unpushed changes

.gitignore

@@ -9,7 +9,5 @@ __pycache__
 xander*.sh
 .huggingface
 tests/
-trainer/
-train.py
 debug/*
 !debug/*.py

README.md

@@ -30,6 +30,7 @@ Algo:
 - the random initialization of the token embeddings has a relatively large impact on the final outcome, there are prob ways to reduce
 this random variance, eg CLIP_similarity pretraining.
 - Improve the img captioning by swapping BLIP for cogVLM: https://github.com/THUDM/CogVLM
+- it looks the like gpu-utilization is only like 65-70% during training: whats the bottleneck? Can we speed this up?
 
 Bugfixing:
 see msgs at: https://discord.com/channels/573691888050241543/1184175211998883950/1217550596878373037
@@ -50,6 +51,7 @@ Bigger improvements:
 
 Tuning Experiments once code is fully ready:
 
+- test if VAE weight_type actually matters for training
 - try-out conditioning noise injection during training to increase robustness
 - re-test / tweak the adaptive learning rates instead of hard-pivot (also test Prodigy vs Adam)
 - right now it looks like the diffusion model gets partially "destroyed" in the beginning of training (outputs from steps 100-200 look terrible), 

io_utils.py

@@ -13,13 +13,36 @@ SDXL_MODEL_CACHE = "./models/juggernaut_v6.safetensors"
 SDXL_URL         = "https://edenartlab-lfs.s3.amazonaws.com/models/checkpoints/juggernautXL_v6.safetensors"
 
 SD15_MODEL_CACHE = "./models/juggernaut_reborn.safetensors"
-# TODO point this url to the correct full folder structure containing the CLIP text-encoder (this wont actually work rn)
 SD15_URL         = "https://edenartlab-lfs.s3.amazonaws.com/models/checkpoints/juggernaut_reborn.safetensors"
 
+SDXL_TURBO_MODEL_CACHE = "./models/SDXL_turbo.safetensors"
+SDXL_TURBO_URL         = "https://huggingface.co/stabilityai/sdxl-turbo/resolve/main/sd_xl_turbo_1.0_fp16.safetensors?download=true"
+
+SDXL_LIGHTNING_MODEL_CACHE = "./models/SDXL_lightning.safetensors"
+SDXL_LIGHTNING_URL         = "https://huggingface.co/ByteDance/SDXL-Lightning/resolve/main/sdxl_lightning_8step.safetensors?download=true"
+
 # Define model paths and URLs in a dictionary
-MODEL_INFO = {
-    "sdxl": {"path": SDXL_MODEL_CACHE, "url": SDXL_URL},
-    "sd15": {"path": SD15_MODEL_CACHE, "url": SD15_URL}
+MODEL_DICT = {
+    "sdxl": {
+        "path": SDXL_MODEL_CACHE, 
+        "url": SDXL_URL, 
+        "version": "sdxl"
+        },
+    "sd15": {
+        "path": SD15_MODEL_CACHE, 
+        "url": SD15_URL, 
+        "version": "sd15"
+        },
+    "sdxl_turbo": {
+        "path": SDXL_TURBO_MODEL_CACHE, 
+        "url": SDXL_TURBO_URL, 
+        "version": "sdxl_turbo"
+        },
+    "sdxl_lightning": {
+        "path": SDXL_LIGHTNING_MODEL_CACHE, 
+        "url": SDXL_LIGHTNING_URL, 
+        "version": "sdxl_lightning"
+        }
 }
 
 def download_weights(url, dest):

lora_utils.py

@@ -1,89 +0,0 @@
-import os, json
-import torch
-from safetensors.torch import load_file
-from typing import Dict
-from peft import PeftModel
-from dataset_and_utils import TokenEmbeddingsHandler
-from safetensors.torch import save_file
-
-'''
-from diffusers.utils import (
-    convert_all_state_dict_to_peft,
-    convert_state_dict_to_diffusers,
-    convert_unet_state_dict_to_peft
-)
-'''
-
-def patch_pipe_with_lora(pipe, lora_path):
-    """
-    update the pipe with the lora model and the token embeddings
-    """
-
-    pipe.unet = PeftModel.from_pretrained(pipe.unet, lora_path)
-    pipe.unet.merge_adapter()
-    
-    # Load the textual_inversion token embeddings into the pipeline:
-    try: #SDXL
-        handler = TokenEmbeddingsHandler([pipe.text_encoder, pipe.text_encoder_2], [pipe.tokenizer, pipe.tokenizer_2])
-    except: #SD15
-        handler = TokenEmbeddingsHandler([pipe.text_encoder, None], [pipe.tokenizer, None])
-
-    embeddings_path = [f for f in os.listdir(lora_path) if f.endswith("embeddings.safetensors")][0]
-    handler.load_embeddings(os.path.join(lora_path, embeddings_path))
-
-    return pipe
-
-
-def unet_attn_processors_state_dict(unet) -> Dict[str, torch.tensor]:
-    """
-    Returns:
-        a state dict containing just the attention processor parameters.
-    """
-    attn_processors = unet.attn_processors
-
-    attn_processors_state_dict = {}
-
-    for attn_processor_key, attn_processor in attn_processors.items():
-        for parameter_key, parameter in attn_processor.state_dict().items():
-            attn_processors_state_dict[
-                f"{attn_processor_key}.{parameter_key}"
-            ] = parameter
-
-    return attn_processors_state_dict
-
-
-def save_lora(output_dir, global_step, unet, embedding_handler, token_dict, args_dict, seed, is_lora, unet_lora_parameters, unet_param_to_optimize_names):
-    """
-    Save the LORA model to output_dir, optionally with some example images
-
-    """
-    print(f"Saving checkpoint at step.. {global_step}")
-    os.makedirs(output_dir, exist_ok=True)
-
-    args_dict["n_training_steps"] = global_step
-    args_dict["total_n_imgs_seen"] = global_step * args_dict["train_batch_size"]
-
-    if not is_lora:
-        lora_tensors = {
-            name: param
-            for name, param in unet.named_parameters()
-            if name in unet_param_to_optimize_names
-        }
-        save_file(lora_tensors, f"{output_dir}/unet.safetensors",)
-    elif len(unet_lora_parameters) > 0:
-        unet.save_pretrained(save_directory = output_dir)
-
-    try:
-        concept_name = args_dict["name"].lower()
-    except:
-        concept_name = "eden_concept_lora"
-
-    # Make sure all weird delimiter characters are removed from concept_name before using it as a filepath:
-    concept_name = concept_name.replace(" ", "_").replace("/", "_").replace("\\", "_").replace(":", "_").replace("*", "_").replace("?", "_").replace("\"", "_").replace("<", "_").replace(">", "_").replace("|", "_")
-
-    embedding_handler.save_embeddings(f"{output_dir}/{concept_name}_embeddings.safetensors",)
-
-    with open(f"{output_dir}/special_params.json", "w") as f:
-        json.dump(token_dict, f)
-    with open(f"{output_dir}/training_args.json", "w") as f:
-        json.dump(args_dict, f, indent=4)

trainer/__init__.py

@@ -0,0 +1,2 @@
+from .trainer import Trainer
+from .config import TrainerConfig

trainer/config.py

@@ -0,0 +1,61 @@
+from typing import Optional, List, Dict, Any
+from pydantic import BaseModel, Field
+import random
+import json
+from typing import Literal
+import torch
+
+precision_map = {
+    "fp16": torch.float16,
+    "bf16": torch.bfloat16,
+    "fp32": torch.float32
+}
+
+class TrainerConfig(BaseModel, extra = "forbid"):
+    pretrained_model: Dict[str, str] # should be a dict with keys "path" and "version"
+    name: str='unnamed',
+    trigger_text: str='a photo of TOK, ',
+    instance_data_dir: str = "./dataset/zeke/captions.csv"
+    concept_mode: Literal["face", "concept", "object", "style"]
+    output_dir: str = "lora_output"
+    seed: Optional[int] = Field(default_factory=lambda: random.randint(0, 2**32 - 1))
+    resolution: int = 960
+    crops_coords_top_left_h: int = 0
+    crops_coords_top_left_w: int = 0
+    train_batch_size: int = 1
+    train_dataset_cache: bool = True
+    num_train_epochs: int = 10000
+    max_train_steps: Optional[int] = None
+    checkpointing_steps: int = 500000
+    gradient_accumulation_steps: int = 1
+    unet_learning_rate: float = 1.0
+    textual_inversion_lr: float = 1e-3
+    textual_inversion_weight_decay: float = 3e-4
+    prodigy_d_coef: float = 0.5,
+    l1_penalty: float = 0.0
+    lora_weight_decay: float = 0.005
+    scale_lr_based_on_grad_acc: bool = False
+    lr_scheduler_name: str = "constant"
+    lr_warmup_steps: int = 50
+    lr_num_cycles: int = 1
+    lr_power: float = 1.0
+    snr_gamma: float = 5.0
+    dataloader_num_workers: int = 0
+    allow_tf32: bool = True
+    precision: Literal["bf16", "fp16", "fp32"] = "bf16"
+    optimizer_name: Literal["prodigy", "adamw"] = "prodigy"
+    device: str = "cuda"
+    token_dict: Dict[str, str] = {"TOK": "<s0><s1>"}
+    inserting_list_tokens: List[str] = ["<s0><s1>"]
+    verbose: bool = True
+    is_lora: bool = True
+    lora_rank: int = 12
+    lora_alpha: int = 12
+    args_dict: Dict[str, Any] = {}
+    debug: bool = False
+    hard_pivot: bool = True
+    off_ratio_power: float = 0.1
+
+    def save_as_json(self, file_path: str) -> None:
+        with open(file_path, 'w') as f:
+            json.dump(self.dict(), f, indent=4)

trainer/dataset_and_utils.py

@@ -35,7 +35,7 @@ def plot_torch_hist(parameters, epoch, checkpoint_dir, name, bins=100, min_val=-
     plt.xlim(min_val, max_val)
     plt.xlabel('Weight Value')
     plt.ylabel('Count')
-    plt.title(f'Epoch {epoch} {name} Histogram (std = {np.std(all_params_cpu):.4f})')
+    plt.title(f'Epoch {epoch} {name} Histogram (std = {np.std(all_params_cpu):.4f}, min = {np.min(all_params_cpu):.2f}, max = {np.max(all_params_cpu):.2f})')
     plt.savefig(f"{checkpoint_dir}/{name}_histogram_{epoch:04d}.png")
     plt.close()
 
