Add concepts learning via textual inversion

configs/finetune-textual-inversion.toml

@@ -0,0 +1,63 @@
+script = "finetune-ldm-textual_inversion.py" # not used for now
+
+[wandb]
+mode = "offline" # "online", "offline", "disabled"
+entity = "acme"
+project = "test-textual-inversion"
+
+[models]
+unet = {checkpoint = "/path/to/stable-diffusion-1-5/unet.safetensors"}
+text_encoder = {checkpoint = "/path/to/stable-diffusion-1-5/CLIPTextEncoderL.safetensors"}
+lda = {checkpoint = "/path/to/stable-diffusion-1-5/lda.safetensors"}
+
+[latent_diffusion]
+unconditional_sampling_probability = 0.05
+offset_noise = 0.1
+
+[textual_inversion]
+placeholder_token = "<cat-toy>"
+initializer_token = "toy"
+# style_mode = true
+
+[training]
+duration = "2000:step"
+seed = 0
+gpu_index = 0
+batch_size = 4
+gradient_accumulation = "1:step"
+evaluation_interval = "250:step"
+evaluation_seed = 1
+
+[optimizer]
+optimizer = "AdamW" # "SGD", "Adam", "AdamW", "AdamW8bit", "Lion8bit"
+learning_rate = 5e-4
+betas = [0.9, 0.999]
+eps = 1e-8
+weight_decay = 1e-2
+
+[scheduler]
+scheduler_type = "ConstantLR"
+update_interval = "1:step"
+
+[dropout]
+dropout_probability = 0
+use_gyro_dropout = false
+
+[dataset]
+hf_repo = "acme/cat-toy"
+revision = "main"
+horizontal_flip = true
+random_crop = true
+resize_image_max_size = 512
+
+[checkpointing]
+# save_folder = "/path/to/ckpts"
+save_interval = "250:step"
+
+[test_diffusion]
+num_inference_steps = 30
+use_short_prompts = false
+prompts = [
+    "<cat-toy>",
+    # "green grass, <cat-toy>"
+]

