# Copyright 2023 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import inspect import re import warnings from collections import OrderedDict from dataclasses import dataclass, field from types import UnionType from typing import Any, Literal, Type, Union, get_args, get_origin import PIL.Image import torch from packaging.specifiers import InvalidSpecifier, SpecifierSet from ..configuration_utils import ConfigMixin, FrozenDict from ..loaders.single_file_utils import _is_single_file_path_or_url from ..utils import DIFFUSERS_LOAD_ID_FIELDS, is_torch_available, logging from ..utils.import_utils import _is_package_available if is_torch_available(): pass logger = logging.get_logger(__name__) # pylint: disable=invalid-name # Template for modular pipeline model card description with placeholders MODULAR_MODEL_CARD_TEMPLATE = """{model_description} ## Example Usage [TODO] ## Pipeline Architecture This modular pipeline is composed of the following blocks: {blocks_description} {trigger_inputs_section} ## Model Components {components_description} {configs_section} {io_specification_section} """ class InsertableDict(OrderedDict): def insert(self, key, value, index): items = list(self.items()) # Remove key if it already exists to avoid duplicates items = [(k, v) for k, v in items if k != key] # Insert at the specified index items.insert(index, (key, value)) # Clear and update self self.clear() self.update(items) # Return self for method chaining return self def __repr__(self): if not self: return "InsertableDict()" items = [] for i, (key, value) in enumerate(self.items()): if isinstance(value, type): # For classes, show class name and obj_repr = f"" else: # For objects (instances) and other types, show class name and module obj_repr = f"" items.append(f"{i}: ({repr(key)}, {obj_repr})") return "InsertableDict([\n " + ",\n ".join(items) + "\n])" # YiYi TODO: # 1. validate the dataclass fields # 2. improve the docstring and potentially add a validator for load methods, make sure they are valid inputs to pass to from_pretrained() @dataclass class ComponentSpec: """Specification for a pipeline component. A component can be created in two ways: 1. From scratch using __init__ with a config dict 2. using `from_pretrained` Attributes: name: Name of the component type_hint: Type of the component (e.g. UNet2DConditionModel) description: Optional description of the component config: Optional config dict for __init__ creation pretrained_model_name_or_path: Optional pretrained_model_name_or_path path for from_pretrained creation subfolder: Optional subfolder in pretrained_model_name_or_path variant: Optional variant in pretrained_model_name_or_path revision: Optional revision in pretrained_model_name_or_path default_creation_method: Preferred creation method - "from_config" or "from_pretrained" """ name: str | None = None type_hint: Type | None = None description: str | None = None config: FrozenDict | None = None pretrained_model_name_or_path: str | list[str] | None = field(default=None, metadata={"loading": True}) subfolder: str | None = field(default="", metadata={"loading": True}) variant: str | None = field(default=None, metadata={"loading": True}) revision: str | None = field(default=None, metadata={"loading": True}) default_creation_method: Literal["from_config", "from_pretrained"] = "from_pretrained" # Deprecated repo: str | list[str] | None = field(default=None, metadata={"loading": False}) def __post_init__(self): repo_value = self.repo if repo_value is not None and self.pretrained_model_name_or_path is None: object.__setattr__(self, "pretrained_model_name_or_path", repo_value) def __hash__(self): """Make ComponentSpec hashable, using load_id as the hash value.""" return hash((self.name, self.load_id, self.default_creation_method)) def __eq__(self, other): """Compare ComponentSpec objects based on name and load_id.""" if not isinstance(other, ComponentSpec): return False return ( self.name == other.name and self.load_id == other.load_id and self.default_creation_method == other.default_creation_method ) @classmethod def from_component(cls, name: str, component: Any) -> Any: """Create a ComponentSpec from a Component. Currently supports: - Components created with `ComponentSpec.load()` method - Components that are ConfigMixin subclasses but not nn.Modules (e.g. schedulers, guiders) Args: name: Name of the component component: Component object to create spec from Returns: ComponentSpec object Raises: ValueError: If component is not supported (e.g. nn.Module without load_id, non-ConfigMixin) """ # Check if component was created with ComponentSpec.load() if hasattr(component, "_diffusers_load_id") and component._diffusers_load_id != "null": # component has a usable load_id -> from_pretrained, no warning needed default_creation_method = "from_pretrained" else: # Component doesn't have a usable load_id, check if it's a nn.Module if isinstance(component, torch.nn.Module): raise ValueError( "Cannot create ComponentSpec from a nn.Module that was not created with `ComponentSpec.load()` method." ) # ConfigMixin objects without weights (e.g. scheduler & guider) can be recreated with from_config elif isinstance(component, ConfigMixin): # warn if component was not created with `ComponentSpec` if not hasattr(component, "_diffusers_load_id"): logger.warning( "Component was not created using `ComponentSpec`, defaulting to `from_config` creation method" ) default_creation_method = "from_config" else: # Not a ConfigMixin and not created with `ComponentSpec.load()` method -> throw error raise ValueError( f"Cannot create ComponentSpec from {name}({component.