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326 lines
11 KiB
Python
326 lines
11 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 tests.utils import ensure_similar_images
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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 import SDXLIPAdapter
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from refiners.foundationals.latent_diffusion.lora import SDLoraManager
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from refiners.foundationals.latent_diffusion.stable_diffusion_xl.model import StableDiffusion_XL
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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(scope="module")
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def ref_path(test_e2e_path: Path) -> Path:
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return test_e2e_path / "test_doc_examples_ref"
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@pytest.fixture(scope="module")
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def sdxl_text_encoder_weights(test_weights_path: Path) -> Path:
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path = test_weights_path / "DoubleCLIPTextEncoder.safetensors"
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if not path.is_file():
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warn(message=f"could not find weights at {path}, skipping")
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pytest.skip(allow_module_level=True)
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return path
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@pytest.fixture(scope="module")
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def sdxl_lda_fp16_fix_weights(test_weights_path: Path) -> Path:
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path = test_weights_path / "sdxl-lda-fp16-fix.safetensors"
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if not path.is_file():
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warn(message=f"could not find weights at {path}, skipping")
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pytest.skip(allow_module_level=True)
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return path
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@pytest.fixture(scope="module")
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def sdxl_unet_weights(test_weights_path: Path) -> Path:
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path = test_weights_path / "sdxl-unet.safetensors"
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if not path.is_file():
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warn(message=f"could not find weights at {path}, skipping")
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pytest.skip(allow_module_level=True)
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return path
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@pytest.fixture(scope="module")
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def sdxl_ip_adapter_plus_weights(test_weights_path: Path) -> Path:
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path = test_weights_path / "ip-adapter-plus_sdxl_vit-h.safetensors"
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if not path.is_file():
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warn(f"could not find weights at {path}, skipping")
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pytest.skip(allow_module_level=True)
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return path
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@pytest.fixture(scope="module")
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def image_encoder_weights(test_weights_path: Path) -> Path:
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path = test_weights_path / "CLIPImageEncoderH.safetensors"
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if not path.is_file():
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warn(f"could not find weights at {path}, skipping")
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pytest.skip(allow_module_level=True)
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return path
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@pytest.fixture
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def scifi_lora_weights(test_weights_path: Path) -> Path:
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path = test_weights_path / "loras" / "Sci-fi_Environments_sdxl.safetensors"
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if not path.is_file():
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warn(message=f"could not find weights at {path}, skipping")
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pytest.skip(allow_module_level=True)
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return path
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@pytest.fixture
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def pixelart_lora_weights(test_weights_path: Path) -> Path:
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path = test_weights_path / "loras" / "pixel-art-xl-v1.1.safetensors"
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if not path.is_file():
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warn(message=f"could not find weights at {path}, skipping")
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pytest.skip(allow_module_level=True)
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return path
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@pytest.fixture
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def sdxl(
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sdxl_text_encoder_weights: Path,
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sdxl_lda_fp16_fix_weights: Path,
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sdxl_unet_weights: Path,
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test_device: torch.device,
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) -> StableDiffusion_XL:
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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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sdxl = StableDiffusion_XL(device=test_device, dtype=torch.float16)
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sdxl.clip_text_encoder.load_from_safetensors(tensors_path=sdxl_text_encoder_weights)
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sdxl.lda.load_from_safetensors(tensors_path=sdxl_lda_fp16_fix_weights)
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sdxl.unet.load_from_safetensors(tensors_path=sdxl_unet_weights)
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return sdxl
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@pytest.fixture
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def image_prompt_german_castle(ref_path: Path) -> Image.Image:
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return _img_open(ref_path / "german-castle.jpg").convert("RGB")
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@pytest.fixture
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def expected_image_guide_adapting_sdxl_vanilla(ref_path: Path) -> Image.Image:
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return _img_open(ref_path / "expected_image_guide_adapting_sdxl_vanilla.png").convert("RGB")
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@pytest.fixture
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def expected_image_guide_adapting_sdxl_single_lora(ref_path: Path) -> Image.Image:
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return _img_open(ref_path / "expected_image_guide_adapting_sdxl_single_lora.png").convert("RGB")
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@pytest.fixture
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def expected_image_guide_adapting_sdxl_multiple_loras(ref_path: Path) -> Image.Image:
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return _img_open(ref_path / "expected_image_guide_adapting_sdxl_multiple_loras.png").convert("RGB")
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@pytest.fixture
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def expected_image_guide_adapting_sdxl_loras_ip_adapter(ref_path: Path) -> Image.Image:
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return _img_open(ref_path / "expected_image_guide_adapting_sdxl_loras_ip_adapter.png").convert("RGB")
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@no_grad()
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def test_guide_adapting_sdxl_vanilla(
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test_device: torch.device,
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sdxl: StableDiffusion_XL,
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expected_image_guide_adapting_sdxl_vanilla: 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_image_guide_adapting_sdxl_vanilla
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prompt = "a futuristic castle surrounded by a forest, mountains in the background"
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seed = 42
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sdxl.set_inference_steps(50, first_step=0)
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sdxl.set_self_attention_guidance(enable=True, scale=0.75)
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clip_text_embedding, pooled_text_embedding = sdxl.compute_clip_text_embedding(
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text=prompt + ", best quality, high quality",
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negative_text="monochrome, lowres, bad anatomy, worst quality, low quality",
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)
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time_ids = sdxl.default_time_ids
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manual_seed(seed)
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# The guide uses 2048x2048 but it is too slow for tests.
