mirror of
https://github.com/finegrain-ai/refiners.git
synced 2024-11-23 22:58:45 +00:00
326 lines
11 KiB
Python
326 lines
11 KiB
Python
import gc
|
|
from pathlib import Path
|
|
from warnings import warn
|
|
|
|
import pytest
|
|
import torch
|
|
from PIL import Image
|
|
from tests.utils import ensure_similar_images
|
|
|
|
from refiners.fluxion.utils import load_from_safetensors, manual_seed, no_grad
|
|
from refiners.foundationals.latent_diffusion import SDXLIPAdapter
|
|
from refiners.foundationals.latent_diffusion.lora import SDLoraManager
|
|
from refiners.foundationals.latent_diffusion.stable_diffusion_xl.model import StableDiffusion_XL
|
|
|
|
|
|
def _img_open(path: Path) -> Image.Image:
|
|
return Image.open(path) # type: ignore
|
|
|
|
|
|
@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(scope="module")
|
|
def ref_path(test_e2e_path: Path) -> Path:
|
|
return test_e2e_path / "test_doc_examples_ref"
|
|
|
|
|
|
@pytest.fixture(scope="module")
|
|
def sdxl_text_encoder_weights(test_weights_path: Path) -> Path:
|
|
path = test_weights_path / "DoubleCLIPTextEncoder.safetensors"
|
|
if not path.is_file():
|
|
warn(message=f"could not find weights at {path}, skipping")
|
|
pytest.skip(allow_module_level=True)
|
|
return path
|
|
|
|
|
|
@pytest.fixture(scope="module")
|
|
def sdxl_lda_fp16_fix_weights(test_weights_path: Path) -> Path:
|
|
path = test_weights_path / "sdxl-lda-fp16-fix.safetensors"
|
|
if not path.is_file():
|
|
warn(message=f"could not find weights at {path}, skipping")
|
|
pytest.skip(allow_module_level=True)
|
|
return path
|
|
|
|
|
|
@pytest.fixture(scope="module")
|
|
def sdxl_unet_weights(test_weights_path: Path) -> Path:
|
|
path = test_weights_path / "sdxl-unet.safetensors"
|
|
if not path.is_file():
|
|
warn(message=f"could not find weights at {path}, skipping")
|
|
pytest.skip(allow_module_level=True)
|
|
return path
|
|
|
|
|
|
@pytest.fixture(scope="module")
|
|
def sdxl_ip_adapter_plus_weights(test_weights_path: Path) -> Path:
|
|
path = test_weights_path / "ip-adapter-plus_sdxl_vit-h.safetensors"
|
|
if not path.is_file():
|
|
warn(f"could not find weights at {path}, skipping")
|
|
pytest.skip(allow_module_level=True)
|
|
return path
|
|
|
|
|
|
@pytest.fixture(scope="module")
|
|
def image_encoder_weights(test_weights_path: Path) -> Path:
|
|
path = test_weights_path / "CLIPImageEncoderH.safetensors"
|
|
if not path.is_file():
|
|
warn(f"could not find weights at {path}, skipping")
|
|
pytest.skip(allow_module_level=True)
|
|
return path
|
|
|
|
|
|
@pytest.fixture
|
|
def scifi_lora_weights(test_weights_path: Path) -> Path:
|
|
path = test_weights_path / "loras" / "Sci-fi_Environments_sdxl.safetensors"
|
|
if not path.is_file():
|
|
warn(message=f"could not find weights at {path}, skipping")
|
|
pytest.skip(allow_module_level=True)
|
|
return path
|
|
|
|
|
|
@pytest.fixture
|
|
def pixelart_lora_weights(test_weights_path: Path) -> Path:
|
|
path = test_weights_path / "loras" / "pixel-art-xl-v1.1.safetensors"
|
|
if not path.is_file():
|
|
warn(message=f"could not find weights at {path}, skipping")
|
|
pytest.skip(allow_module_level=True)
|
|
return path
|
|
|
|
|
|
@pytest.fixture
|
|
def sdxl(
|
|
sdxl_text_encoder_weights: Path,
|
|
sdxl_lda_fp16_fix_weights: Path,
|
|
sdxl_unet_weights: Path,
|
|
test_device: torch.device,
|
|
) -> StableDiffusion_XL:
|
|
if test_device.type == "cpu":
|
|
warn(message="not running on CPU, skipping")
|
|
pytest.skip()
|
|
|
|
