I trained a model on childhood photos to simulate memory recall
热度趋势
趋势数据积累中
百分比基于当前可用热度信号，而非评论数或独立用户人数。
OpenAI 相关模型动态已经出现，适合跟踪能力变化、生态影响和后续可用性。
一位开发者利用其有限的家庭档案中的60张童年照片，对SDXL模型进行了微调，以模拟记忆回忆。该模型并非忠实地重建这些图像，而是生成不稳定的变体，产生似曾相识但可能从未存在过的空间、面孔和片段。该开发者计划通过YouTube、Instagram、Patreon和Uisato Studio分享更多实验、项目文件和教程。
I fine-tuned the good-old SDXL on 60 photographs from my childhood, using a limited family archive as the dataset through which to revisit that period of my life. Rather than reconstructing those images faithfully, the model produces unstable variations: spaces, faces and fragments that feel familiar without necessarily having existed.
This speculative study treats generative hallucination as an analogue for recollection: not the retrieval of a preserved image, but the reconstruction of a past from incomplete traces. This resonates with contemporary accounts of episodic memory as a reconstructive rather than reproductive process. The model becomes a kind of externalized mnemonic apparatus, situated somewhere between archive, memory and imagination.
Tools used: Kohya, WarpFusion, TouchDesigner, Premiere, After Effects, Ableton Live, Expressive Osmose, Soma Cosmos.
More experiments, project files, and tutorials, through YouTube, Instagram, Patreon, and Uisato Studio.