From 3680f9d196a8f80e4870b9f557c4f54bb615d0d3 Mon Sep 17 00:00:00 2001 From: Doryan Kaced Date: Fri, 25 Aug 2023 20:27:29 +0200 Subject: [PATCH] Add support for learned concepts e.g. via textual inversion --- src/refiners/foundationals/clip/concepts.py | 121 ++++++++++++++++++ tests/conftest.py | 4 + tests/e2e/test_diffusion.py | 49 +++++++ tests/e2e/test_diffusion_ref/README.md | 33 ++++- ...expected_textual_inversion_random_init.png | Bin 0 -> 460754 bytes tests/foundationals/clip/test_concepts.py | 113 ++++++++++++++++ .../clip/test_concepts_ref/README.md | 6 + .../cat-toy/learned_embeds.bin | Bin 0 -> 3819 bytes .../gta5-artwork/learned_embeds.bin | Bin 0 -> 3819 bytes 9 files changed, 325 insertions(+), 1 deletion(-) create mode 100644 src/refiners/foundationals/clip/concepts.py create mode 100644 tests/e2e/test_diffusion_ref/expected_textual_inversion_random_init.png create mode 100644 tests/foundationals/clip/test_concepts.py create mode 100644 tests/foundationals/clip/test_concepts_ref/README.md create mode 100644 tests/foundationals/clip/test_concepts_ref/cat-toy/learned_embeds.bin create mode 100644 tests/foundationals/clip/test_concepts_ref/gta5-artwork/learned_embeds.bin diff --git a/src/refiners/foundationals/clip/concepts.py b/src/refiners/foundationals/clip/concepts.py new file mode 100644 index 0000000..586f5eb --- /dev/null +++ b/src/refiners/foundationals/clip/concepts.py @@ -0,0 +1,121 @@ +from refiners.adapters.adapter import Adapter +from refiners.foundationals.clip.text_encoder import CLIPTextEncoder, TokenEncoder +from refiners.foundationals.clip.tokenizer import CLIPTokenizer +import refiners.fluxion.layers as fl +from typing import cast + +from torch import Tensor, cat, zeros +import torch.nn.functional as F +from torch.nn import Parameter +import re + + +class ConceptExtender: + """ + Extends the vocabulary of a CLIPTextEncoder with one or multiple new concepts, e.g. obtained via the Textual Inversion technique. + + Example: + import torch + from refiners.foundationals.clip.concepts import ConceptExtender + from refiners.foundationals.clip.text_encoder import CLIPTextEncoderL + from refiners.fluxion.utils import load_from_safetensors + + encoder = CLIPTextEncoderL(device="cuda") + tensors = load_from_safetensors("CLIPTextEncoderL.safetensors") + encoder.load_state_dict(tensors) + + cat_embedding = torch.load("cat_embedding.bin")[""] + dog_embedding = torch.load("dog_embedding.bin")[""] + + extender = ConceptExtender(encoder) + extender.add_concept(token="", embedding=cat_embedding) + extender.inject() + # New concepts can be added at any time + extender.add_concept(token="", embedding=dog_embedding) + + # Now the encoder can be used with the new concepts + """ + + def __init__(self, target: CLIPTextEncoder) -> None: + try: + token_encoder, self.token_encoder_parent = next(target.walk(TokenEncoder)) + except StopIteration: + raise RuntimeError("TokenEncoder not found.") + + try: + clip_tokenizer, self.clip_tokenizer_parent = next(target.walk(CLIPTokenizer)) + except StopIteration: + raise RuntimeError("Tokenizer not found.") + + self.embedding_extender = EmbeddingExtender(token_encoder) + self.token_extender = TokenExtender(clip_tokenizer) + + def add_concept(self, token: str, embedding: Tensor) -> None: + self.embedding_extender.add_embedding(embedding) + self.token_extender.add_token(token, self.embedding_extender.num_embeddings - 1) + + def inject(self) -> None: + self.embedding_extender.inject(self.token_encoder_parent) + self.token_extender.inject(self.clip_tokenizer_parent) + + def eject(self) -> None: + self.embedding_extender.eject() + self.token_extender.eject() + + +class EmbeddingExtender(fl.Chain, Adapter[TokenEncoder]): + old_weight: Parameter + new_weight: Parameter + weight: Tensor + + def __init__( + self, + target: TokenEncoder, + ) -> None: + with self.setup_adapter(target): + super().