mirror of
https://github.com/huggingface/candle.git
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Add inpainting to the stable diffusion example (#2735)
* Update the stable diffusion example with inpainting support for 1.5, 2 and XL. * Apply cargo fmt. * Clippy fixes. --------- Co-authored-by: laurent <laurent.mazare@gmail.com>
This commit is contained in:
mneilly
@ -5,10 +5,12 @@ extern crate accelerate_src;
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extern crate intel_mkl_src;
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use candle_transformers::models::stable_diffusion;
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use std::ops::Div;
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use anyhow::{Error as E, Result};
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use candle::{DType, Device, IndexOp, Module, Tensor, D};
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use clap::Parser;
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use rand::Rng;
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use stable_diffusion::vae::AutoEncoderKL;
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use tokenizers::Tokenizer;
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@ -49,6 +51,10 @@ struct Args {
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#[arg(long, value_name = "FILE")]
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clip_weights: Option<String>,
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/// The CLIP2 weight file, in .safetensors format.
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#[arg(long, value_name = "FILE")]
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clip2_weights: Option<String>,
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/// The VAE weight file, in .safetensors format.
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#[arg(long, value_name = "FILE")]
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vae_weights: Option<String>,
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@ -93,6 +99,11 @@ struct Args {
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#[arg(long)]
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guidance_scale: Option<f64>,
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/// Path to the mask image for inpainting.
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#[arg(long, value_name = "FILE")]
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mask_path: Option<String>,
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/// Path to the image used to initialize the latents. For inpainting, this is the image to be masked.
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#[arg(long, value_name = "FILE")]
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img2img: Option<String>,
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@ -105,13 +116,20 @@ struct Args {
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/// The seed to use when generating random samples.
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#[arg(long)]
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seed: Option<u64>,
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/// Force the saved image to update only the masked region
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#[arg(long)]
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only_update_masked: bool,
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}
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#[derive(Debug, Clone, Copy, clap::ValueEnum, PartialEq, Eq)]
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enum StableDiffusionVersion {
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V1_5,
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V1_5Inpaint,
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V2_1,
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V2Inpaint,
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Xl,
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XlInpaint,
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Turbo,
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}
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@ -128,16 +146,25 @@ enum ModelFile {
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impl StableDiffusionVersion {
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fn repo(&self) -> &'static str {
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match self {
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Self::XlInpaint => "diffusers/stable-diffusion-xl-1.0-inpainting-0.1",
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Self::Xl => "stabilityai/stable-diffusion-xl-base-1.0",
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Self::V2Inpaint => "stabilityai/stable-diffusion-2-inpainting",
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Self::V2_1 => "stabilityai/stable-diffusion-2-1",
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Self::V1_5 => "runwayml/stable-diffusion-v1-5",
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Self::V1_5Inpaint => "stable-diffusion-v1-5/stable-diffusion-inpainting",
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Self::Turbo => "stabilityai/sdxl-turbo",
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}
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}
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fn unet_file(&self, use_f16: bool) -> &'static str {
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match self {
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Self::V1_5 | Self::V2_1 | Self::Xl | Self::Turbo => {
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Self::V1_5
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| Self::V1_5Inpaint
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| Self::V2_1
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| Self::V2Inpaint
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| Self::Xl
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| Self::XlInpaint
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| Self::Turbo => {
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if use_f16 {
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"unet/diffusion_pytorch_model.fp16.safetensors"
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} else {
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@ -149,7 +176,13 @@ impl StableDiffusionVersion {
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fn vae_file(&self, use_f16: bool) -> &'static str {
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match self {
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Self::V1_5 | Self::V2_1 | Self::Xl | Self::Turbo => {
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Self::V1_5
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| Self::V1_5Inpaint
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| Self::V2_1
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| Self::V2Inpaint
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| Self::Xl
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| Self::XlInpaint
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| Self::Turbo => {
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if use_f16 {
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"vae/diffusion_pytorch_model.fp16.safetensors"
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} else {
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@ -161,7 +194,13 @@ impl StableDiffusionVersion {
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fn clip_file(&self, use_f16: bool) -> &'static str {
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match self {
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Self::V1_5 | Self::V2_1 | Self::Xl | Self::Turbo => {
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Self::V1_5
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| Self::V1_5Inpaint
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| Self::V2_1
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| Self::V2Inpaint
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| Self::Xl
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| Self::XlInpaint
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| Self::Turbo => {
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if use_f16 {
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"text_encoder/model.fp16.safetensors"
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} else {
