Comments (3)
Ok, I recompiled VAEEncoder myself with fp32 precision and the problem gone
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If you would like to get back to float16, you could also use madebyollin/sdxl-vae-fp16-fix as the VAE for SDXL (further instructions in the XL README section)
from ml-stable-diffusion.
yes, I know, but when I applied the fix using custom vae argument, I only got VAEDecoder as fp16, VAEEncoder still in fp32
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Related Issues (20)
- Model conversion failed HOT 3
- Conversion will be much slower on Python 3.9.6 HOT 3
- Only one controlnet is supported
- Memory Issues on 4 GB iOS/iPadOS devices HOT 4
- loadResources took long time to load UNET HOT 6
- mixed_bit_compression_pre_analysis: Weird recipe results HOT 4
- Colorspace HOT 1
- Memory βLeakβ when using cpu+gpu HOT 6
- SDXL produces black output images when given a starting image HOT 3
- Memory Crash on iPhone12 Pro Max HOT 10
- Adding multiple ControlNets has no effect HOT 1
- Flag quantize-nbits returns error HOT 9
- SDXL Turbo support? HOT 5
- EnumeratedShapes horrible performance
- How to modify or update the model weight in swift? HOT 2
- Crashes on getting `currentImages`
- When will training be implemented?
- Torch2coreml multiprocessing
- Won't work on GPU on macOS 14.4 Beta 1 due to CoreML bug HOT 3
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