refiners/tests/e2e/test_diffusion.py

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import torch
import pytest
from typing import Iterator
from warnings import warn
from PIL import Image
from pathlib import Path
from refiners.fluxion.utils import load_from_safetensors, image_to_tensor, manual_seed
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from refiners.foundationals.latent_diffusion import (
StableDiffusion_1,
StableDiffusion_1_Inpainting,
SD1UNet,
SD1ControlnetAdapter,
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)
from refiners.foundationals.latent_diffusion.lora import SD1LoraAdapter
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from refiners.foundationals.latent_diffusion.schedulers import DDIM
from refiners.foundationals.latent_diffusion.self_attention_injection import SelfAttentionInjection
from refiners.foundationals.clip.concepts import ConceptExtender
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from tests.utils import ensure_similar_images
@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
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_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")
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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 lora_data_pokemon(ref_path: Path, test_weights_path: Path) -> tuple[Image.Image, Path]:
expected_image = Image.open(ref_path / "expected_lora_pokemon.png").convert("RGB")
weights_path = test_weights_path / "loras" / "pcuenq_pokemon_lora.safetensors"
return expected_image, weights_path
@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")
@pytest.fixture
def text_embedding_textual_inversion(test_textual_inversion_path: Path) -> torch.Tensor:
return torch.load(test_textual_inversion_path / "gta5-artwork" / "learned_embeds.bin")["<gta5-artwork>"] # type: ignore
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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
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_state_dict(load_from_safetensors(text_encoder_weights))
sd15.lda.load_state_dict(load_from_safetensors(lda_weights))
sd15.unet.load_state_dict(load_from_safetensors(unet_weights_std))
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_state_dict(load_from_safetensors(text_encoder_weights))
sd15.lda.load_state_dict(load_from_safetensors(lda_weights))
sd15.unet.load_state_dict(load_from_safetensors(unet_weights_std))
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()
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unet = SD1UNet(in_channels=9, clip_embedding_dim=768)
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sd15 = StableDiffusion_1_Inpainting(unet=unet, 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_weights))
sd15.unet.load_state_dict(load_from_safetensors(unet_weights_inpainting))
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()
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unet = SD1UNet(in_channels=9, clip_embedding_dim=768)
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sd15 = StableDiffusion_1_Inpainting(unet=unet, device=test_device, dtype=torch.float16)
sd15.clip_text_encoder.load_state_dict(load_from_safetensors(text_encoder_weights))
sd15.lda.load_state_dict(load_from_safetensors(lda_weights))
sd15.unet.load_state_dict(load_from_safetensors(unet_weights_inpainting))
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_scheduler = DDIM(num_inference_steps=20)
sd15 = StableDiffusion_1(scheduler=ddim_scheduler, 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_weights))
sd15.unet.load_state_dict(load_from_safetensors(unet_weights_std))
return sd15
@torch.no_grad()
def test_diffusion_std_random_init(
sd15_std: StableDiffusion_1, expected_image_std_random_init: Image.Image, test_device: torch.device
):
sd15 = sd15_std
n_steps = 30
prompt = "a cute cat, detailed high-quality professional image"
negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality"
with torch.no_grad():
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clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
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sd15.set_num_inference_steps(n_steps)
manual_seed(2)
x = torch.randn(1, 4, 64, 64, device=test_device)
with torch.no_grad():
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)
@torch.no_grad()
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
n_steps = 30
prompt = "a cute cat, detailed high-quality professional image"
negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality"
with torch.no_grad():
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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_num_inference_steps(n_steps)
manual_seed(2)
x = torch.randn(1, 4, 64, 64, device=test_device, dtype=torch.float16)
with torch.no_grad():
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, min_psnr=35, min_ssim=0.98)
@torch.no_grad()
def test_diffusion_std_init_image(
sd15_std: StableDiffusion_1,
cutecat_init: Image.Image,
expected_image_std_init_image: Image.Image,
):
sd15 = sd15_std
n_steps = 35
first_step = 5
prompt = "a cute cat, detailed high-quality professional image"
negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality"
with torch.no_grad():
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clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
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sd15.set_num_inference_steps(n_steps)
manual_seed(2)
x = sd15.init_latents((512, 512), cutecat_init, first_step=first_step)
with torch.no_grad():
for step in sd15.steps[first_step:]:
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_init_image)
@torch.no_grad()
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
n_steps = 30
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"
with torch.no_grad():
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clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
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sd15.set_num_inference_steps(n_steps)
