mirror of
https://github.com/finegrain-ai/refiners.git
synced 2024-11-15 09:38:14 +00:00
129 lines
4.9 KiB
Python
129 lines
4.9 KiB
Python
from pathlib import Path
|
|
|
|
import pytest
|
|
import torch
|
|
import transformers # type: ignore
|
|
from diffusers import StableDiffusionPipeline # type: ignore
|
|
|
|
import refiners.fluxion.layers as fl
|
|
from refiners.fluxion.utils import load_from_safetensors, load_tensors, no_grad
|
|
from refiners.foundationals.clip.concepts import ConceptExtender, TokenExtender
|
|
from refiners.foundationals.clip.text_encoder import CLIPTextEncoderL
|
|
from refiners.foundationals.clip.tokenizer import CLIPTokenizer
|
|
|
|
PROMPTS = [
|
|
"a cute cat", # a simple prompt
|
|
"This artwork is inspired by <gta5-artwork> and uses a <cat-toy> as a prop", # prompt with two added concepts
|
|
]
|
|
|
|
|
|
@pytest.fixture(scope="module")
|
|
def our_encoder_with_new_concepts(
|
|
sd15_text_encoder_weights_path: Path,
|
|
test_device: torch.device,
|
|
cat_embedding_textual_inversion: torch.Tensor,
|
|
gta5_artwork_embedding_textual_inversion: torch.Tensor,
|
|
) -> CLIPTextEncoderL:
|
|
encoder = CLIPTextEncoderL(device=test_device)
|
|
tensors = load_from_safetensors(sd15_text_encoder_weights_path)
|
|
encoder.load_state_dict(tensors)
|
|
concept_extender = ConceptExtender(encoder)
|
|
concept_extender.add_concept("<cat-toy>", cat_embedding_textual_inversion)
|
|
concept_extender.add_concept("<gta5-artwork>", gta5_artwork_embedding_textual_inversion)
|
|
concept_extender.inject()
|
|
return encoder
|
|
|
|
|
|
@pytest.fixture(scope="module")
|
|
def ref_sd15_with_new_concepts(
|
|
sd15_diffusers_runwayml_path: str,
|
|
test_textual_inversion_path: Path,
|
|
test_device: torch.device,
|
|
use_local_weights: bool,
|
|
) -> StableDiffusionPipeline:
|
|
pipe = StableDiffusionPipeline.from_pretrained( # type: ignore
|
|
sd15_diffusers_runwayml_path,
|
|
local_files_only=use_local_weights,
|
|
).to(test_device) # type: ignore
|
|
assert isinstance(pipe, StableDiffusionPipeline)
|
|
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 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 load_tensors(test_textual_inversion_path / "gta5-artwork" / "learned_embeds.bin")["<gta5-artwork>"]
|
|
|
|
|
|
@pytest.fixture(scope="module")
|
|
def cat_embedding_textual_inversion(test_textual_inversion_path: Path) -> torch.Tensor:
|
|
return load_tensors(test_textual_inversion_path / "cat-toy" / "learned_embeds.bin")["<cat-toy>"]
|
|
|
|
|
|
def test_tokenizer_with_special_character():
|
|
clip_tokenizer_chain = fl.Chain(CLIPTokenizer())
|
|
original_clip_tokenizer = clip_tokenizer_chain.layer("CLIPTokenizer", CLIPTokenizer)
|
|
token_extender = TokenExtender(original_clip_tokenizer)
|
|
new_token_id = max(original_clip_tokenizer.token_to_id_mapping.values()) + 42
|
|
token_extender.add_token("*", new_token_id)
|
|
token_extender.inject(clip_tokenizer_chain)
|
|
|
|
adapted_clip_tokenizer = clip_tokenizer_chain.ensure_find(CLIPTokenizer)
|
|
|
|
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,
|
|
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.ensure_find(CLIPTokenizer)
|
|
our_tokens = tokenizer(prompt)
|
|
assert torch.equal(our_tokens, ref_tokens)
|
|
|
|
with 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
|