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Getting errors using Guernika Model Converter on .ckpt files

by radfaraf - opened Jan 2, 2023

Jan 2, 2023

I tried converting the .ckpt files from here:

https://civitai.com/models/1254/elldreths-dream-mix (I used the All settings)
AND
https://civitai.com/models/2968/wlop (I used the CPU and NE setting only just to try something different)

Waited about 20 minutes of processing and eventually see a folder created for them to output the files, but then it errors and no files are created:
Screenshot showing error:
https://prnt.sc/GAgfHFaZgPL4

GuiyeC

Guernika org Jan 2, 2023

Thank you for the info, I'll take a look and I should probably add the error message on that alert.

radfaraf

Jan 2, 2023

Also strange I noticed after I start the conversion the program opens a second copy of itself after a short while, is that on purpose?

GuiyeC

Guernika org Jan 2, 2023

Not on purpose, it should only launch one instance but I noticed the same happened to me, this is my first PyInstaller app so I'll have to work on it a bit more, I'm trying to convert the models you shared, might take a while but if you want to test with better output you can right click the app "Show Package Contents" then go to Contents -> MacOS and double click the "Guernika Model Converter" there, it should launch with a terminal window showing all the output of the python scripts, could be helpful.

radfaraf

Jan 2, 2023

Here's a log:

