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When using SDXL+animatediff+Tgate, the following error occurs: #10
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Error occurred when executing SamplerCustom: Allocation on device 0 would exceed allowed memory. (out of memory) File "/kaggle/working/ComfyUI/execution.py", line 151, in recursive_execute |
Is it normal when t-gate is not used? Because I see that the error location is not on t-gate. |
I also find it strange, but after testing two large models, I discovered that as long as Tgate is disabled, the error no longer occurs. |
It might also be due to a plugin called Style Aligned, there are some compatibility issues.But I can't give up on Style Aligned. |
Can you provide the |
I might not have expressed myself clearly, let me add some clarification: |
Or. |
Thanks for the information, I will take a look at this issue |
It seems to be a problem with animatediff. Both animatediff and T-GATE hook
Thank you for your issue. Compatibility with animatediff will be on the agenda. |
@hahy36 hey, I updated repo yestoday. You can try the new node and new parameters. |
It has been resolved. Great job! |
Error occurred when executing SamplerCustom:
Allocation on device 0 would exceed allowed memory. (out of memory)
Currently allocated : 12.81 GiB
Requested : 480.00 MiB
Device limit : 14.75 GiB
Free (according to CUDA): 27.06 MiB
PyTorch limit (set by user-supplied memory fraction)
: 17179869184.00 GiB
File "/kaggle/working/ComfyUI/execution.py", line 151, in recursive_execute
output_data, output_ui = get_output_data(obj, input_data_all)
File "/kaggle/working/ComfyUI/execution.py", line 81, in get_output_data
return_values = map_node_over_list(obj, input_data_all, obj.FUNCTION, allow_interrupt=True)
File "/kaggle/working/ComfyUI/execution.py", line 74, in map_node_over_list
results.append(getattr(obj, func)(**slice_dict(input_data_all, i)))
File "/kaggle/working/ComfyUI/comfy_extras/nodes_custom_sampler.py", line 374, in sample
samples = comfy.sample.sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent_image, noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=noise_seed)
File "/kaggle/working/ComfyUI/custom_nodes/ComfyUI-Advanced-ControlNet/adv_control/control_reference.py", line 47, in refcn_sample
return orig_comfy_sample(model, *args, **kwargs)
File "/kaggle/working/ComfyUI/custom_nodes/ComfyUI-AnimateDiff-Evolved/animatediff/sampling.py", line 419, in motion_sample
latents = orig_comfy_sample(model, noise, *args, **kwargs)
File "/kaggle/working/ComfyUI/comfy/sample.py", line 42, in sample_custom
samples = comfy.samplers.sample(model, noise, positive, negative, cfg, model.load_device, sampler, sigmas, model_options=model.model_options, latent_image=latent_image, denoise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed)
File "/kaggle/working/ComfyUI/comfy/samplers.py", line 663, in sample
return cfg_guider.sample(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed)
File "/kaggle/working/ComfyUI/comfy/samplers.py", line 650, in sample
output = self.inner_sample(noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed)
File "/kaggle/working/ComfyUI/comfy/samplers.py", line 629, in inner_sample
samples = sampler.sample(self, sigmas, extra_args, callback, noise, latent_image, denoise_mask, disable_pbar)
File "/kaggle/working/ComfyUI/comfy/samplers.py", line 534, in sample
samples = self.sampler_function(model_k, noise, sigmas, extra_args=extra_args, callback=k_callback, disable=disable_pbar, **self.extra_options)
File "/kaggle/venv/venv/lib/python3.10/site-packages/torch/utils/contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "/kaggle/working/ComfyUI/custom_nodes/ComfyUI-sampler-lcm-alternative/sampler_lcm_alt.py", line 32, in sample_lcm_cycle
return sample_lcm_backbone(model, x, sigmas, extra_args, callback, disable, noise_sampler, loop_control, ancestral)
