TypeError: SparseMoeBlock.forward() missing 1 required positional argument: 'scale' about segmoe HOT 5 CLOSED

bin C:\Users\Nagol\AppData\Local\Programs\Python\Python310\lib\site-packages\bitsandbytes\libbitsandbytes_cuda121.dll

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TypeError                                 Traceback (most recent call last)
Cell In[2], line 7
      5 prompt = "cosmic canvas, orange city background, painting of a chubby cat"
      6 negative_prompt = "nsfw, bad quality, worse quality"
----> 7 img = pipeline(
      8     prompt=prompt,
      9     negative_prompt=negative_prompt,
     10     height=1024,
     11     width=1024,
     12     num_inference_steps=25,
     13     guidance_scale=7.5,
     14 ).images[0]
     15 img

File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\segmoe\main.py:866, in SegMoEPipeline.__call__(self, *args, **kwds)
    860 """
    861 Inference the SegMoEPipeline.
    862 
    863 Calls diffusers.DiffusionPipeline forward with the keyword arguments. See https://github.com/segmind/segmoe#usage for detailed usage.
    864 """
    865 kwds["scale"]=7.5
--> 866 return self.pipe(*args, **kwds)

File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\torch\utils\_contextlib.py:115, in context_decorator.<locals>.decorate_context(*args, **kwargs)
    112 @functools.wraps(func)
    113 def decorate_context(*args, **kwargs):
    114     with ctx_factory():
--> 115         return func(*args, **kwargs)

File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\diffusers\pipelines\stable_diffusion\pipeline_stable_diffusion.py:1027, in StableDiffusionPipeline.__call__(self, prompt, height, width, num_inference_steps, timesteps, guidance_scale, negative_prompt, num_images_per_prompt, eta, generator, latents, prompt_embeds, negative_prompt_embeds, ip_adapter_image, output_type, return_dict, cross_attention_kwargs, guidance_rescale, clip_skip, callback_on_step_end, callback_on_step_end_tensor_inputs, **kwargs)
   1024 latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
   1026 # predict the noise residual
-> 1027 noise_pred = self.unet(
   1028     latent_model_input,
   1029     t,
   1030     encoder_hidden_states=prompt_embeds,
   1031     timestep_cond=timestep_cond,
   1032     cross_attention_kwargs=self.cross_attention_kwargs,
   1033     added_cond_kwargs=added_cond_kwargs,
   1034     return_dict=False,
   1035 )[0]
   1037 # perform guidance
   1038 if self.do_classifier_free_guidance:

File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\torch\nn\modules\module.py:1518, in Module._wrapped_call_impl(self, *args, **kwargs)
   1516     return self._compiled_call_impl(*args, **kwargs)  # type: ignore[misc]
   1517 else:
-> 1518     return self._call_impl(*args, **kwargs)

File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\torch\nn\modules\module.py:1527, in Module._call_impl(self, *args, **kwargs)
   1522 # If we don't have any hooks, we want to skip the rest of the logic in
   1523 # this function, and just call forward.
   1524 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
   1525         or _global_backward_pre_hooks or _global_backward_hooks
   1526         or _global_forward_hooks or _global_forward_pre_hooks):
-> 1527     return forward_call(*args, **kwargs)
   1529 try:
   1530     result = None

File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\diffusers\models\unets\unet_2d_condition.py:1121, in UNet2DConditionModel.forward(self, sample, timestep, encoder_hidden_states, class_labels, timestep_cond, attention_mask, cross_attention_kwargs, added_cond_kwargs, down_block_additional_residuals, mid_block_additional_residual, down_intrablock_additional_residuals, encoder_attention_mask, return_dict)
   1118     if is_adapter and len(down_intrablock_additional_residuals) > 0:
   1119         additional_residuals["additional_residuals"] = down_intrablock_additional_residuals.pop(0)
-> 1121     sample, res_samples = downsample_block(
   1122         hidden_states=sample,
   1123         temb=emb,
   1124         encoder_hidden_states=encoder_hidden_states,
   1125         attention_mask=attention_mask,
   1126         cross_attention_kwargs=cross_attention_kwargs,
   1127         encoder_attention_mask=encoder_attention_mask,
   1128         **additional_residuals,
   1129     )
   1130 else:
   1131     sample, res_samples = downsample_block(hidden_states=sample, temb=emb, scale=lora_scale)

