[Bug]: RX580 RuntimeError: Input type (float) and bias type (struct c10::Half) should be the same

[ares20ene-dotcom]

Checklist

• The issue exists after disabling all extensions
• The issue exists on a clean installation of webui
• The issue is caused by an extension, but I believe it is caused by a bug in the webui
• The issue exists in the current version of the webui
• The issue has not been reported before recently
• The issue has been reported before but has not been fixed yet

What happened?

Error after trying to generate an image

Steps to reproduce the problem

1. execute webui.bat
2. type a prompt
3. get error RuntimeError: Input type (float) and bias type (struct c10::Half) should be the same

What should have happened?

WebUI should have generated an image, instead I got an error

What browsers do you use to access the UI ?

Other, Google Chrome

Sysinfo

sysinfo.txt

Console logs

venv "C:\Users\Miguel\stable-diffusion-webui-amdgpu\venv\Scripts\Python.exe"
Python 3.10.6 (tags/v3.10.6:9c7b4bd, Aug  1 2022, 21:53:49) [MSC v.1932 64 bit (AMD64)]
Version: v1.10.1-amd-51-ge61adddd
Commit hash: e61adddd295d3438036a87460cde6f437e26b559
ROCm: agents=['gfx803']
ROCm: version=None, using agent gfx803
ZLUDA support: experimental
Failed to install ZLUDA: 'NoneType' object is not subscriptable
Using CPU-only torch
W1216 02:45:44.677986 20024 venv\Lib\site-packages\torch\distributed\elastic\multiprocessing\redirects.py:29] NOTE: Redirects are currently not supported in Windows or MacOs.
C:\Users\Miguel\stable-diffusion-webui-amdgpu\venv\lib\site-packages\timm\models\layers\__init__.py:48: FutureWarning: Importing from timm.models.layers is deprecated, please import via timm.layers
  warnings.warn(f"Importing from {__name__} is deprecated, please import via timm.layers", FutureWarning)
no module 'xformers'. Processing without...
no module 'xformers'. Processing without...
No module 'xformers'. Proceeding without it.
C:\Users\Miguel\stable-diffusion-webui-amdgpu\venv\lib\site-packages\pytorch_lightning\utilities\distributed.py:258: LightningDeprecationWarning: `pytorch_lightning.utilities.distributed.rank_zero_only` has been deprecated in v1.8.1 and will be removed in v2.0.0. You can import it from `pytorch_lightning.utilities` instead.
  rank_zero_deprecation(
Launching Web UI with arguments:
Warning: caught exception 'Torch not compiled with CUDA enabled', memory monitor disabled
C:\Users\Miguel\stable-diffusion-webui-amdgpu\venv\lib\site-packages\torch\amp\autocast_mode.py:266: UserWarning: User provided device_type of 'cuda', but CUDA is not available. Disabling
  warnings.warn(
ONNX: version=1.23.0 provider=CPUExecutionProvider, available=['DmlExecutionProvider', 'CPUExecutionProvider']
Loading weights [6ce0161689] from C:\Users\Miguel\stable-diffusion-webui-amdgpu\models\Stable-diffusion\v1-5-pruned-emaonly.safetensors
Creating model from config: C:\Users\Miguel\stable-diffusion-webui-amdgpu\configs\v1-inference.yaml
Running on local URL:  http://127.0.0.1:7860

