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More shape fixes.

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This commit is contained in:
laurent
2023-09-19 13:43:19 +01:00
parent 3769d8bf71
commit ec895453cd
2 changed files with 15 additions and 7 deletions
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21
candle-examples/examples/wuerstchen/main.rs
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@ -16,6 +16,7 @@ use tokenizers::Tokenizer;
const PRIOR_GUIDANCE_SCALE: f64 = 8.0; const PRIOR_GUIDANCE_SCALE: f64 = 8.0;
const RESOLUTION_MULTIPLE: f64 = 42.67; const RESOLUTION_MULTIPLE: f64 = 42.67;
const LATENT_DIM_SCALE: f64 = 10.67;
const PRIOR_CIN: usize = 16; const PRIOR_CIN: usize = 16;
const DECODER_CIN: usize = 4; const DECODER_CIN: usize = 4;
@ -270,11 +271,11 @@ fn run(args: Args) -> Result<()> {
println!("generated text embeddings {text_embeddings:?}"); println!("generated text embeddings {text_embeddings:?}");
println!("Building the prior."); println!("Building the prior.");
// https://huggingface.co/warp-ai/wuerstchen-prior/blob/main/prior/config.json
let latent_height = (height as f64 / RESOLUTION_MULTIPLE).ceil() as usize;
let latent_width = (width as f64 / RESOLUTION_MULTIPLE).ceil() as usize;
let b_size = 1; let b_size = 1;
let effnet = { let image_embeddings = {
// https://huggingface.co/warp-ai/wuerstchen-prior/blob/main/prior/config.json
let latent_height = (height as f64 / RESOLUTION_MULTIPLE).ceil() as usize;
let latent_width = (width as f64 / RESOLUTION_MULTIPLE).ceil() as usize;
let mut latents = Tensor::randn( let mut latents = Tensor::randn(
0f32, 0f32,
1f32, 1f32,
@ -296,6 +297,7 @@ fn run(args: Args) -> Result<()> {
let timesteps = prior_scheduler.timesteps(); let timesteps = prior_scheduler.timesteps();
println!("prior denoising"); println!("prior denoising");
for (index, &t) in timesteps.iter().enumerate() { for (index, &t) in timesteps.iter().enumerate() {
continue;
let start_time = std::time::Instant::now(); let start_time = std::time::Instant::now();
if index == timesteps.len() - 1 { if index == timesteps.len() - 1 {
continue; continue;
@ -343,6 +345,10 @@ fn run(args: Args) -> Result<()> {
}; };
for idx in 0..num_samples { for idx in 0..num_samples {
// https://huggingface.co/warp-ai/wuerstchen/blob/main/model_index.json
let latent_height = (image_embeddings.dim(2)? as f64 * LATENT_DIM_SCALE) as usize;
let latent_width = (image_embeddings.dim(3)? as f64 * LATENT_DIM_SCALE) as usize;
let mut latents = Tensor::randn( let mut latents = Tensor::randn(
0f32, 0f32,
1f32, 1f32,
@ -350,7 +356,7 @@ fn run(args: Args) -> Result<()> {
&device, &device,
)?; )?;
println!("diffusion process with prior {effnet:?}"); println!("diffusion process with prior {image_embeddings:?}");
let scheduler = wuerstchen::ddpm::DDPMWScheduler::new(60, Default::default())?; let scheduler = wuerstchen::ddpm::DDPMWScheduler::new(60, Default::default())?;
let timesteps = scheduler.timesteps(); let timesteps = scheduler.timesteps();
for (index, &t) in timesteps.iter().enumerate() { for (index, &t) in timesteps.iter().enumerate() {
@ -358,8 +364,9 @@ fn run(args: Args) -> Result<()> {
if index == timesteps.len() - 1 { if index == timesteps.len() - 1 {
continue; continue;
} }
let ratio = (Tensor::ones(2, DType::F32, &device)? * t)?; let ratio = (Tensor::ones(1, DType::F32, &device)? * t)?;
let noise_pred = decoder.forward(&latents, &ratio, &effnet, Some(&text_embeddings))?; let noise_pred =
decoder.forward(&latents, &ratio, &image_embeddings, Some(&text_embeddings))?;
latents = scheduler.step(&noise_pred, t, &latents)?; latents = scheduler.step(&noise_pred, t, &latents)?;
let dt = start_time.elapsed().as_secs_f32(); let dt = start_time.elapsed().as_secs_f32();
println!("step {}/{} done, {:.2}s", index + 1, timesteps.len(), dt); println!("step {}/{} done, {:.2}s", index + 1, timesteps.len(), dt);

1
candle-transformers/src/models/wuerstchen/diffnext.rs
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@ -335,6 +335,7 @@ impl WDiffNeXt {
level_outputs.push(xs.clone()) level_outputs.push(xs.clone())
} }
level_outputs.reverse(); level_outputs.reverse();
let mut xs = level_outputs[0].clone();
for (i, up_block) in self.up_blocks.iter().enumerate() { for (i, up_block) in self.up_blocks.iter().enumerate() {
let effnet_c = match &self.effnet_mappers[self.down_blocks.len() + i] { let effnet_c = match &self.effnet_mappers[self.down_blocks.len() + i] {