File:Demonstration of inpainting and outpainting using Stable Diffusion (step 3 of 4).png

原始文件 (2,048 × 3,584像素,文件大小:4.43 MB,MIME类型:image/png


摘要

描述

Demonstration of the usage of inpainting and outpainting techniques on algorithmically-generated artworks created using the Stable Diffusion V1-4 AI diffusion model. Not only is Stable Diffusion capable of generating new images from scratch via text prompt, it is also capable of providing guided image synthesis for enhancing existing images, through the use of the model's diffusion-denoising mechanism.

This image aims to illustrate the process in which Stable Diffusion can be used to perform both inpainting and outpainting, as one part out of four images showing each step of the procedure.

Procedure/Methodology

All artworks created using a single NVIDIA RTX 3090. Front-end used for the entire generation process is Stable Diffusion web UI created by AUTOMATIC1111.

First image: Generation via text prompt

An initial 512x768 image was algorithmically-generated with Stable Diffusion via txt2img using the following prompts:

Prompt: busty young girl, art style of artgerm and greg rutkowski

Negative prompt: (((deformed))), [blurry], bad anatomy, disfigured, poorly drawn face, mutation, mutated, (extra_limb), (ugly), (poorly drawn hands), messy drawing, two heads, four breasts

Settings: Steps: 50, Sampler: Euler a, CFG scale: 7, Seed: 4027103558, Size: 512x768

Then, two passes of the SD upscale script using "Real-ESRGAN 4x plus anime 6B" were run within img2img. The first pass used a tile overlap of 64, denoising strength of 0.3, 50 sampling steps with Euler a, and a CFG scale of 7. The second pass used a tile overlap of 128, denoising strength of 0.1, 10 sampling steps with Euler a, and a CFG scale of 7. This creates our initial 2048x3072 image to begin working with. Unfortunately for her (and fortunately for the purpose of this demonstration), it appears that the AI neglected to give this woman one of her arms.

Second image: Outpainting

Using the "Outpainting mk2" script within img2img, the bottom of the image was extended by 512 pixels (via two passes, each pass extending 256 pixels), using 100 sampling steps with Euler a, denoising strength of 0.8, CFG scale of 7.5, mask blur of 4, fall-off exponent value of 1.8, colour variation set to 0.03. The prompts used were identical to those utilised during the first step. This subsequently increases the image's dimensions to 2048x3584, while also revealing the woman's midriff, belly button and skirt, which were previously absent from the original AI-generated image.

Third image: Preparation for inpainting

In GIMP, I drew a very shoddy attempt at a human arm using the standard paintbrush. This will provide a guide for the AI model to generate a new arm.

Final image: Inpainting

Using the inpaint feature for img2img, I drew a mask over the arm drawn in the previous step, along with a portion of the shoulder. The following settings were used for all passes:

  • Inpaint masked
  • Masked content: original
  • Inpaint at full resolution, padding at 256 pixels
  • Steps: 80, Sampler: Euler a

An initial pass was run using the following prompts:

Prompt: perfect arm, young woman's arm, (((anterior elbow))), (((inside of elbow))), bent arm, slender arm, realistic arm, wrinkled short sleeve of white blouse, woman's shoulder, brown hair on top of sleeve, (((pale skin))), skin on arm, smooth skin, art style of artgerm and greg rutkowski

Negative prompt: (((torn blouse))), (((torn sleeve))), (((deformed))), [blurry], bad anatomy, disfigured, multiple arms, mutation, mutated, (extra_limb), (ugly), (poorly drawn hands), messy drawing

Settings: CFG scale: 17, Denoising strength: 0.6, Seed: 525737653

This created the arm; another subsequent pass was then done to fine-tune deformations and blemishes around the newly generated arm along the sleeve. Drawing a new mask over the shoulder, the following prompt was used:

Prompt: brown hair on top of sleeve and arm, wrinkled short sleeve of white blouse, young woman's upper arm beside her chest, woman's shoulder, skin under sleeve, art style of artgerm and greg rutkowski

Negative prompt: (((deformed))), [blurry], bad anatomy, disfigured, multiple arms, mutation, mutated, (extra_limb), (ugly), (poorly drawn hands), messy drawing

Settings: CFG scale: 7, Denoising strength: 0.4, Seed: 653575127

The outcome of this pass resulted in the final image.