@@ -344,7 +344,7 @@ class TokenEmbeddingsHandler:
             if text_encoder is None:
                 continue
             trainable_embeddings.append(text_encoder.text_model.embeddings.token_embedding.weight.data[self.train_ids])
-
+        
         return trainable_embeddings
 
     def find_nearest_tokens(self, query_embedding, tokenizer, text_encoder, idx, distance_metric, top_k = 5):
@@ -486,7 +486,6 @@ class TokenEmbeddingsHandler:
 
         print("Initializing new tokens...")
         print(inserting_toks)
-        torch.manual_seed(seed)
 
         idx = 0
         for tokenizer, text_encoder in zip(self.tokenizers, self.text_encoders):
@@ -541,6 +540,7 @@ class TokenEmbeddingsHandler:
             else:
 
                 if 1: # random initialization:
+                    torch.manual_seed(seed)
                     init_embeddings = (torch.randn(len(self.train_ids), text_encoder.text_model.config.hidden_size).to(device=self.device).to(dtype=self.dtype) * std_token_embedding * 1.0)
                 else: 
                     # Test code to initialize the new tokens with some specific tokens

trainer/trainer.py

@@ -0,0 +1,590 @@
+import os
+import math
+import random
+import numpy as np
+import torch
+import fnmatch
+from peft import LoraConfig, get_peft_model
+from diffusers.optimization import get_scheduler
+from tqdm import tqdm
+import shutil
+import time
+import gc
+import prodigyopt
+
+from .config import (
+    TrainerConfig, 
+    precision_map
+)
+from .dataset_and_utils import (
+    load_models, 
+    TokenEmbeddingsHandler,
+    PreprocessedDataset,
+    plot_torch_hist,
+    plot_loss,
+    plot_lrs
+)
+from .utils.model_info import print_trainable_parameters
+from .utils.snr import compute_snr
+from .utils.learning_rate import get_avg_lr
+from .utils.lora import save_lora
+from .utils.rendering import render_images
+from io_utils import download_weights
+
+from preprocess import preprocess
+
+class Trainer:
+    def __init__(self, args):
+        self.args = args
+
+        random.seed(args.seed)
+        torch.manual_seed(args.seed)
+        np.random.seed(args.seed)
+        torch.cuda.manual_seed(args.seed)
+        torch.cuda.manual_seed_all(args.seed)
+
+        #torch.backends.cudnn.deterministic = True
+
+        print("Trainer initialized!")
+
+    def train(self):
+
+        if self.args.concept_mode == "style": # for styles you usually want the LoRA matrices to absorb a lot (instead of just the token embedding)
+            self.args.l1_penalty = 0.05
+
+        args = self.args
+
+        if args.allow_tf32:
+            torch.backends.cuda.matmul.allow_tf32 = True
+
+        weight_dtype = precision_map[args.precision]
+
+        print(f"Loading models with weight_dtype: {weight_dtype}")
+        if args.scale_lr_based_on_grad_acc:
+
+            unet_learning_rate = (
+                args.unet_learning_rate * args.gradient_accumulation_steps * args.train_batch_size
+            )
+
+        # Download the weights if they don't exist locally
+        if not os.path.exists(args.pretrained_model['path']):
+            download_weights(args.pretrained_model['url'], args.pretrained_model['path'])
+
+        (   
+            pipe,
+            tokenizer_one,
+            tokenizer_two,
+            noise_scheduler,
+            text_encoder_one,
+            text_encoder_two,
+            vae,
+            unet,
+        ) = load_models(
+            pretrained_model = args.pretrained_model, 
+            device=args.device, 
+            weight_dtype=weight_dtype
+        )
+
+        # Initialize new tokens for training.
+        embedding_handler = TokenEmbeddingsHandler(
+            [text_encoder_one, text_encoder_two], [tokenizer_one, tokenizer_two]
+        )
+    
+        starting_toks = None
+        embedding_handler.initialize_new_tokens(
+            inserting_toks=args.inserting_list_tokens, 
+            starting_toks=starting_toks,
+            seed=args.seed
+        )
+        text_encoders = [text_encoder_one, text_encoder_two]
+
+        unet_param_to_optimize = []
+        text_encoder_parameters = []
+        for text_encoder in text_encoders:
+            if text_encoder is not  None:
+                for name, param in text_encoder.named_parameters():
+                    if "token_embedding" in name:
+                        param.requires_grad = True
+                        text_encoder_parameters.append(param)
+                    else:
+                        param.requires_grad = False
+
+        unet_param_to_optimize_names = []
+        unet_lora_parameters = []
+
+        if not args.is_lora:
+            WHITELIST_PATTERNS = [
+                # "*.attn*.weight",
+                # "*ff*.weight",
+                "*"
+            ]
+            BLACKLIST_PATTERNS = ["*.norm*.weight", "*time*"]
+            for name, param in unet.named_parameters():
+                if any(
+                    fnmatch.fnmatch(name, pattern) for pattern in WHITELIST_PATTERNS
+                ) and not any(
+                    fnmatch.fnmatch(name, pattern) for pattern in BLACKLIST_PATTERNS
+                ):
+                    param.requires_grad_(True)
+                    unet_param_to_optimize_names.append(name)
+                    print(f"Training: {name}")
+                else:
+                    param.requires_grad_(False)
+
+            # Optimizer creation
+            params_to_optimize = [
+                {
+                    "params": text_encoder_parameters,
+                    "lr": args.textual_inversion_lr,
+                    "weight_decay": args.textual_inversion_weight_decay,
+                },
+            ]
+
+            params_to_optimize_prodigy = [
+                {
+                    "params": unet_param_to_optimize,
+                    "lr": unet_learning_rate,
+                    "weight_decay": args.lora_weight_decay,
+                },
+            ]
+
+        else:
+            
+            # Do lora-training instead.
+            unet.requires_grad_(False)
+            # https://huggingface.co/docs/peft/main/en/developer_guides/lora#rank-stabilized-lora
+
+            use_dora = True
+             
+            unet_lora_config = LoraConfig(
+                r=args.lora_rank,
+                lora_alpha=args.lora_alpha,
+                init_lora_weights="gaussian",
+                target_modules=["to_k", "to_q", "to_v", "to_out.0"],
+                use_dora=use_dora,
+            )
+
+            if use_dora:
+                print(f"Disabling L1 penalty for DORA training")
+                args.l1_penalty = 0.0
+            
+            unet = get_peft_model(unet, unet_lora_config)
+            print_trainable_parameters(unet, name = 'unet')
+
+            unet_lora_parameters = list(filter(lambda p: p.requires_grad, unet.parameters()))
+
+            # Loop over the unet_lora_parameters and print their names and shapes:
+            for name, param in unet.named_parameters():
+                if param.requires_grad:
+                    print(name, param.shape)
+
+            params_to_optimize = [{
+                    "params": text_encoder_parameters,
+                    "lr": args.textual_inversion_lr,
+                    "weight_decay": args.textual_inversion_weight_decay,
+                }]
+
+            params_to_optimize_prodigy = [{
+                    "params": unet_lora_parameters,
+                    "lr": 1.0,
+                    "weight_decay": args.lora_weight_decay,
+                }]
+
+        if args.optimizer_name == "adamw":
+            optimizer = torch.optim.AdamW(
+                params_to_optimize,
+                weight_decay=0.0, # this wd doesn't matter, I think
+            )
+            optimizer_prod = None
+        elif args.optimizer_name == "prodigy":        
+
+            # Note: the specific settings of Prodigy seem to matter A LOT
+            optimizer_prod = prodigyopt.Prodigy(
+                            params_to_optimize_prodigy,
+                            d_coef = args.prodigy_d_coef,
+                            lr=1.0,
+                            decouple=True,
+                            use_bias_correction=True,
+                            safeguard_warmup=True,
+                            weight_decay=args.lora_weight_decay,
+                            betas=(0.9, 0.99),
+                            growth_rate=1.025,  # this slows down the lr_rampup
+                            #growth_rate=1.05,  # this slows down the lr_rampup
+                        )
+            
+            optimizer = torch.optim.AdamW(
+                params_to_optimize,
+                weight_decay=args.textual_inversion_weight_decay,
+            )
+            
+        train_dataset = PreprocessedDataset(
+            args.instance_data_dir,
+            tokenizer_one,
+            tokenizer_two,
+            vae,
+            do_cache=args.train_dataset_cache,
+            substitute_caption_map=args.token_dict,
+        )
+
+        print(f"# PTI : Loaded dataset, do_cache: {args.train_dataset_cache}")
+        train_dataloader = torch.utils.data.DataLoader(
+            train_dataset,
+            batch_size=args.train_batch_size,
+            shuffle=True,
+            num_workers=args.dataloader_num_workers,
+        )
+
+        num_update_steps_per_epoch = math.ceil(
+            len(train_dataloader) / args.gradient_accumulation_steps
+        )
+        if args.max_train_steps is None:
+            max_train_steps = num_train_epochs * num_update_steps_per_epoch
+        else:
+            max_train_steps = args.max_train_steps
+
+        lr_scheduler = get_scheduler(
+            args.lr_scheduler_name,
+            optimizer=optimizer,
+            num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,
+            num_training_steps=max_train_steps * args.gradient_accumulation_steps,
+            num_cycles=args.lr_num_cycles,
+            power=args.lr_power,
+        )
+
+        num_update_steps_per_epoch = math.ceil(
+            len(train_dataloader) / args.gradient_accumulation_steps
+        )
+        num_train_epochs = math.ceil(max_train_steps / num_update_steps_per_epoch)
+
+        total_batch_size = args.train_batch_size * args.gradient_accumulation_steps
+
+        if args.verbose:
+            print(f"# PTI :  Running training ")
+            print(f"# PTI :  Num examples = {len(train_dataset)}")
+            print(f"# PTI :  Num batches each epoch = {len(train_dataloader)}")
+            print(f"# PTI :  Num Epochs = {num_train_epochs}")
+            print(f"# PTI :  Instantaneous batch size per device = {args.train_batch_size}")
+            print(f"# PTI :  Total train batch size (distributed & accumulation) = {total_batch_size}")
+            print(f"# PTI :  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
+            print(f"# PTI :  Total optimization steps = {max_train_steps}")
+
+        global_step = 0
+        first_epoch = 0
+        last_save_step = 0
+
+        progress_bar = tqdm(range(global_step, max_train_steps), position=0, leave=True)
+        checkpoint_dir = os.path.join(args.output_dir, "checkpoints")
+        if os.path.exists(checkpoint_dir):
+            shutil.rmtree(checkpoint_dir)
+        os.makedirs(f"{checkpoint_dir}")
+
+        # Experimental TODO: warmup the token embeddings using CLIP-similarity optimization
+        #embedding_handler.pre_optimize_token_embeddings(train_dataset)
+    
+        ti_lrs, lora_lrs = [], []
+        losses = []
+        start_time, images_done = time.time(), 0
+
+        for epoch in range(first_epoch, num_train_epochs):
+            unet.train()
+            progress_bar.set_description(f"# PTI :step: {global_step}, epoch: {epoch}")
+
+            for step, batch in enumerate(train_dataloader):
+                progress_bar.update(1)
+
+                if args.hard_pivot:
+                    if epoch >= num_train_epochs // 2:
+                        if optimizer is not None:
+                            print("----------------------")
+                            print("# PTI :  Pivot halfway")
+                            print("----------------------")
+                            # remove text encoder parameters from the optimizer
+                            optimizer.param_groups = None
+                            # remove the optimizer state corresponding to text_encoder_parameters
+                            for param in text_encoder_parameters:
+                                if param in optimizer.state:
+                                    del optimizer.state[param]
+                            optimizer = None
+
+                else: # Update learning rates gradually:
+                    finegrained_epoch = epoch + step / len(train_dataloader)
+                    completion_f = finegrained_epoch / num_train_epochs
+                    # param_groups[1] goes from ti_lr to 0.0 over the course of training
+                    optimizer.param_groups[0]['lr'] = args.textual_inversion_lr * (1 - completion_f) ** 2.0
+
+            
+                try: #sdxl
+                    (tok1, tok2), vae_latent, mask = batch
+                except: #sd15
