From b1bb1511fe8b7a9b385ddfa47ef853c32f753610 Mon Sep 17 00:00:00 2001 From: Ryan Dick Date: Tue, 18 Jun 2024 13:33:10 -0400 Subject: [PATCH] Delete rough notes. --- .../multi_diffusion_pipeline.py | 26 ------------------- 1 file changed, 26 deletions(-) diff --git a/invokeai/backend/stable_diffusion/multi_diffusion_pipeline.py b/invokeai/backend/stable_diffusion/multi_diffusion_pipeline.py index a73a2b70db..2f945cfeca 100644 --- a/invokeai/backend/stable_diffusion/multi_diffusion_pipeline.py +++ b/invokeai/backend/stable_diffusion/multi_diffusion_pipeline.py @@ -27,32 +27,6 @@ class MultiDiffusionRegionConditioning: class MultiDiffusionPipeline(StableDiffusionGeneratorPipeline): """A Stable Diffusion pipeline that uses Multi-Diffusion (https://arxiv.org/pdf/2302.08113) for denoising.""" - # Plan: - # - latents_from_embeddings(...) will accept all of the same global params, but the "local" params will be bundled - # together with tile locations. - # - What is "local"?: - # - conditioning_data could be local, but for upscaling will be global - # - control_data makes more sense as global, then we split it up as we split up the latents - # - ip_adapter_data sort of has 3 modes to consider: - # - global style: applied in the same way to all tiles - # - local style: apply different IP-Adapters to each tile - # - global structure: we want to crop the input image and run the IP-Adapter on each separately - # - t2i_adapter_data won't be supported at first - it's not popular enough - # - All the inpainting params are global and need to be cropped accordingly - # - Local: - # - latents - # - conditioning_data - # - noise - # - control_data - # - ip_adapter_data (skip for now) - # - t2i_adapter_data (skip for now) - # - mask - # - masked_latents - # - is_gradient_mask ??? - # - Can we support inpainting models in this node? - # - TBD, need to think about this more - # - step(...) remains mostly unmodified, is not overriden in this sub-class. - # - May need a cleaner AddsMaskGuidance implementation to handle this plan... we'll see. def multi_diffusion_denoise( self, multi_diffusion_conditioning: list[MultiDiffusionRegionConditioning],