File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\torch\nn\modules\module.py:1518, in Module._wrapped_call_impl(self, *args, **kwargs)
   1516     return self._compiled_call_impl(*args, **kwargs)  # type: ignore[misc]
   1517 else:
-> 1518     return self._call_impl(*args, **kwargs)

File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\torch\nn\modules\module.py:1527, in Module._call_impl(self, *args, **kwargs)
   1522 # If we don't have any hooks, we want to skip the rest of the logic in
   1523 # this function, and just call forward.
   1524 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
   1525         or _global_backward_pre_hooks or _global_backward_hooks
   1526         or _global_forward_hooks or _global_forward_pre_hooks):
-> 1527     return forward_call(*args, **kwargs)
   1529 try:
   1530     result = None

File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\diffusers\models\unets\unet_2d_blocks.py:1199, in CrossAttnDownBlock2D.forward(self, hidden_states, temb, encoder_hidden_states, attention_mask, cross_attention_kwargs, encoder_attention_mask, additional_residuals)
   1197 else:
   1198     hidden_states = resnet(hidden_states, temb, scale=lora_scale)
-> 1199     hidden_states = attn(
   1200         hidden_states,
   1201         encoder_hidden_states=encoder_hidden_states,
   1202         cross_attention_kwargs=cross_attention_kwargs,
   1203         attention_mask=attention_mask,
   1204         encoder_attention_mask=encoder_attention_mask,
   1205         return_dict=False,
   1206     )[0]
   1208 # apply additional residuals to the output of the last pair of resnet and attention blocks
   1209 if i == len(blocks) - 1 and additional_residuals is not None:

File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\torch\nn\modules\module.py:1518, in Module._wrapped_call_impl(self, *args, **kwargs)
   1516     return self._compiled_call_impl(*args, **kwargs)  # type: ignore[misc]
   1517 else:
-> 1518     return self._call_impl(*args, **kwargs)

File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\torch\nn\modules\module.py:1527, in Module._call_impl(self, *args, **kwargs)
   1522 # If we don't have any hooks, we want to skip the rest of the logic in
   1523 # this function, and just call forward.
   1524 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
   1525         or _global_backward_pre_hooks or _global_backward_hooks
   1526         or _global_forward_hooks or _global_forward_pre_hooks):
-> 1527     return forward_call(*args, **kwargs)
   1529 try:
   1530     result = None

File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\diffusers\models\transformers\transformer_2d.py:391, in Transformer2DModel.forward(self, hidden_states, encoder_hidden_states, timestep, added_cond_kwargs, class_labels, cross_attention_kwargs, attention_mask, encoder_attention_mask, return_dict)
    379         hidden_states = torch.utils.checkpoint.checkpoint(
    380             create_custom_forward(block),
    381             hidden_states,
   (...)
    388             **ckpt_kwargs,
    389         )
    390     else:
--> 391         hidden_states = block(
    392             hidden_states,
    393             attention_mask=attention_mask,
    394             encoder_hidden_states=encoder_hidden_states,
    395             encoder_attention_mask=encoder_attention_mask,
    396             timestep=timestep,
    397             cross_attention_kwargs=cross_attention_kwargs,
    398             class_labels=class_labels,
    399         )
    401 # 3. Output
    402 if self.is_input_continuous:

File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\torch\nn\modules\module.py:1518, in Module._wrapped_call_impl(self, *args, **kwargs)
   1516     return self._compiled_call_impl(*args, **kwargs)  # type: ignore[misc]
   1517 else:
-> 1518     return self._call_impl(*args, **kwargs)