File "/kaggle/venv/venv/lib/python3.10/site-packages/torch/utils/contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "/kaggle/working/ComfyUI/custom_nodes/ComfyUI-sampler-lcm-alternative/sampler_lcm_alt.py", line 38, in sample_lcm_backbone
denoised = model(x, sigmas[i] * s_in, **extra_args)
File "/kaggle/working/ComfyUI/comfy/samplers.py", line 272, in call
out = self.inner_model(x, sigma, model_options=model_options, seed=seed)
File "/kaggle/working/ComfyUI/comfy/samplers.py", line 616, in call
return self.predict_noise(*args, **kwargs)
File "/kaggle/working/ComfyUI/comfy/samplers.py", line 619, in predict_noise
return sampling_function(self.inner_model, x, timestep, self.conds.get("negative", None), self.conds.get("positive", None), self.cfg, model_options=model_options, seed=seed)
File "/kaggle/working/ComfyUI/custom_nodes/ComfyUI-AnimateDiff-Evolved/animatediff/sampling.py", line 456, in evolved_sampling_function
cond_pred, uncond_pred = sliding_calc_conds_batch(model, [cond, uncond], x, timestep, model_options)
File "/kaggle/working/ComfyUI/custom_nodes/ComfyUI-AnimateDiff-Evolved/animatediff/sampling.py", line 593, in sliding_calc_conds_batch
sub_conds_out = calc_cond_uncond_batch_wrapper(model, sub_conds, sub_x, sub_timestep, model_options)
File "/kaggle/working/ComfyUI/custom_nodes/ComfyUI-AnimateDiff-Evolved/animatediff/sampling.py", line 653, in calc_cond_uncond_batch_wrapper
return comfy.samplers.calc_cond_batch(model, conds, x_in, timestep, model_options)
File "/kaggle/working/ComfyUI/comfy/samplers.py", line 218, in calc_cond_batch
output = model.apply_model(input_x, timestep, **c).chunk(batch_chunks)
File "/kaggle/working/ComfyUI/comfy/model_base.py", line 97, in apply_model
model_output = self.diffusion_model(xc, t, context=context, control=control, transformer_options=transformer_options, **extra_conds).float()
File "/kaggle/venv/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/kaggle/venv/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
return forward_call(*args, **kwargs)
File "/kaggle/working/ComfyUI/comfy/ldm/modules/diffusionmodules/openaimodel.py", line 885, in forward
h = forward_timestep_embed(module, h, emb, context, transformer_options, output_shape, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator)
File "/kaggle/working/ComfyUI/custom_nodes/ComfyUI-AnimateDiff-Evolved/animatediff/sampling.py", line 130, in forward_timestep_embed
x = layer(x, emb)
File "/kaggle/venv/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/kaggle/venv/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
return forward_call(*args, **kwargs)
File "/kaggle/working/ComfyUI/comfy/ldm/modules/diffusionmodules/openaimodel.py", line 230, in forward
return checkpoint(
File "/kaggle/working/ComfyUI/comfy/ldm/modules/diffusionmodules/util.py", line 191, in checkpoint
return func(*inputs)
File "/kaggle/working/ComfyUI/comfy/ldm/modules/diffusionmodules/openaimodel.py", line 243, in _forward
h = self.in_layers(x)
File "/kaggle/venv/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/kaggle/venv/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
return forward_call(*args, **kwargs)
File "/kaggle/venv/venv/lib/python3.10/site-packages/torch/nn/modules/container.py", line 217, in forward
input = module(input)
File "/kaggle/venv/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/kaggle/venv/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
return forward_call(*args, **kwargs)
File "/kaggle/working/ComfyUI/comfy/ops.py", line 94, in forward
return super().forward(*args, **kwargs)
File "/kaggle/working/ComfyUI/custom_nodes/ComfyUI-AnimateDiff-Evolved/animatediff/sampling.py", line 171, in groupnorm_mm_forward
input = group_norm(input, self.num_groups, weight, bias, self.eps)
File "/kaggle/venv/venv/lib/python3.10/site-packages/torch/nn/functional.py", line 2561, in group_norm
return torch.group_norm(input, num_groups, weight, bias, eps, torch.backends.cudnn.enabled)
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