Merge remote-tracking branch 'upstream/main' into hf_model_download
This commit is contained in:
Damian Stewart
commit
4e6d8f1157
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@ -41,7 +41,8 @@ Resuming from a checkpoint, 50 epochs, 6 batch size, 3e-6 learning rate, cosine
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--sample_steps 200 ^
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--lr 3e-6 ^
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--ckpt_every_n_minutes 10 ^
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--useadam8bit
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--useadam8bit ^
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--ed1_mode
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Training from SD2 512 base model, 18 epochs, 4 batch size, 1.2e-6 learning rate, constant LR, generate samples evern 100 steps, 30 minute checkpoint interval, adam8bit, using imagesin the x:\mydata folder, training at resolution class of 640:
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36
train.py
36
train.py
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@ -46,13 +46,12 @@ from accelerate.utils import set_seed
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import wandb
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from torch.utils.tensorboard import SummaryWriter
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from tqdm.auto import tqdm
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import keyboard
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from data.every_dream import EveryDreamBatch
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from utils.huggingface_downloader import try_download_model_from_hf
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from utils.convert_diffusers_to_stable_diffusion import convert as converter
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from utils.convert_diff_to_ckpt import convert as converter
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from utils.gpu import GPU
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@ -281,7 +280,12 @@ def main(args):
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"""
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log_time = setup_local_logger(args)
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args = setup_args(args)
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if args.notebook:
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from tqdm.notebook import tqdm
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else:
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from tqdm.auto import tqdm
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seed = args.seed if args.seed != -1 else random.randint(0, 2**30)
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set_seed(seed)
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gpu = GPU()
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@ -295,7 +299,7 @@ def main(args):
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os.makedirs(log_folder)
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@torch.no_grad()
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def __save_model(save_path, unet, text_encoder, tokenizer, scheduler, vae, save_ckpt_dir):
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def __save_model(save_path, unet, text_encoder, tokenizer, scheduler, vae, save_ckpt_dir, save_full_precision=False):
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"""
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Save the model to disk
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"""
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@ -320,9 +324,11 @@ def main(args):
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sd_ckpt_full = os.path.join(save_ckpt_dir, sd_ckpt_path)
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else:
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sd_ckpt_full = os.path.join(os.curdir, sd_ckpt_path)
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half = not save_full_precision
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logging.info(f" * Saving SD model to {sd_ckpt_full}")
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converter(model_path=save_path, checkpoint_path=sd_ckpt_full, half=True)
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converter(model_path=save_path, checkpoint_path=sd_ckpt_full, half=half)
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# optimizer_path = os.path.join(save_path, "optimizer.pt")
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# if self.save_optimizer_flag:
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@ -585,7 +591,7 @@ def main(args):
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logging.error(f"{Fore.LIGHTRED_EX} CTRL-C received, attempting to save model to {interrupted_checkpoint_path}{Style.RESET_ALL}")
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logging.error(f"{Fore.LIGHTRED_EX} ************************************************************************{Style.RESET_ALL}")
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time.sleep(2) # give opportunity to ctrl-C again to cancel save
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__save_model(interrupted_checkpoint_path, unet, text_encoder, tokenizer, noise_scheduler, vae, args.save_ckpt_dir)
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__save_model(interrupted_checkpoint_path, unet, text_encoder, tokenizer, noise_scheduler, vae, args.save_ckpt_dir, args.save_full_precision)
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exit(_SIGTERM_EXIT_CODE)
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signal.signal(signal.SIGINT, sigterm_handler)
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@ -781,7 +787,7 @@ def main(args):
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append_epoch_log(global_step=global_step, epoch_pbar=epoch_pbar, gpu=gpu, log_writer=log_writer, **logs)
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torch.cuda.empty_cache()
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if keyboard.is_pressed("ctrl+alt+page up") or ((global_step + 1) % args.sample_steps == 0):
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if (not args.notebook and keyboard.is_pressed("ctrl+alt+page up")) or ((global_step + 1) % args.sample_steps == 0):
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pipe = __create_inference_pipe(unet=unet, text_encoder=text_encoder, tokenizer=tokenizer, scheduler=sample_scheduler, vae=vae)
