Arguments: (0, 'analog style, photorealistic, RAW, 8k, perfect eyes', '', 'None', 'None', <PIL.Image.Image image mode=RGB size=1920x1080 at 0x1301DAB99F0>, None, None, None, None, 0, 20, 0, 4, 0, 1, False, False, 1, 1, 7, 0.5, -1.0, -1.0, 0, 0, 0, False, 512, 512, 0, False, 32, 0, '', '', 0, '<ul>\n<li><code>CFG Scale</code> should be 2 or lower.</li>\n</ul>\n', True, True, '', '', True, 50, True, 1, 0, False, 4, 1, '<p style="margin-bottom:0.75em">Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8</p>', 128, 8, ['left', 'right', 'up', 'down'], 1, 0.05, 128, 4, 0, ['left', 'right', 'up', 'down'], False, False, False, False, '', '<p style="margin-bottom:0.75em">Will upscale the image by the selected scale factor; use width and height sliders to set tile size</p>', 64, 0, 2, 1, '', 0, '', True, False, False) {}
Traceback (most recent call last):
  File "D:\AI\stable-diffusion-webui\modules\call_queue.py", line 45, in f
    res = list(func(*args, **kwargs))
  File "D:\AI\stable-diffusion-webui\modules\call_queue.py", line 28, in f
    res = func(*args, **kwargs)
  File "D:\AI\stable-diffusion-webui\modules\img2img.py", line 152, in img2img
    processed = process_images(p)
  File "D:\AI\stable-diffusion-webui\modules\processing.py", line 464, in process_images
    res = process_images_inner(p)
  File "D:\AI\stable-diffusion-webui\modules\processing.py", line 532, in process_images_inner
    p.init(p.all_prompts, p.all_seeds, p.all_subseeds)
  File "D:\AI\stable-diffusion-webui\modules\processing.py", line 877, in init
    self.init_latent = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image))
  File "D:\AI\stable-diffusion-webui\venv\lib\site-packages\torch\autograd\grad_mode.py", line 27, in decorate_context
    return func(*args, **kwargs)
  File "D:\AI\stable-diffusion-webui\repositories\stable-diffusion-stability-ai\ldm\models\diffusion\ddpm.py", line 830, in encode_first_stage
    return self.first_stage_model.encode(x)
  File "D:\AI\stable-diffusion-webui\repositories\stable-diffusion-stability-ai\ldm\models\autoencoder.py", line 83, in encode
    h = self.encoder(x)
  File "D:\AI\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1130, in _call_impl
    return forward_call(*input, **kwargs)
  File "D:\AI\stable-diffusion-webui\repositories\stable-diffusion-stability-ai\ldm\modules\diffusionmodules\model.py", line 536, in forward
    h = self.mid.attn_1(h)
  File "D:\AI\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1130, in _call_impl
    return forward_call(*input, **kwargs)
  File "D:\AI\stable-diffusion-webui\repositories\stable-diffusion-stability-ai\ldm\modules\diffusionmodules\model.py", line 258, in forward
    out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op)
  File "D:\AI\stable-diffusion-webui\venv\lib\site-packages\xformers\ops.py", line 858, in memory_efficient_attention
    ).op
  File "D:\AI\stable-diffusion-webui\venv\lib\site-packages\xformers\ops.py", line 726, in op
    raise NotImplementedError(f"No operator found for this attention: {self}")
NotImplementedError: No operator found for this attention: AttentionOpDispatch(dtype=torch.float32, device=device(type='cpu'), k=512, has_dropout=False, attn_bias_type=<class 'NoneType'>, kv_len=4096, q_len=4096, kv=512, batch_size=1, num_heads=1)