__class__.__name__}). Currently ComponentSpec.from_component() only supports: " f" - components created with `ComponentSpec.load()` method" f" - components that are a subclass of ConfigMixin but not a nn.Module (e.g. guider, scheduler)." ) type_hint = component.__class__ if isinstance(component, ConfigMixin) and default_creation_method == "from_config": config = component.config else: config = None if hasattr(component, "_diffusers_load_id") and component._diffusers_load_id != "null": load_spec = cls.decode_load_id(component._diffusers_load_id) else: load_spec = {} return cls( name=name, type_hint=type_hint, config=config, default_creation_method=default_creation_method, **load_spec ) @classmethod def loading_fields(cls) -> list[str]: """ Return the names of all loading‐related fields (i.e. those whose field.metadata["loading"] is True). """ return DIFFUSERS_LOAD_ID_FIELDS.copy() @property def load_id(self) -> str: """ Unique identifier for this spec's pretrained load, composed of pretrained_model_name_or_path|subfolder|variant|revision (no empty segments). """ if self.default_creation_method == "from_config": return "null" parts = [getattr(self, k) for k in self.loading_fields()] parts = ["null" if p is None else p for p in parts] return "|".join(parts) @classmethod def decode_load_id(cls, load_id: str) -> dict[str, str | None]: """ Decode a load_id string back into a dictionary of loading fields and values. Args: load_id: The load_id string to decode, format: "pretrained_model_name_or_path|subfolder|variant|revision" where None values are represented as "null" Returns: Dict mapping loading field names to their values. e.g. { "pretrained_model_name_or_path": "path/to/repo", "subfolder": "subfolder", "variant": "variant", "revision": "revision" } If a segment value is "null", it's replaced with None. Returns None if load_id is "null" (indicating component not created with `load` method). """ # Get all loading fields in order loading_fields = cls.loading_fields() result = dict.fromkeys(loading_fields) if load_id == "null": return result # Split the load_id parts = load_id.split("|") # Map parts to loading fields by position for i, part in enumerate(parts): if i < len(loading_fields): # Convert "null" string back to None result[loading_fields[i]] = None if part == "null" else part return result # YiYi TODO: I think we should only support ConfigMixin for this method (after we make guider and image_processors config mixin) # otherwise we cannot do spec -> spec.create() -> component -> ComponentSpec.from_component(component) # the config info is lost in the process # remove error check in from_component spec and ModularPipeline.update_components() if we remove support for non configmixin in `create()` method def create(self, config: FrozenDict | dict[str, Any] | None = None, **kwargs) -> Any: """Create component using from_config with config.""" if self.type_hint is None or not isinstance(self.type_hint, type): raise ValueError("`type_hint` is required when using from_config creation method.") config = config or self.config or {} if issubclass(self.type_hint, ConfigMixin): component = self.type_hint.from_config(config, **kwargs) else: signature_params = inspect.signature(self.type_hint.__init__).parameters init_kwargs = {} for k, v in config.items(): if k in signature_params: init_kwargs[k] = v for k, v in kwargs.items(): if k in signature_params: init_kwargs[k] = v component = self.type_hint(**init_kwargs) component._diffusers_load_id = "null" if hasattr(component, "config"): self.config = component.config return component # YiYi TODO: add guard for type of model, if it is supported by from_pretrained def load(self, **kwargs) -> Any: """Load component using from_pretrained.""" # select loading fields from kwargs passed from user: e.g. pretrained_model_name_or_path, subfolder, variant, revision, note the list could change passed_loading_kwargs = {key: kwargs.pop(key) for key in self.loading_fields() if key in kwargs} # merge loading field value in the spec with user passed values to create load_kwargs load_kwargs = {key: passed_loading_kwargs.get(key, getattr(self, key)) for key in self.loading_fields()} pretrained_model_name_or_path = load_kwargs.pop("pretrained_model_name_or_path", None) if pretrained_model_name_or_path is None: raise ValueError( "`pretrained_model_name_or_path` info is required when using `load` method (you can directly set it in `pretrained_model_name_or_path` field of the ComponentSpec or pass it as an argument)" ) is_single_file = _is_single_file_path_or_url(pretrained_model_name_or_path) if is_single_file and self.type_hint is None: raise ValueError( f"`type_hint` is required when loading a single file model but is missing for component: {self.name}" ) from diffusers import AutoModel # `torch_dtype` is not an accepted parameter for tokenizers and processors. # As a result, it gets stored in `init_kwargs`, which are written to the config # during save. This causes JSON serialization to fail when saving the component. if self.type_hint is not None and not issubclass(self.type_hint, (torch.nn.Module, AutoModel)): kwargs.pop("torch_dtype", None) if self.type_hint is None: try: component = AutoModel.from_pretrained(pretrained_model_name_or_path, **load_kwargs, **kwargs) except Exception as e: raise ValueError(f"Unable to load {self.name} without `type_hint`: {e}") # update type_hint if AutoModel load successfully self.type_hint = component.