sdxl = StableDiffusion_XL(device=test_device, dtype=torch.float16)
|
|
|
|
sdxl.clip_text_encoder.load_from_safetensors(tensors_path=sdxl_text_encoder_weights)
|
|
sdxl.lda.load_from_safetensors(tensors_path=sdxl_lda_fp16_fix_weights)
|
|
sdxl.unet.load_from_safetensors(tensors_path=sdxl_unet_weights)
|
|
|
|
return sdxl
|
|
|
|
|
|
@pytest.fixture
|
|
def image_prompt_german_castle(ref_path: Path) -> Image.Image:
|
|
return _img_open(ref_path / "german-castle.jpg").convert("RGB")
|
|
|
|
|
|
@pytest.fixture
|
|
def expected_image_guide_adapting_sdxl_vanilla(ref_path: Path) -> Image.Image:
|
|
return _img_open(ref_path / "expected_image_guide_adapting_sdxl_vanilla.png").convert("RGB")
|
|
|
|
|
|
@pytest.fixture
|
|
def expected_image_guide_adapting_sdxl_single_lora(ref_path: Path) -> Image.Image:
|
|
return _img_open(ref_path / "expected_image_guide_adapting_sdxl_single_lora.png").convert("RGB")
|
|
|
|
|
|
@pytest.fixture
|
|
def expected_image_guide_adapting_sdxl_multiple_loras(ref_path: Path) -> Image.Image:
|
|
return _img_open(ref_path / "expected_image_guide_adapting_sdxl_multiple_loras.png").convert("RGB")
|
|
|
|
|
|
@pytest.fixture
|
|
def expected_image_guide_adapting_sdxl_loras_ip_adapter(ref_path: Path) -> Image.Image:
|
|
return _img_open(ref_path / "expected_image_guide_adapting_sdxl_loras_ip_adapter.png").convert("RGB")
|
|
|
|
|
|
@no_grad()
|
|
def test_guide_adapting_sdxl_vanilla(
|
|
test_device: torch.device,
|
|
sdxl: StableDiffusion_XL,
|
|
expected_image_guide_adapting_sdxl_vanilla: Image.Image,
|
|
) -> None:
|
|
if test_device.type == "cpu":
|
|
warn(message="not running on CPU, skipping")
|
|
pytest.skip()
|
|
|
|
expected_image = expected_image_guide_adapting_sdxl_vanilla
|
|
|
|
prompt = "a futuristic castle surrounded by a forest, mountains in the background"
|
|
seed = 42
|
|
sdxl.set_inference_steps(50, first_step=0)
|
|
sdxl.set_self_attention_guidance(enable=True, scale=0.75)
|
|
|
|
clip_text_embedding, pooled_text_embedding = sdxl.compute_clip_text_embedding(
|
|
text=prompt + ", best quality, high quality",
|
|
negative_text="monochrome, lowres, bad anatomy, worst quality, low quality",
|
|
)
|
|
time_ids = sdxl.default_time_ids
|
|
|
|
manual_seed(seed)
|
|
# The guide uses 2048x2048 but it is too slow for tests.
|
|
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.decode_latents(x)
|
|
ensure_similar_images(predicted_image, expected_image)
|
|
|
|
|
|
@no_grad()
|
|
def test_guide_adapting_sdxl_single_lora(
|
|
test_device: torch.device,
|
|
sdxl: StableDiffusion_XL,
|
|
scifi_lora_weights: Path,
|
|
expected_image_guide_adapting_sdxl_single_lora: Image.Image,
|
|
) -> None:
|
|
if test_device.type == "cpu":
|
|
warn(message="not running on CPU, skipping")
|
|
pytest.skip()
|
|
|
|
expected_image = expected_image_guide_adapting_sdxl_single_lora
|
|
|
|
prompt = "a futuristic castle surrounded by a forest, mountains in the background"
|
|
seed = 42
|
|
sdxl.set_inference_steps(50, first_step=0)
|
|
sdxl.set_self_attention_guidance(enable=True, scale=0.75)
|
|
|
|
manager = SDLoraManager(sdxl)
|
|
manager.add_loras("scifi-lora", load_from_safetensors(scifi_lora_weights))
|
|
|
|
clip_text_embedding, pooled_text_embedding = sdxl.compute_clip_text_embedding(
|
|
text=prompt + ", best quality, high quality",
|
|
negative_text="monochrome, lowres, bad anatomy, worst quality, low quality",
|
|
)
|
|
time_ids = sdxl.default_time_ids
|
|
|
|
manual_seed(seed)
|
|
