bulutharbeli
commented
1 year ago
Traceback (most recent call last): File "I:\AI\stable-diffusion-webui\modules\call_queue.py", line 57, in f res = list(func(*args, **kwargs)) File "I:\AI\stable-diffusion-webui\modules\call_queue.py", line 36, in f res = func(*args, **kwargs) File "I:\AI\stable-diffusion-webui\modules\txt2img.py", line 55, in txt2img processed = processing.process_images(p) File "I:\AI\stable-diffusion-webui\modules\processing.py", line 732, in process_images res = process_images_inner(p) File "I:\AI\stable-diffusion-webui\extensions\sd-webui-controlnet\scripts\batch_hijack.py", line 42, in processing_process_images_hijack return getattr(processing, '__controlnet_original_process_images_inner')(p, *args, **kwargs) File "I:\AI\stable-diffusion-webui\modules\processing.py", line 867, 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 "I:\AI\stable-diffusion-webui\extensions\sd-webui-controlnet\scripts\hook.py", line 451, in process_sample return process.sample_before_CN_hack(*args, **kwargs) File "I:\AI\stable-diffusion-webui\modules\processing.py", line 1140, in sample samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x)) File "I:\AI\stable-diffusion-webui\modules\sd_samplers_kdiffusion.py", line 235, 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 "I:\AI\stable-diffusion-webui\modules\sd_samplers_common.py", line 261, in launch_sampling return func() File "I:\AI\stable-diffusion-webui\modules\sd_samplers_kdiffusion.py", line 235, 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 "I:\AI\stable-diffusion-webui\venv\lib\site-packages\torch\autograd\grad_mode.py", line 27, in decorate_context return func(*args, **kwargs) File "I:\AI\stable-diffusion-webui\repositories\k-diffusion\k_diffusion\sampling.py", line 518, in sample_dpmpp_2s_ancestral denoised = model(x, sigmas[i] * s_in, **extra_args) File "I:\AI\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl return forward_call(*input, **kwargs) File "I:\AI\stable-diffusion-webui\modules\sd_samplers_cfg_denoiser.py", line 188, in forward x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(c_crossattn, image_cond_in[a:b])) File "I:\AI\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl return forward_call(*input, **kwargs) File "I:\AI\stable-diffusion-webui\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 "I:\AI\stable-diffusion-webui\repositories\k-diffusion\k_diffusion\external.py", line 138, in get_eps return self.inner_model.apply_model(*args, **kwargs) File "I:\AI\stable-diffusion-webui\modules\sd_models_xl.py", line 37, in apply_model return self.model(x, t, cond) File "I:\AI\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl return forward_call(*input, **kwargs) File "I:\AI\stable-diffusion-webui\modules\sd_hijack_utils.py", line 17, in <lambda> setattr(resolved_obj, func_path[-1], lambda *args, **kwargs: self(*args, **kwargs)) File "I:\AI\stable-diffusion-webui\modules\sd_hijack_utils.py", line 28, in __call__ return self.__orig_func(*args, **kwargs) File "I:\AI\stable-diffusion-webui\repositories\generative-models\sgm\modules\diffusionmodules\wrappers.py", line 28, in forward return self.diffusion_model( File "I:\AI\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl return forward_call(*input, **kwargs) File "I:\AI\stable-diffusion-webui\extensions\sd-webui-controlnet\scripts\hook.py", line 858, in forward_webui raise e File "I:\AI\stable-diffusion-webui\extensions\sd-webui-controlnet\scripts\hook.py", line 855, in forward_webui return forward(*args, **kwargs) File "I:\AI\stable-diffusion-webui\extensions\sd-webui-controlnet\scripts\hook.py", line 762, in forward h = module(h, emb, context) File "I:\AI\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1212, in _call_impl result = forward_call(*input, **kwargs) File "I:\AI\stable-diffusion-webui\repositories\generative-models\sgm\modules\diffusionmodules\openaimodel.py", line 100, in forward x = layer(x, context) File "I:\AI\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl return forward_call(*input, **kwargs) File "I:\AI\stable-diffusion-webui\repositories\generative-models\sgm\modules\attention.py", line 627, in forward x = block(x, context=context[i]) File "I:\AI\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl return forward_call(*input, **kwargs) File "I:\AI\stable-diffusion-webui\repositories\generative-models\sgm\modules\attention.py", line 459, in forward return checkpoint( File "I:\AI\stable-diffusion-webui\repositories\generative-models\sgm\modules\diffusionmodules\util.py", line 165, in checkpoint return CheckpointFunction.apply(func, len(inputs), *args) File "I:\AI\stable-diffusion-webui\repositories\generative-models\sgm\modules\diffusionmodules\util.py", line 182, in forward output_tensors = ctx.run_function(*ctx.input_tensors) File "I:\AI\stable-diffusion-webui\repositories\generative-models\sgm\modules\attention.py", line 478, in _forward self.attn2( File "I:\AI\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl return forward_call(*input, **kwargs) File "I:\AI\stable-diffusion-webui\extensions\sd-webui-controlnet\scripts\controlmodel_ipadapter.py", line 240, in attn_forward_hacked out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False) AttributeError: module 'torch.nn.functional' has no attribute 'scaled_dot_product_attention'