File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\torch\nn\modules\module.py:1527, in Module._call_impl(self, *args, **kwargs)
   1522 # If we don't have any hooks, we want to skip the rest of the logic in
   1523 # this function, and just call forward.
   1524 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
   1525         or _global_backward_pre_hooks or _global_backward_hooks
   1526         or _global_forward_hooks or _global_forward_pre_hooks):
-> 1527     return forward_call(*args, **kwargs)
   1529 try:
   1530     result = None

File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\diffusers\models\attention.py:329, in BasicTransformerBlock.forward(self, hidden_states, attention_mask, encoder_hidden_states, encoder_attention_mask, timestep, cross_attention_kwargs, class_labels, added_cond_kwargs)
    326 cross_attention_kwargs = cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {}
    327 gligen_kwargs = cross_attention_kwargs.pop("gligen", None)
--> 329 attn_output = self.attn1(
    330     norm_hidden_states,
    331     encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
    332     attention_mask=attention_mask,
    333     **cross_attention_kwargs,
    334 )
    335 if self.norm_type == "ada_norm_zero":
    336     attn_output = gate_msa.unsqueeze(1) * attn_output

File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\torch\nn\modules\module.py:1518, in Module._wrapped_call_impl(self, *args, **kwargs)
   1516     return self._compiled_call_impl(*args, **kwargs)  # type: ignore[misc]
   1517 else:
-> 1518     return self._call_impl(*args, **kwargs)

File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\torch\nn\modules\module.py:1527, in Module._call_impl(self, *args, **kwargs)
   1522 # If we don't have any hooks, we want to skip the rest of the logic in
   1523 # this function, and just call forward.
   1524 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
   1525         or _global_backward_pre_hooks or _global_backward_hooks
   1526         or _global_forward_hooks or _global_forward_pre_hooks):
-> 1527     return forward_call(*args, **kwargs)
   1529 try:
   1530     result = None

File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\diffusers\models\attention_processor.py:512, in Attention.forward(self, hidden_states, encoder_hidden_states, attention_mask, **cross_attention_kwargs)
    493 r"""
    494 The forward method of the `Attention` class.
    495 
   (...)
    507     `torch.Tensor`: The output of the attention layer.
    508 """
    509 # The `Attention` class can call different attention processors / attention functions
    510 # here we simply pass along all tensors to the selected processor class
    511 # For standard processors that are defined here, `**cross_attention_kwargs` is empty
--> 512 return self.processor(
    513     self,
    514     hidden_states,
    515     encoder_hidden_states=encoder_hidden_states,
    516     attention_mask=attention_mask,
    517     **cross_attention_kwargs,
    518 )

File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\diffusers\models\attention_processor.py:1224, in AttnProcessor2_0.__call__(self, attn, hidden_states, encoder_hidden_states, attention_mask, temb, scale)
   1221     hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
   1223 args = () if USE_PEFT_BACKEND else (scale,)
-> 1224 query = attn.to_q(hidden_states, *args)
   1226 if encoder_hidden_states is None:
   1227     encoder_hidden_states = hidden_states

File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\torch\nn\modules\module.py:1518, in Module._wrapped_call_impl(self, *args, **kwargs)
   1516     return self._compiled_call_impl(*args, **kwargs)  # type: ignore[misc]
   1517 else:
-> 1518     return self._call_impl(*args, **kwargs)

File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\torch\nn\modules\module.py:1527, in Module._call_impl(self, *args, **kwargs)
   1522 # If we don't have any hooks, we want to skip the rest of the logic in
   1523 # this function, and just call forward.
   1524 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
   1525         or _global_backward_pre_hooks or _global_backward_hooks
   1526         or _global_forward_hooks or _global_forward_pre_hooks):
-> 1527     return forward_call(*args, **kwargs)
   1529 try:
   1530     result = None

TypeError: SparseMoeBlock.forward() missing 1 required positional argument: 'scale'