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File:X-Y plot of algorithmically-generated AI art of European-style castle in Japan demonstrating DDIM diffusion steps.png

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摘要

描述

An X/Y plot of algorithmically-generated AI artworks depicting a European-style castle in Japan, created using the Stable Diffusion V1-5 AI diffusion model. This plot serves to demonstrate the U-Net denoising process, using the DDIM sampling method. Diffusion models algorithmically generate images by repeatedly removing Gaussian noise, step-by-step, and then decoding the denoised output into pixel space. Shown here are a smaller subset of steps within a 40-step generation process.

Procedure/Methodology

These images were generated using an NVIDIA RTX 4090; since Ada Lovelace chipsets (using compute capability 8.9, which requires CUDA 11.8) are not fully supported by the pyTorch dependency libraries currently used by Stable Diffusion, I've used a custom build of xformers, along with pyTorch cu116 and cuDNN v8.6, as a temporary workaround. Front-end used for the entire generation process is Stable Diffusion web UI created by AUTOMATIC1111.

A batch of 512x768 images were generated with txt2img using the following prompts:

Prompt: a (european castle:1.3) in japan. by Albert Bierstadt, ray traced, octane render, 8k

Negative prompt: None

Settings: Sampler: DDIM, CFG scale: 7, Size: 512x768

During the generation of this batch, the X/Y plot was generated using the "X/Y plot" txt2img script, along with the following settings:

  • X-axis: Steps: 1, 2, 3, 5, 8, 10, 15, 20, 30, 40
  • Y-axis: None
日期
來源 自己的作品
作者 Benlisquare
授權許可
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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.
其他版本
Using DDIM sampling method
Using Euler ancestral sampling method

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檔案來源 Chinese (Taiwan) (已轉換拼寫)

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日期/時間縮⁠圖尺寸使用者備⁠註
目前2022年10月31日 (一) 22:55於 2022年10月31日 (一) 22:55 版本的縮圖2,560 × 1,734（7.11 MB）Benlisquarerearrange images into a 5-by-2 to optimise space
2022年10月31日 (一) 22:48於 2022年10月31日 (一) 22:48 版本的縮圖5,120 × 867（6.63 MB）Benlisquare{{Information |Description=An X/Y plot of algorithmically-generated AI artworks depicting a European-style castle in Japan, created using the [https://huggingface.co/runwayml/stable-diffusion-v1-5 Stable Diffusion V1-5] AI diffusion model. This plot serves to demonstrate the noise diffusion process, using the DDIM sampling method. Diffusion models algorithmically generate images by repeatedly applying Gaussian noise, step-by-step, and then decoding the denoised output into pixel space. Shown...

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