```shell
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Cell In[26], line 2
      1 generator = torch.Generator(device="cuda").manual_seed(4)
----> 2 images = pipe(
      3     prompt="a cute gummy bear waving",
      4     image=image,
      5     mask_image=mask_image,
      6     ip_adapter_image=ip_image,
      7     generator=generator,
      8     num_inference_steps=10,
      9 ).images
     10 images[0]

File /opt/conda/envs/pytorch/lib/python3.10/site-packages/torch/autograd/grad_mode.py:27, in _DecoratorContextManager.__call__.<locals>.decorate_context(*args, **kwargs)
     24 @functools.wraps(func)
     25 def decorate_context(*args, **kwargs):
     26     with self.clone():
---> 27         return func(*args, **kwargs)

File /opt/conda/envs/pytorch/lib/python3.10/site-packages/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py:1384, in StableDiffusionInpaintPipeline.__call__(self, prompt, image, mask_image, masked_image_latents, height, width, padding_mask_crop, strength, num_inference_steps, timesteps, sigmas, guidance_scale, negative_prompt, num_images_per_prompt, eta, generator, latents, prompt_embeds, negative_prompt_embeds, ip_adapter_image, ip_adapter_image_embeds, output_type, return_dict, cross_attention_kwargs, clip_skip, callback_on_step_end, callback_on_step_end_tensor_inputs, **kwargs)
   1381     latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1)
   1383 # predict the noise residual
-> 1384 noise_pred = self.unet(
   1385     latent_model_input,
   1386     t,
   1387     encoder_hidden_states=prompt_embeds,
   1388     timestep_cond=timestep_cond,
   1389     cross_attention_kwargs=self.cross_attention_kwargs,
   1390     added_cond_kwargs=added_cond_kwargs,
   1391     return_dict=False,
   1392 )[0]
   1394 # perform guidance
   1395 if self.do_classifier_free_guidance:

File /opt/conda/envs/pytorch/lib/python3.10/site-packages/torch/nn/modules/module.py:1194, in Module._call_impl(self, *input, **kwargs)
   1190 # If we don't have any hooks, we want to skip the rest of the logic in
   1191 # this function, and just call forward.
   1192 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1193         or _global_forward_hooks or _global_forward_pre_hooks):
-> 1194     return forward_call(*input, **kwargs)
   1195 # Do not call functions when jit is used
   1196 full_backward_hooks, non_full_backward_hooks = [], []

File /opt/conda/envs/pytorch/lib/python3.10/site-packages/accelerate/hooks.py:166, in add_hook_to_module.<locals>.new_forward(module, *args, **kwargs)
    164         output = module._old_forward(*args, **kwargs)
    165 else:
--> 166     output = module._old_forward(*args, **kwargs)
    167 return module._hf_hook.post_forward(module, output)

File /opt/conda/envs/pytorch/lib/python3.10/site-packages/diffusers/models/unets/unet_2d_condition.py:1209, in UNet2DConditionModel.forward(self, sample, timestep, encoder_hidden_states, class_labels, timestep_cond, attention_mask, cross_attention_kwargs, added_cond_kwargs, down_block_additional_residuals, mid_block_additional_residual, down_intrablock_additional_residuals, encoder_attention_mask, return_dict)
   1206     if is_adapter and len(down_intrablock_additional_residuals) > 0:
   1207         additional_residuals["additional_residuals"] = down_intrablock_additional_residuals.pop(0)
-> 1209     sample, res_samples = downsample_block(
   1210         hidden_states=sample,
   1211         temb=emb,
   1212         encoder_hidden_states=encoder_hidden_states,
   1213         attention_mask=attention_mask,
   1214         cross_attention_kwargs=cross_attention_kwargs,
   1215         encoder_attention_mask=encoder_attention_mask,
   1216         **additional_residuals,
   1217     )
   1218 else:
   1219     sample, res_samples = downsample_block(hidden_states=sample, temb=emb)

File /opt/conda/envs/pytorch/lib/python3.10/site-packages/torch/nn/modules/module.py:1194, in Module._call_impl(self, *input, **kwargs)
   1190 # If we don't have any hooks, we want to skip the rest of the logic in
   1191 # this function, and just call forward.
   1192 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1193         or _global_forward_hooks or _global_forward_pre_hooks):
-> 1194     return forward_call(*input, **kwargs)
   1195 # Do not call functions when jit is used
   1196 full_backward_hooks, non_full_backward_hooks = [], []

File /opt/conda/envs/pytorch/lib/python3.10/site-packages/diffusers/models/unets/unet_2d_blocks.py:1288, in CrossAttnDownBlock2D.forward(self, hidden_states, temb, encoder_hidden_states, attention_mask, cross_attention_kwargs, encoder_attention_mask, additional_residuals)
   1286 else:
   1287     hidden_states = resnet(hidden_states, temb)
-> 1288     hidden_states = attn(
   1289         hidden_states,
   1290         encoder_hidden_states=encoder_hidden_states,
   1291         cross_attention_kwargs=cross_attention_kwargs,
   1292         attention_mask=attention_mask,
   1293         encoder_attention_mask=encoder_attention_mask,
   1294         return_dict=False,
   1295     )[0]
   1297 # apply additional residuals to the output of the last pair of resnet and attention blocks
   1298 if i == len(blocks) - 1 and additional_residuals is not None:

File /opt/conda/envs/pytorch/lib/python3.10/site-packages/torch/nn/modules/module.py:1194, in Module._call_impl(self, *input, **kwargs)
   1190 # If we don't have any hooks, we want to skip the rest of the logic in
   1191 # this function, and just call forward.
