add tests for LCM and LCM-LoRA

(As of now LoRA with guidance > 1 and especially base do not pass with those tolerances.)

tests/e2e/test_lcm.py

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+import gc
+from pathlib import Path
+from warnings import warn
+
+import pytest
+import torch
+from PIL import Image
+
+from refiners.fluxion.utils import load_from_safetensors, manual_seed, no_grad
+from refiners.foundationals.latent_diffusion.lcm_lora import add_lcm_lora
+from refiners.foundationals.latent_diffusion.lora import SDLoraManager
+from refiners.foundationals.latent_diffusion.solvers import LCMSolver
+from refiners.foundationals.latent_diffusion.stable_diffusion_xl.lcm import LcmAdapter
+from refiners.foundationals.latent_diffusion.stable_diffusion_xl.model import StableDiffusion_XL
+from tests.utils import ensure_similar_images
+
+
+@pytest.fixture(autouse=True)
+def ensure_gc():
+    # Avoid GPU OOMs
+    # See https://github.com/pytest-dev/pytest/discussions/8153#discussioncomment-214812
+    gc.collect()
+
+
+@pytest.fixture
+def sdxl_lda_fp16_fix_weights(test_weights_path: Path) -> Path:
+    r = test_weights_path / "sdxl-lda-fp16-fix.safetensors"
+    if not r.is_file():
+        warn(f"could not find weights at {r}, skipping")
+        pytest.skip(allow_module_level=True)
+    return r
+
+
+@pytest.fixture
+def sdxl_unet_weights(test_weights_path: Path) -> Path:
+    r = test_weights_path / "sdxl-unet.safetensors"
+    if not r.is_file():
+        warn(f"could not find weights at {r}, skipping")
+        pytest.skip(allow_module_level=True)
+    return r
+
+
+@pytest.fixture
+def sdxl_lcm_unet_weights(test_weights_path: Path) -> Path:
+    r = test_weights_path / "sdxl-lcm-unet.safetensors"
+    if not r.is_file():
+        warn(f"could not find weights at {r}, skipping")
+        pytest.skip(allow_module_level=True)
+    return r
+
+
+@pytest.fixture
+def sdxl_text_encoder_weights(test_weights_path: Path) -> Path:
+    r = test_weights_path / "DoubleCLIPTextEncoder.safetensors"
+    if not r.is_file():
+        warn(f"could not find weights at {r}, skipping")
+        pytest.skip(allow_module_level=True)
+    return r
+
+
+@pytest.fixture
+def sdxl_lcm_lora_weights(test_weights_path: Path) -> Path:
+    r = test_weights_path / "sdxl-lcm-lora.safetensors"
+    if not r.is_file():
+        warn(f"could not find weights at {r}, skipping")
+        pytest.skip(allow_module_level=True)
+    return r
+
+
+@pytest.fixture(scope="module")
+def ref_path(test_e2e_path: Path) -> Path:
+    return test_e2e_path / "test_lcm_ref"
+
+
+@pytest.fixture
+def expected_lcm_base(ref_path: Path) -> Image.Image:
+    return Image.open(ref_path / "expected_lcm_base.png").convert("RGB")
+
+
+@pytest.fixture
+def expected_lcm_lora_1_0(ref_path: Path) -> Image.Image:
+    return Image.open(ref_path / "expected_lcm_lora_1_0.png").convert("RGB")
+
+
+@pytest.fixture
+def expected_lcm_lora_1_2(ref_path: Path) -> Image.Image:
+    return Image.open(ref_path / "expected_lcm_lora_1_2.png").convert("RGB")
+
+
+@no_grad()
+def test_lcm_base(
+    test_device: torch.device,
+    sdxl_lda_fp16_fix_weights: Path,
+    sdxl_lcm_unet_weights: Path,
+    sdxl_text_encoder_weights: Path,
+    expected_lcm_base: Image.Image,
+) -> None:
+    if test_device.type == "cpu":
+        warn(message="not running on CPU, skipping")
+        pytest.skip()
+
+    solver = LCMSolver(num_inference_steps=4)
+    sdxl = StableDiffusion_XL(device=test_device, dtype=torch.float16, solver=solver)
+    sdxl.classifier_free_guidance = False
+
+    # With standard LCM the condition scale is passed to the adapter,
+    # not in the diffusion loop.
+    LcmAdapter(sdxl.unet, condition_scale=8.0).inject()
+
+    sdxl.clip_text_encoder.load_from_safetensors(sdxl_text_encoder_weights)
+    sdxl.lda.load_from_safetensors(sdxl_lda_fp16_fix_weights)
+    sdxl.unet.load_from_safetensors(sdxl_lcm_unet_weights)
+
+    prompt = "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k"
+    expected_image = expected_lcm_base
+
+    # *NOT* compute_clip_text_embedding! We disable classifier-free guidance.
+    clip_text_embedding, pooled_text_embedding = sdxl.clip_text_encoder(prompt)
+    time_ids = sdxl.default_time_ids
+
+    manual_seed(2)
+    x = sdxl.init_latents((1024, 1024)).to(sdxl.device, sdxl.dtype)
+
+    for step in sdxl.steps:
+        x = sdxl(
+            x,
+            step=step,
+            clip_text_embedding=clip_text_embedding,
