feat: LVM - Added support for Image Generation models

Features:

* Generate images from text prompt (prompt, negative prompt, width, height, seed, guidance scale)
* Edit an existing images using a text prompt (and optional mask)
* Upscale image
* Show image (works in notebook environments)
* Save image (saved image also includes the image generation parameters)

Example usage:
```python
model = ImageGenerationModel.from_pretrained("imagegeneration@002")
images = model.generate_images(
    prompt="Astronaut riding a horse",
    # Optional:
    number_of_images=1,
    width=1024,
    height=768,
    seed=1,
    guidance_scale=15,
)
images[0].show()
images[0].save("image1.png")
```
PiperOrigin-RevId: 557736987

tests/system/aiplatform/test_vision_models.py

@@ -84,3 +84,77 @@ def test_multi_modal_embedding_model(self):
         # The service is expected to return the embeddings of size 1408
         assert len(embeddings.image_embedding) == 1408
         assert len(embeddings.text_embedding) == 1408
+
+    def test_image_generation_model_generate_images(self):
+        """Tests the image generation model generating images."""
+        model = vision_models.ImageGenerationModel.from_pretrained(
+            "imagegeneration@001"
+        )
+
+        width = 1024
+        height = 768
+        number_of_images = 4
+        seed = 1
+        guidance_scale = 15
+
+        prompt1 = "Astronaut riding a horse"
+        negative_prompt1 = "bad quality"
+        image_response = model.generate_images(
+            prompt=prompt1,
+            # Optional:
+            negative_prompt=negative_prompt1,
+            number_of_images=number_of_images,
+            width=width,
+            height=height,
+            seed=seed,
+            guidance_scale=guidance_scale,
+        )
+
+        assert len(image_response.images) == number_of_images
+        for idx, image in enumerate(image_response):
+            assert image._pil_image.size == (width, height)
+            assert image.generation_parameters
+            assert image.generation_parameters["prompt"] == prompt1
+            assert image.generation_parameters["negative_prompt"] == negative_prompt1
+            assert image.generation_parameters["width"] == width
+            assert image.generation_parameters["height"] == height
+            assert image.generation_parameters["seed"] == seed
+            assert image.generation_parameters["guidance_scale"] == guidance_scale
+            assert image.generation_parameters["index_of_image_in_batch"] == idx
+
+        # Test saving and loading images
+        with tempfile.TemporaryDirectory() as temp_dir:
+            image_path = os.path.join(temp_dir, "image.png")
+            image_response[0].save(location=image_path)
+            image1 = vision_models.GeneratedImage.load_from_file(image_path)
+            assert image1._pil_image.size == (width, height)
+            assert image1.generation_parameters
+            assert image1.generation_parameters["prompt"] == prompt1
+
+            # Preparing mask
+            mask_path = os.path.join(temp_dir, "mask.png")
+            mask_pil_image = PIL_Image.new(mode="RGB", size=(width, height))
+            mask_pil_image.save(mask_path, format="PNG")
+            mask_image = vision_models.Image.load_from_file(mask_path)
+
+        # Test generating image from base image
+        prompt2 = "Ancient book style"
+        image_response2 = model.edit_image(
+            prompt=prompt2,
+            # Optional:
+            number_of_images=number_of_images,
+            seed=seed,
+            guidance_scale=guidance_scale,
+            base_image=image1,
+            mask=mask_image,
+        )
+        assert len(image_response2.images) == number_of_images
+        for idx, image in enumerate(image_response2):
+            assert image._pil_image.size == (width, height)
+            assert image.generation_parameters
+            assert image.generation_parameters["prompt"] == prompt2
+            assert image.generation_parameters["seed"] == seed
+            assert image.generation_parameters["guidance_scale"] == guidance_scale
+            assert image.generation_parameters["index_of_image_in_batch"] == idx
+            assert "base_image_hash" in image.generation_parameters
+            assert "mask_hash" in image.generation_parameters