__class__ else: # determine load method load_method = ( getattr(self.type_hint, "from_single_file") if is_single_file else getattr(self.type_hint, "from_pretrained") ) try: component = load_method(pretrained_model_name_or_path, **load_kwargs, **kwargs) except Exception as e: raise ValueError(f"Unable to load {self.name} using load method: {e}") self.pretrained_model_name_or_path = pretrained_model_name_or_path for k, v in load_kwargs.items(): setattr(self, k, v) component._diffusers_load_id = self.load_id return component @dataclass class ConfigSpec: """Specification for a pipeline configuration parameter.""" name: str default: Any description: str | None = None # ====================================================== # InputParam and OutputParam templates # ====================================================== INPUT_PARAM_TEMPLATES = { "prompt": { "type_hint": str, "required": True, "description": "The prompt or prompts to guide image generation.", }, "negative_prompt": { "type_hint": str, "description": "The prompt or prompts not to guide the image generation.", }, "max_sequence_length": { "type_hint": int, "default": 512, "description": "Maximum sequence length for prompt encoding.", }, "height": { "type_hint": int, "description": "The height in pixels of the generated image.", }, "width": { "type_hint": int, "description": "The width in pixels of the generated image.", }, "num_inference_steps": { "type_hint": int, "default": 50, "description": "The number of denoising steps.", }, "num_images_per_prompt": { "type_hint": int, "default": 1, "description": "The number of images to generate per prompt.", }, "generator": { "type_hint": torch.Generator, "description": "Torch generator for deterministic generation.", }, "sigmas": { "type_hint": list[float], "description": "Custom sigmas for the denoising process.", }, "strength": { "type_hint": float, "default": 0.9, "description": "Strength for img2img/inpainting.", }, "image": { "type_hint": PIL.Image.Image | list[PIL.Image.Image], "required": True, "description": "Reference image(s) for denoising. Can be a single image or list of images.", }, "latents": { "type_hint": torch.Tensor, "description": "Pre-generated noisy latents for image generation.", }, "timesteps": { "type_hint": torch.Tensor, "description": "Timesteps for the denoising process.", }, "output_type": { "type_hint": str, "default": "pil", "description": "Output format: 'pil', 'np', 'pt'.", }, "attention_kwargs": { "type_hint": dict[str, Any], "description": "Additional kwargs for attention processors.", }, "denoiser_input_fields": { "name": None, "kwargs_type": "denoiser_input_fields", "description": "conditional model inputs for the denoiser: e.g. prompt_embeds, negative_prompt_embeds, etc.", }, # inpainting "mask_image": { "type_hint": PIL.Image.Image, "required": True, "description": "Mask image for inpainting.", }, "padding_mask_crop": { "type_hint": int, "description": "Padding for mask cropping in inpainting.", }, # controlnet "control_image": { "type_hint": PIL.Image.Image, "required": True, "description": "Control image for ControlNet conditioning.", }, "control_guidance_start": { "type_hint": float, "default": 0.0, "description": "When to start applying ControlNet.", }, "control_guidance_end": { "type_hint": float, "default": 1.0, "description": "When to stop applying ControlNet.", }, "controlnet_conditioning_scale": { "type_hint": float, "default": 1.0, "description": "Scale for ControlNet conditioning.", }, "layers": { "type_hint": int, "default": 4, "description": "Number of layers to extract from the image", }, # common intermediate inputs "prompt_embeds": { "type_hint": torch.Tensor, "required": True, "description": "text embeddings used to guide the image generation. Can be generated from text_encoder step.", }, "prompt_embeds_mask": { "type_hint": torch.Tensor, "required": True, "description": "mask for the text embeddings. Can be generated from text_encoder step.", }, "negative_prompt_embeds": { "type_hint": torch.Tensor, "description": "negative text embeddings used to guide the image generation. Can be generated from text_encoder step.", }, "negative_prompt_embeds_mask": { "type_hint": torch.Tensor, "description": "mask for the negative text embeddings. Can be generated from text_encoder step.", }, "image_latents": { "type_hint": torch.Tensor, "required": True, "description": "image latents used to guide the image generation. Can be generated from vae_encoder step.", }, "batch_size": { "type_hint": int, "default": 1, "description": "Number of prompts, the final batch size of model inputs should be batch_size * num_images_per_prompt. Can be generated in input step.", }, "dtype": { "type_hint": torch.dtype, "default": torch.float32, "description": "The dtype of the model inputs, can be generated in input step.", }, } OUTPUT_PARAM_TEMPLATES = { "images": { "type_hint": list[PIL.Image.Image], "description": "Generated images.", }, "videos": { "type_hint": list[PIL.Image.Image], "description": "The generated videos.", }, "latents": { "type_hint": torch.Tensor, "description": "Denoised latents.", }, # intermediate outputs "prompt_embeds": { "type_hint": torch.Tensor, "kwargs_type": "denoiser_input_fields", "description": "The prompt embeddings.", }, "prompt_embeds_mask": { "type_hint": torch.Tensor, "kwargs_type": "denoiser_input_fields", "description": "The