When opening the URL (http://0.0.0.0:7860) I get the "can't reach this page" message. I don't get any errors while loading, apart from the "No module named 'triton'" one, which I assume is normal on Windows. I did have to install xformers for cuda 11.8 before though, as I the 12.1 ver. that downloaded automatically wasn't compatible with the PyTorch version. I have Python 3.9.13 installed in the venv.
Here's the log after I run webui.py:
A matching Triton is not available, some optimizations will not be enabled
Traceback (most recent call last):
File "G:\stable diffusion stuff\CharacterGen\CharacterGen\lib\site-packages\xformers__init__.py", line 57, in _is_triton_available
import triton # noqa
ModuleNotFoundError: No module named 'triton'
G:\stable diffusion stuff\CharacterGen\CharacterGen\lib\site-packages\diffusers\utils\outputs.py:63: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
torch.utils._pytree._register_pytree_node(
G:\stable diffusion stuff\CharacterGen\CharacterGen\lib\site-packages\huggingface_hub\file_download.py:1132: FutureWarning: resume_download is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use force_download=True.
warnings.warn(
Some weights of UNetMV2DConditionModel were not initialized from the model checkpoint at stabilityai/stable-diffusion-2-1 and are newly initialized: ['class_embedding.linear_1.weight', 'class_embedding.linear_2.weight', 'camera_embedding.2.bias', 'camera_embedding.0.bias', 'camera_embedding.0.weight', 'camera_embedding.2.weight', 'class_embedding.linear_1.bias', 'class_embedding.linear_2.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
Some weights of the model checkpoint at stabilityai/stable-diffusion-2-1 were not used when initializing UNetMV2DRefModel: ['up_blocks.3.attentions.2.transformer_blocks.0.norm2.weight', 'conv_out.bias', 'up_blocks.3.attentions.2.transformer_blocks.0.ff.net.2.bias', 'up_blocks.3.attentions.2.transformer_blocks.0.ff.net.0.proj.weight', 'up_blocks.3.attentions.2.transformer_blocks.0.attn1.to_q.weight', 'up_blocks.3.attentions.2.transformer_blocks.0.attn1.to_k.weight', 'up_blocks.3.attentions.2.transformer_blocks.0.norm3.weight', 'up_blocks.3.attentions.2.transformer_blocks.0.attn2.to_v.weight', 'up_blocks.3.attentions.2.transformer_blocks.0.ff.net.2.weight', 'up_blocks.3.attentions.2.transformer_blocks.0.attn1.to_out.0.bias', 'conv_out.weight', 'up_blocks.3.attentions.2.transformer_blocks.0.attn2.to_q.weight', 'up_blocks.3.attentions.2.transformer_blocks.0.norm3.bias', 'conv_norm_out.bias', 'up_blocks.3.attentions.2.transformer_blocks.0.norm2.bias', 'up_blocks.3.attentions.2.proj_out.weight', 'up_blocks.3.attentions.2.transformer_blocks.0.attn1.to_out.0.weight', 'up_blocks.3.attentions.2.transformer_blocks.0.attn1.to_v.weight', 'up_blocks.3.attentions.2.transformer_blocks.0.attn2.to_k.weight', 'up_blocks.3.attentions.2.proj_out.bias', 'up_blocks.3.attentions.2.transformer_blocks.0.attn2.to_out.0.weight', 'up_blocks.3.attentions.2.transformer_blocks.0.ff.net.0.proj.bias', 'conv_norm_out.weight', 'up_blocks.3.attentions.2.transformer_blocks.0.attn2.to_out.0.bias']
This IS expected if you are initializing UNetMV2DRefModel 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 UNetMV2DRefModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Some weights of UNetMV2DRefModel were not initialized from the model checkpoint at stabilityai/stable-diffusion-2-1 and are newly initialized: ['class_embedding.linear_1.weight', 'class_embedding.linear_2.weight', 'camera_embedding.2.bias', 'camera_embedding.0.bias', 'camera_embedding.0.weight', 'camera_embedding.2.weight', 'class_embedding.linear_1.bias', 'class_embedding.linear_2.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
G:\stable diffusion stuff\CharacterGen\CharacterGen\lib\site-packages\diffusers\utils\outputs.py:63: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
torch.utils._pytree._register_pytree_node(
Loading system
G:\stable diffusion stuff\CharacterGen\CharacterGen\lib\site-packages\torch\utils\cpp_extension.py:1967: UserWarning: TORCH_CUDA_ARCH_LIST is not set, all archs for visible cards are included for compilation.