Torch version 1.13.0 has not been tested with coremltools. You may run into unexpected errors. Torch 1.12.1 is the most recent version that has been tested.
2023-01-02 18:05:04.438 Guernika Model Converter[1645:21862] +[CATransaction synchronize] called within transaction
2023-01-02 18:05:04.702 Guernika Model Converter[1645:21862] +[CATransaction synchronize] called within transaction
2023-01-02 18:05:34.758 Guernika Model Converter[1645:21862] +[CATransaction synchronize] called within transaction
2023-01-02 18:05:39.160 Guernika Model Converter[1645:21862] +[CATransaction synchronize] called within transaction
2023-01-02 18:05:47.889 Guernika Model Converter[1645:21862] +[CATransaction synchronize] called within transaction
2023-01-02 18:05:53.117 Guernika Model Converter[1645:21862] +[CATransaction synchronize] called within transaction
2023-01-02 18:06:03.700 Guernika Model Converter[1645:21862] +[CATransaction synchronize] called within transaction
2023-01-02 18:06:36.963 Guernika Model Converter[1645:21862] +[CATransaction synchronize] called within transaction
2023-01-02 18:06:46.755 Guernika Model Converter[1645:21862] +[CATransaction synchronize] called within transaction
2023-01-02 18:07:14.505 Guernika Model Converter[1645:21862] +[CATransaction synchronize] called within transaction
2023-01-02 18:07:15.689 Guernika Model Converter[1645:21862] +[CATransaction synchronize] called within transaction
2023-01-02 18:07:15.796 Guernika Model Converter[1645:21862] +[CATransaction synchronize] called within transaction
2023-01-02 18:07:15.899 Guernika Model Converter[1645:21862] +[CATransaction synchronize] called within transaction
2023-01-02 18:07:16.005 Guernika Model Converter[1645:21862] +[CATransaction synchronize] called within transaction
2023-01-02 18:07:16.040 Guernika Model Converter[1645:21862] +[CATransaction synchronize] called within transaction
2023-01-02 18:07:16.690 Guernika Model Converter[1645:21862] +[CATransaction synchronize] called within transaction
2023-01-02 18:07:39.022 Guernika Model Converter[1645:21862] +[CATransaction synchronize] called within transaction
2023-01-02 18:07:43.987 Guernika Model Converter[1645:21862] +[CATransaction synchronize] called within transaction
2023-01-02 18:07:47.804 Guernika Model Converter[1645:21862] +[CATransaction synchronize] called within transaction
2023-01-02 18:07:52.819 Guernika Model Converter[1645:21862] +[CATransaction synchronize] called within transaction
2023-01-02 18:08:22.899 Guernika Model Converter[1645:21862] +[CATransaction synchronize] called within transaction
2023-01-02 18:08:37.131 Guernika Model Converter[1645:21862] +[CATransaction synchronize] called within transaction
INFO:python_coreml_stable_diffusion.torch2coreml:Initializing StableDiffusionPipeline from /Volumes/2TB SSD/Stable-diffusion/elldrethSDreamMix_v10.ckpt..
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
100 1873 100 1873 0 0 5501 0 --:--:-- --:--:-- --:--:-- 5557
Some weights of the model checkpoint at openai/clip-vit-large-patch14 were not used when initializing CLIPTextModel: ['vision_model.encoder.layers.5.self_attn.out_proj.bias', 'vision_model.encoder.layers.18.mlp.fc2.bias', 'vision_model.encoder.layers.14.self_attn.q_proj.weight', 'vision_model.encoder.layers.18.mlp.fc2.weight', 'vision_model.encoder.layers.7.mlp.fc1.bias', 'vision_model.encoder.layers.21.self_attn.out_proj.bias', 'vision_model.encoder.layers.21.mlp.fc2.bias', 'vision_model.encoder.layers.18.layer_norm1.bias', 'vision_model.encoder.layers.11.self_attn.out_proj.bias', 'vision_model.encoder.layers.3.self_attn.k_proj.bias', 'vision_model.encoder.layers.0.self_attn.out_proj.weight', 'vision_model.encoder.layers.16.mlp.fc1.weight', 'vision_model.encoder.layers.23.self_attn.v_proj.bias', 'vision_model.encoder.layers.10.layer_norm2.weight', 'vision_model.encoder.layers.20.self_attn.q_proj.bias', 'vision_model.encoder.layers.18.self_attn.q_proj.bias', 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'vision_model.encoder.layers.7.self_attn.out_proj.weight', 'vision_model.encoder.layers.3.layer_norm1.bias', 'vision_model.encoder.layers.12.self_attn.q_proj.bias', 'vision_model.encoder.layers.18.self_attn.v_proj.bias', 'vision_model.encoder.layers.23.self_attn.out_proj.bias', 