Use output_size in repeat_interleave (huggingface#11030)

Author: hlky

src/diffusers/models/attention_processor.py

@@ -741,10 +741,14 @@ def prepare_attention_mask(
 
         if out_dim == 3:
             if attention_mask.shape[0] < batch_size * head_size:
-                attention_mask = attention_mask.repeat_interleave(head_size, dim=0)
+                attention_mask = attention_mask.repeat_interleave(
+                    head_size, dim=0, output_size=attention_mask.shape[0] * head_size
+                )
         elif out_dim == 4:
             attention_mask = attention_mask.unsqueeze(1)
-            attention_mask = attention_mask.repeat_interleave(head_size, dim=1)
+            attention_mask = attention_mask.repeat_interleave(
+                head_size, dim=1, output_size=attention_mask.shape[1] * head_size
+            )
 
         return attention_mask
 
@@ -3704,8 +3708,10 @@ def __call__(
         if kv_heads != attn.heads:
             # if GQA or MQA, repeat the key/value heads to reach the number of query heads.
             heads_per_kv_head = attn.heads // kv_heads
-            key = torch.repeat_interleave(key, heads_per_kv_head, dim=1)
-            value = torch.repeat_interleave(value, heads_per_kv_head, dim=1)
+            key = torch.repeat_interleave(key, heads_per_kv_head, dim=1, output_size=key.shape[1] * heads_per_kv_head)
+            value = torch.repeat_interleave(
+                value, heads_per_kv_head, dim=1, output_size=value.shape[1] * heads_per_kv_head
+            )
 
         if attn.norm_q is not None:
             query = attn.norm_q(query)

src/diffusers/models/autoencoders/autoencoder_dc.py

@@ -190,7 +190,7 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
             x = F.pixel_shuffle(x, self.factor)
 
         if self.shortcut:
-            y = hidden_states.repeat_interleave(self.repeats, dim=1)
+            y = hidden_states.repeat_interleave(self.repeats, dim=1, output_size=hidden_states.shape[1] * self.repeats)
             y = F.pixel_shuffle(y, self.factor)
             hidden_states = x + y
         else:
@@ -361,7 +361,9 @@ def __init__(
 
     def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
         if self.in_shortcut:
-            x = hidden_states.repeat_interleave(self.in_shortcut_repeats, dim=1)
+            x = hidden_states.repeat_interleave(
+                self.in_shortcut_repeats, dim=1, output_size=hidden_states.shape[1] * self.in_shortcut_repeats
+            )
             hidden_states = self.conv_in(hidden_states) + x
         else:
             hidden_states = self.conv_in(hidden_states)

src/diffusers/models/autoencoders/autoencoder_kl_allegro.py

@@ -103,7 +103,7 @@ def forward(self, hidden_states: torch.Tensor, batch_size: int) -> torch.Tensor:
         if self.down_sample:
             identity = hidden_states[:, :, ::2]
         elif self.up_sample:
-            identity = hidden_states.repeat_interleave(2, dim=2)
+            identity = hidden_states.repeat_interleave(2, dim=2, output_size=hidden_states.shape[2] * 2)
         else:
             identity = hidden_states
 

src/diffusers/models/autoencoders/autoencoder_kl_mochi.py

@@ -426,7 +426,9 @@ def forward(self, inputs: torch.Tensor) -> torch.Tensor:
         w = w.repeat(num_channels)[None, :, None, None, None]  # [1, num_channels * num_freqs, 1, 1, 1]
 
         # Interleaved repeat of input channels to match w
-        h = inputs.repeat_interleave(num_freqs, dim=1)  # [B, C * num_freqs, T, H, W]
+        h = inputs.repeat_interleave(
+            num_freqs, dim=1, output_size=inputs.shape[1] * num_freqs
+        )  # [B, C * num_freqs, T, H, W]
         # Scale channels by frequency.
         h = w * h
 

src/diffusers/models/controlnets/controlnet_sparsectrl.py

@@ -687,7 +687,7 @@ def forward(
         t_emb = t_emb.to(dtype=sample.dtype)
 
         emb = self.time_embedding(t_emb, timestep_cond)
-        emb = emb.repeat_interleave(sample_num_frames, dim=0)
+        emb = emb.repeat_interleave(sample_num_frames, dim=0, output_size=emb.shape[0] * sample_num_frames)
 
         # 2. pre-process
         batch_size, channels, num_frames, height, width = sample.shape

src/diffusers/models/embeddings.py

@@ -139,7 +139,9 @@ def get_3d_sincos_pos_embed(
 
     # 3. Concat
     pos_embed_spatial = pos_embed_spatial[None, :, :]
-    pos_embed_spatial = pos_embed_spatial.repeat_interleave(temporal_size, dim=0)  # [T, H*W, D // 4 * 3]
+    pos_embed_spatial = pos_embed_spatial.repeat_interleave(
+        temporal_size, dim=0, output_size=pos_embed_spatial.shape[0] * temporal_size
+    )  # [T, H*W, D // 4 * 3]
 
     pos_embed_temporal = pos_embed_temporal[:, None, :]
     pos_embed_temporal = pos_embed_temporal.repeat_interleave(
@@ -1154,8 +1156,8 @@ def get_1d_rotary_pos_embed(
     freqs = torch.outer(pos, freqs)  # type: ignore   # [S, D/2]
     if use_real and repeat_interleave_real:
         # flux, hunyuan-dit, cogvideox
-        freqs_cos = freqs.cos().repeat_interleave(2, dim=1).float()  # [S, D]
-        freqs_sin = freqs.sin().repeat_interleave(2, dim=1).float()  # [S, D]
+        freqs_cos = freqs.cos().repeat_interleave(2, dim=1, output_size=freqs.shape[1] * 2).float()  # [S, D]
+        freqs_sin = freqs.sin().repeat_interleave(2, dim=1, output_size=freqs.shape[1] * 2).float()  # [S, D]
         return freqs_cos, freqs_sin
     elif use_real:
         # stable audio, allegro

