refiners/tests/e2e/test_diffusion.py

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import gc
from pathlib import Path
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from typing import Iterator
from warnings import warn
import pytest
import torch
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from PIL import Image
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from refiners.fluxion.utils import image_to_tensor, load_from_safetensors, load_tensors, manual_seed, no_grad
from refiners.foundationals.clip.concepts import ConceptExtender
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from refiners.foundationals.latent_diffusion import (
SD1ControlnetAdapter,
SD1IPAdapter,
SD1T2IAdapter,
SD1UNet,
SDFreeUAdapter,
SDXLIPAdapter,
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SDXLT2IAdapter,
StableDiffusion_1,
StableDiffusion_1_Inpainting,
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)
from refiners.foundationals.latent_diffusion.lora import SDLoraManager
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from refiners.foundationals.latent_diffusion.multi_diffusion import DiffusionTarget
from refiners.foundationals.latent_diffusion.reference_only_control import ReferenceOnlyControlAdapter
from refiners.foundationals.latent_diffusion.restart import Restart
from refiners.foundationals.latent_diffusion.solvers import DDIM, Euler, NoiseSchedule
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from refiners.foundationals.latent_diffusion.stable_diffusion_1.multi_diffusion import SD1MultiDiffusion
from refiners.foundationals.latent_diffusion.stable_diffusion_xl.model import StableDiffusion_XL
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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()
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@pytest.fixture(scope="module")
def ref_path(test_e2e_path: Path) -> Path:
return test_e2e_path / "test_diffusion_ref"
@pytest.fixture(scope="module")
def cutecat_init(ref_path: Path) -> Image.Image:
return Image.open(ref_path / "cutecat_init.png").convert("RGB")
@pytest.fixture(scope="module")
def kitchen_dog(ref_path: Path) -> Image.Image:
return Image.open(ref_path / "kitchen_dog.png").convert("RGB")
@pytest.fixture(scope="module")
def kitchen_dog_mask(ref_path: Path) -> Image.Image:
return Image.open(ref_path / "kitchen_dog_mask.png").convert("RGB")
@pytest.fixture(scope="module")
def woman_image(ref_path: Path) -> Image.Image:
return Image.open(ref_path / "woman.png").convert("RGB")
@pytest.fixture(scope="module")
def statue_image(ref_path: Path) -> Image.Image:
return Image.open(ref_path / "statue.png").convert("RGB")
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@pytest.fixture
def expected_image_std_random_init(ref_path: Path) -> Image.Image:
return Image.open(ref_path / "expected_std_random_init.png").convert("RGB")
@pytest.fixture
def expected_image_std_random_init_euler(ref_path: Path) -> Image.Image:
return Image.open(ref_path / "expected_std_random_init_euler.png").convert("RGB")
@pytest.fixture
def expected_karras_random_init(ref_path: Path) -> Image.Image:
return Image.open(ref_path / "expected_karras_random_init.png").convert("RGB")
@pytest.fixture
def expected_image_std_random_init_sag(ref_path: Path) -> Image.Image:
return Image.open(ref_path / "expected_std_random_init_sag.png").convert("RGB")
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@pytest.fixture
def expected_image_std_init_image(ref_path: Path) -> Image.Image:
return Image.open(ref_path / "expected_std_init_image.png").convert("RGB")
@pytest.fixture
def expected_image_std_inpainting(ref_path: Path) -> Image.Image:
return Image.open(ref_path / "expected_std_inpainting.png").convert("RGB")
@pytest.fixture
def expected_image_controlnet_stack(ref_path: Path) -> Image.Image:
return Image.open(ref_path / "expected_controlnet_stack.png").convert("RGB")
@pytest.fixture
def expected_image_ip_adapter_woman(ref_path: Path) -> Image.Image:
return Image.open(ref_path / "expected_image_ip_adapter_woman.png").convert("RGB")
@pytest.fixture
def expected_image_ip_adapter_multi(ref_path: Path) -> Image.Image:
return Image.open(ref_path / "expected_image_ip_adapter_multi.png").convert("RGB")
@pytest.fixture
def expected_image_ip_adapter_plus_statue(ref_path: Path) -> Image.Image:
return Image.open(ref_path / "expected_image_ip_adapter_plus_statue.png").convert("RGB")
@pytest.fixture
def expected_image_sdxl_ip_adapter_woman(ref_path: Path) -> Image.Image:
return Image.open(ref_path / "expected_image_sdxl_ip_adapter_woman.png").convert("RGB")
@pytest.fixture
def expected_image_sdxl_ip_adapter_plus_woman(ref_path: Path) -> Image.Image:
return Image.open(ref_path / "expected_image_sdxl_ip_adapter_plus_woman.png").convert("RGB")
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@pytest.fixture
def expected_image_ip_adapter_controlnet(ref_path: Path) -> Image.Image:
return Image.open(ref_path / "expected_ip_adapter_controlnet.png").convert("RGB")
@pytest.fixture
def expected_sdxl_ddim_random_init(ref_path: Path) -> Image.Image:
return Image.open(fp=ref_path / "expected_cutecat_sdxl_ddim_random_init.png").convert(mode="RGB")
@pytest.fixture
def expected_sdxl_ddim_random_init_sag(ref_path: Path) -> Image.Image:
return Image.open(fp=ref_path / "expected_cutecat_sdxl_ddim_random_init_sag.png").convert(mode="RGB")
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@pytest.fixture(scope="module", params=["canny", "depth", "lineart", "normals", "sam"])
def controlnet_data(
ref_path: Path, test_weights_path: Path, request: pytest.FixtureRequest
) -> Iterator[tuple[str, Image.Image, Image.Image, Path]]:
cn_name: str = request.param
condition_image = Image.open(ref_path / f"cutecat_guide_{cn_name}.png").convert("RGB")
expected_image = Image.open(ref_path / f"expected_controlnet_{cn_name}.png").convert("RGB")
weights_fn = {
"depth": "lllyasviel_control_v11f1p_sd15_depth",
"canny": "lllyasviel_control_v11p_sd15_canny",
"lineart": "lllyasviel_control_v11p_sd15_lineart",
"normals": "lllyasviel_control_v11p_sd15_normalbae",
"sam": "mfidabel_controlnet-segment-anything",
}
weights_path = test_weights_path / "controlnet" / f"{weights_fn[cn_name]}.safetensors"
yield (cn_name, condition_image, expected_image, weights_path)
@pytest.fixture(scope="module")
def controlnet_data_canny(ref_path: Path, test_weights_path: Path) -> tuple[str, Image.Image, Image.Image, Path]:
cn_name = "canny"
condition_image = Image.open(ref_path / f"cutecat_guide_{cn_name}.png").convert("RGB")
expected_image = Image.open(ref_path / f"expected_controlnet_{cn_name}.png").convert("RGB")
weights_path = test_weights_path / "controlnet" / "lllyasviel_control_v11p_sd15_canny.safetensors"
return cn_name, condition_image, expected_image, weights_path
@pytest.fixture(scope="module")
def controlnet_data_depth(ref_path: Path, test_weights_path: Path) -> tuple[str, Image.Image, Image.Image, Path]:
cn_name = "depth"
condition_image = Image.open(ref_path / f"cutecat_guide_{cn_name}.png").convert("RGB")
expected_image = Image.open(ref_path / f"expected_controlnet_{cn_name}.png").convert("RGB")
weights_path = test_weights_path / "controlnet" / "lllyasviel_control_v11f1p_sd15_depth.safetensors"
return cn_name, condition_image, expected_image, weights_path
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@pytest.fixture(scope="module")
def t2i_adapter_data_depth(ref_path: Path, test_weights_path: Path) -> tuple[str, Image.Image, Image.Image, Path]:
name = "depth"
condition_image = Image.open(ref_path / f"cutecat_guide_{name}.png").convert("RGB")
expected_image = Image.open(ref_path / f"expected_t2i_adapter_{name}.png").convert("RGB")
weights_path = test_weights_path / "T2I-Adapter" / "t2iadapter_depth_sd15v2.safetensors"
