AnimateDiff Flicker-Free Workflow Update Ver.10
Added 2024-01-15 15:12:29 +0000 UTC
Introducing our new and improved AnimateDiff Flicker-Free workflow version 10.
Stable Diffusion Animation for easy background and character outfit color changes! With a streamlined interface, enhanced modularity, and the introduction of unique image styles through the IPA group, our updated workflow provides a seamless and intuitive experience. Transform your animations with ease, thanks to the cleaner layout and organized diagram. Unlock your creativity and explore limitless possibilities as we support you every step of the way. Get ready to elevate your animations to new heights with Stable Diffusion Animation.
Tutorial : https://www.patreon.com/posts/96537669
Happy animating!
Comments
ok i've found actually the solution by fixing the install of KJNodes in the ComfyUI Manager
Alexandre Hertoghe
2024-02-14 21:38:07 +0000 UTCHello, i've followed your advice for this error : "When loading the graph, the following node types were not found: SetNode GetNode Nodes that have failed to load will show as red on the graph." I've download your customs nodes at this adress "https://github.com/kijai/ComfyUI-KJNodes", i've cloned it in customs nodes folder then installed the requirement but i keep getting the red nodes. Any ideas where does this come from?
Alexandre Hertoghe
2024-02-14 21:32:05 +0000 UTCYes maybe your Reactor installation have problem , try update the Comfyui or just disable it if you don't want this to run
Benjamin Law
2024-01-25 06:14:56 +0000 UTCCan you Help with this Errrr please , or i should disable the reactor otherwise I get err No module named 'onnx.onnx_cpp2py_export.defs'; 'onnx.onnx_cpp2py_export' is not a package File "/home/ubuntu/ComfyUI/execution.py", line 154, in recursive_execute output_data, output_ui = get_output_data(obj, input_data_all) File "/home/ubuntu/ComfyUI/execution.py", line 84, in get_output_data return_values = map_node_over_list(obj, input_data_all, obj.FUNCTION, allow_interrupt=True) File "/home/ubuntu/ComfyUI/execution.py", line 77, in map_node_over_list results.append(getattr(obj, func)(**slice_dict(input_data_all, i))) File "/home/ubuntu/user_data/comfyui/custom_nodes/ComfyUI_IPAdapter_plus/IPAdapterPlus.py", line 537, in load_insight_face raise Exception(e)
Ali Heidari
2024-01-25 00:55:23 +0000 UTCUm... Not sure about this. By see the error reason. Looks like the Torch have some problem.
Benjamin Law
2024-01-16 19:18:21 +0000 UTCCheck out the video that I talk about this update.
Benjamin Law
2024-01-16 18:54:45 +0000 UTCany idea how to handle the cuda backend errors for the impact detailer node? am running a 3090 24gb vram and 64gb system ram Torch version: 2.1.2+cu118 Error occurred when executing ImpactSimpleDetectorSEGS_for_AD: Could not run 'torchvision::nms' with arguments from the 'CUDA' backend. This could be because the operator doesn't exist for this backend, or was omitted during the selective/custom build process (if using custom build). If you are a Facebook employee using PyTorch on mobile, please visit https://fburl.com/ptmfixes for possible resolutions. 'torchvision::nms' is only available for these backends: [CPU, QuantizedCPU, BackendSelect, Python, FuncTorchDynamicLayerBackMode, Functionalize, Named, Conjugate, Negative, ZeroTensor, ADInplaceOrView, AutogradOther, AutogradCPU, AutogradCUDA, AutogradXLA, AutogradMPS, AutogradXPU, AutogradHPU, AutogradLazy, AutogradMeta, Tracer, AutocastCPU, AutocastCUDA, FuncTorchBatched, FuncTorchVmapMode, Batched, VmapMode, FuncTorchGradWrapper, PythonTLSSnapshot, FuncTorchDynamicLayerFrontMode, PreDispatch, PythonDispatcher]. CPU: registered at C:\actions-runner\_work\vision\vision\pytorch\vision\torchvision\csrc\ops\cpu\nms_kernel.cpp:112 [kernel] QuantizedCPU: registered at C:\actions-runner\_work\vision\vision\pytorch\vision\torchvision\csrc\ops\quantized\cpu\qnms_kernel.cpp:124 [kernel] BackendSelect: fallthrough registered at ..