+                    tok1, vae_latent, mask = batch
+                    tok2 = None
+
+                vae_latent = vae_latent.to(weight_dtype)
+
+                # tokens to text embeds
+                prompt_embeds_list = []
+                for tok, text_encoder in zip((tok1, tok2), text_encoders):
+                    if tok is None:
+                        continue
+
+                    prompt_embeds_out = text_encoder(
+                        tok.to(text_encoder.device),
+                        output_hidden_states=True,
+                    )
+
+                    pooled_prompt_embeds = prompt_embeds_out[0]
+                    prompt_embeds = prompt_embeds_out.hidden_states[-2]
+                    bs_embed, seq_len, _ = prompt_embeds.shape
+                    prompt_embeds = prompt_embeds.view(bs_embed, seq_len, -1)
+                    prompt_embeds_list.append(prompt_embeds)
+
+                prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)
+                pooled_prompt_embeds = pooled_prompt_embeds.view(bs_embed, -1)
+
+                # Create Spatial-dimensional conditions.
+                original_size = (args.resolution, args.resolution)
+                target_size   = (args.resolution, args.resolution)
+                crops_coords_top_left = (
+                    args.crops_coords_top_left_h, 
+                    args.crops_coords_top_left_w
+                )
+                add_time_ids = list(original_size + crops_coords_top_left + target_size)
+                add_time_ids = torch.tensor([add_time_ids])
+                add_time_ids = add_time_ids.to(
+                    args.device, 
+                    dtype=prompt_embeds.dtype
+                ).repeat(
+                    bs_embed, 1
+                )
+
+                # Sample noise that we'll add to the latents:
+                noise = torch.randn_like(vae_latent)
+
+                noise_offset = 0.05 # TODO, turn this into an input arg and do a grid search
+                if noise_offset > 0.0:
+                    # https://www.crosslabs.org//blog/diffusion-with-offset-noise
+                    noise += noise_offset * torch.randn(
+                        (noise.shape[0], noise.shape[1], 1, 1), device=noise.device)
+
+                bsz = vae_latent.shape[0]
+
+                timesteps = torch.randint(
+                    0,
+                    noise_scheduler.config.num_train_timesteps,
+                    (bsz,),
+                    device=vae_latent.device,
+                ).long()
+
+                noisy_model_input = noise_scheduler.add_noise(vae_latent, noise, timesteps)
+
+                noise_sigma = 0.0
+                if noise_sigma > 0.0: # experimental: apply random noise to the conditioning vectors as a form of regularization
+                    prompt_embeds[0,1:-2,:] += torch.randn_like(prompt_embeds[0,1:-2,:]) * noise_sigma
+
+                # Predict the noise residual
+                model_pred = unet(
+                    noisy_model_input,
+                    timesteps,
+                    prompt_embeds,
+                    added_cond_kwargs={"text_embeds": pooled_prompt_embeds, "time_ids": add_time_ids},
+                ).sample
+
+                # Get the unet prediction target depending on the prediction type:
+                if noise_scheduler.config.prediction_type == "epsilon":
+                    target = noise
+                else:
+                    raise NotImplementedError(f"Not implemented for noise_scheduler.config.prediction_type: {noise_scheduler.config.prediction_type}")
+
+                # Compute the loss:
+                if args.snr_gamma is None:
+                    loss = (model_pred - target).pow(2) * mask
+
+                    # modulate loss by the inverse of the mask's mean value
+                    mean_mask_values = mask.mean(dim=list(range(1, len(loss.shape))))
+                    mean_mask_values = mean_mask_values / mean_mask_values.mean()
+                    loss = loss.mean(dim=list(range(1, len(loss.shape)))) / mean_mask_values
+
+                    # Average the normalized errors across the batch
+                    loss = loss.mean()
+
+                else:
+                    # Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556.
+                    # Since we predict the noise instead of x_0, the original formulation is slightly changed.
+                    # This is discussed in Section 4.2 of the same paper.
+                    snr = compute_snr(noise_scheduler, timesteps)
+                    base_weight = (
+                        torch.stack([snr, args.snr_gamma * torch.ones_like(timesteps)], dim=1).min(dim=1)[0] / snr
+                    )
+                    if noise_scheduler.config.prediction_type == "v_prediction":
+                        # Velocity objective needs to be floored to an SNR weight of one.
+                        mse_loss_weights = base_weight + 1
+                    else:
+                        # Epsilon and sample both use the same loss weights.
+                        mse_loss_weights = base_weight
+
+                    mse_loss_weights = mse_loss_weights / mse_loss_weights.mean()
+                    loss = (model_pred - target).pow(2) * mask
+                    loss = loss.mean(dim=list(range(1, len(loss.shape)))) * mse_loss_weights
+
+                    if 1: # modulate loss by the inverse of the mask's mean value
+                        mean_mask_values = mask.mean(dim=list(range(1, len(loss.shape))))
+                        mean_mask_values = mean_mask_values / mean_mask_values.mean()
+                        loss = loss.mean(dim=list(range(1, len(loss.shape)))) / mean_mask_values
+
+                    loss = loss.mean()
+
+                if args.l1_penalty > 0.0:
+                    # Compute normalized L1 norm (mean of abs sum) of all lora parameters:
+                    l1_norm = sum(p.abs().sum() for p in unet_lora_parameters) / sum(p.numel() for p in unet_lora_parameters)
+                    loss += args.l1_penalty * l1_norm
+
+                    # Print the relative L1 norm:
+                    if global_step % 50 == 0:
+                        print(f" ---- L1 norm: {l1_norm.item():.4f}")
+                        print(f" ---- L1 loss: {args.l1_penalty * l1_norm.item():.4f}")
+                        print(f" ---- Total loss: {loss.item():.4f}")
+
+                losses.append(loss.item())
+
+                loss = loss / args.gradient_accumulation_steps
+                loss.backward()
+
+                '''
+                apart from the usual gradient accumulation steps,
+                we also do a backward pass after computing the last forward pass in the epoch (last_batch == True)
+                this is to make sure that we're not missing out on any data 
+                '''
+                last_batch = (step + 1 == len(train_dataloader))
+                if (step + 1) % args.gradient_accumulation_steps == 0 or last_batch:
+                    if optimizer is not None:
+                        optimizer.step()
+                        optimizer.zero_grad()
+                    
+                    if optimizer_prod is not None:
+                        optimizer_prod.step()
+                        optimizer_prod.zero_grad()
+
+                    # after every optimizer step, we reset the non-trainable embeddings to the original embeddings
+                    embedding_handler.retract_embeddings(print_stds = (global_step % 50 == 0))
+                    embedding_handler.fix_embedding_std(args.off_ratio_power)
+            
+                # Track the learning rates for final plotting:
+                lora_lrs.append(get_avg_lr(optimizer_prod))
+                try:
+                    ti_lrs.append(optimizer.param_groups[0]['lr'])
+                except:
+                    ti_lrs.append(0.0)
+
+                # Print some statistics:
+                if (global_step % args.checkpointing_steps == 0): # and (global_step > 0):
+                    output_save_dir = f"{checkpoint_dir}/checkpoint-{global_step}"
+                    save_lora(
+                        output_dir=output_save_dir, 
+                        global_step=global_step, 
+                        unet=unet, 
+                        embedding_handler=embedding_handler,
+                        token_dict=args.token_dict,
+                        args_dict=args.args_dict,
+                        is_lora= args.is_lora, 
+                        unet_lora_parameters=unet_lora_parameters, 
+                        unet_param_to_optimize_names=unet_param_to_optimize_names
+                    )
+
+                    args.save_as_json(os.path.join(output_save_dir,"training_args.json"))
+                    last_save_step = global_step
+
+                    validation_prompts = render_images(
+                        pipe, target_size, 
+                        output_save_dir, 
+                        global_step, 
+                        args.seed, 
+                        args.is_lora, 
+                        args.pretrained_model, 
+                        n_imgs = 4
+                    )
+
+                    if args.debug:
+                        token_embeddings = embedding_handler.get_trainable_embeddings()
+                        for i, token_embeddings_i in enumerate(token_embeddings):
+                            plot_torch_hist(
+                                token_embeddings_i[0], 
+                                global_step, 
+                                args.output_dir, 
+                                f"embeddings_weights_token_0_{i}", 
+                                min_val=-0.05, 
+                                max_val=0.05, 
+                                ymax_f = 0.05
+                            )
+                            plot_torch_hist(
+                                token_embeddings_i[1], 
+                                global_step, 
+                                args.output_dir, 
+                                f"embeddings_weights_token_1_{i}", 
+                                min_val=-0.05, 
+                                max_val=0.05, 
+                                ymax_f = 0.05
+                            )
+                        
+                        embedding_handler.print_token_info()
+                        plot_torch_hist(
+                            unet_lora_parameters, 
+                            global_step, 
+                            args.output_dir, 
+                            "lora_weights", 
+                            min_val=-0.3, 
+                            max_val=0.3, 
+                            ymax_f = 0.05
+                        )
+                        plot_loss(losses, save_path=f'{args.output_dir}/losses.png')
+                        plot_lrs(lora_lrs, ti_lrs, save_path=f'{args.output_dir}/learning_rates.png')
+                        gc.collect()
+                        torch.cuda.empty_cache()
+                
+                images_done += args.train_batch_size
+                global_step += 1
+
+                if global_step % 100 == 0:
+                    print(f" ---- avg training fps: {images_done / (time.time() - start_time):.2f}", end="\r")
+
+        if args.debug:
+            plot_loss(losses, save_path=f'{args.output_dir}/losses.png')
+            plot_lrs(lora_lrs, ti_lrs, save_path=f'{args.output_dir}/learning_rates.png')
+            plot_torch_hist(unet_lora_parameters, global_step, args.output_dir, "lora_weights", min_val=-0.3, max_val=0.3, ymax_f = 0.05)
+            plot_torch_hist(embedding_handler.get_trainable_embeddings(), global_step, args.output_dir, "embeddings_weights", min_val=-0.05, max_val=0.05, ymax_f = 0.05)      
+
+        # final_save
+        if (global_step - last_save_step) > 51:
+            output_save_dir = f"{checkpoint_dir}/checkpoint-{global_step}"
+        else:
+            output_save_dir = f"{checkpoint_dir}/checkpoint-{last_save_step}"
+
+        if not os.path.exists(output_save_dir):
+            save_lora(
+                output_dir=output_save_dir, 
+                global_step=global_step, 
+                unet=unet, 
+                embedding_handler=embedding_handler,
+                token_dict=args.token_dict,
+                args_dict=args.args_dict,
+                is_lora= args.is_lora, 
+                unet_lora_parameters=unet_lora_parameters, 
+                unet_param_to_optimize_names=unet_param_to_optimize_names
+            )
+
+            args.save_as_json(os.path.join(output_save_dir,"training_args.json"))
+            validation_prompts = render_images(pipe, target_size, output_save_dir, global_step, args.seed, args.is_lora, args.pretrained_model, n_imgs = 4, n_steps = 35)
+        else:
+            print(f"Skipping final save, {output_save_dir} already exists")
+
+        del unet
+        del vae
+        del text_encoder_one
+        del text_encoder_two
+        del tokenizer_one
+        del tokenizer_two
+        del embedding_handler
+        del pipe
+        gc.collect()
+        torch.cuda.empty_cache()
+
+        return output_save_dir

