diffusers
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Published
3 months ago
•
huggingface
huggingface
Error load inpainting model in float16
Describe the bug
Following this tutorial: https://huggingface.co/runwayml/stable-diffusion-inpainting
I tried to made to load the model with float16, but I have follow error: RuntimeError: "LayerNormKernelImpl" not implemented for 'Half'
If I try without float16, the model load correctly
Reproduction
from diffusers import StableDiffusionInpaintPipeline
pipe = StableDiffusionInpaintPipeline.from_pretrained( "runwayml/stable-diffusion-inpainting", revision="fp16", torch_dtype=torch.float16, ) prompt = "Face of a yellow cat, high resolution, sitting on a park bench" #image and mask_image should be PIL images. #The mask structure is white for inpainting and black for keeping as is image = pipe(prompt=prompt, image=img, mask_image=mask).images[0]
Logs
/opt/conda/lib/python3.7/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/python3.7/site-packages/diffusers/pipelines/stable_diffusion/pipeline_stable_diff │
│ usion_inpaint.py:786 in __call__ │
│ │
│ 783 │ │ │ do_classifier_free_guidance, │
│ 784 │ │ │ negative_prompt, │
│ 785 │ │ │ prompt_embeds=prompt_embeds, │
│ ❱ 786 │ │ │ negative_prompt_embeds=negative_prompt_embeds, │
│ 787 │ │ ) │
│ 788 │ │ │
│ 789 │ │ # 4. Preprocess mask and image │
│ │
│ /opt/conda/lib/python3.7/site-packages/diffusers/pipelines/stable_diffusion/pipeline_stable_diff │
│ usion_inpaint.py:396 in _encode_prompt │
│ │
│ 393 │ │ │ │
│ 394 │ │ │ prompt_embeds = self.text_encoder( │
│ 395 │ │ │ │ text_input_ids.to(device), │
│ ❱ 396 │ │ │ │ attention_mask=attention_mask, │
│ 397 │ │ │ ) │
│ 398 │ │ │ prompt_embeds = prompt_embeds[0] │
│ 399 │
│ │
│ /opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py:1194 in _call_impl │
│ │
│ 1191 │ │ # this function, and just call forward. │
│ 1192 │ │ if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks o │
│ 1193 │ │ │ │ or _global_forward_hooks or _global_forward_pre_hooks): │
│ ❱ 1194 │ │ │ return forward_call(*input, **kwargs) │
│ 1195 │ │ # Do not call functions when jit is used │
│ 1196 │ │ full_backward_hooks, non_full_backward_hooks = [], [] │
│ 1197 │ │ if self._backward_hooks or _global_backward_hooks: │
│ │
│ /opt/conda/lib/python3.7/site-packages/transformers/models/clip/modeling_clip.py:822 in forward │
│ │
│ 819 │ │ │ position_ids=position_ids, │
│ 820 │ │ │ output_attentions=output_attentions, │
│ 821 │ │ │ output_hidden_states=output_hidden_states, │
│ ❱ 822 │ │ │ return_dict=return_dict, │
│ 823 │ │ ) │
│ 824 │
│ 825 │
│ │
│ /opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py:1194 in _call_impl │
│ │
│ 1191 │ │ # this function, and just call forward. │
│ 1192 │ │ if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks o │
│ 1193 │ │ │ │ or _global_forward_hooks or _global_forward_pre_hooks): │
│ ❱ 1194 │ │ │ return forward_call(*input, **kwargs) │
│ 1195 │ │ # Do not call functions when jit is used │
│ 1196 │ │ full_backward_hooks, non_full_backward_hooks = [], [] │
│ 1197 │ │ if self._backward_hooks or _global_backward_hooks: │
│ │
│ /opt/conda/lib/python3.7/site-packages/transformers/models/clip/modeling_clip.py:731 in forward │
│ │
│ 728 │ │ │ causal_attention_mask=causal_attention_mask, │
│ 729 │ │ │ output_attentions=output_attentions, │
│ 730 │ │ │ output_hidden_states=output_hidden_states, │
│ ❱ 731 │ │ │ return_dict=return_dict, │
│ 732 │ │ ) │
│ 733 │ │ │
│ 734 │ │ last_hidden_state = encoder_outputs[0] │
│ │
