本文比较了DreamBooth(Imagen)、DreamBooth(Stable Diffusion)以及Textual Inversion(Stable Diffusion)的DINO、CLIP-I和CLIP-T指标,如下表: 4.3消融实验 Prior Preservation Loss PPL substantially counteracts language drift and helps retain the ability to generate diverse images of the prior class. model trained ...
用少量数据,fine-tuning diffusion model,并保持原来模型的语义。(另外一篇的做法是训练新的prompt,而这篇是finetune模型过拟合) Method 和另外一篇的区别: 他们提出的方法是学习一个pseudo-words来表达新概念——这其实就是寻找一个最好的embedding,但是这会导致概念的表达局限于原来模型的domain。 相比之下,本文的...
This post will show you how to fine-tune a Stable Diffusion model on a Sapphire Rapids CPU cluster. We will usetextual inversion, a technique that only requires a small number of example images. We'll use only five! Let's get started. Setting Up the Cluster Our friends atIntelprovided...
you can fine-tune the stable diffusion model on your own dataset with as little as five images. For example, on the left are training images of a dog named Doppler used to fine-tune the model, in the middle and right are images generated by the fine-tu...
If you'd like, you can fine-tune the model some more. Here's a lovely example generated by a 3,000-step model (about an hour of training). ## Conclusion Thanks to Hugging Face and Intel, you can now use Xeon CPU servers to generate high-quality images adapted to your business...
It is possible to fine-tune either a schnell or dev model, but we recommend training the dev model. dev has a more limited license for use, but it is also far more powerful in terms of prompt understanding, spelling, and object composition compared to schnell. schnell however should be fa...
9月26日上午10点,「AI新青年讲座」第226讲邀请到FateZero 一作、香港科技大学在读博士戚晨洋参与,主讲《无需 Finetune 的文本驱动视频编辑算法 FateZero》。 讲者 戚晨洋,香港科技大学在读博士,指导老师为陈启峰教授;研究兴趣为图像视频处理和生成,在 ICCV、CVPR、ACM- MM 等会议上共发表5篇一作/共同一作论文...
finetune代码地址:https://github.com/justinpinkney/stable-diffusion 按照这个代码readme里的要求装好环境。同时下载好stable diffusion预训练好的模型 sd-v1-4-full-ema.ckpt ,放到目录里。 模型下载地址:CompVis/stable-diffusion-v-1-4-original · Hugging Face ...
Using reinforcement learning with human feedback (RLHF) has shown significant promise in fine-tuning diffusion models. Previous methods start by training a reward model that aligns with human preferences, then leverage RL techniques to fine-tune the underlying models. However, crafting an efficient ...
To address these limitations, we introduce a novel memory-efficient fine-tuning method specifically designed for quantized diffusion models, dubbed TuneQDM. Our approach introduces quantization scales as separable functions to consider inter-channel weight patterns. Then, it optimizes these scales in a ...