return name, condition_image, expected_image, weights_path
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@pytest.fixture(scope="module")
def t2i_adapter_xl_data_canny(ref_path: Path, test_weights_path: Path) -> tuple[str, Image.Image, Image.Image, Path]:
name = "canny"
condition_image = Image.open(ref_path / f"fairy_guide_{name}.png").convert("RGB")
expected_image = Image.open(ref_path / f"expected_t2i_adapter_xl_{name}.png").convert("RGB")
weights_path = test_weights_path / "T2I-Adapter" / "t2i-adapter-canny-sdxl-1.0.safetensors"
if not weights_path.is_file():
warn(f"could not find weights at {weights_path}, skipping")
pytest.skip(allow_module_level=True)
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return name, condition_image, expected_image, weights_path
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@pytest.fixture(scope="module")
def lora_data_pokemon(ref_path: Path, test_weights_path: Path) -> tuple[Image.Image, dict[str, torch.Tensor]]:
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expected_image = Image.open(ref_path / "expected_lora_pokemon.png").convert("RGB")
weights_path = test_weights_path / "loras" / "pokemon-lora" / "pytorch_lora_weights.bin"
if not weights_path.is_file():
warn(f"could not find weights at {weights_path}, skipping")
pytest.skip(allow_module_level=True)
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tensors = load_tensors(weights_path)
return expected_image, tensors
@pytest.fixture(scope="module")
def lora_data_dpo(ref_path: Path, test_weights_path: Path) -> tuple[Image.Image, dict[str, torch.Tensor]]:
expected_image = Image.open(ref_path / "expected_sdxl_dpo_lora.png").convert("RGB")
weights_path = test_weights_path / "loras" / "dpo-lora" / "pytorch_lora_weights.safetensors"
if not weights_path.is_file():
warn(f"could not find weights at {weights_path}, skipping")
pytest.skip(allow_module_level=True)
tensors = load_from_safetensors(weights_path)
return expected_image, tensors
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@pytest.fixture(scope="module")
def lora_sliders(test_weights_path: Path) -> tuple[dict[str, dict[str, torch.Tensor]], dict[str, float]]:
weights_path = test_weights_path / "loras" / "sliders"
if not weights_path.is_dir():
warn(f"could not find weights at {weights_path}, skipping")
pytest.skip(allow_module_level=True)
return {
"age": load_tensors(weights_path / "age.pt"), # type: ignore
"cartoon_style": load_tensors(weights_path / "cartoon_style.pt"), # type: ignore
"eyesize": load_tensors(weights_path / "eyesize.pt"), # type: ignore
}, {
"age": 0.3,
"cartoon_style": -0.2,
"dpo": 1.4,
"eyesize": -0.2,
}
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@pytest.fixture
def scene_image_inpainting_refonly(ref_path: Path) -> Image.Image:
return Image.open(ref_path / "inpainting-scene.png").convert("RGB")
@pytest.fixture
def mask_image_inpainting_refonly(ref_path: Path) -> Image.Image:
return Image.open(ref_path / "inpainting-mask.png").convert("RGB")
@pytest.fixture
def target_image_inpainting_refonly(ref_path: Path) -> Image.Image:
return Image.open(ref_path / "inpainting-target.png").convert("RGB")
@pytest.fixture
def expected_image_inpainting_refonly(ref_path: Path) -> Image.Image:
return Image.open(ref_path / "expected_inpainting_refonly.png").convert("RGB")
@pytest.fixture
def expected_image_refonly(ref_path: Path) -> Image.Image:
return Image.open(ref_path / "expected_refonly.png").convert("RGB")
@pytest.fixture
def condition_image_refonly(ref_path: Path) -> Image.Image:
return Image.open(ref_path / "cyberpunk_guide.png").convert("RGB")
@pytest.fixture
def expected_image_textual_inversion_random_init(ref_path: Path) -> Image.Image:
return Image.open(ref_path / "expected_textual_inversion_random_init.png").convert("RGB")
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@pytest.fixture
def expected_multi_diffusion(ref_path: Path) -> Image.Image:
return Image.open(fp=ref_path / "expected_multi_diffusion.png").convert(mode="RGB")
@pytest.fixture
def expected_restart(ref_path: Path) -> Image.Image:
return Image.open(fp=ref_path / "expected_restart.png").convert(mode="RGB")
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@pytest.fixture
def expected_freeu(ref_path: Path) -> Image.Image:
return Image.open(fp=ref_path / "expected_freeu.png").convert(mode="RGB")
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@pytest.fixture
def expected_sdxl_multi_loras(ref_path: Path) -> Image.Image:
return Image.open(fp=ref_path / "expected_sdxl_multi_loras.png").convert(mode="RGB")
@pytest.fixture
def hello_world_assets(ref_path: Path) -> tuple[Image.Image, Image.Image, Image.Image, Image.Image]:
assets = Path(__file__).parent.parent.parent / "assets"
dropy = assets / "dropy_logo.png"
image_prompt = assets / "dragon_quest_slime.jpg"
condition_image = assets / "dropy_canny.png"
return (
Image.open(fp=dropy).convert(mode="RGB"),
Image.open(fp=image_prompt).convert(mode="RGB"),
Image.open(fp=condition_image).convert(mode="RGB"),
Image.open(fp=ref_path / "expected_dropy_slime_9752.png").convert(mode="RGB"),
)
@pytest.fixture
def text_embedding_textual_inversion(test_textual_inversion_path: Path) -> torch.Tensor:
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return load_tensors(test_textual_inversion_path / "gta5-artwork" / "learned_embeds.bin")["<gta5-artwork>"]
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@pytest.fixture(scope="module")
def text_encoder_weights(test_weights_path: Path) -> Path:
text_encoder_weights = test_weights_path / "CLIPTextEncoderL.safetensors"
if not text_encoder_weights.is_file():
warn(f"could not find weights at {text_encoder_weights}, skipping")
pytest.skip(allow_module_level=True)
return text_encoder_weights
@pytest.fixture(scope="module")
def lda_weights(test_weights_path: Path) -> Path:
lda_weights = test_weights_path / "lda.safetensors"
if not lda_weights.is_file():
warn(f"could not find weights at {lda_weights}, skipping")
pytest.skip(allow_module_level=True)
return lda_weights
@pytest.fixture(scope="module")
def unet_weights_std(test_weights_path: Path) -> Path:
unet_weights_std = test_weights_path / "unet.safetensors"
if not unet_weights_std.is_file():
warn(f"could not find weights at {unet_weights_std}, skipping")
pytest.skip(allow_module_level=True)
return unet_weights_std
@pytest.fixture(scope="module")
def unet_weights_inpainting(test_weights_path: Path) -> Path:
unet_weights_inpainting = test_weights_path / "inpainting" / "unet.safetensors"
if not unet_weights_inpainting.is_file():
warn(f"could not find weights at {unet_weights_inpainting}, skipping")
pytest.skip(allow_module_level=True)
return unet_weights_inpainting
@pytest.fixture(scope="module")
def lda_ft_mse_weights(test_weights_path: Path) -> Path:
lda_weights = test_weights_path / "lda_ft_mse.safetensors"
if not lda_weights.is_file():
warn(f"could not find weights at {lda_weights}, skipping")
pytest.skip(allow_module_level=True)
return lda_weights
@pytest.fixture(scope="module")
def ip_adapter_weights(test_weights_path: Path) -> Path:
ip_adapter_weights = test_weights_path / "ip-adapter_sd15.safetensors"
if not ip_adapter_weights.is_file():
warn(f"could not find weights at {ip_adapter_weights}, skipping")
pytest.skip(allow_module_level=True)
return ip_adapter_weights
@pytest.fixture(scope="module")
def ip_adapter_plus_weights(test_weights_path: Path) -> Path:
ip_adapter_weights = test_weights_path / "ip-adapter-plus_sd15.safetensors"
if not ip_adapter_weights.is_file():
warn(f"could not find weights at {ip_adapter_weights}, skipping")
pytest.skip(allow_module_level=True)
return ip_adapter_weights
@pytest.fixture(scope="module")
def sdxl_ip_adapter_weights(test_weights_path: Path) -> Path:
