follow-up refactor on lumina2 (#10776)

* up

src/diffusers/models/transformers/transformer_lumina2.py

@@ -242,97 +242,85 @@ class Lumina2RotaryPosEmbed(nn.Module):
 
     def _precompute_freqs_cis(self, axes_dim: List[int], axes_lens: List[int], theta: int) -> List[torch.Tensor]:
         freqs_cis = []
-        # Use float32 for MPS compatibility
-        dtype = torch.float32 if torch.backends.mps.is_available() else torch.float64
+        freqs_dtype = torch.float32 if torch.backends.mps.is_available() else torch.float64
         for i, (d, e) in enumerate(zip(axes_dim, axes_lens)):
-            emb = get_1d_rotary_pos_embed(d, e, theta=self.theta, freqs_dtype=dtype)
+            emb = get_1d_rotary_pos_embed(d, e, theta=self.theta, freqs_dtype=freqs_dtype)
             freqs_cis.append(emb)
         return freqs_cis
 
     def _get_freqs_cis(self, ids: torch.Tensor) -> torch.Tensor:
+        device = ids.device
+        if ids.device.type == "mps":
+            ids = ids.to("cpu")
+
         result = []
         for i in range(len(self.axes_dim)):
             freqs = self.freqs_cis[i].to(ids.device)
             index = ids[:, :, i : i + 1].repeat(1, 1, freqs.shape[-1]).to(torch.int64)
             result.append(torch.gather(freqs.unsqueeze(0).repeat(index.shape[0], 1, 1), dim=1, index=index))
-        return torch.cat(result, dim=-1)
+        return torch.cat(result, dim=-1).to(device)
 
     def forward(self, hidden_states: torch.Tensor, attention_mask: torch.Tensor):
-        batch_size = len(hidden_states)
-        p_h = p_w = self.patch_size
-        device = hidden_states[0].device
+        batch_size, channels, height, width = hidden_states.shape
+        p = self.patch_size
+        post_patch_height, post_patch_width = height // p, width // p
+        image_seq_len = post_patch_height * post_patch_width
+        device = hidden_states.device
 
+        encoder_seq_len = attention_mask.shape[1]
         l_effective_cap_len = attention_mask.sum(dim=1).tolist()
-        # TODO: this should probably be refactored because all subtensors of hidden_states will be of same shape
-        img_sizes = [(img.size(1), img.size(2)) for img in hidden_states]
-        l_effective_img_len = [(H // p_h) * (W // p_w) for (H, W) in img_sizes]
-
-        max_seq_len = max((cap_len + img_len for cap_len, img_len in zip(l_effective_cap_len, l_effective_img_len)))
-        max_img_len = max(l_effective_img_len)
+        seq_lengths = [cap_seq_len + image_seq_len for cap_seq_len in l_effective_cap_len]
+        max_seq_len = max(seq_lengths)
 
+        # Create position IDs
         position_ids = torch.zeros(batch_size, max_seq_len, 3, dtype=torch.int32, device=device)
 
-        for i in range(batch_size):
-            cap_len = l_effective_cap_len[i]
-            img_len = l_effective_img_len[i]
-            H, W = img_sizes[i]
-            H_tokens, W_tokens = H // p_h, W // p_w
-            assert H_tokens * W_tokens == img_len
+        for i, (cap_seq_len, seq_len) in enumerate(zip(l_effective_cap_len, seq_lengths)):
+            # add caption position ids
+            position_ids[i, :cap_seq_len, 0] = torch.arange(cap_seq_len, dtype=torch.int32, device=device)
+            position_ids[i, cap_seq_len:seq_len, 0] = cap_seq_len
 
