Stability AI released Stable Video Diffusion, a latent diffusion model for high-resolution video generation from text and images.
7.02 sec
and an average inference time of 34 sec
for generating a video of 4 sec
with 6fps
model.py
def initialize
: In this function, you will initialize your model and define any variable
that you want to use during inference. We are using torch.compile
which improves the latency but requires a large GPU(A10/A100). If you are using Nvidia T4 then remove those lines and use model CPU offloading to reduce memory usage.
def infer
: This function gets called for every request that you send. Here you can define all the steps that are required for the inference. You can also pass custom values for inference, for example fps_id
is fixed in the tutorial, you can pass it through inputs
parameter.
def finalize
: This function cleans up all the allocated memory.
inferless remote-run
(installation guide here) command to test your model or any custom Python script in a remote GPU environment directly from your local machine. Make sure that you use Python3.10
for seamless experience.
inferless
library and initialize Cls(gpu="A100")
. The available GPU options are T4
, A10
and A100
.initialize
and infer
functions with @app.load
and @app.infer
respectively.my_local_entry
) with @inferless.local_entry_point
.
Within this function, instantiate your model class, convert any incoming parameters into a RequestObjects
object, and invoke the model’s infer
method.app.py
and your inferless-runtime-config.yaml
and run:
inputs
dictionary.
If you want to exclude certain files or directories from being uploaded, use the --exclude
or -e
flag.
Add a custom model
button that you see on the top right. An import wizard will open up.
--gpu A100
: Specifies the GPU type for deployment. Available options include A10
, A100
, and T4
.--runtime inferless-runtime-config.yaml
: Defines the runtime configuration file. If not specified, the default Inferless runtime is used.