encoder attention mask.", }, "negative_prompt_embeds": { "type_hint": torch.Tensor, "kwargs_type": "denoiser_input_fields", "description": "The negative prompt embeddings.", }, "negative_prompt_embeds_mask": { "type_hint": torch.Tensor, "kwargs_type": "denoiser_input_fields", "description": "The negative prompt embeddings mask.", }, "image_latents": { "type_hint": torch.Tensor, "description": "The latent representation of the input image.", }, } @dataclass class InputParam: """Specification for an input parameter.""" name: str = None type_hint: Any = None default: Any = None required: bool = False description: str = "" kwargs_type: str = None metadata: dict[str, Any] = None def __repr__(self): return f"<{self.name}: {'required' if self.required else 'optional'}, default={self.default}>" @classmethod def template(cls, template_name: str, note: str = None, **overrides) -> "InputParam": """Get template for name if exists, otherwise raise ValueError.""" if template_name not in INPUT_PARAM_TEMPLATES: raise ValueError(f"InputParam template for {template_name} not found") template_kwargs = INPUT_PARAM_TEMPLATES[template_name].copy() # Determine the actual param name: # 1. From overrides if provided # 2. From template if present # 3. Fall back to template_name name = overrides.pop("name", template_kwargs.pop("name", template_name)) if note and "description" in template_kwargs: template_kwargs["description"] = f"{template_kwargs['description']} ({note})" template_kwargs.update(overrides) return cls(name=name, **template_kwargs) @dataclass class OutputParam: """Specification for an output parameter.""" name: str type_hint: Any = None description: str = "" kwargs_type: str = None metadata: dict[str, Any] = None def __repr__(self): return ( f"<{self.name}: {self.type_hint.__name__ if hasattr(self.type_hint, '__name__') else str(self.type_hint)}>" ) @classmethod def template(cls, template_name: str, note: str = None, **overrides) -> "OutputParam": """Get template for name if exists, otherwise raise ValueError.""" if template_name not in OUTPUT_PARAM_TEMPLATES: raise ValueError(f"OutputParam template for {template_name} not found") template_kwargs = OUTPUT_PARAM_TEMPLATES[template_name].copy() # Determine the actual param name: # 1. From overrides if provided # 2. From template if present # 3. Fall back to template_name name = overrides.pop("name", template_kwargs.pop("name", template_name)) if note and "description" in template_kwargs: template_kwargs["description"] = f"{template_kwargs['description']} ({note})" template_kwargs.update(overrides) return cls(name=name, **template_kwargs) def format_inputs_short(inputs): """ Format input parameters into a string representation, with required params first followed by optional ones. Args: inputs: list of input parameters with 'required' and 'name' attributes, and 'default' for optional params Returns: str: Formatted string of input parameters Example: >>> inputs = [ ... InputParam(name="prompt", required=True), ... InputParam(name="image", required=True), ... InputParam(name="guidance_scale", required=False, default=7.5), ... InputParam(name="num_inference_steps", required=False, default=50) ... ] >>> format_inputs_short(inputs) 'prompt, image, guidance_scale=7.5, num_inference_steps=50' """ required_inputs = [param for param in inputs if param.required] optional_inputs = [param for param in inputs if not param.required] required_str = ", ".join(param.name for param in required_inputs) optional_str = ", ".join(f"{param.name}={param.default}" for param in optional_inputs) inputs_str = required_str if optional_str: inputs_str = f"{inputs_str}, {optional_str}" if required_str else optional_str return inputs_str def format_intermediates_short(intermediate_inputs, required_intermediate_inputs, intermediate_outputs): """ Formats intermediate inputs and outputs of a block into a string representation. Args: intermediate_inputs: list of intermediate input parameters required_intermediate_inputs: list of required intermediate input names intermediate_outputs: list of intermediate output parameters Returns: str: Formatted string like: Intermediates: - inputs: Required(latents), dtype - modified: latents # variables that appear in both inputs and outputs - outputs: images # new outputs only """ # Handle inputs input_parts = [] for inp in intermediate_inputs: if inp.name in required_intermediate_inputs: input_parts.append(f"Required({inp.name})") else: if inp.name is None and inp.kwargs_type is not None: inp_name = "*_" + inp.kwargs_type else: inp_name = inp.name input_parts.append(inp_name) # Handle modified variables (appear in both inputs and outputs) inputs_set = {inp.name for inp in intermediate_inputs} modified_parts = [] new_output_parts = [] for out in intermediate_outputs: if out.name in inputs_set: modified_parts.append(out.name) else: new_output_parts.append(out.name) result = [] if input_parts: result.append(f" - inputs: {', '.join(input_parts)}") if modified_parts: result.append(f" - modified: {', '.join(modified_parts)}") if new_output_parts: result.append(f" - outputs: {', '.join(new_output_parts)}") return "\n".join(result) if result else " (none)" def format_params(params, header="Args", indent_level=4, max_line_length=115): """Format a list of InputParam or OutputParam objects into a readable string representation. Args: params: list of InputParam or OutputParam objects to format header: Header text to use (e.g. "Args" or "Returns") indent_level: Number of spaces to indent each parameter line (default: 4) max_line_length: Maximum length for each line before wrapping (default: 115) Returns: A formatted string representing all parameters """ if not params: return "" base_indent = " " * indent_level param_indent = " " * (indent_level + 4) desc_indent = " " * (indent_level + 8) formatted_params = [] def get_type_str(type_hint): if isinstance(type_hint, UnionType) or get_origin(type_hint) is Union: type_strs = [t.