trainer/utils/learning_rate.py

@@ -0,0 +1,23 @@
+def get_avg_lr(optimizer):
+    # Calculate the weighted average effective learning rate
+    total_lr = 0
+    total_params = 0
+    for group in optimizer.param_groups:
+        d = group['d']
+        lr = group['lr']
+        bias_correction = 1  # Default value
+        if group['use_bias_correction']:
+            beta1, beta2 = group['betas']
+            k = group['k']
+            bias_correction = ((1 - beta2**(k+1))**0.5) / (1 - beta1**(k+1))
+
+        effective_lr = d * lr * bias_correction
+
+        # Count the number of parameters in this group
+        num_params = sum(p.numel() for p in group['params'] if p.requires_grad)
+        total_lr += effective_lr * num_params
+        total_params += num_params
+
+    if total_params == 0:
+        return 0.0
+    else: return total_lr / total_params

trainer/utils/lora.py

@@ -0,0 +1,174 @@
+import os, json
+import torch
+from safetensors.torch import load_file
+from typing import Dict
+from peft import PeftModel
+from ..dataset_and_utils import TokenEmbeddingsHandler
+from safetensors.torch import save_file
+from .string import replace_in_string
+
+'''
+from diffusers.utils import (
+    convert_all_state_dict_to_peft,
+    convert_state_dict_to_diffusers,
+    convert_unet_state_dict_to_peft
+)
+'''
+
+def prepare_prompt_for_lora(prompt, lora_path, interpolation=False, verbose=True):
+    if "_no_token" in lora_path:
+        return prompt
+        
+    orig_prompt = prompt
+
+    # Helper function to read JSON
+    def read_json_from_path(path):
+        with open(path, "r") as f:
+            return json.load(f)
+
+    # Check existence of "special_params.json"
+    if not os.path.exists(os.path.join(lora_path, "special_params.json")):
+        raise ValueError("This concept is from an old lora trainer that was deprecated. Please retrain your concept for better results!")
+
+    token_map = read_json_from_path(os.path.join(lora_path, "special_params.json"))
+    training_args = read_json_from_path(os.path.join(lora_path, "training_args.json"))
+    
+    try:
+        lora_name = str(training_args["name"])
+    except: # fallback for old loras that dont have the name field:
+        return training_args["trigger_text"] + ", " + prompt
+
+    lora_name_encapsulated = "<" + lora_name + ">"
+    trigger_text = training_args["trigger_text"]
+
+    try:
+        mode = training_args["concept_mode"]
+    except KeyError:
+        try:
+            mode = training_args["mode"]
+        except KeyError:
+            mode = "object"
+
+    # Handle different modes
+    if mode != "style":
+        replacements = {
+            "<concept>": trigger_text,
+            "<concepts>": trigger_text + "'s",
+            lora_name_encapsulated: trigger_text,
+            lora_name_encapsulated.lower(): trigger_text,
+            lora_name: trigger_text,
+            lora_name.lower(): trigger_text,
+        }
+        prompt = replace_in_string(prompt, replacements)
+        if trigger_text not in prompt:
+            prompt = trigger_text + ", " + prompt
+    else:
+        style_replacements = {
+            "in the style of <concept>": "in the style of TOK",
+            f"in the style of {lora_name_encapsulated}": "in the style of TOK",
+            f"in the style of {lora_name_encapsulated.lower()}": "in the style of TOK",
+            f"in the style of {lora_name}": "in the style of TOK",
+            f"in the style of {lora_name.lower()}": "in the style of TOK"
+        }
+        prompt = replace_in_string(prompt, style_replacements)
+        if "in the style of TOK" not in prompt:
+            prompt = "in the style of TOK, " + prompt
+        
+    # Final cleanup
+    prompt = replace_in_string(prompt, {"<concept>": "TOK", lora_name_encapsulated: "TOK"})
+
+    if interpolation and mode != "style":
+        prompt = "TOK, " + prompt
+
+    # Replace tokens based on token map
+    prompt = replace_in_string(prompt, token_map)
+
+    # Fix common mistakes
+    fix_replacements = {
+        r",,": ",",
+        r"\s\s+": " ",  # Replaces one or more whitespace characters with a single space
+        r"\s\.": ".",
+        r"\s,": ","
+    }
+    prompt = replace_in_string(prompt, fix_replacements)
+
+    if verbose:
+        print('-------------------------')
+        print("Adjusted prompt for LORA:")
+        print(orig_prompt)
+        print('-- to:')
+        print(prompt)
+        print('-------------------------')
+
+    return prompt
+
+def patch_pipe_with_lora(pipe, lora_path):
+    """
+    update the pipe with the lora model and the token embeddings
+    """
+
+    pipe.unet = PeftModel.from_pretrained(pipe.unet, lora_path)
+    pipe.unet.merge_adapter()
+    
+    # Load the textual_inversion token embeddings into the pipeline:
+    try: #SDXL
+        handler = TokenEmbeddingsHandler([pipe.text_encoder, pipe.text_encoder_2], [pipe.tokenizer, pipe.tokenizer_2])
+    except: #SD15
+        handler = TokenEmbeddingsHandler([pipe.text_encoder, None], [pipe.tokenizer, None])
+
+    embeddings_path = [f for f in os.listdir(lora_path) if f.endswith("embeddings.safetensors")][0]
+    handler.load_embeddings(os.path.join(lora_path, embeddings_path))
+
+    return pipe
+
+
+def unet_attn_processors_state_dict(unet) -> Dict[str, torch.tensor]:
+    """
+    Returns:
+        a state dict containing just the attention processor parameters.
+    """
+    attn_processors = unet.attn_processors
+
+    attn_processors_state_dict = {}
+
+    for attn_processor_key, attn_processor in attn_processors.items():
+        for parameter_key, parameter in attn_processor.state_dict().items():
+            attn_processors_state_dict[
+                f"{attn_processor_key}.{parameter_key}"
+            ] = parameter
+
+    return attn_processors_state_dict
+
+
+def save_lora(output_dir, global_step, unet, embedding_handler, token_dict, args_dict, is_lora, unet_lora_parameters, unet_param_to_optimize_names):
+    """
+    Save the LORA model to output_dir, optionally with some example images
+
+    """
+    print(f"Saving checkpoint at step.. {global_step}")
+    os.makedirs(output_dir, exist_ok=True)
+
+    if not is_lora:
+        lora_tensors = {
+            name: param
+            for name, param in unet.named_parameters()
+            if name in unet_param_to_optimize_names
+        }
+        save_file(lora_tensors, f"{output_dir}/unet.safetensors",)
+    elif len(unet_lora_parameters) > 0:
+        unet.save_pretrained(save_directory = output_dir)
+
+    try:
+        concept_name = args_dict["name"].lower()
+    except:
+        concept_name = "eden_concept_lora"
+
+    # Make sure all weird delimiter characters are removed from concept_name before using it as a filepath:
+    concept_name = concept_name.replace(" ", "_").replace("/", "_").replace("\\", "_").replace(":", "_").replace("*", "_").replace("?", "_").replace("\"", "_").replace("<", "_").replace(">", "_").replace("|", "_")
+
+    embedding_handler.save_embeddings(f"{output_dir}/{concept_name}_embeddings.safetensors",)
+
+    with open(f"{output_dir}/special_params.json", "w") as f:
+        json.dump(token_dict, f)
+    with open(f"{output_dir}/training_args.json", "w") as f:
+        json.dump(args_dict, f, indent=4)

