Converting DreamShaper_8 results in invalid model about ml-stable-diffusion HOT 4 OPEN

Looks like there is an inconsistent latent dimension that results in an inconsistent output resolution. Can you try reexporting with --latent-h 64 --latent-w 64 to make sure HF hub config files are overridden? (This will enforce 64x64 for latents and 512x512 for the output space)

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Thanks for your answer! I tried converting the model using

python -m python_coreml_stable_diffusion.torch2coreml --convert-unet --convert-text-encoder --convert-vae-decoder --convert-safety-checker --model-version digiplay/DreamShaper_8 -o <OUTPUTNAME> --bundle-resources-for-swift-cli --latent-h 64 --latent-w 64

and inference with

./StableDiffusionSample --resource-path <PATHTOMODEL> --compute-units cpuAndGPU --scheduler dpmpp "car"

and it now WORKS! THANKS!

But: omitting the cpuAndGPU flag so the model is run with the ANE sadly results in:

Error: Unable to compute the asynchronous prediction using ML Program. It can be an invalid input data or broken/unsupported model.

Tested this on a MacBook Pro M1 Max with macOS Sonoma 14.0 (23A344). StableDiffusionSample has been compiled with Xcode 15.

from ml-stable-diffusion.

Great! For ANE inference, you will need --compute-units cpuAndNeuralEngine

from ml-stable-diffusion.

Awesome, that works great. Thanks for your support. My models now run on CPU+GPU and ANE (if enough RAM is present).

But: Is it normal that on every app relaunch, ANECompilerService takes approx. 1 to 3 minutes to compile my model for ANE?

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