sd15.set_inpainting_conditions(kitchen_dog, kitchen_dog_mask)
manual_seed(2)
x = torch.randn(1, 4, 64, 64, device=test_device)
with torch.no_grad():
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)
# 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)
@torch.no_grad()
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
n_steps = 30
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"
with torch.no_grad():
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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_num_inference_steps(n_steps)
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)
with torch.no_grad():
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)
# 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)
@torch.no_grad()
def test_diffusion_controlnet(
sd15_std: StableDiffusion_1,
controlnet_data: tuple[str, Image.Image, Image.Image, Path],
test_device: torch.device,
):
sd15 = sd15_std
n_steps = 30
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"
with torch.no_grad():
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clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
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sd15.set_num_inference_steps(n_steps)
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)
with torch.no_grad():
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)
ensure_similar_images(predicted_image, expected_image, min_psnr=35, min_ssim=0.98)
@torch.no_grad()
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()
n_steps = 30
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"
with torch.no_grad():
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clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
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sd15.set_num_inference_steps(n_steps)
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)
with torch.no_grad():
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)
ensure_similar_images(predicted_image, expected_image, min_psnr=35, min_ssim=0.98)
@torch.no_grad()
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
n_steps = 30
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"
with torch.no_grad():
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clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
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sd15.set_num_inference_steps(n_steps)
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)
with torch.no_grad():
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)
ensure_similar_images(predicted_image, expected_image, min_psnr=35, min_ssim=0.98)
@torch.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
n_steps = 30
_, 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"
with torch.no_grad():
clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
sd15.set_num_inference_steps(n_steps)
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)
with torch.no_grad():
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)
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@torch.no_grad()
def test_diffusion_lora(
sd15_std: StableDiffusion_1,
lora_data_pokemon: tuple[Image.Image, Path],
test_device: torch.device,
):
sd15 = sd15_std
n_steps = 30
expected_image, lora_weights_path = lora_data_pokemon
if not lora_weights_path.is_file():
warn(f"could not find weights at {lora_weights_path}, skipping")
pytest.skip(allow_module_level=True)
prompt = "a cute cat"
with torch.no_grad():
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clip_text_embedding = sd15.compute_clip_text_embedding(prompt)
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sd15.set_num_inference_steps(n_steps)
SD1LoraAdapter.from_safetensors(target=sd15, checkpoint_path=lora_weights_path, scale=1.0).inject()
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manual_seed(2)
x = torch.randn(1, 4, 64, 64, device=test_device)
with torch.no_grad():
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, min_psnr=35, min_ssim=0.98)
@torch.no_grad()
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"
with torch.no_grad():
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clip_text_embedding = sd15.compute_clip_text_embedding(prompt)
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sai = SelfAttentionInjection(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)
with torch.no_grad():
for step in sd15.steps:
noise = torch.randn(2, 4, 64, 64, device=test_device)
noised_guide = sd15.scheduler.add_noise(guide, noise, step)
sai.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)
ensure_similar_images(predicted_image, expected_image_refonly, min_psnr=35, min_ssim=0.99)
@torch.no_grad()
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
n_steps = 30
prompt = "" # unconditional
with torch.no_grad():
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clip_text_embedding = sd15.compute_clip_text_embedding(prompt)
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sai = SelfAttentionInjection(sd15.unet).inject()
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sd15.set_num_inference_steps(n_steps)
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)
with torch.no_grad():
for step in sd15.steps:
noise = torch.randn_like(guide)
noised_guide = sd15.scheduler.add_noise(guide, noise, step)
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# 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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sai.set_controlnet_condition(noised_guide)
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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_image_inpainting_refonly, min_psnr=35, min_ssim=0.99)
@torch.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()
n_steps = 30
prompt = "a cute cat on a <gta5-artwork>"
with torch.no_grad():
clip_text_embedding = sd15.compute_clip_text_embedding(prompt)
sd15.set_num_inference_steps(n_steps)
manual_seed(2)
x = torch.randn(1, 4, 64, 64, device=test_device)
with torch.no_grad():
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)