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.decode_latents(x)
|
|
ensure_similar_images(predicted_image, expected_image)
|
|
|
|
|
|
@no_grad()
|
|
def test_guide_adapting_sdxl_multiple_loras(
|
|
test_device: torch.device,
|
|
sdxl: StableDiffusion_XL,
|
|
scifi_lora_weights: Path,
|
|
pixelart_lora_weights: Path,
|
|
expected_image_guide_adapting_sdxl_multiple_loras: Image.Image,
|
|
) -> None:
|
|
if test_device.type == "cpu":
|
|
warn(message="not running on CPU, skipping")
|
|
pytest.skip()
|
|
|
|
expected_image = expected_image_guide_adapting_sdxl_multiple_loras
|
|
|
|
prompt = "a futuristic castle surrounded by a forest, mountains in the background"
|
|
seed = 42
|
|
sdxl.set_inference_steps(50, first_step=0)
|
|
sdxl.set_self_attention_guidance(enable=True, scale=0.75)
|
|
|
|
manager = SDLoraManager(sdxl)
|
|
manager.add_loras("scifi-lora", load_from_safetensors(scifi_lora_weights))
|
|
manager.add_loras("pixel-art-lora", load_from_safetensors(pixelart_lora_weights), scale=1.4)
|
|
|
|
clip_text_embedding, pooled_text_embedding = sdxl.compute_clip_text_embedding(
|
|
text=prompt + ", best quality, high quality",
|
|
negative_text="monochrome, lowres, bad anatomy, worst quality, low quality",
|
|
)
|
|
time_ids = sdxl.default_time_ids
|
|
|
|
manual_seed(seed)
|
|
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.decode_latents(x)
|
|
ensure_similar_images(predicted_image, expected_image)
|
|
|
|
|
|
@no_grad()
|
|
def test_guide_adapting_sdxl_loras_ip_adapter(
|
|
test_device: torch.device,
|
|
sdxl: StableDiffusion_XL,
|
|
sdxl_ip_adapter_plus_weights: Path,
|
|
image_encoder_weights: Path,
|
|
scifi_lora_weights: Path,
|
|
pixelart_lora_weights: Path,
|
|
image_prompt_german_castle: Image.Image,
|
|
expected_image_guide_adapting_sdxl_loras_ip_adapter: Image.Image,
|
|
) -> None:
|
|
if test_device.type == "cpu":
|
|
warn(message="not running on CPU, skipping")
|
|
pytest.skip()
|
|
|
|
expected_image = expected_image_guide_adapting_sdxl_loras_ip_adapter
|
|
|
|
prompt = "a futuristic castle surrounded by a forest, mountains in the background"
|
|
seed = 42
|
|
sdxl.set_inference_steps(50, first_step=0)
|
|
sdxl.set_self_attention_guidance(enable=True, scale=0.75)
|
|
|
|
manager = SDLoraManager(sdxl)
|
|
manager.add_loras("scifi-lora", load_from_safetensors(scifi_lora_weights), scale=1.5)
|
|
manager.add_loras("pixel-art-lora", load_from_safetensors(pixelart_lora_weights), scale=1.55)
|
|
|
|
ip_adapter = SDXLIPAdapter(
|
|
target=sdxl.unet,
|
|
weights=load_from_safetensors(sdxl_ip_adapter_plus_weights),
|
|
scale=1.0,
|
|
fine_grained=True,
|
|
)
|
|
ip_adapter.clip_image_encoder.load_from_safetensors(image_encoder_weights)
|
|
ip_adapter.inject()
|
|
|
|
clip_text_embedding, pooled_text_embedding = sdxl.compute_clip_text_embedding(
|
|
text=prompt + ", best quality, high quality",
|
|
negative_text="monochrome, lowres, bad anatomy, worst quality, low quality",
|
|
)
|
|
time_ids = sdxl.default_time_ids
|
|
|
|
image_prompt_preprocessed = ip_adapter.preprocess_image(image_prompt_german_castle)
|
|
clip_image_embedding = ip_adapter.compute_clip_image_embedding(image_prompt_preprocessed)
|
|
ip_adapter.set_clip_image_embedding(clip_image_embedding)
|
|
|
|
manual_seed(seed)
|
|
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.decode_latents(x)
|
|
ensure_similar_images(predicted_image, expected_image)
|
|
|
|
|
|
# We do not (yet) test the last example using T2i-Adapter with Zoe Depth.
|