import torch from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel from pydantic import field_validator from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation from invokeai.app.invocations.constants import DEFAULT_PRECISION, LATENT_SCALE_FACTOR, SCHEDULER_NAME_VALUES from invokeai.app.invocations.fields import ( ConditioningField, FieldDescriptions, ImageField, Input, InputField, LatentsField, UIType, ) from invokeai.app.invocations.image_to_latents import ImageToLatentsInvocation from invokeai.app.invocations.denoise_latents import DenoiseLatentsInvocation, get_scheduler from invokeai.app.invocations.model import UNetField, VAEField from invokeai.app.invocations.noise import get_noise from invokeai.app.invocations.primitives import LatentsOutput 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.backend.tiles.tiles import calc_tiles_min_overlap from invokeai.backend.util.devices import TorchDevice @invocation( "tiled_stable_diffusion_refine", title="Tiled Stable Diffusion Refine", tags=["upscale", "denoise"], category="latents", version="1.0.0", ) class TiledStableDiffusionRefine(BaseInvocation): """A tiled Stable Diffusion pipeline for refining high resolution images. This invocation is intended to be used to refine an image after upscaling i.e. it is the second step in a typical "tiled upscaling" workflow. """ # Implementation order: # - Basic tiled denoising. Support text prompts, but no other features. # - Support LoRA + TI # - Support ControlNet # - IP-Adapter? (It has to run on each tile independently. Could be complicated to support batching.) image: ImageField = InputField(description="Image to be refined.") positive_conditioning: ConditioningField | list[ConditioningField] = InputField( description=FieldDescriptions.positive_cond, input=Input.Connection ) negative_conditioning: ConditioningField | list[ConditioningField] = InputField( description=FieldDescriptions.negative_cond, input=Input.Connection ) noise: LatentsField | None = InputField( default=None, description=FieldDescriptions.noise, input=Input.Connection, ) steps: int = InputField(default=10, gt=0, description=FieldDescriptions.steps) cfg_scale: float | list[float] = InputField(default=7.5, description=FieldDescriptions.cfg_scale, title="CFG Scale") denoising_start: float = InputField( default=0.0, ge=0, le=1, description=FieldDescriptions.denoising_start, ) denoising_end: float = InputField(default=1.0, ge=0, le=1, description=FieldDescriptions.denoising_end) scheduler: SCHEDULER_NAME_VALUES = InputField( default="euler", description=FieldDescriptions.scheduler, ui_type=UIType.Scheduler, ) unet: UNetField = InputField( description=FieldDescriptions.unet, 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 ) latents: LatentsField | None = InputField( default=None, description=FieldDescriptions.latents, input=Input.Connection, ) vae: VAEField = InputField( description=FieldDescriptions.vae, input=Input.Connection, ) vae_fp32: bool = InputField( default=DEFAULT_PRECISION == torch.float32, description="Whether to use float32 precision when running the VAE." ) @field_validator("cfg_scale") def ge_one(cls, v: list[float] | float) -> list[float] | float: """Validate that all cfg_scale values are >= 1""" if isinstance(v, list): for i in v: if i < 1: raise ValueError("cfg_scale must be greater than 1") else: if v < 1: raise ValueError("cfg_scale must be greater than 1") return v @torch.no_grad() def invoke(self, context: InvocationContext) -> LatentsOutput: # TODO(ryand): Expose the seed parameter. seed = 0 # 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) # Calculate the tile locations to cover the image. # TODO(ryand): Expose these tiling parameters. (Keep in mind the multiple-of constraints on these params.) tiles = calc_tiles_min_overlap( image_height=input_image.height, image_width=input_image.width, tile_height=512, tile_width=512, min_overlap=128, ) # 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] = [] for tile in tiles: image_tile = input_image_torch[ :, :, tile.coords.top : tile.coords.bottom, tile.coords.left : tile.coords.right, ] image_tiles.append(image_tile) # VAE-encode each image tile independently. # TODO(ryand): Is there any advantage to VAE-encoding the entire image before splitting it into tiles? vae_info = context.models.load(self.vae.vae) latent_tiles: list[torch.Tensor] = [] for image_tile in image_tiles: latent_tiles.append( ImageToLatentsInvocation.vae_encode( vae_info=vae_info, upcast=self.vae_fp32, tiled=False, image_tensor=image_tile ) ) # 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 noise_tiles = 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, ) # Load the UNet model. unet_info = context.models.load(self.unet.unet) refined_latent_tiles: list[torch.Tensor] = [] with unet_info as unet: assert isinstance(unet, UNet2DConditionModel) scheduler = get_scheduler( context=context, scheduler_info=self.unet.scheduler, scheduler_name=self.scheduler, seed=seed, ) pipeline = DenoiseLatentsInvocation.create_pipeline(unet=unet, scheduler=scheduler) for latent_tile in latent_tiles: name = context.tensors.save(tensor=result_latents) return LatentsOutput.build(latents_name=name, latents=result_latents, seed=None)