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refactor based on dhruv's feedback: remove the class method
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yiyixuxu
@@ -324,6 +324,133 @@ class ConfigSpec:
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description: Optional[str] = None
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# ======================================================
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# InputParam and OutputParam templates
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# ======================================================
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INPUT_PARAM_TEMPLATES = {
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"prompt": {
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"type_hint": str,
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"required": True,
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"description": "The prompt or prompts to guide image generation.",
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},
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"negative_prompt": {
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"type_hint": str,
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"default": None,
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"description": "The prompt or prompts not to guide the image generation.",
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},
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"max_sequence_length": {
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"type_hint": int,
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"default": 512,
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"description": "Maximum sequence length for prompt encoding.",
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},
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"height": {
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"type_hint": int,
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"description": "The height in pixels of the generated image.",
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},
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"width": {
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"type_hint": int,
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"description": "The width in pixels of the generated image.",
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},
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"num_inference_steps": {
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"type_hint": int,
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"default": 50,
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"description": "The number of denoising steps.",
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},
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"num_images_per_prompt": {
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"type_hint": int,
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"default": 1,
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"description": "The number of images to generate per prompt.",
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},
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"generator": {
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"type_hint": torch.Generator,
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"default": None,
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"description": "Torch generator for deterministic generation.",
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},
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"sigmas": {
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"type_hint": List[float],
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"default": None,
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"description": "Custom sigmas for the denoising process.",
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},
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"strength": {
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"type_hint": float,
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"default": 0.9,
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"description": "Strength for img2img/inpainting.",
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},
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"image": {
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"type_hint": PIL.Image.Image,
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"required": True,
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"description": "Input image for img2img, editing, or conditioning.",
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},
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"mask_image": {
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"type_hint": PIL.Image.Image,
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"required": True,
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"description": "Mask image for inpainting.",
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},
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"control_image": {
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"type_hint": PIL.Image.Image,
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"required": True,
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"description": "Control image for ControlNet conditioning.",
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},
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"padding_mask_crop": {
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"type_hint": int,
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"default": None,
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"description": "Padding for mask cropping in inpainting.",
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},
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"latents": {
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"type_hint": torch.Tensor,
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"default": None,
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"description": "Pre-generated noisy latents for image generation.",
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},
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"timesteps": {
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"type_hint": torch.Tensor,
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"default": None,
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"description": "Timesteps for the denoising process.",
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},
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"output_type": {
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"type_hint": str,
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"default": "pil",
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"description": "Output format: 'pil', 'np', 'pt'.",
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},
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"attention_kwargs": {
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"type_hint": Dict[str, Any],
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"default": None,
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"description": "Additional kwargs for attention processors.",
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},
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"denoiser_input_fields": {
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"kwargs_type": "denoiser_input_fields",
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"type_hint": torch.Tensor,
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"description": "conditional model inputs for the denoiser: e.g. prompt_embeds, negative_prompt_embeds, etc.",
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},
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"control_guidance_start": {
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"type_hint": float,
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"default": 0.0,
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"description": "When to start applying ControlNet.",
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},
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"control_guidance_end": {
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"type_hint": float,
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"default": 1.0,
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"description": "When to stop applying ControlNet.",
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},
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"controlnet_conditioning_scale": {
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"type_hint": float,
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"default": 1.0,
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"description": "Scale for ControlNet conditioning.",
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},
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}
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OUTPUT_PARAM_TEMPLATES = {
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"images": {
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"type_hint": List[PIL.Image.Image],
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"description": "Generated images.",
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},
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"latents": {
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"type_hint": torch.Tensor,
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"description": "Denoised latents.",
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},
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}
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# YiYi Notes: both inputs and intermediate_inputs are InputParam objects
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# however some fields are not relevant for intermediate_inputs
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# e.g. unlike inputs, required only used in docstring for intermediate_inputs, we do not check if a required intermediate inputs is passed
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@@ -344,190 +471,22 @@ class InputParam:
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return f"<{self.name}: {'required' if self.required else 'optional'}, default={self.default}>"
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@classmethod
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def template(cls, name: str) -> Optional["InputParam"]:
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"""Get template for name if exists, otherwise None."""
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if hasattr(cls, name) and callable(getattr(cls, name)):
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return getattr(cls, name)()
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return None
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# ======================================================
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# InputParam templates
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# ======================================================
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@classmethod
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def prompt(cls) -> "InputParam":
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return cls(
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name="prompt", type_hint=str, required=True, description="The prompt or prompts to guide image generation."
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)
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@classmethod
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def negative_prompt(cls) -> "InputParam":
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return cls(
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name="negative_prompt",
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type_hint=str,
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default=None,
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description="The prompt or prompts not to guide the image generation.",
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)
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@classmethod
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def max_sequence_length(cls, default: int = 512) -> "InputParam":
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return cls(
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name="max_sequence_length",
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type_hint=int,
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default=default,
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description="Maximum sequence length for prompt encoding.",
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)
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@classmethod
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def height(cls, default: Optional[int] = None) -> "InputParam":
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return cls(
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name="height", type_hint=int, default=default, description="The height in pixels of the generated image."
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)
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@classmethod
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def width(cls, default: Optional[int] = None) -> "InputParam":
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return cls(
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name="width", type_hint=int, default=default, description="The width in pixels of the generated image."
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)
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@classmethod
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def num_inference_steps(cls, default: int = 50) -> "InputParam":
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return cls(
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name="num_inference_steps", type_hint=int, default=default, description="The number of denoising steps."
