update

src/diffusers/pipelines/mochi/pipeline_mochi.py

@@ -139,6 +139,7 @@ def retrieve_timesteps(
 
 class MochiPipeline(
     DiffusionPipeline,
+    TextualInversionLoaderMixin
 ):
     r"""
     The Flux pipeline for text-to-image generation.
@@ -187,14 +188,17 @@ class MochiPipeline(
             transformer=transformer,
             scheduler=scheduler,
         )
-        self.vae_scale_factor = (
-            2 ** (len(self.vae.config.block_out_channels)) if hasattr(self, "vae") and self.vae is not None else 16
-        )
+        #TODO: determine these scaling factors from model parameters
+        self.vae_spatial_scale_factor = 8
+        self.vae_temporal_scale_factor = 6
+        self.patch_size = 2
+        
         self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
         self.tokenizer_max_length = (
             self.tokenizer.model_max_length if hasattr(self, "tokenizer") and self.tokenizer is not None else 77
         )
-        self.default_sample_size = 64
+        self.default_height = 64
+        self.default_width = 64
 
     def _get_t5_prompt_embeds(
         self,
@@ -235,7 +239,7 @@ class MochiPipeline(
                 f" {max_sequence_length} tokens: {removed_text}"
             )
 
-        prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=prompt_attention_mask,  output_hidden_states=False)[0]
+        prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=prompt_attention_mask,  output_hidden_states=False).last_hidden_state
 
         dtype = self.text_encoder.dtype
         prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
@@ -246,7 +250,32 @@ class MochiPipeline(
         prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
         prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
 
-        return prompt_embeds
+        return prompt_embeds, prompt_attention_mask
+
+    def _pack_indices(self, attention_mask, latent_frames_dim, latent_height_dim, latent_width_dim):
+        N = latent_frames_dim * latent_height_dim * latent_width_dim // (self.patch_size**2)
+
+        # Create an expanded token mask saying which tokens are valid across both visual and text tokens.
+        assert N > 0 and len(attention_mask) == 1
+        attention_mask = attention_mask[0]
+
+        mask = F.pad(attention_mask, (N, 0), value=True)  # (B, N + L)
+        seqlens_in_batch = mask.sum(dim=-1, dtype=torch.int32)  # (B,)
+        valid_token_indices = torch.nonzero(
+            mask.flatten(), as_tuple=False
+        ).flatten()  # up to (B * (N + L),)
+
+        assert valid_token_indices.size(0) >= attention_mask.size(0) * N  # At least (B * N,)
+        cu_seqlens = F.pad(
+            torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0)
+        )
+        max_seqlen_in_batch = seqlens_in_batch.max().item()
+
+        return {
+            "cu_seqlens_kv": cu_seqlens,
+            "max_seqlen_in_batch_kv": max_seqlen_in_batch,
+            "valid_token_indices_kv": valid_token_indices,
+        }
 
     def encode_prompt(
         self,