NotImplementedError: Module [ModuleList] is missing the required "forward" function

[jinwonkim93]

Hi have you encounter this problem? I have download latest diffusers. my torch version is 1.12.1

Some weights of the model checkpoint at openai/clip-vit-large-patch14 were not used when initializing CLIPTextModel: ['vision_model.encoder.layers.13.self_attn.k_proj.weight', 'vision_model.encoder.layers.12.self_attn.k_proj.bias', 'vision_model.encoder.layers.23.self_attn.q_proj.weight', 'vision_model.encoder.layers.6.mlp.fc1.weight', 'vision_model.post_layernorm.bias', 'vision_model.encoder.layers.0.self_attn.q_proj.weight', 'vision_model.encoder.layers.22.layer_norm2.bias', 'vision_model.encoder.layers.1.self_attn.k_proj.bias', 'vision_model.encoder.layers.13.mlp.fc2.weight', 'vision_model.encoder.layers.12.layer_norm2.bias', 'vision_model.encoder.layers.14.self_attn.k_proj.weight', 'vision_model.encoder.layers.8.layer_norm1.bias', 'vision_model.encoder.layers.10.self_attn.q_proj.weight', 'vision_model.encoder.layers.1.layer_norm2.bias', 'vision_model.encoder.layers.17.mlp.fc1.bias', 'vision_model.encoder.layers.12.self_attn.out_proj.weight', 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'vision_model.encoder.layers.16.self_attn.out_proj.weight', 'vision_model.encoder.layers.4.self_attn.k_proj.bias', 'vision_model.encoder.layers.20.self_attn.v_proj.bias', 'vision_model.encoder.layers.10.mlp.fc1.bias', 'vision_model.encoder.layers.18.layer_norm2.bias', 'vision_model.encoder.layers.10.self_attn.q_proj.bias', 'vision_model.encoder.layers.5.layer_norm1.weight', 'vision_model.encoder.layers.3.layer_norm1.bias', 'vision_model.encoder.layers.7.mlp.fc1.bias', 'vision_model.encoder.layers.1.self_attn.out_proj.bias']

