CompVis

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stable-diffusion-v1-4

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CompVis 1,111

same issue to you

same issue to you

same issue

same issue!

Same issue

same here too.

in
3
4 # make sure you're logged in with huggingface-cli login
----> 5 pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", revision="fp16", torch_dtype=torch.float16)

/usr/local/lib/python3.8/dist-packages/diffusers/pipeline_utils.py in from_pretrained(cls, pretrained_model_name_or_path, **kwargs)
453 load_method_name = importable_classes[class_name][1]
454
--> 455 load_method = getattr(class_obj, load_method_name)
456
457 loading_kwargs = {}

TypeError: getattr(): attribute name must be string

I had the issue. I updated the version of the diffusers to 0.8.0 instead of 0.3.0 and it works now.

thanks phoenire
I was running 0.4.0. I changed it to 0.8.0
it seems to have changed the error a bit but the issue remains:

TypeError Traceback (most recent call last)
in
3
4 # make sure you're logged in with huggingface-cli login
----> 5 pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", revision="fp16", torch_dtype=torch.float16)

/usr/local/lib/python3.8/dist-packages/diffusers/pipeline_utils.py in from_pretrained(cls, pretrained_model_name_or_path, **kwargs)
453
454 if custom_pipeline is not None:
--> 455 allow_patterns += [CUSTOM_PIPELINE_FILE_NAME]
456
457 if cls != DiffusionPipeline:

TypeError: getattr(): attribute name must be string

same here, after updateing diffusers i'm getting the following now:

Traceback (most recent call last):
File "E:\stable_diffusion_amd\diffusers-dml\examples\inference\dml_onnx.py", line 206, in
pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", scheduler=lms, use_auth_token=True)
File "E:\stable_diffusion_amd\amd_venv\lib\site-packages\diffusers\pipeline_utils.py", line 679, in from_pretrained
model = pipeline_class(**init_kwargs)
File "E:\stable_diffusion_amd\diffusers-dml\examples\inference\dml_onnx.py", line 28, in init
scheduler = scheduler.set_format(format)
AttributeError: 'LMSDiscreteScheduler' object has no attribute 'set_format'

running with onyx on Windows/AMD

UPDATE: I reseted the runtime environment and ran it at 0.8.0 and it worked

I had the issue. I updated the version of the diffusers to 0.8.0 instead of 0.3.0 and it works now.

This works for me too!

Could you please share a notebook withe the 0.8.0 updated and running here
I tried to replace it and the code keep having the error
ypeError: getattr(): attribute name must be string

Same issue; where is 0.8.0 located?

Restart the Runtime and update the version of diffusion to 0.8.0, it works!

I am not getting that error after running:
!pip install --upgrade diffusers transformers scipy

READ SETUP of StableDiffusionPipeline
in the "SETUP #2" just under the name of the google GPU you connect with you will see:
!pip install diffusers==0.4.0 highlight the "4" and change it to - !pip install diffusers==0.8.0

edit: you probably will need to disconnect and rerun

This getattr() error is caused by the latest update on the transformers package (4.25.1/4.25.0: Dec.2, 2022). To fix it, simply downgrade the transformers package to an earlier version, e.g., pip install transformers==4.24.0, then everything goes fine.

I had the issue. I updated the version of the diffusers to 0.8.0 instead of 0.3.0 and it works now.

This worked for me as well

I had the issue. I updated the version of the diffusers to 0.8.0 instead of 0.3.0 and it works now.

ur a day saver @hargup , appreciated : )

I am not getting that error after running:
!pip install --upgrade diffusers transformers scipy

this worked

This getattr() error is caused by the latest update on the transformers package (4.25.1/4.25.0: Dec.2, 2022). To fix it, simply downgrade the transformers package to an earlier version, e.g., pip install transformers==4.24.0, then everything goes fine.

when a guide provides a statement like: "with a simple _________" you know life as you know is forever changed.

Now 0.8.0 is having the same type error

0.9.0 works

0.9.0 works!

I reseted the runtime environment and 0.9.0 works ~

Now it doesn't require me to log in..... which is odd. How do I make sure I am logged in with my token so that there is a record that the creations I make are a result of my prompt engineering?

It's not working for me either--setting the diffuser to 0.7.0, 0.8.0, or 0.9.0 is unsuccessful; setting the transformers to an earlier version is unsuccessful. Running !pip install --upgrade diffusers transformers scipy results in both of those being replaced with later versions, but no positive effect.

Traceback (most recent call last):
File "C:\Users\Fiojja\stable-diffusion-amd\diffusers\examples\inference\dml_onnx.py", line 206, in
pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", scheduler=lms, use_auth_token=True)
File "C:\Python310\lib\site-packages\diffusers\pipeline_utils.py", line 730, in from_pretrained
model = pipeline_class(**init_kwargs)
File "C:\Users\Fiojja\stable-diffusion-amd\diffusers\examples\inference\dml_onnx.py", line 28, in init
scheduler = scheduler.set_format(format)
AttributeError: 'LMSDiscreteScheduler' object has no attribute 'set_format'

The newest is 0.10.0

0.9.0 stopped working for me as well, but 0.10.0 works

The newest transformers has a bug as fyviezhao said, so download transformers==4.24.0

This getattr() error is caused by the latest update on the transformers package (4.25.1/4.25.0: Dec.2, 2022). To fix it, simply downgrade the transformers package to an earlier version, e.g., pip install transformers==4.24.0, then everything goes fine.

when a guide provides a statement like: "with a simple _________" you know life as you know is forever changed.