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@ -173,7 +212,13 @@ impl StableDiffusionVersion {
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fn clip2_file(&self, use_f16: bool) -> &'static str {
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match self {
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Self::V1_5 | Self::V2_1 | Self::Xl | Self::Turbo => {
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Self::V1_5
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| Self::V1_5Inpaint
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| Self::V2_1
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| Self::V2Inpaint
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| Self::Xl
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| Self::XlInpaint
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| Self::Turbo => {
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if use_f16 {
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"text_encoder_2/model.fp16.safetensors"
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} else {
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@ -198,10 +243,13 @@ impl ModelFile {
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let (repo, path) = match self {
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Self::Tokenizer => {
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let tokenizer_repo = match version {
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StableDiffusionVersion::V1_5 | StableDiffusionVersion::V2_1 => {
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"openai/clip-vit-base-patch32"
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}
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StableDiffusionVersion::Xl | StableDiffusionVersion::Turbo => {
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StableDiffusionVersion::V1_5
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| StableDiffusionVersion::V2_1
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| StableDiffusionVersion::V1_5Inpaint
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| StableDiffusionVersion::V2Inpaint => "openai/clip-vit-base-patch32",
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StableDiffusionVersion::Xl
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| StableDiffusionVersion::XlInpaint
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| StableDiffusionVersion::Turbo => {
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// This seems similar to the patch32 version except some very small
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// difference in the split regex.
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"openai/clip-vit-large-patch14"
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@ -299,6 +347,7 @@ fn text_embeddings(
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uncond_prompt: &str,
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tokenizer: Option<String>,
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clip_weights: Option<String>,
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clip2_weights: Option<String>,
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sd_version: StableDiffusionVersion,
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sd_config: &stable_diffusion::StableDiffusionConfig,
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use_f16: bool,
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@ -342,7 +391,11 @@ fn text_embeddings(
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} else {
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ModelFile::Clip2
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};
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let clip_weights = clip_weights_file.get(clip_weights, sd_version, false)?;
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let clip_weights = if first {
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clip_weights_file.get(clip_weights, sd_version, use_f16)?
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} else {
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clip_weights_file.get(clip2_weights, sd_version, use_f16)?
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};
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let clip_config = if first {
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&sd_config.clip
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} else {
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@ -399,6 +452,82 @@ fn image_preprocess<T: AsRef<std::path::Path>>(path: T) -> anyhow::Result<Tensor
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Ok(img)
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}
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/// Convert the mask image to a single channel tensor. Also ensure the image is a multiple of 32 in both dimensions.
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fn mask_preprocess<T: AsRef<std::path::Path>>(path: T) -> anyhow::Result<Tensor> {
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let img = image::open(path)?.to_luma8();
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let (new_width, new_height) = {
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let (width, height) = img.dimensions();
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(width - width % 32, height - height % 32)
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};
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let img = image::imageops::resize(
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&img,
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new_width,
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new_height,
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image::imageops::FilterType::CatmullRom,
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)
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.into_raw();
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let mask = Tensor::from_vec(img, (new_height as usize, new_width as usize), &Device::Cpu)?
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.unsqueeze(0)?
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.to_dtype(DType::F32)?
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.div(255.0)?
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.unsqueeze(0)?;
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Ok(mask)
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}
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/// Generates the mask latents, scaled mask and mask_4 for inpainting. Returns a tuple of None if inpainting is not
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/// being used.
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#[allow(clippy::too_many_arguments)]
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fn inpainting_tensors(
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sd_version: StableDiffusionVersion,
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mask_path: Option<String>,
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dtype: DType,
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device: &Device,
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use_guide_scale: bool,
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vae: &AutoEncoderKL,
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image: Option<Tensor>,
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vae_scale: f64,
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) -> Result<(Option<Tensor>, Option<Tensor>, Option<Tensor>)> {
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match sd_version {
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StableDiffusionVersion::XlInpaint
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| StableDiffusionVersion::V2Inpaint
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| StableDiffusionVersion::V1_5Inpaint => {
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let inpaint_mask = mask_path.ok_or_else(|| {
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anyhow::anyhow!("An inpainting model was requested but mask-path is not provided.")
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})?;
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// Get the mask image with shape [1, 1, 128, 128]
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let mask = mask_preprocess(inpaint_mask)?
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.to_device(device)?