+            pooled_text_embedding=pooled_text_embedding,
+            time_ids=time_ids,
+        )
+    predicted_image = sdxl.lda.latents_to_image(x)
+
+    ensure_similar_images(predicted_image, expected_image, min_psnr=35, min_ssim=0.98)
+
+
+@no_grad()
+@pytest.mark.parametrize("condition_scale", [1.0, 1.2])
+def test_lcm_lora_with_guidance(
+    test_device: torch.device,
+    sdxl_lda_fp16_fix_weights: Path,
+    sdxl_unet_weights: Path,
+    sdxl_text_encoder_weights: Path,
+    sdxl_lcm_lora_weights: Path,
+    expected_lcm_lora_1_0: Image.Image,
+    expected_lcm_lora_1_2: Image.Image,
+    condition_scale: float,
+) -> None:
+    if test_device.type == "cpu":
+        warn(message="not running on CPU, skipping")
+        pytest.skip()
+
+    solver = LCMSolver(num_inference_steps=4)
+    sdxl = StableDiffusion_XL(device=test_device, dtype=torch.float16, solver=solver)
+
+    sdxl.clip_text_encoder.load_from_safetensors(sdxl_text_encoder_weights)
+    sdxl.lda.load_from_safetensors(sdxl_lda_fp16_fix_weights)
+    sdxl.unet.load_from_safetensors(sdxl_unet_weights)
+
+    manager = SDLoraManager(sdxl)
+    add_lcm_lora(manager, "lcm", load_from_safetensors(sdxl_lcm_lora_weights))
+
+    prompt = "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k"
+    expected_image = expected_lcm_lora_1_0 if condition_scale == 1.0 else expected_lcm_lora_1_2
+
+    # *NOT* clip_text_encoder! We use classifier-free guidance here.
+    clip_text_embedding, pooled_text_embedding = sdxl.compute_clip_text_embedding(prompt)
+    time_ids = sdxl.default_time_ids
+    assert time_ids.shape == (2, 6)  # CFG
+
+    manual_seed(2)
+    x = sdxl.init_latents((1024, 1024)).to(sdxl.device, sdxl.dtype)
+
+    for step in sdxl.steps:
+        x = sdxl(
+            x,
+            step=step,
+            clip_text_embedding=clip_text_embedding,
+            pooled_text_embedding=pooled_text_embedding,
+            time_ids=time_ids,
+            condition_scale=condition_scale,
+        )
+    predicted_image = sdxl.lda.latents_to_image(x)
+
+    psnr = 35 if condition_scale == 1.0 else 33
+    ensure_similar_images(predicted_image, expected_image, min_psnr=psnr, min_ssim=0.98)
+
+
+@no_grad()
+def test_lcm_lora_without_guidance(
+    test_device: torch.device,
+    sdxl_lda_fp16_fix_weights: Path,
+    sdxl_unet_weights: Path,
+    sdxl_text_encoder_weights: Path,
+    sdxl_lcm_lora_weights: Path,
+    expected_lcm_lora_1_0: Image.Image,
+) -> None:
+    if test_device.type == "cpu":
+        warn(message="not running on CPU, skipping")
+        pytest.skip()
+
+    solver = LCMSolver(num_inference_steps=4)
+    sdxl = StableDiffusion_XL(device=test_device, dtype=torch.float16, solver=solver)
+    sdxl.classifier_free_guidance = False
+
+    sdxl.clip_text_encoder.load_from_safetensors(sdxl_text_encoder_weights)
+    sdxl.lda.load_from_safetensors(sdxl_lda_fp16_fix_weights)
+    sdxl.unet.load_from_safetensors(sdxl_unet_weights)
+
+    manager = SDLoraManager(sdxl)
+    add_lcm_lora(manager, "lcm", load_from_safetensors(sdxl_lcm_lora_weights))
+
+    prompt = "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k"
+    expected_image = expected_lcm_lora_1_0
+
+    # *NOT* compute_clip_text_embedding! We disable classifier-free guidance.
+    clip_text_embedding, pooled_text_embedding = sdxl.clip_text_encoder(prompt)
+    time_ids = sdxl.default_time_ids
+    assert time_ids.shape == (1, 6)  # no CFG
+
+    manual_seed(2)
+    x = sdxl.init_latents((1024, 1024)).to(sdxl.device, sdxl.dtype)
+
+    for step in sdxl.steps:
+        x = sdxl(
+            x,
+            step=step,
+            clip_text_embedding=clip_text_embedding,
+            pooled_text_embedding=pooled_text_embedding,
+            time_ids=time_ids,
+            condition_scale=0.0,
+        )
+    predicted_image = sdxl.lda.latents_to_image(x)
+
+    ensure_similar_images(predicted_image, expected_image, min_psnr=35, min_ssim=0.98)

tests/e2e/test_lcm_ref/README.md

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+# Note about this data
+
+## Expected outputs
+
+`expected_*.png` files in this folder are all the output of the same diffusion run with Diffusers (see [here](https://huggingface.co/docs/diffusers/main/en/using-diffusers/lcm) and [here](https://huggingface.co/docs/diffusers/main/en/using-diffusers/inference_with_lcm_lora), but note that the generator must be properly seeded).