diff --git a/src/main.rs b/src/main.rs
index ef26557..cf64b59 100644
--- a/src/main.rs
+++ b/src/main.rs
@@ -9,7 +9,7 @@ mod model;
 mod postprocessing;
 mod preprocessing;
 
-use model::{ModelInfo, create_session, get_model_path, infer_input_info};
+use model::{ModelInfo, create_session, get_model_path};
 use postprocessing::{apply_mask, create_side_by_side};
 use preprocessing::preprocess_image;
 
@@ -54,7 +54,7 @@ fn main() -> Result<(), Box<dyn Error>> {
     println!("Loaded image: {}x{}\n", img_width, img_height);
 
     // Get model info
-    let model_info = ModelInfo::BIREFNET_LITE;
+    let model_info = ModelInfo::BRIA;
     println!("Using model: {}", model_info.name);
 
     // Get or download model
@@ -62,16 +62,14 @@ fn main() -> Result<(), Box<dyn Error>> {
 
     // Create ONNX Runtime session
     let mut session = create_session(&model_path)?;
-
-    let input_info = infer_input_info(&session, img_width, img_height)?;
+    let input_name = session.inputs()[0].name().to_string();
 
     // Preprocess
-    let input_tensor = preprocess_image(&img, input_info.shape.width, input_info.shape.height)?;
+    let input_tensor = preprocess_image(&img, 1024, 1024)?;
 
     // Run inference
     println!("Running inference...");
-    let outputs =
-        session.run(ort::inputs![input_info.name => Tensor::from_array(input_tensor)?])?;
+    let outputs = session.run(ort::inputs![input_name => Tensor::from_array(input_tensor)?])?;
 
     // Extract mask output
     let mask_output = &outputs[0];
diff --git a/src/model.rs b/src/model.rs
index ec8bad7..3b38490 100644
--- a/src/model.rs
+++ b/src/model.rs
@@ -1,8 +1,5 @@
 use ort::execution_providers::CUDAExecutionProvider;
-use ort::{
-    session::{Session, builder::GraphOptimizationLevel},
-    value::ValueType,
-};
+use ort::session::{Session, builder::GraphOptimizationLevel};
 use sha2::{Digest, Sha256};
 use std::error::Error;
 use std::fs;
@@ -23,6 +20,12 @@ impl ModelInfo {
         url: "https://github.com/danielgatis/rembg/releases/download/v0.0.0/BiRefNet-general-bb_swin_v1_tiny-epoch_232.onnx",
         sha256: Some("5600024376f572a557870a5eb0afb1e5961636bef4e1e22132025467d0f03333"),
     };
+
+    pub const BRIA: ModelInfo = ModelInfo {
+        name: "bria",
+        url: "https://github.com/danielgatis/rembg/releases/download/v0.0.0/bria-rmbg-2.0.onnx",
+        sha256: Some("5b486f08200f513f460da46dd701db5fbb47d79b4be4b708a19444bcd4e79958"),
+    };
 }
 
 /// Get the cache directory for models
@@ -158,74 +161,6 @@ pub fn get_model_path(
     Ok(model_path)
 }
 
-#[derive(Debug, Clone, Copy, PartialEq, Eq)]
-pub struct InputShape {
-    pub batch: u32,
-    pub channels: u32,
-    pub height: u32,
-    pub width: u32,
-}
-
-#[derive(Debug, Clone, PartialEq, Eq)]
-pub struct InputInfo {
-    pub name: String,
-    pub shape: InputShape,
-}
-
-pub fn infer_input_info(
-    session: &Session,
-    image_width: u32,
-    image_height: u32,
-) -> Result<InputInfo, Box<dyn Error>> {
-    let input = &session.inputs()[0];
-    let input_name = input.name().to_string();
-    let shape = match input.dtype() {
-        ValueType::Tensor { shape, .. } => shape,
-        _ => return Err("Expected tensor input".into()),
-    };
-
-    // Validate shape has 4 dimensions
-    if shape.len() != 4 {
-        return Err(format!("Expected 4D tensor, got {} dimensions", shape.len()).into());
-    }
-
-    // Process each dimension, replacing -1 with appropriate defaults
-    let batch = if shape[0] == -1 {
-        1
-    } else {
-        shape[0].try_into().map_err(|_| "Invalid batch dimension")?
-    };
-    let channels = if shape[1] == -1 {
-        3
-    } else {
-        shape[1]
-            .try_into()
-            .map_err(|_| "Invalid channels dimension")?
-    };
-    let height = if shape[2] == -1 {
-        image_height.min(4096)
-    } else {
-        shape[2]
-            .try_into()
-            .map_err(|_| "Invalid height dimension")?
-    };
-    let width = if shape[3] == -1 {
-        image_width.min(4096)
-    } else {
-        shape[3].try_into().map_err(|_| "Invalid width dimension")?
-    };
-
-    Ok(InputInfo {
-        name: input_name,
-        shape: InputShape {
-            batch,
-            channels,
-            height,
-            width,
-        },
-    })
-}
-
 /// Create an ONNX Runtime session from model path with CUDA backend
 pub fn create_session(model_path: &Path) -> Result<Session, Box<dyn Error>> {
     println!("Loading model into ONNX Runtime with CUDA backend...");