生成图像时报错
Isotr0py opened this issue · 2 comments
Isotr0py commented
- 生成图像时最后vae decode的时候报错
- 复现：运行Colab example的Generate Images部分
╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮
│ in <cell line: 16>:16 │
│ │
│ /usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py:115 in decorate_context │
│ │
│ 112 │ @functools.wraps(func) │
│ 113 │ def decorate_context(*args, **kwargs): │
│ 114 │ │ with ctx_factory(): │
│ ❱ 115 │ │ │ return func(*args, **kwargs) │
│ 116 │ │
│ 117 │ return decorate_context │
│ 118 │
│ │
│ /usr/local/lib/python3.10/dist-packages/hcpdiff/visualizer.py:202 in vis_images │
│ │
│ 199 │ │ │ │ for feeder in self.pipe.unet.input_feeder: │
│ 200 │ │ │ │ │ feeder(ex_input_dict) │
│ 201 │ │ │ │
│ ❱ 202 │ │ │ images = self.pipe(prompt_embeds=emb_p, negative_prompt_embeds=emb_n, **kwar │
│ 203 │ │ return images │
│ 204 │ │
│ 205 │ def save_images(self, images, root, prompt, negative_prompt='', save_cfg=True): │
│ │
│ /usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py:115 in decorate_context │
│ │
│ 112 │ @functools.wraps(func) │
│ 113 │ def decorate_context(*args, **kwargs): │
│ 114 │ │ with ctx_factory(): │
│ ❱ 115 │ │ │ return func(*args, **kwargs) │
│ 116 │ │
│ 117 │ return decorate_context │
│ 118 │
│ │
│ /usr/local/lib/python3.10/dist-packages/diffusers/pipelines/stable_diffusion/pipeline_stable_dif │
│ fusion.py:755 in __call__ │
│ │
│ 752 │ │ │ │ │ │ callback(i, t, latents) │
│ 753 │ │ │
│ 754 │ │ if not output_type == "latent": │
│ ❱ 755 │ │ │ image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dic │
│ 756 │ │ │ image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embe │
│ 757 │ │ else: │
│ 758 │ │ │ image = latents │
│ │
│ /usr/local/lib/python3.10/dist-packages/diffusers/utils/accelerate_utils.py:46 in wrapper │
│ │
│ 43 │ def wrapper(self, *args, **kwargs): │
│ 44 │ │ if hasattr(self, "_hf_hook") and hasattr(self._hf_hook, "pre_forward"): │
│ 45 │ │ │ self._hf_hook.pre_forward(self) │
│ ❱ 46 │ │ return method(self, *args, **kwargs) │
│ 47 │ │
│ 48 │ return wrapper │
│ 49 │
│ │
│ /usr/local/lib/python3.10/dist-packages/diffusers/models/autoencoder_kl.py:191 in decode │
│ │
│ 188 │ │ │ decoded_slices = [self._decode(z_slice).sample for z_slice in z.split(1)] │
│ 189 │ │ │ decoded = torch.cat(decoded_slices) │
│ 190 │ │ else: │
│ ❱ 191 │ │ │ decoded = self._decode(z).sample │
│ 192 │ │ │
│ 193 │ │ if not return_dict: │
│ 194 │ │ │ return (decoded,) │
│ │
│ /usr/local/lib/python3.10/dist-packages/diffusers/models/autoencoder_kl.py:177 in _decode │
│ │
│ 174 │ │ if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > │
│ 175 │ │ │ return self.tiled_decode(z, return_dict=return_dict) │
│ 176 │ │ │
│ ❱ 177 │ │ z = self.post_quant_conv(z) │
│ 178 │ │ dec = self.decoder(z) │
│ 179 │ │ │
│ 180 │ │ if not return_dict: │
│ │
│ /usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py:1501 in _call_impl │
│ │
│ 1498 │ │ if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks │
│ 1499 │ │ │ │ or _global_backward_pre_hooks or _global_backward_hooks │
│ 1500 │ │ │ │ or _global_forward_hooks or _global_forward_pre_hooks): │
│ ❱ 1501 │ │ │ return forward_call(*args, **kwargs) │
│ 1502 │ │ # Do not call functions when jit is used │
│ 1503 │ │ full_backward_hooks, non_full_backward_hooks = [], [] │
│ 1504 │ │ backward_pre_hooks = [] │
│ │
│ /usr/local/lib/python3.10/dist-packages/torch/nn/modules/conv.py:463 in forward │
│ │
│ 460 │ │ │ │ │ │ self.padding, self.dilation, self.groups) │
│ 461 │ │
│ 462 │ def forward(self, input: Tensor) -> Tensor: │
│ ❱ 463 │ │ return self._conv_forward(input, self.weight, self.bias) │
│ 464 │
│ 465 class Conv3d(_ConvNd): │
│ 466 │ __doc__ = r"""Applies a 3D convolution over an input signal composed of several inpu │
│ │
│ /usr/local/lib/python3.10/dist-packages/torch/nn/modules/conv.py:459 in _conv_forward │
│ │
│ 456 │ │ │ return F.conv2d(F.pad(input, self._reversed_padding_repeated_twice, mode=sel │
│ 457 │ │ │ │ │ │ │ weight, bias, self.stride, │
│ 458 │ │ │ │ │ │ │ _pair(0), self.dilation, self.groups) │
│ ❱ 459 │ │ return F.conv2d(input, weight, bias, self.stride, │
│ 460 │ │ │ │ │ │ self.padding, self.dilation, self.groups) │
│ 461 │ │
│ 462 │ def forward(self, input: Tensor) -> Tensor: │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
RuntimeError: Input type (torch.cuda.HalfTensor) and weight type (torch.HalfTensor) should be the same
Isotr0py commented
注释掉下面代码中第206行的to_cpu(self.pipe.text_encoder)能正常出图：
https://github.com/7eu7d7/HCP-Diffusion/blob/c7dff0c185a34c6c20cf4dba0e0206fd25468589/hcpdiff/visualizer.py#L190-L215
IrisRainbowNeko commented
新版已修复