FreemanT/dreambooth-training
0
1# Script for converting a HF Diffusers saved pipeline to a Stable Diffusion checkpoint.2# *Only* converts the UNet, VAE, and Text Encoder.3# Does not convert optimizer state or any other thing.4 5import argparse6import os.path as osp7import re8 9import torch10import gc11 12# =================#13# UNet Conversion #14# =================#15 16unet_conversion_map = [17 # (stable-diffusion, HF Diffusers)18 ("time_embed.0.weight", "time_embedding.linear_1.weight"),19 ("time_embed.0.bias", "time_embedding.linear_1.bias"),20 ("time_embed.2.weight", "time_embedding.linear_2.weight"),21 ("time_embed.2.bias", "time_embedding.linear_2.bias"),22 ("input_blocks.0.0.weight", "conv_in.weight"),23 ("input_blocks.0.0.bias", "conv_in.bias"),24 ("out.0.weight", "conv_norm_out.weight"),25 ("out.0.bias", "conv_norm_out.bias"),26 ("out.2.weight", "conv_out.weight"),27 ("out.2.bias", "conv_out.bias"),28]29 30unet_conversion_map_resnet = [31 # (stable-diffusion, HF Diffusers)32 ("in_layers.0", "norm1"),33 ("in_layers.2", "conv1"),34 ("out_layers.0", "norm2"),35 ("out_layers.3", "conv2"),36 ("emb_layers.1", "time_emb_proj"),37 ("skip_connection", "conv_shortcut"),38]39 40unet_conversion_map_layer = []41# hardcoded number of downblocks and resnets/attentions...42# would need smarter logic for other networks.43for i in range(4):44 # loop over downblocks/upblocks45 46 for j in range(2):47 # loop over resnets/attentions for downblocks48 hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}."49 sd_down_res_prefix = f"input_blocks.{3*i + j + 1}.0."50 unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))51 52 if i < 3:53 # no attention layers in down_blocks.354 hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}."55 sd_down_atn_prefix = f"input_blocks.{3*i + j + 1}.1."56 unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))57 58 for j in range(3):59 # loop over resnets/attentions for upblocks60 hf_up_res_prefix = f"up_blocks.{i}.resnets.{j}."61 sd_up_res_prefix = f"output_blocks.{3*i + j}.0."62 unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))63 64 if i > 0:65 # no attention layers in up_blocks.066 hf_up_atn_prefix = f"up_blocks.{i}.attentions.{j}."67 sd_up_atn_prefix = f"output_blocks.{3*i + j}.1."68 unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))69 70 if i < 3:71 # no downsample in down_blocks.372 hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv."73 sd_downsample_prefix = f"input_blocks.{3*(i+1)}.0.op."74 unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))75 76 # no upsample in up_blocks.377 hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."78 sd_upsample_prefix = f"output_blocks.{3*i + 2}.{1 if i == 0 else 2}."79 unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))80 81hf_mid_atn_prefix = "mid_block.attentions.0."82sd_mid_atn_prefix = "middle_block.1."83unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))84 85for j in range(2):86 hf_mid_res_prefix = f"mid_block.resnets.{j}."87 sd_mid_res_prefix = f"middle_block.{2*j}."88 unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))89 90 91def convert_unet_state_dict(unet_state_dict):92 # buyer beware: this is a *brittle* function,93 # and correct output requires that all of these pieces interact in94 # the exact order in which I have arranged them.95 mapping = {k: k for k in unet_state_dict.keys()}96 for sd_name, hf_name in unet_conversion_map:97 mapping[hf_name] = sd_name98 for k, v in mapping.items():99 if "resnets" in k:100 for sd_part, hf_part in unet_conversion_map_resnet:101 v = v.replace(hf_part, sd_part)102 mapping[k] = v103 for k, v in mapping.items():104 for sd_part, hf_part in unet_conversion_map_layer:105 v = v.replace(hf_part, sd_part)106 mapping[k] = v107 new_state_dict = {v: unet_state_dict[k] for k, v in mapping.items()}108 return new_state_dict109 110 111# ================#112# VAE Conversion #113# ================#114 115vae_conversion_map = [116 # (stable-diffusion, HF Diffusers)117 ("nin_shortcut", "conv_shortcut"),118 ("norm_out", "conv_norm_out"),119 ("mid.attn_1.", "mid_block.attentions.0."),120]121 122for i in range(4):123 # down_blocks have two resnets124 for j in range(2):125 hf_down_prefix = f"encoder.down_blocks.{i}.resnets.{j}."126 sd_down_prefix = f"encoder.down.{i}.block.{j}."127 vae_conversion_map.append((sd_down_prefix, hf_down_prefix))128 129 if i < 3:130 hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0."131 sd_downsample_prefix = f"down.{i}.downsample."132 vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))133 134 hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."135 sd_upsample_prefix = f"up.{3-i}.upsample."136 vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))137 138 # up_blocks have three resnets139 # also, up blocks in hf are numbered in reverse from sd140 for j in range(3):141 hf_up_prefix = f"decoder.up_blocks.{i}.resnets.{j}."142 sd_up_prefix = f"decoder.up.{3-i}.block.{j}."143 vae_conversion_map.append((sd_up_prefix, hf_up_prefix))144 145# this part accounts for mid blocks in both the encoder and the decoder146for i in range(2):147 hf_mid_res_prefix = f"mid_block.resnets.