | |
| # proj_attn.weight has to be converted from conv 1D to linear | |
| 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 create_ldm_bert_config(original_config): | |
| bert_params = original_config.model.parms.cond_stage_config.params | |
| config = LDMBertConfig( | |
| d_model=bert_params.n_embed, | |
| encoder_layers=bert_params.n_layer, | |
| encoder_ffn_dim=bert_params.n_embed * 4, | |
| ) | |
| return config | |
| def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False): | |
| """ | |
| Takes a state dict and a config, and returns a converted 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( | |
| "In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA" | |
| " weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag." | |
| ) | |
| 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( | |
| "In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA" | |
| " weights (usually better for inference), please make sure to add the `--extract_ema` flag." | |
| ) | |
| for key in keys: | |
| if key.startswith(unet_key): | |
| unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(key) | |
| new_checkpoint = {} | |
| new_checkpoint["time_embedding.linear_1.weight"] = unet_state_dict["time_embed.0.weight"] | |
| new_checkpoint["time_embedding.linear_1.bias"] = unet_state_dict["time_embed.0.bias"] | |
| new_checkpoint["time_embedding.linear_2.weight"] = unet_state_dict["time_embed.2.weight"] | |
| new_checkpoint["time_embedding.linear_2.bias"] = unet_state_dict["time_embed.2.bias"] | |
| new_checkpoint["conv_in.weight"] = unet_state_dict["input_blocks.0.0.weight"] | |
| new_checkpoint["conv_in.bias"] = unet_state_dict["input_blocks.0.0.bias"] | |
| new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"] | |
| new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"] | |
| new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"] | |
| new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"] | |
| # Retrieves the keys for the input blocks only | |
| num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "input_blocks" in layer}) | |
| input_blocks = { | |
| layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}" in key] | |
| for layer_id in range(num_input_blocks) | |
| } | |
| # Retrieves the keys for the middle blocks only | |
| num_middle_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "middle_block" in layer}) | |
| middle_blocks = { | |
| layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}" in key] | |
| for layer_id in range(num_middle_blocks) | |
| } | |
| # Retrieves the keys for the output blocks only | |
| num_output_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "output_blocks" in layer}) | |
| output_blocks = { | |
| layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}" in key] | |
| for layer_id in range(num_output_blocks) | |
| } | |
| for i in range(1, num_input_blocks): | |
| block_id = (i - 1) // (config["layers_per_block"] + 1) | |
| layer_in_block_id = (i - 1) % (config["layers_per_block"] + 1) | |
| resnets = [ | |
| key for key in input_blocks[i] if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key | |
| ] | |
| attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key] | |
| if f"input_blocks.{i}.0.op.weight" in unet_state_dict: | |
| new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = unet_state_dict.pop( | |
| f"input_blocks.{i}.0.op.weight" | |
| ) | |
| 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 = [] | |
| 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): | |
| # 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) | |
| 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_ldm_bert_checkpoint(checkpoint, config): | |
| def _copy_attn_layer(hf_attn_layer, pt_attn_layer): | |
| hf_attn_layer.q_proj.weight.data = pt_attn_layer.to_q.weight | |
| hf_attn_layer.k_proj.weight.data = pt_attn_layer.to_k.weight | |
| hf_attn_layer.v_proj.weight.data = pt_attn_layer.to_v.weight | |
| hf_attn_layer.out_proj.weight = pt_attn_layer.to_out.weight | |
| hf_attn_layer.out_proj.bias = pt_attn_layer.to_out.bias | |
| def _copy_linear(hf_linear, pt_linear): | |
| hf_linear.weight = pt_linear.weight | |
| hf_linear.bias = pt_linear.bias | |
| def _copy_layer(hf_layer, pt_layer): | |
| # copy layer norms | |
| _copy_linear(hf_layer.self_attn_layer_norm, pt_layer[0][0]) | |
| _copy_linear(hf_layer.final_layer_norm, pt_layer[1][0]) | |
| # copy attn | |
| _copy_attn_layer(hf_layer.self_attn, pt_layer[0][1]) | |
| # copy MLP | |
| pt_mlp = pt_layer[1][1] | |
| _copy_linear(hf_layer.fc1, pt_mlp.net[0][0]) | |
| _copy_linear(hf_layer.fc2, pt_mlp.net[2]) | |
| def _copy_layers(hf_layers, pt_layers): | |
| for i, hf_layer in enumerate(hf_layers): | |
| if i != 0: | |
| i += i | |
