Refactor TiledStableDiffusionRefineInvocation to more closely mirror TiledMultiDiffusionDenoiseLatents. The biggest improvement is in the handling of the ControlNets - global ControlNet info can now be passed in and it is tiled within the node.

invokeai/app/invocations/tiled_stable_diffusion_refine.py

@@ -2,14 +2,14 @@ from contextlib import ExitStack
 from typing import Iterator, Tuple
 
 import numpy as np
-import numpy.typing as npt
 import torch
 from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel
 from PIL import Image
 from pydantic import field_validator
 
-from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
+from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
 from invokeai.app.invocations.constants import DEFAULT_PRECISION, LATENT_SCALE_FACTOR, SCHEDULER_NAME_VALUES
+from invokeai.app.invocations.controlnet_image_processors import ControlField
 from invokeai.app.invocations.denoise_latents import DenoiseLatentsInvocation, get_scheduler
 from invokeai.app.invocations.fields import (
     ConditioningField,
@@ -17,22 +17,24 @@ from invokeai.app.invocations.fields import (
     ImageField,
     Input,
     InputField,
+    LatentsField,
     UIType,
 )
 from invokeai.app.invocations.image_to_latents import ImageToLatentsInvocation
 from invokeai.app.invocations.latents_to_image import LatentsToImageInvocation
-from invokeai.app.invocations.model import ModelIdentifierField, UNetField, VAEField
+from invokeai.app.invocations.model import UNetField, VAEField
-from invokeai.app.invocations.noise import get_noise
 from invokeai.app.invocations.primitives import ImageOutput
+from invokeai.app.invocations.tiled_multi_diffusion_denoise_latents import crop_controlnet_data
 from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.app.util.controlnet_utils import CONTROLNET_MODE_VALUES, CONTROLNET_RESIZE_VALUES, prepare_control_image
 from invokeai.backend.lora import LoRAModelRaw
 from invokeai.backend.model_patcher import ModelPatcher
 from invokeai.backend.stable_diffusion.diffusers_pipeline import ControlNetData, image_resized_to_grid_as_tensor
-from invokeai.backend.tiles.tiles import calc_tiles_with_overlap, merge_tiles_with_linear_blending
+from invokeai.backend.tiles.tiles import (
-from invokeai.backend.tiles.utils import Tile
+    calc_tiles_min_overlap,
+    merge_tiles_with_linear_blending,
+)
+from invokeai.backend.tiles.utils import TBLR, Tile
 from invokeai.backend.util.devices import TorchDevice
-from invokeai.backend.util.hotfixes import ControlNetModel
 
 
 @invocation(
@@ -40,6 +42,7 @@ from invokeai.backend.util.hotfixes import ControlNetModel
     title="Tiled Stable Diffusion Refine",
     tags=["upscale", "denoise"],
     category="latents",
+    classification=Classification.Beta,
     version="1.0.0",
 )
 class TiledStableDiffusionRefineInvocation(BaseInvocation):
@@ -55,13 +58,21 @@ class TiledStableDiffusionRefineInvocation(BaseInvocation):
     negative_conditioning: ConditioningField = InputField(
         description=FieldDescriptions.negative_cond, input=Input.Connection
     )
-    # TODO(ryand): Add multiple-of validation.
+    noise: LatentsField = InputField(
-    tile_height: int = InputField(default=512, gt=0, description="Height of the tiles.")
+        description=FieldDescriptions.noise,
-    tile_width: int = InputField(default=512, gt=0, description="Width of the tiles.")
+        input=Input.Connection,
+    )
+    tile_height: int = InputField(
+        default=1024, gt=0, multiple_of=LATENT_SCALE_FACTOR, description="Height of the tiles in image space."
+    )
+    tile_width: int = InputField(
+        default=1024, gt=0, multiple_of=LATENT_SCALE_FACTOR, description="Width of the tiles in image space."
+    )
     tile_overlap: int = InputField(
-        default=16,
+        default=32,
+        multiple_of=LATENT_SCALE_FACTOR,
         gt=0,
-        description="Target overlap between adjacent tiles (the last row/column may overlap more than this).",
+        description="Target overlap between adjacent tiles in image space.",
     )
     steps: int = InputField(default=18, gt=0, description=FieldDescriptions.steps)
     cfg_scale: float | list[float] = InputField(default=6.0, description=FieldDescriptions.cfg_scale, title="CFG Scale")
@@ -92,16 +103,10 @@ 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
+    control: ControlField | list[ControlField] | None = InputField(
-    # don't want to use the image field. Figure out how best to handle this.
+        default=None,
-    # TODO(ryand): Currently, there is no ControlNet preprocessor applied to the tile images. In other words, we pretty
+        input=Input.Connection,
-    # 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
     )
-    control_weight: float = InputField(default=0.6)
 
     @field_validator("cfg_scale")
     def ge_one(cls, v: list[float] | float) -> list[float] | float:
@@ -115,90 +120,72 @@ class TiledStableDiffusionRefineInvocation(BaseInvocation):
                 raise ValueError("cfg_scale must be greater than 1")
         return v
 