To create a public link, set `share=True` in `launch()`.
Startup time: 12.7s (prepare environment: 18.2s, initialize shared: 1.9s, load scripts: 0.5s, create ui: 0.6s, gradio launch: 0.3s).
Applying attention optimization: InvokeAI... done.
Model loaded in 17.4s (load weights from disk: 0.7s, create model: 0.7s, apply weights to model: 14.3s, apply half(): 0.4s, load VAE: 0.1s, calculate empty prompt: 1.0s).
  0%|                                                                                           | 0/20 [00:00<?, ?it/s]
*** Error completing request
*** Arguments: ('task(t0m08iidph7n921)', <gradio.routes.Request object at 0x000001F8C4AD37F0>, 'dragon', '', [], 1, 1, 7, 512, 512, False, 0.7, 2, 'Latent', 0, 0, 0, 'Use same checkpoint', 'Use same sampler', 'Use same scheduler', '', '', [], 0, 20, 'DPM++ 2M', 'Automatic', False, '', 0.8, -1, False, -1, 0, 0, 0, False, False, 'positive', 'comma', 0, False, False, 'start', '', 1, '', [], 0, '', [], 0, '', [], True, False, False, False, False, False, False, 0, False) {}
    Traceback (most recent call last):
      File "C:\Users\Miguel\stable-diffusion-webui-amdgpu\modules\call_queue.py", line 74, in f
        res = list(func(*args, **kwargs))
      File "C:\Users\Miguel\stable-diffusion-webui-amdgpu\modules\call_queue.py", line 53, in f
        res = func(*args, **kwargs)
      File "C:\Users\Miguel\stable-diffusion-webui-amdgpu\modules\call_queue.py", line 37, in f
        res = func(*args, **kwargs)
      File "C:\Users\Miguel\stable-diffusion-webui-amdgpu\modules\txt2img.py", line 109, in txt2img
        processed = processing.process_images(p)
      File "C:\Users\Miguel\stable-diffusion-webui-amdgpu\modules\processing.py", line 849, in process_images
        res = process_images_inner(p)
      File "C:\Users\Miguel\stable-diffusion-webui-amdgpu\modules\processing.py", line 1083, in process_images_inner
        samples_ddim = p.sample(conditioning=p.c, unconditional_conditioning=p.uc, seeds=p.seeds, subseeds=p.subseeds, subseed_strength=p.subseed_strength, prompts=p.prompts)
      File "C:\Users\Miguel\stable-diffusion-webui-amdgpu\modules\processing.py", line 1441, in sample
        samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
      File "C:\Users\Miguel\stable-diffusion-webui-amdgpu\modules\sd_samplers_kdiffusion.py", line 233, in sample
        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
      File "C:\Users\Miguel\stable-diffusion-webui-amdgpu\modules\sd_samplers_common.py", line 272, in launch_sampling
        return func()
      File "C:\Users\Miguel\stable-diffusion-webui-amdgpu\modules\sd_samplers_kdiffusion.py", line 233, in <lambda>
        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
      File "C:\Users\Miguel\stable-diffusion-webui-amdgpu\venv\lib\site-packages\torch\utils\_contextlib.py", line 116, in decorate_context
        return func(*args, **kwargs)
      File "C:\Users\Miguel\stable-diffusion-webui-amdgpu\repositories\k-diffusion\k_diffusion\sampling.py", line 594, in sample_dpmpp_2m
        denoised = model(x, sigmas[i] * s_in, **extra_args)
      File "C:\Users\Miguel\stable-diffusion-webui-amdgpu\venv\lib\site-packages\torch\nn\modules\module.py", line 1751, in _wrapped_call_impl
        return self._call_impl(*args, **kwargs)
      File "C:\Users\Miguel\stable-diffusion-webui-amdgpu\venv\lib\site-packages\torch\nn\modules\module.py", line 1762, in _call_impl
        return forward_call(*args, **kwargs)
      File "C:\Users\Miguel\stable-diffusion-webui-amdgpu\modules\sd_samplers_cfg_denoiser.py", line 249, in forward
        x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict(cond_in, image_cond_in))
      File "C:\Users\Miguel\stable-diffusion-webui-amdgpu\venv\lib\site-packages\torch\nn\modules\module.py", line 1751, in _wrapped_call_impl
        return self._call_impl(*args, **kwargs)
      File "C:\Users\Miguel\stable-diffusion-webui-amdgpu\venv\lib\site-packages\torch\nn\modules\module.py", line 1762, in _call_impl
        return forward_call(*args, **kwargs)
      File "C:\Users\Miguel\stable-diffusion-webui-amdgpu\repositories\k-diffusion\k_diffusion\external.py", line 112, in forward
        eps = self.get_eps(input * c_in, self.sigma_to_t(sigma), **kwargs)
      File "C:\Users\Miguel\stable-diffusion-webui-amdgpu\repositories\k-diffusion\k_diffusion\external.py", line 138, in get_eps