here is also ss my setup
Traceback (most recent call last): File "I:\AI\stable-diffusion-webui\modules\call_queue.py", line 57, in f res = list(func(*args, **kwargs)) File "I:\AI\stable-diffusion-webui\modules\call_queue.py", line 36, in f res = func(*args, **kwargs) File "I:\AI\stable-diffusion-webui\modules\txt2img.py", line 55, in txt2img processed = processing.process_images(p) File "I:\AI\stable-diffusion-webui\modules\processing.py", line 732, in process_images res = process_images_inner(p) File "I:\AI\stable-diffusion-webui\extensions\sd-webui-controlnet\scripts\batch_hijack.py", line 42, in processing_process_images_hijack return getattr(processing, '__controlnet_original_process_images_inner')(p, *args, **kwargs) File "I:\AI\stable-diffusion-webui\modules\processing.py", line 867, 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 "I:\AI\stable-diffusion-webui\extensions\sd-webui-controlnet\scripts\hook.py", line 451, in process_sample return process.sample_before_CN_hack(*args, **kwargs) File "I:\AI\stable-diffusion-webui\modules\processing.py", line 1140, in sample samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x)) File "I:\AI\stable-diffusion-webui\modules\sd_samplers_kdiffusion.py", line 235, 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 "I:\AI\stable-diffusion-webui\modules\sd_samplers_common.py", line 261, in launch_sampling return func() File "I:\AI\stable-diffusion-webui\modules\sd_samplers_kdiffusion.py", line 235, 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 "I:\AI\stable-diffusion-webui\venv\lib\site-packages\torch\autograd\grad_mode.py", line 27, in decorate_context return func(*args, **kwargs) File "I:\AI\stable-diffusion-webui\repositories\k-diffusion\k_diffusion\sampling.py", line 518, in sample_dpmpp_2s_ancestral denoised = model(x, sigmas[i] * s_in, **extra_args) File "I:\AI\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl return forward_call(*input, **kwargs) File "I:\AI\stable-diffusion-webui\modules\sd_samplers_cfg_denoiser.py", line 188, in forward x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(c_crossattn, image_cond_in[a:b])) File "I:\AI\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl return forward_call(*input, **kwargs) File "I:\AI\stable-diffusion-webui\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 "I:\AI\stable-diffusion-webui\repositories\k-diffusion\k_diffusion\external.py", line 138, in get_eps return self.inner_model.apply_model(*args, **kwargs) File "I:\AI\stable-diffusion-webui\modules\sd_models_xl.py", line 37, in apply_model return self.model(x, t, cond) File "I:\AI\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl return forward_call(*input, **kwargs) File "I:\AI\stable-diffusion-webui\modules\sd_hijack_utils.py", line 17, in <lambda> setattr(resolved_obj, func_path[-1], lambda *args, **kwargs: self(*args, **kwargs)) File "I:\AI\stable-diffusion-webui\modules\sd_hijack_utils.py", line 28, in __call__ return self.__orig_func(*args, **kwargs) File "I:\AI\stable-diffusion-webui\repositories\generative-models\sgm\modules\diffusionmodules\wrappers.py", line 28, in forward return self.diffusion_model( File "I:\AI\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl return forward_call(*input, **kwargs) File "I:\AI\stable-diffusion-webui\extensions\sd-webui-controlnet\scripts\hook.py", line 858, in forward_webui raise e File "I:\AI\stable-diffusion-webui\extensions\sd-webui-controlnet\scripts\hook.py", line 855, in forward_webui return forward(*args, **kwargs) File "I:\AI\stable-diffusion-webui\extensions\sd-webui-controlnet\scripts\hook.py", line 762, in forward h = module(h, emb, context) File "I:\AI\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1212, in _call_impl result = forward_call(*input, **kwargs) File "I:\AI\stable-diffusion-webui\repositories\generative-models\sgm\modules\diffusionmodules\openaimodel.py", line 100, in forward x = layer(x, context) File "I:\AI\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl return forward_call(*input, **kwargs) File "I:\AI\stable-diffusion-webui\repositories\generative-models\sgm\modules\attention.py", line 627, in forward x = block(x, context=context[i]) File "I:\AI\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl return forward_call(*input, **kwargs) File "I:\AI\stable-diffusion-webui\repositories\generative-models\sgm\modules\attention.py", line 459, in forward return checkpoint( File "I:\AI\stable-diffusion-webui\repositories\generative-models\sgm\modules\diffusionmodules\util.py", line 165, in checkpoint return CheckpointFunction.apply(func, len(inputs), *args) File "I:\AI\stable-diffusion-webui\repositories\generative-models\sgm\modules\diffusionmodules\util.py", line 182, in forward output_tensors = ctx.run_function(*ctx.input_tensors) File "I:\AI\stable-diffusion-webui\repositories\generative-models\sgm\modules\attention.py", line 478, in _forward self.attn2( File "I:\AI\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl return forward_call(*input, **kwargs) File "I:\AI\stable-diffusion-webui\extensions\sd-webui-controlnet\scripts\controlmodel_ipadapter.py", line 240, in attn_forward_hacked out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False) AttributeError: module 'torch.nn.functional' has no attribute 'scaled_dot_product_attention'