scripts/training/finetune-ldm-textual-inversion.py

@@ -0,0 +1,164 @@
+from typing import Any
+from pydantic import BaseModel
+from loguru import logger
+from torch.utils.data import Dataset
+from torch import randn, Tensor
+import random
+
+from refiners.foundationals.clip.concepts import ConceptExtender, EmbeddingExtender
+from refiners.foundationals.clip.text_encoder import CLIPTextEncoder, TokenEncoder
+from refiners.foundationals.clip.tokenizer import CLIPTokenizer
+from refiners.fluxion.utils import save_to_safetensors
+from refiners.training_utils.callback import Callback
+from refiners.training_utils.latent_diffusion import (
+    FinetuneLatentDiffusionConfig,
+    TextEmbeddingLatentsBatch,
+    LatentDiffusionTrainer,
+    LatentDiffusionConfig,
+    TextEmbeddingLatentsDataset,
+)
+
+
+IMAGENET_TEMPLATES_SMALL = [
+    "a photo of a {}",
+    "a rendering of a {}",
+    "a cropped photo of the {}",
+    "the photo of a {}",
+    "a photo of a clean {}",
+    "a photo of a dirty {}",
+    "a dark photo of the {}",
+    "a photo of my {}",
+    "a photo of the cool {}",
+    "a close-up photo of a {}",
+    "a bright photo of the {}",
+    "a cropped photo of a {}",
+    "a photo of the {}",
+    "a good photo of the {}",
+    "a photo of one {}",
+    "a close-up photo of the {}",
+    "a rendition of the {}",
+    "a photo of the clean {}",
+    "a rendition of a {}",
+    "a photo of a nice {}",
+    "a good photo of a {}",
+    "a photo of the nice {}",
+    "a photo of the small {}",
+    "a photo of the weird {}",
+    "a photo of the large {}",
+    "a photo of a cool {}",
+    "a photo of a small {}",
+]
+
+IMAGENET_STYLE_TEMPLATES_SMALL = [
+    "a painting in the style of {}",
+    "a rendering in the style of {}",
+    "a cropped painting in the style of {}",
+    "the painting in the style of {}",
+    "a clean painting in the style of {}",
+    "a dirty painting in the style of {}",
+    "a dark painting in the style of {}",
+    "a picture in the style of {}",
+    "a cool painting in the style of {}",
+    "a close-up painting in the style of {}",
+    "a bright painting in the style of {}",
+    "a cropped painting in the style of {}",
+    "a good painting in the style of {}",
+    "a close-up painting in the style of {}",
+    "a rendition in the style of {}",
+    "a nice painting in the style of {}",
+    "a small painting in the style of {}",
+    "a weird painting in the style of {}",
+    "a large painting in the style of {}",
+]
+
+
+class TextualInversionDataset(TextEmbeddingLatentsDataset):
+    templates: list[str] = []
+    placeholder_token: str = ""
+
+    def __init__(self, trainer: "LatentDiffusionTrainer[Any]") -> None:
+        super().__init__(trainer)
+        self.templates = (
+            IMAGENET_STYLE_TEMPLATES_SMALL if self.config.textual_inversion.style_mode else IMAGENET_TEMPLATES_SMALL
+        )
+        self.placeholder_token = self.config.textual_inversion.placeholder_token
+
+    def get_caption(self, index: int) -> str:
+        # Ignore the dataset caption, if any: use a template instead
+        return random.choice(self.templates).format(self.placeholder_token)
+
+
+class TextualInversionConfig(BaseModel):
+    # The new token to be learned
+    placeholder_token: str = "*"
+    # The token to be used as initializer; if None, a random vector is used
+    initializer_token: str | None = None
+    style_mode: bool = False
+
+    def apply_textual_inversion_to_target(self, text_encoder: CLIPTextEncoder) -> None:
+        adapter = ConceptExtender(target=text_encoder)
+        tokenizer = text_encoder.find(layer_type=CLIPTokenizer)
+        assert tokenizer is not None, "Tokenizer not found in text encoder."
+        token_encoder = text_encoder.find(layer_type=TokenEncoder)
+        assert token_encoder is not None, "Token encoder not found in text encoder."
+        if self.initializer_token is not None:
+            bpe = tokenizer.byte_pair_encoding(token=self.initializer_token)
+            assert " " not in bpe, "This initializer_token is not a single token."
+            token = Tensor([tokenizer.token_to_id_mapping[bpe]]).int().to(text_encoder.device)
+            init_embedding = token_encoder(token).squeeze(0)
+        else:
+            token_encoder = text_encoder.find(layer_type=TokenEncoder)
+            assert token_encoder is not None, "Token encoder not found in text encoder."
+            init_embedding = randn(token_encoder.embedding_dim)
+        adapter.add_concept(self.placeholder_token, init_embedding)
+        adapter.inject()
+
+
+class TextualInversionLatentDiffusionConfig(FinetuneLatentDiffusionConfig):
+    latent_diffusion: LatentDiffusionConfig
+    textual_inversion: TextualInversionConfig
+
+    def model_post_init(self, __context: Any) -> None:
+        # Pydantic v2 does post init differently, so we need to override this method too.
+        logger.info("Freezing models to train only the new embedding.")
+        self.models["unet"].train = False
+        self.models["text_encoder"].train = False
+        self.models["lda"].train = False
+
+
+class TextualInversionLatentDiffusionTrainer(LatentDiffusionTrainer[TextualInversionLatentDiffusionConfig]):
+    def __init__(
+        self,
+        config: TextualInversionLatentDiffusionConfig,
+        callbacks: "list[Callback[Any]] | None" = None,
+    ) -> None:
+        super().__init__(config=config, callbacks=callbacks)
+        self.callbacks.extend((LoadTextualInversion(), SaveTextualInversion()))
+
+    def load_dataset(self) -> Dataset[TextEmbeddingLatentsBatch]:
+        return TextualInversionDataset(trainer=self)
+
+
+class LoadTextualInversion(Callback[TextualInversionLatentDiffusionTrainer]):
+    def on_train_begin(self, trainer: TextualInversionLatentDiffusionTrainer) -> None:
+        trainer.config.textual_inversion.apply_textual_inversion_to_target(text_encoder=trainer.text_encoder)
+
+
+class SaveTextualInversion(Callback[TextualInversionLatentDiffusionTrainer]):
+    def on_checkpoint_save(self, trainer: TextualInversionLatentDiffusionTrainer) -> None:
+        embedding_extender = trainer.text_encoder.find(layer_type=EmbeddingExtender)
+        assert embedding_extender is not None, "Embedding extender not found in text encoder."
+        tensors = {trainer.config.textual_inversion.placeholder_token: embedding_extender.new_weight.squeeze(0)}
+
+        save_to_safetensors(
+            path=trainer.ensure_checkpoints_save_folder / f"step{trainer.clock.step}.safetensors", tensors=tensors
+        )
+
+
+if __name__ == "__main__":
+    import sys
+
+    config_path = sys.argv[1]
+    config = TextualInversionLatentDiffusionConfig.load_from_toml(toml_path=config_path)
+    trainer = TextualInversionLatentDiffusionTrainer(config=config)
+    trainer.train()

src/refiners/foundationals/clip/concepts.py

@@ -71,7 +71,6 @@ class ConceptExtender(fl.Chain, Adapter[CLIPTextEncoder]):
 class EmbeddingExtender(fl.Chain, Adapter[TokenEncoder]):
     old_weight: Parameter
     new_weight: Parameter
-    weight: Tensor
 
     def __init__(
         self,
@@ -83,22 +82,21 @@ class EmbeddingExtender(fl.Chain, Adapter[TokenEncoder]):
         self.new_weight = Parameter(
             zeros([0, target.embedding_dim], device=target.device, dtype=target.dtype)
         )  # requires_grad=True by default
-        self.weight = cat([self.old_weight, self.new_weight])
 