'vision_model.encoder.layers.6.self_attn.out_proj.bias', 'vision_model.encoder.layers.2.mlp.fc1.bias', 'vision_model.encoder.layers.21.layer_norm2.weight', 'vision_model.encoder.layers.14.self_attn.q_proj.bias', 'vision_model.encoder.layers.13.self_attn.out_proj.weight', 'vision_model.encoder.layers.7.mlp.fc2.weight', 'vision_model.encoder.layers.14.self_attn.v_proj.bias', 'vision_model.encoder.layers.22.mlp.fc2.weight', 'vision_model.encoder.layers.0.self_attn.out_proj.bias', 'vision_model.encoder.layers.3.self_attn.q_proj.bias', 'vision_model.encoder.layers.14.layer_norm2.bias', 'vision_model.encoder.layers.4.mlp.fc2.bias', 'vision_model.encoder.layers.13.layer_norm2.weight', 'vision_model.encoder.layers.6.self_attn.q_proj.bias', 'vision_model.encoder.layers.21.self_attn.k_proj.weight', 'vision_model.encoder.layers.6.layer_norm2.weight', 'vision_model.encoder.layers.6.mlp.fc1.bias', 'vision_model.encoder.layers.22.layer_norm2.weight', 'vision_model.encoder.layers.16.layer_norm2.weight', 'vision_model.encoder.layers.11.self_attn.k_proj.bias', 'vision_model.encoder.layers.4.self_attn.v_proj.bias', 'vision_model.encoder.layers.3.self_attn.out_proj.bias', 'vision_model.encoder.layers.7.layer_norm1.bias', 'vision_model.encoder.layers.16.layer_norm1.bias', 'vision_model.encoder.layers.4.self_attn.k_proj.weight', 'vision_model.encoder.layers.5.mlp.fc1.bias', 'vision_model.encoder.layers.16.layer_norm2.bias', 'vision_model.encoder.layers.10.self_attn.v_proj.weight', 'vision_model.encoder.layers.15.self_attn.k_proj.weight', 'vision_model.encoder.layers.1.layer_norm1.weight', 'vision_model.encoder.layers.18.self_attn.q_proj.weight', 'vision_model.encoder.layers.16.mlp.fc2.weight', 'vision_model.encoder.layers.11.mlp.fc2.bias', 'vision_model.encoder.layers.11.layer_norm2.weight', 'vision_model.encoder.layers.16.self_attn.v_proj.bias', 'vision_model.encoder.layers.0.mlp.fc1.bias', 'vision_model.encoder.layers.15.mlp.fc1.weight', 'vision_model.encoder.layers.15.layer_norm1.bias', 'vision_model.encoder.layers.1.self_attn.v_proj.weight', 'vision_model.embeddings.class_embedding', 'vision_model.encoder.layers.6.mlp.fc2.bias', 'vision_model.encoder.layers.10.mlp.fc1.bias', 'vision_model.encoder.layers.0.self_attn.k_proj.bias', 'vision_model.encoder.layers.23.self_attn.v_proj.weight', 'vision_model.encoder.layers.23.layer_norm2.weight', 'vision_model.encoder.layers.8.self_attn.k_proj.weight', 'vision_model.encoder.layers.15.layer_norm1.weight', 'vision_model.encoder.layers.22.self_attn.q_proj.weight', 'vision_model.encoder.layers.23.mlp.fc1.bias', 'vision_model.encoder.layers.15.mlp.fc2.bias', 'vision_model.encoder.layers.3.mlp.fc2.weight', 'vision_model.encoder.layers.15.layer_norm2.bias', 'vision_model.encoder.layers.0.layer_norm2.weight', 'vision_model.encoder.layers.9.layer_norm2.weight', 'vision_model.encoder.layers.1.self_attn.k_proj.weight', 'vision_model.encoder.layers.17.layer_norm2.bias', 'vision_model.encoder.layers.20.mlp.fc2.bias', 'vision_model.encoder.layers.10.layer_norm2.bias', 'vision_model.encoder.layers.1.self_attn.q_proj.weight', 'vision_model.encoder.layers.2.mlp.fc1.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).
diffusers/utils/deprecation_utils.py:35: FutureWarning: The configuration file of this scheduler: PNDMScheduler {
"_class_name": "PNDMScheduler",
"_diffusers_version": "0.9.0",
"beta_end": 0.012,
"beta_schedule": "scaled_linear",
"beta_start": 0.00085,
"num_train_timesteps": 1000,
"set_alpha_to_one": false,
"skip_prk_steps": true,
"steps_offset": 0,
"trained_betas": null
}
is outdated. steps_offset should be set to 1 instead of 0. Please make sure to update the config accordingly as leaving steps_offset might led to incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for the scheduler/scheduler_config.json file
INFO:python_coreml_stable_diffusion.torch2coreml:Done.
INFO:python_coreml_stable_diffusion.torch2coreml:Converting vae_decoder
diffusers/models/resnet.py:109: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
diffusers/models/resnet.py:122: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
INFO:python_coreml_stable_diffusion.torch2coreml:Converting vae_decoder to CoreML..