ip_adapter_weights = test_weights_path / "ip-adapter_sdxl_vit-h.safetensors"
if not ip_adapter_weights.is_file():
warn(f"could not find weights at {ip_adapter_weights}, skipping")
pytest.skip(allow_module_level=True)
return ip_adapter_weights
@pytest.fixture(scope="module")
def sdxl_ip_adapter_plus_weights(test_weights_path: Path) -> Path:
ip_adapter_weights = test_weights_path / "ip-adapter-plus_sdxl_vit-h.safetensors"
if not ip_adapter_weights.is_file():
warn(f"could not find weights at {ip_adapter_weights}, skipping")
pytest.skip(allow_module_level=True)
return ip_adapter_weights
@pytest.fixture(scope="module")
def image_encoder_weights(test_weights_path: Path) -> Path:
image_encoder_weights = test_weights_path / "CLIPImageEncoderH.safetensors"
if not image_encoder_weights.is_file():
warn(f"could not find weights at {image_encoder_weights}, skipping")
pytest.skip(allow_module_level=True)
return image_encoder_weights
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@pytest.fixture
def sd15_std(
text_encoder_weights: Path, lda_weights: Path, unet_weights_std: Path, test_device: torch.device
) -> StableDiffusion_1:
if test_device.type == "cpu":
warn("not running on CPU, skipping")
pytest.skip()
sd15 = StableDiffusion_1(device=test_device)
sd15.clip_text_encoder.load_from_safetensors(text_encoder_weights)
sd15.lda.load_from_safetensors(lda_weights)
sd15.unet.load_from_safetensors(unet_weights_std)
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return sd15
@pytest.fixture
def sd15_std_float16(
text_encoder_weights: Path, lda_weights: Path, unet_weights_std: Path, test_device: torch.device
) -> StableDiffusion_1:
if test_device.type == "cpu":
warn("not running on CPU, skipping")
pytest.skip()
sd15 = StableDiffusion_1(device=test_device, dtype=torch.float16)
sd15.clip_text_encoder.load_from_safetensors(text_encoder_weights)
sd15.lda.load_from_safetensors(lda_weights)
sd15.unet.load_from_safetensors(unet_weights_std)
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return sd15
@pytest.fixture
def sd15_inpainting(
text_encoder_weights: Path, lda_weights: Path, unet_weights_inpainting: Path, test_device: torch.device
) -> StableDiffusion_1_Inpainting:
if test_device.type == "cpu":
warn("not running on CPU, skipping")
pytest.skip()
unet = SD1UNet(in_channels=9)
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sd15 = StableDiffusion_1_Inpainting(unet=unet, device=test_device)
sd15.clip_text_encoder.load_from_safetensors(text_encoder_weights)
sd15.lda.load_from_safetensors(lda_weights)
sd15.unet.load_from_safetensors(unet_weights_inpainting)
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return sd15
@pytest.fixture
def sd15_inpainting_float16(
text_encoder_weights: Path, lda_weights: Path, unet_weights_inpainting: Path, test_device: torch.device
) -> StableDiffusion_1_Inpainting:
if test_device.type == "cpu":
warn("not running on CPU, skipping")
pytest.skip()
unet = SD1UNet(in_channels=9)
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sd15 = StableDiffusion_1_Inpainting(unet=unet, device=test_device, dtype=torch.float16)
sd15.clip_text_encoder.load_from_safetensors(text_encoder_weights)
sd15.lda.load_from_safetensors(lda_weights)
sd15.unet.load_from_safetensors(unet_weights_inpainting)
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return sd15
@pytest.fixture
def sd15_ddim(
text_encoder_weights: Path, lda_weights: Path, unet_weights_std: Path, test_device: torch.device
) -> StableDiffusion_1:
if test_device.type == "cpu":
warn("not running on CPU, skipping")
pytest.skip()
ddim_solver = DDIM(num_inference_steps=20)
sd15 = StableDiffusion_1(solver=ddim_solver, device=test_device)
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sd15.clip_text_encoder.load_from_safetensors(text_encoder_weights)
sd15.lda.load_from_safetensors(lda_weights)
sd15.unet.load_from_safetensors(unet_weights_std)
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return sd15
@pytest.fixture
def sd15_ddim_karras(
text_encoder_weights: Path, lda_weights: Path, unet_weights_std: Path, test_device: torch.device
) -> StableDiffusion_1:
if test_device.type == "cpu":
warn("not running on CPU, skipping")
pytest.skip()
ddim_solver = DDIM(num_inference_steps=20, noise_schedule=NoiseSchedule.KARRAS)
sd15 = StableDiffusion_1(solver=ddim_solver, device=test_device)
sd15.clip_text_encoder.load_from_safetensors(text_encoder_weights)
sd15.lda.load_from_safetensors(lda_weights)
sd15.unet.load_from_safetensors(unet_weights_std)
return sd15
@pytest.fixture
def sd15_euler(
text_encoder_weights: Path, lda_weights: Path, unet_weights_std: Path, test_device: torch.device
) -> StableDiffusion_1:
if test_device.type == "cpu":
warn("not running on CPU, skipping")
pytest.skip()
euler_solver = Euler(num_inference_steps=30)
sd15 = StableDiffusion_1(solver=euler_solver, device=test_device)
sd15.clip_text_encoder.load_from_safetensors(text_encoder_weights)
sd15.lda.load_from_safetensors(lda_weights)
sd15.unet.load_from_safetensors(unet_weights_std)
return sd15
@pytest.fixture
def sd15_ddim_lda_ft_mse(
text_encoder_weights: Path, lda_ft_mse_weights: Path, unet_weights_std: Path, test_device: torch.device
) -> StableDiffusion_1:
if test_device.type == "cpu":
warn("not running on CPU, skipping")
pytest.skip()
ddim_solver = DDIM(num_inference_steps=20)
sd15 = StableDiffusion_1(solver=ddim_solver, device=test_device)
sd15.clip_text_encoder.load_state_dict(load_from_safetensors(text_encoder_weights))
sd15.lda.load_state_dict(load_from_safetensors(lda_ft_mse_weights))
sd15.unet.load_state_dict(load_from_safetensors(unet_weights_std))
return sd15
@pytest.fixture
def sdxl_lda_weights(test_weights_path: Path) -> Path:
sdxl_lda_weights = test_weights_path / "sdxl-lda.safetensors"
if not sdxl_lda_weights.is_file():
warn(message=f"could not find weights at {sdxl_lda_weights}, skipping")
pytest.skip(allow_module_level=True)
return sdxl_lda_weights
@pytest.fixture
def sdxl_lda_fp16_fix_weights(test_weights_path: Path) -> Path:
sdxl_lda_weights = test_weights_path / "sdxl-lda-fp16-fix.safetensors"
if not sdxl_lda_weights.is_file():
warn(message=f"could not find weights at {sdxl_lda_weights}, skipping")
pytest.skip(allow_module_level=True)
return sdxl_lda_weights
@pytest.fixture
def sdxl_unet_weights(test_weights_path: Path) -> Path:
sdxl_unet_weights = test_weights_path / "sdxl-unet.safetensors"
if not sdxl_unet_weights.is_file():
warn(message=f"could not find weights at {sdxl_unet_weights}, skipping")
pytest.skip(allow_module_level=True)
return sdxl_unet_weights
@pytest.fixture
def sdxl_text_encoder_weights(test_weights_path: Path) -> Path:
sdxl_double_text_encoder_weights = test_weights_path / "DoubleCLIPTextEncoder.safetensors"
if not sdxl_double_text_encoder_weights.is_file():
warn(message=f"could not find weights at {sdxl_double_text_encoder_weights}, skipping")
pytest.skip(allow_module_level=True)
return sdxl_double_text_encoder_weights
@pytest.fixture
def sdxl_ddim(
sdxl_text_encoder_weights: Path, sdxl_lda_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()
solver = DDIM(num_inference_steps=30)
sdxl = StableDiffusion_XL(solver=solver, device=test_device)
sdxl.clip_text_encoder.load_from_safetensors(tensors_path=sdxl_text_encoder_weights)
sdxl.lda.load_from_safetensors(tensors_path=sdxl_lda_weights)
sdxl.unet.load_from_safetensors(tensors_path=sdxl_unet_weights)
return sdxl
@pytest.fixture
def sdxl_ddim_lda_fp16_fix(
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()
solver = DDIM(num_inference_steps=30)
sdxl = StableDiffusion_XL(solver=solver, device=test_device)
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
@no_grad()
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def test_diffusion_std_random_init(
