VRAM error after last update

[Samotta]

Hi, after the last update, i am no longer able to generate images:
every time i try to generate an image (no matters the size) i get "There is not enough GPU video memory available!" which is really strange cause i have always been able to generate images without using arguments.
I am currently using a RX590 8GB.
The error i get is the following one:

Error completing request██████████▍ | 4/20 [00:08<00:35, 2.23s/it] Arguments: ('task(5sosf4qb01nxeoo)', 'beautiful girl', '', [], 20, 0, False, False, 1, 1, 7, -1.0, -1.0, 0, 0, 0, False, 512, 512, False, 0.7, 2, 'Latent', 0, 0, 0, 0, '', '', [], 0, False, False, 'LoRA', 'None', 1, 1, 'LoRA', 'None', 1, 1, 'LoRA', 'None', 1, 1, 'LoRA', 'None', 1, 1, 'LoRA', 'None', 1, 1, None, 'Refresh models', <controlnet.py.UiControlNetUnit object at 0x000002B7B0B5D690>, False, False, 'positive', 'comma', 0, False, False, '', 1, '', [], 0, '', [], 0, '', [], True, False, False, False, 0, None, None, False, 50) {} Traceback (most recent call last): File "E:\stable diffusion\stable-diffusion-webui-directml\modules\call_queue.py", line 57, in f res = list(func(*args, **kwargs)) File "E:\stable diffusion\stable-diffusion-webui-directml\modules\call_queue.py", line 37, in f res = func(*args, **kwargs) File "E:\stable diffusion\stable-diffusion-webui-directml\modules\txt2img.py", line 57, in txt2img processed = processing.process_images(p) File "E:\stable diffusion\stable-diffusion-webui-directml\modules\processing.py", line 611, in process_images res = process_images_inner(p) File "E:\stable diffusion\stable-diffusion-webui-directml\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 "E:\stable diffusion\stable-diffusion-webui-directml\modules\processing.py", line 729, 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 "E:\stable diffusion\stable-diffusion-webui-directml\modules\processing.py", line 977, in sample samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x)) File "E:\stable diffusion\stable-diffusion-webui-directml\modules\sd_samplers_kdiffusion.py", line 383, in sample samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={ File "E:\stable diffusion\stable-diffusion-webui-directml\modules\sd_samplers_kdiffusion.py", line 257, in launch_sampling return func() File "E:\stable diffusion\stable-diffusion-webui-directml\modules\sd_samplers_kdiffusion.py", line 383, in <lambda> samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={ File "E:\stable diffusion\stable-diffusion-webui-directml\venv\lib\site-packages\torch\utils\_contextlib.py", line 115, in decorate_context return func(*args, **kwargs) File "E:\stable diffusion\stable-diffusion-webui-directml\repositories\k-diffusion\k_diffusion\sampling.py", line 145, in sample_euler_ancestral denoised = model(x, sigmas[i] * s_in, **extra_args) File "E:\stable diffusion\stable-diffusion-webui-directml\venv\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl return forward_call(*args, **kwargs) File "E:\stable diffusion\stable-diffusion-webui-directml\modules\sd_samplers_kdiffusion.py", line 137, in forward x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict([cond_in], image_cond_in)) File "E:\stable diffusion\stable-diffusion-webui-directml\venv\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl return forward_call(*args, **kwargs) File "E:\stable diffusion\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 "E:\stable diffusion\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 "E:\stable diffusion\stable-diffusion-webui-directml\modules\sd_hijack_utils.py", line 17, in <lambda> setattr(resolved_obj, func_path[-1], lambda *args, **kwargs: self(*args, **kwargs)) File "E:\stable diffusion\stable-diffusion-webui-directml\modules\sd_hijack_utils.py", line 28, in __call__ return self.__orig_func(*args, **kwargs) File "E:\stable diffusion\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 "E:\stable diffusion\stable-diffusion-webui-directml\venv\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl return forward_call(*args, **kwargs) File "E:\stable diffusion\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 "E:\stable diffusion\stable-diffusion-webui-directml\venv\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl return forward_call(*args, **kwargs) File "E:\stable diffusion\stable-diffusion-webui-directml\repositories\stable-diffusion-stability-ai\ldm\modules\diffusionmodules\openaimodel.py", line 802, in forward h = module(h, emb, context) File "E:\stable diffusion\stable-diffusion-webui-directml\venv\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl return forward_call(*args, **kwargs) File "E:\stable diffusion\stable-diffusion-webui-directml\repositories\stable-diffusion-stability-ai\ldm\modules\diffusionmodules\openaimodel.py", line 84, in forward x = layer(x, context) File "E:\stable diffusion\stable-diffusion-webui-directml\venv\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl return forward_call(*args, **kwargs) File "E:\stable diffusion\stable-diffusion-webui-directml\repositories\stable-diffusion-stability-ai\ldm\modules\attention.py", line 334, in forward x = block(x, context=context[i]) File "E:\stable diffusion\stable-diffusion-webui-directml\venv\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl return forward_call(*args, **kwargs) File "E:\stable diffusion\stable-diffusion-webui-directml\repositories\stable-diffusion-stability-ai\ldm\modules\attention.py", line 269, in forward return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint) File "E:\stable diffusion\stable-diffusion-webui-directml\repositories\stable-diffusion-stability-ai\ldm\modules\diffusionmodules\util.py", line 121, in checkpoint return CheckpointFunction.apply(func, len(inputs), *args) File "E:\stable diffusion\stable-diffusion-webui-directml\venv\lib\site-packages\torch\autograd\function.py", line 506, in apply return super().apply(*args, **kwargs) # type: ignore[misc] File "E:\stable diffusion\stable-diffusion-webui-directml\repositories\stable-diffusion-stability-ai\ldm\modules\diffusionmodules\util.py", line 136, in forward output_tensors = ctx.run_function(*ctx.input_tensors) File "E:\stable diffusion\stable-diffusion-webui-directml\repositories\stable-diffusion-stability-ai\ldm\modules\attention.py", line 272, in _forward x = self.attn1(self.norm1(x), context=context if self.disable_self_attn else None) + x File "E:\stable diffusion\stable-diffusion-webui-directml\venv\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl return forward_call(*args, **kwargs) File "E:\stable diffusion\stable-diffusion-webui-directml\modules\sd_hijack_optimizations.py", line 365, in split_cross_attention_forward_invokeAI r = einsum_op(q, k, v) File "E:\stable diffusion\stable-diffusion-webui-directml\modules\sd_hijack_optimizations.py", line 340, in einsum_op return einsum_op_dml(q, k, v) File "E:\stable diffusion\stable-diffusion-webui-directml\modules\sd_hijack_optimizations.py", line 328, in einsum_op_dml return einsum_op_tensor_mem(q, k, v, (mem_reserved - mem_active) if mem_reserved > mem_active else 1) File "E:\stable diffusion\stable-diffusion-webui-directml\modules\sd_hijack_optimizations.py", line 313, in einsum_op_tensor_mem return einsum_op_slice_1(q, k, v, max(q.shape[1] // div, 1)) File "E:\stable diffusion\stable-diffusion-webui-directml\modules\sd_hijack_optimizations.py", line 288, in einsum_op_slice_1 r[:, i:end] = einsum_op_compvis(q[:, i:end], k, v) RuntimeError: Could not allocate tensor with 4915840 bytes. There is not enough GPU video memory available!