Obtaining file:///Users/user/Documents/ml-stable-diffusion
  Preparing metadata (setup.py) ... done
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Installing collected packages: python-coreml-stable-diffusion
  Attempting uninstall: python-coreml-stable-diffusion
    Found existing installation: python-coreml-stable-diffusion 1.0.0
    Uninstalling python-coreml-stable-diffusion-1.0.0:
      Successfully uninstalled python-coreml-stable-diffusion-1.0.0
  Running setup.py develop for python-coreml-stable-diffusion
Successfully installed python-coreml-stable-diffusion-1.0.0
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--------------------------------------------------------------------------------
SD to Core ML script by φ Zabriskije
--------------------------------------------------------------------------------

Enter model name: v1-5-pruned
--------------------------------------------------------------------------------
SD to Core ML script by φ Zabriskije
--------------------------------------------------------------------------------

Model: v1-5-pruned

1) CKPT → All
2) CKPT → Diffusers
3) SafeTensors → All
4) SafeTensors → Diffusers
5) Diffusers → ORIGINAL
6) Diffusers → ORIGINAL 512x768
7) Diffusers → ORIGINAL 768x512
8) Diffusers → SPLIT_EINSUM
9) Change model name

Pick a number: 3

[1/5] Converting v1-5-pruned to Diffusers...

Checkpoint v1-5-pruned.safetensors has both EMA and non-EMA weights.
In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag.
`text_config_dict` is provided which will be used to initialize `CLIPTextConfig`. The value `text_config["id2label"]` will be overriden.
`text_config_dict` is provided which will be used to initialize `CLIPTextConfig`. The value `text_config["bos_token_id"]` will be overriden.
`text_config_dict` is provided which will be used to initialize `CLIPTextConfig`. The value `text_config["eos_token_id"]` will be overriden.
/Users/user/miniconda3/envs/coreml_stable_diffusion/lib/python3.8/site-packages/transformers/models/clip/feature_extraction_clip.py:28: FutureWarning: The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please use CLIPImageProcessor instead.
  warnings.warn(

[1/5] Conversion of v1-5-pruned to Diffusers completed in 0min and 25s

[2/5] Converting v1-5-pruned to ORIGINAL...

