reset

Author: zRzRzRzRzRzRzR

tests/models/test_modeling_common.py

@@ -292,9 +292,9 @@ def test_one_request_upon_cached(self):
                 )
 
             download_requests = [r.method for r in m.request_history]
-            assert download_requests.count("HEAD") == 3, (
-                "3 HEAD requests one for config, one for model, and one for shard index file."
-            )
+            assert (
+                download_requests.count("HEAD") == 3
+            ), "3 HEAD requests one for config, one for model, and one for shard index file."
             assert download_requests.count("GET") == 2, "2 GET requests one for config, one for model"
 
             with requests_mock.mock(real_http=True) as m:
@@ -306,9 +306,9 @@ def test_one_request_upon_cached(self):
                 )
 
             cache_requests = [r.method for r in m.request_history]
-            assert "HEAD" == cache_requests[0] and len(cache_requests) == 2, (
-                "We should call only `model_info` to check for commit hash and  knowing if shard index is present."
-            )
+            assert (
+                "HEAD" == cache_requests[0] and len(cache_requests) == 2
+            ), "We should call only `model_info` to check for commit hash and  knowing if shard index is present."
 
     def test_weight_overwrite(self):
         with tempfile.TemporaryDirectory() as tmpdirname, self.assertRaises(ValueError) as error_context:

tests/models/transformers/test_models_transformer_sd3.py

@@ -91,9 +91,9 @@ def test_xformers_enable_works(self):
 
         model.enable_xformers_memory_efficient_attention()
 
-        assert model.transformer_blocks[0].attn.processor.__class__.__name__ == "XFormersJointAttnProcessor", (
-            "xformers is not enabled"
-        )
+        assert (
+            model.transformer_blocks[0].attn.processor.__class__.__name__ == "XFormersJointAttnProcessor"
+        ), "xformers is not enabled"
 
     @unittest.skip("SD3Transformer2DModel uses a dedicated attention processor. This test doesn't apply")
     def test_set_attn_processor_for_determinism(self):
@@ -165,9 +165,9 @@ def test_xformers_enable_works(self):
 
         model.enable_xformers_memory_efficient_attention()
 
-        assert model.transformer_blocks[0].attn.processor.__class__.__name__ == "XFormersJointAttnProcessor", (
-            "xformers is not enabled"
-        )
+        assert (
+            model.transformer_blocks[0].attn.processor.__class__.__name__ == "XFormersJointAttnProcessor"
+        ), "xformers is not enabled"
 
     @unittest.skip("SD3Transformer2DModel uses a dedicated attention processor. This test doesn't apply")
     def test_set_attn_processor_for_determinism(self):

tests/models/unets/test_models_unet_2d_condition.py

@@ -651,22 +651,22 @@ def test_model_xattn_mask(self, mask_dtype):
 
             keepall_mask = torch.ones(*cond.shape[:-1], device=cond.device, dtype=mask_dtype)
             full_cond_keepallmask_out = model(**{**inputs_dict, "encoder_attention_mask": keepall_mask}).sample
-            assert full_cond_keepallmask_out.allclose(full_cond_out, rtol=1e-05, atol=1e-05), (
-                "a 'keep all' mask should give the same result as no mask"
-            )
+            assert full_cond_keepallmask_out.allclose(
+                full_cond_out, rtol=1e-05, atol=1e-05
+            ), "a 'keep all' mask should give the same result as no mask"
 
             trunc_cond = cond[:, :-1, :]
             trunc_cond_out = model(**{**inputs_dict, "encoder_hidden_states": trunc_cond}).sample
-            assert not trunc_cond_out.allclose(full_cond_out, rtol=1e-05, atol=1e-05), (
-                "discarding the last token from our cond should change the result"
-            )
+            assert not trunc_cond_out.allclose(
+                full_cond_out, rtol=1e-05, atol=1e-05
+            ), "discarding the last token from our cond should change the result"
 
             batch, tokens, _ = cond.shape
             mask_last = (torch.arange(tokens) < tokens - 1).expand(batch, -1).to(cond.device, mask_dtype)
             masked_cond_out = model(**{**inputs_dict, "encoder_attention_mask": mask_last}).sample
-            assert masked_cond_out.allclose(trunc_cond_out, rtol=1e-05, atol=1e-05), (
-                "masking the last token from our cond should be equivalent to truncating that token out of the condition"
-            )
+            assert masked_cond_out.allclose(
+                trunc_cond_out, rtol=1e-05, atol=1e-05
+            ), "masking the last token from our cond should be equivalent to truncating that token out of the condition"
 