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pipe = pipe.to(device)
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@ -803,12 +809,12 @@ def main(args):
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last_epoch_saved_time = time.time()
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logging.info(f"Saving model, {args.ckpt_every_n_minutes} mins at step {global_step}")
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save_path = os.path.join(f"{log_folder}/ckpts/{args.project_name}-ep{epoch:02}-gs{global_step:05}")
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__save_model(save_path, unet, text_encoder, tokenizer, noise_scheduler, vae, args.save_ckpt_dir)
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__save_model(save_path, unet, text_encoder, tokenizer, noise_scheduler, vae, args.save_ckpt_dir, args.save_full_precision)
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if epoch > 0 and epoch % args.save_every_n_epochs == 0 and step == 1 and epoch < args.max_epochs - 1:
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logging.info(f" Saving model, {args.save_every_n_epochs} epochs at step {global_step}")
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save_path = os.path.join(f"{log_folder}/ckpts/{args.project_name}-ep{epoch:02}-gs{global_step:05}")
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__save_model(save_path, unet, text_encoder, tokenizer, noise_scheduler, vae, args.save_ckpt_dir)
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__save_model(save_path, unet, text_encoder, tokenizer, noise_scheduler, vae, args.save_ckpt_dir, args.save_full_precision)
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del batch
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global_step += 1
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@ -829,7 +835,7 @@ def main(args):
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# end of training
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save_path = os.path.join(f"{log_folder}/ckpts/last-{args.project_name}-ep{epoch:02}-gs{global_step:05}")
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__save_model(save_path, unet, text_encoder, tokenizer, noise_scheduler, vae, args.save_ckpt_dir)
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__save_model(save_path, unet, text_encoder, tokenizer, noise_scheduler, vae, args.save_ckpt_dir, args.save_full_precision)
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total_elapsed_time = time.time() - training_start_time
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logging.info(f"{Fore.CYAN}Training complete{Style.RESET_ALL}")
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@ -839,7 +845,7 @@ def main(args):
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except Exception as ex:
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logging.error(f"{Fore.LIGHTYELLOW_EX}Something went wrong, attempting to save model{Style.RESET_ALL}")
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save_path = os.path.join(f"{log_folder}/ckpts/errored-{args.project_name}-ep{epoch:02}-gs{global_step:05}")
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__save_model(save_path, unet, text_encoder, tokenizer, noise_scheduler, vae, args.save_ckpt_dir)
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__save_model(save_path, unet, text_encoder, tokenizer, noise_scheduler, vae, args.save_ckpt_dir, args.save_full_precision)
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raise ex
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logging.info(f"{Fore.LIGHTWHITE_EX} ***************************{Style.RESET_ALL}")
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@ -854,6 +860,12 @@ def update_old_args(t_args):
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if not hasattr(t_args, "shuffle_tags"):
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print(f" Config json is missing 'shuffle_tags'")
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t_args.__dict__["shuffle_tags"] = False
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if not hasattr(t_args, "save_full_precision"):
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print(f" Config json is missing 'save_full_precision'")
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t_args.__dict__["save_full_precision"] = False
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if not hasattr(t_args, "notebook"):
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print(f" Config json is missing 'notebook'")
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t_args.__dict__["notebook"] = False
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if __name__ == "__main__":
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supported_resolutions = [256, 384, 448, 512, 576, 640, 704, 768, 832, 896, 960, 1024, 1088, 1152]
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@ -909,6 +921,8 @@ if __name__ == "__main__":
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argparser.add_argument("--useadam8bit", action="store_true", default=False, help="Use AdamW 8-Bit optimizer, recommended!")
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argparser.add_argument("--wandb", action="store_true", default=False, help="enable wandb logging instead of tensorboard, requires env var WANDB_API_KEY")
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argparser.add_argument("--write_schedule", action="store_true", default=False, help="write schedule of images and their batches to file (def: False)")
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argparser.add_argument("--save_full_precision", action="store_true", default=False, help="save ckpts at full FP32")
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argparser.add_argument("--notebook", action="store_true", default=False, help="disable keypresses and uses tqdm.notebook for jupyter notebook (def: False)")
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args = argparser.parse_args()
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@ -0,0 +1,321 @@
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# from https://github.com/huggingface/diffusers/blob/main/scripts/convert_diffusers_to_original_stable_diffusion.py
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# modified to be callable
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# coding=utf-8
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# Copyright 2022 The HuggingFace Inc. team.