__init__(fl.Lambda(func=self.lookup)) + self.old_weight = cast(Parameter, target.weight) + 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) + + 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] + + +class TokenExtender(fl.Chain, Adapter[CLIPTokenizer]): + def __init__(self, target: CLIPTokenizer) -> None: + with self.setup_adapter(target): + super().__init__( + CLIPTokenizer( + vocabulary_path=target.vocabulary_path, + sequence_length=target.sequence_length, + start_of_text_token_id=target.start_of_text_token_id, + end_of_text_token_id=target.end_of_text_token_id, + pad_token_id=target.pad_token_id, + ) + ) + + def add_token(self, token: str, token_id: int) -> None: + 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 + tokenizer.token_pattern = re.compile(new_pattern, re.IGNORECASE) + # Define the keyword as its own smallest subtoken + tokenizer.byte_pair_encoding_cache[token] = token diff --git a/tests/conftest.py b/tests/conftest.py index e9e3ae1..cf7a28b 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -23,3 +23,7 @@ def test_weights_path() -> Path: @fixture(scope="session") def test_e2e_path() -> Path: return PARENT_PATH / "e2e" + +@fixture(scope="session") +def test_textual_inversion_path() -> Path: + return PARENT_PATH / "foundationals" / "clip" / "test_concepts_ref" diff --git a/tests/e2e/test_diffusion.py b/tests/e2e/test_diffusion.py index 31b0ec6..4a2ed9d 100644 --- a/tests/e2e/test_diffusion.py +++ b/tests/e2e/test_diffusion.py @@ -14,6 +14,7 @@ from refiners.foundationals.latent_diffusion.stable_diffusion_1.controlnet impor from refiners.foundationals.latent_diffusion.lora import LoraWeights from refiners.foundationals.latent_diffusion.schedulers import DDIM from refiners.foundationals.latent_diffusion.self_attention_injection import SelfAttentionInjection +from refiners.foundationals.clip.concepts import ConceptExtender from tests.utils import ensure_similar_images @@ -118,6 +119,16 @@ def condition_image_refonly(ref_path: Path) -> Image.Image: return Image.open(ref_path / "cyberpunk_guide.png").convert("RGB") +@pytest.fixture +def expected_image_textual_inversion_random_init(ref_path: Path) -> Image.Image: + return Image.open(ref_path / "expected_textual_inversion_random_init.png").convert("RGB") + + +@pytest.fixture +def text_embedding_textual_inversion(test_textual_inversion_path: Path) -> torch.Tensor: + return torch.load(test_textual_inversion_path / "gta5-artwork" / "learned_embeds.bin")[""] # type: ignore + + @pytest.fixture(scope="module") def text_encoder_weights(test_weights_path: Path) -> Path: text_encoder_weights = test_weights_path / "CLIPTextEncoderL.safetensors" @@ -689,3 +700,41 @@ def test_diffusion_inpainting_refonly( predicted_image = sd15.lda.decode_latents(x) ensure_similar_images(predicted_image, expected_image_inpainting_refonly, min_psnr=35, min_ssim=0.99) + + +@torch.no_grad() +def test_diffusion_textual_inversion_random_init( + sd15_std: StableDiffusion_1, + expected_image_textual_inversion_random_init: Image.Image, + text_embedding_textual_inversion: torch.Tensor, + test_device: torch.device, +): + sd15 = sd15_std + + conceptExtender = ConceptExtender(sd15.clip_text_encoder) + conceptExtender.add_concept("", text_embedding_textual_inversion) + conceptExtender.inject() + + n_steps = 30 + + prompt = "a cute cat on a " + + with torch.no_grad(): + clip_text_embedding = sd15.compute_clip_text_embedding(prompt) + + sd15.set_num_inference_steps(n_steps) + + manual_seed(2) + x = torch.randn(1, 4, 64, 64, device=test_device) + + with