fix vid2vid

src/diffusers/pipelines/cogvideo/pipeline_cogvideox_video2video.py

@@ -15,7 +15,7 @@
 
 import inspect
 import math
-from typing import Callable, Dict, List, Optional, Tuple, Union
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 
 import torch
 from PIL import Image
@@ -539,6 +539,10 @@ class CogVideoXVideoToVideoPipeline(DiffusionPipeline, CogVideoXLoraLoaderMixin)
     def num_timesteps(self):
         return self._num_timesteps
 
+    @property
+    def attention_kwargs(self):
+        return self._attention_kwargs
+
     @property
     def interrupt(self):
         return self._interrupt
@@ -565,12 +569,12 @@ class CogVideoXVideoToVideoPipeline(DiffusionPipeline, CogVideoXLoraLoaderMixin)
         negative_prompt_embeds: Optional[torch.FloatTensor] = None,
         output_type: str = "pil",
         return_dict: bool = True,
+        attention_kwargs: Optional[Dict[str, Any]] = None,
         callback_on_step_end: Optional[
             Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]
         ] = None,
         callback_on_step_end_tensor_inputs: List[str] = ["latents"],
         max_sequence_length: int = 226,
-        lora_scale: Optional[float] = None,
     ) -> Union[CogVideoXPipelineOutput, Tuple]:
         """
         Function invoked when calling the pipeline for generation.
@@ -626,6 +630,10 @@ class CogVideoXVideoToVideoPipeline(DiffusionPipeline, CogVideoXLoraLoaderMixin)
             return_dict (`bool`, *optional*, defaults to `True`):
                 Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead
                 of a plain tuple.
+            attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+                `self.processor` in
+                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
             callback_on_step_end (`Callable`, *optional*):
                 A function that calls at the end of each denoising steps during the inference. The function is called
                 with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
@@ -666,6 +674,7 @@ class CogVideoXVideoToVideoPipeline(DiffusionPipeline, CogVideoXLoraLoaderMixin)
             negative_prompt_embeds,
         )
         self._guidance_scale = guidance_scale
+        self._attention_kwargs = attention_kwargs
         self._interrupt = False
 
         # 2. Default call parameters
@@ -693,7 +702,6 @@ class CogVideoXVideoToVideoPipeline(DiffusionPipeline, CogVideoXLoraLoaderMixin)
             negative_prompt_embeds=negative_prompt_embeds,
             max_sequence_length=max_sequence_length,
             device=device,
-            lora_scale=lora_scale,
         )
         if do_classifier_free_guidance:
             prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
@@ -755,6 +763,7 @@ class CogVideoXVideoToVideoPipeline(DiffusionPipeline, CogVideoXLoraLoaderMixin)
                     encoder_hidden_states=prompt_embeds,
                     timestep=timestep,
                     image_rotary_emb=image_rotary_emb,
+                    attention_kwargs=attention_kwargs,
                     return_dict=False,
                 )[0]
                 noise_pred = noise_pred.float()