sd15_std: StableDiffusion_1, expected_image_std_random_init: Image.Image, test_device: torch.device
):
sd15 = sd15_std
prompt = "a cute cat, detailed high-quality professional image"
negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality"
clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
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sd15.set_inference_steps(30)
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manual_seed(2)
x = torch.randn(1, 4, 64, 64, device=test_device)
for step in sd15.steps:
x = sd15(
x,
step=step,
clip_text_embedding=clip_text_embedding,
condition_scale=7.5,
)
predicted_image = sd15.lda.decode_latents(x)
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ensure_similar_images(predicted_image, expected_image_std_random_init)
@no_grad()
def test_diffusion_std_random_init_euler(
sd15_euler: StableDiffusion_1, expected_image_std_random_init_euler: Image.Image, test_device: torch.device
):
sd15 = sd15_euler
euler_solver = sd15_euler.solver
assert isinstance(euler_solver, Euler)
prompt = "a cute cat, detailed high-quality professional image"
negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality"
clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
sd15.set_inference_steps(30)
manual_seed(2)
x = torch.randn(1, 4, 64, 64, device=test_device)
x = x * euler_solver.init_noise_sigma
for step in sd15.steps:
x = sd15(
x,
step=step,
clip_text_embedding=clip_text_embedding,
condition_scale=7.5,
)
predicted_image = sd15.lda.decode_latents(x)
ensure_similar_images(predicted_image, expected_image_std_random_init_euler)
@no_grad()
def test_diffusion_karras_random_init(
sd15_ddim_karras: StableDiffusion_1, expected_karras_random_init: Image.Image, test_device: torch.device
):
sd15 = sd15_ddim_karras
prompt = "a cute cat, detailed high-quality professional image"
negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality"
clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
manual_seed(2)
x = torch.randn(1, 4, 64, 64, device=test_device)
for step in sd15.steps:
x = sd15(
x,
step=step,
clip_text_embedding=clip_text_embedding,
condition_scale=7.5,
)
predicted_image = sd15.lda.decode_latents(x)
ensure_similar_images(predicted_image, expected_karras_random_init, min_psnr=35, min_ssim=0.98)
@no_grad()
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def test_diffusion_std_random_init_float16(
sd15_std_float16: StableDiffusion_1, expected_image_std_random_init: Image.Image, test_device: torch.device
):
sd15 = sd15_std_float16
prompt = "a cute cat, detailed high-quality professional image"
negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality"
clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
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assert clip_text_embedding.dtype == torch.float16
sd15.set_inference_steps(30)
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manual_seed(2)
x = torch.randn(1, 4, 64, 64, device=test_device, dtype=torch.float16)
for step in sd15.steps:
x = sd15(
x,
step=step,
clip_text_embedding=clip_text_embedding,
condition_scale=7.5,
)
predicted_image = sd15.lda.decode_latents(x)
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ensure_similar_images(predicted_image, expected_image_std_random_init, min_psnr=35, min_ssim=0.98)
@no_grad()
def test_diffusion_std_random_init_sag(
sd15_std: StableDiffusion_1, expected_image_std_random_init_sag: Image.Image, test_device: torch.device
):
sd15 = sd15_std
prompt = "a cute cat, detailed high-quality professional image"
negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality"
clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
sd15.set_inference_steps(30)
sd15.set_self_attention_guidance(enable=True, scale=0.75)
manual_seed(2)
x = torch.randn(1, 4, 64, 64, device=test_device)
for step in sd15.steps:
x = sd15(
x,
step=step,
clip_text_embedding=clip_text_embedding,
condition_scale=7.5,
)
predicted_image = sd15.lda.decode_latents(x)
ensure_similar_images(predicted_image, expected_image_std_random_init_sag)
@no_grad()
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def test_diffusion_std_init_image(
sd15_std: StableDiffusion_1,
cutecat_init: Image.Image,
expected_image_std_init_image: Image.Image,
):
sd15 = sd15_std
prompt = "a cute cat, detailed high-quality professional image"
negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality"
clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
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sd15.set_inference_steps(35, first_step=5)
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manual_seed(2)
x = sd15.init_latents((512, 512), cutecat_init)
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for step in sd15.steps:
x = sd15(
x,
step=step,
clip_text_embedding=clip_text_embedding,
condition_scale=7.5,
)
predicted_image = sd15.lda.decode_latents(x)
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ensure_similar_images(predicted_image, expected_image_std_init_image)
@no_grad()
def test_rectangular_init_latents(
sd15_std: StableDiffusion_1,
cutecat_init: Image.Image,
):
sd15 = sd15_std
# Just check latents initialization with a non-square image (and not the entire diffusion)
width, height = 512, 504
rect_init_image = cutecat_init.crop((0, 0, width, height))
x = sd15.init_latents((height, width), rect_init_image)
assert sd15.lda.decode_latents(x).size == (width, height)
@no_grad()
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def test_diffusion_inpainting(
sd15_inpainting: StableDiffusion_1_Inpainting,
kitchen_dog: Image.Image,
kitchen_dog_mask: Image.Image,
expected_image_std_inpainting: Image.Image,
test_device: torch.device,
):
sd15 = sd15_inpainting
prompt = "a large white cat, detailed high-quality professional image, sitting on a chair, in a kitchen"
negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality"
clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
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sd15.set_inference_steps(30)
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sd15.set_inpainting_conditions(kitchen_dog, kitchen_dog_mask)
manual_seed(2)
x = torch.randn(1, 4, 64, 64, device=test_device)
for step in sd15.steps:
x = sd15(
x,
step=step,
clip_text_embedding=clip_text_embedding,
condition_scale=7.5,
)
predicted_image = sd15.lda.decode_latents(x)
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# PSNR and SSIM values are large because with float32 we get large differences even v.s. ourselves.
ensure_similar_images(predicted_image, expected_image_std_inpainting, min_psnr=25, min_ssim=0.95)
@no_grad()
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def test_diffusion_inpainting_float16(
sd15_inpainting_float16: StableDiffusion_1_Inpainting,
kitchen_dog: Image.Image,
kitchen_dog_mask: Image.Image,
expected_image_std_inpainting: Image.Image,
test_device: torch.device,
):
sd15 = sd15_inpainting_float16
prompt = "a large white cat, detailed high-quality professional image, sitting on a chair, in a kitchen"
negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality"
clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
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assert clip_text_embedding.dtype == torch.float16
sd15.set_inference_steps(30)
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sd15.set_inpainting_conditions(kitchen_dog, kitchen_dog_mask)
manual_seed(2)
x = torch.randn(1, 4, 64, 64, device=test_device, dtype=torch.float16)
for step in sd15.steps:
x = sd15(
x,
step=step,
clip_text_embedding=clip_text_embedding,
condition_scale=7.5,
)
predicted_image = sd15.lda.decode_latents(x)
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# PSNR and SSIM values are large because float16 is even worse than float32.
