runtime error

ion_model.encoder.layers.8.self_attn.q_proj.weight', 'vision_model.encoder.layers.3.self_attn.out_proj.bias', 'vision_model.encoder.layers.15.self_attn.q_proj.weight'] - This IS expected if you are initializing CLIPTextModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model). - This IS NOT expected if you are initializing CLIPTextModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). Loading model: SD2 superresolution... x4-upscaler-ema.safetensors exists Traceback (most recent call last): File "/home/user/app/app.py", line 68, in <module> sr_model = models.sd2_sr.load_model(device='cuda:1') File "/home/user/app/lib/models/sd2_sr.py", line 183, in load_model unet.to(dtype=dtype, device=device) File "/home/user/.pyenv/versions/3.9.18/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1160, in to return self._apply(convert) File "/home/user/.pyenv/versions/3.9.18/lib/python3.9/site-packages/torch/nn/modules/module.py", line 810, in _apply module._apply(fn) File "/home/user/.pyenv/versions/3.9.18/lib/python3.9/site-packages/torch/nn/modules/module.py", line 810, in _apply module._apply(fn) File "/home/user/.pyenv/versions/3.9.18/lib/python3.9/site-packages/torch/nn/modules/module.py", line 833, in _apply param_applied = fn(param) File "/home/user/.pyenv/versions/3.9.18/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1158, in convert return t.to(device, dtype if t.is_floating_point() or t.is_complex() else None, non_blocking) RuntimeError: CUDA error: invalid device ordinal CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect. For debugging consider passing CUDA_LAUNCH_BLOCKING=1. Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.

Container logs: