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write StyleAligned
e2e test
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@ -30,6 +30,7 @@ from refiners.foundationals.latent_diffusion.solvers import DDIM, Euler, NoiseSc
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from refiners.foundationals.latent_diffusion.stable_diffusion_1.multi_diffusion import SD1MultiDiffusion
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from refiners.foundationals.latent_diffusion.stable_diffusion_xl.control_lora import ControlLoraAdapter
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from refiners.foundationals.latent_diffusion.stable_diffusion_xl.model import StableDiffusion_XL
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from refiners.foundationals.latent_diffusion.style_aligned import StyleAlignedAdapter
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from tests.utils import ensure_similar_images
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@ -150,6 +151,11 @@ def expected_sdxl_euler_random_init(ref_path: Path) -> Image.Image:
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return Image.open(ref_path / "expected_cutecat_sdxl_euler_random_init.png").convert("RGB")
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@pytest.fixture
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def expected_style_aligned(ref_path: Path) -> Image.Image:
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return Image.open(fp=ref_path / "expected_style_aligned.png").convert(mode="RGB")
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@pytest.fixture(scope="module", params=["canny", "depth", "lineart", "normals", "sam"])
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def controlnet_data(
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ref_path: Path, test_weights_path: Path, request: pytest.FixtureRequest
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@ -2140,3 +2146,79 @@ def test_hello_world(
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predicted_image = sdxl.lda.latents_to_image(x)
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ensure_similar_images(predicted_image, expected_image)
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@no_grad()
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def test_style_aligned(
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sdxl_ddim_lda_fp16_fix: StableDiffusion_XL,
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expected_style_aligned: Image.Image,
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):
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sdxl = sdxl_ddim_lda_fp16_fix.to(dtype=torch.float16)
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sdxl.dtype = torch.float16 # FIXME: should not be necessary
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style_aligned_adapter = StyleAlignedAdapter(sdxl.unet)
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style_aligned_adapter.inject()
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set_of_prompts = [
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"a toy train. macro photo. 3d game asset",
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"a toy airplane. macro photo. 3d game asset",
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"a toy bicycle. macro photo. 3d game asset",
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"a toy car. macro photo. 3d game asset",
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"a toy boat. macro photo. 3d game asset",
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]
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# create (context) embeddings from prompts
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# TODO: replace this logic with https://github.com/finegrain-ai/refiners/pull/263 when it gets merged
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unconds: list[torch.Tensor] = []
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conds: list[torch.Tensor] = []
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pooled_unconds: list[torch.Tensor] = []
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pooled_conds: list[torch.Tensor] = []
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for prompt in set_of_prompts:
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clip_text_embedding, pooled_text_embedding = sdxl.compute_clip_text_embedding(text=prompt)
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uncond, cond = clip_text_embedding.chunk(2)
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pooled_uncond, pooled_cond = pooled_text_embedding.chunk(2)
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unconds.append(uncond)
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conds.append(cond)
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pooled_unconds.append(pooled_uncond)
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pooled_conds.append(pooled_cond)
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uncond = torch.cat(unconds, dim=0)
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cond = torch.cat(conds, dim=0)
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pooled_uncond = torch.cat(pooled_unconds, dim=0)
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pooled_cond = torch.cat(pooled_conds, dim=0)
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clip_text_embedding = torch.cat((uncond, cond), dim=0)
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pooled_text_embedding = torch.cat((pooled_uncond, pooled_cond), dim=0)
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time_ids = sdxl.default_time_ids.repeat(len(set_of_prompts), 1)
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# initialize latents
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manual_seed(seed=2)
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x = torch.randn(
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(len(set_of_prompts), 4, 128, 128),
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device=sdxl.device,
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dtype=sdxl.dtype,
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)
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# denoise
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for step in sdxl.steps:
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x = sdxl(
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x,
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step=step,
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clip_text_embedding=clip_text_embedding,
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pooled_text_embedding=pooled_text_embedding,
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time_ids=time_ids,
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)
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# decode latents
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predicted_images = [sdxl.lda.decode_latents(latent.unsqueeze(0)) for latent in x]
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# tile all images horizontally
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merged_image = Image.new("RGB", (1024 * len(predicted_images), 1024))
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for i in range(len(predicted_images)):
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merged_image.paste(predicted_images[i], (i * 1024, 0))
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# compare against reference image
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ensure_similar_images(merged_image, expected_style_aligned, min_psnr=35, min_ssim=0.99)
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@ -56,6 +56,7 @@ Special cases:
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- `expected_controllora_PyraCanny.png`
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- `expected_controllora_PyraCanny+CPDS.png`
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- `expected_controllora_disabled.png`
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- `expected_style_aligned.png`
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## Other images
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BIN
tests/e2e/test_diffusion_ref/expected_style_aligned.png
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tests/e2e/test_diffusion_ref/expected_style_aligned.png
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