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Add naive ControlNet support to TiledStableDiffusionRefineInvocation

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This commit is contained in:
Ryan Dick 2024-06-10 10:52:14 -04:00
parent 93e3a2b504
commit 85db33bc7e
2 changed files with 106 additions and 18 deletions
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120
invokeai/app/invocations/tiled_stable_diffusion_refine.py
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@ -1,4 +1,7 @@
from contextlib import ExitStack
import numpy as np
import numpy.typing as npt
import torch
from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel
from PIL import Image
@ -17,14 +20,16 @@ from invokeai.app.invocations.fields import (
)
from invokeai.app.invocations.image_to_latents import ImageToLatentsInvocation
from invokeai.app.invocations.latents_to_image import LatentsToImageInvocation
from invokeai.app.invocations.model import UNetField, VAEField
from invokeai.app.invocations.model import ModelIdentifierField, UNetField, VAEField
from invokeai.app.invocations.noise import get_noise
from invokeai.app.invocations.primitives import ImageOutput
from invokeai.app.services.shared.invocation_context import InvocationContext
from invokeai.backend.stable_diffusion.diffusers_pipeline import image_resized_to_grid_as_tensor
from invokeai.app.util.controlnet_utils import CONTROLNET_MODE_VALUES, CONTROLNET_RESIZE_VALUES, prepare_control_image
from invokeai.backend.stable_diffusion.diffusers_pipeline import ControlNetData, image_resized_to_grid_as_tensor
from invokeai.backend.tiles.tiles import calc_tiles_min_overlap, merge_tiles_with_linear_blending
from invokeai.backend.tiles.utils import Tile
from invokeai.backend.util.devices import TorchDevice
from invokeai.backend.util.hotfixes import ControlNetModel
@invocation(
@ -66,10 +71,6 @@ class TiledStableDiffusionRefineInvocation(BaseInvocation):
input=Input.Connection,
title="UNet",
)
# control: Optional[Union[ControlField, list[ControlField]]] = InputField(
# default=None,
# input=Input.Connection,
# )
cfg_rescale_multiplier: float = InputField(
title="CFG Rescale Multiplier", default=0, ge=0, lt=1, description=FieldDescriptions.cfg_rescale_multiplier
)
@ -80,6 +81,15 @@ class TiledStableDiffusionRefineInvocation(BaseInvocation):
vae_fp32: bool = InputField(
default=DEFAULT_PRECISION == torch.float32, description="Whether to use float32 precision when running the VAE."
)
# HACK(ryand): We probably want to allow the user to control all of the parameters in ControlField. But, we akwardly
# don't want to use the image field. Figure out how best to handle this.
# TODO(ryand): Currently, there is no ControlNet preprocessor applied to the tile images. In other words, we pretty
# much assume that it is a tile ControlNet. We need to decide how we want to handle this. E.g. find a way to support
# CN preprocessors, raise a clear warning when a non-tile CN model is selected, hardcode the supported CN models,
# etc.
control_model: ModelIdentifierField = InputField(
description=FieldDescriptions.controlnet_model, ui_type=UIType.ControlNetModel
)
@field_validator("cfg_scale")
def ge_one(cls, v: list[float] | float) -> list[float] | float:
@ -112,6 +122,41 @@ class TiledStableDiffusionRefineInvocation(BaseInvocation):
right = image_tile.coords.right // LATENT_SCALE_FACTOR
return latents[..., top:bottom, left:right]
def run_controlnet(
self,
image: Image.Image,
controlnet_model: ControlNetModel,
weight: float,
do_classifier_free_guidance: bool,
width: int,
height: int,
device: torch.device,
dtype: torch.dtype,
control_mode: CONTROLNET_MODE_VALUES = "balanced",
resize_mode: CONTROLNET_RESIZE_VALUES = "just_resize_simple",
) -> ControlNetData:
control_image = prepare_control_image(
image=image,
do_classifier_free_guidance=do_classifier_free_guidance,
width=width,
height=height,
device=device,
dtype=dtype,
control_mode=control_mode,
resize_mode=resize_mode,
)
return ControlNetData(
model=controlnet_model,
image_tensor=control_image,
weight=weight,
begin_step_percent=0.0,
end_step_percent=1.0,
control_mode=control_mode,
# Any resizing needed should currently be happening in prepare_control_image(), but adding resize_mode to
# ControlNetData in case needed in the future.
resize_mode=resize_mode,
)
@torch.no_grad()
def invoke(self, context: InvocationContext) -> ImageOutput:
# TODO(ryand): Expose the seed parameter.
@ -119,8 +164,6 @@ class TiledStableDiffusionRefineInvocation(BaseInvocation):
# Load the input image.
input_image = context.images.get_pil(self.image.image_name)
input_image_torch = image_resized_to_grid_as_tensor(input_image.convert("RGB"), multiple_of=LATENT_SCALE_FACTOR)
input_image_torch = input_image_torch.unsqueeze(0) # Add a batch dimension.
# Calculate the tile locations to cover the image.
# TODO(ryand): Expose these tiling parameters. (Keep in mind the multiple-of constraints on these params.)
@ -132,12 +175,15 @@ class TiledStableDiffusionRefineInvocation(BaseInvocation):
min_overlap=128,
)
# Convert the input image to a torch.Tensor.
input_image_torch = image_resized_to_grid_as_tensor(input_image.convert("RGB"), multiple_of=LATENT_SCALE_FACTOR)