trainer/utils/model_info.py

@@ -0,0 +1,13 @@
+def print_trainable_parameters(model, name = ''):
+    trainable_params = 0
+    all_param = 0
+    for _, param in model.named_parameters():
+        all_param += param.numel()
+        if param.requires_grad:
+            trainable_params += param.numel()
+    line_delimiter = "#" * 70
+    print('\n', line_delimiter)
+    print(
+        f"Trainable {name} params: {trainable_params/1000000:.1f}M || All params: {all_param/1000000:.1f}M || trainable = {100 * trainable_params / all_param:.2f}%"
+    )
+    print(line_delimiter, '\n')

trainer/utils/rendering.py

@@ -0,0 +1,120 @@
+import random
+import json
+import os
+import gc
+import torch
+from ..dataset_and_utils import load_models
+from .lora import patch_pipe_with_lora, prepare_prompt_for_lora
+from ..val_prompts import val_prompts
+from diffusers import EulerDiscreteScheduler
+from PIL import Image
+
+def make_validation_img_grid(img_folder):
+    """
+
+    find all the .jpg imgs in img_folder (template = *.jpg)
+    if >=4 validation imgs, create a 2x2 grid of them
+    otherwise just return the first validation img
+
+    """
+    
+    # Find all validation images
+    validation_imgs = sorted([f for f in os.listdir(img_folder) if f.endswith(".jpg")])
+
+    if len(validation_imgs) < 4:
+        # If less than 4 validation images, return path of the first one
+        return os.path.join(img_folder, validation_imgs[0])
+    else:
+        # If >= 4 validation images, create 2x2 grid
+        imgs = [Image.open(os.path.join(img_folder, img)) for img in validation_imgs[:4]]
+
+        # Assuming all images are the same size, get dimensions of first image
+        width, height = imgs[0].size
+
+        # Create an empty image with 2x2 grid size
+        grid_img = Image.new("RGB", (2 * width, 2 * height))
+
+        # Paste the images into the grid
+        for i in range(2):
+            for j in range(2):
+                grid_img.paste(imgs.pop(0), (i * width, j * height))
+
+        # Save the new image
+        grid_img_path = os.path.join(img_folder, "validation_grid.jpg")
+        grid_img.save(grid_img_path)
+
+        return grid_img_path
+
+@torch.no_grad()
+def render_images(training_pipeline, render_size, lora_path, train_step, seed, is_lora, pretrained_model, lora_scale = 0.7, n_steps = 25, n_imgs = 4, device = "cuda:0"):
+
+    random.seed(seed)
+    
+    with open(os.path.join(lora_path, "training_args.json"), "r") as f:
+        training_args = json.load(f)
+        concept_mode = training_args["concept_mode"]
+
+    if concept_mode == "style":
+        validation_prompts_raw = random.sample(val_prompts['style'], n_imgs)
+        validation_prompts_raw[0] = ''
+
+    elif concept_mode == "face":
+        validation_prompts_raw = random.sample(val_prompts['face'], n_imgs)
+        validation_prompts_raw[0] = '<concept>'
+    else:
+        validation_prompts_raw = random.sample(val_prompts['object'], n_imgs)
+        validation_prompts_raw[0] = '<concept>'
+
+
+    reload_entire_pipeline = False
+    if reload_entire_pipeline: # reload the entire pipeline from disk and load in the lora module
+        print(f"Reloading entire pipeline from disk..")
+        gc.collect()
+        torch.cuda.empty_cache()
+
+        (pipeline,
+            tokenizer_one,
+            tokenizer_two,
+            noise_scheduler,
+            text_encoder_one,
+            text_encoder_two,
+            vae,
+            unet) = load_models(pretrained_model, device, torch.float16)
+
+        pipeline = pipeline.to(device)
+        pipeline = patch_pipe_with_lora(pipeline, lora_path)
+
+    else:
+        print(f"Re-using training pipeline for inference, just swapping the scheduler..")
+        pipeline = training_pipeline
+        training_scheduler = pipeline.scheduler
+    
+    pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config)
+    validation_prompts = [prepare_prompt_for_lora(prompt, lora_path) for prompt in validation_prompts_raw]
+    generator = torch.Generator(device=device).manual_seed(0)
+    pipeline_args = {
+                "negative_prompt": "nude, naked, poorly drawn face, ugly, tiling, out of frame, extra limbs, disfigured, deformed body, blurry, blurred, watermark, text, grainy, signature, cut off, draft", 
+                "num_inference_steps": n_steps,
+                "guidance_scale": 7,
+                "height": render_size[0],
+                "width": render_size[1],
+                }
+
+    if is_lora > 0:
+        cross_attention_kwargs = {"scale": lora_scale}
+    else:
+        cross_attention_kwargs = None
+
+    for i in range(n_imgs):
+        pipeline_args["prompt"] = validation_prompts[i]
+        print(f"Rendering validation img with prompt: {validation_prompts[i]}")
+        image = pipeline(**pipeline_args, generator=generator, cross_attention_kwargs = cross_attention_kwargs).images[0]
+        image.save(os.path.join(lora_path, f"img_{train_step:04d}_{i}.jpg"), format="JPEG", quality=95)
+
+    # create img_grid:
+    img_grid_path = make_validation_img_grid(lora_path)
+
+    if not reload_entire_pipeline: # restore the training scheduler
+        pipeline.scheduler = training_scheduler
+
+    return validation_prompts_raw

trainer/utils/snr.py

@@ -0,0 +1,24 @@
+def compute_snr(noise_scheduler, timesteps):
+    """
+    Computes SNR as per
+    https://github.com/TiankaiHang/Min-SNR-Diffusion-Training/blob/521b624bd70c67cee4bdf49225915f5945a872e3/guided_diffusion/gaussian_diffusion.py#L847-L849
+    """
+    alphas_cumprod = noise_scheduler.alphas_cumprod
+    sqrt_alphas_cumprod = alphas_cumprod**0.5
+    sqrt_one_minus_alphas_cumprod = (1.0 - alphas_cumprod) ** 0.5
+
+    # Expand the tensors.
+    # Adapted from https://github.com/TiankaiHang/Min-SNR-Diffusion-Training/blob/521b624bd70c67cee4bdf49225915f5945a872e3/guided_diffusion/gaussian_diffusion.py#L1026
+    sqrt_alphas_cumprod = sqrt_alphas_cumprod.to(device=timesteps.device)[timesteps].float()
+    while len(sqrt_alphas_cumprod.shape) < len(timesteps.shape):
+        sqrt_alphas_cumprod = sqrt_alphas_cumprod[..., None]
+    alpha = sqrt_alphas_cumprod.expand(timesteps.shape)
+
+    sqrt_one_minus_alphas_cumprod = sqrt_one_minus_alphas_cumprod.to(device=timesteps.device)[timesteps].float()
+    while len(sqrt_one_minus_alphas_cumprod.shape) < len(timesteps.shape):
+        sqrt_one_minus_alphas_cumprod = sqrt_one_minus_alphas_cumprod[..., None]
+    sigma = sqrt_one_minus_alphas_cumprod.expand(timesteps.shape)
+
+    # Compute SNR.
+    snr = (alpha / sigma) ** 2
+    return snr

trainer/utils/string.py

@@ -0,0 +1,14 @@
+import re
+
+def replace_in_string(s, replacements):
+    while True:
+        replaced = False
+        for target, replacement in replacements.items():
+            new_s = re.sub(target, replacement, s, flags=re.IGNORECASE)
+            if new_s != s:
+                s = new_s
+                replaced = True
+        if not replaced:
+            break
+    return s
+