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.to_dtype(dtype)?;
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// Generate the masked image from the image and the mask with shape [1, 3, 1024, 1024]
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let xmask = mask.le(0.5)?.repeat(&[1, 3, 1, 1])?.to_dtype(dtype)?;
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let image = &image
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.ok_or_else(|| anyhow::anyhow!(
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"An inpainting model was requested but img2img which is used as the input image is not provided."
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))?;
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let masked_img = (image * xmask)?;
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// Scale down the mask
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let shape = masked_img.shape();
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let (w, h) = (shape.dims()[3] / 8, shape.dims()[2] / 8);
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let mask = mask.interpolate2d(w, h)?;
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// shape: [1, 4, 128, 128]
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let mask_latents = vae.encode(&masked_img)?;
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let mask_latents = (mask_latents.sample()? * vae_scale)?.to_device(device)?;
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let mask_4 = mask.as_ref().repeat(&[1, 4, 1, 1])?;
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let (mask_latents, mask) = if use_guide_scale {
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(
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Tensor::cat(&[&mask_latents, &mask_latents], 0)?,
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Tensor::cat(&[&mask, &mask], 0)?,
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)
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} else {
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(mask_latents, mask)
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};
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Ok((Some(mask_latents), Some(mask), Some(mask_4)))
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}
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_ => Ok((None, None, None)),
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}
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}
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fn run(args: Args) -> Result<()> {
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use tracing_chrome::ChromeLayerBuilder;
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use tracing_subscriber::prelude::*;
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@ -417,12 +546,14 @@ fn run(args: Args) -> Result<()> {
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bsize,
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sd_version,
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clip_weights,
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clip2_weights,
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vae_weights,
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unet_weights,
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tracing,
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use_f16,
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guidance_scale,
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use_flash_attn,
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mask_path,
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img2img,
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img2img_strength,
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seed,
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@ -445,7 +576,10 @@ fn run(args: Args) -> Result<()> {
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Some(guidance_scale) => guidance_scale,
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None => match sd_version {
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StableDiffusionVersion::V1_5
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| StableDiffusionVersion::V1_5Inpaint
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| StableDiffusionVersion::V2_1
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| StableDiffusionVersion::V2Inpaint
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| StableDiffusionVersion::XlInpaint
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| StableDiffusionVersion::Xl => 7.5,
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StableDiffusionVersion::Turbo => 0.,
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},
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@ -454,20 +588,23 @@ fn run(args: Args) -> Result<()> {
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Some(n_steps) => n_steps,
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None => match sd_version {
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StableDiffusionVersion::V1_5
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| StableDiffusionVersion::V1_5Inpaint
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| StableDiffusionVersion::V2_1
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| StableDiffusionVersion::V2Inpaint
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| StableDiffusionVersion::XlInpaint
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| StableDiffusionVersion::Xl => 30,
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StableDiffusionVersion::Turbo => 1,
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},
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};
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let dtype = if use_f16 { DType::F16 } else { DType::F32 };
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let sd_config = match sd_version {
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StableDiffusionVersion::V1_5 => {
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StableDiffusionVersion::V1_5 | StableDiffusionVersion::V1_5Inpaint => {
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stable_diffusion::StableDiffusionConfig::v1_5(sliced_attention_size, height, width)
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}
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StableDiffusionVersion::V2_1 => {
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StableDiffusionVersion::V2_1 | StableDiffusionVersion::V2Inpaint => {
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stable_diffusion::StableDiffusionConfig::v2_1(sliced_attention_size, height, width)
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}
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StableDiffusionVersion::Xl => {
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StableDiffusionVersion::Xl | StableDiffusionVersion::XlInpaint => {
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stable_diffusion::StableDiffusionConfig::sdxl(sliced_attention_size, height, width)
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}
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StableDiffusionVersion::Turbo => stable_diffusion::StableDiffusionConfig::sdxl_turbo(
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@ -479,13 +616,16 @@ fn run(args: Args) -> Result<()> {
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let mut scheduler = sd_config.build_scheduler(n_steps)?;
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let device = candle_examples::device(cpu)?;
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if let Some(seed) = seed {
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device.set_seed(seed)?;
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}
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// If a seed is not given, generate a random seed and print it
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let seed = seed.unwrap_or(rand::thread_rng().gen_range(0u64..u64::MAX));
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println!("Using seed {seed}");
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device.set_seed(seed)?;
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let use_guide_scale = guidance_scale > 1.0;
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let which = match sd_version {
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StableDiffusionVersion::Xl | StableDiffusionVersion::Turbo => vec![true, false],
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StableDiffusionVersion::Xl
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| StableDiffusionVersion::XlInpaint
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| StableDiffusionVersion::Turbo => vec![true, false],
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_ => vec![true],
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};
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let text_embeddings = which
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@ -496,6 +636,7 @@ fn run(args: Args) -> Result<()> {
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&uncond_prompt,
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tokenizer.clone(),
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clip_weights.clone(),
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clip2_weights.clone(),
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sd_version,
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&sd_config,
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use_f16,
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@ -514,16 +655,26 @@ fn run(args: Args) -> Result<()> {
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println!("Building the autoencoder.");
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let vae_weights = ModelFile::Vae.get(vae_weights, sd_version, use_f16)?;
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let vae = sd_config.build_vae(vae_weights, &device, dtype)?;
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let init_latent_dist = match &img2img {
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None => None,
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let (image, init_latent_dist) = match &img2img {
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None => (None, None),
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Some(image) => {
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let image = image_preprocess(image)?.to_device(&device)?;
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Some(vae.encode(&image)?)