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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.decode_latents(x)
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ensure_similar_images(predicted_image, expected_image)
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@no_grad()
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def test_guide_adapting_sdxl_single_lora(
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test_device: torch.device,
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sdxl: StableDiffusion_XL,
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scifi_lora_weights: Path,
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expected_image_guide_adapting_sdxl_single_lora: 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_image_guide_adapting_sdxl_single_lora
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prompt = "a futuristic castle surrounded by a forest, mountains in the background"
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seed = 42
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sdxl.set_inference_steps(50, first_step=0)
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sdxl.set_self_attention_guidance(enable=True, scale=0.75)
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manager = SDLoraManager(sdxl)
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manager.add_loras("scifi-lora", load_from_safetensors(scifi_lora_weights))
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clip_text_embedding, pooled_text_embedding = sdxl.compute_clip_text_embedding(
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text=prompt + ", best quality, high quality",
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negative_text="monochrome, lowres, bad anatomy, worst quality, low quality",
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)
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time_ids = sdxl.default_time_ids
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manual_seed(seed)
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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.decode_latents(x)
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ensure_similar_images(predicted_image, expected_image)
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@no_grad()
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def test_guide_adapting_sdxl_multiple_loras(
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test_device: torch.device,
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sdxl: StableDiffusion_XL,
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scifi_lora_weights: Path,
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pixelart_lora_weights: Path,
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expected_image_guide_adapting_sdxl_multiple_loras: 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_image_guide_adapting_sdxl_multiple_loras
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prompt = "a futuristic castle surrounded by a forest, mountains in the background"
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seed = 42
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sdxl.set_inference_steps(50, first_step=0)
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sdxl.set_self_attention_guidance(enable=True, scale=0.75)
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manager = SDLoraManager(sdxl)
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manager.add_loras("scifi-lora", load_from_safetensors(scifi_lora_weights))
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manager.add_loras("pixel-art-lora", load_from_safetensors(pixelart_lora_weights), scale=1.4)
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clip_text_embedding, pooled_text_embedding = sdxl.compute_clip_text_embedding(
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text=prompt + ", best quality, high quality",
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negative_text="monochrome, lowres, bad anatomy, worst quality, low quality",
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)
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time_ids = sdxl.default_time_ids
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manual_seed(seed)
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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.decode_latents(x)
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ensure_similar_images(predicted_image, expected_image)
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@no_grad()
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def test_guide_adapting_sdxl_loras_ip_adapter(
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test_device: torch.device,
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sdxl: StableDiffusion_XL,
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sdxl_ip_adapter_plus_weights: Path,
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image_encoder_weights: Path,
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scifi_lora_weights: Path,
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pixelart_lora_weights: Path,
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image_prompt_german_castle: Image.Image,
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expected_image_guide_adapting_sdxl_loras_ip_adapter: 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_image_guide_adapting_sdxl_loras_ip_adapter
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prompt = "a futuristic castle surrounded by a forest, mountains in the background"
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seed = 42
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sdxl.set_inference_steps(50, first_step=0)
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sdxl.set_self_attention_guidance(enable=True, scale=0.75)
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manager = SDLoraManager(sdxl)
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manager.add_loras("scifi-lora", load_from_safetensors(scifi_lora_weights), scale=1.5)
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manager.add_loras("pixel-art-lora", load_from_safetensors(pixelart_lora_weights), scale=1.55)
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ip_adapter = SDXLIPAdapter(
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target=sdxl.unet,
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weights=load_from_safetensors(sdxl_ip_adapter_plus_weights),
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scale=1.0,
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fine_grained=True,
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)
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ip_adapter.clip_image_encoder.load_from_safetensors(image_encoder_weights)
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ip_adapter.inject()
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clip_text_embedding, pooled_text_embedding = sdxl.compute_clip_text_embedding(
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text=prompt + ", best quality, high quality",
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negative_text="monochrome, lowres, bad anatomy, worst quality, low quality",
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)
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time_ids = sdxl.default_time_ids
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image_prompt_preprocessed = ip_adapter.preprocess_image(image_prompt_german_castle)
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clip_image_embedding = ip_adapter.compute_clip_image_embedding(image_prompt_preprocessed)
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ip_adapter.set_clip_image_embedding(clip_image_embedding)
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manual_seed(seed)
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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.decode_latents(x)
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ensure_similar_images(predicted_image, expected_image)
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# We do not (yet) test the last example using T2i-Adapter with Zoe Depth.
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