DiffRP aims to provide an easy-to-use programming interface for non-differentiable and differentiable rendering pipelines.
Rendered with DiffRP.
The package can be installed by:
pip install diffrpDiffRP depends on PyTorch (torch). The default version pip resolves to may not come with the cuda version you want. It is recommended to install PyTorch before you install DiffRP so you can choose the version you like.
DiffRP rendering is based on CUDA GPUs. You can develop without one, but a CUDA GPU is required to run the code.
If you use rasterization in DiffRP, you need to have the CUDA development kit set up as we use the nvdiffrast backend. See also https://nvlabs.github.io/nvdiffrast/#installation.
If you want to use hardware ray tracing in DiffRP, you need to install torchoptix by pip install torchoptix. See also the hardware and driver requirements https://github.com/eliphatfs/torchoptix?tab=readme-ov-file#requirements.
If you plan on using plugins in DiffRP (currently when you compute tangents), gcc is required in path. This is already fulfilled in most Linux and Mac distributions. For Windows I recommend the Strawberry Perl (https://strawberryperl.com/) distribution of gcc.
Rendering attributes of a mesh programmingly is incredibly simple with DiffRP compared to conventional render engines like Blender and Unity.
In this section, we will get started to render a simple procedural mesh.
First, let's import what we would need:
import diffrp
import trimesh.creation
from diffrp.utils import *We now create a icosphere and render the camera space normal map, where RGB color values in
The first run may take some time due to compiling and importing the nvdiffrast backend. Following calls would be fast.
# create the mesh (cpu)
mesh = trimesh.creation.icosphere(radius=0.8)
# initialize the DiffRP scene
scene = diffrp.Scene()
# register the mesh, load vertices and faces arrays to GPU
scene.add_mesh_object(diffrp.MeshObject(diffrp.DefaultMaterial(), gpu_f32(mesh.vertices), gpu_i32(mesh.faces)))
# default camera at [0, 0, 3.2] looking backwards
camera = diffrp.PerspectiveCamera()
# create the SurfaceDeferredRenderSession, a deferred-rendering rasterization pipeline session
rp = diffrp.SurfaceDeferredRenderSession(scene, camera)
# convert output tensor to PIL Image and save
to_pil(rp.false_color_camera_space_normal()).save("procedural-normals.png")Only 6 lines of code and we are done! The output should look like this:
The GPU buffers can be replaced by arbitrary tensors or parameters, and the process has been fully differentiable.
Please refer to the [Documentation] for more features!