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Start adding the Wuerstchen diffusion pipeline (#843)
* Wuerstchen common bits. * Add the prior layer. * Start adding diffnext.
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55
candle-transformers/src/models/wuerstchen/diffnext.rs
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55
candle-transformers/src/models/wuerstchen/diffnext.rs
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#![allow(unused)]
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use super::common::{GlobalResponseNorm, ResBlock, TimestepBlock, WLayerNorm};
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use candle::{DType, Module, Result, Tensor, D};
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use candle_nn::VarBuilder;
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#[derive(Debug)]
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pub struct ResBlockStageB {
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depthwise: candle_nn::Conv2d,
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norm: WLayerNorm,
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channelwise_lin1: candle_nn::Linear,
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channelwise_grn: GlobalResponseNorm,
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channelwise_lin2: candle_nn::Linear,
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}
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impl ResBlockStageB {
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pub fn new(c: usize, c_skip: usize, ksize: usize, vb: VarBuilder) -> Result<Self> {
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let cfg = candle_nn::Conv2dConfig {
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groups: c,
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padding: ksize / 2,
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..Default::default()
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};
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let depthwise = candle_nn::conv2d(c, c, ksize, cfg, vb.pp("depthwise"))?;
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let norm = WLayerNorm::new(c, vb.pp("norm"))?;
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let channelwise_lin1 = candle_nn::linear(c + c_skip, c * 4, vb.pp("channelwise.0"))?;
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let channelwise_grn = GlobalResponseNorm::new(4 * c, vb.pp("channelwise.2"))?;
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let channelwise_lin2 = candle_nn::linear(c * 4, c, vb.pp("channelwise.4"))?;
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Ok(Self {
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depthwise,
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norm,
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channelwise_lin1,
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channelwise_grn,
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channelwise_lin2,
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})
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}
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pub fn forward(&self, xs: &Tensor, x_skip: Option<&Tensor>) -> Result<Tensor> {
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let x_res = xs;
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let xs = xs.apply(&self.depthwise)?.apply(&self.norm)?;
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let xs = match x_skip {
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None => xs.clone(),
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Some(x_skip) => Tensor::cat(&[&xs, x_skip], 1)?,
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};
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let xs = xs
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.permute((0, 2, 3, 1))?
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.apply(&self.channelwise_lin1)?
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.gelu()?
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.apply(&self.channelwise_grn)?
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.apply(&self.channelwise_lin2)?
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.permute((0, 3, 1, 2))?;
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xs + x_res
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}
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}
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#[derive(Debug)]
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pub struct WDiffNeXt {}
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