feffy380
commented
8 months ago Needs more info, like the full error log
Open AndreyRGW opened 8 months ago
feffy380
commented
8 months ago Needs more info, like the full error log
AndreyRGW
commented
7 months ago 0%| | 0/8 [00:00<?, ?it/s]
*** Error completing request
*** Arguments: ('task(3eofx1k8bts0fu1)', <gradio.routes.Request object at 0x00000254978CF070>, 'Playstation 1 Graphics, PS1 Game, N64style\nscifi horror game, a city, early winter, dark blue hues, fog.\n<lora:PS1Redmond-PS1Game-Playstation1Graphics:0.7> <lora:N64style:0.75>', 'bad art, ugly, deformed, ugly, watermark, text, colorful, high contrast', [], 1, 1, 2, 360, 640, True, 0.501, 2.5, 'Latent (nearest-exact)', 4, 0, 0, 'Use same checkpoint', 'DPM++ 2M', 'Use same scheduler', '', '', [], 0, 8, 'Euler a', 'Uniform', False, '', 0.8, 473607612, False, -1, 0, 0, 0, False, False, 20, 4, 4, 0.4, 0.95, 2, 2, 0.4, 0.5, False, 1, False, UiControlNetUnit(enabled=False, module='none', model='None', weight=1, image=None, resize_mode='Crop and Resize', low_vram=False, processor_res=-1, threshold_a=-1, threshold_b=-1, guidance_start=0, guidance_end=1, pixel_perfect=False, control_mode='Balanced', inpaint_crop_input_image=False, hr_option='Both', save_detected_map=True, advanced_weighting=None), UiControlNetUnit(enabled=False, module='none', model='None', weight=1, image=None, resize_mode='Crop and Resize', low_vram=False, processor_res=-1, threshold_a=-1, threshold_b=-1, guidance_start=0, guidance_end=1, pixel_perfect=False, control_mode='Balanced', inpaint_crop_input_image=False, hr_option='Both', save_detected_map=True, advanced_weighting=None), UiControlNetUnit(enabled=False, module='none', model='None', weight=1, image=None, resize_mode='Crop and Resize', low_vram=False, processor_res=-1, threshold_a=-1, threshold_b=-1, guidance_start=0, guidance_end=1, pixel_perfect=False, control_mode='Balanced', inpaint_crop_input_image=False, hr_option='Both', save_detected_map=True, advanced_weighting=None), False, '1.5', 0, False, 0.01, 0.5, -0.13, 0, 0, 0, 0, False, 0, 1, 0, 'Version 2', 1.2, 0.9, 0, 0.5, 0, 1, 1.4, 0.2, 0, 0.5, 0, 1, 1, 1, 0, 0.5, 0, 1, 0, False, 'Default', 'Default', 1, False, 0, False, 0, 0, 0, 0, False, True, 3, 4, 0.15, 0.3, 'bicubic', 1, 2, True, False, None, False, '0', '0', 'inswapper_128.onnx', 'CodeFormer', 1, True, 'None', 1, 1, False, True, 1, 0, 0, False, 0.5, True, False, 'CUDA', False, 0, 'None', '', None, False, False, 0.5, 0, False, 0, 0, False, False, 0, 0, 1, 0, 0, 0, False, False, 'Straight Abs.', 'Flat', False, False, 'positive', 'comma', 0, False, False, 'start', '', 1, '', [], 0, '', [], 0, '', [], True, False, False, False, False, False, False, 0, False, None, None, False, None, None, False, None, None, False, 50, 7, 1.5, True, '16bpc', '.tiff', 1.2) {}
Traceback (most recent call last):
File "C:\wbcnvme\stable-diffusion-webui\modules\call_queue.py", line 57, in f
res = list(func(*args, **kwargs))
File "C:\wbcnvme\stable-diffusion-webui\modules\call_queue.py", line 36, in f
res = func(*args, **kwargs)
File "C:\wbcnvme\stable-diffusion-webui\modules\txt2img.py", line 109, in txt2img
processed = processing.process_images(p)
File "C:\wbcnvme\stable-diffusion-webui\modules\processing.py", line 794, in process_images
res = process_images_inner(p)
File "C:\wbcnvme\stable-diffusion-webui\extensions\sd-webui-controlnet\scripts\batch_hijack.py", line 59, in processing_process_images_hijack
return getattr(processing, '__controlnet_original_process_images_inner')(p, *args, **kwargs)
File "C:\wbcnvme\stable-diffusion-webui\modules\processing.py", line 930, 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:\wbcnvme\stable-diffusion-webui\modules\processing.py", line 1266, in sample
samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
File "C:\wbcnvme\stable-diffusion-webui\modules\sd_samplers_kdiffusion.py", line 218, 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:\wbcnvme\stable-diffusion-webui\modules\sd_samplers_common.py", line 272, in launch_sampling
return func()
File "C:\wbcnvme\stable-diffusion-webui\modules\sd_samplers_kdiffusion.py", line 218, 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:\wbcnvme\stable-diffusion-webui\venv\lib\site-packages\torch\utils\_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "C:\wbcnvme\stable-diffusion-webui\repositories\k-diffusion\k_diffusion\sampling.py", line 145, in sample_euler_ancestral
denoised = model(x, sigmas[i] * s_in, **extra_args)
File "C:\wbcnvme\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "C:\wbcnvme\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1527, in _call_impl
return forward_call(*args, **kwargs)
File "C:\wbcnvme\stable-diffusion-webui\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:\wbcnvme\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "C:\wbcnvme\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1527, in _call_impl
return forward_call(*args, **kwargs)
