Class ImageGenerationModel (1.93.1)

ImageGenerationModel(model_id: str, endpoint_name: typing.Optional[str] = None)

model = ImageGenerationModel.from_pretrained("imagegeneration@002")
response = model.generate_images(
 prompt="Astronaut riding a horse",
 # Optional:
 number_of_images=1,
 seed=0,
)
response[0].show()
response[0].save("image1.png")

ImageGenerationModel(model_id: str, endpoint_name: typing.Optional[str] = None)

edit_image(
 *,
 prompt: str,
 base_image: typing.Optional[vertexai.vision_models.Image] = None,
 mask: typing.Optional[vertexai.vision_models.Image] = None,
 reference_images: typing.Optional[
 typing.List[vertexai.vision_models.ReferenceImage]
 ] = None,
 negative_prompt: typing.Optional[str] = None,
 number_of_images: int = 1,
 guidance_scale: typing.Optional[float] = None,
 edit_mode: typing.Optional[
 typing.Literal[
 "inpainting-insert", "inpainting-remove", "outpainting", "product-image"
 ]
 ] = None,
 mask_mode: typing.Optional[
 typing.Literal["background", "foreground", "semantic"]
 ] = None,
 segmentation_classes: typing.Optional[typing.List[str]] = None,
 mask_dilation: typing.Optional[float] = None,
 product_position: typing.Optional[typing.Literal["fixed", "reposition"]] = None,
 output_mime_type: typing.Optional[typing.Literal["image/png", "image/jpeg"]] = None,
 compression_quality: typing.Optional[float] = None,
 language: typing.Optional[str] = None,
 seed: typing.Optional[int] = None,
 output_gcs_uri: typing.Optional[str] = None,
 safety_filter_level: typing.Optional[
 typing.Literal["block_most", "block_some", "block_few", "block_fewest"]
 ] = None,
 person_generation: typing.Optional[
 typing.Literal["dont_allow", "allow_adult", "allow_all"]
 ] = None
) -> vertexai.preview.vision_models.ImageGenerationResponse

from_pretrained(model_name: str) -> vertexai._model_garden._model_garden_models.T

generate_images(
 prompt: str,
 *,
 negative_prompt: typing.Optional[str] = None,
 number_of_images: int = 1,
 aspect_ratio: typing.Optional[
 typing.Literal["1:1", "9:16", "16:9", "4:3", "3:4"]
 ] = None,
 guidance_scale: typing.Optional[float] = None,
 language: typing.Optional[str] = None,
 seed: typing.Optional[int] = None,
 output_gcs_uri: typing.Optional[str] = None,
 add_watermark: typing.Optional[bool] = True,
 safety_filter_level: typing.Optional[
 typing.Literal["block_most", "block_some", "block_few", "block_fewest"]
 ] = None,
 person_generation: typing.Optional[
 typing.Literal["dont_allow", "allow_adult", "allow_all"]
 ] = None
) -> vertexai.preview.vision_models.ImageGenerationResponse

upscale_image(
 image: typing.Union[
 vertexai.vision_models.Image, vertexai.preview.vision_models.GeneratedImage
 ],
 new_size: typing.Optional[int] = 2048,
 upscale_factor: typing.Optional[typing.Literal["x2", "x4"]] = None,
 output_mime_type: typing.Optional[
 typing.Literal["image/png", "image/jpeg"]
 ] = "image/png",
 output_compression_quality: typing.Optional[int] = None,
 output_gcs_uri: typing.Optional[str] = None,
) -> vertexai.vision_models.Image

# Upscale a generated image
model = ImageGenerationModel.from_pretrained("imagegeneration@002")
response = model.generate_images(
 prompt="Astronaut riding a horse",
)
model.upscale_image(image=response[0])
# Upscale a new 1024x1024 image
my_image = Image.load_from_file("my-image.png")
model.upscale_image(image=my_image)
# Upscale a new arbitrary sized image using a x2 or x4 upscaling factor
my_image = Image.load_from_file("my-image.png")
model.upscale_image(image=my_image, upscale_factor="x2")
# Upscale an image and get the result in JPEG format
my_image = Image.load_from_file("my-image.png")
model.upscale_image(image=my_image, output_mime_type="image/jpeg",
output_compression_quality=90)