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https://github.com/invoke-ai/InvokeAI
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Connect TiledMultiDiffusionDenoiseLatents to the MultiDiffusionPipeline backend.
This commit is contained in:
Ryan Dick
Kent Keirsey
@ -1,6 +1,6 @@
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from __future__ import annotations
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import copy
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from dataclasses import dataclass
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from typing import Any, Callable, Optional
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import torch
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@ -11,7 +11,15 @@ from invokeai.backend.stable_diffusion.diffusers_pipeline import (
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StableDiffusionGeneratorPipeline,
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)
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from invokeai.backend.stable_diffusion.diffusion.conditioning_data import TextConditioningData
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from invokeai.backend.tiles.utils import Tile
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from invokeai.backend.tiles.utils import TBLR
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@dataclass
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class MultiDiffusionRegionConditioning:
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# Region coords in latent space.
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region: TBLR
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text_conditioning_data: TextConditioningData
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control_data: list[ControlNetData]
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class MultiDiffusionPipeline(StableDiffusionGeneratorPipeline):
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@ -45,15 +53,13 @@ class MultiDiffusionPipeline(StableDiffusionGeneratorPipeline):
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# - May need a cleaner AddsMaskGuidance implementation to handle this plan... we'll see.
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def multi_diffusion_denoise(
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self,
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regions: list[Tile],
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multi_diffusion_conditioning: list[MultiDiffusionRegionConditioning],
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latents: torch.Tensor,
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scheduler_step_kwargs: dict[str, Any],
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conditioning_data: TextConditioningData,
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noise: Optional[torch.Tensor],
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timesteps: torch.Tensor,
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init_timestep: torch.Tensor,
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callback: Callable[[PipelineIntermediateState], None],
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control_data: list[ControlNetData] | None = None,
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) -> torch.Tensor:
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# TODO(ryand): Figure out why this condition is necessary, and document it. My guess is that it's to handle
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# cases where densoisings_start and denoising_end are set such that there are no timesteps.
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@ -74,21 +80,14 @@ class MultiDiffusionPipeline(StableDiffusionGeneratorPipeline):
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# cropping into regions.
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self._adjust_memory_efficient_attention(latents)
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use_regional_prompting = (
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conditioning_data.cond_regions is not None or conditioning_data.uncond_regions is not None
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)
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if use_regional_prompting:
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raise NotImplementedError("Regional prompting is not yet supported in Multi-Diffusion.")
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# Populate a weighted mask that will be used to combine the results from each region after every step.
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# For now, we assume that each regions has the same weight (1.0).
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region_weight_mask = torch.zeros(
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(1, 1, latent_height, latent_width), device=latents.device, dtype=latents.dtype
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)
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for region in regions:
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region_weight_mask[
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:, :, region.coords.top : region.coords.bottom, region.coords.left : region.coords.right
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] += 1.0
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for region_conditioning in multi_diffusion_conditioning:
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region = region_conditioning.region
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region_weight_mask[:, :, region.top : region.bottom, region.left : region.right] += 1.0
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callback(
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PipelineIntermediateState(
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@ -103,39 +102,36 @@ class MultiDiffusionPipeline(StableDiffusionGeneratorPipeline):
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for i, t in enumerate(self.progress_bar(timesteps)):
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batched_t = t.expand(batch_size)
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prev_samples_by_region: list[torch.Tensor] = []
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pred_original_by_region: list[torch.Tensor | None] = []
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for region in regions:
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merged_latents = torch.zeros_like(latents)
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merged_pred_original: torch.Tensor | None = None
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for region_conditioning in multi_diffusion_conditioning:
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# Run a denoising step on the region.