     # Use F.embedding instead of nn.Embedding to make sure that gradients can only be computed for the new embeddings
     def lookup(self, x: Tensor) -> Tensor:
-        return F.embedding(x, self.weight)
+        # Concatenate old and new weights for dynamic embedding updates during training
+        return F.embedding(x, cat([self.old_weight, self.new_weight]))
 
     def add_embedding(self, embedding: Tensor) -> None:
         assert embedding.shape == (self.old_weight.shape[1],)
         self.new_weight = Parameter(
             cat([self.new_weight, embedding.unsqueeze(0).to(self.new_weight.device, self.new_weight.dtype)])
         )
-        self.weight = cat([self.old_weight, self.new_weight])
 
     @property
     def num_embeddings(self) -> int:
-        return self.weight.shape[0]
+        return self.old_weight.shape[0] + self.new_weight.shape[0]
 
 
 class TokenExtender(fl.Chain, Adapter[CLIPTokenizer]):
@@ -115,12 +113,13 @@ class TokenExtender(fl.Chain, Adapter[CLIPTokenizer]):
             )
 
     def add_token(self, token: str, token_id: int) -> None:
+        token = token.lower()
         tokenizer = self.find(layer_type=CLIPTokenizer)
         assert tokenizer is not None, "Tokenizer not found."
         assert token_id not in tokenizer.token_to_id_mapping.values()
         tokenizer.token_to_id_mapping[token] = token_id
         current_pattern = tokenizer.token_pattern.pattern
-        new_pattern = token + "|" + current_pattern
+        new_pattern = re.escape(token) + "|" + current_pattern
         tokenizer.token_pattern = re.compile(new_pattern, re.IGNORECASE)
         # Define the keyword as its own smallest subtoken
         tokenizer.byte_pair_encoding_cache[token] = token

src/refiners/training_utils/latent_diffusion.py

@@ -95,9 +95,15 @@ class TextEmbeddingLatentsDataset(Dataset[TextEmbeddingLatentsBatch]):
     def process_caption(self, caption: str) -> str:
         return caption if random.random() > self.config.latent_diffusion.unconditional_sampling_probability else ""
 
+    def get_caption(self, index: int) -> str:
+        return self.dataset[index]["caption"]
+
+    def get_image(self, index: int) -> Image.Image:
+        return self.dataset[index]["image"]
+
     def __getitem__(self, index: int) -> TextEmbeddingLatentsBatch:
-        item = self.dataset[index]
+        caption = self.get_caption(index=index)
-        caption, image = item["caption"], item["image"]
+        image = self.get_image(index=index)
         resized_image = self.resize_image(
             image=image,
             min_size=self.config.dataset.resize_image_min_size,

tests/foundationals/clip/test_concepts.py

@@ -4,10 +4,11 @@ import pytest
 from warnings import warn
 from pathlib import Path
 
-from refiners.foundationals.clip.concepts import ConceptExtender
+from refiners.foundationals.clip.concepts import ConceptExtender, TokenExtender
 from refiners.foundationals.clip.text_encoder import CLIPTextEncoderL
 from refiners.foundationals.clip.tokenizer import CLIPTokenizer
 from refiners.fluxion.utils import load_from_safetensors
+import refiners.fluxion.layers as fl
 
 from diffusers import StableDiffusionPipeline  # type: ignore
 import transformers  # type: ignore
@@ -84,6 +85,28 @@ def cat_embedding_textual_inversion(test_textual_inversion_path: Path) -> torch.
     return torch.load(test_textual_inversion_path / "cat-toy" / "learned_embeds.bin")["<cat-toy>"]  # type: ignore
 
 
+def test_tokenizer_with_special_character():
+    clip_tokenizer = fl.Chain(CLIPTokenizer())
+    token_extender = TokenExtender(clip_tokenizer.CLIPTokenizer)
+    new_token_id = max(clip_tokenizer.CLIPTokenizer.token_to_id_mapping.values()) + 42
+    token_extender.add_token("*", new_token_id)
+    token_extender.inject(clip_tokenizer)
+
+    adapted_clip_tokenizer = clip_tokenizer.find(layer_type=CLIPTokenizer)
+    assert adapted_clip_tokenizer is not None
+
+    assert torch.allclose(
+        adapted_clip_tokenizer.encode("*"),
+        torch.Tensor(
+            [
+                adapted_clip_tokenizer.start_of_text_token_id,
+                new_token_id,
+                adapted_clip_tokenizer.end_of_text_token_id,
+            ]
+        ).to(torch.int64),
+    )
+
+
 def test_encoder(
     prompt: str,
     ref_tokenizer_with_new_concepts: transformers.CLIPTokenizer,