torch.no_grad(): + for step in sd15.steps: + x = sd15( + x, + step=step, + clip_text_embedding=clip_text_embedding, + condition_scale=7.5, + ) + predicted_image = sd15.lda.decode_latents(x) + + ensure_similar_images(predicted_image, expected_image_textual_inversion_random_init, min_psnr=35, min_ssim=0.98) diff --git a/tests/e2e/test_diffusion_ref/README.md b/tests/e2e/test_diffusion_ref/README.md index f1024bb..59447dd 100644 --- a/tests/e2e/test_diffusion_ref/README.md +++ b/tests/e2e/test_diffusion_ref/README.md @@ -15,7 +15,7 @@ from diffusers import StableDiffusionPipeline pipe = StableDiffusionPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float32, -).to("cuda) +).to("cuda") pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) prompt = "a cute cat, detailed high-quality professional image" @@ -80,3 +80,34 @@ Special cases: init_latents = self.vae.config.scaling_factor * init_latents ``` + +## Textual Inversion + +- `expected_textual_inversion_random_init.png` has been generated with StableDiffusionPipeline, e.g.: + +```py +import torch + +from diffusers import DPMSolverMultistepScheduler +from diffusers import StableDiffusionPipeline + +pipe = StableDiffusionPipeline.from_pretrained( + "runwayml/stable-diffusion-v1-5", + torch_dtype=torch.float32, +).to("cuda") +pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) +pipe.load_textual_inversion("sd-concepts-library/gta5-artwork") + +prompt = "a cute cat on a " +negative_prompt = "" + +torch.manual_seed(2) +output = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + num_inference_steps=30, + guidance_scale=7.5, +) + +output.images[0].save("expected_textual_inversion_random_init.png") +``` diff --git a/tests/e2e/test_diffusion_ref/expected_textual_inversion_random_init.png b/tests/e2e/test_diffusion_ref/expected_textual_inversion_random_init.png new file mode 100644 index 0000000000000000000000000000000000000000..ec28972804af3a4853b61c920d580196fba2c1e9 GIT binary patch literal 460754 zcmV)6K*+y|P)F(JxyYsXUyMMu+{UdfC z_Uze*IXip0ce>eRvq1oaF31unl$KekRYZokapfo6Y#-ds-EV>uKxIbW`w15&e)cnS zcl^Kp>7N&3pfy5-0ssOC00a$No_u{txjNgtzM@2mpu(Ai^RD2HCMOMa$4z8Qd9-{+DllX9~xkefp`t z`pO^Pc>e>g#zrg(2#^RP8D<0m0YLAw&}$U&tpf1f3V$JhgufsB!R(GWY9J5?(mxmh1OyO75J^ac+qwm`o$ioLTUzUQ z(YCb#;+6}tb?qDr0vY3sMHXqD2%-=IVwg1%iETpt3)8P6`mZqNI=*2d0sT~x@gEXl z=oB*`01E;NA^?a8W_=8Q2C>M8CJKs<28QcFLL<<>EVjW9ul^Dq7^TDihA1qUy?zxmETTsyqql+B|X 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zDXx`t9FwoA(9~J_Y*~T8K~9EPHb$j#!!`_U*W7J(zxm}a?zVRye)?l_4!{|quRNCh z31X@yYUH`1=JxJzdUn!0I42;o9QQkY&=M{@vsLZTQ>ty%x-`ZtdHM7QQPF%}$0!jv*QN?AD6l2oS1?9icznj&RTglQ5m6Ey%A ze8Y~*ykM~#LE_TfGx=%CNH|V0#0OYvoS`KU`3Sg>JijvZ9 zKXz@^_>S0LzrK3;`t6T@^21~VF;#6F+7?grkKFu&F${XS9jBtS+w7-QIdf}a$}UXt^`RGz=4^lpnLyA` z4T and uses a as a prop", # prompt with two added concepts +] + + +@pytest.fixture(scope="module") +def our_encoder_with_new_concepts( + test_weights_path: Path, + test_device: torch.device, + cat_embedding_textual_inversion: torch.Tensor, + gta5_artwork_embedding_textual_inversion: torch.Tensor, +) -> CLIPTextEncoderL: + weights = test_weights_path / "CLIPTextEncoderL.safetensors" + if not weights.is_file(): + warn(f"could not find weights at {weights}, skipping") + pytest.skip(allow_module_level=True) + encoder = CLIPTextEncoderL(device=test_device) + tensors = load_from_safetensors(weights) + encoder.load_state_dict(tensors) + concept_extender = ConceptExtender(encoder) + concept_extender.add_concept("", cat_embedding_textual_inversion) + concept_extender.add_concept("", gta5_artwork_embedding_textual_inversion) + concept_extender.inject() + return encoder + + +@pytest.fixture(scope="module") +def ref_sd15_with_new_concepts(runwayml_weights_path: Path, test_textual_inversion_path: Path, test_device: torch.device): + pipe = StableDiffusionPipeline.from_pretrained(runwayml_weights_path, torch_dtype=torch.float16).to(test_device) # type: ignore + pipe.load_textual_inversion(test_textual_inversion_path / "cat-toy") # type: ignore + pipe.load_textual_inversion(test_textual_inversion_path / "gta5-artwork") # type: ignore + return pipe + + +@pytest.fixture(scope="module") +def runwayml_weights_path(test_weights_path: Path): + r = test_weights_path / "runwayml" / "stable-diffusion-v1-5" + if not r.is_dir(): + warn(f"could not find RunwayML weights at {r}, skipping") + pytest.skip(allow_module_level=True) + return r + + +@pytest.fixture(scope="module") +def ref_tokenizer_with_new_concepts(ref_sd15_with_new_concepts: StableDiffusionPipeline) -> transformers.CLIPTokenizer: + return ref_sd15_with_new_concepts.tokenizer # type: ignore + + +@pytest.fixture(scope="module") +def ref_encoder_with_new_concepts(ref_sd15_with_new_concepts: StableDiffusionPipeline) -> transformers.CLIPTextModel: + return ref_sd15_with_new_concepts.text_encoder + + +@pytest.fixture(params=PROMPTS) +def prompt(request: pytest.FixtureRequest): + return request.param + + +@pytest.fixture(scope="module") +def gta5_artwork_embedding_textual_inversion(test_textual_inversion_path: Path) -> torch.Tensor: + return torch.load(test_textual_inversion_path / "gta5-artwork" / "learned_embeds.bin")[""] # type: ignore + + +@pytest.fixture(scope="module") +def cat_embedding_textual_inversion(test_textual_inversion_path: Path) -> torch.Tensor: + return torch.load(test_textual_inversion_path / "cat-toy" / "learned_embeds.bin")[""] # type: ignore + + +def test_encoder( + prompt: str, + ref_tokenizer_with_new_concepts: transformers.CLIPTokenizer, + ref_encoder_with_new_concepts: transformers.CLIPTextModel, + our_encoder_with_new_concepts: CLIPTextEncoderL, + test_device: torch.device, +): + ref_tokens = ref_tokenizer_with_new_concepts( # type: ignore + prompt, + padding="max_length", + max_length=ref_tokenizer_with_new_concepts.model_max_length, # type: ignore + truncation=True, + return_tensors="pt", + ).input_ids + assert isinstance(ref_tokens, torch.Tensor) + tokenizer = our_encoder_with_new_concepts.find(layer_type=CLIPTokenizer) + assert tokenizer is not None + our_tokens = tokenizer(prompt) + assert torch.equal(our_tokens, ref_tokens) + + with torch.no_grad(): + ref_embeddings = ref_encoder_with_new_concepts(ref_tokens.to(test_device))[0] + our_embeddings = our_encoder_with_new_concepts(prompt) + + assert ref_embeddings.shape == (1, 77, 768) + assert our_embeddings.shape == (1, 77, 768) + + # See `test_encoder` in test_text_encoder.py for details about the tolerance (0.04) + assert (our_embeddings - ref_embeddings).abs().max() < 0.04 diff --git a/tests/foundationals/clip/test_concepts_ref/README.md b/tests/foundationals/clip/test_concepts_ref/README.md new file mode 100644 index 0000000..f8fe00e --- /dev/null +++ b/tests/foundationals/clip/test_concepts_ref/README.md @@ -0,0 +1,6 @@ +# Note about this data + +## Textual Inversion Concepts + +- `textual_inversion/cat-toy` comes from https://huggingface.co/sd-concepts-library/cat-toy +- `textual_inversion/gta5-artwork` comes from https://huggingface.co/sd-concepts-library/gta5-artwork diff --git a/tests/foundationals/clip/test_concepts_ref/cat-toy/learned_embeds.bin b/tests/foundationals/clip/test_concepts_ref/cat-toy/learned_embeds.bin new file mode 100644 index 0000000000000000000000000000000000000000..310bf764d0c727d5e1a42de25c8e248329c2e61a GIT binary patch literal 3819 zcmaKvd035W_s4hBZq%%jikwDia3T%Qx+4^JY1mQ9DQO@g)mCIE6sJ-OafCEeGG(Z@ 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