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Merge pull request #10 from atoma-network/inference-models
feat: add stable diffusion
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,2 +1,3 @@ | ||
| Cargo.lock | ||
| target/ | ||
| .vscode/ |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,6 +1,5 @@ | ||
| pub mod apis; | ||
| pub mod model_thread; | ||
| pub mod models; | ||
| pub mod service; | ||
| pub mod specs; | ||
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| pub mod apis; | ||
| pub mod models; |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1 +1,84 @@ | ||
| use std::{fs::File, io::Write, path::PathBuf}; | ||
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| use candle::{ | ||
| utils::{cuda_is_available, metal_is_available}, | ||
| DType, Device, Tensor, | ||
| }; | ||
| use tracing::info; | ||
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| use crate::bail; | ||
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| use super::ModelError; | ||
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| pub mod mamba; | ||
| pub mod stable_diffusion; | ||
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| pub fn device() -> Result<Device, candle::Error> { | ||
| if cuda_is_available() { | ||
| info!("Using CUDA"); | ||
| Device::new_cuda(0) | ||
| } else if metal_is_available() { | ||
| info!("Using Metal"); | ||
| Device::new_metal(0) | ||
| } else { | ||
| info!("Using Cpu"); | ||
| Ok(Device::Cpu) | ||
| } | ||
| } | ||
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| pub fn hub_load_safetensors( | ||
| repo: &hf_hub::api::sync::ApiRepo, | ||
| json_file: &str, | ||
| ) -> Result<Vec<std::path::PathBuf>, ModelError> { | ||
| let json_file = repo.get(json_file).map_err(candle::Error::wrap)?; | ||
| let json_file = std::fs::File::open(json_file)?; | ||
| let json: serde_json::Value = | ||
| serde_json::from_reader(&json_file).map_err(candle::Error::wrap)?; | ||
| let weight_map = match json.get("weight_map") { | ||
| None => bail!("no weight map in {json_file:?}"), | ||
| Some(serde_json::Value::Object(map)) => map, | ||
| Some(_) => bail!("weight map in {json_file:?} is not a map"), | ||
| }; | ||
| let mut safetensors_files = std::collections::HashSet::new(); | ||
| for value in weight_map.values() { | ||
| if let Some(file) = value.as_str() { | ||
| safetensors_files.insert(file.to_string()); | ||
| } | ||
| } | ||
| let safetensors_files = safetensors_files | ||
| .iter() | ||
| .map(|v| repo.get(v).map_err(candle::Error::wrap)) | ||
| .collect::<candle::Result<Vec<_>>>()?; | ||
| Ok(safetensors_files) | ||
| } | ||
|
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| pub fn save_image<P: AsRef<std::path::Path>>(img: &Tensor, p: P) -> Result<(), ModelError> { | ||
| let p = p.as_ref(); | ||
| let (channel, height, width) = img.dims3()?; | ||
| if channel != 3 { | ||
| bail!("save_image expects an input of shape (3, height, width)") | ||
| } | ||
| let img = img.permute((1, 2, 0))?.flatten_all()?; | ||
| let pixels = img.to_vec1::<u8>()?; | ||
| let image: image::ImageBuffer<image::Rgb<u8>, Vec<u8>> = | ||
| match image::ImageBuffer::from_raw(width as u32, height as u32, pixels) { | ||
| Some(image) => image, | ||
| None => bail!("error saving image {p:?}"), | ||
| }; | ||
| image.save(p).map_err(candle::Error::wrap)?; | ||
| Ok(()) | ||
| } | ||
|
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| pub fn save_tensor_to_file(tensor: &Tensor, filename: &str) -> Result<(), candle::Error> { | ||
| let json_output = serde_json::to_string( | ||
| &tensor | ||
| .to_device(&Device::Cpu)? | ||
| .flatten_all()? | ||
| .to_dtype(DType::F64)? | ||
| .to_vec1::<f64>()?, | ||
| ) | ||
| .unwrap(); | ||
| let mut file = File::create(PathBuf::from(filename))?; | ||
| file.write_all(json_output.as_bytes())?; | ||
| Ok(()) | ||
| } |
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