ensure_similar_images(predicted_image, expected_image_std_inpainting, min_psnr=20, min_ssim=0.92)
@no_grad()
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def test_diffusion_controlnet(
sd15_std: StableDiffusion_1,
controlnet_data: tuple[str, Image.Image, Image.Image, Path],
test_device: torch.device,
):
sd15 = sd15_std
cn_name, condition_image, expected_image, cn_weights_path = controlnet_data
if not cn_weights_path.is_file():
warn(f"could not find weights at {cn_weights_path}, skipping")
pytest.skip(allow_module_level=True)
prompt = "a cute cat, detailed high-quality professional image"
negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality"
clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
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sd15.set_inference_steps(30)
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controlnet = SD1ControlnetAdapter(
sd15.unet, name=cn_name, scale=0.5, weights=load_from_safetensors(cn_weights_path)
).inject()
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cn_condition = image_to_tensor(condition_image.convert("RGB"), device=test_device)
manual_seed(2)
x = torch.randn(1, 4, 64, 64, device=test_device)
for step in sd15.steps:
controlnet.set_controlnet_condition(cn_condition)
x = sd15(
x,
step=step,
clip_text_embedding=clip_text_embedding,
condition_scale=7.5,
)
predicted_image = sd15.lda.decode_latents(x)
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ensure_similar_images(predicted_image, expected_image, min_psnr=35, min_ssim=0.98)
@no_grad()
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def test_diffusion_controlnet_structural_copy(
sd15_std: StableDiffusion_1,
controlnet_data_canny: tuple[str, Image.Image, Image.Image, Path],
test_device: torch.device,
):
sd15_base = sd15_std
sd15 = sd15_base.structural_copy()
cn_name, condition_image, expected_image, cn_weights_path = controlnet_data_canny
if not cn_weights_path.is_file():
warn(f"could not find weights at {cn_weights_path}, skipping")
pytest.skip(allow_module_level=True)
prompt = "a cute cat, detailed high-quality professional image"
negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality"
clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
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sd15.set_inference_steps(30)
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controlnet = SD1ControlnetAdapter(
sd15.unet, name=cn_name, scale=0.5, weights=load_from_safetensors(cn_weights_path)
).inject()
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cn_condition = image_to_tensor(condition_image.convert("RGB"), device=test_device)
manual_seed(2)
x = torch.randn(1, 4, 64, 64, device=test_device)
for step in sd15.steps:
controlnet.set_controlnet_condition(cn_condition)
x = sd15(
x,
step=step,
clip_text_embedding=clip_text_embedding,
condition_scale=7.5,
)
predicted_image = sd15.lda.decode_latents(x)
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ensure_similar_images(predicted_image, expected_image, min_psnr=35, min_ssim=0.98)
@no_grad()
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def test_diffusion_controlnet_float16(
sd15_std_float16: StableDiffusion_1,
controlnet_data_canny: tuple[str, Image.Image, Image.Image, Path],
test_device: torch.device,
):
sd15 = sd15_std_float16
cn_name, condition_image, expected_image, cn_weights_path = controlnet_data_canny
if not cn_weights_path.is_file():
warn(f"could not find weights at {cn_weights_path}, skipping")
pytest.skip(allow_module_level=True)
prompt = "a cute cat, detailed high-quality professional image"
negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality"
clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
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sd15.set_inference_steps(30)
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controlnet = SD1ControlnetAdapter(
sd15.unet, name=cn_name, scale=0.5, weights=load_from_safetensors(cn_weights_path)
).inject()
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cn_condition = image_to_tensor(condition_image.convert("RGB"), device=test_device, dtype=torch.float16)
manual_seed(2)
x = torch.randn(1, 4, 64, 64, device=test_device, dtype=torch.float16)
for step in sd15.steps:
controlnet.set_controlnet_condition(cn_condition)
x = sd15(
x,
step=step,
clip_text_embedding=clip_text_embedding,
condition_scale=7.5,
)
predicted_image = sd15.lda.decode_latents(x)
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ensure_similar_images(predicted_image, expected_image, min_psnr=35, min_ssim=0.98)
@no_grad()
def test_diffusion_controlnet_stack(
sd15_std: StableDiffusion_1,
controlnet_data_depth: tuple[str, Image.Image, Image.Image, Path],
controlnet_data_canny: tuple[str, Image.Image, Image.Image, Path],
expected_image_controlnet_stack: Image.Image,
test_device: torch.device,
):
sd15 = sd15_std
_, depth_condition_image, _, depth_cn_weights_path = controlnet_data_depth
_, canny_condition_image, _, canny_cn_weights_path = controlnet_data_canny
if not canny_cn_weights_path.is_file():
warn(f"could not find weights at {canny_cn_weights_path}, skipping")
pytest.skip(allow_module_level=True)
if not depth_cn_weights_path.is_file():
warn(f"could not find weights at {depth_cn_weights_path}, skipping")
pytest.skip(allow_module_level=True)
prompt = "a cute cat, detailed high-quality professional image"
negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality"
clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
sd15.set_inference_steps(30)
depth_controlnet = SD1ControlnetAdapter(
sd15.unet, name="depth", scale=0.3, weights=load_from_safetensors(depth_cn_weights_path)
).inject()
canny_controlnet = SD1ControlnetAdapter(
sd15.unet, name="canny", scale=0.7, weights=load_from_safetensors(canny_cn_weights_path)
).inject()
depth_cn_condition = image_to_tensor(depth_condition_image.convert("RGB"), device=test_device)
canny_cn_condition = image_to_tensor(canny_condition_image.convert("RGB"), device=test_device)
manual_seed(2)
x = torch.randn(1, 4, 64, 64, device=test_device)
for step in sd15.steps:
depth_controlnet.set_controlnet_condition(depth_cn_condition)
canny_controlnet.set_controlnet_condition(canny_cn_condition)
x = sd15(
x,
step=step,
clip_text_embedding=clip_text_embedding,
condition_scale=7.5,
)
predicted_image = sd15.lda.decode_latents(x)
ensure_similar_images(predicted_image, expected_image_controlnet_stack, min_psnr=35, min_ssim=0.98)
@no_grad()
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def test_diffusion_lora(
sd15_std: StableDiffusion_1,
lora_data_pokemon: tuple[Image.Image, dict[str, torch.Tensor]],
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test_device: torch.device,
) -> None:
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sd15 = sd15_std
expected_image, lora_weights = lora_data_pokemon
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prompt = "a cute cat"
clip_text_embedding = sd15.compute_clip_text_embedding(prompt)
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sd15.set_inference_steps(30)
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SDLoraManager(sd15).add_loras("pokemon", lora_weights, scale=1)
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manual_seed(2)
x = torch.randn(1, 4, 64, 64, device=test_device)
for step in sd15.steps:
x = sd15(
x,
step=step,
clip_text_embedding=clip_text_embedding,
condition_scale=7.5,
)
predicted_image = sd15.lda.decode_latents(x)
