From 383c3c8a0416990b9e1db98603d32c94627f9e0a Mon Sep 17 00:00:00 2001 From: Pierre Chapuis Date: Fri, 16 Feb 2024 13:53:21 +0100 Subject: [PATCH] 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 | 235 ++++++++++++++++++ tests/e2e/test_lcm_ref/README.md | 5 + tests/e2e/test_lcm_ref/expected_lcm_base.png | Bin 0 -> 1630905 bytes .../test_lcm_ref/expected_lcm_lora_1_0.png | Bin 0 -> 1691267 bytes .../test_lcm_ref/expected_lcm_lora_1_2.png | Bin 0 -> 1696232 bytes 5 files changed, 240 insertions(+) create mode 100644 tests/e2e/test_lcm.py create mode 100644 tests/e2e/test_lcm_ref/README.md create mode 100644 tests/e2e/test_lcm_ref/expected_lcm_base.png create mode 100644 tests/e2e/test_lcm_ref/expected_lcm_lora_1_0.png create mode 100644 tests/e2e/test_lcm_ref/expected_lcm_lora_1_2.png diff --git a/tests/e2e/test_lcm.py b/tests/e2e/test_lcm.py new file mode 100644 index 0000000..df7aa4d --- /dev/null +++ b/tests/e2e/test_lcm.py @@ -0,0 +1,235 @@ +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) diff --git a/tests/e2e/test_lcm_ref/README.md b/tests/e2e/test_lcm_ref/README.md new file mode 100644 index 0000000..2d9817c --- /dev/null +++ b/tests/e2e/test_lcm_ref/README.md @@ -0,0 +1,5 @@ +# Note about this data + +## Expected outputs + +`expected_*.png` files in this folder are all the output of the same diffusion run with Diffusers (see 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