     # see diffusers.models.attention_processor::Attention#prepare_attention_mask
     # note: we may not need to fix mask padding to work for stable-diffusion cross-attn masks.
@@ -694,9 +694,9 @@ def test_model_xattn_padding(self):
 
             trunc_mask = torch.zeros(batch, tokens - 1, device=cond.device, dtype=torch.bool)
             trunc_mask_out = model(**{**inputs_dict, "encoder_attention_mask": trunc_mask}).sample
-            assert trunc_mask_out.allclose(keeplast_out), (
-                "a mask with fewer tokens than condition, will be padded with 'keep' tokens. a 'discard-all' mask missing the final token is thus equivalent to a 'keep last' mask."
-            )
+            assert trunc_mask_out.allclose(
+                keeplast_out
+            ), "a mask with fewer tokens than condition, will be padded with 'keep' tokens. a 'discard-all' mask missing the final token is thus equivalent to a 'keep last' mask."
 
     def test_custom_diffusion_processors(self):
         # enable deterministic behavior for gradient checkpointing
@@ -1111,12 +1111,12 @@ def test_load_attn_procs_raise_warning(self):
             with torch.no_grad():
                 lora_sample_2 = model(**inputs_dict).sample
 
-        assert not torch.allclose(non_lora_sample, lora_sample_1, atol=1e-4, rtol=1e-4), (
-            "LoRA injected UNet should produce different results."
-        )
-        assert torch.allclose(lora_sample_1, lora_sample_2, atol=1e-4, rtol=1e-4), (
-            "Loading from a saved checkpoint should produce identical results."
-        )
+        assert not torch.allclose(
+            non_lora_sample, lora_sample_1, atol=1e-4, rtol=1e-4
+        ), "LoRA injected UNet should produce different results."
+        assert torch.allclose(
+            lora_sample_1, lora_sample_2, atol=1e-4, rtol=1e-4
+        ), "Loading from a saved checkpoint should produce identical results."
 
     @require_peft_backend
     def test_save_attn_procs_raise_warning(self):

tests/others/test_image_processor.py

@@ -65,9 +65,9 @@ def test_vae_image_processor_pt(self):
             )
             out_np = self.to_np(out)
             in_np = (input_np * 255).round() if output_type == "pil" else input_np
-            assert np.abs(in_np - out_np).max() < 1e-6, (
-                f"decoded output does not match input for output_type {output_type}"
-            )
+            assert (
+                np.abs(in_np - out_np).max() < 1e-6
+            ), f"decoded output does not match input for output_type {output_type}"
 
     def test_vae_image_processor_np(self):
         image_processor = VaeImageProcessor(do_resize=False, do_normalize=True)
@@ -78,9 +78,9 @@ def test_vae_image_processor_np(self):
 
             out_np = self.to_np(out)
             in_np = (input_np * 255).round() if output_type == "pil" else input_np
-            assert np.abs(in_np - out_np).max() < 1e-6, (
-                f"decoded output does not match input for output_type {output_type}"
-            )
+            assert (
+                np.abs(in_np - out_np).max() < 1e-6
+            ), f"decoded output does not match input for output_type {output_type}"
 
     def test_vae_image_processor_pil(self):
         image_processor = VaeImageProcessor(do_resize=False, do_normalize=True)
@@ -93,9 +93,9 @@ def test_vae_image_processor_pil(self):
             for i, o in zip(input_pil, out):
                 in_np = np.array(i)
                 out_np = self.to_np(out) if output_type == "pil" else (self.to_np(out) * 255).round()
-                assert np.abs(in_np - out_np).max() < 1e-6, (
-                    f"decoded output does not match input for output_type {output_type}"
-                )
+                assert (
+                    np.abs(in_np - out_np).max() < 1e-6
+                ), f"decoded output does not match input for output_type {output_type}"
 