__name__ if hasattr(t, "__name__") else str(t) for t in get_args(type_hint)] return " | ".join(type_strs) return type_hint.__name__ if hasattr(type_hint, "__name__") else str(type_hint) def wrap_text(text, indent, max_length): """Wrap text while preserving markdown links and maintaining indentation.""" words = text.split() lines = [] current_line = [] current_length = 0 for word in words: word_length = len(word) + (1 if current_line else 0) if current_line and current_length + word_length > max_length: lines.append(" ".join(current_line)) current_line = [word] current_length = len(word) else: current_line.append(word) current_length += word_length if current_line: lines.append(" ".join(current_line)) return f"\n{indent}".join(lines) # Add the header formatted_params.append(f"{base_indent}{header}:") for param in params: # Format parameter name and type type_str = get_type_str(param.type_hint) if param.type_hint != Any else "" # YiYi Notes: remove this line if we remove kwargs_type name = f"**{param.kwargs_type}" if param.name is None and param.kwargs_type is not None else param.name param_str = f"{param_indent}{name} (`{type_str}`" # Add optional tag and default value if parameter is an InputParam and optional if hasattr(param, "required"): if not param.required: param_str += ", *optional*" if param.default is not None: param_str += f", defaults to {param.default}" param_str += "):" # Add description on a new line with additional indentation and wrapping if param.description: desc = re.sub(r"\[(.*?)\]\((https?://[^\s\)]+)\)", r"[\1](\2)", param.description) wrapped_desc = wrap_text(desc, desc_indent, max_line_length) param_str += f"\n{desc_indent}{wrapped_desc}" else: param_str += f"\n{desc_indent}TODO: Add description." formatted_params.append(param_str) return "\n".join(formatted_params) def format_input_params(input_params, indent_level=4, max_line_length=115): """Format a list of InputParam objects into a readable string representation. Args: input_params: list of InputParam objects to format indent_level: Number of spaces to indent each parameter line (default: 4) max_line_length: Maximum length for each line before wrapping (default: 115) Returns: A formatted string representing all input parameters """ return format_params(input_params, "Inputs", indent_level, max_line_length) def format_output_params(output_params, indent_level=4, max_line_length=115): """Format a list of OutputParam objects into a readable string representation. Args: output_params: list of OutputParam objects to format indent_level: Number of spaces to indent each parameter line (default: 4) max_line_length: Maximum length for each line before wrapping (default: 115) Returns: A formatted string representing all output parameters """ return format_params(output_params, "Outputs", indent_level, max_line_length) def format_params_markdown(params, header="Inputs"): """Format a list of InputParam or OutputParam objects as a markdown bullet-point list. Suitable for model cards rendered on Hugging Face Hub. Args: params: list of InputParam or OutputParam objects to format header: Header text (e.g. "Inputs" or "Outputs") Returns: A formatted markdown string, or empty string if params is empty. """ if not params: return "" def get_type_str(type_hint): if isinstance(type_hint, UnionType) or get_origin(type_hint) is Union: type_strs = [t.__name__ if hasattr(t, "__name__") else str(t) for t in get_args(type_hint)] return " | ".join(type_strs) return type_hint.__name__ if hasattr(type_hint, "__name__") else str(type_hint) lines = [f"**{header}:**\n"] if header else [] for param in params: type_str = get_type_str(param.type_hint) if param.type_hint != Any else "" name = f"**{param.kwargs_type}" if param.name is None and param.kwargs_type is not None else param.name param_str = f"- `{name}` (`{type_str}`" if hasattr(param, "required") and not param.required: param_str += ", *optional*" if param.default is not None: param_str += f", defaults to `{param.default}`" param_str += ")" desc = param.description if param.description else "No description provided" param_str += f": {desc}" lines.append(param_str) return "\n".join(lines) def format_components(components, indent_level=4, max_line_length=115, add_empty_lines=True): """Format a list of ComponentSpec objects into a readable string representation. Args: components: list of ComponentSpec objects to format indent_level: Number of spaces to indent each component line (default: 4) max_line_length: Maximum length for each line before wrapping (default: 115) add_empty_lines: Whether to add empty lines between components (default: True) Returns: A formatted string representing all components """ if not components: return "" base_indent = " " * indent_level component_indent = " " * (indent_level + 4) formatted_components = [] # Add the header formatted_components.append(f"{base_indent}Components:") if add_empty_lines: formatted_components.append("") # Add each component with optional empty lines between them for i, component in enumerate(components): # Get type name, handling special cases type_name = ( component.type_hint.