File:Loss, in first-person view.png
原始文件 (1,536 × 2,048像素,文件大小:3.47 MB,MIME类型:image/png)
摘要
描述Loss, in first-person view.png |
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:
|
日期 | |
来源 | 自己的作品 |
作者 | Benlisquare |
授权 (二次使用本文件) |
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.
|
许可协议
Public domainPublic domainfalsefalse |
这个作品属于公有领域,因为其由计算机算法或人工智能生成,不包含足够的人类作者信息来支持版权主张。
|
法律免责声明 大多数图像生成人工智能模型都是使用受版权保护的作品进行训练的。在某些情况下,此类模型可以生成包含受版权保护的训练图像的主要版权元素的图像,从而使这些输出物成为衍生作品。因此,上传到维基共享资源的人工智能生成的艺术作品可能会侵犯原始作品作者的权利。有关更多详细信息,请参阅共享资源:人工智能生成媒体文件。 azərbaycanca ∙ Deutsch ∙ English ∙ español ∙ français ∙ galego ∙ हिन्दी ∙ 日本語 ∙ português do Brasil ∙ русский ∙ slovenščina ∙ Türkçe ∙ Tiếng Việt ∙ 中文 ∙ 中文(简体) ∙ 中文(繁體) ∙ +/− |
此文件中描述的项目
描绘内容
创作作者 简体中文(已转写)
某些值没有维基数据项目
知识共享署名-相同方式共享4.0国际 简体中文(已转写)
GNU自由文档许可证1.2或更高版本 简体中文(已转写)
3 12 2022
媒体类型 简体中文(已转写)
image/png
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 | 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... |
文件用途
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:
- www.wikidata.org上的用途