input_image_torch = input_image_torch.unsqueeze(0) # Add a batch dimension.
# Validate our assumptions about the shape of input_image_torch.
assert input_image_torch.dim() == 4 # We expect: (batch_size, channels, height, width).
assert input_image_torch.shape[:2] == (1, 3)
# Split the input image into tiles.
image_tiles: list[torch.Tensor] = []
# Split the input image into tiles in torch.Tensor format.
image_tiles_torch: list[torch.Tensor] = []
for tile in tiles:
image_tile = input_image_torch[
:,
@ -145,17 +191,30 @@ class TiledStableDiffusionRefineInvocation(BaseInvocation):
tile.coords.top : tile.coords.bottom,
tile.coords.left : tile.coords.right,
]
image_tiles.append(image_tile)
image_tiles_torch.append(image_tile)
# Split the input image into tiles in numpy format.
# TODO(ryand): We currently maintain both np.ndarray and torch.Tensor tiles. Ideally, all operations should work
# with torch.Tensor tiles.
input_image_np = np.array(input_image)
image_tiles_np: list[npt.NDArray[np.uint8]] = []
for tile in tiles:
image_tile_np = input_image_np[
tile.coords.top : tile.coords.bottom,
tile.coords.left : tile.coords.right,
:,
]
image_tiles_np.append(image_tile_np)
# VAE-encode each image tile independently.
# TODO(ryand): Is there any advantage to VAE-encoding the entire image before splitting it into tiles? What
# about for decoding?
vae_info = context.models.load(self.vae.vae)
latent_tiles: list[torch.Tensor] = []
for image_tile in image_tiles:
for image_tile_torch in image_tiles_torch:
latent_tiles.append(
ImageToLatentsInvocation.vae_encode(
vae_info=vae_info, upcast=self.vae_fp32, tiled=False, image_tensor=image_tile
vae_info=vae_info, upcast=self.vae_fp32, tiled=False, image_tensor=image_tile_torch
)
)
@ -181,7 +240,7 @@ class TiledStableDiffusionRefineInvocation(BaseInvocation):
unet_info = context.models.load(self.unet.unet)
refined_latent_tiles: list[torch.Tensor] = []
with unet_info as unet:
with ExitStack() as exit_stack, unet_info as unet:
assert isinstance(unet, UNet2DConditionModel)
scheduler = get_scheduler(
context=context,
@ -206,10 +265,39 @@ class TiledStableDiffusionRefineInvocation(BaseInvocation):
cfg_rescale_multiplier=self.cfg_rescale_multiplier,
)
# Load the ControlNet model.
# TODO(ryand): Support multiple ControlNet models.
controlnet_model = exit_stack.enter_context(context.models.load(self.control_model))
assert isinstance(controlnet_model, ControlNetModel)
# Denoise (i.e. "refine") each tile independently.
for latent_tile, noise_tile in zip(latent_tiles, noise_tiles, strict=True):
for image_tile_np, latent_tile, noise_tile in zip(image_tiles_np, latent_tiles, noise_tiles, strict=True):
assert latent_tile.shape == noise_tile.shape
# Prepare a PIL Image for ControlNet processing.
# TODO(ryand): This is a bit awkward that we have to prepare both torch.Tensor and PIL.Image versions of
# the tiles. Ideally, the ControlNet code should be able to work with Tensors.
image_tile_pil = Image.fromarray(image_tile_np)
# Run the ControlNet on the image tile.
height, width, _ = image_tile_np.shape
# The height and width must be evenly divisible by LATENT_SCALE_FACTOR. This is enforced earlier, but we
# validate this assumption here.
assert height % LATENT_SCALE_FACTOR == 0
assert width % LATENT_SCALE_FACTOR == 0
controlnet_data = self.run_controlnet(
image=image_tile_pil,
controlnet_model=controlnet_model,
weight=1.0,
do_classifier_free_guidance=True,
width=width,
height=height,
device=controlnet_model.device,
dtype=controlnet_model.dtype,
control_mode="balanced",
resize_mode="just_resize_simple",
)
num_inference_steps, timesteps, init_timestep, scheduler_step_kwargs = (
DenoiseLatentsInvocation.init_scheduler(
scheduler,
@ -236,7 +324,7 @@ class TiledStableDiffusionRefineInvocation(BaseInvocation):
num_inference_steps=num_inference_steps,
scheduler_step_kwargs=scheduler_step_kwargs,
conditioning_data=conditioning_data,
control_data=None,
control_data=[controlnet_data],
ip_adapter_data=None,
t2i_adapter_data=None,
callback=lambda x: None,

4
invokeai/app/util/controlnet_utils.py
View File

@ -289,7 +289,7 @@ def prepare_control_image(
width: int,
height: int,
num_channels: int = 3,
device: str = "cuda",
device: str | torch.device = "cuda",
dtype: torch.dtype = torch.float16,
control_mode: CONTROLNET_MODE_VALUES = "balanced",
resize_mode: CONTROLNET_RESIZE_VALUES = "just_resize_simple",
@ -304,7 +304,7 @@ def prepare_control_image(
num_channels (int, optional): The target number of image channels. This is achieved by converting the input
image to RGB, then naively taking the first `num_channels` channels. The primary use case is converting a
RGB image to a single-channel grayscale image. Raises if `num_channels` cannot be achieved. Defaults to 3.
device (str, optional): The target device for the output image. Defaults to "cuda".
device (str | torch.Device, optional): The target device for the output image. Defaults to "cuda".
dtype (_type_, optional): The dtype for the output image. Defaults to torch.float16.
do_classifier_free_guidance (bool, optional): If True, repeat the output image along the batch dimension.
Defaults to True.