__name__ if hasattr(component.type_hint, "__name__") else str(component.type_hint) ) component_desc = f"{component_indent}{component.name} (`{type_name}`)" if component.description: component_desc += f": {component.description}" # Get the loading fields dynamically loading_field_values = [] for field_name in component.loading_fields(): field_value = getattr(component, field_name) if field_value: loading_field_values.append(f"{field_name}={field_value}") # Add loading field information if available if loading_field_values: component_desc += f" [{', '.join(loading_field_values)}]" formatted_components.append(component_desc) # Add an empty line after each component except the last one if add_empty_lines and i < len(components) - 1: formatted_components.append("") return "\n".join(formatted_components) def format_configs(configs, indent_level=4, max_line_length=115, add_empty_lines=True): """Format a list of ConfigSpec objects into a readable string representation. Args: configs: list of ConfigSpec objects to format indent_level: Number of spaces to indent each config line (default: 4) max_line_length: Maximum length for each line before wrapping (default: 115) add_empty_lines: Whether to add empty lines between configs (default: True) Returns: A formatted string representing all configs """ if not configs: return "" base_indent = " " * indent_level config_indent = " " * (indent_level + 4) formatted_configs = [] # Add the header formatted_configs.append(f"{base_indent}Configs:") if add_empty_lines: formatted_configs.append("") # Add each config with optional empty lines between them for i, config in enumerate(configs): config_desc = f"{config_indent}{config.name} (default: {config.default})" if config.description: config_desc += f": {config.description}" formatted_configs.append(config_desc) # Add an empty line after each config except the last one if add_empty_lines and i < len(configs) - 1: formatted_configs.append("") return "\n".join(formatted_configs) def format_workflow(workflow_map): """Format a workflow map into a readable string representation. Args: workflow_map: Dictionary mapping workflow names to trigger inputs Returns: A formatted string representing all workflows """ if workflow_map is None: return "" lines = ["Supported workflows:"] for workflow_name, trigger_inputs in workflow_map.items(): required_inputs = [k for k, v in trigger_inputs.items() if v] if required_inputs: inputs_str = ", ".join(f"`{t}`" for t in required_inputs) lines.append(f" - `{workflow_name}`: requires {inputs_str}") else: lines.append(f" - `{workflow_name}`: default (no additional inputs required)") return "\n".join(lines) def make_doc_string( inputs, outputs, description="", class_name=None, expected_components=None, expected_configs=None, ): """ Generates a formatted documentation string describing the pipeline block's parameters and structure. Args: inputs: list of input parameters intermediate_inputs: list of intermediate input parameters outputs: list of output parameters description (str, *optional*): Description of the block class_name (str, *optional*): Name of the class to include in the documentation expected_components (list[ComponentSpec], *optional*): list of expected components expected_configs (list[ConfigSpec], *optional*): list of expected configurations Returns: str: A formatted string containing information about components, configs, call parameters, intermediate inputs/outputs, and final outputs. """ output = "" # Add class name if provided if class_name: output += f"class {class_name}\n\n" # Add description if description: desc_lines = description.strip().split("\n") aligned_desc = "\n".join(" " + line.rstrip() for line in desc_lines) output += aligned_desc + "\n\n" # Add components section if provided if expected_components and len(expected_components) > 0: components_str = format_components(expected_components, indent_level=2, add_empty_lines=False) output += components_str + "\n\n" # Add configs section if provided if expected_configs and len(expected_configs) > 0: configs_str = format_configs(expected_configs, indent_level=2, add_empty_lines=False) output += configs_str + "\n\n" # Add inputs section output += format_input_params(inputs, indent_level=2) # Add outputs section output += "\n\n" output += format_output_params(outputs, indent_level=2) return output def _validate_requirements(reqs): if reqs is None: normalized_reqs = {} else: if not isinstance(reqs, dict): raise ValueError( "Requirements must be provided as a dictionary mapping package names to version specifiers." ) normalized_reqs = _normalize_requirements(reqs) if not normalized_reqs: return {} final: dict[str, str] = {} for req, specified_ver in normalized_reqs.items(): req_available, req_actual_ver = _is_package_available(req) if not req_available: logger.warning(f"{req} was specified in the requirements but wasn't found in the current environment.") if specified_ver: try: specifier = SpecifierSet(specified_ver) except InvalidSpecifier as err: raise ValueError(f"Requirement specifier '{specified_ver}' for {req} is invalid.") from err if req_actual_ver == "N/A": logger.warning( f"Version of {req} could not be determined to validate requirement '{specified_ver}'. Things might work unexpected." ) elif not specifier.contains(req_actual_ver, prereleases=True): logger.warning( f"{req} requirement '{specified_ver}' is