-            position_ids[i, :cap_len, 0] = torch.arange(cap_len, dtype=torch.int32, device=device)
-            position_ids[i, cap_len : cap_len + img_len, 0] = cap_len
+            # add image position ids
             row_ids = (
-                torch.arange(H_tokens, dtype=torch.int32, device=device).view(-1, 1).repeat(1, W_tokens).flatten()
+                torch.arange(post_patch_height, dtype=torch.int32, device=device)
+                .view(-1, 1)
+                .repeat(1, post_patch_width)
+                .flatten()
             )
             col_ids = (
-                torch.arange(W_tokens, dtype=torch.int32, device=device).view(1, -1).repeat(H_tokens, 1).flatten()
+                torch.arange(post_patch_width, dtype=torch.int32, device=device)
+                .view(1, -1)
+                .repeat(post_patch_height, 1)
+                .flatten()
             )
-            position_ids[i, cap_len : cap_len + img_len, 1] = row_ids
-            position_ids[i, cap_len : cap_len + img_len, 2] = col_ids
+            position_ids[i, cap_seq_len:seq_len, 1] = row_ids
+            position_ids[i, cap_seq_len:seq_len, 2] = col_ids
 
+        # Get combined rotary embeddings
         freqs_cis = self._get_freqs_cis(position_ids)
 
-        cap_freqs_cis_shape = list(freqs_cis.shape)
-        cap_freqs_cis_shape[1] = attention_mask.shape[1]
-        cap_freqs_cis = torch.zeros(*cap_freqs_cis_shape, device=device, dtype=freqs_cis.dtype)
-
-        img_freqs_cis_shape = list(freqs_cis.shape)
-        img_freqs_cis_shape[1] = max_img_len
-        img_freqs_cis = torch.zeros(*img_freqs_cis_shape, device=device, dtype=freqs_cis.dtype)
-
-        for i in range(batch_size):
-            cap_len = l_effective_cap_len[i]
-            img_len = l_effective_img_len[i]
-            cap_freqs_cis[i, :cap_len] = freqs_cis[i, :cap_len]
-            img_freqs_cis[i, :img_len] = freqs_cis[i, cap_len : cap_len + img_len]
-
-        flat_hidden_states = []
-        for i in range(batch_size):
-            img = hidden_states[i]
-            C, H, W = img.size()
-            img = img.view(C, H // p_h, p_h, W // p_w, p_w).permute(1, 3, 2, 4, 0).flatten(2).flatten(0, 1)
-            flat_hidden_states.append(img)
-        hidden_states = flat_hidden_states
-        padded_img_embed = torch.zeros(
-            batch_size, max_img_len, hidden_states[0].shape[-1], device=device, dtype=hidden_states[0].dtype
+        # create separate rotary embeddings for captions and images
+        cap_freqs_cis = torch.zeros(
+            batch_size, encoder_seq_len, freqs_cis.shape[-1], device=device, dtype=freqs_cis.dtype
         )
-        padded_img_mask = torch.zeros(batch_size, max_img_len, dtype=torch.bool, device=device)
-        for i in range(batch_size):
-            padded_img_embed[i, : l_effective_img_len[i]] = hidden_states[i]
-            padded_img_mask[i, : l_effective_img_len[i]] = True
-
-        return (
-            padded_img_embed,
-            padded_img_mask,
-            img_sizes,
-            l_effective_cap_len,
-            l_effective_img_len,
-            freqs_cis,
-            cap_freqs_cis,
-            img_freqs_cis,
-            max_seq_len,
+        img_freqs_cis = torch.zeros(
+            batch_size, image_seq_len, freqs_cis.shape[-1], device=device, dtype=freqs_cis.dtype
+        )
+
+        for i, (cap_seq_len, seq_len) in enumerate(zip(l_effective_cap_len, seq_lengths)):
+            cap_freqs_cis[i, :cap_seq_len] = freqs_cis[i, :cap_seq_len]
+            img_freqs_cis[i, :image_seq_len] = freqs_cis[i, cap_seq_len:seq_len]
+
+        # image patch embeddings
+        hidden_states = (
+            hidden_states.view(batch_size, channels, post_patch_height, p, post_patch_width, p)
+            .permute(0, 2, 4, 3, 5, 1)
+            .flatten(3)
+            .flatten(1, 2)
         )
 