        return self.inner_model.apply_model(*args, **kwargs)
      File "C:\Users\Miguel\stable-diffusion-webui-amdgpu\modules\sd_hijack_utils.py", line 22, in <lambda>
        setattr(resolved_obj, func_path[-1], lambda *args, **kwargs: self(*args, **kwargs))
      File "C:\Users\Miguel\stable-diffusion-webui-amdgpu\modules\sd_hijack_utils.py", line 34, in __call__
        return self.__sub_func(self.__orig_func, *args, **kwargs)
      File "C:\Users\Miguel\stable-diffusion-webui-amdgpu\modules\sd_hijack_unet.py", line 50, in apply_model
        result = orig_func(self, x_noisy.to(devices.dtype_unet), t.to(devices.dtype_unet), cond, **kwargs)
      File "C:\Users\Miguel\stable-diffusion-webui-amdgpu\modules\sd_hijack_utils.py", line 22, in <lambda>
        setattr(resolved_obj, func_path[-1], lambda *args, **kwargs: self(*args, **kwargs))
      File "C:\Users\Miguel\stable-diffusion-webui-amdgpu\modules\sd_hijack_utils.py", line 36, in __call__
        return self.__orig_func(*args, **kwargs)
      File "C:\Users\Miguel\stable-diffusion-webui-amdgpu\repositories\stable-diffusion-stability-ai\ldm\models\diffusion\ddpm.py", line 858, in apply_model
        x_recon = self.model(x_noisy, t, **cond)
      File "C:\Users\Miguel\stable-diffusion-webui-amdgpu\venv\lib\site-packages\torch\nn\modules\module.py", line 1751, in _wrapped_call_impl
        return self._call_impl(*args, **kwargs)
      File "C:\Users\Miguel\stable-diffusion-webui-amdgpu\venv\lib\site-packages\torch\nn\modules\module.py", line 1762, in _call_impl
        return forward_call(*args, **kwargs)
      File "C:\Users\Miguel\stable-diffusion-webui-amdgpu\repositories\stable-diffusion-stability-ai\ldm\models\diffusion\ddpm.py", line 1335, in forward
        out = self.diffusion_model(x, t, context=cc)
      File "C:\Users\Miguel\stable-diffusion-webui-amdgpu\venv\lib\site-packages\torch\nn\modules\module.py", line 1751, in _wrapped_call_impl
        return self._call_impl(*args, **kwargs)
      File "C:\Users\Miguel\stable-diffusion-webui-amdgpu\venv\lib\site-packages\torch\nn\modules\module.py", line 1762, in _call_impl
        return forward_call(*args, **kwargs)
      File "C:\Users\Miguel\stable-diffusion-webui-amdgpu\modules\sd_unet.py", line 91, in UNetModel_forward
        return original_forward(self, x, timesteps, context, *args, **kwargs)
      File "C:\Users\Miguel\stable-diffusion-webui-amdgpu\repositories\stable-diffusion-stability-ai\ldm\modules\diffusionmodules\openaimodel.py", line 797, in forward
        h = module(h, emb, context)
      File "C:\Users\Miguel\stable-diffusion-webui-amdgpu\venv\lib\site-packages\torch\nn\modules\module.py", line 1751, in _wrapped_call_impl
        return self._call_impl(*args, **kwargs)
      File "C:\Users\Miguel\stable-diffusion-webui-amdgpu\venv\lib\site-packages\torch\nn\modules\module.py", line 1762, in _call_impl
        return forward_call(*args, **kwargs)
      File "C:\Users\Miguel\stable-diffusion-webui-amdgpu\repositories\stable-diffusion-stability-ai\ldm\modules\diffusionmodules\openaimodel.py", line 86, in forward
        x = layer(x)
      File "C:\Users\Miguel\stable-diffusion-webui-amdgpu\venv\lib\site-packages\torch\nn\modules\module.py", line 1751, in _wrapped_call_impl
        return self._call_impl(*args, **kwargs)
      File "C:\Users\Miguel\stable-diffusion-webui-amdgpu\venv\lib\site-packages\torch\nn\modules\module.py", line 1762, in _call_impl
        return forward_call(*args, **kwargs)
      File "C:\Users\Miguel\stable-diffusion-webui-amdgpu\extensions-builtin\Lora\networks.py", line 599, in network_Conv2d_forward
        return originals.Conv2d_forward(self, input)
      File "C:\Users\Miguel\stable-diffusion-webui-amdgpu\venv\lib\site-packages\torch\nn\modules\conv.py", line 554, in forward
        return self._conv_forward(input, self.weight, self.bias)
      File "C:\Users\Miguel\stable-diffusion-webui-amdgpu\venv\lib\site-packages\torch\nn\modules\conv.py", line 549, in _conv_forward
        return F.conv2d(
    RuntimeError: Input type (float) and bias type (struct c10::Half) should be the same

---

Additional information

No response

[lshqqytiger]

DirectML is recommended for RX 500 series.
Nevertheless, if you want to try ZLUDA for better performance, see https://github.com/vladmandic/sdnext/wiki/ZLUDA.