trainer_pti.py

@@ -1,783 +1,70 @@
-import fnmatch
-import json
-import math
+from trainer import TrainerConfig, Trainer
+from preprocess import preprocess
 import os
-import sys
-import random
-import time
-import shutil
-import gc
-import numpy as np
-from typing import List, Optional
-
-import torch
-import torch.utils.checkpoint
-import torch.nn.functional as F
-
-from peft import LoraConfig, get_peft_model
-from diffusers.optimization import get_scheduler
-from diffusers import EulerDiscreteScheduler
-from tqdm import tqdm
-
-from dataset_and_utils import *
-from lora_utils import *
-from io_utils import make_validation_img_grid
-import matplotlib.pyplot as plt
-
-
-def print_trainable_parameters(model, name = ''):
-    trainable_params = 0
-    all_param = 0
-    for _, param in model.named_parameters():
-        all_param += param.numel()
-        if param.requires_grad:
-            trainable_params += param.numel()
-    line_delimiter = "#" * 70
-    print('\n', line_delimiter)
-    print(
-        f"Trainable {name} params: {trainable_params/1000000:.1f}M || All params: {all_param/1000000:.1f}M || trainable = {100 * trainable_params / all_param:.2f}%"
-    )
-    print(line_delimiter, '\n')
-
-
-def compute_snr(noise_scheduler, timesteps):
-    """
-    Computes SNR as per
-    https://github.com/TiankaiHang/Min-SNR-Diffusion-Training/blob/521b624bd70c67cee4bdf49225915f5945a872e3/guided_diffusion/gaussian_diffusion.py#L847-L849
-    """
-    alphas_cumprod = noise_scheduler.alphas_cumprod
-    sqrt_alphas_cumprod = alphas_cumprod**0.5
-    sqrt_one_minus_alphas_cumprod = (1.0 - alphas_cumprod) ** 0.5
-
-    # Expand the tensors.
-    # Adapted from https://github.com/TiankaiHang/Min-SNR-Diffusion-Training/blob/521b624bd70c67cee4bdf49225915f5945a872e3/guided_diffusion/gaussian_diffusion.py#L1026
-    sqrt_alphas_cumprod = sqrt_alphas_cumprod.to(device=timesteps.device)[timesteps].float()
-    while len(sqrt_alphas_cumprod.shape) < len(timesteps.shape):
-        sqrt_alphas_cumprod = sqrt_alphas_cumprod[..., None]
-    alpha = sqrt_alphas_cumprod.expand(timesteps.shape)
-
-    sqrt_one_minus_alphas_cumprod = sqrt_one_minus_alphas_cumprod.to(device=timesteps.device)[timesteps].float()
-    while len(sqrt_one_minus_alphas_cumprod.shape) < len(timesteps.shape):
-        sqrt_one_minus_alphas_cumprod = sqrt_one_minus_alphas_cumprod[..., None]
-    sigma = sqrt_one_minus_alphas_cumprod.expand(timesteps.shape)
-
-    # Compute SNR.
-    snr = (alpha / sigma) ** 2
-    return snr
-
-def get_avg_lr(optimizer):
-    # Calculate the weighted average effective learning rate
-    total_lr = 0
-    total_params = 0
-    for group in optimizer.param_groups:
-        d = group['d']
-        lr = group['lr']
-        bias_correction = 1  # Default value
-        if group['use_bias_correction']:
-            beta1, beta2 = group['betas']
-            k = group['k']
-            bias_correction = ((1 - beta2**(k+1))**0.5) / (1 - beta1**(k+1))
-
-        effective_lr = d * lr * bias_correction
-
-        # Count the number of parameters in this group
-        num_params = sum(p.numel() for p in group['params'] if p.requires_grad)
-        total_lr += effective_lr * num_params
-        total_params += num_params
-
-    if total_params == 0:
-        return 0.0
-    else: return total_lr / total_params
-
-
-import re
-
-def replace_in_string(s, replacements):
-    while True:
-        replaced = False
-        for target, replacement in replacements.items():
-            new_s = re.sub(target, replacement, s, flags=re.IGNORECASE)
-            if new_s != s:
-                s = new_s
-                replaced = True
-        if not replaced:
-            break
-    return s
-
-def prepare_prompt_for_lora(prompt, lora_path, interpolation=False, verbose=True):
-    if "_no_token" in lora_path:
-        return prompt
-        
-    orig_prompt = prompt
-
-    # Helper function to read JSON
-    def read_json_from_path(path):
-        with open(path, "r") as f:
-            return json.load(f)
-
-    # Check existence of "special_params.json"
-    if not os.path.exists(os.path.join(lora_path, "special_params.json")):
-        raise ValueError("This concept is from an old lora trainer that was deprecated. Please retrain your concept for better results!")
-
-    token_map = read_json_from_path(os.path.join(lora_path, "special_params.json"))
-    training_args = read_json_from_path(os.path.join(lora_path, "training_args.json"))
-    
-    try:
-        lora_name = str(training_args["name"])
-    except: # fallback for old loras that dont have the name field:
-        return training_args["trigger_text"] + ", " + prompt
-
-    lora_name_encapsulated = "<" + lora_name + ">"
-    trigger_text = training_args["trigger_text"]
-
-    try:
-        mode = training_args["concept_mode"]
-    except KeyError:
-        try:
-            mode = training_args["mode"]
-        except KeyError:
-            mode = "object"
-
-    # Handle different modes
-    if mode != "style":
-        replacements = {
-            "<concept>": trigger_text,
-            "<concepts>": trigger_text + "'s",
-            lora_name_encapsulated: trigger_text,
-            lora_name_encapsulated.lower(): trigger_text,
-            lora_name: trigger_text,
-            lora_name.lower(): trigger_text,
-        }
-        prompt = replace_in_string(prompt, replacements)
-        if trigger_text not in prompt:
-            prompt = trigger_text + ", " + prompt
-    else:
-        style_replacements = {
-            "in the style of <concept>": "in the style of TOK",
-            f"in the style of {lora_name_encapsulated}": "in the style of TOK",
-            f"in the style of {lora_name_encapsulated.lower()}": "in the style of TOK",
-            f"in the style of {lora_name}": "in the style of TOK",
-            f"in the style of {lora_name.lower()}": "in the style of TOK"
-        }
-        prompt = replace_in_string(prompt, style_replacements)
-        if "in the style of TOK" not in prompt:
-            prompt = "in the style of TOK, " + prompt
-        
-    # Final cleanup
-    prompt = replace_in_string(prompt, {"<concept>": "TOK", lora_name_encapsulated: "TOK"})
-
-    if interpolation and mode != "style":
-        prompt = "TOK, " + prompt
-
-    # Replace tokens based on token map
-    prompt = replace_in_string(prompt, token_map)
-
-    # Fix common mistakes
-    fix_replacements = {
-        r",,": ",",
-        r"\s\s+": " ",  # Replaces one or more whitespace characters with a single space
-        r"\s\.": ".",
-        r"\s,": ","
-    }
-    prompt = replace_in_string(prompt, fix_replacements)
-
-    if verbose:
-        print('-------------------------')
-        print("Adjusted prompt for LORA:")
-        print(orig_prompt)
-        print('-- to:')
-        print(prompt)
-        print('-------------------------')
-
-    return prompt
-
-from val_prompts import val_prompts
-@torch.no_grad()
-def render_images(training_pipeline, render_size, lora_path, train_step, seed, is_lora, pretrained_model, lora_scale = 0.7, n_steps = 25, n_imgs = 4, device = "cuda:0"):
-
-    random.seed(seed)
-
-    with open(os.path.join(lora_path, "training_args.json"), "r") as f:
-        training_args = json.load(f)
-        concept_mode = training_args["concept_mode"]
-
-    if concept_mode == "style":
-        validation_prompts_raw = random.sample(val_prompts['style'], n_imgs)
-        validation_prompts_raw[0] = ''
-
-    elif concept_mode == "face":
-        validation_prompts_raw = random.sample(val_prompts['face'], n_imgs)
-        validation_prompts_raw[0] = '<concept>'
-    else:
-        validation_prompts_raw = random.sample(val_prompts['object'], n_imgs)
-        validation_prompts_raw[0] = '<concept>'
-
-
-    reload_entire_pipeline = False
-    if reload_entire_pipeline: # reload the entire pipeline from disk and load in the lora module
-        print(f"Reloading entire pipeline from disk..")
-        gc.collect()
-        torch.cuda.empty_cache()
-
-        (pipeline,
-            tokenizer_one,
-            tokenizer_two,
-            noise_scheduler,
-            text_encoder_one,
-            text_encoder_two,
-            vae,
-            unet) = load_models(pretrained_model, device, torch.float16)
-
-        pipeline = pipeline.to(device)
-        pipeline = patch_pipe_with_lora(pipeline, lora_path)
-
-    else:
-        print(f"Re-using training pipeline for inference, just swapping the scheduler..")
-        pipeline = training_pipeline
-        training_scheduler = pipeline.scheduler
-    
-    pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config)
-    validation_prompts = [prepare_prompt_for_lora(prompt, lora_path) for prompt in validation_prompts_raw]
-    generator = torch.Generator(device=device).manual_seed(0)
-    pipeline_args = {
-                "negative_prompt": "nude, naked, poorly drawn face, ugly, tiling, out of frame, extra limbs, disfigured, deformed body, blurry, blurred, watermark, text, grainy, signature, cut off, draft", 
-                "num_inference_steps": n_steps,
-                "guidance_scale": 7,
-                "height": render_size[0],
-                "width": render_size[1],
-                }
-
-    if is_lora > 0:
-        cross_attention_kwargs = {"scale": lora_scale}
-    else:
-        cross_attention_kwargs = None
-
-    for i in range(n_imgs):
-        pipeline_args["prompt"] = validation_prompts[i]
-        print(f"Rendering validation img with prompt: {validation_prompts[i]}")
-        image = pipeline(**pipeline_args, generator=generator, cross_attention_kwargs = cross_attention_kwargs).images[0]
-        image.save(os.path.join(lora_path, f"img_{train_step:04d}_{i}.jpg"), format="JPEG", quality=95)
-
-    # create img_grid:
-    img_grid_path = make_validation_img_grid(lora_path)
-
-    if not reload_entire_pipeline: # restore the training scheduler
-        pipeline.scheduler = training_scheduler
-
-    return validation_prompts_raw
-
-
-def main(
-    pretrained_model,
-    instance_data_dir: Optional[str] = "./dataset/zeke/captions.csv",
-    output_dir: str = "lora_output",
-    seed: Optional[int] = random.randint(0, 2**32 - 1),
-    resolution: int = 768,
-    crops_coords_top_left_h: int = 0,
-    crops_coords_top_left_w: int = 0,
-    train_batch_size: int = 1,
-    do_cache: bool = True,
-    num_train_epochs: int = 10000,
-    max_train_steps: Optional[int] = None,
-    checkpointing_steps: int = 500000,  # default to no checkpoints
-    gradient_accumulation_steps: int = 1,  # todo
-    unet_learning_rate: float = 1.0,
-    ti_lr: float = 3e-4,
-    lora_lr: float = 1.0,
-    prodigy_d_coef: float = 0.33,
-    l1_penalty: float = 0.0,
-    lora_weight_decay: float = 0.005,
-    ti_weight_decay: float = 0.001,
-    scale_lr: bool = False,
-    lr_scheduler: str = "constant",
-    lr_warmup_steps: int = 50,
-    lr_num_cycles: int = 1,
-    lr_power: float = 1.0,
-    snr_gamma: float = 5.0,
