Remove some unusued bits. (#1067)

This commit is contained in:
Laurent Mazare
2023-10-09 20:49:57 +02:00
committed by GitHub
parent 4d04ac83c7
commit b34d7f0248
5 changed files with 2 additions and 18 deletions

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@ -25,7 +25,6 @@ impl ParamsConv1D {
}
}
#[allow(unused)]
#[derive(Debug, Clone, PartialEq, Eq, Hash)]
pub enum CudnnFwdAlgo {
ImplicitGemm,
@ -51,7 +50,7 @@ pub struct ParamsConv2D {
pub(crate) padding: usize,
pub(crate) stride: usize,
pub(crate) dilation: usize,
pub(crate) cudnn_fwd_algo: Option<CudnnFwdAlgo>,
pub cudnn_fwd_algo: Option<CudnnFwdAlgo>,
}
impl ParamsConv2D {

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@ -104,7 +104,6 @@ enum StableDiffusionVersion {
Xl,
}
#[allow(unused)]
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
enum ModelFile {
Tokenizer,

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@ -1,5 +1,3 @@
#![allow(unused)]
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
@ -10,7 +8,7 @@ use candle_transformers::models::stable_diffusion;
use candle_transformers::models::wuerstchen;
use anyhow::{Error as E, Result};
use candle::{DType, Device, IndexOp, Module, Tensor, D};
use candle::{DType, Device, IndexOp, Tensor};
use clap::Parser;
use tokenizers::Tokenizer;
@ -80,14 +78,6 @@ struct Args {
/// The file specifying the tokenizer to used for prior tokenization.
prior_tokenizer: Option<String>,
/// The size of the sliced attention or 0 for automatic slicing (disabled by default)
#[arg(long)]
sliced_attention_size: Option<usize>,
/// The number of steps to run the diffusion for.
#[arg(long, default_value_t = 30)]
n_steps: usize,
/// The number of samples to generate.
#[arg(long, default_value_t = 1)]
num_samples: i64,
@ -220,10 +210,8 @@ fn run(args: Args) -> Result<()> {
cpu,
height,
width,
n_steps,
tokenizer,
final_image,
sliced_attention_size,
num_samples,
clip_weights,
prior_weights,

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@ -3,7 +3,6 @@ use candle_nn::VarBuilder;
use candle_wasm_example_sam as sam;
use wasm_bindgen::prelude::*;
#[allow(unused)]
struct Embeddings {
original_width: u32,
original_height: u32,

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@ -84,7 +84,6 @@ pub struct Segment {
pub dr: DecodingResult,
}
#[allow(unused)]
pub struct Decoder {
model: Model,
rng: rand::rngs::StdRng,