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step_output = self._region_step(
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region=region,
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region_conditioning=region_conditioning,
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t=batched_t,
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latents=latents,
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conditioning_data=conditioning_data,
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step_index=i,
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total_step_count=len(timesteps),
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scheduler_step_kwargs=scheduler_step_kwargs,
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control_data=control_data,
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)
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prev_samples_by_region.append(step_output.prev_sample)
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pred_original_by_region.append(getattr(step_output, "pred_original_sample", None))
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# Merge the prev_sample results from each region.
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merged_latents = torch.zeros_like(latents)
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for region_idx, region in enumerate(regions):
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merged_latents[
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:, :, region.coords.top : region.coords.bottom, region.coords.left : region.coords.right
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] += prev_samples_by_region[region_idx]
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# Store the results from the region.
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region = region_conditioning.region
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merged_latents[:, :, region.top : region.bottom, region.left : region.right] += step_output.prev_sample
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pred_orig_sample = getattr(step_output, "pred_original_sample", None)
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if pred_orig_sample is not None:
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# If one region has pred_original_sample, then we can assume that all regions will have it, because
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# they all use the same scheduler.
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if merged_pred_original is None:
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merged_pred_original = torch.zeros_like(latents)
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merged_pred_original[:, :, region.top : region.bottom, region.left : region.right] += (
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pred_orig_sample
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)
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# Normalize the merged results.
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latents = merged_latents / region_weight_mask
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# Merge the predicted_original results from each region.
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predicted_original = None
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if all(pred_original_by_region):
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merged_pred_original = torch.zeros_like(latents)
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for region_idx, region in enumerate(regions):
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merged_pred_original[
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:, :, region.coords.top : region.coords.bottom, region.coords.left : region.coords.right
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] += pred_original_by_region[region_idx]
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if merged_pred_original is not None:
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predicted_original = merged_pred_original / region_weight_mask
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callback(
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@ -154,44 +150,38 @@ class MultiDiffusionPipeline(StableDiffusionGeneratorPipeline):
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@torch.inference_mode()
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def _region_step(
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self,
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region: Tile,
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region_conditioning: MultiDiffusionRegionConditioning,
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t: torch.Tensor,
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latents: torch.Tensor,
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conditioning_data: TextConditioningData,
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step_index: int,
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total_step_count: int,
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scheduler_step_kwargs: dict[str, Any],
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control_data: list[ControlNetData] | None = None,
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):
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use_regional_prompting = (
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region_conditioning.text_conditioning_data.cond_regions is not None
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or region_conditioning.text_conditioning_data.uncond_regions is not None
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)
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if use_regional_prompting:
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raise NotImplementedError("Regional prompting is not yet supported in Multi-Diffusion.")
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# Crop the inputs to the region.
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region_latents = latents[
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:, :, region.coords.top : region.coords.bottom, region.coords.left : region.coords.right
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:,
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:,
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region_conditioning.region.top : region_conditioning.region.bottom,
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region_conditioning.region.left : region_conditioning.region.right,
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]
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region_control_data: list[ControlNetData] | None = None
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if control_data is not None:
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region_control_data = [self._crop_controlnet_data(c, region) for c in control_data]
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# Run the denoising step on the region.
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return self.step(
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t=t,
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latents=region_latents,
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conditioning_data=conditioning_data,
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conditioning_data=region_conditioning.text_conditioning_data,
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step_index=step_index,
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total_step_count=total_step_count,
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scheduler_step_kwargs=scheduler_step_kwargs,
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mask_guidance=None,
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mask=None,
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masked_latents=None,
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control_data=region_control_data,
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control_data=region_conditioning.control_data,
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)
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def _crop_controlnet_data(self, control_data: ControlNetData, region: Tile) -> ControlNetData:
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"""Crop a ControlNetData object to a region."""
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# Create a shallow copy of the control_data object.
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control_data_copy = copy.copy(control_data)
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# The ControlNet reference image is the only attribute that needs to be cropped.
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control_data_copy.image_tensor = control_data.image_tensor[
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:, :, region.coords.top : region.coords.bottom, region.coords.left : region.coords.right
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]
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return control_data_copy
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