     def test_preprocess_input_3d(self):
         image_processor = VaeImageProcessor(do_resize=False, do_normalize=False)
@@ -293,9 +293,9 @@ def test_vae_image_processor_resize_pt(self):
         scale = 2
         out_pt = image_processor.resize(image=input_pt, height=h // scale, width=w // scale)
         exp_pt_shape = (b, c, h // scale, w // scale)
-        assert out_pt.shape == exp_pt_shape, (
-            f"resized image output shape '{out_pt.shape}' didn't match expected shape '{exp_pt_shape}'."
-        )
+        assert (
+            out_pt.shape == exp_pt_shape
+        ), f"resized image output shape '{out_pt.shape}' didn't match expected shape '{exp_pt_shape}'."
 
     def test_vae_image_processor_resize_np(self):
         image_processor = VaeImageProcessor(do_resize=True, vae_scale_factor=1)
@@ -305,6 +305,6 @@ def test_vae_image_processor_resize_np(self):
         input_np = self.to_np(input_pt)
         out_np = image_processor.resize(image=input_np, height=h // scale, width=w // scale)
         exp_np_shape = (b, h // scale, w // scale, c)
-        assert out_np.shape == exp_np_shape, (
-            f"resized image output shape '{out_np.shape}' didn't match expected shape '{exp_np_shape}'."
-        )
+        assert (
+            out_np.shape == exp_np_shape
+        ), f"resized image output shape '{out_np.shape}' didn't match expected shape '{exp_np_shape}'."

tests/pipelines/amused/test_amused.py

@@ -125,7 +125,8 @@ def test_inference_batch_consistent(self, batch_sizes=[2]):
         self._test_inference_batch_consistent(batch_sizes=batch_sizes, batch_generator=False)
 
     @unittest.skip("aMUSEd does not support lists of generators")
-    def test_inference_batch_single_identical(self): ...
+    def test_inference_batch_single_identical(self):
+        ...
 
 
 @slow

tests/pipelines/amused/test_amused_img2img.py

@@ -126,7 +126,8 @@ def test_inference_batch_consistent(self, batch_sizes=[2]):
         self._test_inference_batch_consistent(batch_sizes=batch_sizes, batch_generator=False)
 
     @unittest.skip("aMUSEd does not support lists of generators")
-    def test_inference_batch_single_identical(self): ...
+    def test_inference_batch_single_identical(self):
+        ...
 
 
 @slow

tests/pipelines/amused/test_amused_inpaint.py

@@ -130,7 +130,8 @@ def test_inference_batch_consistent(self, batch_sizes=[2]):
         self._test_inference_batch_consistent(batch_sizes=batch_sizes, batch_generator=False)
 
     @unittest.skip("aMUSEd does not support lists of generators")
-    def test_inference_batch_single_identical(self): ...
+    def test_inference_batch_single_identical(self):
+        ...
 
 
 @slow

tests/pipelines/aura_flow/test_pipeline_aura_flow.py

@@ -139,9 +139,9 @@ def test_fused_qkv_projections(self):
         # TODO (sayakpaul): will refactor this once `fuse_qkv_projections()` has been added
         # to the pipeline level.
         pipe.transformer.fuse_qkv_projections()
-        assert check_qkv_fusion_processors_exist(pipe.transformer), (
-            "Something wrong with the fused attention processors. Expected all the attention processors to be fused."
-        )
+        assert check_qkv_fusion_processors_exist(
+            pipe.transformer
+        ), "Something wrong with the fused attention processors. Expected all the attention processors to be fused."
         assert check_qkv_fusion_matches_attn_procs_length(
             pipe.transformer, pipe.transformer.original_attn_processors
         ), "Something wrong with the attention processors concerning the fused QKV projections."
@@ -155,15 +155,15 @@ def test_fused_qkv_projections(self):
         image = pipe(**inputs).images
         image_slice_disabled = image[0, -3:, -3:, -1]
 
