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[Kogiss]

hey guys, i'm trying to use the stable diffusion on an AMD

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*** Error completing request
*** Arguments: ('task(vij4a5eytyvjluh)', <gradio.routes.Request object at 0x000001B7D96D76D0>, 'wolf', '', [], 1, 1, 7, 512, 512, True, 0.7, 2, 'Latent', 0, 0, 0, 'Use same checkpoint', 'DPM++ 2M', 'Use same scheduler', '', '', [], 0, 150, 'DPM++ 2M', 'Karras', True, 'bb95FurryMix_v110.safetensors [4362cfddc4]', 0.8, 1471682, 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\dra19\OneDrive\Bureau\Test\stable-diffusion-webui-directml\modules\call_queue.py", line 57, in f
res = list(func(*args, **kwargs))
File "C:\Users\dra19\OneDrive\Bureau\Test\stable-diffusion-webui-directml\modules\call_queue.py", line 36, in f
res = func(*args, **kwargs)
File "C:\Users\dra19\OneDrive\Bureau\Test\stable-diffusion-webui-directml\modules\txt2img.py", line 109, in txt2img
processed = processing.process_images(p)
File "C:\Users\dra19\OneDrive\Bureau\Test\stable-diffusion-webui-directml\modules\processing.py", line 847, in process_images
res = process_images_inner(p)
File "C:\Users\dra19\OneDrive\Bureau\Test\stable-diffusion-webui-directml\modules\processing.py", line 1075, 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\dra19\OneDrive\Bureau\Test\stable-diffusion-webui-directml\modules\processing.py", line 1422, in sample
samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
File "C:\Users\dra19\OneDrive\Bureau\Test\stable-diffusion-webui-directml\modules\sd_samplers_kdiffusion.py", line 221, 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\dra19\OneDrive\Bureau\Test\stable-diffusion-webui-directml\modules\sd_samplers_common.py", line 272, in launch_sampling
return func()
File "C:\Users\dra19\OneDrive\Bureau\Test\stable-diffusion-webui-directml\modules\sd_samplers_kdiffusion.py", line 221, in
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\dra19\OneDrive\Bureau\Test\stable-diffusion-webui-directml\venv\lib\site-packages\torch\utils_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "C:\Users\dra19\OneDrive\Bureau\Test\stable-diffusion-webui-directml\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\dra19\OneDrive\Bureau\Test\stable-diffusion-webui-directml\venv\lib\site-packages\torch\nn\modules\module.py", line 1532, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "C:\Users\dra19\OneDrive\Bureau\Test\stable-diffusion-webui-directml\venv\lib\site-packages\torch\nn\modules\module.py", line 1541, in _call_impl
return forward_call(*args, **kwargs)
File "C:\Users\dra19\OneDrive\Bureau\Test\stable-diffusion-webui-directml\modules\sd_samplers_cfg_denoiser.py", line 237, in forward
x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict(cond_in, image_cond_in))
File "C:\Users\dra19\OneDrive\Bureau\Test\stable-diffusion-webui-directml\venv\lib\site-packages\torch\nn\modules\module.py", line 1532, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "C:\Users\dra19\OneDrive\Bureau\Test\stable-diffusion-webui-directml\venv\lib\site-packages\torch\nn\modules\module.py", line 1541, in _call_impl
return forward_call(*args, **kwargs)
File "C:\Users\dra19\OneDrive\Bureau\Test\stable-diffusion-webui-directml\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\dra19\OneDrive\Bureau\Test\stable-diffusion-webui-directml\repositories\k-diffusion\k_diffusion\external.py", line 138, in get_eps
return self.inner_model.apply_model(*args, **kwargs)
File "C:\Users\dra19\OneDrive\Bureau\Test\stable-diffusion-webui-directml\modules\sd_hijack_utils.py", line 18, in
setattr(resolved_obj, func_path[-1], lambda *args, **kwargs: self(*args, **kwargs))
File "C:\Users\dra19\OneDrive\Bureau\Test\stable-diffusion-webui-directml\modules\sd_hijack_utils.py", line 32, in call
return self.__orig_func(*args, **kwargs)
File "C:\Users\dra19\OneDrive\Bureau\Test\stable-diffusion-webui-directml\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\dra19\OneDrive\Bureau\Test\stable-diffusion-webui-directml\venv\lib\site-packages\torch\nn\modules\module.py", line 1532, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "C:\Users\dra19\OneDrive\Bureau\Test\stable-diffusion-webui-directml\venv\lib\site-packages\torch\nn\modules\module.py", line 1541, in _call_impl
return forward_call(*args, **kwargs)
File "C:\Users\dra19\OneDrive\Bureau\Test\stable-diffusion-webui-directml\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\dra19\OneDrive\Bureau\Test\stable-diffusion-webui-directml\venv\lib\site-packages\torch\nn\modules\module.py", line 1532, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "C:\Users\dra19\OneDrive\Bureau\Test\stable-diffusion-webui-directml\venv\lib\site-packages\torch\nn\modules\module.py", line 1541, in _call_impl
return forward_call(*args, **kwargs)
File "C:\Users\dra19\OneDrive\Bureau\Test\stable-diffusion-webui-directml\modules\sd_unet.py", line 91, in UNetModel_forward
return original_forward(self, x, timesteps, context, *args, **kwargs)
File "C:\Users\dra19\OneDrive\Bureau\Test\stable-diffusion-webui-directml\repositories\stable-diffusion-stability-ai\ldm\modules\diffusionmodules\openaimodel.py", line 789, in forward
emb = self.time_embed(t_emb)
File "C:\Users\dra19\OneDrive\Bureau\Test\stable-diffusion-webui-directml\venv\lib\site-packages\torch\nn\modules\module.py", line 1532, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "C:\Users\dra19\OneDrive\Bureau\Test\stable-diffusion-webui-directml\venv\lib\site-packages\torch\nn\modules\module.py", line 1582, in _call_impl
result = forward_call(*args, **kwargs)
File "C:\Users\dra19\OneDrive\Bureau\Test\stable-diffusion-webui-directml\venv\lib\site-packages\torch\nn\modules\container.py", line 217, in forward
input = module(input)
File "C:\Users\dra19\OneDrive\Bureau\Test\stable-diffusion-webui-directml\venv\lib\site-packages\torch\nn\modules\module.py", line 1532, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "C:\Users\dra19\OneDrive\Bureau\Test\stable-diffusion-webui-directml\venv\lib\site-packages\torch\nn\modules\module.py", line 1541, in _call_impl
return forward_call(*args, **kwargs)
File "C:\Users\dra19\OneDrive\Bureau\Test\stable-diffusion-webui-directml\extensions-builtin\Lora\networks.py", line 503, in network_Linear_forward
return originals.Linear_forward(self, input)
File "C:\Users\dra19\OneDrive\Bureau\Test\stable-diffusion-webui-directml\venv\lib\site-packages\torch\nn\modules\linear.py", line 116, in forward
return F.linear(input, self.weight, self.bias)
RuntimeError: mat1 and mat2 must have the same dtype, but got Float and Half

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I need some guidance for this