• 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). 0% 0/30 [00:01<?, ?it/s] ╭──────────────────────────── Traceback (most recent call last) ────────────────────────────╮ │ :1 in │ │ │ │ /opt/conda/lib/python3.8/site-packages/torch/autograd/grad_mode.py:27 in decorate_context │ │ │ │ 24 │ │ @functools.wraps(func) │ │ 25 │ │ def decorate_context(*args, kwargs): │ │ 26 │ │ │ with self.clone(): │ │ ❱ 27 │ │ │ │ return func(*args, *kwargs) │ │ 28 │ │ return cast(F, decorate_context) │ │ 29 │ │ │ 30 │ def _wrap_generator(self, func): │ │ │ │ /opt/conda/lib/python3.8/site-packages/torch/amp/autocast_mode.py:12 in decorate_autocast │ │ │ │ 9 │ @functools.wraps(func) │ │ 10 │ def decorate_autocast(args, kwargs): │ │ 11 │ │ with autocast_instance: │ │ ❱ 12 │ │ │ return func(*args, kwargs) │ │ 13 │ decorate_autocast.__script_unsupported = '@autocast() decorator is not supporte │ │ 14 │ return decorate_autocast │ │ 15 │ │ │ │ /workspace/docker/jw93/paint-with-words-sd/paint_with_words/paint_with_words.py:244 in │ │ paint_with_words │ │ │ │ 241 │ │ │ │ 242 │ │ latent_model_input = scheduler.scale_model_input(latents, t) │ │ 243 │ │ │ │ ❱ 244 │ │ noise_pred_text = unet( │ │ 245 │ │ │ latent_model_input, │ │ 246 │ │ │ t, │ │ 247 │ │ │ encoder_hidden_states={ │ │ │ │ /opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py:1130 in _call_impl │ │ │ │ 1127 │ │ # this function, and just call forward. │ │ 1128 │ │ if not (self._backward_hooks or self._forward_hooks or self._forward_pre_h │ │ 1129 │ │ │ │ or _global_forward_hooks or _global_forward_pre_hooks): │ │ ❱ 1130 │ │ │ return forward_call(*input, *kwargs) │ │ 1131 │ │ # Do not call functions when jit is used │ │ 1132 │ │ full_backward_hooks, non_full_backward_hooks = [], [] │ │ 1133 │ │ if self._backward_hooks or _global_backward_hooks: │ │ │ │ /opt/conda/lib/python3.8/site-packages/diffusers/models/unet_2d_condition.py:307 in │ │ forward │ │ │ │ 304 │ │ down_block_res_samples = (sample,) │ │ 305 │ │ for downsample_block in self.down_blocks: │ │ 306 │ │ │ if hasattr(downsample_block, "attentions") and downsample_block.attenti │ │ ❱ 307 │ │ │ │ sample, res_samples = downsample_block( │ │ 308 │ │ │ │ │ hidden_states=sample, │ │ 309 │ │ │ │ │ temb=emb, │ │ 310 │ │ │ │ │ encoder_hidden_states=encoder_hidden_states, │ │ │ │ /opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py:1130 in _call_impl │ │ │ │ 1127 │ │ # this function, and just call forward. │ │ 1128 │ │ if not (self._backward_hooks or self._forward_hooks or self._forward_pre_h │ │ 1129 │ │ │ │ or _global_forward_hooks or _global_forward_pre_hooks): │ │ ❱ 1130 │ │ │ return forward_call(input, kwargs) │ │ 1131 │ │ # Do not call functions when jit is used │ │ 1132 │ │ full_backward_hooks, non_full_backward_hooks = [], [] │ │ 1133 │ │ if self._backward_hooks or _global_backward_hooks: │ │ │ │ /opt/conda/lib/python3.8/site-packages/diffusers/models/unet_2d_blocks.py:598 in forward │ │ │ │ 595 │ │ │ │ )[0] │ │ 596 │ │ │ else: │ │ 597 │ │ │ │ hidden_states = resnet(hidden_states, temb) │ │ ❱ 598 │ │ │ │ hidden_states = attn(hidden_states, encoder_hiddenstates=encoder │ │ 599 │ │ │ │ │ 600 │ │ │ output_states += (hidden_states,) │ │ 601 │ │ │ │ /opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py:1130 in _call_impl │ │ │ │ 1127 │ │ # this function, and just call forward. │ │ 1128 │ │ if not (self._backward_hooks or self._forward_hooks or self._forward_pre_h │ │ 1129 │ │ │ │ or _global_forward_hooks or _global_forward_pre_hooks): │ │ ❱ 1130 │ │ │ return forward_call(*input, kwargs) │ │ 1131 │ │ # Do not call functions when jit is used │ │ 1132 │ │ full_backward_hooks, non_full_backward_hooks = [], [] │ │ 1133 │ │ if self._backward_hooks or _global_backward_hooks: │ │ │ │ /opt/conda/lib/python3.8/site-packages/diffusers/models/attention.py:202 in forward │ │ │ │ 199 │ │ │ │ 200 │ │ # 2. Blocks │ │ 201 │ │ for block in self.transformer_blocks: │ │ ❱ 202 │ │ │ hidden_states = block(hidden_states, context=encoder_hidden_states, tim │ │ 203 │ │ │ │ 204 │ │ # 3. Output │ │ 205 │ │ if self.is_input_continuous: │ │ │ │ /opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py:1130 in _call_impl │ │ │ │ 1127 │ │ # this function, and just call forward. │ │ 1128 │ │ if not (self._backward_hooks or self._forward_hooks or self._forward_pre_h │ │ 1129 │ │ │ │ or _global_forward_hooks or _global_forward_pre_hooks): │ │ ❱ 1130 │ │ │ return forward_call(*input, *kwargs) │ │ 1131 │ │ # Do not call functions when jit is used │ │ 1132 │ │ full_backward_hooks, non_full_backward_hooks = [], [] │ │ 1133 │ │ if self._backward_hooks or _global_backward_hooks: │ │ │ │ /opt/conda/lib/python3.8/site-packages/diffusers/models/attention.py:404 in forward │ │ │ │ 401 │ │ norm_hidden_states = ( │ │ 402 │ │ │ self.norm1(hidden_states, timestep) if self.use_ada_layer_norm else sel │ │ 403 │ │ ) │ │ ❱ 404 │ │ hidden_states = self.attn1(norm_hidden_states) + hidden_states │ │ 405 │ │ │ │ 406 │ │ # 2. Cross-Attention │ │ 407 │ │ norm_hidden_states = ( │ │ │ │ /workspace/docker/jw93/paint-with-words-sd/paint_with_words/paint_with_words.py:83 in │ │ inj_forward │ │ │ │ 80 │ │ │ 81 │ hidden_states = self.reshape_batch_dim_to_heads(hidden_states) │ │ 82 │ │ │ ❱ 83 │ return self.to_out(hidden_states) │ │ 84 │ │ 85 │ │ 86 def _load_tools(device: str, scheduler_type): │ │ │ │ /opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py:1130 in _call_impl │ │ │ │ 1127 │ │ # this function, and just call forward. │ │ 1128 │ │ if not (self._backward_hooks or self._forward_hooks or self._forward_pre_h │ │ 1129 │ │ │ │ or _global_forward_hooks or _global_forward_pre_hooks): │ │ ❱ 1130 │ │ │ return forward_call(input, kwargs) │ │ 1131 │ │ # Do not call functions when jit is used │ │ 1132 │ │ full_backward_hooks, non_full_backward_hooks = [], [] │ │ 1133 │ │ if self._backward_hooks or _global_backward_hooks: │ │ │ │ /opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py:201 in │ │ _forward_unimplemented │ │ │ │ 198 │ │ instead of this since the former takes care of running the │ │ 199 │ │ registered hooks while the latter silently ignores them. │ │ 200 │ """ │ │ ❱ 201 │ raise NotImplementedError(f"Module [{type(self).name}] is missing the requ │ │ 202 │ │ 203 │ │ 204 class Module: │ ╰───────────────────────────────────────────────────────────────────────────────────────────╯ NotImplementedError: Module [ModuleList] is missing the required "forward" function

[cloneofsimo]

Mitigated by #3, Thanks!