If this is not desired, please set os.environ['TORCH_CUDA_ARCH_LIST'].
warnings.warn(
./3D_Stage/models/lrm.ckpt
finish loading!!
Running on local URL: http://0.0.0.0:7860
When opening the URL (http://0.0.0.0:7860) I get the "can't reach this page" message. I don't get any errors while loading, apart from the "No module named 'triton'" one, which I assume is normal on Windows. I did have to install xformers for cuda 11.8 before though, as I the 12.1 ver. that downloaded automatically wasn't compatible with the PyTorch version. I have Python 3.9.13 installed in the venv. Here's the log after I run webui.py:
A matching Triton is not available, some optimizations will not be enabled Traceback (most recent call last): File "G:\stable diffusion stuff\CharacterGen\CharacterGen\lib\site-packages\xformers__init__.py", line 57, in _is_triton_available import triton # noqa ModuleNotFoundError: No module named 'triton' G:\stable diffusion stuff\CharacterGen\CharacterGen\lib\site-packages\diffusers\utils\outputs.py:63: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead. torch.utils._pytree._register_pytree_node( G:\stable diffusion stuff\CharacterGen\CharacterGen\lib\site-packages\huggingface_hub\file_download.py:1132: FutureWarning:
resume_download
is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, useforce_download=True
. warnings.warn( Some weights of UNetMV2DConditionModel were not initialized from the model checkpoint at stabilityai/stable-diffusion-2-1 and are newly initialized: ['class_embedding.linear_1.weight', 'class_embedding.linear_2.weight', 'camera_embedding.2.bias', 'camera_embedding.0.bias', 'camera_embedding.0.weight', 'camera_embedding.2.weight', 'class_embedding.linear_1.bias', 'class_embedding.linear_2.bias'] You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. Some weights of the model checkpoint at stabilityai/stable-diffusion-2-1 were not used when initializing UNetMV2DRefModel: ['up_blocks.3.attentions.2.transformer_blocks.0.norm2.weight', 'conv_out.bias', 'up_blocks.3.attentions.2.transformer_blocks.0.ff.net.2.bias', 'up_blocks.3.attentions.2.transformer_blocks.0.ff.net.0.proj.weight', 'up_blocks.3.attentions.2.transformer_blocks.0.attn1.to_q.weight', 'up_blocks.3.attentions.2.transformer_blocks.0.attn1.to_k.weight', 'up_blocks.3.attentions.2.transformer_blocks.0.norm3.weight', 'up_blocks.3.attentions.2.transformer_blocks.0.attn2.to_v.weight', 'up_blocks.3.attentions.2.transformer_blocks.0.ff.net.2.weight', 'up_blocks.3.attentions.2.transformer_blocks.0.attn1.to_out.0.bias', 'conv_out.weight', 'up_blocks.3.attentions.2.transformer_blocks.0.attn2.to_q.weight', 'up_blocks.3.attentions.2.transformer_blocks.0.norm3.bias', 'conv_norm_out.bias', 'up_blocks.3.attentions.2.transformer_blocks.0.norm2.bias', 'up_blocks.3.attentions.2.proj_out.weight', 'up_blocks.3.attentions.2.transformer_blocks.0.attn1.to_out.0.weight', 'up_blocks.3.attentions.2.transformer_blocks.0.attn1.to_v.weight', 'up_blocks.3.attentions.2.transformer_blocks.0.attn2.to_k.weight', 'up_blocks.3.attentions.2.proj_out.bias', 'up_blocks.3.attentions.2.transformer_blocks.0.attn2.to_out.0.weight', 'up_blocks.3.attentions.2.transformer_blocks.0.ff.net.0.proj.bias', 'conv_norm_out.weight', 'up_blocks.3.attentions.2.transformer_blocks.0.attn2.to_out.0.bias']