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mkshing
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e4t-diffusion
Implementation of Encoder-based Domain Tuning for Fast Personalization of Text-to-Image Models
https://arxiv.org/abs/2302.12228
MIT License
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images and resources for pre-training
#31
yudongjian
opened
7 months ago
0
Question about settings and results.
#30
zhanjiahui
opened
10 months ago
0
{placeholder_token} vs *s in inference
#29
bsun0802
opened
10 months ago
1
Problem with running the code both in colab and local machine
#28
alimohammadiamirhossein
opened
11 months ago
1
Upgrade to SD 1.5 or later
#27
blistick
opened
1 year ago
0
can you release the cat model
#26
silence401
opened
1 year ago
0
Need help... OOM with 2 RTX3090 (bs=2)
#25
CHR-ray
opened
1 year ago
1
BUG in inference.py
#24
CHR-ray
opened
1 year ago
1
Bugs in the pretrain script
#23
shenzy08
opened
1 year ago
0
when I finetuned the model
#22
zhangqizky
opened
1 year ago
2
how can i get the unet.pt file?
#21
Parisa-Bagherzadeh
closed
1 year ago
1
can a get a cat model?
#20
redredbluee
closed
1 year ago
1
Handle case where ckpt_path is None, i.e. when running pretrain_e4t.py
#19
brianfitzgerald
opened
1 year ago
0
I fine-tuned the same yannlecun image as yours, and the generated results are different from yours. I used the pre-trained model e4t-diffusion-ffhq-celebahq-v1 you released. Why are the results different?
#18
wangzhaolei
closed
1 year ago
8
Prompt used during pretraining
#17
Actis92
opened
1 year ago
1
finetuning-free personalized text-to-image model
#16
iRedBean
closed
1 year ago
1
The performance of my pretraining model is very bad.
#15
dong-y21
closed
1 year ago
7
CUDA out of memory when running pretrain of branch v1 even if I set batch_size to 1. I wonder how much GPU memory is required to train this model. And does this mean I can't train on the V100(32G)?
#14
dong-y21
closed
1 year ago
5
I tried to train faces use FFHQ and celebA dataset. Why is the image sampled in the training completely different from the input image face? When I input an image during the inference, the generated face is completely different from the input face. And why domain_embed_scale=0.1 in training? Thankyou!
#13
wangzhaolei
closed
1 year ago
2
big changes for better results
#12
mkshing
closed
1 year ago
0
fix a bug in pipe
#11
mkshing
closed
1 year ago
0
question about inference.py
#10
laolongboy
closed
1 year ago
2
how to process dataset
#9
lsjiiia
closed
1 year ago
1
request a pretrained model
#8
laolongboy
closed
1 year ago
2
a Pre-training problem
#7
YibooZhao
closed
1 year ago
13
Problem due to wrong dimensions in E4TEncoder
#6
Actis92
closed
1 year ago
2
Can you specify versions in requirements.txt?
#5
cutlass90
closed
1 year ago
2
fix clip pooling part to align with the paper
#4
mkshing
closed
1 year ago
0
difference in the structure of encoder
#3
XavierXiao
closed
1 year ago
6
When can we use the pertained model?
#2
songyiren98
closed
1 year ago
1
Create LICENSE
#1
mkshing
closed
1 year ago
0