Converting PyTorch Frontend ==> MIL Ops: 0%| | 0/353 [00:00<?, ? ops/s]WARNING:python_coreml_stable_diffusion.torch2coreml:Casted the beta(value=0.0) argument of baddbmm op from int32 to float32 dtype for conversion!
Converting PyTorch Frontend ==> MIL Ops: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋| 352/353 [00:00<00:00, 2370.68 ops/s]
Running MIL Common passes: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 39/39 [00:00<00:00, 79.24 passes/s]
Running MIL FP16ComputePrecision pass: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1.87 passes/s]
Running MIL Clean up passes: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 11/11 [00:02<00:00, 5.23 passes/s]
Torch version 1.13.0 has not been tested with coremltools. You may run into unexpected errors. Torch 1.12.1 is the most recent version that has been tested.
INFO:python_coreml_stable_diffusion.torch2coreml:Saved vae_decoder model to /var/folders/jc/w_542bpx5m545pgd5y6_8yzc0000gn/T/Stable_Diffusion_version_elldrethSDreamMix_v10_vae_decoder.mlpackage
INFO:python_coreml_stable_diffusion.torch2coreml:Saved vae_decoder into /var/folders/jc/w_542bpx5m545pgd5y6_8yzc0000gn/T/Stable_Diffusion_version_elldrethSDreamMix_v10_vae_decoder.mlpackage
INFO:python_coreml_stable_diffusion.torch2coreml:Converted vae_decoder
INFO:python_coreml_stable_diffusion.torch2coreml:Converting vae_encoder
diffusers/models/resnet.py:182: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
diffusers/models/resnet.py:187: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
INFO:python_coreml_stable_diffusion.torch2coreml:Converting vae_encoder to CoreML..
Converting PyTorch Frontend ==> MIL Ops: 0%| | 0/281 [00:00<?, ? ops/s]WARNING:python_coreml_stable_diffusion.torch2coreml:Casted the beta(value=0.0) argument of baddbmm op from int32 to float32 dtype for conversion!
Converting PyTorch Frontend ==> MIL Ops: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌| 280/281 [00:00<00:00, 3289.24 ops/s]
Running MIL Common passes: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 39/39 [00:00<00:00, 132.70 passes/s]
Running MIL FP16ComputePrecision pass: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 3.05 passes/s]
Running MIL Clean up passes: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 11/11 [00:01<00:00, 9.79 passes/s]
INFO:python_coreml_stable_diffusion.torch2coreml:Saved vae_encoder model to /var/folders/jc/w_542bpx5m545pgd5y6_8yzc0000gn/T/Stable_Diffusion_version_elldrethSDreamMix_v10_vae_encoder.mlpackage
INFO:python_coreml_stable_diffusion.torch2coreml:Saved vae_encoder into /var/folders/jc/w_542bpx5m545pgd5y6_8yzc0000gn/T/Stable_Diffusion_version_elldrethSDreamMix_v10_vae_encoder.mlpackage
INFO:python_coreml_stable_diffusion.torch2coreml:Converted vae_encoder
INFO:python_coreml_stable_diffusion.torch2coreml:Converting unet
INFO:python_coreml_stable_diffusion.torch2coreml:Attention implementation in effect: AttentionImplementations.SPLIT_EINSUM
INFO:python_coreml_stable_diffusion.torch2coreml:Sample inputs spec: {'sample': (torch.Size([2, 4, 64, 64]), torch.float32), 'timestep': (torch.Size([2]), torch.float32), 'encoder_hidden_states': (torch.Size([2, 768, 1, 77]), torch.float32)}
INFO:python_coreml_stable_diffusion.torch2coreml:JIT tracing..
python_coreml_stable_diffusion/layer_norm.py:61: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
INFO:python_coreml_stable_diffusion.torch2coreml:Done.
INFO:python_coreml_stable_diffusion.torch2coreml:Converting unet to CoreML..
WARNING:coremltools:Tuple detected at graph output. This will be flattened in the converted model.
Converting PyTorch Frontend ==> MIL Ops: 0%| | 0/7876 [00:00<?, ? ops/s]WARNING:coremltools:Saving value type of int64 into a builtin type of int32, might lose precision!
Converting PyTorch Frontend ==> MIL Ops: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉| 7874/7876 [00:01<00:00, 5789.89 ops/s]
Running MIL Common passes: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 39/39 [00:07<00:00, 4.93 passes/s]
Running MIL FP16ComputePrecision pass: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:22<00:00, 22.21s/ passes]
Running MIL Clean up passes: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 11/11 [02:38<00:00, 14.43s/ passes]
