mirror of
https://github.com/finegrain-ai/refiners.git
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256 lines
8.1 KiB
Python
256 lines
8.1 KiB
Python
import gc
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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 PIL import Image
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from refiners.fluxion.utils import load_from_safetensors, manual_seed, no_grad
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from refiners.foundationals.latent_diffusion.lora import SDLoraManager
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from refiners.foundationals.latent_diffusion.solvers import Euler, ModelPredictionType, SolverParams, TimestepSpacing
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from refiners.foundationals.latent_diffusion.stable_diffusion_xl.lcm_lora import add_lcm_lora
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from refiners.foundationals.latent_diffusion.stable_diffusion_xl.model import StableDiffusion_XL
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from tests.utils import ensure_similar_images
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def _img_open(path: Path) -> Image.Image:
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return Image.open(path) # type: ignore
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@pytest.fixture(autouse=True)
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def ensure_gc():
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# Avoid GPU OOMs
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# See https://github.com/pytest-dev/pytest/discussions/8153#discussioncomment-214812
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gc.collect()
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@pytest.fixture
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def sdxl_lda_fp16_fix_weights(test_weights_path: Path) -> Path:
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r = test_weights_path / "sdxl-lda-fp16-fix.safetensors"
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if not r.is_file():
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warn(f"could not find 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
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def sdxl_unet_weights(test_weights_path: Path) -> Path:
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r = test_weights_path / "sdxl-unet.safetensors"
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if not r.is_file():
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warn(f"could not find 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
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def sdxl_lightning_4step_unet_weights(test_weights_path: Path) -> Path:
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r = test_weights_path / "sdxl_lightning_4step_unet.safetensors"
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if not r.is_file():
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warn(f"could not find 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
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def sdxl_lightning_1step_unet_weights(test_weights_path: Path) -> Path:
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r = test_weights_path / "sdxl_lightning_1step_unet_x0.safetensors"
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if not r.is_file():
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warn(f"could not find 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
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def sdxl_text_encoder_weights(test_weights_path: Path) -> Path:
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r = test_weights_path / "DoubleCLIPTextEncoder.safetensors"
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if not r.is_file():
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warn(f"could not find 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
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def sdxl_lightning_4step_lora_weights(test_weights_path: Path) -> Path:
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r = test_weights_path / "sdxl_lightning_4step_lora.safetensors"
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if not r.is_file():
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warn(f"could not find 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 ref_path(test_e2e_path: Path) -> Path:
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return test_e2e_path / "test_lightning_ref"
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@pytest.fixture
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def expected_lightning_base_4step(ref_path: Path) -> Image.Image:
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return _img_open(ref_path / "expected_lightning_base_4step.png").convert("RGB")
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@pytest.fixture
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def expected_lightning_base_1step(ref_path: Path) -> Image.Image:
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return _img_open(ref_path / "expected_lightning_base_1step.png").convert("RGB")
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@pytest.fixture
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def expected_lightning_lora_4step(ref_path: Path) -> Image.Image:
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return _img_open(ref_path / "expected_lightning_lora_4step.png").convert("RGB")
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@no_grad()
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def test_lightning_base_4step(
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test_device: torch.device,
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sdxl_lda_fp16_fix_weights: Path,
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sdxl_lightning_4step_unet_weights: Path,
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sdxl_text_encoder_weights: Path,
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expected_lightning_base_4step: Image.Image,
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) -> None:
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if test_device.type == "cpu":
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warn(message="not running on CPU, skipping")
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pytest.skip()
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unet_weights = sdxl_lightning_4step_unet_weights
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expected_image = expected_lightning_base_4step
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solver = Euler(
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num_inference_steps=4,
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params=SolverParams(
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timesteps_spacing=TimestepSpacing.TRAILING,
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model_prediction_type=ModelPredictionType.NOISE,
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),
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)
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sdxl = StableDiffusion_XL(device=test_device, dtype=torch.float16, solver=solver)
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sdxl.classifier_free_guidance = False
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sdxl.clip_text_encoder.load_from_safetensors(sdxl_text_encoder_weights)
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sdxl.lda.load_from_safetensors(sdxl_lda_fp16_fix_weights)
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sdxl.unet.load_from_safetensors(unet_weights)
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prompt = "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k"
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clip_text_embedding, pooled_text_embedding = sdxl.compute_clip_text_embedding(prompt)
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time_ids = sdxl.default_time_ids
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manual_seed(0)
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x = sdxl.init_latents((1024, 1024)).to(sdxl.device, sdxl.dtype)
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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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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_lightning_base_1step(
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test_device: torch.device,
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sdxl_lda_fp16_fix_weights: Path,
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sdxl_lightning_1step_unet_weights: Path,
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sdxl_text_encoder_weights: Path,
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expected_lightning_base_1step: Image.Image,
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) -> None:
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if test_device.type == "cpu":
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warn(message="not running on CPU, skipping")
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pytest.skip()
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unet_weights = sdxl_lightning_1step_unet_weights
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expected_image = expected_lightning_base_1step
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solver = Euler(
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num_inference_steps=1,
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params=SolverParams(
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timesteps_spacing=TimestepSpacing.TRAILING,
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model_prediction_type=ModelPredictionType.SAMPLE, # 1 step special case
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),
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)
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sdxl = StableDiffusion_XL(device=test_device, dtype=torch.float16, solver=solver)
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sdxl.classifier_free_guidance = False
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sdxl.clip_text_encoder.load_from_safetensors(sdxl_text_encoder_weights)
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sdxl.lda.load_from_safetensors(sdxl_lda_fp16_fix_weights)
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sdxl.unet.load_from_safetensors(unet_weights)
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prompt = "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k"
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clip_text_embedding, pooled_text_embedding = sdxl.compute_clip_text_embedding(prompt)
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time_ids = sdxl.default_time_ids
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manual_seed(0)
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x = sdxl.init_latents((1024, 1024)).to(sdxl.device, sdxl.dtype)
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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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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_lightning_lora_4step(
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test_device: torch.device,
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sdxl_lda_fp16_fix_weights: Path,
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sdxl_unet_weights: Path,
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sdxl_text_encoder_weights: Path,
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sdxl_lightning_4step_lora_weights: Path,
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expected_lightning_lora_4step: Image.Image,
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) -> None:
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if test_device.type == "cpu":
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warn(message="not running on CPU, skipping")
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pytest.skip()
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expected_image = expected_lightning_lora_4step
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solver = Euler(
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num_inference_steps=4,
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params=SolverParams(
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timesteps_spacing=TimestepSpacing.TRAILING,
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model_prediction_type=ModelPredictionType.NOISE,
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),
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)
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sdxl = StableDiffusion_XL(device=test_device, dtype=torch.float16, solver=solver)
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sdxl.classifier_free_guidance = False
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sdxl.clip_text_encoder.load_from_safetensors(sdxl_text_encoder_weights)
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sdxl.lda.load_from_safetensors(sdxl_lda_fp16_fix_weights)
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sdxl.unet.load_from_safetensors(sdxl_unet_weights)
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manager = SDLoraManager(sdxl)
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add_lcm_lora(manager, load_from_safetensors(sdxl_lightning_4step_lora_weights), name="lightning")
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prompt = "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k"
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clip_text_embedding, pooled_text_embedding = sdxl.compute_clip_text_embedding(prompt)
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time_ids = sdxl.default_time_ids
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manual_seed(0)
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x = sdxl.init_latents((1024, 1024)).to(sdxl.device, sdxl.dtype)
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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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predicted_image = sdxl.lda.latents_to_image(x)
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ensure_similar_images(predicted_image, expected_image)
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