Merge branch 'main' into fix-clip-lora-tests

Author: sayakpaul

src/diffusers/models/transformers/transformer_qwenimage.py

@@ -23,7 +23,7 @@
 
 from ...configuration_utils import ConfigMixin, register_to_config
 from ...loaders import FromOriginalModelMixin, PeftAdapterMixin
-from ...utils import apply_lora_scale, deprecate, logging
+from ...utils import apply_lora_scale, logging
 from ...utils.torch_utils import lru_cache_unless_export, maybe_allow_in_graph
 from .._modeling_parallel import ContextParallelInput, ContextParallelOutput
 from ..attention import AttentionMixin, FeedForward
@@ -241,38 +241,21 @@ def _get_device_freqs(self, device: torch.device) -> tuple[torch.Tensor, torch.T
     def forward(
         self,
         video_fhw: tuple[int, int, int, list[tuple[int, int, int]]],
-        txt_seq_lens: list[int] | None = None,
         device: torch.device = None,
         max_txt_seq_len: int | torch.Tensor | None = None,
     ) -> tuple[torch.Tensor, torch.Tensor]:
         """
         Args:
             video_fhw (`tuple[int, int, int]` or `list[tuple[int, int, int]]`):
                 A list of 3 integers [frame, height, width] representing the shape of the video.
-            txt_seq_lens (`list[int]`, *optional*, **Deprecated**):
-                Deprecated parameter. Use `max_txt_seq_len` instead. If provided, the maximum value will be used.
             device: (`torch.device`, *optional*):
                 The device on which to perform the RoPE computation.
             max_txt_seq_len (`int` or `torch.Tensor`, *optional*):
                 The maximum text sequence length for RoPE computation. This should match the encoder hidden states
                 sequence length. Can be either an int or a scalar tensor (for torch.compile compatibility).
         """
-        # Handle deprecated txt_seq_lens parameter
-        if txt_seq_lens is not None:
-            deprecate(
-                "txt_seq_lens",
-                "0.39.0",
-                "Passing `txt_seq_lens` is deprecated and will be removed in version 0.39.0. "
-                "Please use `max_txt_seq_len` instead. "
-                "The new parameter accepts a single int or tensor value representing the maximum text sequence length.",
-                standard_warn=False,
-            )
-            if max_txt_seq_len is None:
-                # Use max of txt_seq_lens for backward compatibility
-                max_txt_seq_len = max(txt_seq_lens) if isinstance(txt_seq_lens, list) else txt_seq_lens
-
         if max_txt_seq_len is None:
-            raise ValueError("Either `max_txt_seq_len` or `txt_seq_lens` (deprecated) must be provided.")
+            raise ValueError("`max_txt_seq_len` must be provided.")
 
         # Validate batch inference with variable-sized images
         if isinstance(video_fhw, list) and len(video_fhw) > 1:
@@ -855,7 +838,6 @@ def forward(
         encoder_hidden_states_mask: torch.Tensor = None,
         timestep: torch.LongTensor = None,
         img_shapes: list[tuple[int, int, int]] | None = None,
-        txt_seq_lens: list[int] | None = None,
         guidance: torch.Tensor = None,  # TODO: this should probably be removed
         attention_kwargs: dict[str, Any] | None = None,
         controlnet_block_samples=None,
@@ -878,9 +860,6 @@ def forward(
                 Used to indicate denoising step.
             img_shapes (`list[tuple[int, int, int]]`, *optional*):
                 Image shapes for RoPE computation.
-            txt_seq_lens (`list[int]`, *optional*, **Deprecated**):
-                Deprecated parameter. Use `encoder_hidden_states_mask` instead. If provided, the maximum value will be
-                used to compute RoPE sequence length.
             guidance (`torch.Tensor`, *optional*):
                 Guidance tensor for conditional generation.
             attention_kwargs (`dict`, *optional*):
@@ -897,16 +876,6 @@ def forward(
             If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
             `tuple` where the first element is the sample tensor.
         """
-        if txt_seq_lens is not None:
-            deprecate(
-                "txt_seq_lens",
-                "0.39.0",
-                "Passing `txt_seq_lens` is deprecated and will be removed in version 0.39.0. "
-                "Please use `encoder_hidden_states_mask` instead. "
-                "The mask-based approach is more flexible and supports variable-length sequences.",
-                standard_warn=False,
-            )
-
         hidden_states = self.img_in(hidden_states)
 
         timestep = timestep.to(hidden_states.dtype)

tests/models/transformers/test_models_transformer_qwenimage.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import warnings
 
 import pytest
 import torch
@@ -176,31 +175,6 @@ def test_non_contiguous_attention_mask(self, batch_size):
 
         assert output.sample.shape[1] == inputs["hidden_states"].shape[1]
 
-    def test_txt_seq_lens_deprecation(self):
-        init_dict = self.get_init_dict()
-        inputs = self.get_dummy_inputs()
-        model = self.model_class(**init_dict).to(torch_device)
-
-        txt_seq_lens = [inputs["encoder_hidden_states"].shape[1]]
-
-        inputs_with_deprecated = inputs.copy()
-        inputs_with_deprecated.pop("encoder_hidden_states_mask")
-        inputs_with_deprecated["txt_seq_lens"] = txt_seq_lens
-
-        with warnings.catch_warnings(record=True) as w:
-            warnings.simplefilter("always")
-            with torch.no_grad():
-                output = model(**inputs_with_deprecated)
-
-            future_warnings = [x for x in w if issubclass(x.category, FutureWarning)]
-            assert len(future_warnings) > 0, "Expected FutureWarning to be raised"
-
-            warning_message = str(future_warnings[0].message)
-            assert "txt_seq_lens" in warning_message
-            assert "deprecated" in warning_message
-
-        assert output.sample.shape[1] == inputs["hidden_states"].shape[1]
-
     def test_layered_model_with_mask(self):
         init_dict = {
             "patch_size": 2,