Perfect

This worked for me i think its the order of installation
put transformers last, in other orders i had the error too

!pip install diffusers==0.3.0
!pip install scipy ftfy
!pip install transformers==4.24.0
!pip install "ipywidgets>=7,<8"

This worked for me i think its the order of installation
put transformers last, in other orders i had the error too

!pip install diffusers==0.3.0
!pip install scipy ftfy
!pip install transformers==4.24.0
!pip install "ipywidgets>=7,<8"

Thanks , It works

I am not getting that error after running:
!pip install --upgrade diffusers transformers scipy

this almost worked then this popped up:
what popped up before that was it crying that i had to install "accelerate" so I did then this popped up I don't want to reinstall again ;\

(amd_venv) e:\ai\diffusers-dml\examples\inference>python dml_onnx.py
Fetching 16 files: 100%|███████████████████████████████████████████████████████████████████████| 16/16 [00:00<?, ?it/s]
text_config_dict is provided which will be used to initialize CLIPTextConfig. The value text_config["id2label"] will be overriden.
e:\ai\amd_venv\lib\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(
Traceback (most recent call last):
File "e:\ai\diffusers-dml\examples\inference\dml_onnx.py", line 206, in
pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", scheduler=lms, use_auth_token=True)
File "e:\ai\amd_venv\lib\site-packages\diffusers\pipelines\pipeline_utils.py", line 965, in from_pretrained
model = pipeline_class(**init_kwargs)
File "e:\ai\diffusers-dml\examples\inference\dml_onnx.py", line 28, in init
scheduler = scheduler.set_format(format)
AttributeError: 'LMSDiscreteScheduler' object has no attribute 'set_format'

small edit: I did a full reinstall and tried "pip install --upgrade diffusers transformers scipy ftfy" (i added ftfy after trying it once) but now im getting this:

(amd_venv) E:\ai\diffusers-dml\diffusers-dml\examples\inference>python save_onnx.py
Some weights of the model checkpoint at openai/clip-vit-large-patch14 were not used when initializing CLIPTextModel: ['vision_model.encoder.layers.7.mlp.fc1.weight', 'vision_model.encoder.layers.5.layer_norm2.bias', 'vision_model.encoder.layers.15.self_attn.v_proj.weight', 'vision_model.encoder.layers.20.layer_norm1.bias', 'vision_model.encoder.layers.21.self_attn.v_proj.bias', 'vision_model.encoder.layers.5.self_attn.out_proj.weight', 'vision_model.encoder.layers.15.mlp.fc1.weight', 'vision_model.encoder.layers.6.self_attn.out_proj.bias', 'vision_model.encoder.layers.2.layer_norm2.weight', 'vision_model.encoder.layers.1.mlp.fc1.bias', 'vision_model.encoder.layers.1.self_attn.v_proj.weight', 'vision_model.encoder.layers.7.mlp.fc1.bias', 'vision_model.encoder.layers.8.self_attn.v_proj.weight', 'vision_model.encoder.layers.12.layer_norm1.bias', 'vision_model.encoder.layers.11.self_attn.out_proj.weight', 'vision_model.encoder.layers.1.self_attn.out_proj.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).
  Fetching 16 files: 100%|███████████████████████████████████████████████████████████████████████| 16/16 [00:00<?, ?it/s]
  Cannot initialize model with low cpu memory usage because accelerate was not found in the environment. Defaulting to low_cpu_mem_usage=False. It is strongly recommended to install accelerate for faster and less memory-intense model loading. You can do so with:

pip install accelerate

.
text_config_dict is provided which will be used to initialize CLIPTextConfig. The value text_config["id2label"] will be overriden.
E:\ai\amd_venv\lib\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(
E:\ai\amd_venv\lib\site-packages\diffusers\models\unet_2d_condition.py:526: 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 any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):
E:\ai\amd_venv\lib\site-packages\diffusers\models\resnet.py:185: 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
E:\ai\amd_venv\lib\site-packages\diffusers\models\resnet.py:190: 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
E:\ai\amd_venv\lib\site-packages\diffusers\models\resnet.py:112: 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
E:\ai\amd_venv\lib\site-packages\diffusers\models\resnet.py:125: 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:
Traceback (most recent call last):
File "E:\ai\diffusers-dml\diffusers-dml\examples\inference\save_onnx.py", line 66, in
convert_to_onnx(pipe.unet, pipe.vae.post_quant_conv, pipe.vae.decoder, text_encoder, height=512, width=512)
File "E:\ai\diffusers-dml\diffusers-dml\examples\inference\save_onnx.py", line 41, in convert_to_onnx
traced_model = torch.jit.trace(unet, check_inputs[0], check_inputs=[check_inputs[1]], strict=True)
File "E:\ai\amd_venv\lib\site-packages\torch\jit_trace.py", line 794, in trace
return trace_module(
File "E:\ai\amd_venv\lib\site-packages\torch\jit_trace.py", line 1056, in trace_module
module._c._create_method_from_trace(
RuntimeError: Encountering a dict at the output of the tracer might cause the trace to be incorrect, this is only valid if the container structure does not change based on the module's inputs. Consider using a constant container instead (e.g. for list, use a tuple instead. for dict, use a NamedTuple instead). If you absolutely need this and know the side effects, pass strict=False to trace() to allow this behavior.

I'm sorry if this is long but most of it can be ignored as far as I know when it talks about "some weights were not used"

I got the error with Diffuser == 0.4.0 and these command help to solve problem:
pip install --upgrade diffusers transformers scipy
=> restart Runtime
Work!