   1192 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1193         or _global_forward_hooks or _global_forward_pre_hooks):
-> 1194     return forward_call(*input, **kwargs)
   1195 # Do not call functions when jit is used
   1196 full_backward_hooks, non_full_backward_hooks = [], []

File /opt/conda/envs/pytorch/lib/python3.10/site-packages/diffusers/models/transformers/transformer_2d.py:440, in Transformer2DModel.forward(self, hidden_states, encoder_hidden_states, timestep, added_cond_kwargs, class_labels, cross_attention_kwargs, attention_mask, encoder_attention_mask, return_dict)
    428         hidden_states = torch.utils.checkpoint.checkpoint(
    429             create_custom_forward(block),
    430             hidden_states,
   (...)
    437             **ckpt_kwargs,
    438         )
    439     else:
--> 440         hidden_states = block(
    441             hidden_states,
    442             attention_mask=attention_mask,
    443             encoder_hidden_states=encoder_hidden_states,
    444             encoder_attention_mask=encoder_attention_mask,
    445             timestep=timestep,
    446             cross_attention_kwargs=cross_attention_kwargs,
    447             class_labels=class_labels,
    448         )
    450 # 3. Output
    451 if self.is_input_continuous:

File /opt/conda/envs/pytorch/lib/python3.10/site-packages/torch/nn/modules/module.py:1194, in Module._call_impl(self, *input, **kwargs)
   1190 # If we don't have any hooks, we want to skip the rest of the logic in
   1191 # this function, and just call forward.
   1192 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1193         or _global_forward_hooks or _global_forward_pre_hooks):
-> 1194     return forward_call(*input, **kwargs)
   1195 # Do not call functions when jit is used
   1196 full_backward_hooks, non_full_backward_hooks = [], []

File /opt/conda/envs/pytorch/lib/python3.10/site-packages/diffusers/models/attention.py:490, in BasicTransformerBlock.forward(self, hidden_states, attention_mask, encoder_hidden_states, encoder_attention_mask, timestep, cross_attention_kwargs, class_labels, added_cond_kwargs)
    487     if self.pos_embed is not None and self.norm_type != "ada_norm_single":
    488         norm_hidden_states = self.pos_embed(norm_hidden_states)
--> 490     attn_output = self.attn2(
    491         norm_hidden_states,
    492         encoder_hidden_states=encoder_hidden_states,
    493         attention_mask=encoder_attention_mask,
    494         **cross_attention_kwargs,
    495     )
    496     hidden_states = attn_output + hidden_states
    498 # 4. Feed-forward
    499 # i2vgen doesn't have this norm 🤷‍♂️

File /opt/conda/envs/pytorch/lib/python3.10/site-packages/torch/nn/modules/module.py:1194, in Module._call_impl(self, *input, **kwargs)
   1190 # If we don't have any hooks, we want to skip the rest of the logic in
   1191 # this function, and just call forward.
   1192 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1193         or _global_forward_hooks or _global_forward_pre_hooks):
-> 1194     return forward_call(*input, **kwargs)
   1195 # Do not call functions when jit is used
   1196 full_backward_hooks, non_full_backward_hooks = [], []

File /opt/conda/envs/pytorch/lib/python3.10/site-packages/diffusers/models/attention_processor.py:559, in Attention.forward(self, hidden_states, encoder_hidden_states, attention_mask, **cross_attention_kwargs)
    554     logger.warning(
    555         f"cross_attention_kwargs {unused_kwargs} are not expected by {self.processor.__class__.__name__} and will be ignored."
    556     )
    557 cross_attention_kwargs = {k: w for k, w in cross_attention_kwargs.items() if k in attn_parameters}
--> 559 return self.processor(
    560     self,
    561     hidden_states,
    562     encoder_hidden_states=encoder_hidden_states,
    563     attention_mask=attention_mask,
    564     **cross_attention_kwargs,
    565 )

File /opt/conda/envs/pytorch/lib/python3.10/site-packages/diffusers/models/attention_processor.py:1355, in XFormersAttnProcessor.__call__(self, attn, hidden_states, encoder_hidden_states, attention_mask, temb, *args, **kwargs)
   1351     batch_size, channel, height, width = hidden_states.shape
   1352     hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
   1354 batch_size, key_tokens, _ = (
-> 1355     hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
   1356 )
   1358 attention_mask = attn.prepare_attention_mask(attention_mask, key_tokens, batch_size)
   1359 if attention_mask is not None:
   1360     # expand our mask's singleton query_tokens dimension:
   1361     #   [batch*heads,            1, key_tokens] ->
   (...)
   1364     #   [batch*heads, query_tokens, key_tokens]
   1365     # we do this explicitly because xformers doesn't broadcast the singleton dimension for us.

AttributeError: 'tuple' object has no attribute 'shape'

pipe.enable_model_cpu_offload()
pipe.enable_xformers_memory_efficient_attention()