add a test for IP-Adapter + ControlNet

This commit is contained in:
Pierre Chapuis 2023-09-13 14:13:41 +02:00
parent cf9efb57c8
commit c421cfd56c
3 changed files with 71 additions and 1 deletions

View file

@ -80,6 +80,11 @@ 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_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")
@ -1076,6 +1081,71 @@ def test_diffusion_sdxl_ip_adapter(
ensure_similar_images(predicted_image, expected_image_sdxl_ip_adapter_woman)
@torch.no_grad()
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)
n_steps = 50
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()
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))
negative_text_embedding, conditional_text_embedding = clip_text_embedding.chunk(2)
negative_image_embedding, conditional_image_embedding = clip_image_embedding.chunk(2)
clip_text_embedding = torch.cat(
(
torch.cat([negative_text_embedding, negative_image_embedding], dim=1),
torch.cat([conditional_text_embedding, conditional_image_embedding], dim=1),
)
)
depth_controlnet = SD1ControlnetAdapter(
sd15.unet,
name="depth",
scale=1.0,
weights=load_from_safetensors(depth_cn_weights_path),
).inject()
depth_cn_condition = image_to_tensor(
depth_condition_image.convert("RGB"),
device=test_device,
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)
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)
@torch.no_grad()
def test_sdxl_random_init(
sdxl_ddim: StableDiffusion_XL, expected_sdxl_ddim_random_init: Image.Image, test_device: torch.device

View file

@ -35,7 +35,7 @@ output.images[0].save("std_random_init_expected.png")
Special cases:
- `expected_refonly.png` has been generated [with Stable Diffusion web UI](https://github.com/AUTOMATIC1111/stable-diffusion-webui).
- `expected_inpainting_refonly.png`, `expected_image_ip_adapter_woman.png`, `expected_image_sdxl_ip_adapter_woman.png` have been generated with refiners itself (and inspected so that they look reasonable).
- `expected_inpainting_refonly.png`, `expected_image_ip_adapter_woman.png`, `expected_image_sdxl_ip_adapter_woman.png` and `expected_ip_adapter_controlnet.png` have been generated with refiners itself (and inspected so that they look reasonable).
## Other images

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