diff --git a/doc/TRAINING.md b/doc/TRAINING.md
index aece041..89df00b 100644
--- a/doc/TRAINING.md
+++ b/doc/TRAINING.md
@@ -41,7 +41,8 @@ Resuming from a checkpoint, 50 epochs, 6 batch size, 3e-6 learning rate, cosine
     --sample_steps 200 ^
     --lr 3e-6 ^
     --ckpt_every_n_minutes 10 ^
-    --useadam8bit
+    --useadam8bit ^
+    --ed1_mode
 
 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:
 
diff --git a/train.py b/train.py
index 166f254..96c75dc 100644
--- a/train.py
+++ b/train.py
@@ -46,13 +46,12 @@ from accelerate.utils import set_seed
 
 import wandb
 from torch.utils.tensorboard import SummaryWriter
-from tqdm.auto import tqdm
 
 import keyboard
 
 from data.every_dream import EveryDreamBatch
 from utils.huggingface_downloader import try_download_model_from_hf
-from utils.convert_diffusers_to_stable_diffusion import convert as converter
+from utils.convert_diff_to_ckpt import convert as converter
 from utils.gpu import GPU
 
 
@@ -281,7 +280,12 @@ def main(args):
     """
     log_time = setup_local_logger(args)
     args = setup_args(args)
-    
+
+    if args.notebook:
+        from tqdm.notebook import tqdm
+    else:
+        from tqdm.auto import tqdm      
+
     seed = args.seed if args.seed != -1 else random.randint(0, 2**30)
     set_seed(seed)
     gpu = GPU()
@@ -295,7 +299,7 @@ def main(args):
         os.makedirs(log_folder)
 
     @torch.no_grad()
-    def __save_model(save_path, unet, text_encoder, tokenizer, scheduler, vae, save_ckpt_dir):
+    def __save_model(save_path, unet, text_encoder, tokenizer, scheduler, vae, save_ckpt_dir, save_full_precision=False):
         """
         Save the model to disk
         """
@@ -320,9 +324,11 @@ def main(args):
             sd_ckpt_full = os.path.join(save_ckpt_dir, sd_ckpt_path)
         else:
             sd_ckpt_full = os.path.join(os.curdir, sd_ckpt_path)
+        
+        half = not save_full_precision
 
         logging.info(f" * Saving SD model to {sd_ckpt_full}")
-        converter(model_path=save_path, checkpoint_path=sd_ckpt_full, half=True)
+        converter(model_path=save_path, checkpoint_path=sd_ckpt_full, half=half)
         # optimizer_path = os.path.join(save_path, "optimizer.pt")
 
         # if self.save_optimizer_flag:
@@ -585,7 +591,7 @@ def main(args):
             logging.error(f"{Fore.LIGHTRED_EX} CTRL-C received, attempting to save model to {interrupted_checkpoint_path}{Style.RESET_ALL}")
             logging.error(f"{Fore.LIGHTRED_EX} ************************************************************************{Style.RESET_ALL}")
             time.sleep(2) # give opportunity to ctrl-C again to cancel save
-            __save_model(interrupted_checkpoint_path, unet, text_encoder, tokenizer, noise_scheduler, vae, args.save_ckpt_dir)
+            __save_model(interrupted_checkpoint_path, unet, text_encoder, tokenizer, noise_scheduler, vae, args.save_ckpt_dir, args.save_full_precision)
         exit(_SIGTERM_EXIT_CODE)
 
     signal.signal(signal.SIGINT, sigterm_handler)
@@ -781,7 +787,7 @@ def main(args):
                     append_epoch_log(global_step=global_step, epoch_pbar=epoch_pbar, gpu=gpu, log_writer=log_writer, **logs)
                     torch.cuda.empty_cache()
 
-                if keyboard.is_pressed("ctrl+alt+page up") or ((global_step + 1) % args.sample_steps == 0):
+                if (not args.notebook and keyboard.is_pressed("ctrl+alt+page up")) or ((global_step + 1) % args.sample_steps == 0):
                     pipe = __create_inference_pipe(unet=unet, text_encoder=text_encoder, tokenizer=tokenizer, scheduler=sample_scheduler, vae=vae)
                     pipe = pipe.to(device)
 
