Comments (11)
thank you for the contribution
from text2video-zero.
seeds are random oops, Hi @kolabearafk 👋 Hi @MaayanBogin 👋 Hi @toughyear 👋 thanks for the info ❤ maybe it's fixed now, please try it again.
from text2video-zero.
Thank you for the Colab version. But why aren't the results from this repo and the results from the colab not matching? The examples from the Colab version seem to be lacking the temporal consistency? Please see below for the example with the prompt "a horse is galloping on a street".
HorseGalloping.mp4
from text2video-zero.
Thank you for the Colab version. But why aren't the results from this repo and the results from the colab not matching? The examples from the Colab version seem to be lacking the temporal consistency? Please see below for the example with the prompt "a horse is galloping on a street".
same here
from text2video-zero.
+1 to @kolabearafk. There is no temporal consistency. I also see a Picsart watermark at the bottom right.
from text2video-zero.
@camenduru trying. Will update.
from text2video-zero.
Slightly better results. The pose and depth seem to stay consistent. The art style keeps changing.
PROMPT: Pixel art of a dancing bearded fat farmer
Time on a Python 3 Google Colab Compute Engine backend (GPU): ~ 173 seconds
I assumed it might help to prompt a common, easily repeatable character. There are still temporal consistency issues -
PROMPT: Pixel art of a santa claus dancing, white background
Time on a Python 3 Google Colab Compute Engine backend (GPU): ~ X seconds
Not using Pixel art in prompt produces slightly better results -
I would attribute this to mainly having a popular character that has a single source of truth for its looks.
PROMPT: iron man dancing, white background
Time on a Python 3 Google Colab Compute Engine backend (GPU): ~ 89 seconds
Obviously, these are the current shortcomings of the model only. Your colab works great. ZeroShot text-to-video models will get better with time.
Let me know if I can help with anything @camenduru
from text2video-zero.
🔥 thanks @toughyear ❤ please join our discord server https://discord.gg/k5BwmmvJJU maybe we can make some little improvements together
from text2video-zero.
temporal consistency problem coming from xformers 😭 without xformers it is working, but then the model does not fit in the free T4 GPU 😭
from text2video-zero.
with custom model https://huggingface.co/plasmo/woolitize-768sd1-5
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joined @camenduru
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Related Issues (20)
- About the process of generating latent code HOT 1
- Which conference do you submit this paper?
- How much memory GPU is required for text-to-video generation?
- FACE + Hand Guidance
- Dependencies in `requirements.txt` have module conflicts.
- Image prompt in video generator
- Ai video prompt not working
- How to only use dreambooth without controlnet for zero-shot T2V?
- ValueError: cross_attention_dim must be specified for CrossAttnDownBlock2D HOT 1
- About the Background Smoothing
- AttributeError: while importing model
- Does it support Stable diffusion XL
- Please continue development in the vein of Animatediff, but undo diffusers work
- Inference on CPU HOT 1
- Not reproducible
- Questions about cross frame attention
- Hello, How can I contact lead developer of this project? HOT 1
- AssertionError: Torch not compiled with CUDA enabled HOT 1
- Video generation takes too long even with GPU HOT 3
- No module named 'distutils.cmd'
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