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# Modifications for EveryDream Copyright 2022 Victor C Hall
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# Script for converting a HF Diffusers saved pipeline to a Stable Diffusion checkpoint.
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# *Only* converts the UNet, VAE, and Text Encoder.
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# Does not convert optimizer state or any other thing.
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import os.path as osp
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import re
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import torch
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# =================#
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# UNet Conversion #
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# =================#
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unet_conversion_map = [
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# (stable-diffusion, HF Diffusers)
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("time_embed.0.weight", "time_embedding.linear_1.weight"),
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("time_embed.0.bias", "time_embedding.linear_1.bias"),
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("time_embed.2.weight", "time_embedding.linear_2.weight"),
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("time_embed.2.bias", "time_embedding.linear_2.bias"),
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("input_blocks.0.0.weight", "conv_in.weight"),
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("input_blocks.0.0.bias", "conv_in.bias"),
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("out.0.weight", "conv_norm_out.weight"),
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("out.0.bias", "conv_norm_out.bias"),
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("out.2.weight", "conv_out.weight"),
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("out.2.bias", "conv_out.bias"),
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]
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unet_conversion_map_resnet = [
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# (stable-diffusion, HF Diffusers)
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("in_layers.0", "norm1"),
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("in_layers.2", "conv1"),
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("out_layers.0", "norm2"),
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("out_layers.3", "conv2"),
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("emb_layers.1", "time_emb_proj"),
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("skip_connection", "conv_shortcut"),
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]
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unet_conversion_map_layer = []
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# hardcoded number of downblocks and resnets/attentions...
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# would need smarter logic for other networks.
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for i in range(4):
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# loop over downblocks/upblocks
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for j in range(2):
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# loop over resnets/attentions for downblocks
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hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}."
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sd_down_res_prefix = f"input_blocks.{3*i + j + 1}.0."
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unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))
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if i < 3:
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# no attention layers in down_blocks.3
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hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}."
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sd_down_atn_prefix = f"input_blocks.{3*i + j + 1}.1."
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unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))
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for j in range(3):
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# loop over resnets/attentions for upblocks
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hf_up_res_prefix = f"up_blocks.{i}.resnets.{j}."
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sd_up_res_prefix = f"output_blocks.{3*i + j}.0."
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unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))
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if i > 0:
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# no attention layers in up_blocks.0
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hf_up_atn_prefix = f"up_blocks.{i}.attentions.{j}."
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sd_up_atn_prefix = f"output_blocks.{3*i + j}.1."
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unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))
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if i < 3:
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# no downsample in down_blocks.3
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hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv."
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sd_downsample_prefix = f"input_blocks.{3*(i+1)}.0.op."
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unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))
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# no upsample in up_blocks.3
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hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
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sd_upsample_prefix = f"output_blocks.{3*i + 2}.{1 if i == 0 else 2}."
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unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))
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hf_mid_atn_prefix = "mid_block.attentions.0."
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sd_mid_atn_prefix = "middle_block.1."
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unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))
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for j in range(2):
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hf_mid_res_prefix = f"mid_block.resnets.{j}."
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sd_mid_res_prefix = f"middle_block.{2*j}."
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unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
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def convert_unet_state_dict(unet_state_dict):
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# buyer beware: this is a *brittle* function,
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# and correct output requires that all of these pieces interact in
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# the exact order in which I have arranged them.
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mapping = {k: k for k in unet_state_dict.keys()}
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for sd_name, hf_name in unet_conversion_map:
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mapping[hf_name] = sd_name
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for k, v in mapping.items():
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if "resnets" in k:
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for sd_part, hf_part in unet_conversion_map_resnet:
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v = v.replace(hf_part, sd_part)
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mapping[k] = v
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for k, v in mapping.items():
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for sd_part, hf_part in unet_conversion_map_layer:
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v = v.replace(hf_part, sd_part)
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mapping[k] = v
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new_state_dict = {v: unet_state_dict[k] for k, v in mapping.items()}
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return new_state_dict
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# ================#
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# VAE Conversion #
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# ================#
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vae_conversion_map = [
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# (stable-diffusion, HF Diffusers)
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("nin_shortcut", "conv_shortcut"),
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("norm_out", "conv_norm_out"),
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("mid.attn_1.", "mid_block.attentions.0."),
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]
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for i in range(4):
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# down_blocks have two resnets
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for j in range(2):
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hf_down_prefix = f"encoder.down_blocks.{i}.resnets.{j}."
