From f00877a13bce50b02cfc3790f2d18a325e9ff95b Mon Sep 17 00:00:00 2001
From: Volpeon <git@volpeon.ink>
Date: Sat, 14 Jan 2023 22:42:44 +0100
Subject: Update

---
 training/util.py | 112 -------------------------------------------------------
 1 file changed, 112 deletions(-)

(limited to 'training/util.py')

diff --git a/training/util.py b/training/util.py
index a292edd..f46cc61 100644
--- a/training/util.py
+++ b/training/util.py
@@ -14,29 +14,6 @@ from models.clip.tokenizer import MultiCLIPTokenizer
 from models.clip.embeddings import ManagedCLIPTextEmbeddings, patch_managed_embeddings
 
 
-class TrainingStrategy():
-    @property
-    def main_model(self) -> torch.nn.Module:
-        ...
-
-    @contextmanager
-    def on_train(self, epoch: int):
-        yield
-
-    @contextmanager
-    def on_eval(self):
-        yield
-
-    def on_before_optimize(self, epoch: int):
-        ...
-
-    def on_after_optimize(self, lr: float):
-        ...
-
-    def on_log():
-        return {}
-
-
 def save_args(basepath: Path, args, extra={}):
     info = {"args": vars(args)}
     info["args"].update(extra)
@@ -44,95 +21,6 @@ def save_args(basepath: Path, args, extra={}):
         json.dump(info, f, indent=4)
 
 
-def generate_class_images(
-    accelerator,
-    text_encoder,
-    vae,
-    unet,
-    tokenizer,
-    scheduler,
-    data_train,
-    sample_batch_size,
-    sample_image_size,
-    sample_steps
-):
-    missing_data = [item for item in data_train if not item.class_image_path.exists()]
-
-    if len(missing_data) == 0:
-        return
-
-    batched_data = [
-        missing_data[i:i+sample_batch_size]
-        for i in range(0, len(missing_data), sample_batch_size)
-    ]
-
-    pipeline = VlpnStableDiffusion(
-        text_encoder=text_encoder,
-        vae=vae,
-        unet=unet,
-        tokenizer=tokenizer,
-        scheduler=scheduler,
-    ).to(accelerator.device)
-    pipeline.set_progress_bar_config(dynamic_ncols=True)
-
-    with torch.inference_mode():
-        for batch in batched_data:
-            image_name = [item.class_image_path for item in batch]
-            prompt = [item.cprompt for item in batch]
-            nprompt = [item.nprompt for item in batch]
-
-            images = pipeline(
-                prompt=prompt,
-                negative_prompt=nprompt,
-                height=sample_image_size,
-                width=sample_image_size,
-                num_inference_steps=sample_steps
-            ).images
-
-            for i, image in enumerate(images):
-                image.save(image_name[i])
-
-    del pipeline
-
-    if torch.cuda.is_available():
-        torch.cuda.empty_cache()
-
-
-def get_models(pretrained_model_name_or_path: str):
-    tokenizer = MultiCLIPTokenizer.from_pretrained(pretrained_model_name_or_path, subfolder='tokenizer')
-    text_encoder = CLIPTextModel.from_pretrained(pretrained_model_name_or_path, subfolder='text_encoder')
-    vae = AutoencoderKL.from_pretrained(pretrained_model_name_or_path, subfolder='vae')
-    unet = UNet2DConditionModel.from_pretrained(pretrained_model_name_or_path, subfolder='unet')
-    noise_scheduler = DDPMScheduler.from_pretrained(pretrained_model_name_or_path, subfolder='scheduler')
-    sample_scheduler = DPMSolverMultistepScheduler.from_pretrained(
-        pretrained_model_name_or_path, subfolder='scheduler')
-
-    embeddings = patch_managed_embeddings(text_encoder)
-
-    return tokenizer, text_encoder, vae, unet, noise_scheduler, sample_scheduler, embeddings
-
-
-def add_placeholder_tokens(
-    tokenizer: MultiCLIPTokenizer,
-    embeddings: ManagedCLIPTextEmbeddings,
-    placeholder_tokens: list[str],
-    initializer_tokens: list[str],
-    num_vectors: Union[list[int], int]
-):
-    initializer_token_ids = [
-        tokenizer.encode(token, add_special_tokens=False)
-        for token in initializer_tokens
-    ]
-    placeholder_token_ids = tokenizer.add_multi_tokens(placeholder_tokens, num_vectors)
-
-    embeddings.resize(len(tokenizer))
-
-    for (placeholder_token_id, initializer_token_id) in zip(placeholder_token_ids, initializer_token_ids):
-        embeddings.add_embed(placeholder_token_id, initializer_token_id)
-
-    return placeholder_token_ids, initializer_token_ids
-
-
 class AverageMeter:
     def __init__(self, name=None):
         self.name = name
-- 
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