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103 lines
3.8 KiB
Python
103 lines
3.8 KiB
Python
from typing import Any, Protocol, cast
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from pathlib import Path
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from warnings import warn
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import pytest
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import torch
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from torch import Tensor
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from refiners.fluxion.utils import manual_seed
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import refiners.fluxion.layers as fl
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from refiners.foundationals.clip.text_encoder import CLIPTextEncoderG, CLIPTextEncoderL
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from refiners.foundationals.latent_diffusion.stable_diffusion_xl.text_encoder import DoubleTextEncoder
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class DiffusersSDXL(Protocol):
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unet: fl.Module
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text_encoder: fl.Module
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text_encoder_2: fl.Module
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tokenizer: fl.Module
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tokenizer_2: fl.Module
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vae: fl.Module
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def __call__(self, prompt: str, *args: Any, **kwargs: Any) -> Any:
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...
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def encode_prompt(
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self,
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prompt: str,
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prompt_2: str | None = None,
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negative_prompt: str | None = None,
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negative_prompt_2: str | None = None,
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) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
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...
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@pytest.fixture(scope="module")
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def stabilityai_sdxl_base_path(test_weights_path: Path) -> Path:
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r = test_weights_path / "stabilityai" / "stable-diffusion-xl-base-1.0"
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if not r.is_dir():
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warn(message=f"could not find Stability SDXL base weights at {r}, skipping")
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pytest.skip(allow_module_level=True)
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return r
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@pytest.fixture(scope="module")
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def diffusers_sdxl(stabilityai_sdxl_base_path: Path) -> Any:
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from diffusers import DiffusionPipeline # type: ignore
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return DiffusionPipeline.from_pretrained(pretrained_model_name_or_path=stabilityai_sdxl_base_path) # type: ignore
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@pytest.fixture(scope="module")
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def double_text_encoder(test_weights_path: Path) -> DoubleTextEncoder:
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text_encoder_l = CLIPTextEncoderL()
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text_encoder_g_with_projection = CLIPTextEncoderG()
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text_encoder_g_with_projection.append(module=fl.Linear(in_features=1280, out_features=1280, bias=False))
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text_encoder_l_path = test_weights_path / "CLIPTextEncoderL.safetensors"
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text_encdoer_g_path = test_weights_path / "CLIPTextEncoderGWithProjection.safetensors"
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text_encoder_l.load_from_safetensors(tensors_path=text_encoder_l_path)
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text_encoder_g_with_projection.load_from_safetensors(tensors_path=text_encdoer_g_path)
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linear = text_encoder_g_with_projection.pop(index=-1)
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assert isinstance(linear, fl.Linear)
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double_text_encoder = DoubleTextEncoder(
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text_encoder_l=text_encoder_l, text_encoder_g=text_encoder_g_with_projection, projection=linear
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)
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return double_text_encoder
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@torch.no_grad()
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def test_double_text_encoder(diffusers_sdxl: DiffusersSDXL, double_text_encoder: DoubleTextEncoder) -> None:
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manual_seed(seed=0)
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prompt = "A photo of a pizza."
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prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds = (
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diffusers_sdxl.encode_prompt(prompt=prompt, negative_prompt="")
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)
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double_embedding, pooled_embedding = double_text_encoder(prompt)
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assert double_embedding.shape == torch.Size([1, 77, 2048])
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assert pooled_embedding.shape == torch.Size([1, 1280])
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embedding_1, embedding_2 = cast(
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tuple[Tensor, Tensor], prompt_embeds.split(split_size=[768, 1280], dim=-1) # type: ignore
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)
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rembedding_1, rembedding_2 = cast(
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tuple[Tensor, Tensor], double_embedding.split(split_size=[768, 1280], dim=-1) # type: ignore
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)
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assert torch.allclose(input=embedding_1, other=rembedding_1, rtol=1e-3, atol=1e-3)
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assert torch.allclose(input=embedding_2, other=rembedding_2, rtol=1e-3, atol=1e-3)
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assert torch.allclose(input=pooled_embedding, other=pooled_prompt_embeds, rtol=1e-3, atol=1e-3)
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negative_double_embedding, negative_pooled_embedding = double_text_encoder("")
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assert torch.allclose(input=negative_double_embedding, other=negative_prompt_embeds, rtol=1e-3, atol=1e-3)
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assert torch.allclose(input=negative_pooled_embedding, other=negative_pooled_prompt_embeds, rtol=1e-3, atol=1e-3)
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