| pt_layer = pt_layers[i : i + 2] | |
| _copy_layer(hf_layer, pt_layer) | |
| hf_model = LDMBertModel(config).eval() | |
| # copy embeds | |
| hf_model.model.embed_tokens.weight = checkpoint.transformer.token_emb.weight | |
| hf_model.model.embed_positions.weight.data = checkpoint.transformer.pos_emb.emb.weight | |
| # copy layer norm | |
| _copy_linear(hf_model.model.layer_norm, checkpoint.transformer.norm) | |
| # copy hidden layers | |
| _copy_layers(hf_model.model.layers, checkpoint.transformer.attn_layers.layers) | |
| _copy_linear(hf_model.to_logits, checkpoint.transformer.to_logits) | |
| return hf_model | |
| 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 | |
| def convert_paint_by_example_checkpoint(checkpoint): | |
| config = CLIPVisionConfig.from_pretrained("openai/clip-vit-large-patch14") | |
| model = PaintByExampleImageEncoder(config) | |
| 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] | |
| # load clip vision | |
| model.model.load_state_dict(text_model_dict) | |
| # load mapper | |
| keys_mapper = { | |
| k[len("cond_stage_model.mapper.res") :]: v | |
| for k, v in checkpoint.items() | |
| if k.startswith("cond_stage_model.mapper") | |
| } | |
| MAPPING = { | |
| "attn.c_qkv": ["attn1.to_q", "attn1.to_k", "attn1.to_v"], | |
| "attn.c_proj": ["attn1.to_out.0"], | |
| "ln_1": ["norm1"], | |
| "ln_2": ["norm3"], | |
| "mlp.c_fc": ["ff.net.0.proj"], | |
| "mlp.c_proj": ["ff.net.2"], | |
| } | |
| mapped_weights = {} | |
| for key, value in keys_mapper.items(): | |
| prefix = key[: len("blocks.i")] | |
| suffix = key.split(prefix)[-1].split(".")[-1] | |
| name = key.split(prefix)[-1].split(suffix)[0][1:-1] | |
| mapped_names = MAPPING[name] | |
| num_splits = len(mapped_names) | |
| for i, mapped_name in enumerate(mapped_names): | |
| new_name = ".".join([prefix, mapped_name, suffix]) | |
| shape = value.shape[0] // num_splits | |
| mapped_weights[new_name] = value[i * shape : (i + 1) * shape] | |
| model.mapper.load_state_dict(mapped_weights) | |
| # load final layer norm | |
| model.final_layer_norm.load_state_dict( | |
| { | |
| "bias": checkpoint["cond_stage_model.final_ln.bias"], | |
| "weight": checkpoint["cond_stage_model.final_ln.weight"], | |
| } | |
| ) | |
| # load final proj | |
| model.proj_out.load_state_dict( | |
| { | |
| "bias": checkpoint["proj_out.bias"], | |
| "weight": checkpoint["proj_out.weight"], | |
| } | |
| ) | |
| # load uncond vector | |
| model.uncond_vector.data = torch.nn.Parameter(checkpoint["learnable_vector"]) | |
| return model | |
| def convert_open_clip_checkpoint(checkpoint): | |
| text_model = CLIPTextModel.from_pretrained("stabilityai/stable-diffusion-2", subfolder="text_encoder") | |
| # SKIP for now - need openclip -> HF conversion script here | |
| # keys = list(checkpoint.keys()) | |
| # | |
| # text_model_dict = {} | |
| # for key in keys: | |
| # if key.startswith("cond_stage_model.model.transformer"): | |
| # text_model_dict[key[len("cond_stage_model.model.transformer.") :]] = checkpoint[key] | |
| # | |
| # text_model.load_state_dict(text_model_dict) | |
| return text_model | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument( | |
| "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert." | |
| ) | |
| parser.add_argument( | |
| "--vae_checkpoint_path", default=None, type=str, required=False, help="Path to the 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-ancest', 'dpm']", | |
| ) | |
| parser.add_argument( | |
| "--pipeline_type", | |
| default=None, | |
| type=str, | |
| help="The pipeline type. If `None` pipeline will be automatically inferred.", | |
| ) | |
| parser.add_argument( | |
| "--image_size", | |
| default=None, | |
| type=int, | |
| help=( | |
| "The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2" | |
| " Base. Use 768 for Stable Diffusion v2." | |
| ), | |
| ) | |
| parser.add_argument( | |
| "--prediction_type", | |
| default=None, | |
| type=str, | |
| help=( | |
| "The prediction type that the model was trained on. Use 'epsilon' for Stable Diffusion v1.X and Stable" | |
| " Siffusion v2 Base. Use 'v-prediction' for Stable Diffusion v2." | |
| ), | |
| ) | |
| 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, required=True, help="Path to the output model.") | |
| args = parser.parse_args() | |
| image_size = args.image_size | |
| prediction_type = args.prediction_type | |
| checkpoint = torch.load(args.checkpoint_path) | |
| # dummy | |
| if 'state_dict' not in checkpoint.keys(): | |
| checkpoint = { | |
| 'state_dict': checkpoint | |
| } | |
| if 'global_step' not in checkpoint.keys(): | |
| checkpoint['global_step'] = 678999 | |
| global_step = checkpoint["global_step"] | |
| checkpoint = checkpoint["state_dict"] | |
| if args.vae_checkpoint_path is not None: | |