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ensure_similar_images(predicted_image, expected_image, min_psnr=35, min_ssim=0.98)
@no_grad()
def test_diffusion_sdxl_lora(
sdxl_ddim: StableDiffusion_XL,
lora_data_dpo: tuple[Image.Image, dict[str, torch.Tensor]],
) -> None:
sdxl = sdxl_ddim
expected_image, lora_weights = lora_data_dpo
# parameters are the same as https://huggingface.co/radames/sdxl-DPO-LoRA
# except that we are using DDIM instead of sde-dpmsolver++
seed = 12341234123
guidance_scale = 7.5
lora_scale = 1.4
prompt = "professional portrait photo of a girl, photograph, highly detailed face, depth of field, moody light, golden hour, style by Dan Winters, Russell James, Steve McCurry, centered, extremely detailed, Nikon D850, award winning photography"
negative_prompt = "3d render, cartoon, drawing, art, low light, blur, pixelated, low resolution, black and white"
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SDLoraManager(sdxl).add_loras("dpo", lora_weights, scale=lora_scale)
clip_text_embedding, pooled_text_embedding = sdxl.compute_clip_text_embedding(
text=prompt, negative_text=negative_prompt
)
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time_ids = sdxl.default_time_ids
sdxl.set_inference_steps(40)
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manual_seed(seed=seed)
x = torch.randn(1, 4, 128, 128, device=sdxl.device, dtype=sdxl.dtype)
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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=guidance_scale,
)
predicted_image = sdxl.lda.decode_latents(x)
ensure_similar_images(predicted_image, expected_image, min_psnr=35, min_ssim=0.98)
@no_grad()
def test_diffusion_sdxl_multiple_loras(
sdxl_ddim: StableDiffusion_XL,
lora_data_dpo: tuple[Image.Image, dict[str, torch.Tensor]],
lora_sliders: tuple[dict[str, dict[str, torch.Tensor]], dict[str, float]],
expected_sdxl_multi_loras: Image.Image,
) -> None:
sdxl = sdxl_ddim
expected_image = expected_sdxl_multi_loras
_, dpo = lora_data_dpo
loras, scales = lora_sliders
loras["dpo"] = dpo
SDLoraManager(sdxl).add_multiple_loras(loras, scales)
# parameters are the same as https://huggingface.co/radames/sdxl-DPO-LoRA
# except that we are using DDIM instead of sde-dpmsolver++
n_steps = 40
seed = 12341234123
guidance_scale = 4
prompt = "professional portrait photo of a girl, photograph, highly detailed face, depth of field, moody light, golden hour, style by Dan Winters, Russell James, Steve McCurry, centered, extremely detailed, Nikon D850, award winning photography"
negative_prompt = "3d render, cartoon, drawing, art, low light, blur, pixelated, low resolution, black and white"
clip_text_embedding, pooled_text_embedding = sdxl.compute_clip_text_embedding(
text=prompt, negative_text=negative_prompt
)
time_ids = sdxl.default_time_ids
sdxl.set_inference_steps(n_steps)
manual_seed(seed=seed)
x = torch.randn(1, 4, 128, 128, device=sdxl.device, dtype=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=guidance_scale,
)
predicted_image = sdxl.lda.decode_latents(x)
ensure_similar_images(predicted_image, expected_image, min_psnr=35, min_ssim=0.98)
@no_grad()
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def test_diffusion_refonly(
sd15_ddim: StableDiffusion_1,
condition_image_refonly: Image.Image,
expected_image_refonly: Image.Image,
test_device: torch.device,
):
sd15 = sd15_ddim
prompt = "Chicken"
clip_text_embedding = sd15.compute_clip_text_embedding(prompt)
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refonly_adapter = ReferenceOnlyControlAdapter(sd15.unet).inject()
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guide = sd15.lda.encode_image(condition_image_refonly)
guide = torch.cat((guide, guide))
manual_seed(2)
x = torch.randn(1, 4, 64, 64, device=test_device)
for step in sd15.steps:
noise = torch.randn(2, 4, 64, 64, device=test_device)
noised_guide = sd15.solver.add_noise(guide, noise, step)
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refonly_adapter.set_controlnet_condition(noised_guide)
x = sd15(
x,
step=step,
clip_text_embedding=clip_text_embedding,
condition_scale=7.5,
)
torch.randn(2, 4, 64, 64, device=test_device) # for SD Web UI reproductibility only
predicted_image = sd15.lda.decode_latents(x)
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# min_psnr lowered to 33 because this reference image was generated without noise removal (see #192)
ensure_similar_images(predicted_image, expected_image_refonly, min_psnr=33, min_ssim=0.99)
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@no_grad()
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def test_diffusion_inpainting_refonly(
sd15_inpainting: StableDiffusion_1_Inpainting,
scene_image_inpainting_refonly: Image.Image,
target_image_inpainting_refonly: Image.Image,
mask_image_inpainting_refonly: Image.Image,
expected_image_inpainting_refonly: Image.Image,
test_device: torch.device,
):
sd15 = sd15_inpainting
prompt = "" # unconditional
clip_text_embedding = sd15.compute_clip_text_embedding(prompt)
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refonly_adapter = ReferenceOnlyControlAdapter(sd15.unet).inject()
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sd15.set_inference_steps(30)
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sd15.set_inpainting_conditions(target_image_inpainting_refonly, mask_image_inpainting_refonly)
guide = sd15.lda.encode_image(scene_image_inpainting_refonly)
guide = torch.cat((guide, guide))
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manual_seed(2)
x = torch.randn(1, 4, 64, 64, device=test_device)
for step in sd15.steps:
noise = torch.randn_like(guide)
noised_guide = sd15.solver.add_noise(guide, noise, step)
# See https://github.com/Mikubill/sd-webui-controlnet/pull/1275 ("1.1.170 reference-only begin to support
# inpaint variation models")
noised_guide = torch.cat([noised_guide, torch.zeros_like(noised_guide)[:, 0:1, :, :], guide], dim=1)
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refonly_adapter.set_controlnet_condition(noised_guide)
x = sd15(
x,
step=step,
clip_text_embedding=clip_text_embedding,
condition_scale=7.5,
)
predicted_image = sd15.lda.decode_latents(x)
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ensure_similar_images(predicted_image, expected_image_inpainting_refonly, min_psnr=35, min_ssim=0.99)
@no_grad()
def test_diffusion_textual_inversion_random_init(
sd15_std: StableDiffusion_1,
expected_image_textual_inversion_random_init: Image.Image,
text_embedding_textual_inversion: torch.Tensor,
test_device: torch.device,
):
sd15 = sd15_std
conceptExtender = ConceptExtender(sd15.clip_text_encoder)
conceptExtender.add_concept("<gta5-artwork>", text_embedding_textual_inversion)
conceptExtender.inject()
prompt = "a cute cat on a <gta5-artwork>"
clip_text_embedding = sd15.compute_clip_text_embedding(prompt)
sd15.set_inference_steps(30)
manual_seed(2)
x = torch.randn(1, 4, 64, 64, device=test_device)
for step in sd15.steps:
x = sd15(
x,
step=step,
clip_text_embedding=clip_text_embedding,
condition_scale=7.5,
)
predicted_image = sd15.lda.decode_latents(x)
ensure_similar_images(predicted_image, expected_image_textual_inversion_random_init, min_psnr=35, min_ssim=0.98)
@no_grad()
def test_diffusion_ip_adapter(
sd15_ddim_lda_ft_mse: StableDiffusion_1,
ip_adapter_weights: Path,
image_encoder_weights: Path,
woman_image: Image.Image,
expected_image_ip_adapter_woman: Image.Image,
test_device: torch.device,
):
sd15 = sd15_ddim_lda_ft_mse.to(dtype=torch.float16)
