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

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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
P'unchaw
Pukyu Kikinpa rurasqan
Ruraq Benlisquare
Saqillay
(Kay willañiqita musuqmanta llamk'achiy)
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.
Huk musuqchasqakuna
Using DDIM sampling method
Using Euler ancestral sampling method

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w:es:Creative Commons
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Este archivo está disponible bajo la licencia Creative Commons Attribution-Share Alike 4.0 International.
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GNU head Saqillayqa huñikusqam kay atiqllata iskaychaypaq, mast'ariypaq icha wakinchaypaq kay saqillaypa phatankunakama: GNU Free Documentation License, musuqchasqa 1.2 icha ima qhipaqnin kaq musuqchasqapas Free Software Foundation nisqap uyaychasqan; mana "mana wakinchana rakinakuna", "ñawpaq qata p'anqa" icha "qhipaq qata p'anqa" nisqa qillqayuq. Saqillaypa iskaychasqanqa GNU Free Documentation License nisqa rakipi ch'aqtasqam.
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nombre del autor Español Simi: Benlisquare
nombre de usuario en Wikimedia Español Simi: Benlisquare
URL Español Simi: https://commons.wikimedia.org/wiki/User:Benlisquare

fecha de fundación o creación Español Simi

1 nuw 2022

tipo de archivo Español Simi

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P'unchaw/PachaUchuylla rikchachaChhikanyachikuqkunaRuraqWillapuy
kunan22:55 31 ukt 202222:55 31 ukt 2022-pa musuqchasqanmanta uchuylla rikchacha2560 × 1734 (7,11 MB)Benlisquarerearrange images into a 5-by-2 to optimise space
22:48 31 ukt 202222:48 31 ukt 2022-pa musuqchasqanmanta uchuylla rikchacha5120 × 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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