{i}."148 sd_mid_res_prefix = f"mid.block_{i+1}."149 vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))150 151 152vae_conversion_map_attn = [153 # (stable-diffusion, HF Diffusers)154 ("norm.", "group_norm."),155 ("q.", "query."),156 ("k.", "key."),157 ("v.", "value."),158 ("proj_out.", "proj_attn."),159]160 161 162def reshape_weight_for_sd(w):163 # convert HF linear weights to SD conv2d weights164 return w.reshape(*w.shape, 1, 1)165 166 167def convert_vae_state_dict(vae_state_dict):168 mapping = {k: k for k in vae_state_dict.keys()}169 for k, v in mapping.items():170 for sd_part, hf_part in vae_conversion_map:171 v = v.replace(hf_part, sd_part)172 mapping[k] = v173 for k, v in mapping.items():174 if "attentions" in k:175 for sd_part, hf_part in vae_conversion_map_attn:176 v = v.replace(hf_part, sd_part)177 mapping[k] = v178 new_state_dict = {v: vae_state_dict[k] for k, v in mapping.items()}179 weights_to_convert = ["q", "k", "v", "proj_out"]180 print("Converting to CKPT ...") 181 for k, v in new_state_dict.items():182 for weight_name in weights_to_convert:183 if f"mid.attn_1.{weight_name}.weight" in k:184 print(f"Reshaping {k} for SD format")185 new_state_dict[k] = reshape_weight_for_sd(v)186 return new_state_dict187 188 189# =========================#190# Text Encoder Conversion #191# =========================#192 193 194textenc_conversion_lst = [195 # (stable-diffusion, HF Diffusers)196 ("resblocks.", "text_model.encoder.layers."),197 ("ln_1", "layer_norm1"),198 ("ln_2", "layer_norm2"),199 (".c_fc.", ".fc1."),200 (".c_proj.", ".fc2."),201 (".attn", ".self_attn"),202 ("ln_final.", "transformer.text_model.final_layer_norm."),203 ("token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"),204 ("positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"),205]206protected = {re.escape(x[1]): x[0] for x in textenc_conversion_lst}207textenc_pattern = re.compile("|".join(protected.keys()))208 209# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp210code2idx = {"q": 0, "k": 1, "v": 2}211 212 213def convert_text_enc_state_dict_v20(text_enc_dict):214 new_state_dict = {}215 capture_qkv_weight = {}216 capture_qkv_bias = {}217 for k, v in text_enc_dict.items():218 if (219 k.endswith(".self_attn.q_proj.weight")220 or k.endswith(".self_attn.k_proj.weight")221 or k.endswith(".self_attn.v_proj.weight")222 ):223 k_pre = k[: -len(".q_proj.weight")]224 k_code = k[-len("q_proj.weight")]225 if k_pre not in capture_qkv_weight:226 capture_qkv_weight[k_pre] = [None, None, None]227 capture_qkv_weight[k_pre][code2idx[k_code]] = v228 continue229 230 if (231 k.endswith(".self_attn.q_proj.bias")232 or k.endswith(".self_attn.k_proj.bias")233 or k.endswith(".self_attn.v_proj.bias")234 ):235 k_pre = k[: -len(".q_proj.bias")]236 k_code = k[-len("q_proj.bias")]237 if k_pre not in capture_qkv_bias:238 capture_qkv_bias[k_pre] = [None, None, None]239 capture_qkv_bias[k_pre][code2idx[k_code]] = v240 continue241 242 relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k)243 new_state_dict[relabelled_key] = v244 245 for k_pre, tensors in capture_qkv_weight.items():246 if None in tensors:247 raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing")248 relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre)249 new_state_dict[relabelled_key + ".in_proj_weight"] = torch.cat(tensors)250 251 for k_pre, tensors in capture_qkv_bias.items():252 if None in tensors:253 raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing")254 relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre)255 new_state_dict[relabelled_key + ".in_proj_bias"] = torch.cat(tensors)256 257 return new_state_dict258 259 260def convert_text_enc_state_dict(text_enc_dict):261 return text_enc_dict262 263 264def convert(model_path, checkpoint_path): 265 unet_path = osp.join(model_path, "unet", "diffusion_pytorch_model.bin")266 vae_path = osp.join(model_path, "vae", "diffusion_pytorch_model.bin")267 text_enc_path = osp.join(model_path, "text_encoder", "pytorch_model.bin")268 269 # Convert the UNet model270 unet_state_dict = torch.load(unet_path, map_location="cpu")271 unet_state_dict = convert_unet_state_dict(unet_state_dict)272 unet_state_dict = {"model.diffusion_model." + k: v for k, v in unet_state_dict.items()}273 274 # Convert the VAE model275 vae_state_dict = torch.load(vae_path, map_location="cpu")276 vae_state_dict = convert_vae_state_dict(vae_state_dict)277 vae_state_dict = {"first_stage_model." + k: v for k, v in vae_state_dict.items()}278 279 # Convert the text encoder model280 text_enc_dict = torch.load(text_enc_path, map_location="cpu")281 282 # Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper283 is_v20_model = "text_model.encoder.layers.22.layer_norm2.bias" in text_enc_dict284 285 if is_v20_model:286 # Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm287 text_enc_dict = {"transformer." + k: v for k, v in text_enc_dict.items()}288 text_enc_dict = convert_text_enc_state_dict_v20(text_enc_dict)289 text_enc_dict = {"cond_stage_model.model." + k: v for k, v in text_enc_dict.items()}290 else:291 text_enc_dict = convert_text_enc_state_dict(text_enc_dict)292 text_enc_dict = {"cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()}293 294 # Put together new checkpoint295 state_dict = {**unet_state_dict, **vae_state_dict, **text_enc_dict}296 state_dict = {k: v.half() for k, v in state_dict.items()}297 state_dict = {"state_dict": state_dict}298 torch.save(state_dict, checkpoint_path)299 del state_dict, text_enc_dict, vae_state_dict, unet_state_dict300 torch.cuda.empty_cache()301 gc.collect()302