File:Loss, in first-person view.png
原始檔案 （1,536 × 2,048 像素，檔案大小：3.47 MB，MIME 類型：image/png）
摘要
| 描述 |
A collection of four algorithmically-generated AI artwork panels serving as a parody of "Loss" by Tim Buckley, depicting a reinterpretation of the events described in "Loss" from a first-person perspective, created using a custom merged Stable Diffusion AI diffusion model checkpoint featuring wd-v1-3-full.ckpt merged with F111 and Stable Diffusion V1-5 at 0.5 sigmoid, and then merged with R34_e4 at 0.25 weighted sum.
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. Four 768x1024 images were generated with txt2img using the following prompts:
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| 日期 | |
| 來源 | 自己的作品 |
| 作者 | Benlisquare |
| 授權許可 (重用此檔案) |
As the creator of the output images, I release this image under the licence displayed within the template below.
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.
R34_e4 and F111 are custom-trained derivative models of Stable Diffusion 1.4. The CreativeML OpenRAIL-M License applies to all downstream derivative versions of the model, as stipulated under the preamble. wd-v1-3-full.ckpt is released under the CreativeML OpenRAIL-M License.
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.
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已授權您依據自由軟體基金會發行的無固定段落、封面文字和封底文字GNU自由文件授權條款1.2版或任意後續版本，對本檔進行複製、傳播和／或修改。該協議的副本列在GNU自由文件授權條款中。 |
檔案歷史
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| 日期/時間 | 縮圖 | 尺寸 | 用戶 | 備註 | |
|---|---|---|---|---|---|
| 目前 | 2023年8月8日 (二) 10:34 | | 1,536 × 2,048（3.47 MB） | Obscure2020 | Optimized with OxiPNG and ZopfliPNG. |
| 2022年12月3日 (六) 22:16 |  | 1,536 × 2,048（4.07 MB） | Benlisquare | {{Information |Description=A collection of four algorithmically-generated AI artwork panels serving as a parody of "Loss" by Tim Buckley, depicting a reinterpretation of the events described in "Loss" from a first-person perspective, created using a custom merged Stable Diffusion AI diffusion model checkpoint featuring [https://huggingface.co/hakurei/waifu-diffusion wd-v1-3-full.ckpt] merged with [https://ai.zeipher.com/ F111] and [https://hugging... |
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