### # Parts of this file are copied or derived from: # https://github.com/d8ahazard/sd_dreambooth_extension/blob/main/dreambooth/diff_to_sd.py # https://github.com/huggingface/diffusers/blob/main/scripts/convert_original_stable_diffusion_to_diffusers.py # # Originally by https://github.com/d8ahazard and https://github.com/huggingface # Those portions *are not* covered by the MIT licensed used for the rest of the onnx-web project. # In particular, you cannot use this converter for commercial purposes without permission. # # d8ahazard code used under No-Commercial License with Limited Commercial Use # https://github.com/d8ahazard/sd_dreambooth_extension/blob/main/license.md # HuggingFace code used under the Apache License, Version 2.0 # https://github.com/huggingface/diffusers/blob/main/LICENSE ### import json import os import re import shutil import sys import traceback from logging import getLogger from typing import Dict, List import torch from diffusers import ( AutoencoderKL, DDIMScheduler, DPMSolverMultistepScheduler, EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, HeunDiscreteScheduler, LDMTextToImagePipeline, LMSDiscreteScheduler, PaintByExamplePipeline, PNDMScheduler, StableDiffusionPipeline, UNet2DConditionModel, ) from diffusers.pipelines.latent_diffusion.pipeline_latent_diffusion import ( LDMBertConfig, LDMBertModel, ) from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker from huggingface_hub import HfApi, hf_hub_download from huggingface_hub.utils.tqdm import tqdm from transformers import ( AutoFeatureExtractor, BertTokenizerFast, CLIPTextModel, CLIPTokenizer, CLIPVisionConfig, ) from .diffusion_stable import convert_diffusion_stable from .utils import ConversionContext, ModelDict, load_tensor, load_yaml, sanitize_name logger = getLogger(__name__) class TrainingConfig(): """ From https://github.com/d8ahazard/sd_dreambooth_extension/blob/main/dreambooth/db_config.py """ adamw_weight_decay: float = 0.01 attention: str = "default" cache_latents: bool = True center_crop: bool = True freeze_clip_normalization: bool = False clip_skip: int = 1 concepts_list: List[Dict] = [] concepts_path: str = "" custom_model_name: str = "" epoch: int = 0 epoch_pause_frequency: int = 0 epoch_pause_time: int = 0 gradient_accumulation_steps: int = 1 gradient_checkpointing: bool = True gradient_set_to_none: bool = True graph_smoothing: int = 50 half_model: bool = False train_unfrozen: bool = False has_ema: bool = False hflip: bool = False initial_revision: int = 0 learning_rate: float = 5e-6 learning_rate_min: float = 1e-6 lifetime_revision: int = 0 lora_learning_rate: float = 1e-4 lora_model_name: str = "" lora_rank: int = 4 lora_txt_learning_rate: float = 5e-5 lora_txt_weight: float = 1.0 lora_weight: float = 1.0 lr_cycles: int = 1 lr_factor: float = 0.5 lr_power: float = 1.0 lr_scale_pos: float = 0.5 lr_scheduler: str = "constant_with_warmup" lr_warmup_steps: int = 0 max_token_length: int = 75 mixed_precision: str = "fp16" model_name: str = "" model_dir: str = "" model_path: str = "" num_train_epochs: int = 100 pad_tokens: bool = True pretrained_model_name_or_path: str = "" pretrained_vae_name_or_path: str = "" prior_loss_scale: bool = False prior_loss_target: int = 100 prior_loss_weight: float = 1.0 prior_loss_weight_min: float = 0.1 resolution: int = 512 revision: int = 0 sample_batch_size: int = 1 sanity_prompt: str = "" sanity_seed: int = 420420 save_ckpt_after: bool = True save_ckpt_cancel: bool = False save_ckpt_during: bool = True save_embedding_every: int = 25 save_lora_after: bool = True save_lora_cancel: bool = False save_lora_during: bool = True save_preview_every: int = 5 save_safetensors: bool = False save_state_after: bool = False save_state_cancel: bool = False save_state_during: bool = False scheduler: str = "ddim" shuffle_tags: bool = False snapshot: str = "" src: str = "" stop_text_encoder: float = 1.0 train_batch_size: int = 1 train_imagic: bool = False train_unet: bool = True use_8bit_adam: bool = True use_concepts: bool = False use_ema: bool = True use_lora: bool = False use_subdir: bool = False v2: bool = False def __init__( self, ctx: ConversionContext, model_name: str = "", scheduler: str = "ddim", v2: bool = False, src: str = "", resolution: int = 512, **kwargs, ): model_name = sanitize_name(model_name) model_dir = os.path.join(ctx.cache_path, model_name) working_dir = os.path.join(model_dir, "working") if not os.path.exists(working_dir): os.makedirs(working_dir) self.model_name = model_name self.model_dir = model_dir self.pretrained_model_name_or_path = working_dir self.resolution = resolution self.src = src self.scheduler = scheduler self.v2 = v2 # avoid pydantic dep for this one fn for k, v in kwargs.items(): setattr(self, k, v) def save(self, backup=False): """ Save the config file """ models_path = self.model_dir config_file = os.path.join(models_path, "db_config.json") if backup: backup_dir = os.path.join(models_path, "backups") if not os.path.exists(backup_dir): os.makedirs(backup_dir) config_file = os.path.join(models_path, "backups", f"db_config_{self.revision}.json") with open(config_file, "w") as outfile: json.dump(self.__dict__, outfile, indent=4) def load_params(self, params_dict): for key, value in params_dict.items(): if "db_" in key: key = key.replace("db_", "") if hasattr(self, key): setattr(self, key, value) # 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. """ 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): """ 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 = 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()) has_ema = False 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: has_ema = True 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 = {"time_embedding.linear_1.weight": unet_state_dict["time_embed.0.weight"], "time_embedding.linear_1.bias": unet_state_dict["time_embed.0.bias"], "time_embedding.linear_2.weight": unet_state_dict["time_embed.2.weight"], "time_embedding.linear_2.bias": unet_state_dict["time_embed.2.bias"], "conv_in.weight": unet_state_dict["input_blocks.0.0.weight"], "conv_in.bias": unet_state_dict["input_blocks.0.0.bias"], "conv_norm_out.weight": unet_state_dict["out.0.weight"], "conv_norm_out.bias": unet_state_dict["out.0.bias"], "conv_out.weight": unet_state_dict["out.2.weight"], "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 ) output_block_list = {k: sorted(v) for k, v in output_block_list.items()} if ["conv.bias", "conv.weight"] in output_block_list.values(): index = list(output_block_list.values()).index(["conv.bias", "conv.weight"]) new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.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] # From Bmalthais # if v2: # linear_transformer_to_conv(new_checkpoint) return new_checkpoint, has_ema def convert_ldm_vae_checkpoint(checkpoint, config, first_stage=True): # extract state dict for VAE vae_state_dict = {} vae_key = "first_stage_model." keys = list(checkpoint.keys()) for key in keys: if first_stage: if key.startswith(vae_key): vae_state_dict[key.replace(vae_key, "")] = checkpoint.get(key) else: vae_state_dict[key] = checkpoint.get(key) new_checkpoint = {"encoder.conv_in.weight": vae_state_dict["encoder.conv_in.weight"], "encoder.conv_in.bias": vae_state_dict["encoder.conv_in.bias"], "encoder.conv_out.weight": vae_state_dict["encoder.conv_out.weight"], "encoder.conv_out.bias": vae_state_dict["encoder.conv_out.bias"], "encoder.conv_norm_out.weight": vae_state_dict["encoder.norm_out.weight"], "encoder.conv_norm_out.bias": vae_state_dict["encoder.norm_out.bias"], "decoder.conv_in.weight": vae_state_dict["decoder.conv_in.weight"], "decoder.conv_in.bias": vae_state_dict["decoder.conv_in.bias"], "decoder.conv_out.weight": vae_state_dict["decoder.conv_out.weight"], "decoder.conv_out.bias": vae_state_dict["decoder.conv_out.bias"], "decoder.conv_norm_out.weight": vae_state_dict["decoder.norm_out.weight"], "decoder.conv_norm_out.bias": vae_state_dict["decoder.norm_out.bias"], "quant_conv.weight": vae_state_dict["quant_conv.weight"], "quant_conv.bias": vae_state_dict["quant_conv.bias"], "post_quant_conv.weight": vae_state_dict["post_quant_conv.weight"], "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"): if