test: remove dedicated logging regressions

Author: wunianze666-netizen

tests/models/autoencoders/test_models_vq.py

@@ -13,14 +13,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import logging
-
-import numpy as np
 import pytest
 import torch
 
 from diffusers import VQModel
-from diffusers.models.autoencoders.vae import VectorQuantizer
 from diffusers.utils.torch_utils import randn_tensor
 
 from ...testing_utils import backend_manual_seed, enable_full_determinism, torch_device
@@ -68,19 +64,6 @@ def get_dummy_inputs(self) -> dict:
 
 
 class TestVQModel(VQModelTesterConfig, ModelTesterMixin):
-    def test_vector_quantizer_logs_remap_configuration(self, caplog, capsys, tmp_path):
-        remap_path = tmp_path / "used.npy"
-        np.save(remap_path, np.array([0, 2, 4], dtype=np.int64))
-
-        logger_name = "diffusers.models.autoencoders.vae"
-        with caplog.at_level(logging.INFO, logger=logger_name):
-            VectorQuantizer(n_e=8, vq_embed_dim=4, beta=0.25, remap=str(remap_path), unknown_index="extra")
-
-        assert [(record.name, record.levelno, record.getMessage()) for record in caplog.records] == [
-            (logger_name, logging.INFO, "Remapping 8 indices to 4 indices. Using 3 for unknown indices.")
-        ]
-        assert capsys.readouterr().out == ""
-
     @pytest.mark.skipif(
         torch_device not in ["cuda", "xpu"],
         reason="float16 and bfloat16 can only be use for inference with an accelerator",

tests/pipelines/longcat_image/test_prepare_pos_ids.py

@@ -12,8 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import logging
-
 import pytest
 import torch
 from PIL import Image
@@ -151,34 +149,6 @@ def get_dummy_inputs(self):
 
 
 class TestWanAnimatePipeline(WanAnimatePipelineTesterConfig, PipelineTesterMixin):
-    def test_prepare_prev_segment_cond_latents_logs_interpolation_warning(self, caplog, capsys):
-        pipe = self.get_pipeline()
-        prev_segment_cond_video = torch.zeros((1, 3, 1, 8, 8), dtype=torch.float32)
-        caplog.clear()
-
-        logger_name = "diffusers.pipelines.wan.pipeline_wan_animate"
-        with caplog.at_level(logging.WARNING, logger=logger_name):
-            pipe.prepare_prev_segment_cond_latents(
-                prev_segment_cond_video=prev_segment_cond_video,
-                batch_size=1,
-                segment_frame_length=5,
-                height=16,
-                width=16,
-                prev_segment_cond_frames=1,
-                task="animate",
-                dtype=torch.float32,
-                device=torch.device("cpu"),
-            )
-
-        assert [(record.name, record.levelno, record.getMessage()) for record in caplog.records] == [
-            (
-                logger_name,
-                logging.WARNING,
-                "Interpolating prev segment cond video from (8, 8) to (16, 16)",
-            )
-        ]
-        assert capsys.readouterr().out == ""
-
     def test_inference(self):
         # Basic inference in animation mode. Run on CPU.
         pipe = self.get_pipeline()