File "C:\wbcnvme\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 "C:\wbcnvme\stable-diffusion-webui\repositories\k-diffusion\k_diffusion\external.py", line 138, in get_eps
return self.inner_model.apply_model(*args, **kwargs)
File "C:\wbcnvme\stable-diffusion-webui\modules\sd_models_xl.py", line 44, in apply_model
return self.model(x, t, cond)
File "C:\wbcnvme\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "C:\wbcnvme\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1527, in _call_impl
return forward_call(*args, **kwargs)
File "C:\wbcnvme\stable-diffusion-webui\modules\sd_hijack_utils.py", line 18, in <lambda>
setattr(resolved_obj, func_path[-1], lambda *args, **kwargs: self(*args, **kwargs))
File "C:\wbcnvme\stable-diffusion-webui\modules\sd_hijack_utils.py", line 32, in __call__
return self.__orig_func(*args, **kwargs)
File "C:\wbcnvme\stable-diffusion-webui\repositories\generative-models\sgm\modules\diffusionmodules\wrappers.py", line 28, in forward
return self.diffusion_model(
File "C:\wbcnvme\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "C:\wbcnvme\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1568, in _call_impl
result = forward_call(*args, **kwargs)
File "C:\wbcnvme\stable-diffusion-webui\modules\sd_unet.py", line 91, in UNetModel_forward
return original_forward(self, x, timesteps, context, *args, **kwargs)
File "C:\wbcnvme\stable-diffusion-webui\repositories\generative-models\sgm\modules\diffusionmodules\openaimodel.py", line 993, in forward
h = module(h, emb, context)
File "C:\wbcnvme\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "C:\wbcnvme\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1527, in _call_impl
return forward_call(*args, **kwargs)
File "C:\wbcnvme\stable-diffusion-webui\repositories\generative-models\sgm\modules\diffusionmodules\openaimodel.py", line 100, in forward
x = layer(x, context)
File "C:\wbcnvme\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "C:\wbcnvme\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1527, in _call_impl
return forward_call(*args, **kwargs)
File "C:\wbcnvme\stable-diffusion-webui\repositories\generative-models\sgm\modules\attention.py", line 627, in forward
x = block(x, context=context[i])
File "C:\wbcnvme\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "C:\wbcnvme\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1527, in _call_impl
return forward_call(*args, **kwargs)
File "C:\wbcnvme\stable-diffusion-webui\repositories\generative-models\sgm\modules\attention.py", line 459, in forward
return checkpoint(
File "C:\wbcnvme\stable-diffusion-webui\repositories\generative-models\sgm\modules\diffusionmodules\util.py", line 165, in checkpoint
return CheckpointFunction.apply(func, len(inputs), *args)
File "C:\wbcnvme\stable-diffusion-webui\venv\lib\site-packages\torch\autograd\function.py", line 539, in apply
return super().apply(*args, **kwargs) # type: ignore[misc]
File "C:\wbcnvme\stable-diffusion-webui\repositories\generative-models\sgm\modules\diffusionmodules\util.py", line 182, in forward
output_tensors = ctx.run_function(*ctx.input_tensors)
File "C:\wbcnvme\stable-diffusion-webui\repositories\generative-models\sgm\modules\attention.py", line 467, in _forward
self.attn1(
File "C:\wbcnvme\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "C:\wbcnvme\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1568, in _call_impl
result = forward_call(*args, **kwargs)
File "C:\wbcnvme\stable-diffusion-webui\extensions-builtin\hypertile\hypertile.py", line 307, in wrapper
out = params.forward(x, *args[1:], **kwargs)
File "C:\wbcnvme\stable-diffusion-webui\modules\sd_hijack_optimizations.py", line 496, in xformers_attention_forward
out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=get_xformers_flash_attention_op(q, k, v))
File "C:\wbcnvme\stable-diffusion-webui\venv\lib\site-packages\xformers\ops\fmha\__init__.py", line 223, in memory_efficient_attention
return _memory_efficient_attention(
File "C:\wbcnvme\stable-diffusion-webui\venv\lib\site-packages\xformers\ops\fmha\__init__.py", line 321, in _memory_efficient_attention
return _memory_efficient_attention_forward(
File "C:\wbcnvme\stable-diffusion-webui\venv\lib\site-packages\xformers\ops\fmha\__init__.py", line 334, in _memory_efficient_attention_forward
inp.validate_inputs()
File "C:\wbcnvme\stable-diffusion-webui\venv\lib\site-packages\xformers\ops\fmha\common.py", line 197, in validate_inputs
raise ValueError(
ValueError: Incompatible shapes for attention inputs:
query.shape: torch.Size([4, 460, 10, 64])
key.shape : torch.Size([2, 220, 10, 64])
value.shape: torch.Size([2, 220, 10, 64])
HINT: We don't support broadcasting, please use `expand` yourself before calling `memory_efficient_attention` if you need to
---I think it's working with torch sdp
couldnt-find-good-name
commented
3 months ago I think Xformers is causing the issue, Hence in SD forge i dont have this issue but in SD A1111 i have this issue. Both my settings/configs are same in SD forge & A1111 only difference in SD forge , i dont have Xformers installed
Running on sdxl lightning