feat(nodes): default to CPU noise

invokeai/app/invocations/noise.py

@@ -0,0 +1,134 @@
+# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654) & the InvokeAI Team
+
+import math
+from typing import Literal
+
+from pydantic import Field, validator
+import torch
+from invokeai.app.invocations.latent import LatentsField
+
+from invokeai.app.util.misc import SEED_MAX, get_random_seed
+from ...backend.util.devices import choose_torch_device, torch_dtype
+from .baseinvocation import (
+    BaseInvocation,
+    BaseInvocationOutput,
+    InvocationConfig,
+    InvocationContext,
+)
+
+"""
+Utilities
+"""
+
+
+def get_noise(
+    width: int,
+    height: int,
+    device: torch.device,
+    seed: int = 0,
+    latent_channels: int = 4,
+    downsampling_factor: int = 8,
+    use_cpu: bool = True,
+    perlin: float = 0.0,
+):
+    """Generate noise for a given image size."""
+    noise_device_type = "cpu" if (use_cpu or device.type == "mps") else device.type
+
+    # limit noise to only the diffusion image channels, not the mask channels
+    input_channels = min(latent_channels, 4)
+    generator = torch.Generator(device=noise_device_type).manual_seed(seed)
+
+    noise_tensor = torch.randn(
+        [
+            1,
+            input_channels,
+            height // downsampling_factor,
+            width // downsampling_factor,
+        ],
+        dtype=torch_dtype(device),
+        device=noise_device_type,
+        generator=generator,
+    ).to(device)
+
+    return noise_tensor
+
+
+"""
+Nodes
+"""
+
+
+class NoiseOutput(BaseInvocationOutput):
+    """Invocation noise output"""
+
+    # fmt: off
+    type:  Literal["noise_output"] = "noise_output"
+
+    # Inputs
+    noise: LatentsField            = Field(default=None, description="The output noise")
+    width:                     int = Field(description="The width of the noise in pixels")
+    height:                    int = Field(description="The height of the noise in pixels")
+    # fmt: on
+
+
+def build_noise_output(latents_name: str, latents: torch.Tensor):
+    return NoiseOutput(
+        noise=LatentsField(latents_name=latents_name),
+        width=latents.size()[3] * 8,
+        height=latents.size()[2] * 8,
+    )
+
+
+class NoiseInvocation(BaseInvocation):
+    """Generates latent noise."""
+
+    type: Literal["noise"] = "noise"
+
+    # Inputs
+    seed: int = Field(
+        ge=0,
+        le=SEED_MAX,
+        description="The seed to use",
+        default_factory=get_random_seed,
+    )
+    width: int = Field(
+        default=512,
+        multiple_of=8,
+        gt=0,
+        description="The width of the resulting noise",
+    )
+    height: int = Field(
+        default=512,
+        multiple_of=8,
+        gt=0,
+        description="The height of the resulting noise",
+    )
+    use_cpu: bool = Field(
+        default=True,
+        description="Use CPU for noise generation (for reproducible results across platforms)",
+    )
+
+    # Schema customisation
+    class Config(InvocationConfig):
+        schema_extra = {
+            "ui": {
+                "tags": ["latents", "noise"],
+            },
+        }
+
+    @validator("seed", pre=True)
+    def modulo_seed(cls, v):
+        """Returns the seed modulo SEED_MAX to ensure it is within the valid range."""
+        return v % SEED_MAX
+
+    def invoke(self, context: InvocationContext) -> NoiseOutput:
+        noise = get_noise(
+            width=self.width,
+            height=self.height,
+            device=choose_torch_device(),
+            seed=self.seed,
+            use_cpu=self.use_cpu,
+        )
+        name = f"{context.graph_execution_state_id}__{self.id}"
+        context.services.latents.save(name, noise)
+        return build_noise_output(latents_name=name, latents=noise)