INFO:python_coreml_stable_diffusion.torch2coreml:Saved unet model to /var/folders/jc/w_542bpx5m545pgd5y6_8yzc0000gn/T/Stable_Diffusion_version_elldrethSDreamMix_v10_unet.mlpackage
INFO:python_coreml_stable_diffusion.torch2coreml:Saved unet into /var/folders/jc/w_542bpx5m545pgd5y6_8yzc0000gn/T/Stable_Diffusion_version_elldrethSDreamMix_v10_unet.mlpackage
INFO:python_coreml_stable_diffusion.torch2coreml:Converted unet
INFO:python_coreml_stable_diffusion.torch2coreml:Converting text_encoder
INFO:python_coreml_stable_diffusion.torch2coreml:Sample inputs spec: {'input_ids': (torch.Size([1, 77]), torch.float32)}
INFO:python_coreml_stable_diffusion.torch2coreml:JIT tracing text_encoder..
transformers/models/clip/modeling_clip.py:280: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
transformers/models/clip/modeling_clip.py:288: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
if causal_attention_mask.size() != (bsz, 1, tgt_len, src_len):
transformers/models/clip/modeling_clip.py:320: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
INFO:python_coreml_stable_diffusion.torch2coreml:Done.
INFO:python_coreml_stable_diffusion.torch2coreml:Converting text_encoder to CoreML..
WARNING:coremltools:Tuple detected at graph output. This will be flattened in the converted model.
Converting PyTorch Frontend ==> MIL Ops: 98%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊ | 794/814 [00:00<00:00, 4135.97 ops/s]WARNING:coremltools:Saving value type of int64 into a builtin type of int32, might lose precision!
Converting PyTorch Frontend ==> MIL Ops: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋| 812/814 [00:00<00:00, 3978.24 ops/s]
Running MIL Common passes: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 39/39 [00:00<00:00, 97.36 passes/s]
Running MIL FP16ComputePrecision pass: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1.18 passes/s]
Running MIL Clean up passes: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 11/11 [00:02<00:00, 4.11 passes/s]
INFO:python_coreml_stable_diffusion.torch2coreml:Saved text_encoder model to /var/folders/jc/w_542bpx5m545pgd5y6_8yzc0000gn/T/Stable_Diffusion_version_elldrethSDreamMix_v10_text_encoder.mlpackage
INFO:python_coreml_stable_diffusion.torch2coreml:Saved text_encoder into /var/folders/jc/w_542bpx5m545pgd5y6_8yzc0000gn/T/Stable_Diffusion_version_elldrethSDreamMix_v10_text_encoder.mlpackage
INFO:python_coreml_stable_diffusion.torch2coreml:Converted text_encoder
INFO:python_coreml_stable_diffusion.torch2coreml:Converting safety_checker
INFO:python_coreml_stable_diffusion.torch2coreml:Sample inputs spec: {'clip_input': (torch.Size([1, 3, 224, 224]), torch.float32), 'images': (torch.Size([1, 512, 512, 3]), torch.float32), 'adjustment': (torch.Size([1]), torch.float32)}
INFO:python_coreml_stable_diffusion.torch2coreml:JIT tracing..
INFO:python_coreml_stable_diffusion.torch2coreml:Done.
INFO:python_coreml_stable_diffusion.torch2coreml:Converting safety_checker to CoreML..
WARNING:coremltools:Tuple detected at graph output. This will be flattened in the converted model.
Converting PyTorch Frontend ==> MIL Ops: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊| 1476/1478 [00:00<00:00, 4831.01 ops/s]
Running MIL Common passes: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 39/39 [00:01<00:00, 35.42 passes/s]
Running MIL FP16ComputePrecision pass: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:02<00:00, 2.24s/ passes]
Running MIL Clean up passes: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 11/11 [00:07<00:00, 1.43 passes/s]
INFO:python_coreml_stable_diffusion.torch2coreml:Saved safety_checker model to /var/folders/jc/w_542bpx5m545pgd5y6_8yzc0000gn/T/Stable_Diffusion_version_elldrethSDreamMix_v10_safety_checker.mlpackage
INFO:python_coreml_stable_diffusion.torch2coreml:Converted safety_checker
INFO:python_coreml_stable_diffusion.torch2coreml:Bundling resources for Guernika
INFO:python_coreml_stable_diffusion.torch2coreml:Created /Volumes/2TB SSD/@OUTPUT/elldrethSDreamMix_v10 for Guernika assets
INFO:python_coreml_stable_diffusion.torch2coreml:Compiling /var/folders/jc/w_542bpx5m545pgd5y6_8yzc0000gn/T/Stable_Diffusion_version_elldrethSDreamMix_v10_text_encoder.mlpackage
xcrun: error: unable to find utility "coremlcompiler", not a developer tool or in PATH

GuiyeC

Guernika org Jan 2, 2023

@radfaraf I found this which seems to be exactly your problem, it looks like you may need to install Xcode and run:

sudo xcode-select --switch /Applications/Xcode.app/Contents/Developer/

I'll check to see if there is someway to avoid this, but this does seem to be your problem.

radfaraf

Jan 2, 2023

Installed it will see if it works soon.

radfaraf

Jan 3, 2023

Strange some sort of permission error now:

Torch version 1.13.0 has not been tested with coremltools. You may run into unexpected errors. Torch 1.12.1 is the most recent version that has been tested.
2023-01-02 19:09:00.874 Guernika Model Converter[7095:92309] +[CATransaction synchronize] called within transaction
2023-01-02 19:09:01.139 Guernika Model Converter[7095:92309] +[CATransaction synchronize] called within transaction
2023-01-02 19:09:17.739 Guernika Model Converter[7095:92309] +[CATransaction synchronize] called within transaction
INFO:python_coreml_stable_diffusion.torch2coreml:Initializing StableDiffusionPipeline from /Volumes/2TB SSD/Stable-diffusion/wlop_1.ckpt..
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
100 1873 100 1873 0 0 29141 0 --:--:-- --:--:-- --:--:-- 30704
Some weights of the model checkpoint at openai/clip-vit-large-patch14 were not used when initializing CLIPTextModel: ['vision_model.encoder.layers.11.self_attn.v_proj.bias', 'vision_model.encoder.layers.19.self_attn.out_proj.bias', 'vision_model.encoder.layers.12.layer_norm2.bias', 'vision_model.encoder.layers.17.mlp.fc1.bias', 'vision_model.encoder.layers.18.self_attn.q_proj.bias', 'vision_model.encoder.layers.16.mlp.fc2.bias', 'vision_model.encoder.layers.18.self_attn.v_proj.bias', 'vision_model.encoder.layers.17.self_attn.v_proj.weight', 'vision_model.encoder.layers.1.mlp.fc1.weight', 'vision_model.encoder.layers.22.self_attn.k_proj.weight', 'vision_model.encoder.layers.3.mlp.fc1.bias', 'vision_model.encoder.layers.6.self_attn.k_proj.weight', 'vision_model.encoder.layers.18.layer_norm2.bias', 'vision_model.encoder.layers.6.self_attn.q_proj.weight', 'vision_model.encoder.layers.23.mlp.fc1.weight', 'vision_model.encoder.layers.8.layer_norm1.weight', 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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).
diffusers/utils/deprecation_utils.py:35: FutureWarning: The configuration file of this scheduler: PNDMScheduler {
"_class_name": "PNDMScheduler",
"_diffusers_version": "0.9.0",
"beta_end": 0.012,
"beta_schedule": "scaled_linear",
"beta_start": 0.00085,
"num_train_timesteps": 1000,
"set_alpha_to_one": false,
"skip_prk_steps": true,
"steps_offset": 0,
"trained_betas": null
}
is outdated. steps_offset should be set to 1 instead of 0. Please make sure to update the config accordingly as leaving steps_offset might led to incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for the scheduler/scheduler_config.json file
INFO:python_coreml_stable_diffusion.torch2coreml:Done.
INFO:python_coreml_stable_diffusion.torch2coreml:Converting vae_decoder
diffusers/models/resnet.py:109: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
diffusers/models/resnet.py:122: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
INFO:python_coreml_stable_diffusion.torch2coreml:Converting vae_decoder to CoreML..
Converting PyTorch Frontend ==> MIL Ops: 0%| | 0/353 [00:00<?, ? ops/s]WARNING:python_coreml_stable_diffusion.torch2coreml:Casted the beta(value=0.0) argument of baddbmm op from int32 to float32 dtype for conversion!
Converting PyTorch Frontend ==> MIL Ops: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌| 352/353 [00:00<00:00, 2087.75 ops/s]
Running MIL Common passes: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 39/39 [00:00<00:00, 60.37 passes/s]
Running MIL FP16ComputePrecision pass: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1.39 passes/s]
Running MIL Clean up passes: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 11/11 [00:02<00:00, 4.18 passes/s]
Torch version 1.13.0 has not been tested with coremltools. You may run into unexpected errors. Torch 1.12.1 is the most recent version that has been tested.
INFO:python_coreml_stable_diffusion.torch2coreml:Saved vae_decoder model to /var/folders/jc/w_542bpx5m545pgd5y6_8yzc0000gn/T/Stable_Diffusion_version_wlop_1_vae_decoder.mlpackage
INFO:python_coreml_stable_diffusion.torch2coreml:Saved vae_decoder into /var/folders/jc/w_542bpx5m545pgd5y6_8yzc0000gn/T/Stable_Diffusion_version_wlop_1_vae_decoder.mlpackage
INFO:python_coreml_stable_diffusion.torch2coreml:Converted vae_decoder
INFO:python_coreml_stable_diffusion.torch2coreml:Converting vae_encoder
diffusers/models/resnet.py:182: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
diffusers/models/resnet.py:187: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
INFO:python_coreml_stable_diffusion.torch2coreml:Converting vae_encoder to CoreML..
Converting PyTorch Frontend ==> MIL Ops: 0%| | 0/281 [00:00<?, ? ops/s]WARNING:python_coreml_stable_diffusion.torch2coreml:Casted the beta(value=0.0) argument of baddbmm op from int32 to float32 dtype for conversion!
Converting PyTorch Frontend ==> MIL Ops: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍| 280/281 [00:00<00:00, 3187.36 ops/s]
Running MIL Common passes: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 39/39 [00:00<00:00, 131.09 passes/s]
Running MIL FP16ComputePrecision pass: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 3.09 passes/s]
Running MIL Clean up passes: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 11/11 [00:01<00:00, 9.61 passes/s]
INFO:python_coreml_stable_diffusion.torch2coreml:Saved vae_encoder model to /var/folders/jc/w_542bpx5m545pgd5y6_8yzc0000gn/T/Stable_Diffusion_version_wlop_1_vae_encoder.mlpackage
INFO:python_coreml_stable_diffusion.torch2coreml:Saved vae_encoder into /var/folders/jc/w_542bpx5m545pgd5y6_8yzc0000gn/T/Stable_Diffusion_version_wlop_1_vae_encoder.mlpackage
INFO:python_coreml_stable_diffusion.torch2coreml:Converted vae_encoder
INFO:python_coreml_stable_diffusion.torch2coreml:Converting unet
