I think it worked previously but these days, when i tried SDXL model with SDXL mm and vae, it won't work anymore...
here is my prompt.json.
for SDXL, i just setup with SDLX model, VAE, Motion model.
others are remaining as false...
Generating 1 animations
Running generation 1 of 1
Generation seed: 3313706524245448039
len( region_condi_list )=1
len( region_list )=1
apply_lcm_lora=False
multi_uncond_mode=False
do_classifier_free_guidance=True
condi_size=2
0%| | 0/8 [00:00<?, ?steps/s]Forward upsample size to force interpolation output size.
0%| | 0/8 [00:02<?, ?steps/s]
Input and output must have the same number of spatial dimensions, but got input with spatial dimensions of [29, 16] and output size of torch.Size([16, 57, 32]). Please provide input tensor in (N, C, d1, d2, ...,dK) format and output size in (o1, o2, ...,oK) format.
Traceback (most recent call last):
File "/storage/aj/animatediff-cli-prompt-travel/src/animatediff/front.py", line 233, in execute_impl
generate(stylize_dir=stylize_dir, length=16)
File "/storage/aj/animatediff-cli-prompt-travel/src/animatediff/stylize.py", line 618, in generate
output_0_dir = generate(
File "/storage/aj/animatediff-cli-prompt-travel/src/animatediff/cli.py", line 445, in generate
output = run_inference(
File "/storage/aj/animatediff-cli-prompt-travel/src/animatediff/generate.py", line 1543, in run_inference
pipeline_output = pipeline(
File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "/storage/aj/animatediff-cli-prompt-travel/src/animatediff/pipelines/sdxl_animation.py", line 1937, in __call__
pred_layer = self.unet(
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1527, in _call_impl
return forward_call(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/accelerate/hooks.py", line 165, in new_forward
output = module._old_forward(*args, **kwargs)
File "/storage/aj/animatediff-cli-prompt-travel/src/animatediff/sdxl_models/unet.py", line 1108, in forward
sample = upsample_block(
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1527, in _call_impl
return forward_call(*args, **kwargs)
File "/storage/aj/animatediff-cli-prompt-travel/src/animatediff/sdxl_models/unet_blocks.py", line 939, in forward
hidden_states = upsampler(hidden_states, upsample_size)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1527, in _call_impl
return forward_call(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/diffusers/models/resnet.py", line 189, in forward
hidden_states = F.interpolate(hidden_states, size=output_size, mode="nearest")
File "/usr/local/lib/python3.10/dist-packages/torch/nn/functional.py", line 3916, in interpolate
raise ValueError(
ValueError: Input and output must have the same number of spatial dimensions, but got input with spatial dimensions of [29, 16] and output size of torch.Size([16, 57, 32]). Please provide input tensor in (N, C, d1, d2, ...,dK) format and output size in (o1, o2, ...,oK) format.
i would like to know what does this error stands for?
is there anything i have missed to do execution?
i tried with tensor_interpolation_slerp=true/false, but it didnt solve the issue.
I think it worked previously but these days, when i tried SDXL model with SDXL mm and vae, it won't work anymore... here is my prompt.json. for SDXL, i just setup with SDLX model, VAE, Motion model. others are remaining as false...
this is the error log..
i would like to know what does this error stands for? is there anything i have missed to do execution? i tried with tensor_interpolation_slerp=true/false, but it didnt solve the issue.
thank you