-    dataloader_num_workers: int = 0,
-    allow_tf32: bool = True,
-    mixed_precision: Optional[str] = "bf16",
-    device: str = "cuda:0",
-    token_dict: dict = {"TOKEN": "<s0>"},
-    inserting_list_tokens: List[str] = ["<s0>"],
-    verbose: bool = True,
-    is_lora: bool = True,
-    lora_rank: int = 8,
-    args_dict: dict = {},
-    debug: bool = False,
-    hard_pivot: bool = True,
-    off_ratio_power: float = 0.1,
-) -> None:
-    if allow_tf32:
-        torch.backends.cuda.matmul.allow_tf32 = True
-
-    print("Using seed", seed)
-    torch.manual_seed(seed)
-
-    weight_dtype = torch.float32
-    if mixed_precision == "fp16":
-        weight_dtype = torch.float16
-    elif mixed_precision == "bf16":
-        weight_dtype = torch.bfloat16
-
-    print(f"Loading models with weight_dtype: {weight_dtype}")
-
-    if scale_lr:
-        unet_learning_rate = (
-            unet_learning_rate * gradient_accumulation_steps * train_batch_size
-        )
-
-    (   
-        pipe,
-        tokenizer_one,
-        tokenizer_two,
-        noise_scheduler,
-        text_encoder_one,
-        text_encoder_two,
-        vae,
-        unet,
-    ) = load_models(pretrained_model, device, weight_dtype)
-
-    # Initialize new tokens for training.
-    embedding_handler = TokenEmbeddingsHandler(
-        [text_encoder_one, text_encoder_two], [tokenizer_one, tokenizer_two]
-    )
-    
-    #starting_toks = ["person", "face"]
-    starting_toks = None
-    embedding_handler.initialize_new_tokens(inserting_toks=inserting_list_tokens, starting_toks=starting_toks, seed=seed)
-    text_encoders = [text_encoder_one, text_encoder_two]
-
-    unet_param_to_optimize = []
-    text_encoder_parameters = []
-    for text_encoder in text_encoders:
-        if text_encoder is not  None:
-            for name, param in text_encoder.named_parameters():
-                if "token_embedding" in name:
-                    param.requires_grad = True
-                    text_encoder_parameters.append(param)
-                else:
-                    param.requires_grad = False
-
-    unet_param_to_optimize_names = []
-    unet_lora_parameters = []
-
-    if not is_lora:
-        WHITELIST_PATTERNS = [
-            # "*.attn*.weight",
-            # "*ff*.weight",
-            "*"
-        ]
-        BLACKLIST_PATTERNS = ["*.norm*.weight", "*time*"]
-        for name, param in unet.named_parameters():
-            if any(
-                fnmatch.fnmatch(name, pattern) for pattern in WHITELIST_PATTERNS
-            ) and not any(
-                fnmatch.fnmatch(name, pattern) for pattern in BLACKLIST_PATTERNS
-            ):
-                param.requires_grad_(True)
-                unet_param_to_optimize_names.append(name)
-                print(f"Training: {name}")
-            else:
-                param.requires_grad_(False)
-
-        # Optimizer creation
-        params_to_optimize = [
-            {
-                "params": text_encoder_parameters,
-                "lr": ti_lr,
-                "weight_decay": ti_weight_decay,
-            },
-        ]
-
-        params_to_optimize_prodigy = [
-            {
-                "params": unet_param_to_optimize,
-                "lr": unet_learning_rate,
-                "weight_decay": lora_weight_decay,
-            },
-        ]
-
-    else:
-        
-        # Do lora-training instead.
-        unet.requires_grad_(False)
-        # https://huggingface.co/docs/peft/main/en/developer_guides/lora#rank-stabilized-lora
-        unet_lora_config = LoraConfig(
-            r=lora_rank,
-            lora_alpha=lora_rank,
-            init_lora_weights="gaussian",
-            target_modules=["to_k", "to_q", "to_v", "to_out.0"],
-            #use_rslora=True,
-            use_dora=True,
-        )
-        #unet.add_adapter(unet_lora_config)
-        
-        unet = get_peft_model(unet, unet_lora_config)
-        print_trainable_parameters(unet, name = 'unet')
-
-        unet_lora_parameters = list(filter(lambda p: p.requires_grad, unet.parameters()))
-
-        params_to_optimize = [
-            {
-                "params": text_encoder_parameters,
-                "lr": ti_lr,
-                "weight_decay": ti_weight_decay,
-            },
-        ]
-
-        params_to_optimize_prodigy = [
-            {
-                "params": unet_lora_parameters,
-                "lr": 1.0,
-                "weight_decay": lora_weight_decay,
-            },
-        ]
-    
-    optimizer_type = "prodigy" # hardcode for now
-
-    if optimizer_type != "prodigy":
-        optimizer = torch.optim.AdamW(
-            params_to_optimize,
-            weight_decay=0.0, # this wd doesn't matter, I think
-        )
-    else:        
-        try:
-            import prodigyopt
-        except ImportError:
-            raise ImportError("To use Prodigy, please install the prodigyopt library: `pip install prodigyopt`")
-
-        # Note: the specific settings of Prodigy seem to matter A LOT
-        optimizer_prod = prodigyopt.Prodigy(
-                        params_to_optimize_prodigy,
-                        d_coef = prodigy_d_coef,
-                        lr=1.0,
-                        decouple=True,
-                        use_bias_correction=True,
-                        safeguard_warmup=True,
-                        weight_decay=lora_weight_decay,
-                        betas=(0.9, 0.99),
-                        growth_rate=1.025,  # this slows down the lr_rampup
-                    )
-        
-        optimizer = torch.optim.AdamW(
-            params_to_optimize,
-            weight_decay=ti_weight_decay,
-        )
-        
-    train_dataset = PreprocessedDataset(
-        instance_data_dir,
-        tokenizer_one,
-        tokenizer_two,
-        vae,
-        do_cache=True,
-        substitute_caption_map=token_dict,
-    )
-
-    print(f"# PTI : Loaded dataset, do_cache: {do_cache}")
-    train_dataloader = torch.utils.data.DataLoader(
-        train_dataset,
-        batch_size=train_batch_size,
-        shuffle=True,
-        num_workers=dataloader_num_workers,
-    )
-
-    num_update_steps_per_epoch = math.ceil(
-        len(train_dataloader) / gradient_accumulation_steps
-    )
-    if max_train_steps is None:
-        max_train_steps = num_train_epochs * num_update_steps_per_epoch
-
-    lr_scheduler = get_scheduler(
-        lr_scheduler,
-        optimizer=optimizer,
-        num_warmup_steps=lr_warmup_steps * gradient_accumulation_steps,
-        num_training_steps=max_train_steps * gradient_accumulation_steps,
-        num_cycles=lr_num_cycles,
-        power=lr_power,
-    )
-
-    num_update_steps_per_epoch = math.ceil(
-        len(train_dataloader) / gradient_accumulation_steps
-    )
-    num_train_epochs = math.ceil(max_train_steps / num_update_steps_per_epoch)
-
-    total_batch_size = train_batch_size * gradient_accumulation_steps
-
-    if verbose:
-        print(f"# PTI :  Running training ")
-        print(f"# PTI :  Num examples = {len(train_dataset)}")
-        print(f"# PTI :  Num batches each epoch = {len(train_dataloader)}")
-        print(f"# PTI :  Num Epochs = {num_train_epochs}")
-        print(f"# PTI :  Instantaneous batch size per device = {train_batch_size}")
-        print(
-            f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}"
-        )
-        print(f"# PTI :  Gradient Accumulation steps = {gradient_accumulation_steps}")
-        print(f"# PTI :  Total optimization steps = {max_train_steps}")
-
-    global_step = 0
-    first_epoch = 0
-    last_save_step = 0
-
-    progress_bar = tqdm(range(global_step, max_train_steps), position=0, leave=True)
-    checkpoint_dir = os.path.join(str(output_dir), "checkpoints")
-    if os.path.exists(checkpoint_dir):
-        shutil.rmtree(checkpoint_dir)
-    os.makedirs(f"{checkpoint_dir}")
-
-    # Experimental TODO: warmup the token embeddings using CLIP-similarity optimization
-    #embedding_handler.pre_optimize_token_embeddings(train_dataset)
-    
-    ti_lrs, lora_lrs = [], []
-    losses = []
-    start_time, images_done = time.time(), 0
-
-    for epoch in range(first_epoch, num_train_epochs):
-        unet.train()
-        progress_bar.set_description(f"# PTI :step: {global_step}, epoch: {epoch}")
-
-        for step, batch in enumerate(train_dataloader):
-            progress_bar.update(1)
-
-            if hard_pivot:
-                if epoch >= num_train_epochs // 2:
-                    if optimizer is not None:
-                        print("----------------------")
-                        print("# PTI :  Pivot halfway")
-                        print("----------------------")
-                        # remove text encoder parameters from the optimizer
-                        optimizer.param_groups = None
-                        # remove the optimizer state corresponding to text_encoder_parameters
-                        for param in text_encoder_parameters:
-                            if param in optimizer.state:
-                                del optimizer.state[param]
-                        optimizer = None
-
-            else: # Update learning rates gradually:
-                finegrained_epoch = epoch + step / len(train_dataloader)
-                completion_f = finegrained_epoch / num_train_epochs
-                # param_groups[1] goes from ti_lr to 0.0 over the course of training
-                optimizer.param_groups[0]['lr'] = ti_lr * (1 - completion_f) ** 2.0
-
-            
-            try: #sdxl
-                (tok1, tok2), vae_latent, mask = batch
-            except: #sd15
-                tok1, vae_latent, mask = batch
-                tok2 = None
-
-            vae_latent = vae_latent.to(weight_dtype)
-
-            # tokens to text embeds
-            prompt_embeds_list = []
-            for tok, text_encoder in zip((tok1, tok2), text_encoders):
-                if tok is None:
-                    continue
-
-                prompt_embeds_out = text_encoder(
-                    tok.to(text_encoder.device),
-                    output_hidden_states=True,
-                )
-
-                pooled_prompt_embeds = prompt_embeds_out[0]
-                prompt_embeds = prompt_embeds_out.hidden_states[-2]
-                bs_embed, seq_len, _ = prompt_embeds.shape
-                prompt_embeds = prompt_embeds.view(bs_embed, seq_len, -1)
-                prompt_embeds_list.append(prompt_embeds)
-
-            prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)
-            pooled_prompt_embeds = pooled_prompt_embeds.view(bs_embed, -1)
-
-            # Create Spatial-dimensional conditions.
-            original_size = (resolution, resolution)
-            target_size   = (resolution, resolution)
-            crops_coords_top_left = (crops_coords_top_left_h, crops_coords_top_left_w)
-            add_time_ids = list(original_size + crops_coords_top_left + target_size)
-            add_time_ids = torch.tensor([add_time_ids])