-        assert np.allclose(original_image_slice, image_slice_fused, atol=1e-3, rtol=1e-3), (
-            "Fusion of QKV projections shouldn't affect the outputs."
-        )
-        assert np.allclose(image_slice_fused, image_slice_disabled, atol=1e-3, rtol=1e-3), (
-            "Outputs, with QKV projection fusion enabled, shouldn't change when fused QKV projections are disabled."
-        )
-        assert np.allclose(original_image_slice, image_slice_disabled, atol=1e-2, rtol=1e-2), (
-            "Original outputs should match when fused QKV projections are disabled."
-        )
+        assert np.allclose(
+            original_image_slice, image_slice_fused, atol=1e-3, rtol=1e-3
+        ), "Fusion of QKV projections shouldn't affect the outputs."
+        assert np.allclose(
+            image_slice_fused, image_slice_disabled, atol=1e-3, rtol=1e-3
+        ), "Outputs, with QKV projection fusion enabled, shouldn't change when fused QKV projections are disabled."
+        assert np.allclose(
+            original_image_slice, image_slice_disabled, atol=1e-2, rtol=1e-2
+        ), "Original outputs should match when fused QKV projections are disabled."
 
     @unittest.skip("xformers attention processor does not exist for AuraFlow")
     def test_xformers_attention_forwardGenerator_pass(self):

tests/pipelines/blipdiffusion/test_blipdiffusion.py

@@ -195,6 +195,6 @@ def test_blipdiffusion(self):
             [0.5329548, 0.8372512, 0.33269387, 0.82096875, 0.43657133, 0.3783, 0.5953028, 0.51934963, 0.42142007]
         )
 
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2, (
-            f" expected_slice {image_slice.flatten()}, but got {image_slice.flatten()}"
-        )
+        assert (
+            np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
+        ), f" expected_slice {image_slice.flatten()}, but got {image_slice.flatten()}"

tests/pipelines/cogvideo/test_cogvideox.py

@@ -295,9 +295,9 @@ def test_fused_qkv_projections(self):
         original_image_slice = frames[0, -2:, -1, -3:, -3:]
 
         pipe.fuse_qkv_projections()
-        assert check_qkv_fusion_processors_exist(pipe.transformer), (
-            "Something wrong with the fused attention processors. Expected all the attention processors to be fused."
-        )
+        assert check_qkv_fusion_processors_exist(
+            pipe.transformer
+        ), "Something wrong with the fused attention processors. Expected all the attention processors to be fused."
         assert check_qkv_fusion_matches_attn_procs_length(
             pipe.transformer, pipe.transformer.original_attn_processors
         ), "Something wrong with the attention processors concerning the fused QKV projections."
@@ -311,15 +311,15 @@ def test_fused_qkv_projections(self):
         frames = pipe(**inputs).frames
         image_slice_disabled = frames[0, -2:, -1, -3:, -3:]
 
-        assert np.allclose(original_image_slice, image_slice_fused, atol=1e-3, rtol=1e-3), (
-            "Fusion of QKV projections shouldn't affect the outputs."
-        )
-        assert np.allclose(image_slice_fused, image_slice_disabled, atol=1e-3, rtol=1e-3), (
-            "Outputs, with QKV projection fusion enabled, shouldn't change when fused QKV projections are disabled."
-        )
-        assert np.allclose(original_image_slice, image_slice_disabled, atol=1e-2, rtol=1e-2), (
-            "Original outputs should match when fused QKV projections are disabled."
-        )
+        assert np.allclose(
+            original_image_slice, image_slice_fused, atol=1e-3, rtol=1e-3
+        ), "Fusion of QKV projections shouldn't affect the outputs."
+        assert np.allclose(
+            image_slice_fused, image_slice_disabled, atol=1e-3, rtol=1e-3
+        ), "Outputs, with QKV projection fusion enabled, shouldn't change when fused QKV projections are disabled."
+        assert np.allclose(
+            original_image_slice, image_slice_disabled, atol=1e-2, rtol=1e-2
+        ), "Original outputs should match when fused QKV projections are disabled."
 
 
 @slow