日期
来源 自己的作品
作者 Benlisquare
授权
(二次使用本文件)
Output images

As the creator of the output images, I release this image under the licence displayed within the template below.

Stable Diffusion AI model

The Stable Diffusion AI model is released under the CreativeML OpenRAIL-M License, which "does not impose any restrictions on reuse, distribution, commercialization, adaptation" as long as the model is not being intentionally used to cause harm to individuals, for instance, to deliberately mislead or deceive, and the authors of the AI models claim no rights over any image outputs generated, as stipulated by the license.

Addendum on datasets used to teach AI neural networks
Artworks generated by Stable Diffusion are algorithmically created based on the AI diffusion model's neural network as a result of learning from various datasets; the algorithm does not use preexisting images from the dataset to create the new image. Ergo, generated artworks cannot be considered derivative works of components from within the original dataset, nor can any coincidental resemblance to any particular artist's drawing style fall foul of de minimis. While an artist can claim copyright over individual works, they cannot claim copyright over mere resemblance over an artistic drawing or painting style. In simpler terms, Vincent van Gogh can claim copyright to The Starry Night, however he cannot claim copyright to a picture of a T-34 tank painted with similar brushstroke styles as Gogh's The Starry Night created by someone else.

许可协议

我,本作品著作权人,特此采用以下许可协议发表本作品:
w:zh:知识共享
署名 相同方式共享
本文件采用知识共享署名-相同方式共享 4.0 国际许可协议授权。
您可以自由地:
  • 共享 – 复制、发行并传播本作品
  • 修改 – 改编作品
惟须遵守下列条件:
  • 署名 – 您必须对作品进行署名,提供授权条款的链接,并说明是否对原始内容进行了更改。您可以用任何合理的方式来署名,但不得以任何方式表明许可人认可您或您的使用。
  • 相同方式共享 – 如果您再混合、转换或者基于本作品进行创作,您必须以与原先许可协议相同或相兼容的许可协议分发您贡献的作品。
GNU head 已授权您依据自由软件基金会发行的无固定段落及封面封底文字(Invariant Sections, Front-Cover Texts, and Back-Cover Texts)的GNU自由文件许可协议1.2版或任意后续版本的条款,复制、传播和/或修改本文件。该协议的副本请见“GNU Free Documentation License”。
您可以选择您需要的许可协议。

说明

添加一行文字以描述该文件所表现的内容

此文件中描述的项目

描繪內容

文件历史

点击某个日期/时间查看对应时刻的文件。

日期/时间缩⁠略⁠图大小用户备注
当前2022年9月28日 (三) 20:012022年9月28日 (三) 20:01版本的缩略图2,048 × 3,584(4.43 MB)BenlisquareI didn't like how dark the arm turned out, so I re-did it again. Redrew the makeshift arm in a lighter shade in GIMP, then ran two passes of inpainting: Steps: 80, Sampler: Euler a, CFG scale: 17, Seed: 525737653, Denoising strength: 0.6 to generate arm; Steps: 80, Sampler: Euler a, CFG scale: 7, Seed: 653575127, Denoising strength: 0.4 for cleanup.
2022年9月27日 (二) 14:232022年9月27日 (二) 14:23版本的缩略图2,048 × 3,584(4.42 MB)Benlisquare{{Information |Description=Demonstration of the usage of inpainting and outpainting techniques on algorithmically-generated artworks created using the [https://github.com/CompVis/stable-diffusion Stable Diffusion V1-4] AI diffusion model. Not only is Stable Diffusion capable of generating new images from scratch via text prompt, it is also capable of providing guided image synthesis for enhancing existing images, through the use of the model's diffusion-denoising mechanism. This image aims t...

以下页面使用本文件:

全域文件用途

以下其他wiki使用此文件:

元数据