+        return hidden_states, cap_freqs_cis, img_freqs_cis, freqs_cis, l_effective_cap_len, seq_lengths
+
 
 class Lumina2Transformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin):
     r"""
@@ -472,75 +460,63 @@ class Lumina2Transformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromO
         hidden_states: torch.Tensor,
         timestep: torch.Tensor,
         encoder_hidden_states: torch.Tensor,
-        attention_mask: torch.Tensor,
-        use_mask_in_transformer: bool = True,
+        encoder_attention_mask: torch.Tensor,
         return_dict: bool = True,
     ) -> Union[torch.Tensor, Transformer2DModelOutput]:
-        batch_size = hidden_states.size(0)
-
         # 1. Condition, positional & patch embedding
+        batch_size, _, height, width = hidden_states.shape
+
         temb, encoder_hidden_states = self.time_caption_embed(hidden_states, timestep, encoder_hidden_states)
 
         (
             hidden_states,
-            hidden_mask,
-            hidden_sizes,
-            encoder_hidden_len,
-            hidden_len,
-            joint_rotary_emb,
-            encoder_rotary_emb,
-            hidden_rotary_emb,
-            max_seq_len,
-        ) = self.rope_embedder(hidden_states, attention_mask)
+            context_rotary_emb,
+            noise_rotary_emb,
+            rotary_emb,
+            encoder_seq_lengths,
+            seq_lengths,
+        ) = self.rope_embedder(hidden_states, encoder_attention_mask)
 
         hidden_states = self.x_embedder(hidden_states)
 
         # 2. Context & noise refinement
         for layer in self.context_refiner:
-            # NOTE: mask not used for performance
-            encoder_hidden_states = layer(
-                encoder_hidden_states, attention_mask if use_mask_in_transformer else None, encoder_rotary_emb
-            )
+            encoder_hidden_states = layer(encoder_hidden_states, encoder_attention_mask, context_rotary_emb)
 
         for layer in self.noise_refiner:
-            # NOTE: mask not used for performance
-            hidden_states = layer(
-                hidden_states, hidden_mask if use_mask_in_transformer else None, hidden_rotary_emb, temb
-            )
+            hidden_states = layer(hidden_states, None, noise_rotary_emb, temb)
+
+        # 3. Joint Transformer blocks
+        max_seq_len = max(seq_lengths)
+        use_mask = len(set(seq_lengths)) > 1
+
+        attention_mask = hidden_states.new_zeros(batch_size, max_seq_len, dtype=torch.bool)
+        joint_hidden_states = hidden_states.new_zeros(batch_size, max_seq_len, self.config.hidden_size)
+        for i, (encoder_seq_len, seq_len) in enumerate(zip(encoder_seq_lengths, seq_lengths)):
+            attention_mask[i, :seq_len] = True
+            joint_hidden_states[i, :encoder_seq_len] = encoder_hidden_states[i, :encoder_seq_len]
+            joint_hidden_states[i, encoder_seq_len:seq_len] = hidden_states[i]
+
+        hidden_states = joint_hidden_states
 
-        # 3. Attention mask preparation
-        mask = hidden_states.new_zeros(batch_size, max_seq_len, dtype=torch.bool)
-        padded_hidden_states = hidden_states.new_zeros(batch_size, max_seq_len, self.config.hidden_size)
-        for i in range(batch_size):
-            cap_len = encoder_hidden_len[i]
-            img_len = hidden_len[i]
-            mask[i, : cap_len + img_len] = True
-            padded_hidden_states[i, :cap_len] = encoder_hidden_states[i, :cap_len]
-            padded_hidden_states[i, cap_len : cap_len + img_len] = hidden_states[i, :img_len]
-        hidden_states = padded_hidden_states
-
-        # 4. Transformer blocks
         for layer in self.layers:
-            # NOTE: mask not used for performance
             if torch.is_grad_enabled() and self.gradient_checkpointing:
                 hidden_states = self._gradient_checkpointing_func(
-                    layer, hidden_states, mask if use_mask_in_transformer else None, joint_rotary_emb, temb
+                    layer, hidden_states, attention_mask if use_mask else None, rotary_emb, temb
                 )
             else:
-                hidden_states = layer(hidden_states, mask if use_mask_in_transformer else None, joint_rotary_emb, temb)
+                hidden_states = layer(hidden_states, attention_mask if use_mask else None, rotary_emb, temb)
 
