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I tried running inference with Stable Diffusion (v1) on (Colab) TPUs. It worked; however, a couple of questions:
Instead of just three images, we get this:
This likely has to do with the distribution on the TPU cores. Has anyone looked into filtering the extra images?
The inference time also seems slower (~39 seconds to generate three images). Could it be because of the non-TF ops that run when we call model.text_to_image()?
To mitigate the second issue, @deep-diver and I created SavedModels to eliminate most of the non-TF ops from the pipeline. Details can be found in this notebook. The tokenizer, however, still is not fully TF. But I couldn't load the SavedModels in a TPU strategy scope, leads to:
---------------------------------------------------------------------------
ResourceExhaustedError Traceback (most recent call last)
[<ipython-input-16-b1735bf71429>](https://localhost:8080/#) in <module>
3 for k in gcs_paths:
4 predict_fns.append(
----> 5 load_saved_model(gcs_paths[k])
6 )
11 frames
[/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py](https://localhost:8080/#) in shape(self)
1261 # `_tensor_shape` is declared and defined in the definition of
1262 # `EagerTensor`, in C.
-> 1263 self._tensor_shape = tensor_shape.TensorShape(self._shape_tuple())
1264 except core._NotOkStatusException as e:
1265 raise core._status_to_exception(e) from None
ResourceExhaustedError: Failed to allocate request for 7.03MiB (7372800B) on device ordinal 0
Encountered when executing an operation using EagerExecutor. This error cancels all future operations and poisons their output tensors.
I tried to run your notebook, @sayakpaul, and it just seems to be stuck on the generation step. Seems like Stable Diffision might be broken on TPU. Will try to take a look what's causing this issue.
Colab Notebook: https://colab.research.google.com/drive/1-z5bm5WqnPxYp6Hl4oAyC0ZwkTOvtRzd?usp=sharing
I tried running inference with Stable Diffusion (v1) on (Colab) TPUs. It worked; however, a couple of questions:
Instead of just three images, we get this:
This likely has to do with the distribution on the TPU cores. Has anyone looked into filtering the extra images?
The inference time also seems slower (~39 seconds to generate three images). Could it be because of the non-TF ops that run when we call
model.text_to_image()
?To mitigate the second issue, @deep-diver and I created SavedModels to eliminate most of the non-TF ops from the pipeline. Details can be found in this notebook. The tokenizer, however, still is not fully TF. But I couldn't load the SavedModels in a TPU strategy scope, leads to:
Ccing @LukeWood @ianstenbit.
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