Merge remote-tracking branch 'origin/diffusion-sdxl' into diffusion-lora-ux

studio/backend/core/inference/diffusion.py

@@ -1538,7 +1538,9 @@ class DiffusionBackend:
                         mask_pil = mask_pil.resize(init_pil.size, _PILImage.NEAREST)
                 if init_pil is not None:
                     # Keep the VAE encode dtype consistent with the input image.
-                    self._align_vae_dtype(pipe, getattr(state.family, "denoiser_attr", "transformer"))
+                    self._align_vae_dtype(
+                        pipe, getattr(state.family, "denoiser_attr", "transformer")
+                    )
 
                 # Pipelines vary in which kwargs they accept (img2img derives size from the
                 # input image and may reject width/height; a distilled pipe may take no

studio/backend/core/inference/diffusion_attention.py

@@ -225,7 +225,6 @@ def _reset_global_backend_to_native(logger: Any) -> None:
             AttentionBackendName,
             _AttentionBackendRegistry,
         )
-
         _AttentionBackendRegistry.set_active_backend(AttentionBackendName.NATIVE)
     except Exception:  # noqa: BLE001 — best-effort; leave the global as-is on any change
         pass

studio/backend/core/inference/diffusion_precision.py

@@ -120,7 +120,10 @@ def _cast_fp8(encoder: Any, target: Any) -> None:
     # gets cast to fp8 and, sharing one tensor, drags the embedding to fp8 with it. The
     # embedding then emits fp8 activations that crash the first RMSNorm. Skip the tied
     # projection so the shared tensor stays dense (lm_head is unused for prompt encoding).
-    get_out, get_in = getattr(encoder, "get_output_embeddings", None), getattr(encoder, "get_input_embeddings", None)
+    get_out, get_in = (
+        getattr(encoder, "get_output_embeddings", None),
+        getattr(encoder, "get_input_embeddings", None),
+    )
     out_emb = get_out() if callable(get_out) else None
     in_emb = get_in() if callable(get_in) else None
     if out_emb is not None and in_emb is not None and out_emb.weight is in_emb.weight:

studio/backend/core/inference/diffusion_speed.py

@@ -282,6 +282,8 @@ def _enable_cudnn_benchmark(logger: Any) -> bool:
     except Exception as exc:  # noqa: BLE001 — optimisation only
         _warn(logger, "cudnn_benchmark", exc)
         return False
+
+
 # The TF32 flag values from before the first max load flipped them, so a later
 # non-max load / unload can put the process back exactly as it found it (rather than
 # forcing a hardcoded default that might clobber another component's choice).

studio/backend/core/inference/diffusion_transformer_quant.py

@@ -313,6 +313,7 @@ def _make_quant_config(scheme: str, fast_accum: Optional[bool] = None) -> Any:
             return Float8DynamicActivationFloat8WeightConfig()
     if scheme == TQ_NVFP4:
         from torchao.prototype.mx_formats import NVFP4DynamicActivationNVFP4WeightConfig
+
         # Select the CUTLASS FP4 path, not the default Triton kernel: torchao defaults
         # use_triton_kernel=True, which needs MSLK installed. On a Blackwell box with the
         # CUTLASS FP4 extension but no MSLK, the default would make the smoke probe fail

studio/backend/tests/test_diffusion_backend.py

@@ -889,9 +889,7 @@ def test_load_sdxl_single_file_uses_pipeline_from_single_file(fake_runtime, tmp_
     assert status["loaded"] is True
     assert status["family"] == "sdxl"
     # The whole-pipeline single-file path was taken with the base repo as config.
-    assert _FakePipeline.last_single_file["path"] == str(
-        (tmp_path / "sdxl.safetensors").resolve()
-    )
+    assert _FakePipeline.last_single_file["path"] == str((tmp_path / "sdxl.safetensors").resolve())
     assert _FakePipeline.last_single_file["config"] == "stabilityai/stable-diffusion-xl-base-1.0"
     # The transformer-only single-file build was NOT taken.
     assert _FakeTransformer.last == {}

studio/backend/tests/test_diffusion_routes.py

@@ -487,6 +487,8 @@ def test_invalid_attention_backend_returns_422(client):
         json = {"model_path": "x/z-image", "gguf_filename": "q.gguf", "attention_backend": "bogus"},
     )
     assert resp.status_code == 422
+
+
 def test_prequant_path_doc_describes_allowlist_not_toggle():
     # The field help must match the code: UNSLOTH_ALLOW_LOCAL_PREQUANT_PATH is a
     # directory allowlist, not a =1 toggle (diffusion_prequant._allowed_prequant_roots

studio/backend/tests/test_diffusion_sdxl.py

@@ -46,7 +46,7 @@ def test_sdxl_detection_by_repo_and_override():
     assert detect_family("stabilityai/sdxl-turbo").name == "sdxl"
     assert detect_family("some-org/My-Cool-SDXL-Merge").name == "sdxl"
     assert detect_family("some-org/stable-diffusion-xl-anime").name == "sdxl"
-    assert detect_family("x", override="sdxl").name == "sdxl"
+    assert detect_family("x", override = "sdxl").name == "sdxl"
     # A GGUF DiT family must NOT be swallowed by the SDXL match.
     assert detect_family("unsloth/FLUX.1-schnell-GGUF").name == "flux.1"
 
@@ -91,9 +91,9 @@ class _FakeVae:
         self.moved_to = None
 
     def parameters(self):
-        yield types.SimpleNamespace(dtype=self._dtype)
+        yield types.SimpleNamespace(dtype = self._dtype)
 
-    def to(self, dtype=None):
+    def to(self, dtype = None):
         self.moved_to = dtype
         self._dtype = dtype
 
@@ -101,29 +101,29 @@ class _FakeVae:
 def test_align_vae_dtype_uses_unet_denoiser():
     # For SDXL the denoiser lives at pipe.unet; _align_vae_dtype must read it (a pipe
     # with only .unet and no .transformer) and cast the VAE to the U-Net's dtype.
-    vae = _FakeVae(dtype="float32")
-    pipe = types.SimpleNamespace(unet=types.SimpleNamespace(dtype="bfloat16"), vae=vae)
+    vae = _FakeVae(dtype = "float32")
+    pipe = types.SimpleNamespace(unet = types.SimpleNamespace(dtype = "bfloat16"), vae = vae)
     DiffusionBackend._align_vae_dtype(pipe, "unet")
     assert vae.moved_to == "bfloat16"
 
 
 def test_align_vae_dtype_transformer_default_unchanged():
     # DiT default: reads pipe.transformer; a pipe with no transformer is a safe no-op.
-    vae = _FakeVae(dtype="float32")
-    pipe = types.SimpleNamespace(transformer=types.SimpleNamespace(dtype="bfloat16"), vae=vae)
+    vae = _FakeVae(dtype = "float32")
+    pipe = types.SimpleNamespace(transformer = types.SimpleNamespace(dtype = "bfloat16"), vae = vae)
     DiffusionBackend._align_vae_dtype(pipe)
     assert vae.moved_to == "bfloat16"
     # No denoiser attribute -> no-op (does not raise, does not move the VAE).
-    vae2 = _FakeVae(dtype="float32")
-    DiffusionBackend._align_vae_dtype(types.SimpleNamespace(vae=vae2), "unet")
+    vae2 = _FakeVae(dtype = "float32")
+    DiffusionBackend._align_vae_dtype(types.SimpleNamespace(vae = vae2), "unet")
     assert vae2.moved_to is None
 
 
 def test_sdxl_lora_supported_on_diffusers():
     # SDXL is bf16/bnb-4bit on diffusers -> LoRA is allowed (unlike GGUF-via-diffusers).
     assert diffusion_lora.supports_lora(
-        engine="diffusers", family="sdxl", model_kind="pipeline", transformer_quant=None
+        engine = "diffusers", family = "sdxl", model_kind = "pipeline", transformer_quant = None
     )
     assert diffusion_lora.supports_lora(
-        engine="diffusers", family="sdxl", model_kind="single_file", transformer_quant=None
+        engine = "diffusers", family = "sdxl", model_kind = "single_file", transformer_quant = None
     )