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sd_down_prefix = f"encoder.down.{i}.block.{j}."
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vae_conversion_map.append((sd_down_prefix, hf_down_prefix))
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if i < 3:
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hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0."
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sd_downsample_prefix = f"down.{i}.downsample."
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vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))
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hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
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sd_upsample_prefix = f"up.{3-i}.upsample."
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vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))
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# up_blocks have three resnets
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# also, up blocks in hf are numbered in reverse from sd
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for j in range(3):
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hf_up_prefix = f"decoder.up_blocks.{i}.resnets.{j}."
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sd_up_prefix = f"decoder.up.{3-i}.block.{j}."
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vae_conversion_map.append((sd_up_prefix, hf_up_prefix))
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# this part accounts for mid blocks in both the encoder and the decoder
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for i in range(2):
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hf_mid_res_prefix = f"mid_block.resnets.{i}."
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sd_mid_res_prefix = f"mid.block_{i+1}."
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vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))
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vae_conversion_map_attn = [
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# (stable-diffusion, HF Diffusers)
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("norm.", "group_norm."),
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("q.", "query."),
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("k.", "key."),
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("v.", "value."),
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("proj_out.", "proj_attn."),
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]
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def reshape_weight_for_sd(w):
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# convert HF linear weights to SD conv2d weights
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return w.reshape(*w.shape, 1, 1)
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def convert_vae_state_dict(vae_state_dict):
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mapping = {k: k for k in vae_state_dict.keys()}
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for k, v in mapping.items():
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for sd_part, hf_part in vae_conversion_map:
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v = v.replace(hf_part, sd_part)
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mapping[k] = v
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for k, v in mapping.items():
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if "attentions" in k:
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for sd_part, hf_part in vae_conversion_map_attn:
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v = v.replace(hf_part, sd_part)
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mapping[k] = v
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new_state_dict = {v: vae_state_dict[k] for k, v in mapping.items()}
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weights_to_convert = ["q", "k", "v", "proj_out"]
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for k, v in new_state_dict.items():
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for weight_name in weights_to_convert:
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if f"mid.attn_1.{weight_name}.weight" in k:
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#print(f"Reshaping {k} for SD format")
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new_state_dict[k] = reshape_weight_for_sd(v)
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return new_state_dict
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# =========================#
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# Text Encoder Conversion #
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# =========================#
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textenc_conversion_lst = [
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# (stable-diffusion, HF Diffusers)
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("resblocks.", "text_model.encoder.layers."),
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("ln_1", "layer_norm1"),
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("ln_2", "layer_norm2"),
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(".c_fc.", ".fc1."),
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(".c_proj.", ".fc2."),
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(".attn", ".self_attn"),
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("ln_final.", "transformer.text_model.final_layer_norm."),
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("token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"),
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("positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"),
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]
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protected = {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
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textenc_pattern = re.compile("|".join(protected.keys()))
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|
||||
# 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
|
||||
|
||||
|
||||
def convert(model_path: str, checkpoint_path: str, half: bool):
|
||||
|
||||
assert model_path is not None, "Must provide a model path!"
|
||||
|
||||
assert checkpoint_path is not None, "Must provide a checkpoint path!"