\aten\src\ATen\core\BackendSelectFallbackKernel.cpp:3 [backend fallback] Python: registered at ..\aten\src\ATen\core\PythonFallbackKernel.cpp:153 [backend fallback] FuncTorchDynamicLayerBackMode: registered at ..\aten\src\ATen\functorch\DynamicLayer.cpp:498 [backend fallback] Functionalize: registered at ..\aten\src\ATen\FunctionalizeFallbackKernel.cpp:290 [backend fallback] Named: registered at ..\aten\src\ATen\core\NamedRegistrations.cpp:7 [backend fallback] Conjugate: registered at ..\aten\src\ATen\ConjugateFallback.cpp:17 [backend fallback] Negative: registered at ..\aten\src\ATen\native\NegateFallback.cpp:19 [backend fallback] ZeroTensor: registered at ..\aten\src\ATen\ZeroTensorFallback.cpp:86 [backend fallback] ADInplaceOrView: fallthrough registered at ..\aten\src\ATen\core\VariableFallbackKernel.cpp:86 [backend fallback] AutogradOther: registered at ..\aten\src\ATen\core\VariableFallbackKernel.cpp:53 [backend fallback] AutogradCPU: registered at ..\aten\src\ATen\core\VariableFallbackKernel.cpp:57 [backend fallback] AutogradCUDA: registered at ..\aten\src\ATen\core\VariableFallbackKernel.cpp:65 [backend fallback] AutogradXLA: registered at ..\aten\src\ATen\core\VariableFallbackKernel.cpp:69 [backend fallback] AutogradMPS: registered at ..\aten\src\ATen\core\VariableFallbackKernel.cpp:77 [backend fallback] AutogradXPU: registered at ..\aten\src\ATen\core\VariableFallbackKernel.cpp:61 [backend fallback] AutogradHPU: registered at ..\aten\src\ATen\core\VariableFallbackKernel.cpp:90 [backend fallback] AutogradLazy: registered at ..\aten\src\ATen\core\VariableFallbackKernel.cpp:73 [backend fallback] AutogradMeta: registered at ..\aten\src\ATen\core\VariableFallbackKernel.cpp:81 [backend fallback] Tracer: registered at ..\torch\csrc\autograd\TraceTypeManual.cpp:296 [backend fallback] AutocastCPU: fallthrough registered at ..\aten\src\ATen\autocast_mode.cpp:382 [backend fallback] AutocastCUDA: fallthrough registered at ..\aten\src\ATen\autocast_mode.cpp:249 [backend fallback] FuncTorchBatched: registered at ..\aten\src\ATen\functorch\LegacyBatchingRegistrations.cpp:710 [backend fallback] FuncTorchVmapMode: fallthrough registered at ..\aten\src\ATen\functorch\VmapModeRegistrations.cpp:28 [backend fallback] Batched: registered at ..\aten\src\ATen\LegacyBatchingRegistrations.cpp:1075 [backend fallback] VmapMode: fallthrough registered at ..\aten\src\ATen\VmapModeRegistrations.cpp:33 [backend fallback] FuncTorchGradWrapper: registered at ..\aten\src\ATen\functorch\TensorWrapper.cpp:203 [backend fallback] PythonTLSSnapshot: registered at ..\aten\src\ATen\core\PythonFallbackKernel.cpp:161 [backend fallback] FuncTorchDynamicLayerFrontMode: registered at ..\aten\src\ATen\functorch\DynamicLayer.cpp:494 [backend fallback] PreDispatch: registered at ..\aten\src\ATen\core\PythonFallbackKernel.cpp:165 [backend fallback] PythonDispatcher: registered at ..\aten\src\ATen\core\PythonFallbackKernel.cpp:157 [backend fallback]
Jon Taylor
2024-01-16 18:35:52 +0000 UTChow do we get your set node and get node custom nodes?
Jon Taylor
2024-01-16 17:44:44 +0000 UTCYes, that will happen for me as well. Try less frames , and it will work.
Benjamin Law
2024-01-16 06:34:30 +0000 UTCReally like how this workflow is evolving. I am getting 'out of memory' errors when hitting the face detailer though. I have a 4080 on my laptop with 12Gb Vram, and my system has 32Gb. Error is: "torch.cuda.OutOfMemoryError: Allocation on device 0 would exceed allowed memory. (out of memory) Currently allocated : 17.73 GiB Requested : 2.83 GiB Device limit : 11.99 GiB Free (according to CUDA): 0 bytes PyTorch limit (set by user-supplied memory fraction) : 17179869184.00 GiB" Any tips to allow it to run to completion?
Peter C
2024-01-16 05:26:22 +0000 UTC