# See tencent-ailab/IP-Adapter best practices section:
#
# If you only use the image prompt, you can set the scale=1.0 and text_prompt="" (or some generic text
# prompts, e.g. "best quality", you can also use any negative text prompt).
#
# The prompts below are the ones used by default by IPAdapter's generate method if none are specified
prompt = "best quality, high quality"
negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
ip_adapter = SD1IPAdapter(target=sd15.unet, weights=load_from_safetensors(ip_adapter_weights))
ip_adapter.clip_image_encoder.load_from_safetensors(image_encoder_weights)
ip_adapter.inject()
clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
clip_image_embedding = ip_adapter.compute_clip_image_embedding(ip_adapter.preprocess_image(woman_image))
ip_adapter.set_clip_image_embedding(clip_image_embedding)
sd15.set_inference_steps(50)
manual_seed(2)
x = torch.randn(1, 4, 64, 64, device=test_device, dtype=torch.float16)
for step in sd15.steps:
x = sd15(
x,
step=step,
clip_text_embedding=clip_text_embedding,
condition_scale=7.5,
)
predicted_image = sd15.lda.decode_latents(x)
ensure_similar_images(predicted_image, expected_image_ip_adapter_woman)
@no_grad()
def test_diffusion_ip_adapter_multi(
sd15_ddim_lda_ft_mse: StableDiffusion_1,
ip_adapter_weights: Path,
image_encoder_weights: Path,
woman_image: Image.Image,
statue_image: Image.Image,
expected_image_ip_adapter_multi: Image.Image,
test_device: torch.device,
):
sd15 = sd15_ddim_lda_ft_mse.to(dtype=torch.float16)
prompt = "best quality, high quality"
negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
ip_adapter = SD1IPAdapter(target=sd15.unet, weights=load_from_safetensors(ip_adapter_weights))
ip_adapter.clip_image_encoder.load_from_safetensors(image_encoder_weights)
ip_adapter.inject()
clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
clip_image_embedding = ip_adapter.compute_clip_image_embedding([woman_image, statue_image], weights=[1.0, 1.4])
ip_adapter.set_clip_image_embedding(clip_image_embedding)
sd15.set_inference_steps(50)
manual_seed(2)
x = torch.randn(1, 4, 64, 64, device=test_device, dtype=torch.float16)
for step in sd15.steps:
x = sd15(
x,
step=step,
clip_text_embedding=clip_text_embedding,
condition_scale=7.5,
)
predicted_image = sd15.lda.decode_latents(x)
ensure_similar_images(predicted_image, expected_image_ip_adapter_multi)
@no_grad()
def test_diffusion_sdxl_ip_adapter(
sdxl_ddim: StableDiffusion_XL,
sdxl_ip_adapter_weights: Path,
image_encoder_weights: Path,
woman_image: Image.Image,
expected_image_sdxl_ip_adapter_woman: Image.Image,
test_device: torch.device,
):
sdxl = sdxl_ddim.to(dtype=torch.float16)
prompt = "best quality, high quality"
negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
ip_adapter = SDXLIPAdapter(target=sdxl.unet, weights=load_from_safetensors(sdxl_ip_adapter_weights))
ip_adapter.clip_image_encoder.load_from_safetensors(image_encoder_weights)
ip_adapter.inject()
with no_grad():
clip_text_embedding, pooled_text_embedding = sdxl.compute_clip_text_embedding(
text=prompt, negative_text=negative_prompt
)
clip_image_embedding = ip_adapter.compute_clip_image_embedding(ip_adapter.preprocess_image(woman_image))
ip_adapter.set_clip_image_embedding(clip_image_embedding)
time_ids = sdxl.default_time_ids
sdxl.set_inference_steps(30)
manual_seed(2)
x = torch.randn(1, 4, 128, 128, device=test_device, dtype=torch.float16)
with no_grad():
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=5,
)
# See https://huggingface.co/madebyollin/sdxl-vae-fp16-fix: "SDXL-VAE generates NaNs in fp16 because the
# internal activation values are too big"
sdxl.lda.to(dtype=torch.float32)
predicted_image = sdxl.lda.decode_latents(x.to(dtype=torch.float32))
ensure_similar_images(predicted_image, expected_image_sdxl_ip_adapter_woman)
@no_grad()
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def test_diffusion_ip_adapter_controlnet(
sd15_ddim: StableDiffusion_1,
ip_adapter_weights: Path,
image_encoder_weights: Path,
lora_data_pokemon: tuple[Image.Image, Path],
controlnet_data_depth: tuple[str, Image.Image, Image.Image, Path],
expected_image_ip_adapter_controlnet: Image.Image,
test_device: torch.device,
):
sd15 = sd15_ddim.to(dtype=torch.float16)
input_image, _ = lora_data_pokemon # use the Pokemon LoRA output as input
_, depth_condition_image, _, depth_cn_weights_path = controlnet_data_depth
prompt = "best quality, high quality"
negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
ip_adapter = SD1IPAdapter(target=sd15.unet, weights=load_from_safetensors(ip_adapter_weights))
ip_adapter.clip_image_encoder.load_from_safetensors(image_encoder_weights)
ip_adapter.inject()
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depth_controlnet = SD1ControlnetAdapter(
sd15.unet,
name="depth",
scale=1.0,
weights=load_from_safetensors(depth_cn_weights_path),
).inject()
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clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
clip_image_embedding = ip_adapter.compute_clip_image_embedding(ip_adapter.preprocess_image(input_image))
ip_adapter.set_clip_image_embedding(clip_image_embedding)
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depth_cn_condition = image_to_tensor(
depth_condition_image.convert("RGB"),
device=test_device,
dtype=torch.float16,
)
sd15.set_inference_steps(50)
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manual_seed(2)
x = torch.randn(1, 4, 64, 64, device=test_device, dtype=torch.float16)
for step in sd15.steps:
depth_controlnet.set_controlnet_condition(depth_cn_condition)
x = sd15(
x,
step=step,
clip_text_embedding=clip_text_embedding,
condition_scale=7.5,
)
predicted_image = sd15.lda.decode_latents(x)
ensure_similar_images(predicted_image, expected_image_ip_adapter_controlnet)
@no_grad()
def test_diffusion_ip_adapter_plus(
sd15_ddim_lda_ft_mse: StableDiffusion_1,
ip_adapter_plus_weights: Path,
image_encoder_weights: Path,
statue_image: Image.Image,
expected_image_ip_adapter_plus_statue: Image.Image,
test_device: torch.device,
):
sd15 = sd15_ddim_lda_ft_mse.to(dtype=torch.float16)
prompt = "best quality, high quality"
negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
ip_adapter = SD1IPAdapter(
target=sd15.unet, weights=load_from_safetensors(ip_adapter_plus_weights), fine_grained=True
)
ip_adapter.clip_image_encoder.load_from_safetensors(image_encoder_weights)
ip_adapter.inject()
clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
clip_image_embedding = ip_adapter.compute_clip_image_embedding(ip_adapter.preprocess_image(statue_image))
ip_adapter.set_clip_image_embedding(clip_image_embedding)
sd15.set_inference_steps(50)
manual_seed(42) # seed=42 is used in the official IP-Adapter demo
x = torch.randn(1, 4, 64, 64, device=test_device, dtype=torch.float16)
for step in sd15.steps:
x = sd15(
x,
step=step,
clip_text_embedding=clip_text_embedding,
condition_scale=7.5,
)
predicted_image = sd15.lda.decode_latents(x)
ensure_similar_images(predicted_image, expected_image_ip_adapter_plus_statue, min_psnr=35, min_ssim=0.98)
@no_grad()
def test_diffusion_sdxl_ip_adapter_plus(
sdxl_ddim: StableDiffusion_XL,
sdxl_ip_adapter_plus_weights: Path,
image_encoder_weights: Path,
woman_image: Image.Image,
expected_image_sdxl_ip_adapter_plus_woman: Image.Image,
test_device: torch.device,
):
sdxl = sdxl_ddim.to(dtype=torch.float16)
prompt = "best quality, high quality"
negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
ip_adapter = SDXLIPAdapter(
target=sdxl.unet, weights=load_from_safetensors(sdxl_ip_adapter_plus_weights), 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, negative_text=negative_prompt
)
clip_image_embedding = ip_adapter.compute_clip_image_embedding(ip_adapter.preprocess_image(woman_image))
ip_adapter.set_clip_image_embedding(clip_image_embedding)
time_ids = sdxl.default_time_ids
sdxl.set_inference_steps(30)
manual_seed(2)
x = torch.randn(1, 4, 128, 128, device=test_device, dtype=torch.float16)
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=5,
)
sdxl.lda.to(dtype=torch.float32)
predicted_image = sdxl.lda.decode_latents(x.to(dtype=torch.float32))
ensure_similar_images(predicted_image, expected_image_sdxl_ip_adapter_plus_woman)
@no_grad()
def test_sdxl_random_init(
sdxl_ddim: StableDiffusion_XL, expected_sdxl_ddim_random_init: Image.Image, test_device: torch.device
) -> None:
sdxl = sdxl_ddim
expected_image = expected_sdxl_ddim_random_init
prompt = "a cute cat, detailed high-quality professional image"
negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality"
