when use StableDiffusionXLControlNetPipeline and guess_mode is True, the pipeline not work and the error is :
RuntimeError: Expected size for first two dimensions of batch2 tensor to be: [20, 64] but got: [10, 64].
File /opt/conda/lib/python3.9/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 ~/.local/lib/python3.9/site-packages/diffusers/pipelines/controlnet/pipeline_controlnet_sd_xl.py:936, in StableDiffusionXLControlNetPipeline.__call__(self, prompt, prompt_2, image, height, width, num_inference_steps, guidance_scale, negative_prompt, negative_prompt_2, num_images_per_prompt, eta, generator, latents, prompt_embeds, negative_prompt_embeds, output_type, return_dict, callback, callback_steps, cross_attention_kwargs, controlnet_conditioning_scale, guess_mode, control_guidance_start, control_guidance_end, original_size, crops_coords_top_left, target_size)
933 cond_scale = controlnet_conditioning_scale * controlnet_keep[i]
935 added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
--> 936 down_block_res_samples, mid_block_res_sample = self.controlnet(
937 control_model_input,
938 t,
939 encoder_hidden_states=controlnet_prompt_embeds,
940 controlnet_cond=image,
941 conditioning_scale=cond_scale,
942 guess_mode=guess_mode,
943 added_cond_kwargs=added_cond_kwargs,
944 return_dict=False,
945 )
947 if guess_mode and do_classifier_free_guidance:
948 # Infered ControlNet only for the conditional batch.
949 # To apply the output of ControlNet to both the unconditional and conditional batches,
950 # add 0 to the unconditional batch to keep it unchanged.
951 down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) fordin down_block_res_samples]
File /opt/conda/lib/python3.9/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 ~/.local/lib/python3.9/site-packages/diffusers/models/controlnet.py:760, in ControlNetModel.forward(self, sample, timestep, encoder_hidden_states, controlnet_cond, conditioning_scale, class_labels, timestep_cond, attention_mask, added_cond_kwargs, cross_attention_kwargs, guess_mode, return_dict)
758 fordownsample_blockin self.down_blocks:
759 if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
--> 760 sample, res_samples = downsample_block(
761 hidden_states=sample,
762 temb=emb,
763 encoder_hidden_states=encoder_hidden_states,
764 attention_mask=attention_mask,
765 cross_attention_kwargs=cross_attention_kwargs,
766 )
767 else:
768 sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
File /opt/conda/lib/python3.9/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 ~/.local/lib/python3.9/site-packages/diffusers/models/unet_2d_blocks.py:996, in CrossAttnDownBlock2D.forward(self, hidden_states, temb, encoder_hidden_states, attention_mask, cross_attention_kwargs, encoder_attention_mask, additional_residuals)
994 else:
995 hidden_states = resnet(hidden_states, temb)
--> 996 hidden_states = attn(
997 hidden_states,
998 encoder_hidden_states=encoder_hidden_states,
999 cross_attention_kwargs=cross_attention_kwargs,
1000 attention_mask=attention_mask,
1001 encoder_attention_mask=encoder_attention_mask,
1002 return_dict=False,
1003 )[0]
1005 # apply additional residuals to the output of the last pair of resnet and attention blocks
1006 if i == len(blocks) - 1 and additional_residuals is not None:
File /opt/conda/lib/python3.9/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 ~/.local/lib/python3.9/site-packages/diffusers/models/transformer_2d.py:292, in Transformer2DModel.forward(self, hidden_states, encoder_hidden_states, timestep, class_labels, cross_attention_kwargs, attention_mask, encoder_attention_mask, return_dict)
290 # 2. Blocks
291 forblockin self.transformer_blocks:
--> 292 hidden_states = block(
293 hidden_states,
294 attention_mask=attention_mask,
295 encoder_hidden_states=encoder_hidden_states,
296 encoder_attention_mask=encoder_attention_mask,
297 timestep=timestep,
298 cross_attention_kwargs=cross_attention_kwargs,
299 class_labels=class_labels,
300 )
302 # 3. Output
303 if self.is_input_continuous:
File /opt/conda/lib/python3.9/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 ~/.local/lib/python3.9/site-packages/diffusers/models/attention.py:171, in BasicTransformerBlock.forward(self, hidden_states, attention_mask, encoder_hidden_states, encoder_attention_mask, timestep, cross_attention_kwargs, class_labels)
166 if self.attn2 is not None:
167 norm_hidden_states = (
168 self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)
169 )
--> 171 attn_output = self.attn2(
172 norm_hidden_states,
173 encoder_hidden_states=encoder_hidden_states,
174 attention_mask=encoder_attention_mask,
175 **cross_attention_kwargs,
176 )
177 hidden_states = attn_output + hidden_states
179 # 3. Feed-forward
File /opt/conda/lib/python3.9/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 ~/.local/lib/python3.9/site-packages/diffusers/models/attention_processor.py:322, in Attention.forward(self, hidden_states, encoder_hidden_states, attention_mask, **cross_attention_kwargs)
318 def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None, **cross_attention_kwargs):
319 # The `Attention` class can call different attention processors / attention functions
320 # here we simply pass along all tensors to the selected processor class
321 # For standard processors that are defined here, `**cross_attention_kwargs` is empty
--> 322 return self.processor(
323 self,
324 hidden_states,
325 encoder_hidden_states=encoder_hidden_states,
326 attention_mask=attention_mask,
327 **cross_attention_kwargs,
328 )
File ~/.local/lib/python3.9/site-packages/diffusers/models/attention_processor.py:489, in AttnProcessor.__call__(self, attn, hidden_states, encoder_hidden_states, attention_mask, temb)
486 key = attn.head_to_batch_dim(key)
487 value = attn.head_to_batch_dim(value)
--> 489 attention_probs = attn.get_attention_scores(query, key, attention_mask)
490 hidden_states = torch.bmm(attention_probs, value)
491 hidden_states = attn.batch_to_head_dim(hidden_states)
File ~/.local/lib/python3.9/site-packages/diffusers/models/attention_processor.py:363, in Attention.get_attention_scores(self, query, key, attention_mask)
360 baddbmm_input = attention_mask
361 beta = 1
--> 363 attention_scores = torch.baddbmm(
364 baddbmm_input,
365 query,
366 key.transpose(-1, -2),
367 beta=beta,
368 alpha=self.scale,
369 )
370 del baddbmm_input
372 if self.upcast_softmax:
RuntimeError: Expected size for first two dimensions of batch2 tensor to be: [20, 64] but got: [10, 64].
Describe the bug
when use StableDiffusionXLControlNetPipeline and guess_mode is True, the pipeline not work and the error is :
RuntimeError: Expected size for first two dimensions of batch2 tensor to be: [20, 64] but got: [10, 64].
Reproduction
controlnet_model = ControlNetModel.from_pretrained ("")
sd_pipe = StableDiffusionXLControlNetPipeline.from_pretrained("", controlnet=controlnet_model,torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
Logs
System Info
Name: diffusers
Version: 0.19.1
Summary: Diffusers
Home-page: https://github.com/huggingface/diffusers
Author: The HuggingFace team
Author-email: patrick@huggingface.co
License: Apache
Location: /home/jovyan/.local/lib/python3.9/site-packages
Requires: filelock, huggingface-hub, importlib-metadata, numpy, Pillow, regex, requests, safetensors
Who can help?
No response