Add Stable Diffusion 3 Example #2558
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Add get_qkv_linear to handle different dimensionality in linears Add stable diffusion 3 example Add use_quant_conv and use_post_quant_conv for vae in stable diffusion adapt existing AutoEncoderKLConfig to the change add forward_until_encoder_layer to ClipTextTransformer rename sd3 config to sd3_medium in mmdit; minor clean-up Enable flash-attn for mmdit impl when the feature is enabled. Add sd3 example codebase add document crediting references pass the cargo fmt test pass the clippy test
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@LaurentMazare Thank you for reminding me this. The sample image has been replaced by a JPG. The original PNG should be excluded from Git objects after squash-merging. |
LaurentMazare
approved these changes
Oct 13, 2024
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Merged, thanks a lot! |
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It appears the focus of the community has been largely shifted to Flux.dev1. So the main purpose of this PR is to demonstrate the capability of Candle and serves a smoke-test to the MMDiT (#2397).
As such, I intend to minimize the intrusive change to the existing stable-diffusion codebase, such as using renaming function to adapt the VAE var-builder to the official safetensor weights of SD3 VAE. Still, there are some changes I have to make to
candle_nn::stable_diffusionto support the CLIP and VAE of SD3, including:forward_until_encoder_layertoClipTextTransformer. The Comfy implementation for SD3 uses the penultimate hidden layer of CLIP-l and CLIP-g instead of the final layer (see sd3_clip.py and sdxl_clip.py). This practice, although not mentioned in the SD3 tech report, is referred and specified in Chapter 2.1 of the SDXL tech report.use_quant_convanduse_post_quant_convoptions to theAutoEncoderKL, as SD3's VAE does not have those layers. These changes might be considered unspecific to SD3, asdiffusershas these options supported.get_qkv_linearto load the attention block incandle_nn::stable-diffusion::attention, as some weight of linear layer of VAE in official SD3 Medium safetensors follow the dimension convention of(channel, channel, 1, 1)instead of the regular(channel, channel)that is natually supported bynn::linearconstructor.These changes allows reusing existing CLIP and VAE implementations, but inevitably add complexity to existing codebase. @LaurentMazare Let me know if these intrusive changes are justified. We may consider alternatives like re-implementing VAE and CLIP from scratch.
On top of these changes, I added the support to flash-attention for MMDiT based on whether the feature
flash-attnis enabled. Also done a simple performance benchmark on GPUs like 3090 Ti and 4090.A side note is the T5 implementation on current main branch hasn't supported for FP16. I attempted to insert simple clampings within the FP16 dynamic range but it didn't work well on my GPUs. Looks like I need to wait for a more sophiscated implementation such as #2481. So for now, I use two different VarBuilders, one maps weights in safetensor into FP32 specifically for T5, the other for the rest compoents.