ValueError: not enough values to unpack (expected 2, got 1) #29
Description
I have run the FLUX demo, but it threw an error:
ValueError: not enough values to unpack (expected 2, got 1)
Passing `txt_ids` 3d torch.Tensor is deprecated.Please remove the batch dimension and pass it as a 2d torch Tensor
Passing `img_ids` 3d torch.Tensor is deprecated.Please remove the batch dimension and pass it as a 2d torch Tensor
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[1], line 26
18 ################################################
19
20 # recommend not using batch operations for sd3, as cpu memory could be exceeded.
21 prompts = [
22 # "A photo of a puppy wearing a hat.",
23 "A capybara holding a sign that reads Hello World.",
24 ]
---> 26 images = pipe(
27 prompts,
28 num_inference_steps=15,
29 guidance_scale=4.5,
30 ).images
32 for batch, image in enumerate(images):
33 image.save(f'{batch}-flux-dev.png')
File [~/miniconda3/lib/python3.12/site-packages/torch/utils/_contextlib.py:116](https://a233301-9e7b-63840ca9.bjc1.seetacloud.com:8443/jupyter/lab/tree/autodl-tmp/FlowEdit/~/miniconda3/lib/python3.12/site-packages/torch/utils/_contextlib.py#line=115), in context_decorator.<locals>.decorate_context(*args, **kwargs)
113 @functools.wraps(func)
114 def decorate_context(*args, **kwargs):
115 with ctx_factory():
--> 116 return func(*args, **kwargs)
File [~/miniconda3/lib/python3.12/site-packages/attention_map_diffusers/modules.py:528](https://a233301-9e7b-63840ca9.bjc1.seetacloud.com:8443/jupyter/lab/tree/autodl-tmp/FlowEdit/~/miniconda3/lib/python3.12/site-packages/attention_map_diffusers/modules.py#line=527), in FluxPipeline_call(self, prompt, prompt_2, height, width, num_inference_steps, timesteps, guidance_scale, num_images_per_prompt, generator, latents, prompt_embeds, pooled_prompt_embeds, output_type, return_dict, joint_attention_kwargs, callback_on_step_end, callback_on_step_end_tensor_inputs, max_sequence_length)
525 # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
526 timestep = t.expand(latents.shape[0]).to(latents.dtype)
--> 528 noise_pred = self.transformer(
529 hidden_states=latents,
530 timestep=timestep / 1000,
531 guidance=guidance,
532 pooled_projections=pooled_prompt_embeds,
533 encoder_hidden_states=prompt_embeds,
534 txt_ids=text_ids,
535 img_ids=latent_image_ids,
536 joint_attention_kwargs=self.joint_attention_kwargs,
537 return_dict=False,
538 ##################################################
539 height=2 * (int(height) // (self.vae_scale_factor * 2)) // 2,
540 ##################################################
541 )[0]
543 # compute the previous noisy sample x_t -> x_t-1
544 latents_dtype = latents.dtype
File [~/miniconda3/lib/python3.12/site-packages/torch/nn/modules/module.py:1751](https://a233301-9e7b-63840ca9.bjc1.seetacloud.com:8443/jupyter/lab/tree/autodl-tmp/FlowEdit/~/miniconda3/lib/python3.12/site-packages/torch/nn/modules/module.py#line=1750), in Module._wrapped_call_impl(self, *args, **kwargs)
1749 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc]
1750 else:
-> 1751 return self._call_impl(*args, **kwargs)
File [~/miniconda3/lib/python3.12/site-packages/torch/nn/modules/module.py:1762](https://a233301-9e7b-63840ca9.bjc1.seetacloud.com:8443/jupyter/lab/tree/autodl-tmp/FlowEdit/~/miniconda3/lib/python3.12/site-packages/torch/nn/modules/module.py#line=1761), in Module._call_impl(self, *args, **kwargs)
1757 # If we don't have any hooks, we want to skip the rest of the logic in
1758 # this function, and just call forward.
