Comments (1)
eps696
commented on June 7, 2024
alas, that's a systemic feature, the downside of high resolution rendering. since the optimization with CLIP is limited to the model resolution (224 or 288 px), the image is being drawn by separate pieces (patches) all over the frame, and the patches know nothing about the distant others, hence the repetitions occur.
you may try changing the parameters - progressive_grow, no_text, enhance (and/or noise on continuous mode Illustra script) all somehow affect the result; but there's no guaranteed way at the moment.
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Related Issues (20)
- RuntimeError: CUDA out of memory. Tried to allocate 60.00 MiB (GPU 0; 6.00 GiB total capacity; 4.11 GiB already allocated; 34.82 MiB free; 4.42 GiB reserved in total by PyTorch) HOT 3
- Pytorch Import torch.irfft Update to torch.fft.irfft HOT 1
- Specify GPU HOT 2
- DeepSpeed integration for training on local cheaper GPUs. HOT 3
- Invalid Syntax when trying to run the first time HOT 10
- Error when running "Generate" HOT 2
- Illustrip3D - problems with video output quality HOT 3
- Can't generate video HOT 14
- init_image support HOT 4
- Colab notebook "How to just use Aphantasia" HOT 3
- clip_fft.py won't start HOT 4
- Error HOT 2
- IndexError in IllusTrip3D HOT 1
- TypeError: 'float' object is not subscriptable HOT 3
- integrate with Lightning ecosystem CI HOT 1
- Something changed since 04/19/2022 HOT 1
- Incorporating the -notext option into IllusTrip3D.ipynb HOT 2
- NameError: name 'aug_transform' is not defined HOT 1
- AssertionError: Torch not compiled with CUDA enabled HOT 1
- torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 58.00 MiB (GPU 0; 2.00 GiB total capacity; 1.58 GiB already allocated; 0 bytes free; 1.74 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF HOT 1
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