-    @staticmethod
+    def _scale_tile(self, tile: Tile, scale: int) -> Tile:
-    def crop_latents_to_tile(latents: torch.Tensor, image_tile: Tile) -> torch.Tensor:
+        """Scale the tile by the given factor."""
-        """Crop the latent-space tensor to the area corresponding to the image-space tile.
+        return Tile(
-        The tile coordinates must be divisible by the LATENT_SCALE_FACTOR.
+            coords=TBLR(
-        """
+                top=tile.coords.top * scale,
-        for coord in [image_tile.coords.top, image_tile.coords.left, image_tile.coords.right, image_tile.coords.bottom]:
+                bottom=tile.coords.bottom * scale,
-            if coord % LATENT_SCALE_FACTOR != 0:
+                left=tile.coords.left * scale,
-                raise ValueError(
+                right=tile.coords.right * scale,
-                    f"The tile coordinates must all be divisible by the latent scale factor"
+            ),
-                    f" ({LATENT_SCALE_FACTOR}). {image_tile.coords=}."
+            overlap=TBLR(
-                )
+                top=tile.overlap.top * scale,
-        assert latents.dim() == 4  # We expect: (batch_size, channels, height, width).
+                bottom=tile.overlap.bottom * scale,
-
+                left=tile.overlap.left * scale,
-        top = image_tile.coords.top // LATENT_SCALE_FACTOR
+                right=tile.overlap.right * scale,
-        left = image_tile.coords.left // LATENT_SCALE_FACTOR
+            ),
-        bottom = image_tile.coords.bottom // LATENT_SCALE_FACTOR
-        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.
+        # Convert tile image-space dimensions to latent-space dimensions.
-        seed = 0
+        latent_tile_height = self.tile_height // LATENT_SCALE_FACTOR
+        latent_tile_width = self.tile_width // LATENT_SCALE_FACTOR
+        latent_tile_overlap = self.tile_overlap // LATENT_SCALE_FACTOR
 
         # Load the input image.
         input_image = context.images.get_pil(self.image.image_name)
-
-        # Calculate the tile locations to cover the image.
-        # We have selected this tiling strategy to make it easy to achieve tile coords that are multiples of 8. This
-        # facilitates conversions between image space and latent space.
-        # TODO(ryand): Expose these tiling parameters. (Keep in mind the multiple-of constraints on these params.)
-        tiles = calc_tiles_with_overlap(
-            image_height=input_image.height,
-            image_width=input_image.width,
-            tile_height=self.tile_height,
-            tile_width=self.tile_width,
-            overlap=self.tile_overlap,
-        )
-
         # 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).
+        batch_size, channels, image_height, image_width = input_image_torch.shape
-        assert input_image_torch.shape[:2] == (1, 3)
+        assert batch_size == 1
+        assert channels == 3
+
+        # Load the noise tensor.
+        noise = context.tensors.load(self.noise.latents_name)
+        if list(noise.shape) != [
+            batch_size,
+            4,
+            image_height // LATENT_SCALE_FACTOR,
+            image_width // LATENT_SCALE_FACTOR,
+        ]:
+            raise ValueError(
+                f"Incompatible noise and image dimensions. Image shape: {input_image_torch.shape}. "
+                f"Noise shape: {noise.shape}. Expected noise shape: [1, 1, "
+                f"{image_height // LATENT_SCALE_FACTOR}, {image_width // LATENT_SCALE_FACTOR}]. "
+            )
+        latent_height, latent_width = noise.shape[2:]
+
+        # Extract the seed from the noise field.
+        assert self.noise.seed is not None
+        seed = self.noise.seed or 0
+
+        # Calculate the tile locations in both latent space and image space.
+        latent_space_tiles = calc_tiles_min_overlap(
+            image_height=latent_height,
+            image_width=latent_width,
+            tile_height=latent_tile_height,
+            tile_width=latent_tile_width,
+            min_overlap=latent_tile_overlap,
+        )
+        image_space_tiles = [self._scale_tile(tile, LATENT_SCALE_FACTOR) for tile in latent_space_tiles]
 
         # Split the input image into tiles in torch.Tensor format.
         image_tiles_torch: list[torch.Tensor] = []
-        for tile in tiles:
+        for tile in image_space_tiles:
             image_tile = input_image_torch[
                 :,
                 :,
@@ -207,22 +194,7 @@ class TiledStableDiffusionRefineInvocation(BaseInvocation):
             ]
             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_torch in image_tiles_torch:
@@ -232,23 +204,16 @@ class TiledStableDiffusionRefineInvocation(BaseInvocation):
                 )
             )
 