scikit-learn version 1.3.0 is not supported. Minimum required version: 0.17. Maximum required version: 1.1.2. Disabling scikit-learn conversion API.
Torch version 2.0.1 has not been tested with coremltools. You may run into unexpected errors. Torch 2.0.0 is the most recent version that has been tested.
INFO:__main__:Initializing StableDiffusionPipeline with v1-5-pruned_diffusers..
Loading pipeline components...:  29%|███▋         | 2/7 [00:00<00:00, 11.39it/s]/Users/user/miniconda3/envs/coreml_stable_diffusion/lib/python3.8/site-packages/transformers/models/clip/feature_extraction_clip.py:28: FutureWarning: The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please use CLIPImageProcessor instead.
  warnings.warn(
Loading pipeline components...: 100%|█████████████| 7/7 [00:03<00:00,  1.81it/s]
INFO:__main__:Done.
INFO:__main__:Attention implementation in effect: AttentionImplementations.ORIGINAL
INFO:__main__:Converting vae_decoder
/Users/user/miniconda3/envs/coreml_stable_diffusion/lib/python3.8/site-packages/diffusers/models/resnet.py:139: 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!
  assert hidden_states.shape[1] == self.channels
/Users/user/miniconda3/envs/coreml_stable_diffusion/lib/python3.8/site-packages/diffusers/models/resnet.py:152: 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 hidden_states.shape[0] >= 64:
INFO:__main__:Converting vae_decoder to CoreML..
Converting PyTorch Frontend ==> MIL Ops: 100%|▉| 368/369 [00:00<00:00, 4158.05 o
Running MIL frontend_pytorch pipeline: 100%|█| 5/5 [00:00<00:00, 385.05 passes/s
Running MIL default pipeline: 100%|████████| 64/64 [00:03<00:00, 16.62 passes/s]
Running MIL backend_mlprogram pipeline: 100%|█| 11/11 [00:00<00:00, 598.44 passe
INFO:__main__:Saved vae_decoder model to v1-5-pruned_original/Stable_Diffusion_version_v1-5-pruned_diffusers_vae_decoder.mlpackage
INFO:__main__:Saved vae_decoder into v1-5-pruned_original/Stable_Diffusion_version_v1-5-pruned_diffusers_vae_decoder.mlpackage
INFO:__main__:Converted vae_decoder
INFO:__main__:Converting vae_encoder
/Users/user/miniconda3/envs/coreml_stable_diffusion/lib/python3.8/site-packages/diffusers/models/resnet.py:215: 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!
  assert hidden_states.shape[1] == self.channels
/Users/user/miniconda3/envs/coreml_stable_diffusion/lib/python3.8/site-packages/diffusers/models/resnet.py:220: 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!
  assert hidden_states.shape[1] == self.channels
INFO:__main__:Converting vae_encoder to CoreML..
Converting PyTorch Frontend ==> MIL Ops: 100%|▉| 295/296 [00:00<00:00, 4919.86 o
Running MIL frontend_pytorch pipeline: 100%|█| 5/5 [00:00<00:00, 563.11 passes/s
Running MIL default pipeline: 100%|████████| 64/64 [00:02<00:00, 27.19 passes/s]
Running MIL backend_mlprogram pipeline: 100%|█| 11/11 [00:00<00:00, 784.46 passe
INFO:__main__:Saved vae_encoder model to v1-5-pruned_original/Stable_Diffusion_version_v1-5-pruned_diffusers_vae_encoder.mlpackage
INFO:__main__:Saved vae_encoder into v1-5-pruned_original/Stable_Diffusion_version_v1-5-pruned_diffusers_vae_encoder.mlpackage
INFO:__main__:Converted vae_encoder
INFO:__main__:Converting unet
/Users/user/Documents/ml-stable-diffusion/python_coreml_stable_diffusion/torch2coreml.py:679: FutureWarning: Accessing config attribute `cross_attention_dim` directly via 'UNet2DConditionModel' object attribute is deprecated. Please access 'cross_attention_dim' over 'UNet2DConditionModel's config object instead, e.g. 'unet.config.cross_attention_dim'.
  args.text_encoder_hidden_size or pipe.unet.cross_attention_dim or pipe.text_encoder.config.hidden_size,
INFO:__main__:Sample UNet 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:__main__:JIT tracing..
/Users/user/Documents/ml-stable-diffusion/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!
  assert inputs.size(1) == self.num_channels
INFO:__main__:Done.
INFO:__main__: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 [00:00<00:00, 4737.51
Running MIL frontend_pytorch pipeline: 100%|█| 5/5 [00:00<00:00, 24.04 passes/s]
Running MIL default pipeline: 100%|████████| 64/64 [01:12<00:00,  1.14s/ passes]
Running MIL backend_mlprogram pipeline: 100%|█| 11/11 [00:00<00:00, 110.07 passe
INFO:__main__:Saved unet model to v1-5-pruned_original/Stable_Diffusion_version_v1-5-pruned_diffusers_unet.mlpackage
INFO:__main__:Saved unet into v1-5-pruned_original/Stable_Diffusion_version_v1-5-pruned_diffusers_unet.mlpackage
INFO:__main__:Converted unet
INFO:__main__:Converting text_encoder
INFO:__main__:Sample inputs spec: {'input_ids': (torch.Size([1, 77]), torch.float32)}
INFO:__main__:JIT tracing text_encoder..
/Users/user/Documents/ml-stable-diffusion/python_coreml_stable_diffusion/torch2coreml.py:282: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
  mask = torch.full((tgt_len, tgt_len), torch.tensor(-1e4, device=device), device=device)
