Fix Hunyuan I2V for transformers>4.47.1

src/diffusers/pipelines/hunyuan_video/pipeline_hunyuan_video_image2video.py

@@ -100,6 +100,50 @@
 }
 
 
+def _expand_input_ids_with_image_tokens(
+    text_input_ids,
+    prompt_attention_mask,
+    max_sequence_length,
+    image_token_index,
+    image_emb_len,
+    image_emb_start,
+    image_emb_end,
+    pad_token_id,
+):
+    special_image_token_mask = text_input_ids == image_token_index
+    num_special_image_tokens = torch.sum(special_image_token_mask, dim=-1)
+    batch_indices, non_image_indices = torch.where(text_input_ids != image_token_index)
+
+    max_expanded_length = max_sequence_length + (num_special_image_tokens.max() * (image_emb_len - 1))
+    new_token_positions = torch.cumsum((special_image_token_mask * (image_emb_len - 1) + 1), -1) - 1
+    text_to_overwrite = new_token_positions[batch_indices, non_image_indices]
+
+    expanded_input_ids = torch.full(
+        (text_input_ids.shape[0], max_expanded_length),
+        pad_token_id,
+        dtype=text_input_ids.dtype,
+        device=text_input_ids.device,
+    )
+    expanded_input_ids[batch_indices, text_to_overwrite] = text_input_ids[batch_indices, non_image_indices]
+    expanded_input_ids[batch_indices, image_emb_start:image_emb_end] = image_token_index
+
+    expanded_attention_mask = torch.zeros(
+        (text_input_ids.shape[0], max_expanded_length),
+        dtype=prompt_attention_mask.dtype,
+        device=prompt_attention_mask.device,
+    )
+    attn_batch_indices, attention_indices = torch.where(expanded_input_ids != pad_token_id)
+    expanded_attention_mask[attn_batch_indices, attention_indices] = 1.0
+    expanded_attention_mask = expanded_attention_mask.to(prompt_attention_mask.dtype)
+    position_ids = (expanded_attention_mask.cumsum(-1) - 1).masked_fill_((expanded_attention_mask == 0), 1)
+
+    return {
+        "input_ids": expanded_input_ids,
+        "attention_mask": expanded_attention_mask,
+        "position_ids": position_ids,
+    }
+
+
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
 def retrieve_timesteps(
     scheduler,
@@ -251,6 +295,12 @@ def _get_llama_prompt_embeds(
         prompt = [prompt_template["template"].format(p) for p in prompt]
 
         crop_start = prompt_template.get("crop_start", None)
+
+        image_emb_len = prompt_template.get("image_emb_len", 576)
+        image_emb_start = prompt_template.get("image_emb_start", 5)
+        image_emb_end = prompt_template.get("image_emb_end", 581)
+        double_return_token_id = prompt_template.get("double_return_token_id", 271)
+
         if crop_start is None:
             prompt_template_input = self.tokenizer(
                 prompt_template["template"],
@@ -280,19 +330,25 @@ def _get_llama_prompt_embeds(
 
         image_embeds = self.image_processor(image, return_tensors="pt").pixel_values.to(device)
 
+        image_token_index = self.text_encoder.config.image_token_index
+        pad_token_id = self.text_encoder.config.pad_token_id
+        expanded_inputs = _expand_input_ids_with_image_tokens(
+            text_input_ids,
+            prompt_attention_mask,
+            max_sequence_length,
+            image_token_index,
+            image_emb_len,
+            image_emb_start,
+            image_emb_end,
+            pad_token_id,
+        )
         prompt_embeds = self.text_encoder(
-            input_ids=text_input_ids,
-            attention_mask=prompt_attention_mask,
-            pixel_values=image_embeds,
+            **expanded_inputs,
+            pixel_value=image_embeds,
             output_hidden_states=True,
         ).hidden_states[-(num_hidden_layers_to_skip + 1)]
         prompt_embeds = prompt_embeds.to(dtype=dtype)
 
-        image_emb_len = prompt_template.get("image_emb_len", 576)
-        image_emb_start = prompt_template.get("image_emb_start", 5)
-        image_emb_end = prompt_template.get("image_emb_end", 581)
-        double_return_token_id = prompt_template.get("double_return_token_id", 271)
-
         if crop_start is not None and crop_start > 0:
             text_crop_start = crop_start - 1 + image_emb_len
             batch_indices, last_double_return_token_indices = torch.where(text_input_ids == double_return_token_id)