INFO:python_coreml_stable_diffusion.torch2coreml:Attention implementation in effect: AttentionImplementations.ORIGINAL
INFO:python_coreml_stable_diffusion.torch2coreml:Sample inputs spec: {'sample': (torch.Size([2, 4, 64, 64]), torch.float32), 'timestep': (torch.Size([2]), torch.float32), 'encoder_hidden_states': (torch.Size([2, 768, 1, 77]), torch.float32)}
INFO:python_coreml_stable_diffusion.torch2coreml:JIT tracing..
python_coreml_stable_diffusion/layer_norm.py:61: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
INFO:python_coreml_stable_diffusion.torch2coreml:Done.
INFO:python_coreml_stable_diffusion.torch2coreml:Converting unet to CoreML..
WARNING:coremltools:Tuple detected at graph output. This will be flattened in the converted model.
Converting PyTorch Frontend ==> MIL Ops: 0%| | 0/3400 [00:00<?, ? ops/s]WARNING:coremltools:Saving value type of int64 into a builtin type of int32, might lose precision!
Converting PyTorch Frontend ==> MIL Ops: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉| 3398/3400 [00:01<00:00, 3238.73 ops/s]
Running MIL Common passes: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 39/39 [00:10<00:00, 3.61 passes/s]
Running MIL FP16ComputePrecision pass: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:27<00:00, 27.12s/ passes]
Running MIL Clean up passes: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 11/11 [01:52<00:00, 10.24s/ passes]
INFO:python_coreml_stable_diffusion.torch2coreml:Saved unet model to /var/folders/jc/w_542bpx5m545pgd5y6_8yzc0000gn/T/Stable_Diffusion_version_wlop_1_unet.mlpackage
INFO:python_coreml_stable_diffusion.torch2coreml:Saved unet into /var/folders/jc/w_542bpx5m545pgd5y6_8yzc0000gn/T/Stable_Diffusion_version_wlop_1_unet.mlpackage
INFO:python_coreml_stable_diffusion.torch2coreml:Converted unet
INFO:python_coreml_stable_diffusion.torch2coreml:Converting text_encoder
INFO:python_coreml_stable_diffusion.torch2coreml:Sample inputs spec: {'input_ids': (torch.Size([1, 77]), torch.float32)}
INFO:python_coreml_stable_diffusion.torch2coreml:JIT tracing text_encoder..
transformers/models/clip/modeling_clip.py:280: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
transformers/models/clip/modeling_clip.py:288: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
if causal_attention_mask.size() != (bsz, 1, tgt_len, src_len):
transformers/models/clip/modeling_clip.py:320: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
INFO:python_coreml_stable_diffusion.torch2coreml:Done.
INFO:python_coreml_stable_diffusion.torch2coreml:Converting text_encoder to CoreML..
WARNING:coremltools:Tuple detected at graph output. This will be flattened in the converted model.
Converting PyTorch Frontend ==> MIL Ops: 91%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍ | 737/814 [00:00<00:00, 3566.20 ops/s]WARNING:coremltools:Saving value type of int64 into a builtin type of int32, might lose precision!
Converting PyTorch Frontend ==> MIL Ops: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋| 812/814 [00:00<00:00, 3551.48 ops/s]
Running MIL Common passes: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 39/39 [00:00<00:00, 71.28 passes/s]
Running MIL FP16ComputePrecision pass: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:01<00:00, 1.18s/ passes]
Running MIL Clean up passes: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 11/11 [00:03<00:00, 3.40 passes/s]
INFO:python_coreml_stable_diffusion.torch2coreml:Saved text_encoder model to /var/folders/jc/w_542bpx5m545pgd5y6_8yzc0000gn/T/Stable_Diffusion_version_wlop_1_text_encoder.mlpackage
INFO:python_coreml_stable_diffusion.torch2coreml:Saved text_encoder into /var/folders/jc/w_542bpx5m545pgd5y6_8yzc0000gn/T/Stable_Diffusion_version_wlop_1_text_encoder.mlpackage
INFO:python_coreml_stable_diffusion.torch2coreml:Converted text_encoder
INFO:python_coreml_stable_diffusion.torch2coreml:Converting safety_checker
INFO:python_coreml_stable_diffusion.torch2coreml:Sample inputs spec: {'clip_input': (torch.Size([1, 3, 224, 224]), torch.float32), 'images': (torch.Size([1, 512, 512, 3]), torch.float32), 'adjustment': (torch.Size([1]), torch.float32)}
INFO:python_coreml_stable_diffusion.torch2coreml:JIT tracing..
INFO:python_coreml_stable_diffusion.torch2coreml:Done.
INFO:python_coreml_stable_diffusion.torch2coreml:Converting safety_checker to CoreML..
WARNING:coremltools:Tuple detected at graph output. This will be flattened in the converted model.
Converting PyTorch Frontend ==> MIL Ops: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊| 1476/1478 [00:00<00:00, 4415.21 ops/s]
Running MIL Common passes: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 39/39 [00:01<00:00, 32.64 passes/s]
Running MIL FP16ComputePrecision pass: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:02<00:00, 2.61s/ passes]
Running MIL Clean up passes: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 11/11 [00:07<00:00, 1.40 passes/s]
INFO:python_coreml_stable_diffusion.torch2coreml:Saved safety_checker model to /var/folders/jc/w_542bpx5m545pgd5y6_8yzc0000gn/T/Stable_Diffusion_version_wlop_1_safety_checker.mlpackage
INFO:python_coreml_stable_diffusion.torch2coreml:Converted safety_checker
INFO:python_coreml_stable_diffusion.torch2coreml:Bundling resources for Guernika
INFO:python_coreml_stable_diffusion.torch2coreml:Compiling /var/folders/jc/w_542bpx5m545pgd5y6_8yzc0000gn/T/Stable_Diffusion_version_wlop_1_text_encoder.mlpackage
2023-01-02 19:25:36.085 coremlcompiler[8736:112819] error creating directory: Error Domain=NSCocoaErrorDomain Code=513 "You don’t have permission to save the file “Stable_Diffusion_version_wlop_1_text_encoder.mlmodelc” in the folder “2TB”." UserInfo={NSFilePath=/Volumes/2TB/Stable_Diffusion_version_wlop_1_text_encoder.mlmodelc, NSUnderlyingError=0x6000038b4000 {Error Domain=NSPOSIXErrorDomain Code=13 "Permission denied"}}
coremlcompiler: error: You don‚Äôt have permission to save the file ‚ÄúStable_Diffusion_version_wlop_1_text_encoder.mlmodelc‚Äù in the folder ‚Äú2TB‚Äù.: unspecified iostream_category error

