mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
044d4c107a
All invocation metadata (type, title, tags and category) are now defined in decorators.
The decorators add the `type: Literal["invocation_type"]: "invocation_type"` field to the invocation.
Category is a new invocation metadata, but it is not used by the frontend just yet.
- `@invocation()` decorator for invocations
```py
@invocation(
"sdxl_compel_prompt",
title="SDXL Prompt",
tags=["sdxl", "compel", "prompt"],
category="conditioning",
)
class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
...
```
- `@invocation_output()` decorator for invocation outputs
```py
@invocation_output("clip_skip_output")
class ClipSkipInvocationOutput(BaseInvocationOutput):
...
```
- update invocation docs
- add category to decorator
- regen frontend types
78 lines
3.2 KiB
Python
78 lines
3.2 KiB
Python
from os.path import exists
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from typing import Optional, Union
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import numpy as np
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from dynamicprompts.generators import CombinatorialPromptGenerator, RandomPromptGenerator
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from pydantic import validator
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from invokeai.app.invocations.primitives import StringCollectionOutput
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from .baseinvocation import BaseInvocation, InputField, InvocationContext, UIComponent, invocation
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@invocation("dynamic_prompt", title="Dynamic Prompt", tags=["prompt", "collection"], category="prompt")
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class DynamicPromptInvocation(BaseInvocation):
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"""Parses a prompt using adieyal/dynamicprompts' random or combinatorial generator"""
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prompt: str = InputField(description="The prompt to parse with dynamicprompts", ui_component=UIComponent.Textarea)
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max_prompts: int = InputField(default=1, description="The number of prompts to generate")
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combinatorial: bool = InputField(default=False, description="Whether to use the combinatorial generator")
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def invoke(self, context: InvocationContext) -> StringCollectionOutput:
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if self.combinatorial:
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generator = CombinatorialPromptGenerator()
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prompts = generator.generate(self.prompt, max_prompts=self.max_prompts)
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else:
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generator = RandomPromptGenerator()
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prompts = generator.generate(self.prompt, num_images=self.max_prompts)
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return StringCollectionOutput(collection=prompts)
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@invocation("prompt_from_file", title="Prompts from File", tags=["prompt", "file"], category="prompt")
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class PromptsFromFileInvocation(BaseInvocation):
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"""Loads prompts from a text file"""
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file_path: str = InputField(description="Path to prompt text file")
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pre_prompt: Optional[str] = InputField(
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default=None, description="String to prepend to each prompt", ui_component=UIComponent.Textarea
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)
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post_prompt: Optional[str] = InputField(
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default=None, description="String to append to each prompt", ui_component=UIComponent.Textarea
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)
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start_line: int = InputField(default=1, ge=1, description="Line in the file to start start from")
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max_prompts: int = InputField(default=1, ge=0, description="Max lines to read from file (0=all)")
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@validator("file_path")
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def file_path_exists(cls, v):
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if not exists(v):
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raise ValueError(FileNotFoundError)
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return v
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def promptsFromFile(
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self,
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file_path: str,
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pre_prompt: Union[str, None],
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post_prompt: Union[str, None],
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start_line: int,
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max_prompts: int,
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):
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prompts = []
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start_line -= 1
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end_line = start_line + max_prompts
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if max_prompts <= 0:
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end_line = np.iinfo(np.int32).max
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with open(file_path) as f:
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for i, line in enumerate(f):
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if i >= start_line and i < end_line:
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prompts.append((pre_prompt or "") + line.strip() + (post_prompt or ""))
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if i >= end_line:
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break
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return prompts
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def invoke(self, context: InvocationContext) -> StringCollectionOutput:
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prompts = self.promptsFromFile(
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self.file_path, self.pre_prompt, self.post_prompt, self.start_line, self.max_prompts
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)
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return StringCollectionOutput(collection=prompts)
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