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Global seed set to 42
>>>>>>>>>>color correction>>>>>>>>>>>
Use adain color correction
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
Loading model from ././weights/mgldvsr_unet.ckpt
Global Step: 42000 [978/1869]
LatentDiffusionVSRTextWT: Running in eps-prediction mode
Setting up MemoryEfficientSelfAttention. Query dim is 1280, using 20 heads.
DiffusionWrapper has 935.32 M params.
making attention of type 'vanilla' with 512 in_channels
Working with z of shape (1, 4, 64, 64) = 16384 dimensions.
making attention of type 'vanilla' with 512 in_channels
Encoder Restored from /flash/zhihaoz/vsr/MGLD-VSR/weights/v2-1_512-ema-pruned.ckpt with 0 missing and 1242 unexpected keys
<<<<<<<<<<<<>>>>>>>>>>>>>>>
Restored from /flash/zhihaoz/vsr/MGLD-VSR/weights/v2-1_512-ema-pruned.ckpt with 588 missing and 38 unexpected keys
Segment shape: torch.Size([5, 3, 2048, 2048])
Segment shape: torch.Size([5, 3, 2048, 2048])
Sampling: 0%| | 0/2 [00:00<?, ?it/s]
>>>>>>>>>>>>>>>>>>>>>>>
seq: road_512 seg: 0 size: torch.Size([5, 3, 2048, 2048])
/flash/zhihaoz/conda/envs/mgldvsr/lib/python3.9/site-packages/torch/functional.py:478: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing a
rgument. (Triggered internally at /opt/conda/conda-bld/pytorch_1659484809662/work/aten/src/ATen/native/TensorShape.cpp:2894.)
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
Global seed set to 42
Sampling t: 0%| | 0/50 [00:00<?, ?it/s]
Sampling: 0%|
Traceback (most recent call last):
File "/flash/zhihaoz/vsr/MGLD-VSR/scripts/vsr_val_ddpm_text_T_vqganfin_oldcanvas_tile.py", line 565, in <module>
main()
File "/flash/zhihaoz/vsr/MGLD-VSR/scripts/vsr_val_ddpm_text_T_vqganfin_oldcanvas_tile.py", line 459, in main
samples, _ = model.sample_canvas(cond=semantic_c,
File "/flash/zhihaoz/conda/envs/mgldvsr/lib/python3.9/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
return func(*args, **kwargs)
File "/flash/zhihaoz/vsr/MGLD-VSR/ldm/models/diffusion/ddpm.py", line 4735, in sample_canvas
return self.p_sample_loop_canvas(cond,
File "/flash/zhihaoz/conda/envs/mgldvsr/lib/python3.9/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
return func(*args, **kwargs)
File "/flash/zhihaoz/vsr/MGLD-VSR/ldm/models/diffusion/ddpm.py", line 4673, in p_sample_loop_canvas
img = self.p_sample_canvas(img, cond, struct_cond, ts, guidance_scale=guidance_scale,
File "/flash/zhihaoz/conda/envs/mgldvsr/lib/python3.9/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
return func(*args, **kwargs)
File "/flash/zhihaoz/vsr/MGLD-VSR/ldm/models/diffusion/ddpm.py", line 4390, in p_sample_canvas
outputs = self.p_mean_variance_canvas(x=x, c=c, struct_cond=struct_cond, t=t, clip_denoised=clip_denoised,
File "/flash/zhihaoz/vsr/MGLD-VSR/ldm/models/diffusion/ddpm.py", line 4256, in p_mean_variance_canvas
model_out = self.apply_model(input_list, t_in[:input_list.size(0)], c[:input_list.size(0)], struct_cond_input, return_ids=return_codebook_ids)
File "/flash/zhihaoz/vsr/MGLD-VSR/ldm/models/diffusion/ddpm.py", line 4080, in apply_model
x_recon = self.model(x_noisy, t, **cond)
File "/flash/zhihaoz/conda/envs/mgldvsr/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "/flash/zhihaoz/vsr/MGLD-VSR/ldm/models/diffusion/ddpm.py", line 4927, in forward
out = self.diffusion_model(x, t, context=cc, struct_cond=struct_cond)
File "/flash/zhihaoz/conda/envs/mgldvsr/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "/flash/zhihaoz/vsr/MGLD-VSR/ldm/modules/diffusionmodules/openaimodel.py", line 2303, in forward
h = module(h, emb, context, struct_cond)
File "/flash/zhihaoz/conda/envs/mgldvsr/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "/flash/zhihaoz/vsr/MGLD-VSR/ldm/modules/diffusionmodules/openaimodel.py", line 145, in forward
x = layer(x, context)
File "/flash/zhihaoz/conda/envs/mgldvsr/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "/flash/zhihaoz/vsr/MGLD-VSR/ldm/modules/attention.py", line 540, in forward
x = block(x, context=context[i])
File "/flash/zhihaoz/conda/envs/mgldvsr/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "/flash/zhihaoz/vsr/MGLD-VSR/ldm/modules/attention.py", line 429, in forward
return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint)
File "/flash/zhihaoz/vsr/MGLD-VSR/ldm/modules/diffusionmodules/util.py", line 116, in checkpoint
return func(*inputs)
File "/flash/zhihaoz/vsr/MGLD-VSR/ldm/modules/attention.py", line 433, in _forward
x = self.attn2(self.norm2(x), context=context) + x
File "/flash/zhihaoz/conda/envs/mgldvsr/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "/flash/zhihaoz/vsr/MGLD-VSR/ldm/modules/attention.py", line 246, in forward
sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
File "/flash/zhihaoz/conda/envs/mgldvsr/lib/python3.9/site-packages/torch/functional.py", line 360, in einsum
return _VF.einsum(equation, operands) # type: ignore[attr-defined]
RuntimeError: einsum(): operands do not broadcast with remapped shapes [original->remapped]: [25, 4096, 64]->[25, 4096, 1, 64] [5, 77, 64]->[5, 1, 77, 64]
The text was updated successfully, but these errors were encountered:
Hi. This might due to the environment problem and I have also encountered the problem on some new environment with new packages. You may refer to issue #9.
Hi,
While running
I got the following error. Do you know what's wrong here? I already tried different image sequences and this error always happens. Btw, I followed this instruction to run your code, https://gist.github.com/meisa233/1549bb95c5c130e3a93fcab17c83e931
The text was updated successfully, but these errors were encountered: