Image Generation

Mix.install(
  [
    {:kino_bumblebee, "~> 0.3.0"},
    {:exla, "~> 0.5.1"}
  ],
  system_env: [
    {"XLA_TARGET", "cuda118"}
  ],
  config: [nx: [default_backend: EXLA.Backend]]
)

Section

repository_id = "CompVis/stable-diffusion-v1-4"
{:ok, tokenizer} = Bumblebee.load_tokenizer({:hf, "openai/clip-vit-large-patch14"})
{:ok, clip} = Bumblebee.load_model({:hf, repository_id, subdir: "text_encoder"})

{:ok, unet} =
  Bumblebee.load_model({:hf, repository_id, subdir: "unet"},
    params_filename: "diffusion_pytorch_model.bin"
  )

{:ok, vae} =
  Bumblebee.load_model({:hf, repository_id, subdir: "vae"},
    architecture: :decoder,
    params_filename: "diffusion_pytorch_model.bin"
  )

{:ok, scheduler} = Bumblebee.load_scheduler({:hf, repository_id, subdir: "scheduler"})

{:ok, featurizer} = Bumblebee.load_featurizer({:hf, repository_id, subdir: "feature_extractor"})

{:ok, safety_checker} = Bumblebee.load_model({:hf, repository_id, subdir: "safety_checker"})

serving =
  Bumblebee.Diffusion.StableDiffusion.text_to_image(clip, unet, vae, tokenizer, scheduler,
    num_steps: 20,
    num_images_per_prompt: 2,
    safety_checker: safety_checker,
    safety_checker_featurizer: featurizer,
    compile: [batch_size: 1, sequence_length: 50],
    defn_options: [compiler: EXLA]
  )

Kino.start_child({Nx.Serving, serving: serving, name: ImageGeneration})

text_input =
  Kino.Input.textarea("Text",
    default: "Elixir changes the world, high quality, detailed, digital art"
  )

form = Kino.Control.form([text: text_input], submit: "Run")
frame = Kino.Frame.new()

Kino.async_listen(form, fn %{data: %{text: text}, origin: origin} ->
  Kino.Frame.render(frame, Kino.Text.new("Running..."))
  output = Nx.Serving.batched_run(ImageGeneration, text)

  for result <- output.results do
    Kino.Image.new(result.image)
  end
  |> Kino.Layout.grid(columns: 2)
  |> then(&amp;Kino.Frame.render(frame, &amp;1, to: origin))
end)

Kino.Layout.grid([form, frame], boxed: true, gap: 16)