/Users/user/miniconda3/envs/coreml_stable_diffusion/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:287: 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):
/Users/user/miniconda3/envs/coreml_stable_diffusion/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:295: 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):
/Users/user/miniconda3/envs/coreml_stable_diffusion/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:327: 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:__main__:Done.
INFO:__main__: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:   0%|        | 0/833 [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:   5%| | 41/833 [00:00<00:00, 7818.08 op
Traceback (most recent call last):
  File "/Users/user/miniconda3/envs/coreml_stable_diffusion/lib/python3.8/runpy.py", line 194, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "/Users/user/miniconda3/envs/coreml_stable_diffusion/lib/python3.8/runpy.py", line 87, in _run_code
    exec(code, run_globals)
  File "/Users/user/Documents/ml-stable-diffusion/python_coreml_stable_diffusion/torch2coreml.py", line 1427, in <module>
    main(args)
  File "/Users/user/Documents/ml-stable-diffusion/python_coreml_stable_diffusion/torch2coreml.py", line 1272, in main
    convert_text_encoder(pipe.text_encoder, pipe.tokenizer, "text_encoder", args)
  File "/Users/user/Documents/ml-stable-diffusion/python_coreml_stable_diffusion/torch2coreml.py", line 356, in convert_text_encoder
    coreml_text_encoder, out_path = _convert_to_coreml(
  File "/Users/user/Documents/ml-stable-diffusion/python_coreml_stable_diffusion/torch2coreml.py", line 122, in _convert_to_coreml
    coreml_model = ct.convert(
  File "/Users/user/miniconda3/envs/coreml_stable_diffusion/lib/python3.8/site-packages/coremltools/converters/_converters_entry.py", line 530, in convert
    mlmodel = mil_convert(
  File "/Users/user/miniconda3/envs/coreml_stable_diffusion/lib/python3.8/site-packages/coremltools/converters/mil/converter.py", line 188, in mil_convert
    return _mil_convert(model, convert_from, convert_to, ConverterRegistry, MLModel, compute_units, **kwargs)
  File "/Users/user/miniconda3/envs/coreml_stable_diffusion/lib/python3.8/site-packages/coremltools/converters/mil/converter.py", line 212, in _mil_convert
    proto, mil_program = mil_convert_to_proto(
  File "/Users/user/miniconda3/envs/coreml_stable_diffusion/lib/python3.8/site-packages/coremltools/converters/mil/converter.py", line 286, in mil_convert_to_proto
    prog = frontend_converter(model, **kwargs)
  File "/Users/user/miniconda3/envs/coreml_stable_diffusion/lib/python3.8/site-packages/coremltools/converters/mil/converter.py", line 108, in __call__
    return load(*args, **kwargs)
  File "/Users/user/miniconda3/envs/coreml_stable_diffusion/lib/python3.8/site-packages/coremltools/converters/mil/frontend/torch/load.py", line 63, in load
    return _perform_torch_convert(converter, debug)
  File "/Users/user/miniconda3/envs/coreml_stable_diffusion/lib/python3.8/site-packages/coremltools/converters/mil/frontend/torch/load.py", line 102, in _perform_torch_convert
    prog = converter.convert()
  File "/Users/user/miniconda3/envs/coreml_stable_diffusion/lib/python3.8/site-packages/coremltools/converters/mil/frontend/torch/converter.py", line 439, in convert
    convert_nodes(self.context, self.graph)
  File "/Users/user/miniconda3/envs/coreml_stable_diffusion/lib/python3.8/site-packages/coremltools/converters/mil/frontend/torch/ops.py", line 92, in convert_nodes
    add_op(context, node)
  File "/Users/user/miniconda3/envs/coreml_stable_diffusion/lib/python3.8/site-packages/coremltools/converters/mil/frontend/torch/ops.py", line 4216, in masked_fill
    res = mb.select(cond=mask, a=value, b=x, name=node.name)
  File "/Users/user/miniconda3/envs/coreml_stable_diffusion/lib/python3.8/site-packages/coremltools/converters/mil/mil/ops/registry.py", line 183, in add_op
    return cls._add_op(op_cls_to_add, **kwargs)
  File "/Users/user/miniconda3/envs/coreml_stable_diffusion/lib/python3.8/site-packages/coremltools/converters/mil/mil/builder.py", line 166, in _add_op
    new_op = op_cls(**kwargs)
  File "/Users/user/miniconda3/envs/coreml_stable_diffusion/lib/python3.8/site-packages/coremltools/converters/mil/mil/operation.py", line 187, in __init__
    self._validate_and_set_inputs(input_kv)
  File "/Users/user/miniconda3/envs/coreml_stable_diffusion/lib/python3.8/site-packages/coremltools/converters/mil/mil/operation.py", line 495, in _validate_and_set_inputs
    self.input_spec.validate_inputs(self.name, self.op_type, input_kvs)
  File "/Users/user/miniconda3/envs/coreml_stable_diffusion/lib/python3.8/site-packages/coremltools/converters/mil/mil/input_type.py", line 137, in validate_inputs
    raise ValueError(msg)
ValueError: In op, of type select, named mask.3, the named input `b` must have the same data type as the named input `a`. However, b has dtype fp32 whereas a has dtype int32.