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)
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@classmethod
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def num_images_per_prompt(cls, default: int = 1) -> "InputParam":
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return cls(
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name="num_images_per_prompt",
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type_hint=int,
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default=default,
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description="The number of images to generate per prompt.",
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)
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@classmethod
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def generator(cls) -> "InputParam":
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return cls(
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name="generator",
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type_hint=torch.Generator,
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default=None,
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description="Torch generator for deterministic generation.",
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)
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@classmethod
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def sigmas(cls) -> "InputParam":
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return cls(
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name="sigmas", type_hint=List[float], default=None, description="Custom sigmas for the denoising process."
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)
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@classmethod
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def strength(cls, default: float = 0.9) -> "InputParam":
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return cls(name="strength", type_hint=float, default=default, description="Strength for img2img/inpainting.")
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# images
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@classmethod
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def image(cls) -> "InputParam":
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return cls(
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name="image",
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type_hint=PIL.Image.Image,
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required=True,
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description="Input image for img2img, editing, or conditioning.",
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)
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@classmethod
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def mask_image(cls) -> "InputParam":
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return cls(
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name="mask_image", type_hint=PIL.Image.Image, required=True, description="Mask image for inpainting."
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)
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@classmethod
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def control_image(cls) -> "InputParam":
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return cls(
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name="control_image",
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type_hint=PIL.Image.Image,
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required=True,
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description="Control image for ControlNet conditioning.",
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)
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@classmethod
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def padding_mask_crop(cls) -> "InputParam":
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return cls(
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name="padding_mask_crop",
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type_hint=int,
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default=None,
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description="Padding for mask cropping in inpainting.",
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)
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@classmethod
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def latents(cls) -> "InputParam":
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return cls(
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name="latents",
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type_hint=torch.Tensor,
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default=None,
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description="Pre-generated noisy latents for image generation.",
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)
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@classmethod
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def timesteps(cls) -> "InputParam":
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return cls(
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name="timesteps", type_hint=torch.Tensor, default=None, description="Timesteps for the denoising process."
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)
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@classmethod
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def output_type(cls) -> "InputParam":
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return cls(name="output_type", type_hint=str, default="pil", description="Output format: 'pil', 'np', 'pt''.")
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@classmethod
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def attention_kwargs(cls) -> "InputParam":
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return cls(
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name="attention_kwargs",
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type_hint=Dict[str, Any],
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default=None,
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description="Additional kwargs for attention processors.",
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)
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@classmethod
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def denoiser_input_fields(cls) -> "InputParam":
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return cls(
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kwargs_type="denoiser_input_fields",
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type_hint=torch.Tensor,
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description="conditional model inputs for the denoiser: e.g. prompt_embeds, negative_prompt_embeds, etc.",
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)
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# ControlNet
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@classmethod
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def control_guidance_start(cls, default: float = 0.0) -> "InputParam":
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return cls(
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name="control_guidance_start",
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type_hint=float,
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default=default,
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description="When to start applying ControlNet.",
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)
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@classmethod
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def control_guidance_end(cls, default: float = 1.0) -> "InputParam":
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return cls(
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name="control_guidance_end",
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type_hint=float,
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default=default,
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description="When to stop applying ControlNet.",
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)
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@classmethod
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def controlnet_conditioning_scale(cls, default: float = 1.0) -> "InputParam":
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return cls(
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name="controlnet_conditioning_scale",
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type_hint=float,
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default=default,
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description="Scale for ControlNet conditioning.",
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)
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def template(cls, name: str, **overrides) -> "InputParam":
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"""Get template for name if exists, otherwise return basic InputParam with just the name."""
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if name in INPUT_PARAM_TEMPLATES:
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kwargs = {"name": name, **INPUT_PARAM_TEMPLATES[name]}
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# Override with user-provided values
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for key, value in overrides.items():
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kwargs[key] = value
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return cls(**kwargs)
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return cls(name=name, **overrides)
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@dataclass
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class OutputParam:
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"""Specification for an output parameter."""
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name: str
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name: str = None
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type_hint: Any = None
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description: str = ""
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kwargs_type: str = None # YiYi notes: remove this feature (maybe)
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@@ -538,23 +497,15 @@ class OutputParam:
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)
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@classmethod
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def template(cls, name: str) -> Optional["OutputParam"]:
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"""Get template for name if exists, otherwise None."""
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if hasattr(cls, name) and callable(getattr(cls, name)):
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return getattr(cls, name)()
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return None
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# ======================================================
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# OutputParam templates
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# ======================================================
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@classmethod
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def images(cls) -> "OutputParam":
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return cls(name="images", type_hint=List[PIL.Image.Image], description="Generated images.")
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@classmethod
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def latents(cls) -> "OutputParam":
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return cls(name="latents", type_hint=torch.Tensor, description="Denoised latents.")
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def template(cls, name: str, **overrides) -> "OutputParam":
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"""Get template for name if exists, otherwise return basic OutputParam with just the name."""
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if name in OUTPUT_PARAM_TEMPLATES:
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kwargs = {"name": name, **OUTPUT_PARAM_TEMPLATES[name]}
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# Override with user-provided values
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for key, value in overrides.items():
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kwargs[key] = value
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return cls(**kwargs)
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return cls(name=name, **overrides)
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def format_inputs_short(inputs):
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@@ -890,4 +841,4 @@ def make_doc_string(
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output += "\n\n"
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output += format_output_params(outputs, indent_level=2)
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return output
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return output
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