add e2e test for T2I-Adapter XL canny

tests/e2e/test_diffusion.py

@@ -16,6 +16,7 @@ from refiners.foundationals.latent_diffusion import (
     SD1IPAdapter,
     SD1T2IAdapter,
     SDXLIPAdapter,
+    SDXLT2IAdapter,
 )
 from refiners.foundationals.latent_diffusion.lora import SD1LoraAdapter
 from refiners.foundationals.latent_diffusion.multi_diffusion import DiffusionTarget
@@ -129,6 +130,7 @@ def controlnet_data_depth(ref_path: Path, test_weights_path: Path) -> tuple[str,
     weights_path = test_weights_path / "controlnet" / "lllyasviel_control_v11f1p_sd15_depth.safetensors"
     return cn_name, condition_image, expected_image, weights_path
 
+
 @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"
@@ -138,6 +140,15 @@ def t2i_adapter_data_depth(ref_path: Path, test_weights_path: Path) -> tuple[str
     return name, condition_image, expected_image, weights_path
 
 
+@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"
+    return name, condition_image, expected_image, weights_path
+
+
 @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")
@@ -1283,3 +1294,52 @@ def test_t2i_adapter_depth(
     predicted_image = sd15.lda.decode_latents(x)
 
     ensure_similar_images(predicted_image, expected_image)
+
+
+@torch.no_grad()
+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
+    n_steps = 30
+
+    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_num_inference_steps(n_steps)
+
+    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)

tests/e2e/test_diffusion_ref/README.md

@@ -35,7 +35,12 @@ 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` and `expected_ip_adapter_controlnet.png` have been generated with refiners itself (and inspected so that they look reasonable).
+- The following references 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`
+    - `expected_ip_adapter_controlnet.png`
+    - `expected_t2i_adapter_xl_canny.png`
 
 ## Other images
 
@@ -45,13 +50,15 @@ Special cases:
 
 - `kitchen_mask.png` is made manually.
 
-- Controlnet guides have been manually generated using open source software and models, namely:
+- Controlnet guides have been manually generated (x) using open source software and models, namely:
     - Canny: opencv-python
     - Depth: https://github.com/isl-org/ZoeDepth
     - Lineart: https://github.com/lllyasviel/ControlNet-v1-1-nightly/tree/main/annotator/lineart
     - Normals: https://github.com/baegwangbin/surface_normal_uncertainty/tree/fe2b9f1
     - SAM: https://huggingface.co/spaces/mfidabel/controlnet-segment-anything
 
+(x): excepted `fairy_guide_canny.png` which comes from [TencentARC/t2i-adapter-canny-sdxl-1.0](https://huggingface.co/TencentARC/t2i-adapter-canny-sdxl-1.0)
+
 - `cyberpunk_guide.png` [comes from Lexica](https://lexica.art/prompt/5ba40855-0d0c-4322-8722-51115985f573).
 
 - `inpainting-mask.png`, `inpainting-scene.png` and `inpainting-target.png` have been generated as follows: