diff --git a/utils/convert_diffusers_to_original_ms_text_to_video.py b/utils/convert_diffusers_to_original_ms_text_to_video.py new file mode 100644 index 0000000..00242b6 --- /dev/null +++ b/utils/convert_diffusers_to_original_ms_text_to_video.py @@ -0,0 +1,461 @@ +# Script for converting a HF Diffusers saved pipeline to a Stable Diffusion checkpoint. +# *Only* converts the UNet, and Text Encoder. +# Does not convert optimizer state or any other thing. + +import argparse +import os.path as osp +import re + +import torch +from safetensors.torch import load_file, save_file + +# =================# +# UNet Conversion # +# =================# + +print ('Initializing the conversion map') + +unet_conversion_map = [ + # (ModelScope, HF Diffusers) + + # from Vanilla ModelScope/StableDiffusion + ("time_embed.0.weight", "time_embedding.linear_1.weight"), + ("time_embed.0.bias", "time_embedding.linear_1.bias"), + ("time_embed.2.weight", "time_embedding.linear_2.weight"), + ("time_embed.2.bias", "time_embedding.linear_2.bias"), + + + # from Vanilla ModelScope/StableDiffusion + ("input_blocks.0.0.weight", "conv_in.weight"), + ("input_blocks.0.0.bias", "conv_in.bias"), + + + # from Vanilla ModelScope/StableDiffusion + ("out.0.weight", "conv_norm_out.weight"), + ("out.0.bias", "conv_norm_out.bias"), + ("out.2.weight", "conv_out.weight"), + ("out.2.bias", "conv_out.bias"), +] + +unet_conversion_map_resnet = [ + # (ModelScope, HF Diffusers) + + # SD + ("in_layers.0", "norm1"), + ("in_layers.2", "conv1"), + ("out_layers.0", "norm2"), + ("out_layers.3", "conv2"), + ("emb_layers.1", "time_emb_proj"), + ("skip_connection", "conv_shortcut"), + + # MS + #("temopral_conv", "temp_convs"), # ROFL, they have a typo here --kabachuha +] + +unet_conversion_map_layer = [] + +# Convert input TemporalTransformer +unet_conversion_map_layer.append(('input_blocks.0.1', 'transformer_in')) + +# Reference for the default settings + +# "model_cfg": { +# "unet_in_dim": 4, +# "unet_dim": 320, +# "unet_y_dim": 768, +# "unet_context_dim": 1024, +# "unet_out_dim": 4, +# "unet_dim_mult": [1, 2, 4, 4], +# "unet_num_heads": 8, +# "unet_head_dim": 64, +# "unet_res_blocks": 2, +# "unet_attn_scales": [1, 0.5, 0.25], +# "unet_dropout": 0.1, +# "temporal_attention": "True", +# "num_timesteps": 1000, +# "mean_type": "eps", +# "var_type": "fixed_small", +# "loss_type": "mse" +# } + +# hardcoded number of downblocks and resnets/attentions... +# would need smarter logic for other networks. +for i in range(4): + # loop over downblocks/upblocks + + for j in range(2): + # loop over resnets/attentions for downblocks + + # Spacial SD stuff + hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}." + sd_down_res_prefix = f"input_blocks.{3*i + j + 1}.0." + unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix)) + + if i < 3: + # no attention layers in down_blocks.3 + hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}." + sd_down_atn_prefix = f"input_blocks.{3*i + j + 1}.1." + unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix)) + + # Temporal MS stuff + hf_down_res_prefix = f"down_blocks.{i}.temp_convs.{j}." + sd_down_res_prefix = f"input_blocks.{3*i + j + 1}.0.temopral_conv." + unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix)) + + if i < 3: + # no attention layers in down_blocks.3 + hf_down_atn_prefix = f"down_blocks.{i}.temp_attentions.{j}." + sd_down_atn_prefix = f"input_blocks.{3*i + j + 1}.2." + unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix)) + + for j in range(3): + # loop over resnets/attentions for upblocks + + # Spacial SD stuff + hf_up_res_prefix = f"up_blocks.{i}.resnets.{j}." + sd_up_res_prefix = f"output_blocks.{3*i + j}.0." + unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix)) + + if i > 0: + # no attention layers in up_blocks.0 + hf_up_atn_prefix = f"up_blocks.{i}.attentions.