not satisfied by the installed version {req_actual_ver}. Things might work unexpected." ) final[req] = specified_ver return final def _normalize_requirements(reqs): if not reqs: return {} normalized: "OrderedDict[str, str]" = OrderedDict() def _accumulate(mapping: dict[str, Any]): for pkg, spec in mapping.items(): if isinstance(spec, dict): # This is recursive because blocks are composable. This way, we can merge requirements # from multiple blocks. _accumulate(spec) continue pkg_name = str(pkg).strip() if not pkg_name: raise ValueError("Requirement package name cannot be empty.") spec_str = "" if spec is None else str(spec).strip() if spec_str and not spec_str.startswith(("<", ">", "=", "!", "~")): spec_str = f"=={spec_str}" existing_spec = normalized.get(pkg_name) if existing_spec is not None: if not existing_spec and spec_str: normalized[pkg_name] = spec_str elif existing_spec and spec_str and existing_spec != spec_str: try: combined_spec = SpecifierSet(",".join(filter(None, [existing_spec, spec_str]))) except InvalidSpecifier: logger.warning( f"Conflicting requirements for '{pkg_name}' detected: '{existing_spec}' vs '{spec_str}'. Keeping '{existing_spec}'." ) else: normalized[pkg_name] = str(combined_spec) continue normalized[pkg_name] = spec_str _accumulate(reqs) return normalized def combine_inputs(*named_input_lists: list[tuple[str, list[InputParam]]]) -> list[InputParam]: """ Combines multiple lists of InputParam objects from different blocks. For duplicate inputs, updates only if current default value is None and new default value is not None. Warns if multiple non-None default values exist for the same input. Args: named_input_lists: List of tuples containing (block_name, input_param_list) pairs Returns: List[InputParam]: Combined list of unique InputParam objects """ combined_dict = {} # name -> InputParam value_sources = {} # name -> block_name for block_name, inputs in named_input_lists: for input_param in inputs: if input_param.name is None and input_param.kwargs_type is not None: input_name = "*_" + input_param.kwargs_type else: input_name = input_param.name if input_name in combined_dict: current_param = combined_dict[input_name] if ( current_param.default is not None and input_param.default is not None and current_param.default != input_param.default ): warnings.warn( f"Multiple different default values found for input '{input_name}': " f"{current_param.default} (from block '{value_sources[input_name]}') and " f"{input_param.default} (from block '{block_name}'). Using {current_param.default}." ) if current_param.default is None and input_param.default is not None: combined_dict[input_name] = input_param value_sources[input_name] = block_name else: combined_dict[input_name] = input_param value_sources[input_name] = block_name return list(combined_dict.values()) def combine_outputs(*named_output_lists: list[tuple[str, list[OutputParam]]]) -> list[OutputParam]: """ Combines multiple lists of OutputParam objects from different blocks. For duplicate outputs, keeps the first occurrence of each output name. Args: named_output_lists: List of tuples containing (block_name, output_param_list) pairs Returns: List[OutputParam]: Combined list of unique OutputParam objects """ combined_dict = {} # name -> OutputParam for block_name, outputs in named_output_lists: for output_param in outputs: if (output_param.name not in combined_dict) or ( combined_dict[output_param.name].kwargs_type is None and output_param.kwargs_type is not None ): combined_dict[output_param.name] = output_param return list(combined_dict.values()) def generate_modular_model_card_content(blocks) -> dict[str, Any]: """ Generate model card content for a modular pipeline. This function creates a comprehensive model card with descriptions of the pipeline's architecture, components, configurations, inputs, and outputs. Args: blocks: The pipeline's blocks object containing all pipeline specifications Returns: Dict[str, Any]: A dictionary containing formatted content sections: - pipeline_name: Name of the pipeline - model_description: Overall description with pipeline type - blocks_description: Detailed architecture of blocks - components_description: List of required components - configs_section: Configuration parameters section - io_specification_section: Input/Output specification (per-workflow or unified) - trigger_inputs_section: Conditional execution information - tags: List of relevant tags for the model card """ blocks_class_name = blocks.__class__.__name__ pipeline_name = blocks_class_name.replace("Blocks", " Pipeline") description = getattr(blocks, "description", "A modular diffusion pipeline.") # generate blocks architecture description blocks_desc_parts = [] sub_blocks = getattr(blocks, "sub_blocks", None) or {} if sub_blocks: for i, (name, block) in enumerate(sub_blocks.items()): block_class = block.__class__.