radfaraf

Jan 3, 2023

Tried one more time and same error, then I changed the output to my internal drive instead of my thunberbolt SSD and it worked to write the output!

GuiyeC

Guernika org Jan 3, 2023

@radfaraf thank you for all the information! I'm adding a check to make sure Xcode is installed, maybe I can do something about the writing permissions too 🤔
Did you ever run sudo xcode-select --switch /Applications/Xcode.app/Contents/Developer/ or you did not have to do it?

radfaraf

Jan 3, 2023

I didn't have xcode installed at all, so first I installed it. Then I did sudo xcode-select --switch /Applications/Xcode.app/Contents/Developer/ just to be safe from the instruction, so I don't know if it's really needed or not. Then I ran into an issue where it would run and soon after halt because it needed me to accept the license for Xcode. It would show up only in the console and it's possible I only saw it because I used your instructions to launch it where it shows everything in the terminal console. Once I accepted that I had the write error, and once I switched to internal drive it worked. Already converted both those files and tested that they work.

GuiyeC

Guernika org Jan 3, 2023

Well, I'll add a message suggesting to run that just in case then, I'm glad this was helpful. Enjoy!

radfaraf

Jan 3, 2023

add this step to the instruction for those who do a new xcode install as it will help accept the license and won't work without it:

sudo xcodebuild -license accept

radfaraf changed discussion status to closed Jan 3, 2023

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