{j}." + sd_up_atn_prefix = f"output_blocks.{3*i + j}.1." + unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix)) + + # loop over resnets/attentions for upblocks + hf_up_res_prefix = f"up_blocks.{i}.temp_convs.{j}." + sd_up_res_prefix = f"output_blocks.{3*i + j}.0.temopral_conv." + unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix)) + + if i > 0: + # no attention layers in up_blocks.0 + hf_up_atn_prefix = f"up_blocks.{i}.temp_attentions.{j}." + sd_up_atn_prefix = f"output_blocks.{3*i + j}.2." + unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix)) + + # Up/Downsamplers are 2D, so don't need to touch them + if i < 3: + # no downsample in down_blocks.3 + hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv." + sd_downsample_prefix = f"input_blocks.{3*(i+1)}.op." + unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix)) + + # no upsample in up_blocks.3 + hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0." + sd_upsample_prefix = f"output_blocks.{3*i + 2}.{1 if i == 0 else 3}." + unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix)) + + +# Handle the middle block + +# Spacial +hf_mid_atn_prefix = "mid_block.attentions.0." +sd_mid_atn_prefix = "middle_block.1." +unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix)) + +for j in range(2): + hf_mid_res_prefix = f"mid_block.resnets.{j}." + sd_mid_res_prefix = f"middle_block.{3*j}." + unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix)) + +# Temporal +hf_mid_atn_prefix = "mid_block.temp_attentions.0." +sd_mid_atn_prefix = "middle_block.2." +unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix)) + +for j in range(2): + hf_mid_res_prefix = f"mid_block.temp_convs.{j}." + sd_mid_res_prefix = f"middle_block.{3*j}.temopral_conv." + unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix)) + +# The pipeline +def convert_unet_state_dict(unet_state_dict): + print ('Converting the UNET') + # buyer beware: this is a *brittle* function, + # and correct output requires that all of these pieces interact in + # the exact order in which I have arranged them. + mapping = {k: k for k in unet_state_dict.keys()} + + for sd_name, hf_name in unet_conversion_map: + mapping[hf_name] = sd_name + for k, v in mapping.items(): + if "resnets" in k: + for sd_part, hf_part in unet_conversion_map_resnet: + v = v.replace(hf_part, sd_part) + mapping[k] = v + # elif "temp_convs" in k: + # for sd_part, hf_part in unet_conversion_map_resnet: + # v = v.replace(hf_part, sd_part) + # mapping[k] = v + for k, v in mapping.items(): + for sd_part, hf_part in unet_conversion_map_layer: + v = v.replace(hf_part, sd_part) + mapping[k] = v + + + # there must be a pattern, but I don't want to bother atm + do_not_unsqueeze = [f'output_blocks.{i}.1.proj_out.weight' for i in range(3, 12)] + [f'output_blocks.{i}.1.proj_in.weight' for i in range(3, 12)] + ['middle_block.1.proj_in.weight', 'middle_block.1.proj_out.weight'] + [f'input_blocks.{i}.1.proj_out.weight' for i in [1, 2, 4, 5, 7, 8]] + [f'input_blocks.{i}.1.proj_in.weight' for i in [1, 2, 4, 5, 7, 8]] + print (do_not_unsqueeze) + + new_state_dict = {v: (unet_state_dict[k].unsqueeze(-1) if ('proj_' in k and ('bias' not in k) and (k not in do_not_unsqueeze)) else unet_state_dict[k]) for k, v in mapping.items()} + # HACK: idk why the hell it does not work with list comprehension + for k, v in new_state_dict.items(): + has_k = False + for n in do_not_unsqueeze: + if k == n: + has_k = True + + if has_k: + v = v.squeeze(-1) + new_state_dict[k] = v + + return new_state_dict + +# TODO: VAE conversion. We doesn't train it in the most cases, but may be handy for the future --kabachuha + +# =========================# +# Text Encoder Conversion # +# =========================# + +# IT IS THE SAME CLIP ENCODER, SO JUST COPYPASTING IT --kabachuha + +# =========================# +# Text Encoder Conversion # +# =========================# + + +textenc_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[1]): x[0] for x in textenc_conversion_lst} +textenc_pattern = re.compile("|".join(protected.keys())) + +# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp +code2idx = {"q": 0, "k": 1, "v": 2} + + +def convert_text_enc_state_dict_v20(text_enc_dict): + #print ('Converting the text encoder') + new_state_dict = {} + capture_qkv_weight = {} + capture_qkv_bias = {} + for k, v in text_enc_dict.items(): + if ( + k.endswith(".self_attn.q_proj.weight") + or k.endswith(".self_attn.k_proj.weight") + or k.endswith(".self_attn.v_proj.weight") + ): + k_pre = k[: -len(".q_proj.weight")] + k_code = k[-len("q_proj.weight")] + if k_pre not in capture_qkv_weight: + capture_qkv_weight[k_pre] = [None, None, None] + capture_qkv_weight[k_pre][code2idx[k_code]] = v + continue + + if ( + k.endswith(".self_attn.q_proj.bias") + or k.endswith(".self_attn.k_proj.bias") + or k.endswith(".self_attn.v_proj.bias") + ): + k_pre = k[: -len(".q_proj.bias")] + k_code = k[-len("q_proj.bias")] + if k_pre not in capture_qkv_bias: + capture_qkv_bias[k_pre] = [None, None, None] + capture_qkv_bias[k_pre][code2idx[k_code]] = v + continue + + relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k) + new_state_dict[relabelled_key] = v + + for k_pre, tensors in capture_qkv_weight.items(): + if None in tensors: + raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing") + relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre) + new_state_dict[relabelled_key + ".in_proj_weight"] = torch.cat(tensors) + + for k_pre, tensors in capture_qkv_bias.items(): + if None in tensors: + raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing") + relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre) + new_state_dict[relabelled_key + ".in_proj_bias"] = torch.cat(tensors) + + return new_state_dict + + +def convert_text_enc_state_dict(text_enc_dict): + return text_enc_dict + +textenc_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[1]): x[0] for x in textenc_conversion_lst} +textenc_pattern = re.compile("|".join(protected.keys())) + +# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp +code2idx = {"q": 0, "k": 1, "v": 2} + + +def convert_text_enc_state_dict_v20(text_enc_dict): + new_state_dict = {} + capture_qkv_weight = {} + capture_qkv_bias = {} + for k, v in text_enc_dict.items(): + if ( + k.endswith(".self_attn.q_proj.weight") + or k.endswith(".self_attn.k_proj.weight") + or k.endswith(".self_attn.v_proj.weight") + ): + k_pre = k[: -len(".q_proj.weight")] + k_code = k[-len("q_proj.weight")] + if k_pre not in capture_qkv_weight: + capture_qkv_weight[k_pre] = [None, None, None] + capture_qkv_weight[k_pre][code2idx[k_code]] = v + continue + + if ( + k.endswith(".self_attn.q_proj.bias") + or k.endswith(".self_attn.k_proj.bias") + or k.endswith(".self_attn.v_proj.bias") + ): + k_pre = k[: -len(".q_proj.bias")] + k_code = k[-len("q_proj.bias")] + if k_pre not in capture_qkv_bias: + capture_qkv_bias[k_pre] = [None, None, None] + capture_qkv_bias[k_pre][code2idx[k_code]] = v + continue + + relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k) + new_state_dict[relabelled_key] = v + + for k_pre, tensors in capture_qkv_weight.items(): + if None in tensors: + raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing") + relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre) + new_state_dict[relabelled_key + ".in_proj_weight"] = torch.cat(tensors) + + for k_pre, tensors in capture_qkv_bias.items(): + if None in tensors: + raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing") + relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre) + new_state_dict[relabelled_key + ".in_proj_bias"] = torch.cat(tensors) + + return new_state_dict + + +def convert_text_enc_state_dict(text_enc_dict): + return text_enc_dict + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument("--model_path", default=None, type=str, required=True, help="Path to the model to convert.") + parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.") + parser.add_argument("--clip_checkpoint_path", default=None, type=str, required=True, help="Path to the output CLIP model.") + parser.add_argument("--half", action="store_true", help="Save weights in half precision.") + parser.add_argument( + "--use_safetensors", action="store_true", help="Save weights use safetensors, default is ckpt." + ) + + args = parser.parse_args() + + assert args.model_path is not None, "Must provide a model path!" + + assert args.checkpoint_path is not None, "Must provide a checkpoint path!" + + assert args.clip_checkpoint_path is not None, "Must provide a CLIP checkpoint path!" + + # Path for safetensors + unet_path = osp.join(args.model_path, "unet", "diffusion_pytorch_model.safetensors") + #vae_path = osp.join(args.model_path, "vae", "diffusion_pytorch_model.safetensors") + text_enc_path = osp.join(args.model_path, "text_encoder", "model.safetensors") + + # Load models from safetensors if it exists, if it doesn't pytorch + if osp.exists(unet_path): + unet_state_dict = load_file(unet_path, device="cpu") + else: + unet_path = osp.join(args.model_path, "unet", "diffusion_pytorch_model.bin") + unet_state_dict = torch.load(unet_path, map_location="cpu") + + # if osp.exists(vae_path): + # vae_state_dict = load_file(vae_path, device="cpu") + # else: + # vae_path = osp.join(args.model_path, "vae", "diffusion_pytorch_model.bin") + # vae_state_dict = torch.load(vae_path, map_location="cpu") + + if osp.exists(text_enc_path): + text_enc_dict = load_file(text_enc_path, device="cpu") + else: + text_enc_path = osp.join(args.model_path, "text_encoder", "pytorch_model.bin") + text_enc_dict = torch.load(text_enc_path, map_location="cpu") + + # Convert the UNet model + unet_state_dict = convert_unet_state_dict(unet_state_dict) + #unet_state_dict = {"model.diffusion_model." + k: v for k, v in unet_state_dict.items()} + + # Convert the VAE model + # vae_state_dict = convert_vae_state_dict(vae_state_dict) + # vae_state_dict = {"first_stage_model." + k: v for k, v in vae_state_dict.items()} + + # Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper + is_v20_model = "text_model.encoder.layers.22.layer_norm2.bias" in text_enc_dict + + if is_v20_model: + + # MODELSCOPE always uses the 2.X encoder, btw --kabachuha + + # Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm + text_enc_dict = {"transformer." + k: v for k, v in text_enc_dict.items()} + text_enc_dict = convert_text_enc_state_dict_v20(text_enc_dict) + #text_enc_dict = {"cond_stage_model.model." + k: v for k, v in text_enc_dict.items()} + else: + text_enc_dict = convert_text_enc_state_dict(text_enc_dict) + #text_enc_dict = {"cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()} + + # DON'T PUT TOGETHER FOR THE NEW CHECKPOINT AS MODELSCOPE USES THEM IN THE SPLITTED FORM --kabachuha + # Save CLIP and the Diffusion model to their own files + + #state_dict = {**unet_state_dict, **vae_state_dict, **text_enc_dict} + print ('Saving UNET') + state_dict = {**unet_state_dict} + + if args.half: + state_dict = {k: v.half() for k, v in state_dict.items()} + + if args.use_safetensors: + save_file(state_dict, args.checkpoint_path) + else: + #state_dict = {"state_dict": state_dict} + torch.save(state_dict, args.checkpoint_path) + + # TODO: CLIP conversion doesn't work atm + # print ('Saving CLIP') + # state_dict = {**text_enc_dict} + + # if args.half: + # state_dict = {k: v.half() for k, v in state_dict.items()} + + # if args.use_safetensors: + # save_file(state_dict, args.checkpoint_path) + # else: + # #state_dict = {"state_dict": state_dict} + # torch.save(state_dict, args.clip_checkpoint_path) + + print('Operation successfull')