Image Generation

def txt2img(
    prompt: str,
    negative_prompt: str = "",
    width: int = 512,
    height: int = 512,
    steps: int = 20,
    sampler_name: str = None,
    scheduler: str = None,
    cfg_scale: float = 7.0,
    seed: int = -1,
    batch_size: int = 1,
    batch_count: int = 1,
    restore_faces: bool = False,
    tiling: bool = False,
    enable_hr: bool = False,
    hr_scale: float = 2.0,
    hr_upscaler: str = "Latent",
    hr_second_pass_steps: int = 0,
    hr_resize_x: int = 0,
    hr_resize_y: int = 0,
    denoising_strength: float = 0.7,
    override_settings: Dict = None,
    override_settings_restore_afterwards: bool = True,
    script_name: str = None,
    script_args: List = None,
    send_images: bool = True,
    save_images: bool = False,
    alwayson_scripts: Dict = None,
    controlnet_units: List[ControlNetUnit] = None,
    **kwargs
) -> WebUIApiResult:
    """
    Generate images from text prompts.

    Parameters:
    - prompt: Text description of desired image
    - negative_prompt: Text describing what to avoid
    - width, height: Image dimensions (default 512x512)
    - steps: Number of sampling steps (default 20)
    - sampler_name: Sampling method (uses default if None)
    - scheduler: Scheduler type (uses default if None)
    - cfg_scale: Classifier-free guidance scale (default 7.0)
    - seed: Random seed (-1 for random)
    - batch_size: Number of images per batch
    - batch_count: Number of batches to generate
    - restore_faces: Apply face restoration
    - tiling: Enable seamless tiling
    - enable_hr: Enable high-res fix
    - hr_scale: High-res upscaling factor
    - hr_upscaler: High-res upscaling method
    - hr_second_pass_steps: Steps for high-res pass
    - hr_resize_x, hr_resize_y: High-res target dimensions
    - denoising_strength: Denoising strength for high-res pass
    - override_settings: Temporary setting overrides
    - override_settings_restore_afterwards: Restore settings after generation
    - script_name: Script to execute during generation
    - script_args: Arguments for the script
    - send_images: Return images in response
    - save_images: Save images to WebUI output folder
    - alwayson_scripts: Always-on script configurations
    - controlnet_units: ControlNet conditioning units

    Returns:
    WebUIApiResult containing generated images and metadata
    """

import webuiapi

api = webuiapi.WebUIApi()

# Basic text-to-image
result = api.txt2img(
    prompt="a serene mountain landscape, oil painting style",
    negative_prompt="blurry, low quality, deformed",
    width=768,
    height=512,
    steps=30,
    cfg_scale=8.0,
    seed=42
)

# High-resolution generation with face restoration
result = api.txt2img(
    prompt="portrait of a person, detailed face, professional photography",
    width=512,
    height=768,
    enable_hr=True,
    hr_scale=2.0,
    hr_upscaler="R-ESRGAN 4x+",
    restore_faces=True,
    steps=25
)

# Batch generation
result = api.txt2img(
    prompt="abstract art, colorful geometric shapes",
    batch_size=4,
    batch_count=2,  # Generates 8 images total
    seed=-1  # Random seeds for each image
)

def img2img(
    prompt: str,
    images: List[Image.Image],
    negative_prompt: str = "",
    width: int = 512,
    height: int = 512,
    steps: int = 20,
    sampler_name: str = None,
    scheduler: str = None,
    cfg_scale: float = 7.0,
    denoising_strength: float = 0.75,
    seed: int = -1,
    batch_size: int = 1,
    batch_count: int = 1,
    resize_mode: int = 0,
    image_cfg_scale: float = None,
    mask: Image.Image = None,
    mask_blur: int = 0,
    inpainting_fill: int = 1,
    inpaint_full_res: bool = True,
    inpaint_full_res_padding: int = 0,
    inpainting_mask_invert: int = 0,
    initial_noise_multiplier: float = None,
    restore_faces: bool = False,
    tiling: bool = False,
    override_settings: Dict = None,
    override_settings_restore_afterwards: bool = True,
    script_name: str = None,
    script_args: List = None,
    send_images: bool = True,
    save_images: bool = False,
    alwayson_scripts: Dict = None,
    controlnet_units: List[ControlNetUnit] = None,
    **kwargs
) -> WebUIApiResult:
    """
    Generate images from existing images with text prompts.

    Parameters:
    - prompt: Text description for transformation
    - images: List of input images to transform
    - negative_prompt: Text describing what to avoid
    - width, height: Output image dimensions
    - steps: Number of sampling steps
    - sampler_name: Sampling method
    - scheduler: Scheduler type
    - cfg_scale: Classifier-free guidance scale
    - denoising_strength: How much to change the image (0.0-1.0)
    - seed: Random seed (-1 for random)
    - batch_size: Number of images per batch
    - batch_count: Number of batches
    - resize_mode: How to handle size differences (0=Just resize, 1=Crop and resize, 2=Resize and fill, 3=Just resize (latent upscale))
    - image_cfg_scale: CFG scale for image conditioning
    - mask: Mask for inpainting (optional)
    - mask_blur: Mask blur radius
    - inpainting_fill: Fill mode for masked area (0=fill, 1=original, 2=latent noise, 3=latent nothing)
    - inpaint_full_res: Inpaint at full resolution
    - inpaint_full_res_padding: Padding for full resolution inpainting
    - inpainting_mask_invert: Invert mask (0=inpaint masked, 1=inpaint not masked)
    - initial_noise_multiplier: Noise multiplier for initial latent
    - restore_faces: Apply face restoration
    - tiling: Enable seamless tiling
    - override_settings: Temporary setting overrides
    - override_settings_restore_afterwards: Restore settings after
    - script_name: Script to execute
    - script_args: Script arguments
    - send_images: Return images in response
    - save_images: Save images to output folder
    - alwayson_scripts: Always-on script configurations
    - controlnet_units: ControlNet conditioning units

    Returns:
    WebUIApiResult containing transformed images and metadata
    """

from PIL import Image
import webuiapi

api = webuiapi.WebUIApi()

# Load input image
init_image = Image.open("photo.jpg")

# Style transfer
result = api.img2img(
    prompt="oil painting, impressionist style, vibrant colors",
    images=[init_image],
    denoising_strength=0.6,
    width=768,
    height=768,
    resize_mode=1  # Crop and resize
)

# Inpainting with mask
mask_image = Image.open("mask.png")  # White areas will be inpainted

result = api.img2img(
    prompt="beautiful garden with flowers",
    images=[init_image],
    mask=mask_image,
    denoising_strength=1.0,
    inpainting_fill=1,  # Use original image for context
    inpaint_full_res=True
)

# Multiple variations
result = api.img2img(
    prompt="same subject, different lighting",
    images=[init_image],
    denoising_strength=0.4,
    batch_size=3,
    seed=-1  # Different seed for each variation
)

class WebUIApiResult:
    """Result container for image generation operations."""
    images: List[Image.Image]  # Generated images as PIL Image objects
    parameters: Dict  # Parameters used for generation
    info: Dict  # Generation info and metadata
    json: Dict  # Raw API response
    
    @property
    def image(self) -> Image.Image:
        """First generated image (convenience property)."""