-        # 5. Output norm & projection & unpatchify
+        # 4. Output norm & projection
         hidden_states = self.norm_out(hidden_states, temb)
 
-        height_tokens = width_tokens = self.config.patch_size
+        # 5. Unpatchify
+        p = self.config.patch_size
         output = []
-        for i in range(len(hidden_sizes)):
-            height, width = hidden_sizes[i]
-            begin = encoder_hidden_len[i]
-            end = begin + (height // height_tokens) * (width // width_tokens)
+        for i, (encoder_seq_len, seq_len) in enumerate(zip(encoder_seq_lengths, seq_lengths)):
             output.append(
-                hidden_states[i][begin:end]
-                .view(height // height_tokens, width // width_tokens, height_tokens, width_tokens, self.out_channels)
+                hidden_states[i][encoder_seq_len:seq_len]
+                .view(height // p, width // p, p, p, self.out_channels)
                 .permute(4, 0, 2, 1, 3)
                 .flatten(3, 4)
                 .flatten(1, 2)

src/diffusers/pipelines/lumina2/pipeline_lumina2.py

@@ -24,8 +24,6 @@ from ...models import AutoencoderKL
 from ...models.transformers.transformer_lumina2 import Lumina2Transformer2DModel
 from ...schedulers import FlowMatchEulerDiscreteScheduler
 from ...utils import (
-    is_bs4_available,
-    is_ftfy_available,
     is_torch_xla_available,
     logging,
     replace_example_docstring,
@@ -44,12 +42,6 @@ else:
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
-if is_bs4_available():
-    pass
-
-if is_ftfy_available():
-    pass
-
 EXAMPLE_DOC_STRING = """
     Examples:
         ```py
@@ -527,7 +519,6 @@ class Lumina2Text2ImgPipeline(DiffusionPipeline):
         system_prompt: Optional[str] = None,
         cfg_trunc_ratio: float = 1.0,
         cfg_normalization: bool = True,
-        use_mask_in_transformer: bool = True,
         max_sequence_length: int = 256,
     ) -> Union[ImagePipelineOutput, Tuple]:
         """
@@ -599,8 +590,6 @@ class Lumina2Text2ImgPipeline(DiffusionPipeline):
                 The ratio of the timestep interval to apply normalization-based guidance scale.
             cfg_normalization (`bool`, *optional*, defaults to `True`):
                 Whether to apply normalization-based guidance scale.
-            use_mask_in_transformer (`bool`, *optional*, defaults to `True`):
-                Whether to use attention mask in `Lumina2Transformer2DModel`. Set `False` for performance gain.
             max_sequence_length (`int`, defaults to `256`):
                 Maximum sequence length to use with the `prompt`.
 
@@ -706,8 +695,7 @@ class Lumina2Text2ImgPipeline(DiffusionPipeline):
                     hidden_states=latents,
                     timestep=current_timestep,
                     encoder_hidden_states=prompt_embeds,
-                    attention_mask=prompt_attention_mask,
-                    use_mask_in_transformer=use_mask_in_transformer,
+                    encoder_attention_mask=prompt_attention_mask,
                     return_dict=False,
                 )[0]
 
@@ -717,8 +705,7 @@ class Lumina2Text2ImgPipeline(DiffusionPipeline):
                         hidden_states=latents,
                         timestep=current_timestep,
                         encoder_hidden_states=negative_prompt_embeds,
-                        attention_mask=negative_prompt_attention_mask,
-                        use_mask_in_transformer=use_mask_in_transformer,
+                        encoder_attention_mask=negative_prompt_attention_mask,
                         return_dict=False,
                     )[0]
                     noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_cond - noise_pred_uncond)

tests/models/transformers/test_models_transformer_lumina2.py

@@ -51,7 +51,7 @@ class Lumina2Transformer2DModelTransformerTests(ModelTesterMixin, unittest.TestC
             "hidden_states": hidden_states,
             "encoder_hidden_states": encoder_hidden_states,
             "timestep": timestep,
-            "attention_mask": attention_mask,
+            "encoder_attention_mask": attention_mask,
         }
 
     @property