control-lora

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匿名用户2024年07月31日
31阅读
所属分类aipytorch、stable-diffusion、text-to-image
开源地址https://modelscope.cn/models/AI-ModelScope/control-lora
授权协议other

作品详情

Control-LoRA Model Card

Introduction

By adding low-rank parameter efficient fine tuning to ControlNet, we introduce Control-LoRAs. This approach offers a more efficient and compact method to bring model control to a wider variety of consumer GPUs.

For each model below, you'll find:

  • Rank 256 files (reducing the original 4.7GB ControlNet models down to ~738MB Control-LoRA models) and experimental
  • Rank 128 files (reducing to model down to ~377MB)

Each Control-LoRA has been trained on a diverse range of image concepts and aspect ratios.

MiDaS and ClipDrop Depth

canny

This Control-LoRA utilizes a grayscale depth map for guided generation.

Depth estimation is an image processing technique that determines the distance of objects in a scene, providing a depth map that highlights variations in proximity.

The model was trained on the depth results of MiDaS dpt_beit_large_512.

It was further finetuned on the Portrait Depth Estimation model available in the ClipDrop API by Stability AI.

Canny Edge

canny Canny Edge Detection is an image processing technique that identifies abrupt changes in intensity to highlight edges in an image.

This Control-LoRA uses the edges from an image to generate the final image.

Photograph and Sketch Colorizer

photograph colorizer These two Control-LoRAs can be used to colorize images.

Recolor is designed to colorize black and white photographs.

Sketch is designed to color in drawings input as a white-on-black image (either hand-drawn, or created with a pidi edge model).

Revision

photograph colorizer Revision is a novel approach of using images to prompt SDXL.

It uses pooled CLIP embeddings to produce images conceptually similar to the input. It can be used either in addition, or to replace text prompts.

Revision also includes a blending function for combining multiple image or text concepts, as either positive or negative prompts.

Inference

Control-LoRAs have been implemented into ComfyUI and StableSwarmUI

Basic ComfyUI workflows (using the base model only) are available in this HF repo. Custom nodes from Stability are available here.

Recolor example on ComfyUI: comfyui recolor

Canny edge on StableSwarmUI: swarmui recolor

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