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
124 lines
3.3 KiB
Python
124 lines
3.3 KiB
Python
# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654) & the InvokeAI Team
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import torch
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from pydantic import validator
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from invokeai.app.invocations.latent import LatentsField
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from invokeai.app.util.misc import SEED_MAX, get_random_seed
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from ...backend.util.devices import choose_torch_device, torch_dtype
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from .baseinvocation import (
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BaseInvocation,
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BaseInvocationOutput,
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FieldDescriptions,
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InputField,
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InvocationContext,
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OutputField,
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invocation,
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invocation_output,
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)
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"""
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Utilities
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"""
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def get_noise(
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width: int,
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height: int,
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device: torch.device,
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seed: int = 0,
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latent_channels: int = 4,
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downsampling_factor: int = 8,
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use_cpu: bool = True,
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perlin: float = 0.0,
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):
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"""Generate noise for a given image size."""
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noise_device_type = "cpu" if use_cpu else device.type
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# limit noise to only the diffusion image channels, not the mask channels
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input_channels = min(latent_channels, 4)
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generator = torch.Generator(device=noise_device_type).manual_seed(seed)
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noise_tensor = torch.randn(
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[
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1,
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input_channels,
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height // downsampling_factor,
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width // downsampling_factor,
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],
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dtype=torch_dtype(device),
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device=noise_device_type,
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generator=generator,
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).to("cpu")
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return noise_tensor
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"""
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Nodes
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"""
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@invocation_output("noise_output")
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class NoiseOutput(BaseInvocationOutput):
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"""Invocation noise output"""
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noise: LatentsField = OutputField(default=None, description=FieldDescriptions.noise)
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width: int = OutputField(description=FieldDescriptions.width)
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height: int = OutputField(description=FieldDescriptions.height)
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def build_noise_output(latents_name: str, latents: torch.Tensor, seed: int):
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return NoiseOutput(
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noise=LatentsField(latents_name=latents_name, seed=seed),
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width=latents.size()[3] * 8,
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height=latents.size()[2] * 8,
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)
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@invocation("noise", title="Noise", tags=["latents", "noise"], category="latents")
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class NoiseInvocation(BaseInvocation):
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"""Generates latent noise."""
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seed: int = InputField(
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ge=0,
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le=SEED_MAX,
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description=FieldDescriptions.seed,
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default_factory=get_random_seed,
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)
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width: int = InputField(
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default=512,
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multiple_of=8,
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gt=0,
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description=FieldDescriptions.width,
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)
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height: int = InputField(
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default=512,
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multiple_of=8,
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gt=0,
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description=FieldDescriptions.height,
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)
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use_cpu: bool = InputField(
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default=True,
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description="Use CPU for noise generation (for reproducible results across platforms)",
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)
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@validator("seed", pre=True)
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def modulo_seed(cls, v):
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"""Returns the seed modulo (SEED_MAX + 1) to ensure it is within the valid range."""
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return v % (SEED_MAX + 1)
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def invoke(self, context: InvocationContext) -> NoiseOutput:
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noise = get_noise(
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width=self.width,
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height=self.height,
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device=choose_torch_device(),
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seed=self.seed,
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use_cpu=self.use_cpu,
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)
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name = f"{context.graph_execution_state_id}__{self.id}"
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context.services.latents.save(name, noise)
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return build_noise_output(latents_name=name, latents=noise, seed=self.seed)
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