src/diffusers/models/transformers/latte_transformer_3d.py

@@ -227,13 +227,17 @@ def forward(
         # Prepare text embeddings for spatial block
         # batch_size num_tokens hidden_size -> (batch_size * num_frame) num_tokens hidden_size
         encoder_hidden_states = self.caption_projection(encoder_hidden_states)  # 3 120 1152
-        encoder_hidden_states_spatial = encoder_hidden_states.repeat_interleave(num_frame, dim=0).view(
-            -1, encoder_hidden_states.shape[-2], encoder_hidden_states.shape[-1]
-        )
+        encoder_hidden_states_spatial = encoder_hidden_states.repeat_interleave(
+            num_frame, dim=0, output_size=encoder_hidden_states.shape[0] * num_frame
+        ).view(-1, encoder_hidden_states.shape[-2], encoder_hidden_states.shape[-1])
 
         # Prepare timesteps for spatial and temporal block
-        timestep_spatial = timestep.repeat_interleave(num_frame, dim=0).view(-1, timestep.shape[-1])
-        timestep_temp = timestep.repeat_interleave(num_patches, dim=0).view(-1, timestep.shape[-1])
+        timestep_spatial = timestep.repeat_interleave(
+            num_frame, dim=0, output_size=timestep.shape[0] * num_frame
+        ).view(-1, timestep.shape[-1])
+        timestep_temp = timestep.repeat_interleave(
+            num_patches, dim=0, output_size=timestep.shape[0] * num_patches
+        ).view(-1, timestep.shape[-1])
 
         # Spatial and temporal transformer blocks
         for i, (spatial_block, temp_block) in enumerate(
@@ -299,7 +303,9 @@ def forward(
                 ).permute(0, 2, 1, 3)
                 hidden_states = hidden_states.reshape(-1, hidden_states.shape[-2], hidden_states.shape[-1])
 
-        embedded_timestep = embedded_timestep.repeat_interleave(num_frame, dim=0).view(-1, embedded_timestep.shape[-1])
+        embedded_timestep = embedded_timestep.repeat_interleave(
+            num_frame, dim=0, output_size=embedded_timestep.shape[0] * num_frame
+        ).view(-1, embedded_timestep.shape[-1])
         shift, scale = (self.scale_shift_table[None] + embedded_timestep[:, None]).chunk(2, dim=1)
         hidden_states = self.norm_out(hidden_states)
         # Modulation

src/diffusers/models/transformers/prior_transformer.py

@@ -353,7 +353,11 @@ def forward(
             attention_mask = (1 - attention_mask.to(hidden_states.dtype)) * -10000.0
             attention_mask = F.pad(attention_mask, (0, self.additional_embeddings), value=0.0)
             attention_mask = (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype)
-            attention_mask = attention_mask.repeat_interleave(self.config.num_attention_heads, dim=0)
+            attention_mask = attention_mask.repeat_interleave(
+                self.config.num_attention_heads,
+                dim=0,
+                output_size=attention_mask.shape[0] * self.config.num_attention_heads,
+            )
 
         if self.norm_in is not None:
             hidden_states = self.norm_in(hidden_states)

src/diffusers/models/unets/unet_3d_condition.py

@@ -638,8 +638,10 @@ def forward(
         t_emb = t_emb.to(dtype=self.dtype)
 
         emb = self.time_embedding(t_emb, timestep_cond)
-        emb = emb.repeat_interleave(repeats=num_frames, dim=0)
-        encoder_hidden_states = encoder_hidden_states.repeat_interleave(repeats=num_frames, dim=0)
+        emb = emb.repeat_interleave(num_frames, dim=0, output_size=emb.shape[0] * num_frames)
+        encoder_hidden_states = encoder_hidden_states.repeat_interleave(
+            num_frames, dim=0, output_size=encoder_hidden_states.shape[0] * num_frames
+        )
 
         # 2. pre-process
         sample = sample.permute(0, 2, 1, 3, 4).reshape((sample.shape[0] * num_frames, -1) + sample.shape[3:])

src/diffusers/models/unets/unet_i2vgen_xl.py

@@ -592,7 +592,7 @@ def forward(
 
         # 3. time + FPS embeddings.
         emb = t_emb + fps_emb
-        emb = emb.repeat_interleave(repeats=num_frames, dim=0)
+        emb = emb.repeat_interleave(num_frames, dim=0, output_size=emb.shape[0] * num_frames)
 
         # 4. context embeddings.
         # The context embeddings consist of both text embeddings from the input prompt
@@ -620,7 +620,7 @@ def forward(
         image_emb = self.context_embedding(image_embeddings)
         image_emb = image_emb.view(-1, self.config.in_channels, self.config.cross_attention_dim)
         context_emb = torch.cat([context_emb, image_emb], dim=1)
-        context_emb = context_emb.repeat_interleave(repeats=num_frames, dim=0)
+        context_emb = context_emb.repeat_interleave(num_frames, dim=0, output_size=context_emb.shape[0] * num_frames)
 
         image_latents = image_latents.permute(0, 2, 1, 3, 4).reshape(
             image_latents.shape[0] * image_latents.shape[2],