Comments (6)
Hi :)! First thank you for your excellent work!
I am trying to reproduce the results of Latte, and I wonder the total batch size for each dataset (local_batch_size * num_gpus), can you share more information on the experiment setups?
I tried the 1e-r lr with 32 total batchsize with the small version Latte-S, but can't generate good results. And so I wonder is the batch size / model size highly relevant to the final results? Thank you!
Hi, thanks for your interest. I think the learning rate you're using is a little too high. You can refer to Figure 6 of the paper.
from latte.
oh I am sorry for the typo, I use 1e-4 as written in the config
from latte.
oh I am sorry for the typo, I use 1e-4 as written in the config
My local_batch_size
is set to 5. You can see the performance of Latte-S in Figure 6.
from latte.
oh I am sorry for the typo, I use 1e-4 as written in the config
My
local_batch_size
is set to 5. You can see the performance of Latte-S in Figure 6.
I want to confirm whether all the unconditional models are trained on 1 GPU with local_batch_set=5, and max_train_steps=1000000. Now I am trying to reproduce the paper results and not sure how many GPU to use.
from latte.
oh I am sorry for the typo, I use 1e-4 as written in the config
My
local_batch_size
is set to 5. You can see the performance of Latte-S in Figure 6.I want to confirm whether all the unconditional models are trained on 1 GPU with local_batch_set=5, and max_train_steps=1000000. Now I am trying to reproduce the paper results and not sure how many GPU to use.
Hi, I trained all the unconditional models on 8 GPUs with local_batch_set=5.
from latte.
Hi There! ๐
This issue has been marked as stale due to inactivity for 14 days.
We would like to inquire if you still have the same problem or if it has been resolved.
If you need further assistance, please feel free to respond to this comment within the next 7 days. Otherwise, the issue will be automatically closed.
We appreciate your understanding and would like to express our gratitude for your contribution to Latte. Thank you for your support. ๐
from latte.
Related Issues (20)
- T2V training and evaluation HOT 7
- Results & ckpts of different sized Latte on UCF-101 HOT 3
- ่ฎญ็ปๆจกๅๅ็ๆๆ่ฏไผฐ HOT 4
- FFS evaluation HOT 5
- Some problems with reimplementing the training process HOT 20
- the code of variant 4 HOT 1
- Question: evaluate the FVD HOT 6
- Error once speed up training HOT 2
- How to get preprocessed_ffs HOT 4
- Any plan to implement Latte in HuggingFace's diffusers library? HOT 3
- ๆจกๅๅจucf101ไธๆ ๆณๆถๆ HOT 5
- Can Latte train for I2V tasks? HOT 2
- Batch Size Ablations HOT 1
- what the param <input_sq_size> stands for? HOT 2
- Can we use batch_size>1 in sample_t2x.py HOT 7
- Evaluate the `FVD` on FFS HOT 4
- How can I utilize the weights of pre-trained PixArt-ฮฑ to initialize the parameters of the spatial Transformer block in the Latte T2V model? HOT 2
- FVD on UCF-101 HOT 15
- Is there tutorial on transfering t2i to t2v model? HOT 5
- inference memory with torch.set_grad_enabled(True) HOT 2
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
๐ Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. ๐๐๐
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google โค๏ธ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from latte.