1759 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
1760 or _global_backward_pre_hooks or _global_backward_hooks
1761 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1762 return forward_call(*args, **kwargs)
1764 result = None
1765 called_always_called_hooks = set()
File [~/miniconda3/lib/python3.12/site-packages/attention_map_diffusers/modules.py:1074](https://a233301-9e7b-63840ca9.bjc1.seetacloud.com:8443/jupyter/lab/tree/autodl-tmp/FlowEdit/~/miniconda3/lib/python3.12/site-packages/attention_map_diffusers/modules.py#line=1073), in FluxTransformer2DModelForward(self, hidden_states, encoder_hidden_states, pooled_projections, timestep, img_ids, txt_ids, guidance, joint_attention_kwargs, controlnet_block_samples, controlnet_single_block_samples, return_dict, controlnet_blocks_repeat, height, width)
1071 img_ids = img_ids[0]
1073 ids = torch.cat((txt_ids, img_ids), dim=0)
-> 1074 image_rotary_emb = self.pos_embed(ids)
1076 for index_block, block in enumerate(self.transformer_blocks):
1077 if torch.is_grad_enabled() and self.gradient_checkpointing:
File [~/miniconda3/lib/python3.12/site-packages/torch/nn/modules/module.py:1751](https://a233301-9e7b-63840ca9.bjc1.seetacloud.com:8443/jupyter/lab/tree/autodl-tmp/FlowEdit/~/miniconda3/lib/python3.12/site-packages/torch/nn/modules/module.py#line=1750), in Module._wrapped_call_impl(self, *args, **kwargs)
1749 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc]
1750 else:
-> 1751 return self._call_impl(*args, **kwargs)
File [~/miniconda3/lib/python3.12/site-packages/torch/nn/modules/module.py:1762](https://a233301-9e7b-63840ca9.bjc1.seetacloud.com:8443/jupyter/lab/tree/autodl-tmp/FlowEdit/~/miniconda3/lib/python3.12/site-packages/torch/nn/modules/module.py#line=1761), in Module._call_impl(self, *args, **kwargs)
1757 # If we don't have any hooks, we want to skip the rest of the logic in
1758 # this function, and just call forward.
1759 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
1760 or _global_backward_pre_hooks or _global_backward_hooks
1761 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1762 return forward_call(*args, **kwargs)
1764 result = None
1765 called_always_called_hooks = set()
File [~/miniconda3/lib/python3.12/site-packages/diffusers/models/transformers/transformer_flux.py:65](https://a233301-9e7b-63840ca9.bjc1.seetacloud.com:8443/jupyter/lab/tree/autodl-tmp/FlowEdit/~/miniconda3/lib/python3.12/site-packages/diffusers/models/transformers/transformer_flux.py#line=64), in EmbedND.forward(self, ids)
62 def forward(self, ids: torch.Tensor) -> torch.Tensor:
63 n_axes = ids.shape[-1]
64 emb = torch.cat(
---> 65 [rope(ids[..., i], self.axes_dim[i], self.theta) for i in range(n_axes)],
66 dim=-3,
67 )
68 return emb.unsqueeze(1)
File [~/miniconda3/lib/python3.12/site-packages/diffusers/models/transformers/transformer_flux.py:44](https://a233301-9e7b-63840ca9.bjc1.seetacloud.com:8443/jupyter/lab/tree/autodl-tmp/FlowEdit/~/miniconda3/lib/python3.12/site-packages/diffusers/models/transformers/transformer_flux.py#line=43), in rope(pos, dim, theta)
41 scale = torch.arange(0, dim, 2, dtype=torch.float64, device=pos.device) / dim
42 omega = 1.0 / (theta**scale)
---> 44 batch_size, seq_length = pos.shape
45 out = torch.einsum("...n,d->...nd", pos, omega)
46 cos_out = torch.cos(out)
ValueError: not enough values to unpack (expected 2, got 1)