-        # Generate noise with dimensions corresponding to the full image in latent space.
-        # It is important that the noise tensor is generated at the full image dimension and then tiled, rather than
-        # generating for each tile independently. This ensures that overlapping regions between tiles use the same
-        # noise.
-        assert input_image_torch.shape[2] % LATENT_SCALE_FACTOR == 0
-        assert input_image_torch.shape[3] % LATENT_SCALE_FACTOR == 0
-        global_noise = get_noise(
-            width=input_image_torch.shape[3],
-            height=input_image_torch.shape[2],
-            device=TorchDevice.choose_torch_device(),
-            seed=seed,
-            downsampling_factor=LATENT_SCALE_FACTOR,
-            use_cpu=True,
-        )
-
         # Crop the global noise into tiles.
-        noise_tiles = [self.crop_latents_to_tile(latents=global_noise, image_tile=t) for t in tiles]
+        noise_tiles: list[torch.Tensor] = []
+        for tile in latent_space_tiles:
+            noise_tile = noise[
+                :,
+                :,
+                tile.coords.top : tile.coords.bottom,
+                tile.coords.left : tile.coords.right,
+            ]
+            noise_tiles.append(noise_tile)
 
         # Prepare an iterator that yields the UNet's LoRA models and their weights.
         def _lora_loader() -> Iterator[Tuple[LoRAModelRaw, float]]:
@@ -273,53 +238,42 @@ class TiledStableDiffusionRefineInvocation(BaseInvocation):
             pipeline = DenoiseLatentsInvocation.create_pipeline(unet=unet, scheduler=scheduler)
 
             # Prepare the prompt conditioning data. The same prompt conditioning is applied to all tiles.
-            # Assume that all tiles have the same shape.
-            _, _, latent_height, latent_width = latent_tiles[0].shape
             conditioning_data = DenoiseLatentsInvocation.get_conditioning_data(
                 context=context,
                 positive_conditioning_field=self.positive_conditioning,
                 negative_conditioning_field=self.negative_conditioning,
                 unet=unet,
-                latent_height=latent_height,
+                latent_height=latent_tile_height,
-                latent_width=latent_width,
+                latent_width=latent_tile_width,
                 cfg_scale=self.cfg_scale,
                 steps=self.steps,
                 cfg_rescale_multiplier=self.cfg_rescale_multiplier,
             )
 
-            # Load the ControlNet model.
+            controlnet_data = DenoiseLatentsInvocation.prep_control_data(
-            # TODO(ryand): Support multiple ControlNet models.
+                context=context,
-            controlnet_model = exit_stack.enter_context(context.models.load(self.control_model))
+                control_input=self.control,
-            assert isinstance(controlnet_model, ControlNetModel)
+                # NOTE: We use the shape of the global noise tensor here, because this is a global ControlNet. We tile
+                # it later.
+                latents_shape=list(noise.shape),
+                # do_classifier_free_guidance=(self.cfg_scale >= 1.0))
+                do_classifier_free_guidance=True,
+                exit_stack=exit_stack,
+            )
+
+            # Split the controlnet_data into tiles.
+            # controlnet_data_tiles[t][c] is the c'th control data for the t'th tile.
+            controlnet_data_tiles: list[list[ControlNetData]] = []
+            for tile in latent_space_tiles:
+                tile_controlnet_data = [crop_controlnet_data(cn, tile.coords) for cn in controlnet_data or []]
+                controlnet_data_tiles.append(tile_controlnet_data)
 
             # Denoise (i.e. "refine") each tile independently.
-            for image_tile_np, latent_tile, noise_tile in zip(image_tiles_np, latent_tiles, noise_tiles, strict=True):
+            for latent_tile, noise_tile, controlnet_data_tile in zip(
+                latent_tiles, noise_tiles, controlnet_data_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=self.control_weight,
-                    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",
-                )
-
                 timesteps, init_timestep, scheduler_step_kwargs = DenoiseLatentsInvocation.init_scheduler(
                     scheduler,
                     device=unet.device,
@@ -342,7 +296,7 @@ class TiledStableDiffusionRefineInvocation(BaseInvocation):
                     masked_latents=None,
                     scheduler_step_kwargs=scheduler_step_kwargs,
                     conditioning_data=conditioning_data,
-                    control_data=[controlnet_data],
+                    control_data=controlnet_data_tile,
                     ip_adapter_data=None,
                     t2i_adapter_data=None,
                     callback=lambda x: None,
@@ -368,9 +322,11 @@ class TiledStableDiffusionRefineInvocation(BaseInvocation):
         # Merge the refined image tiles back into a single image.
         refined_image_tiles_np = [np.array(t) for t in refined_image_tiles]
         merged_image_np = np.zeros(shape=(input_image.height, input_image.width, 3), dtype=np.uint8)
-        # TODO(ryand): Tune the blend_amount. Should this be exposed as a parameter?
         merge_tiles_with_linear_blending(
-            dst_image=merged_image_np, tiles=tiles, tile_images=refined_image_tiles_np, blend_amount=self.tile_overlap
+            dst_image=merged_image_np,
+            tiles=image_space_tiles,
+            tile_images=refined_image_tiles_np,
+            blend_amount=self.tile_overlap,
         )
 
         # Save the refined image and return its reference.