│ /opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py:1194 in _call_impl │
│ │
│ 1191 │ │ # this function, and just call forward. │
│ 1192 │ │ if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks o │
│ 1193 │ │ │ │ or _global_forward_hooks or _global_forward_pre_hooks): │
│ ❱ 1194 │ │ │ return forward_call(*input, **kwargs) │
│ 1195 │ │ # Do not call functions when jit is used │
│ 1196 │ │ full_backward_hooks, non_full_backward_hooks = [], [] │
│ 1197 │ │ if self._backward_hooks or _global_backward_hooks: │
│ │
│ /opt/conda/lib/python3.7/site-packages/transformers/models/clip/modeling_clip.py:658 in forward │
│ │
│ 655 │ │ │ │ │ hidden_states, │
│ 656 │ │ │ │ │ attention_mask, │
│ 657 │ │ │ │ │ causal_attention_mask, │
│ ❱ 658 │ │ │ │ │ output_attentions=output_attentions, │
│ 659 │ │ │ │ ) │
│ 660 │ │ │ │
│ 661 │ │ │ hidden_states = layer_outputs[0] │
│ │
│ /opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py:1194 in _call_impl │
│ │
│ 1191 │ │ # this function, and just call forward. │
│ 1192 │ │ if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks o │
│ 1193 │ │ │ │ or _global_forward_hooks or _global_forward_pre_hooks): │
│ ❱ 1194 │ │ │ return forward_call(*input, **kwargs) │
│ 1195 │ │ # Do not call functions when jit is used │
│ 1196 │ │ full_backward_hooks, non_full_backward_hooks = [], [] │
│ 1197 │ │ if self._backward_hooks or _global_backward_hooks: │
│ │
│ /opt/conda/lib/python3.7/site-packages/transformers/models/clip/modeling_clip.py:382 in forward │
│ │
│ 379 │ │ """ │
│ 380 │ │ residual = hidden_states │
│ 381 │ │ │
│ ❱ 382 │ │ hidden_states = self.layer_norm1(hidden_states) │
│ 383 │ │ hidden_states, attn_weights = self.self_attn( │
│ 384 │ │ │ hidden_states=hidden_states, │
│ 385 │ │ │ attention_mask=attention_mask, │
│ │
│ /opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py:1194 in _call_impl │
│ │
│ 1191 │ │ # this function, and just call forward. │
│ 1192 │ │ if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks o │
│ 1193 │ │ │ │ or _global_forward_hooks or _global_forward_pre_hooks): │
│ ❱ 1194 │ │ │ return forward_call(*input, **kwargs) │
│ 1195 │ │ # Do not call functions when jit is used │
│ 1196 │ │ full_backward_hooks, non_full_backward_hooks = [], [] │
│ 1197 │ │ if self._backward_hooks or _global_backward_hooks: │
│ │
│ /opt/conda/lib/python3.7/site-packages/torch/nn/modules/normalization.py:191 in forward │
│ │
│ 188 │ │
│ 189 │ def forward(self, input: Tensor) -> Tensor: │
│ 190 │ │ return F.layer_norm( │
│ ❱ 191 │ │ │ input, self.normalized_shape, self.weight, self.bias, self.eps) │
│ 192 │ │
│ 193 │ def extra_repr(self) -> str: │
│ 194 │ │ return '{normalized_shape}, eps={eps}, ' \ │
│ │
│ /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:2515 in layer_norm │
│ │
│ 2512 │ │ return handle_torch_function( │
│ 2513 │ │ │ layer_norm, (input, weight, bias), input, normalized_shape, weight=weight, b │
│ 2514 │ │ ) │
│ ❱ 2515 │ return torch.layer_norm(input, normalized_shape, weight, bias, eps, torch.backends.c │
│ 2516 │
│ 2517 │
│ 2518 def group_norm( │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
RuntimeError: "LayerNormKernelImpl" not implemented for 'Half'
System Info
System Info diffusers version: 0.14.0.dev0 Platform: Linux-5.15.65+-x86_64-with-debian-bullseye-sid Python version: 3.7.12 PyTorch version (GPU?): 1.13.1+cu116 (True) Huggingface_hub version: 0.12.1 Transformers version: 4.26.1 Accelerate version: 0.16.0 xFormers version: 0.0.16 Using GPU in script?: YES Using distributed or parallel set-up in script?: NO