key.find("text_model") == -1: text_model_dict["text_model." + key[len("cond_stage_model.transformer."):]] = checkpoint[key] else: text_model_dict[key[len("cond_stage_model.transformer."):]] = checkpoint[key] text_model.load_state_dict(text_model_dict) return text_model textenc_conversion_lst = [ ('cond_stage_model.model.positional_embedding', "text_model.embeddings.position_embedding.weight"), ('cond_stage_model.model.token_embedding.weight', "text_model.embeddings.token_embedding.weight"), ('cond_stage_model.model.ln_final.weight', 'text_model.final_layer_norm.weight'), ('cond_stage_model.model.ln_final.bias', 'text_model.final_layer_norm.bias') ] textenc_conversion_map = {x[0]: x[1] for x in textenc_conversion_lst} textenc_transformer_conversion_lst = [ # (stable-diffusion, HF Diffusers) ("resblocks.", "text_model.encoder.layers."), ("ln_1", "layer_norm1"), ("ln_2", "layer_norm2"), (".c_fc.", ".fc1."), (".c_proj.", ".fc2."), (".attn", ".self_attn"), ("ln_final.", "transformer.text_model.final_layer_norm."), ("token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"), ("positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"), ] protected = {re.escape(x[0]): x[1] for x in textenc_transformer_conversion_lst} textenc_pattern = re.compile("|".join(protected.keys())) 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") keys = list(checkpoint.keys()) text_model_dict = {} if 'cond_stage_model.model.text_projection' in checkpoint: d_model = int(checkpoint['cond_stage_model.model.text_projection'].shape[0]) else: logger.debug("no projection shape found, setting to 1024") d_model = 1024 text_model_dict["text_model.embeddings.position_ids"] = text_model.text_model.embeddings.get_buffer("position_ids") for key in keys: if "resblocks.23" in key: # Diffusers drops the final layer and only uses the penultimate layer continue if key in textenc_conversion_map: text_model_dict[textenc_conversion_map[key]] = checkpoint[key] if key.startswith("cond_stage_model.model.transformer."): new_key = key[len("cond_stage_model.model.transformer.") :] if new_key.endswith(".in_proj_weight"): new_key = new_key[: -len(".in_proj_weight")] new_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], new_key) text_model_dict[new_key + ".q_proj.weight"] = checkpoint[key][:d_model, :] text_model_dict[new_key + ".k_proj.weight"] = checkpoint[key][d_model : d_model * 2, :] text_model_dict[new_key + ".v_proj.weight"] = checkpoint[key][d_model * 2 :, :] elif new_key.endswith(".in_proj_bias"): new_key = new_key[: -len(".in_proj_bias")] new_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], new_key) text_model_dict[new_key + ".q_proj.bias"] = checkpoint[key][:d_model] text_model_dict[new_key + ".k_proj.bias"] = checkpoint[key][d_model : d_model * 2] text_model_dict[new_key + ".v_proj.bias"] = checkpoint[key][d_model * 2 :] else: new_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], new_key) text_model_dict[new_key] = checkpoint[key] text_model.load_state_dict(text_model_dict) return text_model def replace_symlinks(path, base): if os.path.islink(path): # Get the target of the symlink src = os.readlink(path) blob = os.path.basename(src) path_parts = path.split("/") if "/" in path else path.split("\\") model_name = None dir_name = None save_next = False for part in path_parts: if save_next: model_name = part break if part == "src" or part == "working": dir_name = part save_next = True if model_name is not None and dir_name is not None: blob_path = os.path.join(base, dir_name, model_name, "blobs", blob) else: blob_path = None if blob_path is None: logger.debug("no blob") return os.replace(blob_path, path) elif os.path.isdir(path): # Recursively replace symlinks in the directory for subpath in os.listdir(path): replace_symlinks(os.path.join(path, subpath), base) def download_model(db_config: TrainingConfig, token): hub_url = db_config.src if "http" in hub_url or "huggingface.co" in hub_url: hub_url = "/".join(hub_url.split("/")[-2:]) api = HfApi() repo_info = api.repo_info( repo_id=hub_url, repo_type="model", revision="main", token=token, ) if repo_info.sha is None: logger.warning("unable to fetch repo info: %s", hub_url) return None, None siblings = repo_info.siblings diffusion_dirs = ["text_encoder", "unet", "vae", "tokenizer", "scheduler", "feature_extractor", "safety_checker"] config_file = None model_index = None model_files = [] diffusion_files = [] for sibling in siblings: name = sibling.rfilename if "inference.yaml" in name: config_file = name continue if "model_index.json" in name: model_index = name continue if (".ckpt" in name or ".safetensors" in name) and "/" not in name: model_files.append(name) continue for diffusion_dir in diffusion_dirs: if f"{diffusion_dir}/" in name: diffusion_files.append(name) for diffusion_dir in diffusion_dirs: safe_model = None bin_model = None for diffusion_file in diffusion_files: if diffusion_dir in diffusion_file: if ".safetensors" in diffusion_file: safe_model = diffusion_file if ".bin" in diffusion_file: bin_model = diffusion_file if safe_model and bin_model: diffusion_files.remove(bin_model) model_file = next( (x for x in model_files if ".safetensors" in x and "nonema" in x), next( (x for x in model_files if "nonema" in x), next( (x for x in model_files if ".safetensors" in x), model_files[0] if model_files else None ) ) ) files_to_fetch = None if model_file is not None: files_to_fetch = [model_file] elif len(diffusion_files): files_to_fetch = diffusion_files if model_index is not None: files_to_fetch.append(model_index) if files_to_fetch and config_file: files_to_fetch.append(config_file) logger.info(f"Fetching files: {files_to_fetch}") if not len(files_to_fetch): logger.debug("nothing to fetch") return None, None out_model = None for repo_file in tqdm(files_to_fetch, desc=f"Fetching {len(files_to_fetch)} files"): out = hf_hub_download( hub_url, filename=repo_file, repo_type="model", revision=repo_info.sha, token=token ) replace_symlinks(out, db_config.model_dir) dest = None file_name = os.path.basename(out) if "yaml" in repo_file: dest = os.path.join(db_config.model_dir) if "model_index" in repo_file: dest = db_config.pretrained_model_name_or_path if not dest: for diffusion_dir in diffusion_dirs: if diffusion_dir in out: out_model = db_config.pretrained_model_name_or_path dest = os.path.join(db_config.pretrained_model_name_or_path, diffusion_dir) if not dest: if ".ckpt" in out or ".safetensors" in out: dest = os.path.join(db_config.model_dir, "src") out_model = dest if dest is not None: if not os.path.exists(dest): os.makedirs(dest) dest_file = os.path.join(dest, file_name) if os.path.exists(dest_file): os.remove(dest_file) shutil.copyfile(out, dest_file) return out_model, config_file def get_config_path( model_version: str = "v1", train_type: str = "default", config_base_name: str = "training", prediction_type: str = "epsilon" ): train_type = f"{train_type}" if not prediction_type == "v_prediction" else f"{train_type}-v" parts = os.path.join( os.path.dirname(os.path.realpath(__file__)), "..", "..", "..", "models", "configs", f"{model_version}-{config_base_name}-{train_type}.yaml" ) return os.path.abspath(parts) def get_config_file(train_unfrozen=False, v2=False, prediction_type="epsilon", config_file=None): if config_file is not None: return config_file config_base_name = "training" model_versions = { "v1": "v1", "v2": "v2" } train_types = { "default": "default", "unfrozen": "unfrozen", } model_train_type = train_types["default"] model_version_name = f"{model_versions['v1'] if not v2 else model_versions['v2']}" if train_unfrozen: model_train_type = train_types["unfrozen"] else: model_train_type = train_types["default"] return get_config_path(model_version_name, model_train_type, config_base_name, prediction_type) def extract_checkpoint( ctx: ConversionContext, new_model_name: str, checkpoint_file: str, scheduler_type="ddim", extract_ema=False, train_unfrozen=False, is_512=True, config_file: str = None, vae_file: str = None, ): """ @param new_model_name: The name of the new model @param checkpoint_file: The source checkpoint to use, if not from hub. Needs full path @param scheduler_type: The target scheduler type @param from_hub: Are we making this model from the hub? @param new_model_url: The URL to pull. Should be formatted like compviz/stable-diffusion-2, not a full URL. @param new_model_token: Your huggingface.co token. @param extract_ema: Whether to extract EMA weights if present. @param is_512: Is it a 512 model? @return: db_new_model_name: Gr.dropdown populated with our model name, if applicable. db_config.model_dir: The directory where our model was created. db_config.revision: Model revision db_config.epoch: Model epoch db_config.scheduler: The scheduler being used db_config.src: The source checkpoint, if not from hub. db_has_ema: Whether the model had EMA weights and they were extracted. If weights were not present or you did not extract them and they were, this will be false. db_resolution: The resolution the model trains at. db_v2: Is this a V2 Model? status """ has_ema = False v2 = False revision = 0 epoch = 0 image_size = 512 if is_512 else 768 # Needed for V2 models so we can create the right text encoder. upcast_attention = False msg = None # Create empty config db_config = TrainingConfig(ctx, model_name=new_model_name, scheduler=scheduler_type, src=checkpoint_file) original_config_file = None try: map_location = torch.device("cpu") # Try to determine if v1 or v2 model if we have a ckpt logger.info("loading model from checkpoint") checkpoint = load_tensor(checkpoint_file, map_location=map_location) rev_keys = ["db_global_step", "global_step"] epoch_keys = ["db_epoch", "epoch"] for key in rev_keys: if key in checkpoint: revision = checkpoint[key] break for key in epoch_keys: if key in checkpoint: epoch = checkpoint[key] break 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: if not is_512: # v2.1 needs to upcast attention logger.debug("setting upcast_attention") upcast_attention = True v2 = True else: v2 = False if v2 and not is_512: prediction_type = "v_prediction" else: prediction_type = "epsilon" original_config_file = get_config_file(train_unfrozen, v2, prediction_type, config_file=config_file) logger.info(f"Pred and size are {prediction_type} and {image_size}, using config: {original_config_file}") db_config.resolution = image_size db_config.lifetime_revision = revision db_config.epoch = epoch db_config.v2 = v2 logger.info(f"{'v2' if v2 else 'v1'} model loaded.") # Use existing YAML if present if checkpoint_file is not None: config_check = checkpoint_file.replace(".ckpt", ".yaml") if ".ckpt" in checkpoint_file else checkpoint_file.replace(".safetensors", ".yaml") if os.path.exists(config_check): original_config_file = config_check if original_config_file is None or not os.path.exists(original_config_file): logger.warning("unable to select a config file: %s" % (original_config_file)) return logger.debug("trying to load: %s", original_config_file) original_config = load_yaml(original_config_file) 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, ) # make sure scheduler works correctly with DDIM scheduler.register_to_config(clip_sample=False) if scheduler_type == "pndm": config = dict(scheduler.config) config["skip_prk_steps"] = True scheduler = PNDMScheduler.from_config(config) elif scheduler_type == "lms": scheduler = LMSDiscreteScheduler.from_config(scheduler.config) elif scheduler_type == "heun": scheduler = HeunDiscreteScheduler.from_config(scheduler.config) elif scheduler_type == "euler": scheduler = EulerDiscreteScheduler.from_config(scheduler.config) elif scheduler_type == "euler-ancestral": scheduler = EulerAncestralDiscreteScheduler.from_config(scheduler.config) elif scheduler_type == "dpm": scheduler = DPMSolverMultistepScheduler.from_config(scheduler.config) elif scheduler_type == "ddim": pass else: raise ValueError(f"Scheduler of type {scheduler_type} doesn't exist!") # Convert the UNet2DConditionModel model. logger.info("converting UNet") unet_config = create_unet_diffusers_config(original_config, image_size=image_size) unet_config["upcast_attention"] = upcast_attention unet = UNet2DConditionModel(**unet_config) converted_unet_checkpoint, has_ema = convert_ldm_unet_checkpoint( checkpoint, unet_config, path=checkpoint_file, extract_ema=extract_ema ) db_config.has_ema = has_ema db_config.save() unet.load_state_dict(converted_unet_checkpoint) # Convert the VAE model. logger.info("converting VAE") vae_config = create_vae_diffusers_config(original_config, image_size=image_size) if vae_file is None: converted_vae_checkpoint = convert_ldm_vae_checkpoint(checkpoint, vae_config) else: vae_file = os.path.join(ctx.model_path, vae_file) logger.debug("loading custom VAE: %s", vae_file) vae_checkpoint = load_tensor(vae_file, map_location=map_location) converted_vae_checkpoint = convert_ldm_vae_checkpoint(vae_checkpoint, vae_config, first_stage=False) vae = AutoencoderKL(**vae_config) vae.load_state_dict(converted_vae_checkpoint) # Convert the text model. logger.info("converting text encoder") text_model_type = original_config.model.params.cond_stage_config.target.split(".")[-1] if text_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 text_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 text_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) except Exception: logger.error("exception setting up output: %s", traceback.format_exception(*sys.exc_info())) pipe = None if pipe is None or db_config is None: msg = "pipeline or config is not set, unable to continue." logger.error(msg) return else: logger.info("saving diffusion model") pipe.save_pretrained(db_config.pretrained_model_name_or_path) result_status = f"Checkpoint successfully extracted to {db_config.pretrained_model_name_or_path}" revision = db_config.revision scheduler = db_config.scheduler required_dirs = ["unet", "vae", "text_encoder", "scheduler", "tokenizer"] if original_config_file is not None and os.path.exists(original_config_file): logger.debug("copying original config: %s -> %s", original_config_file, db_config.model_dir) shutil.copy(original_config_file, db_config.model_dir) basename = os.path.basename(original_config_file) new_ex_path = os.path.join(db_config.model_dir, basename) new_name = os.path.join(db_config.model_dir, f"{db_config.model_name}.yaml") logger.debug("copying model config to new name: %s -> %s", new_ex_path, new_name) if os.path.exists(new_name): os.remove(new_name) os.rename(new_ex_path, new_name) for req_dir in required_dirs: full_path = os.path.join(db_config.pretrained_model_name_or_path, req_dir) if not os.path.exists(full_path): result_status = f"Missing model directory, removing model: {full_path}" shutil.rmtree(db_config.model_dir, ignore_errors=False, onerror=None) break remove_dirs = ["logging", "samples"] for rd in remove_dirs: rem_dir = os.path.join(db_config.model_dir, rd) if os.path.exists(rem_dir): shutil.rmtree(rem_dir, True) if not os.path.exists(rem_dir): os.makedirs(rem_dir) logger.info(result_status) def convert_diffusion_original( ctx: ConversionContext, model: ModelDict, source: str, ): name = model["name"] source = source or model["source"] dest = os.path.join(ctx.model_path, name) logger.info("converting original Diffusers checkpoint %s: %s -> %s", name, source, dest) if os.path.exists(dest): logger.info("ONNX pipeline already exists, skipping") return torch_name = name + "-torch" torch_path = os.path.join(ctx.cache_path, torch_name) working_name = os.path.join(ctx.cache_path, torch_name, "working") if os.path.exists(torch_path): logger.info("torch pipeline already exists, reusing: %s", torch_path) else: logger.info("converting original Diffusers check to Torch model: %s -> %s", source, torch_path) extract_checkpoint(ctx, torch_name, source, config_file=model.get("config"), vae_file=model.get("vae")) logger.info("converted original Diffusers checkpoint to Torch model") # VAE has already been converted and will confuse HF repo lookup if "vae" in model: del model["vae"] convert_diffusion_stable(ctx, model, working_name) logger.info("ONNX pipeline saved to %s", name)