|
||||
|
||||
unet_path = osp.join(model_path, "unet", "diffusion_pytorch_model.bin")
|
||||
vae_path = osp.join(model_path, "vae", "diffusion_pytorch_model.bin")
|
||||
text_enc_path = osp.join(model_path, "text_encoder", "pytorch_model.bin")
|
||||
|
||||
# Convert the UNet model
|
||||
unet_state_dict = torch.load(unet_path, map_location="cpu")
|
||||
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 = torch.load(vae_path, map_location="cpu")
|
||||
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()}
|
||||
|
||||
# Convert the text encoder model
|
||||
text_enc_dict = torch.load(text_enc_path, map_location="cpu")
|
||||
|
||||
# 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:
|
||||
# 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()}
|
||||
|
||||
# Put together new checkpoint
|
||||
state_dict = {**unet_state_dict, **vae_state_dict, **text_enc_dict}
|
||||
if half:
|
||||
state_dict = {k: v.half() for k, v in state_dict.items()}
|
||||
state_dict = {"state_dict": state_dict}
|
||||
torch.save(state_dict, checkpoint_path)
|
||||
|
||||
|
|
@ -19,10 +19,11 @@
|
|||
|
||||
# Script for converting a HF Diffusers saved pipeline to a Stable Diffusion checkpoint.
|
||||
# *Only* converts the UNet, VAE, and Text Encoder.
|
||||
# Does not convert optimizer state or any other thing.
|
||||
|
||||
# Does not convert optimizer state or any other thing.
|
||||
|
||||
import argparse
|
||||
import os.path as osp
|
||||
import re
|
||||
|
||||
import torch
|
||||
|
||||
|
|
@ -198,7 +199,7 @@ def convert_vae_state_dict(vae_state_dict):
|
|||
for k, v in new_state_dict.items():
|
||||
for weight_name in weights_to_convert:
|
||||
if f"mid.attn_1.{weight_name}.weight" in k:
|
||||
#print(f"Reshaping {k} for SD format")
|
||||
print(f"Reshaping {k} for SD format")
|
||||
new_state_dict[k] = reshape_weight_for_sd(v)
|
||||
return new_state_dict
|
||||
|
||||
|
|
@ -206,21 +207,95 @@ def convert_vae_state_dict(vae_state_dict):
|
|||
# =========================#
|
||||
# Text Encoder Conversion #
|
||||
# =========================#
|
||||
# pretty much a no-op
|
||||
|
||||
|
||||
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
|
||||
|
||||
def convert(model_path: str, checkpoint_path: str, half: bool):
|
||||
|
||||
assert model_path is not None, "Must provide a model path!"
|
||||
|
||||
assert checkpoint_path is not None, "Must provide a checkpoint path!"
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser()
|
||||
|
||||
unet_path = osp.join(model_path, "unet", "diffusion_pytorch_model.bin")
|
||||
vae_path = osp.join(model_path, "vae", "diffusion_pytorch_model.bin")
|
||||
text_enc_path = osp.join(model_path, "text_encoder", "pytorch_model.bin")
|
||||
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("--half", action="store_true", help="Save weights in half precision.")
|
||||
|
||||
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!"
|
||||
|
||||
|
||||
|
||||
unet_path = osp.join(args.model_path, "unet", "diffusion_pytorch_model.bin")
|
||||
vae_path = osp.join(args.model_path, "vae", "diffusion_pytorch_model.bin")
|
||||
text_enc_path = osp.join(args.model_path, "text_encoder", "pytorch_model.bin")
|
||||
|
||||
# Convert the UNet model
|
||||
unet_state_dict = torch.load(unet_path, map_location="cpu")
|
||||
|
|
@ -234,23 +309,22 @@ def convert(model_path: str, checkpoint_path: str, half: bool):
|
|||
|
||||
# Convert the text encoder model
|
||||
text_enc_dict = torch.load(text_enc_path, map_location="cpu")
|
||||
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()}
|
||||
|
||||
# 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:
|
||||
# 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()}
|
||||
|
||||
# Put together new checkpoint
|
||||
state_dict = {**unet_state_dict, **vae_state_dict, **text_enc_dict}
|
||||
if half:
|
||||
if args.half:
|
||||
state_dict = {k: v.half() for k, v in state_dict.items()}
|
||||
state_dict = {"state_dict": state_dict}
|
||||
torch.save(state_dict, checkpoint_path)
|
||||
print(" * Saved converted checkpoint to", checkpoint_path)
|
||||
|
||||
# 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("--half", action="store_true", help="Save weights in half precision.")
|
||||
|
||||
# args = parser.parse_args()
|
||||
# __convert(args)
|
||||
torch.save(state_dict, args.checkpoint_path)
|
||||
|
|
|
|||
Loading…
Reference in New Issue