From d5d199edc50de0308651259e5a0c1633def13351 Mon Sep 17 00:00:00 2001 From: Pierre Chapuis Date: Fri, 23 Feb 2024 17:34:50 +0100 Subject: [PATCH] add tests for SDXL Lightning --- tests/e2e/test_lightning.py | 251 ++++++++++++++++++ tests/e2e/test_lightning_ref/README.md | 5 + .../expected_lightning_base_1step.png | Bin 0 -> 1579314 bytes .../expected_lightning_base_4step.png | Bin 0 -> 1519009 bytes .../expected_lightning_lora_4step.png | Bin 0 -> 1465709 bytes 5 files changed, 256 insertions(+) create mode 100644 tests/e2e/test_lightning.py create mode 100644 tests/e2e/test_lightning_ref/README.md create mode 100644 tests/e2e/test_lightning_ref/expected_lightning_base_1step.png create mode 100644 tests/e2e/test_lightning_ref/expected_lightning_base_4step.png create mode 100644 tests/e2e/test_lightning_ref/expected_lightning_lora_4step.png diff --git a/tests/e2e/test_lightning.py b/tests/e2e/test_lightning.py new file mode 100644 index 0000000..40793aa --- /dev/null +++ b/tests/e2e/test_lightning.py @@ -0,0 +1,251 @@ +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.lora import SDLoraManager +from refiners.foundationals.latent_diffusion.solvers import Euler, ModelPredictionType, SolverParams, TimestepSpacing +from refiners.foundationals.latent_diffusion.stable_diffusion_xl.lcm_lora import add_lcm_lora +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_lightning_4step_unet_weights(test_weights_path: Path) -> Path: + r = test_weights_path / "sdxl_lightning_4step_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_lightning_1step_unet_weights(test_weights_path: Path) -> Path: + r = test_weights_path / "sdxl_lightning_1step_unet_x0.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_lightning_4step_lora_weights(test_weights_path: Path) -> Path: + r = test_weights_path / "sdxl_lightning_4step_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_lightning_ref" + + +@pytest.fixture +def expected_lightning_base_4step(ref_path: Path) -> Image.Image: + return Image.open(ref_path / "expected_lightning_base_4step.png").convert("RGB") + + +@pytest.fixture +def expected_lightning_base_1step(ref_path: Path) -> Image.Image: + return Image.open(ref_path / "expected_lightning_base_1step.png").convert("RGB") + + +@pytest.fixture +def expected_lightning_lora_4step(ref_path: Path) -> Image.Image: + return Image.open(ref_path / "expected_lightning_lora_4step.png").convert("RGB") + + +@no_grad() +def test_lightning_base_4step( + test_device: torch.device, + sdxl_lda_fp16_fix_weights: Path, + sdxl_lightning_4step_unet_weights: Path, + sdxl_text_encoder_weights: Path, + expected_lightning_base_4step: Image.Image, +) -> None: + if test_device.type == "cpu": + warn(message="not running on CPU, skipping") + pytest.skip() + + unet_weights = sdxl_lightning_4step_unet_weights + expected_image = expected_lightning_base_4step + + solver = Euler( + num_inference_steps=4, + params=SolverParams( + timesteps_spacing=TimestepSpacing.TRAILING, + model_prediction_type=ModelPredictionType.NOISE, + ), + ) + + 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(unet_weights) + + prompt = "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k" + + clip_text_embedding, pooled_text_embedding = sdxl.clip_text_encoder(prompt) + time_ids = sdxl.default_time_ids + + manual_seed(0) + 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) + + +@no_grad() +def test_lightning_base_1step( + test_device: torch.device, + sdxl_lda_fp16_fix_weights: Path, + sdxl_lightning_1step_unet_weights: Path, + sdxl_text_encoder_weights: Path, + expected_lightning_base_1step: Image.Image, +) -> None: + if test_device.type == "cpu": + warn(message="not running on CPU, skipping") + pytest.skip() + + unet_weights = sdxl_lightning_1step_unet_weights + expected_image = expected_lightning_base_1step + + solver = Euler( + num_inference_steps=1, + params=SolverParams( + timesteps_spacing=TimestepSpacing.TRAILING, + model_prediction_type=ModelPredictionType.SAMPLE, # 1 step special case + ), + ) + + 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(unet_weights) + + prompt = "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k" + + clip_text_embedding, pooled_text_embedding = sdxl.clip_text_encoder(prompt) + time_ids = sdxl.default_time_ids + + manual_seed(0) + 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) + + +@no_grad() +def test_lightning_lora_4step( + test_device: torch.device, + sdxl_lda_fp16_fix_weights: Path, + sdxl_unet_weights: Path, + sdxl_text_encoder_weights: Path, + sdxl_lightning_4step_lora_weights: Path, + expected_lightning_lora_4step: Image.Image, +) -> None: + if test_device.type == "cpu": + warn(message="not running on CPU, skipping") + pytest.skip() + + expected_image = expected_lightning_lora_4step + + solver = Euler( + num_inference_steps=4, + params=SolverParams( + timesteps_spacing=TimestepSpacing.TRAILING, + model_prediction_type=ModelPredictionType.NOISE, + ), + ) + + 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, load_from_safetensors(sdxl_lightning_4step_lora_weights), name="lightning") + + prompt = "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k" + + clip_text_embedding, pooled_text_embedding = sdxl.clip_text_encoder(prompt) + time_ids = sdxl.default_time_ids + + manual_seed(0) + 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) diff --git a/tests/e2e/test_lightning_ref/README.md b/tests/e2e/test_lightning_ref/README.md new file mode 100644 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