| vae_checkpoint = torch.load(args.vae_checkpoint_path) | |
| if 'state_dict' in vae_checkpoint.keys(): | |
| vae_checkpoint = vae_checkpoint['state_dict'] | |
| # overwrite ckpt | |
| for k, v in vae_checkpoint.items(): | |
| vae_key = "first_stage_model." + k | |
| checkpoint[vae_key] = v | |
| vae_checkpoint = None | |
| if args.original_config_file is None: | |
| key_name = "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight" | |
| if key_name in checkpoint and checkpoint[key_name].shape[-1] == 1024: | |
| # model_type = "v2" | |
| os.system( | |
| "wget https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/v2-inference-v.yaml" | |
| ) | |
| args.original_config_file = "./v2-inference-v.yaml" | |
| else: | |
| # model_type = "v1" | |
| os.system( | |
| "wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml" | |
| ) | |
| 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 | |
| if ( | |
| "parameterization" in original_config["model"]["params"] | |
| and original_config["model"]["params"]["parameterization"] == "v" | |
| ): | |
| if prediction_type is None: | |
| # NOTE: For stable diffusion 2 base it is recommended to pass `prediction_type=="epsilon"` | |
| # as it relies on a brittle global step parameter here | |
| prediction_type = "epsilon" if global_step == 875000 else "v_prediction" | |
| if image_size is None: | |
| # NOTE: For stable diffusion 2 base one has to pass `image_size==512` | |
| # as it relies on a brittle global step parameter here | |
| image_size = 512 if global_step == 875000 else 768 | |
| else: | |
| if prediction_type is None: | |
| prediction_type = "epsilon" | |
| if image_size is None: | |
| image_size = 512 | |
| 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, | |
| prediction_type=prediction_type, | |
| ) | |
| 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!") | |
| # Convert the UNet2DConditionModel model. | |
| unet_config = create_unet_diffusers_config(original_config, image_size=image_size) | |
| unet = UNet2DConditionModel(**unet_config) | |
| converted_unet_checkpoint = convert_ldm_unet_checkpoint( | |
| checkpoint, unet_config, path=args.checkpoint_path, extract_ema=args.extract_ema | |
| ) | |
| unet.load_state_dict(converted_unet_checkpoint) | |
| # Convert the VAE model. | |
| vae_config = create_vae_diffusers_config(original_config, image_size=image_size) | |
| converted_vae_checkpoint = convert_ldm_vae_checkpoint(checkpoint, vae_config) | |
| vae = AutoencoderKL(**vae_config) | |
| vae.load_state_dict(converted_vae_checkpoint) | |
| # Convert the text model. | |
| model_type = args.pipeline_type | |
| if model_type is None: | |
| model_type = original_config.model.params.cond_stage_config.target.split(".")[-1] | |
| if model_type == "FrozenOpenCLIPEmbedder": | |
| text_model = convert_open_clip_checkpoint(checkpoint) | |
| tokenizer = CLIPTokenizer.from_pretrained("stabilityai/stable-diffusion-2", subfolder="tokenizer") | |
| pipe = StableDiffusionPipeline( | |
| vae=vae, | |
| text_encoder=text_model, | |
| tokenizer=tokenizer, | |
| unet=unet, | |
| scheduler=scheduler, | |
| safety_checker=None, | |
| feature_extractor=None, | |
| requires_safety_checker=False, | |
| ) | |
| elif model_type == "PaintByExample": | |
| vision_model = convert_paint_by_example_checkpoint(checkpoint) | |
| tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14") | |
| feature_extractor = AutoFeatureExtractor.from_pretrained("CompVis/stable-diffusion-safety-checker") | |
| pipe = PaintByExamplePipeline( | |
| vae=vae, | |
| image_encoder=vision_model, | |
| unet=unet, | |
| scheduler=scheduler, | |
| safety_checker=None, | |
| feature_extractor=feature_extractor, | |
| ) | |
| elif model_type == "FrozenCLIPEmbedder": | |
| text_model = convert_ldm_clip_checkpoint(checkpoint) | |
| tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14") | |
| safety_checker = StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker") | |
| feature_extractor = AutoFeatureExtractor.from_pretrained("CompVis/stable-diffusion-safety-checker") | |
| pipe = StableDiffusionPipeline( | |
| vae=vae, | |
| text_encoder=text_model, | |
| tokenizer=tokenizer, | |
| unet=unet, | |
| scheduler=scheduler, | |
| safety_checker=safety_checker, | |
| feature_extractor=feature_extractor, | |
| ) | |
| else: | |
| text_config = create_ldm_bert_config(original_config) | |
| text_model = convert_ldm_bert_checkpoint(checkpoint, text_config) | |
| tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased") | |
| pipe = LDMTextToImagePipeline(vqvae=vae, bert=text_model, tokenizer=tokenizer, unet=unet, scheduler=scheduler) | |
| pipe.save_pretrained(args.dump_path) |
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