@@ -803,12 +809,12 @@ def main(args):
                     last_epoch_saved_time = time.time()
                     logging.info(f"Saving model, {args.ckpt_every_n_minutes} mins at step {global_step}")
                     save_path = os.path.join(f"{log_folder}/ckpts/{args.project_name}-ep{epoch:02}-gs{global_step:05}")
-                    __save_model(save_path, unet, text_encoder, tokenizer, noise_scheduler, vae, args.save_ckpt_dir)
+                    __save_model(save_path, unet, text_encoder, tokenizer, noise_scheduler, vae, args.save_ckpt_dir, args.save_full_precision)
 
                 if epoch > 0 and epoch % args.save_every_n_epochs == 0 and step == 1 and epoch < args.max_epochs - 1:
                     logging.info(f" Saving model, {args.save_every_n_epochs} epochs at step {global_step}")
                     save_path = os.path.join(f"{log_folder}/ckpts/{args.project_name}-ep{epoch:02}-gs{global_step:05}")
-                    __save_model(save_path, unet, text_encoder, tokenizer, noise_scheduler, vae, args.save_ckpt_dir)
+                    __save_model(save_path, unet, text_encoder, tokenizer, noise_scheduler, vae, args.save_ckpt_dir, args.save_full_precision)
 
                 del batch
                 global_step += 1
@@ -829,7 +835,7 @@ def main(args):
         # end of training
 
         save_path = os.path.join(f"{log_folder}/ckpts/last-{args.project_name}-ep{epoch:02}-gs{global_step:05}")
-        __save_model(save_path, unet, text_encoder, tokenizer, noise_scheduler, vae, args.save_ckpt_dir)
+        __save_model(save_path, unet, text_encoder, tokenizer, noise_scheduler, vae, args.save_ckpt_dir, args.save_full_precision)
 
         total_elapsed_time = time.time() - training_start_time
         logging.info(f"{Fore.CYAN}Training complete{Style.RESET_ALL}")
@@ -839,7 +845,7 @@ def main(args):
     except Exception as ex:
         logging.error(f"{Fore.LIGHTYELLOW_EX}Something went wrong, attempting to save model{Style.RESET_ALL}")
         save_path = os.path.join(f"{log_folder}/ckpts/errored-{args.project_name}-ep{epoch:02}-gs{global_step:05}")
-        __save_model(save_path, unet, text_encoder, tokenizer, noise_scheduler, vae, args.save_ckpt_dir)
+        __save_model(save_path, unet, text_encoder, tokenizer, noise_scheduler, vae, args.save_ckpt_dir, args.save_full_precision)
         raise ex
 
     logging.info(f"{Fore.LIGHTWHITE_EX} ***************************{Style.RESET_ALL}")
@@ -854,6 +860,12 @@ def update_old_args(t_args):
     if not hasattr(t_args, "shuffle_tags"):
         print(f" Config json is missing 'shuffle_tags'")
         t_args.__dict__["shuffle_tags"] = False
+    if not hasattr(t_args, "save_full_precision"):
+        print(f" Config json is missing 'save_full_precision'")
+        t_args.__dict__["save_full_precision"] = False
+    if not hasattr(t_args, "notebook"):
+        print(f" Config json is missing 'notebook'")
+        t_args.__dict__["notebook"] = False
 
 if __name__ == "__main__":
     supported_resolutions = [256, 384, 448, 512, 576, 640, 704, 768, 832, 896, 960, 1024, 1088, 1152]
@@ -909,6 +921,8 @@ if __name__ == "__main__":
         argparser.add_argument("--useadam8bit", action="store_true", default=False, help="Use AdamW 8-Bit optimizer, recommended!")
         argparser.add_argument("--wandb", action="store_true", default=False, help="enable wandb logging instead of tensorboard, requires env var WANDB_API_KEY")
         argparser.add_argument("--write_schedule", action="store_true", default=False, help="write schedule of images and their batches to file (def: False)")
+        argparser.add_argument("--save_full_precision", action="store_true", default=False, help="save ckpts at full FP32")
+        argparser.add_argument("--notebook", action="store_true", default=False, help="disable keypresses and uses tqdm.notebook for jupyter notebook (def: False)")
 
         args = argparser.parse_args()
 
