Text-to-image diffusion models are typically trainedto optimize the log-likelihood objective, which presents challengesin meeting specific requirements for downstream tasks, such asimage aesthetics and image-text alignment. Recent research addressesthis issue by refining the diffusion U-Net using human re...
from diffusers import StableDiffusionPipeline from diffusers import DDIMScheduler from peft import PeftModel import torch def load_model_weights(pipe, weight_path, model_type): if model_type == 'text+lora': text_encoder = pipe.text_encoder PeftModel.from_pretrained(text_encoder, weight_path) eli...
model_name_or_path = "CompVis/stable-diffusion-v1-4" text_encoder = CLIPTextModel.from_pretrained(model_name_or_path, subfolder="text_encoder", cache_dir=cache_path) AdvUnlearn (Ours): Unlearned text encoder model_name_or_path = "OPTML-Group/AdvUnlearn" # Nudity-Unlearned text_encoder...
SD-XL技术报告来了,代码模型均开源了 | SD XL相比SD 2.0参数量大了3倍,参数量从865M增加到2.6B,采用混合的text encoder,直接生成1024x1024大小的图像,而且加上refiner模型可以生成2048x2048的图像。 链接 之前已经做了SD的介绍文章文生图模型之Stable Diffusi...,后续我也将增加对SD XL的解读文章。
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While T5 achieves impressive performance on language tasks cast as sequence-to-sequence mapping problems, it is unclear how to produce sentence embeddings from encoder-decoder models. We investigate three methods for extracting T5 sentence embeddings: two utilize only the T5 encoder and one uses the...
1. In our analysis, we employed four distinct approaches to estimate propensity scores (step 1): logistic regression, MARS, supervised deep learning, and autoencoders. During the estimation phase, all propensity score models were constructed using only the main effects of the 50 covariates, ...
Bidirectional Encoder Representations from Transformers (BERT) was developed by Google. Unlike GPT models that predict the next word in a sequence, BERT is designed for tasks requiring understanding the entire sentence context. It's pre-trained on a huge amount of text to learn contextual ...
models on English word- and sequence-level tasks. We outperform encoder-only models by a large margin on word-level tasks and reach a new unsupervised state-of-the-art performance on the Massive Text Embeddings Benchmark (MTEB). Moreover, when combining LLM2Vec with supervised contrastive ...
Therefore, we propose to use reinforcement learning with low-rank adaptation to finetune the text encoderbased on task-specific rewards, referred as TexForce . Wefirst show that finetuning the text encoder can improve the performanceof diffusion models. Then, we illustrate that TexForce can be ...