一、unCLIP model 什么是unCLIP model?我们最熟悉的文生图扩散模型当属Stable Diffusion,它是一种Latent Diffusion Models (LDM)[1],也就是先将图像用encoder编码为latent code,而后对这个latent code加噪去噪,文本条件在去噪过程中通过Unet中的Cross-attention层影响生成结果,最后通过decoder转回像素域。 而Dalle2[2]...
https://www.youtube.com/@atdigitStable Diffusion unCLIP ComfyUI使用技巧, 视频播放量 666、弹幕量 0、点赞数 4、投硬币枚数 2、收藏人数 14、转发人数 0, 视频作者 AI创作大师, 作者简介 分享最新的 Midjourney,Stable Difusion 等优秀的 AI 生成艺术技术。,相关视频
Stable Diffusion Meets Karlo Recently, KakaoBrain openly released Karlo, a pretrained, large-scale replication of unCLIP. We introduce Stable Karlo, a combination of the Karlo CLIP image embedding prior, and Stable Diffusion v2.1-768. To run the model, first download the KARLO checkpoin...
class StableDiffusionProcessing: @@ -190,6 +196,14 @@ def edit_image_conditioning(self, source_image): return conditioning_image def unclip_image_conditioning(self, source_image): c_adm = self.sd_model.embedder(source_image) if self.sd_model.noise_augmentor is not None: noise_level = 0...
Stable Diffusion不认识“Nahida”这个名字,它不知道一个叫“Nahida”的角色到底有什么样的特征。 归根结底可以看成是CLIP这个语言模型不认识“Nahida”这个名字,因此CLIP没办法把这个词转换成合适的语义向量(embedding), Stable Diffusion也就没办法得知对应的视觉信息。
StableDiffusionGLIGENPipeline StableDiffusionGLIGENTextImagePipeline Stable Diffusion XL StableDiffusionXLAdapterPipeline StableDiffusionXLControlNetImg2ImgPipeline StableDiffusionXLControlNetInpaintPipeline StableDiffusionXLControlNetPipeline UnCLIP UnCLIPPipeline UnCLIPImageVariationPipeline Usage Example Most pipelines...
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DALL-E 2 刚出的时候也算非常火,不过这个模型也有 diffusion model 的一些通病,比如会出现不同主体的属性混淆、文本的生成效果比较差等情况。总体来说,个人感觉这个模型不如 Stable Diffusion 优雅,从后续的很多工作也可以看出,基于 Stable Diffusion 继续进行拓展的方法才是主流,基于 DALL-E 2 的方法还是比较少的...
Stable Diffusion Meets Karlo Recently, KakaoBrain openly released Karlo, a pretrained, large-scale replication of unCLIP. We introduce Stable Karlo, a combination of the Karlo CLIP image embedding prior, and Stable Diffusion v2.1-768. To run the model, first download the KARLO checkpoints mkdir ...
stablediffusion /doc / UNCLIP.MD unCLIPis the approach behind OpenAI'sDALL·E 2, trained to invert CLIP image embeddings. We finetuned SD 2.1 to accept a CLIP ViT-L/14 image embedding in addition to the text encodings. This means that the model can be used to produce image variations,...