-            add_time_ids = add_time_ids.to(device, dtype=prompt_embeds.dtype).repeat(
-                bs_embed, 1
-            )
-
-            # Sample noise that we'll add to the latents:
-            noise = torch.randn_like(vae_latent)
-
-            noise_offset = 0.05 # TODO, turn this into an input arg and do a grid search
-            if noise_offset > 0.0:
-                # https://www.crosslabs.org//blog/diffusion-with-offset-noise
-                noise += noise_offset * torch.randn(
-                    (noise.shape[0], noise.shape[1], 1, 1), device=noise.device)
-
-            bsz = vae_latent.shape[0]
-
-            timesteps = torch.randint(
-                0,
-                noise_scheduler.config.num_train_timesteps,
-                (bsz,),
-                device=vae_latent.device,
-            ).long()
-
-            noisy_model_input = noise_scheduler.add_noise(vae_latent, noise, timesteps)
-
-            noise_sigma = 0.0
-            if noise_sigma > 0.0: # experimental: apply random noise to the conditioning vectors as a form of regularization
-                prompt_embeds[0,1:-2,:] += torch.randn_like(prompt_embeds[0,1:-2,:]) * noise_sigma
-
-            # Predict the noise residual
-            model_pred = unet(
-                noisy_model_input,
-                timesteps,
-                prompt_embeds,
-                added_cond_kwargs={"text_embeds": pooled_prompt_embeds, "time_ids": add_time_ids},
-            ).sample
-
-            # Get the unet prediction target depending on the prediction type:
-            if noise_scheduler.config.prediction_type == "epsilon":
-                target = noise
-            elif noise_scheduler.config.prediction_type == "v_prediction":
-                target = noise_scheduler.get_velocity(model_input, noise, timesteps)
-            else:
-                raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
-
-            # Compute the loss:
-            if snr_gamma is None:
-                loss = (model_pred - target).pow(2) * mask
-
-                # modulate loss by the inverse of the mask's mean value
-                mean_mask_values = mask.mean(dim=list(range(1, len(loss.shape))))
-                mean_mask_values = mean_mask_values / mean_mask_values.mean()
-                loss = loss.mean(dim=list(range(1, len(loss.shape)))) / mean_mask_values
-
-                # Average the normalized errors across the batch
-                loss = loss.mean()
-
-            else:
-                # Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556.
-                # Since we predict the noise instead of x_0, the original formulation is slightly changed.
-                # This is discussed in Section 4.2 of the same paper.
-                snr = compute_snr(noise_scheduler, timesteps)
-                base_weight = (
-                    torch.stack([snr, snr_gamma * torch.ones_like(timesteps)], dim=1).min(dim=1)[0] / snr
-                )
-                if noise_scheduler.config.prediction_type == "v_prediction":
-                    # Velocity objective needs to be floored to an SNR weight of one.
-                    mse_loss_weights = base_weight + 1
-                else:
-                    # Epsilon and sample both use the same loss weights.
-                    mse_loss_weights = base_weight
-
-                mse_loss_weights = mse_loss_weights / mse_loss_weights.mean()
-                loss = (model_pred - target).pow(2) * mask
-                loss = loss.mean(dim=list(range(1, len(loss.shape)))) * mse_loss_weights
-
-                if 1: # modulate loss by the inverse of the mask's mean value
-                    mean_mask_values = mask.mean(dim=list(range(1, len(loss.shape))))
-                    mean_mask_values = mean_mask_values / mean_mask_values.mean()
-                    loss = loss.mean(dim=list(range(1, len(loss.shape)))) / mean_mask_values
-
-                loss = loss.mean()
-
-            if l1_penalty > 0.0:
-                # Compute normalized L1 norm (mean of abs sum) of all lora parameters:
-                l1_norm = sum(p.abs().sum() for p in unet_lora_parameters) / sum(p.numel() for p in unet_lora_parameters)
-                loss += l1_penalty * l1_norm
-
-            losses.append(loss.item())
-
-            loss = loss / gradient_accumulation_steps
-            loss.backward()
-
-            '''
-            apart from the usual gradient accumulation steps,
-            we also do a backward pass after computing the last forward pass in the epoch (last_batch == True)
-            this is to make sure that we're not missing out on any data 
-            '''
-            last_batch = (step + 1 == len(train_dataloader))
-            if (step + 1) % gradient_accumulation_steps == 0 or last_batch:
-                if optimizer is not None:
-                    optimizer.step()
-                    optimizer.zero_grad()
-                
-                optimizer_prod.step()
-                optimizer_prod.zero_grad()
-
-                # after every optimizer step, we reset the non-trainable embeddings to the original embeddings
-                embedding_handler.retract_embeddings(print_stds = (global_step % 50 == 0))
-                embedding_handler.fix_embedding_std(off_ratio_power)
-            
-            # Track the learning rates for final plotting:
-            lora_lrs.append(get_avg_lr(optimizer_prod))
-            try:
-                ti_lrs.append(optimizer.param_groups[0]['lr'])
-            except:
-                ti_lrs.append(0.0)
-
-            # Print some statistics:
-            if (global_step % checkpointing_steps == 0):
-                output_save_dir = f"{checkpoint_dir}/checkpoint-{global_step}"
-                save_lora(output_save_dir, global_step, unet, embedding_handler, token_dict, args_dict, seed, is_lora, unet_lora_parameters, unet_param_to_optimize_names)
-                last_save_step = global_step
-
-                if debug:
-                    token_embeddings = embedding_handler.get_trainable_embeddings()
-                    for i, token_embeddings_i in enumerate(token_embeddings):
-                        plot_torch_hist(token_embeddings_i[0], global_step, output_dir, f"embeddings_weights_token_0_{i}", min_val=-0.05, max_val=0.05, ymax_f = 0.05)
-                        plot_torch_hist(token_embeddings_i[1], global_step, output_dir, f"embeddings_weights_token_1_{i}", min_val=-0.05, max_val=0.05, ymax_f = 0.05)
-                    
-                    embedding_handler.print_token_info()
-                    plot_torch_hist(unet_lora_parameters, global_step, output_dir, "lora_weights", min_val=-0.3, max_val=0.3, ymax_f = 0.05)
-                    plot_loss(losses, save_path=f'{output_dir}/losses.png')
-                    plot_lrs(lora_lrs, ti_lrs, save_path=f'{output_dir}/learning_rates.png')
-                    validation_prompts = render_images(pipe, target_size, output_save_dir, global_step, seed, is_lora, pretrained_model, n_imgs = 4)
-                    gc.collect()
-                    torch.cuda.empty_cache()
-            
-            images_done += train_batch_size
-            global_step += 1
-
-            if global_step % 100 == 0:
-                print(f" ---- avg training fps: {images_done / (time.time() - start_time):.2f}", end="\r")
-
-            if global_step % (max_train_steps//20) == 0:
-                progress = (global_step / max_train_steps) + 0.05
-                yield np.min((progress, 1.0))
-
-
-    # final_save
-    if (global_step - last_save_step) > 51:
-        output_save_dir = f"{checkpoint_dir}/checkpoint-{global_step}"
-    else:
-        output_save_dir = f"{checkpoint_dir}/checkpoint-{last_save_step}"
-
-    if debug:
-        plot_loss(losses, save_path=f'{output_dir}/losses.png')
-        plot_lrs(lora_lrs, ti_lrs, save_path=f'{output_dir}/learning_rates.png')
-        plot_torch_hist(unet_lora_parameters, global_step, output_dir, "lora_weights", min_val=-0.3, max_val=0.3, ymax_f = 0.05)
-        plot_torch_hist(embedding_handler.get_trainable_embeddings(), global_step, output_dir, "embeddings_weights", min_val=-0.05, max_val=0.05, ymax_f = 0.05)      
-
-    if not os.path.exists(output_save_dir):
-        save_lora(output_save_dir, global_step, unet, embedding_handler, token_dict, args_dict, seed, is_lora, unet_lora_parameters, unet_param_to_optimize_names)
-        validation_prompts = render_images(pipe, target_size, output_save_dir, global_step, seed, is_lora, pretrained_model, n_imgs = 4, n_steps = 35)
-    else:
-        print(f"Skipping final save, {output_save_dir} already exists")
-
-    del unet
-    del vae
-    del text_encoder_one
-    del text_encoder_two
-    del tokenizer_one
-    del tokenizer_two
-    del embedding_handler
-    del pipe
-    gc.collect()
-    torch.cuda.empty_cache()
-
-    with open(f"{output_save_dir}/training_args.json", "w") as f:
-        args_dict["grid_prompts"] = validation_prompts
-        json.dump(args_dict, f, indent=4)
-
-    return output_save_dir, validation_prompts
-
-
-if __name__ == "__main__":
-    main()
+from io_utils import MODEL_DICT
+
+out_root_dir = "./lora_models"
+run_name     = "face_01"
+concept_mode = "face"
+
+output_dir = os.path.join(out_root_dir, run_name)
+
+input_dir, n_imgs, trigger_text, segmentation_prompt, captions = preprocess(
+    output_dir,
+    concept_mode = concept_mode,
+    input_zip_path = "https://storage.googleapis.com/public-assets-xander/A_workbox/lora_training_sets/xander_5.zip",
+    #caption_text="in the style of TOK, ",
+    caption_text="",
+    mask_target_prompts=None,
+    target_size=1024,
+    crop_based_on_salience=True,
+    use_face_detection_instead=False,
+    temp=0.7,
+    left_right_flip_augmentation=False,
+    augment_imgs_up_to_n = 20,
+    seed = 0,
+    caption_model = "blip"
+)
+
+print('-------------------------------------------')
+print(f"Trigger text: {trigger_text}")
+print(f'n_imgs: {n_imgs}')
+print(f'concept_mode: {concept_mode}')
+print('-------------------------------------------')
+
+
+config = TrainerConfig(
+    pretrained_model = MODEL_DICT['sdxl'],
+    name='unnamed',
+    concept_mode=concept_mode,
+    trigger_text=trigger_text,
+    instance_data_dir = os.path.join(input_dir, "captions.csv"),
+    output_dir = output_dir,
+    resolution= 1024,
+    train_batch_size = 4,
+    max_train_steps = 600,
+    checkpointing_steps = 200,
+    num_train_epochs = 10000,
+    gradient_accumulation_steps = 1,
+    textual_inversion_lr = 5e-4,
+    textual_inversion_weight_decay = 3e-4,
+    lora_weight_decay = 0.00,
+    prodigy_d_coef = 1.0,
+    l1_penalty = 0.0,
+    snr_gamma = 5.0,
+    precision = "bf16",
+    token_dict = {"TOK": "<s0><s1>"},
+    inserting_list_tokens = ["<s0>","<s1>"],
+    is_lora = True,
+    lora_rank = 12,
+    lora_alpha = 12,
+    hard_pivot = False,
+    off_ratio_power = 0.1,
+    args_dict = {},
+    debug = True,
+    seed = 0
+)
+
+trainer = Trainer(config)
+trainer.train()
+print("DONE")