diff --git a/utils/convert_diff_to_ckpt.py b/utils/convert_diff_to_ckpt.py
new file mode 100644
index 0000000..5df3d3f
--- /dev/null
+++ b/utils/convert_diff_to_ckpt.py
@@ -0,0 +1,321 @@
+# from https://github.com/huggingface/diffusers/blob/main/scripts/convert_diffusers_to_original_stable_diffusion.py
+# modified to be callable
+
+# coding=utf-8
+# Copyright 2022 The HuggingFace Inc. team.
+# Modifications for EveryDream Copyright 2022 Victor C Hall
+#
+# 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.
+
+# 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.
+
+import os.path as osp
+import re
+
+import torch
+
+
+# =================#
+# UNet Conversion #
+# =================#
+
+unet_conversion_map = [
+    # (stable-diffusion, HF Diffusers)
+    ("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"),
+    ("input_blocks.0.0.weight", "conv_in.weight"),
+    ("input_blocks.0.0.bias", "conv_in.bias"),
+    ("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 = [
+    # (stable-diffusion, HF Diffusers)
+    ("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"),
+]
+
+unet_conversion_map_layer = []
+# 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
+        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))
+
+    for j in range(3):
+        # loop over resnets/attentions for upblocks
+        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))
+
+    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)}.0.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 2}."
+        unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))
+
+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.{2*j}."
+    unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
+
+
+def convert_unet_state_dict(unet_state_dict):
+    # 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
+    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
+    new_state_dict = {v: unet_state_dict[k] for k, v in mapping.items()}
+    return new_state_dict
+
+
+# ================#
+# VAE Conversion #
+# ================#
+
+vae_conversion_map = [
+    # (stable-diffusion, HF Diffusers)
+    ("nin_shortcut", "conv_shortcut"),
+    ("norm_out", "conv_norm_out"),
+    ("mid.attn_1.", "mid_block.attentions.0."),
+]
+
+for i in range(4):
+    # down_blocks have two resnets
+    for j in range(2):
+        hf_down_prefix = f"encoder.down_blocks.{i}.resnets.{j}."
+        sd_down_prefix = f"encoder.down.{i}.block.{j}."
+        vae_conversion_map.append((sd_down_prefix, hf_down_prefix))
+
+    if i < 3:
+        hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0."
+        sd_downsample_prefix = f"down.{i}.downsample."
+        vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))
+
+        hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
+        sd_upsample_prefix = f"up.{3-i}.upsample."
+        vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))
+
+    # up_blocks have three resnets
+    # also, up blocks in hf are numbered in reverse from sd
+    for j in range(3):
+        hf_up_prefix = f"decoder.up_blocks.{i}.resnets.{j}."
+        sd_up_prefix = f"decoder.up.{3-i}.block.{j}."
+        vae_conversion_map.append((sd_up_prefix, hf_up_prefix))
+
+# this part accounts for mid blocks in both the encoder and the decoder
+for i in range(2):
+    hf_mid_res_prefix = f"mid_block.resnets.{i}."
+    sd_mid_res_prefix = f"mid.block_{i+1}."
+    vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))
+
+
+vae_conversion_map_attn = [
+    # (stable-diffusion, HF Diffusers)
+    ("norm.", "group_norm."),
+    ("q.", "query."),
+    ("k.", "key."),
+    ("v.", "value."),
+    ("proj_out.", "proj_attn."),
+]
+
+
+def reshape_weight_for_sd(w):
+    # convert HF linear weights to SD conv2d weights
+    return w.reshape(*w.shape, 1, 1)
+
+
+def convert_vae_state_dict(vae_state_dict):
+    mapping = {k: k for k in vae_state_dict.keys()}
+    for k, v in mapping.items():
+        for sd_part, hf_part in vae_conversion_map:
+            v = v.replace(hf_part, sd_part)
+        mapping[k] = v
+    for k, v in mapping.items():
+        if "attentions" in k:
+            for sd_part, hf_part in vae_conversion_map_attn:
+                v = v.replace(hf_part, sd_part)
+            mapping[k] = v
+    new_state_dict = {v: vae_state_dict[k] for k, v in mapping.items()}
+    weights_to_convert = ["q", "k", "v", "proj_out"]
+    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")
+                new_state_dict[k] = reshape_weight_for_sd(v)
+    return new_state_dict
+
+
+# =========================#
+# 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):
+    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)
+												
\ No newline at end of file
diff --git a/utils/convert_diffusers_to_stable_diffusion.py b/utils/convert_diffusers_to_stable_diffusion.py
index 98af90d..2ebcf82 100644
--- a/utils/convert_diffusers_to_stable_diffusion.py
+++ b/utils/convert_diffusers_to_stable_diffusion.py
@@ -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)
\ No newline at end of file
+    torch.save(state_dict, args.checkpoint_path)