Geração de Imagens com Bumblebee, Elixir, Livebook

Mix.install(
  [
    {:kino_bumblebee, "~> 0.2.0"},
    {:exla, "~> 0.5.1"}
  ],
  config: [nx: [default_backend: EXLA.Backend]]
)

:ok

Geração de imagens

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"},
    log_params_diff: false
  )

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

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

{: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"},
    log_params_diff: false
  )

serving =
  Bumblebee.Diffusion.StableDiffusion.text_to_image(clip, unet, vae, tokenizer, scheduler,
    num_steps: 40,
    num_images_per_prompt: 2,
    safety_checker: safety_checker,
    safety_checker_featurizer: featurizer,
    compile: [batch_size: 1, sequence_length: 50],
    defn_options: [compiler: EXLA]
  )

text_input =
  Kino.Input.textarea("Text",
    default: "numbat, forest, high quality, detailed, digital art"
  )

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

form
|> Kino.Control.stream()
|> Kino.listen(fn %{data: %{text: text}} ->
  Kino.Frame.render(frame, Kino.Markdown.new("Running..."))
  output = Nx.Serving.run(serving, 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))
end)

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

15:19:53.555 [info] TfrtCpuClient created.

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"},
    log_params_diff: false
  )

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

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

{: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"},
    log_params_diff: false
  )

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]
  )

text_input =
  Kino.Input.textarea("Text",
    default: "numbat, forest, high quality, detailed, digital art"
  )

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

form
|> Kino.Control.stream()
|> Kino.listen(fn %{data: %{text: text}} ->
  Kino.Frame.render(frame, Kino.Markdown.new("Running..."))
  output = Nx.Serving.run(serving, 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))
end)

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