__name__ block_desc = block.description.split("\n")[0] if getattr(block, "description", "") else "" blocks_desc_parts.append(f"{i + 1}. **{name}** (`{block_class}`)") if block_desc: blocks_desc_parts.append(f" - {block_desc}") blocks_description = "\n".join(blocks_desc_parts) if blocks_desc_parts else "No blocks defined." components = getattr(blocks, "expected_components", []) if components: components_str = format_components(components, indent_level=0, add_empty_lines=False) # remove the "Components:" header since template has its own components_description = components_str.replace("Components:\n", "").strip() if components_description: # Convert to enumerated list lines = [line.strip() for line in components_description.split("\n") if line.strip()] enumerated_lines = [f"{i + 1}. {line}" for i, line in enumerate(lines)] components_description = "\n".join(enumerated_lines) else: components_description = "No specific components required." else: components_description = "No specific components required. Components can be loaded dynamically." configs = getattr(blocks, "expected_configs", []) configs_section = "" if configs: configs_str = format_configs(configs, indent_level=0, add_empty_lines=False) configs_description = configs_str.replace("Configs:\n", "").strip() if configs_description: configs_section = f"\n\n## Configuration Parameters\n\n{configs_description}" # Branch on whether workflows are defined has_workflows = getattr(blocks, "_workflow_map", None) is not None if has_workflows: workflow_map = blocks._workflow_map parts = [] # If blocks overrides outputs (e.g. to return just "images" instead of all intermediates), # use that as the shared output for all workflows blocks_outputs = blocks.outputs blocks_intermediate = getattr(blocks, "intermediate_outputs", None) shared_outputs = ( blocks_outputs if blocks_intermediate is not None and blocks_outputs != blocks_intermediate else None ) parts.append("## Workflow Input Specification\n") # Per-workflow details: show trigger inputs with full param descriptions for wf_name, trigger_inputs in workflow_map.items(): trigger_input_names = set(trigger_inputs.keys()) try: workflow_blocks = blocks.get_workflow(wf_name) except Exception: parts.append(f"
\n{wf_name}\n") parts.append("*Could not resolve workflow blocks.*\n") parts.append("
\n") continue wf_inputs = workflow_blocks.inputs # Show only trigger inputs with full parameter descriptions trigger_params = [p for p in wf_inputs if p.name in trigger_input_names] parts.append(f"
\n{wf_name}\n") inputs_str = format_params_markdown(trigger_params, header=None) parts.append(inputs_str if inputs_str else "No additional inputs required.") parts.append("") parts.append("
\n") # Common Inputs & Outputs section (like non-workflow pipelines) all_inputs = blocks.inputs all_outputs = shared_outputs if shared_outputs is not None else blocks.outputs inputs_str = format_params_markdown(all_inputs, "Inputs") outputs_str = format_params_markdown(all_outputs, "Outputs") inputs_description = inputs_str if inputs_str else "No specific inputs defined." outputs_description = outputs_str if outputs_str else "Standard pipeline outputs." parts.append(f"\n## Input/Output Specification\n\n{inputs_description}\n\n{outputs_description}") io_specification_section = "\n".join(parts) # Suppress trigger_inputs_section when workflows are shown (it's redundant) trigger_inputs_section = "" else: # Unified I/O section (original behavior) inputs = blocks.inputs outputs = blocks.outputs inputs_str = format_params_markdown(inputs, "Inputs") outputs_str = format_params_markdown(outputs, "Outputs") inputs_description = inputs_str if inputs_str else "No specific inputs defined." outputs_description = outputs_str if outputs_str else "Standard pipeline outputs." io_specification_section = f"## Input/Output Specification\n\n{inputs_description}\n\n{outputs_description}" trigger_inputs_section = "" if hasattr(blocks, "trigger_inputs") and blocks.trigger_inputs: trigger_inputs_list = sorted([t for t in blocks.trigger_inputs if t is not None]) if trigger_inputs_list: trigger_inputs_str = ", ".join(f"`{t}`" for t in trigger_inputs_list) trigger_inputs_section = f""" ### Conditional Execution This pipeline contains blocks that are selected at runtime based on inputs: - **Trigger Inputs**: {trigger_inputs_str} """ # generate tags based on pipeline characteristics tags = ["modular-diffusers", "diffusers"] if hasattr(blocks, "model_name") and blocks.model_name: tags.append(blocks.model_name) if has_workflows: # Derive tags from workflow names workflow_names = set(blocks._workflow_map.keys()) if any("inpainting" in wf for wf in workflow_names): tags.append("inpainting") if any("image2image" in wf for wf in workflow_names): tags.append("image-to-image") if any("controlnet" in wf for wf in workflow_names): tags.append("controlnet") if any("text2image" in wf for wf in workflow_names): tags.append("text-to-image") elif hasattr(blocks, "trigger_inputs") and blocks.trigger_inputs: triggers = blocks.trigger_inputs if any(t in triggers for t in ["mask", "mask_image"]): tags.append("inpainting") if any(t in triggers for t in ["image", "image_latents"]): tags.append("image-to-image") if any(t in triggers for t in ["control_image", "controlnet_cond"]): tags.append("controlnet") if not any(t in triggers for t in ["image", "mask", "image_latents", "mask_image"]): tags.append("text-to-image") else: tags.append("text-to-image") block_count = len(blocks.sub_blocks) model_description = f"""This is a modular diffusion pipeline built with 🧨 Diffusers' modular pipeline framework. **Pipeline Type**: {blocks_class_name} **Description**: {description} This pipeline uses a {block_count}-block architecture that can be customized and extended.""" return { "pipeline_name": pipeline_name, "model_description": model_description, "blocks_description": blocks_description, "components_description": components_description, "configs_section": configs_section, "io_specification_section": io_specification_section, "trigger_inputs_section": trigger_inputs_section, "tags": tags, }