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let image = image_preprocess(image)?
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.to_device(&device)?
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.to_dtype(dtype)?;
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(Some(image.clone()), Some(vae.encode(&image)?))
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}
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};
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println!("Building the unet.");
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let unet_weights = ModelFile::Unet.get(unet_weights, sd_version, use_f16)?;
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let unet = sd_config.build_unet(unet_weights, &device, 4, use_flash_attn, dtype)?;
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let in_channels = match sd_version {
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StableDiffusionVersion::XlInpaint
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| StableDiffusionVersion::V2Inpaint
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| StableDiffusionVersion::V1_5Inpaint => 9,
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_ => 4,
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};
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let unet = sd_config.build_unet(unet_weights, &device, in_channels, use_flash_attn, dtype)?;
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let t_start = if img2img.is_some() {
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n_steps - (n_steps as f64 * img2img_strength) as usize
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@ -533,11 +684,25 @@ fn run(args: Args) -> Result<()> {
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let vae_scale = match sd_version {
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StableDiffusionVersion::V1_5
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| StableDiffusionVersion::V1_5Inpaint
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| StableDiffusionVersion::V2_1
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| StableDiffusionVersion::V2Inpaint
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| StableDiffusionVersion::XlInpaint
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| StableDiffusionVersion::Xl => 0.18215,
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StableDiffusionVersion::Turbo => 0.13025,
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};
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let (mask_latents, mask, mask_4) = inpainting_tensors(
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sd_version,
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mask_path,
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dtype,
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&device,
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use_guide_scale,
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&vae,
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image,
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vae_scale,
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)?;
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for idx in 0..num_samples {
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let timesteps = scheduler.timesteps().to_vec();
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let latents = match &init_latent_dist {
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@ -576,6 +741,22 @@ fn run(args: Args) -> Result<()> {
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};
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let latent_model_input = scheduler.scale_model_input(latent_model_input, timestep)?;
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let latent_model_input = match sd_version {
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StableDiffusionVersion::XlInpaint
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| StableDiffusionVersion::V2Inpaint
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| StableDiffusionVersion::V1_5Inpaint => Tensor::cat(
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&[
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&latent_model_input,
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mask.as_ref().unwrap(),
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mask_latents.as_ref().unwrap(),
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],
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1,
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)?,
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_ => latent_model_input,
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}
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.to_device(&device)?;
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let noise_pred =
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unet.forward(&latent_model_input, timestep as f64, &text_embeddings)?;
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@ -592,6 +773,18 @@ fn run(args: Args) -> Result<()> {
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let dt = start_time.elapsed().as_secs_f32();
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println!("step {}/{n_steps} done, {:.2}s", timestep_index + 1, dt);
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// Replace all pixels in the unmasked region with the original pixels discarding any changes.
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if args.only_update_masked {
|
||||
let mask = mask_4.as_ref().unwrap();
|
||||
let latent_to_keep = mask_latents
|
||||
.as_ref()
|
||||
.unwrap()
|
||||
.get_on_dim(0, 0)? // shape: [4, H, W]
|
||||
.unsqueeze(0)?; // shape: [1, 4, H, W]
|
||||
|
||||
latents = ((&latents * mask)? + &latent_to_keep * (1.0 - mask))?;
|
||||
}
|
||||
|
||||
if args.intermediary_images {
|
||||
save_image(
|
||||
&vae,
|
||||
|
||||
Reference in New Issue
Block a user