clip_text_embedding, pooled_text_embedding = sdxl.compute_clip_text_embedding(
text=prompt, negative_text=negative_prompt
)
time_ids = sdxl.default_time_ids
sdxl.set_inference_steps(30)
manual_seed(seed=2)
x = torch.randn(1, 4, 128, 128, device=test_device)
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=5,
)
predicted_image = sdxl.lda.decode_latents(x=x)
ensure_similar_images(img_1=predicted_image, img_2=expected_image, min_psnr=35, min_ssim=0.98)
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@no_grad()
def test_sdxl_random_init_sag(
sdxl_ddim: StableDiffusion_XL, expected_sdxl_ddim_random_init_sag: Image.Image, test_device: torch.device
) -> None:
sdxl = sdxl_ddim
expected_image = expected_sdxl_ddim_random_init_sag
prompt = "a cute cat, detailed high-quality professional image"
negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality"
clip_text_embedding, pooled_text_embedding = sdxl.compute_clip_text_embedding(
text=prompt, negative_text=negative_prompt
)
time_ids = sdxl.default_time_ids
sdxl.set_inference_steps(30)
sdxl.set_self_attention_guidance(enable=True, scale=0.75)
manual_seed(seed=2)
x = torch.randn(1, 4, 128, 128, device=test_device)
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=5,
)
predicted_image = sdxl.lda.decode_latents(x=x)
ensure_similar_images(img_1=predicted_image, img_2=expected_image)
@no_grad()
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def test_multi_diffusion(sd15_ddim: StableDiffusion_1, expected_multi_diffusion: Image.Image) -> None:
manual_seed(seed=2)
sd = sd15_ddim
multi_diffusion = SD1MultiDiffusion(sd)
clip_text_embedding = sd.compute_clip_text_embedding(text="a panorama of a mountain")
target_1 = DiffusionTarget(
size=(64, 64),
offset=(0, 0),
clip_text_embedding=clip_text_embedding,
start_step=0,
)
target_2 = DiffusionTarget(
size=(64, 64),
offset=(0, 16),
clip_text_embedding=clip_text_embedding,
start_step=0,
)
noise = torch.randn(1, 4, 64, 80, device=sd.device, dtype=sd.dtype)
x = noise
for step in sd.steps:
x = multi_diffusion(
x,
noise=noise,
step=step,
targets=[target_1, target_2],
)
result = sd.lda.decode_latents(x=x)
ensure_similar_images(img_1=result, img_2=expected_multi_diffusion, min_psnr=35, min_ssim=0.98)
@no_grad()
def test_t2i_adapter_depth(
sd15_std: StableDiffusion_1,
t2i_adapter_data_depth: tuple[str, Image.Image, Image.Image, Path],
test_device: torch.device,
):
sd15 = sd15_std
name, condition_image, expected_image, weights_path = t2i_adapter_data_depth
if not weights_path.is_file():
warn(f"could not find weights at {weights_path}, skipping")
pytest.skip(allow_module_level=True)
prompt = "a cute cat, detailed high-quality professional image"
negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality"
clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
sd15.set_inference_steps(30)
t2i_adapter = SD1T2IAdapter(target=sd15.unet, name=name, weights=load_from_safetensors(weights_path)).inject()
condition = image_to_tensor(condition_image.convert("RGB"), device=test_device)
t2i_adapter.set_condition_features(features=t2i_adapter.compute_condition_features(condition))
manual_seed(2)
x = torch.randn(1, 4, 64, 64, device=test_device)
for step in sd15.steps:
x = sd15(
x,
step=step,
clip_text_embedding=clip_text_embedding,
condition_scale=7.5,
)
predicted_image = sd15.lda.decode_latents(x)
ensure_similar_images(predicted_image, expected_image)
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@no_grad()
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def test_t2i_adapter_xl_canny(
sdxl_ddim: StableDiffusion_XL,
t2i_adapter_xl_data_canny: tuple[str, Image.Image, Image.Image, Path],
test_device: torch.device,
):
sdxl = sdxl_ddim
name, condition_image, expected_image, weights_path = t2i_adapter_xl_data_canny
if not weights_path.is_file():
warn(f"could not find weights at {weights_path}, skipping")
pytest.skip(allow_module_level=True)
prompt = "Mystical fairy in real, magic, 4k picture, high quality"
negative_prompt = (
"extra digit, fewer digits, cropped, worst quality, low quality, glitch, deformed, mutated, ugly, disfigured"
)
clip_text_embedding, pooled_text_embedding = sdxl.compute_clip_text_embedding(
text=prompt, negative_text=negative_prompt
)
time_ids = sdxl.default_time_ids
sdxl.set_inference_steps(30)
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t2i_adapter = SDXLT2IAdapter(target=sdxl.unet, name=name, weights=load_from_safetensors(weights_path)).inject()
t2i_adapter.set_scale(0.8)
condition = image_to_tensor(condition_image.convert("RGB"), device=test_device)
t2i_adapter.set_condition_features(features=t2i_adapter.compute_condition_features(condition))
manual_seed(2)
x = torch.randn(1, 4, condition_image.height // 8, condition_image.width // 8, device=test_device)
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=7.5,
)
predicted_image = sdxl.lda.decode_latents(x)
ensure_similar_images(predicted_image, expected_image)
@no_grad()
def test_restart(
sd15_ddim: StableDiffusion_1,
expected_restart: Image.Image,
test_device: torch.device,
):
sd15 = sd15_ddim
prompt = "a cute cat, detailed high-quality professional image"
negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality"
clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
sd15.set_inference_steps(30)
restart = Restart(ldm=sd15)
manual_seed(2)
x = torch.randn(1, 4, 64, 64, device=test_device)
for step in sd15.steps:
x = sd15(
x,
step=step,
clip_text_embedding=clip_text_embedding,
condition_scale=8,
)
if step == restart.start_step:
x = restart(
x,
clip_text_embedding=clip_text_embedding,
condition_scale=8,
)
predicted_image = sd15.lda.decode_latents(x)
ensure_similar_images(predicted_image, expected_restart, min_psnr=35, min_ssim=0.98)
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@no_grad()
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def test_freeu(
sd15_std: StableDiffusion_1,
expected_freeu: Image.Image,
):
sd15 = sd15_std
prompt = "best quality, high quality cute cat"
negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
sd15.set_inference_steps(50, first_step=1)
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SDFreeUAdapter(
sd15.unet, backbone_scales=[1.2, 1.2, 1.2, 1.4, 1.4, 1.4], skip_scales=[0.9, 0.9, 0.9, 0.2, 0.2, 0.2]
).inject()
manual_seed(9752)
x = sd15.init_latents((512, 512)).to(device=sd15.device, dtype=sd15.dtype)
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for step in sd15.steps:
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x = sd15(
x,
step=step,
clip_text_embedding=clip_text_embedding,
condition_scale=7.5,
)
predicted_image = sd15.lda.decode_latents(x)
ensure_similar_images(predicted_image, expected_freeu)
@no_grad()
def test_hello_world(
sdxl_ddim_lda_fp16_fix: StableDiffusion_XL,
t2i_adapter_xl_data_canny: tuple[str, Image.Image, Image.Image, Path],
sdxl_ip_adapter_weights: Path,
image_encoder_weights: Path,
hello_world_assets: tuple[Image.Image, Image.Image, Image.Image, Image.Image],
) -> None:
sdxl = sdxl_ddim_lda_fp16_fix.to(dtype=torch.float16)
sdxl.dtype = torch.float16 # FIXME: should not be necessary
name, _, _, weights_path = t2i_adapter_xl_data_canny
init_image, image_prompt, condition_image, expected_image = hello_world_assets
if not weights_path.is_file():
warn(f"could not find weights at {weights_path}, skipping")
pytest.skip(allow_module_level=True)
ip_adapter = SDXLIPAdapter(target=sdxl.unet, weights=load_from_safetensors(sdxl_ip_adapter_weights))
ip_adapter.clip_image_encoder.load_from_safetensors(image_encoder_weights)
ip_adapter.inject()
image_embedding = ip_adapter.compute_clip_image_embedding(ip_adapter.preprocess_image(image_prompt))
ip_adapter.set_clip_image_embedding(image_embedding)
# Note: default text prompts for IP-Adapter
clip_text_embedding, pooled_text_embedding = sdxl.compute_clip_text_embedding(
text="best quality, high quality", negative_text="monochrome, lowres, bad anatomy, worst quality, low quality"
)
time_ids = sdxl.default_time_ids
t2i_adapter = SDXLT2IAdapter(target=sdxl.unet, name=name, weights=load_from_safetensors(weights_path)).inject()
condition = image_to_tensor(condition_image.convert("RGB"), device=sdxl.device, dtype=sdxl.dtype)
t2i_adapter.set_condition_features(features=t2i_adapter.compute_condition_features(condition))
ip_adapter.set_scale(0.85)
t2i_adapter.set_scale(0.8)
sdxl.set_inference_steps(50, first_step=1)
sdxl.set_self_attention_guidance(enable=True, scale=0.75)
manual_seed(9752)
x = sdxl.init_latents(size=(1024, 1024), init_image=init_image).to(device=sdxl.device, dtype=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)