adymaharana/story-dalle 🔢❓📝✓ → 🖼️
About
A model trained for the task of story visualization; generating images to pair with captions in a story.
Example Output
Output
Performance Metrics
17.65s
Prediction Time
17.69s
Total Time
All Input Parameters
{
"top_k": 32,
"top_p": 0.2,
"source": "Pororo",
"caption_1": "Pororo is in a party.",
"caption_2": "Pororo is singing a song on the stage in the party",
"caption_3": "Poby is cheering in the audience",
"caption_4": "Crong is dancing in the party",
"n_candidates": 4
}
Input Parameters
- top_k
- the number of highest probability vocabulary tokens to keep for top-k-filtering
- top_p
- Only the most probable tokens with probabilities that add up to `top_p` or higher are kept for generation
- source
- The main character of your story
- caption_1
- First scene in your story
- caption_2
- Second scene in your story
- caption_3
- Third scene in your story
- caption_4
- Final scene in your story
- n_candidates
- Num candidates to generate for each story panel
- supercondition
- Set `supercondition` to True to enable generation using a null hypothesis.
Output Schema
Output
Example Execution Logs
['Pororo is in a party.', 'Pororo is singing a song on the stage in the party.', 'Poby is cheering in the audience.', 'Crong is dancing in the party.'] [1, 1, 1, 1] Pororo 4 Pororo is in a party. Pororo is singing a song on the stage in the party. Poby is cheering in the audience. Crong is dancing in the party. ['Pororo is in a party.', 'Pororo is singing a song on the stage in a party.', 'Poby is cheering in the audience.', 'Crong is dancing in a party.'] torch.Size([16, 64]) torch.Size([16, 256]) torch.Size([16, 1, 1536]) torch.Size([16, 1]) 0%| | 0/256 [00:00<?, ?it/s] 1%| | 2/256 [00:00<00:17, 14.21it/s] 2%|▏ | 4/256 [00:00<00:16, 15.75it/s] 2%|▏ | 6/256 [00:00<00:15, 16.16it/s] 3%|▎ | 8/256 [00:00<00:15, 16.34it/s] 4%|▍ | 10/256 [00:00<00:15, 16.36it/s] 5%|▍ | 12/256 [00:00<00:15, 16.21it/s] 5%|▌ | 14/256 [00:00<00:14, 16.21it/s] 6%|▋ | 16/256 [00:00<00:14, 16.28it/s] 7%|▋ | 18/256 [00:01<00:14, 16.45it/s] 8%|▊ | 20/256 [00:01<00:14, 16.54it/s] 9%|▊ | 22/256 [00:01<00:14, 16.58it/s] 9%|▉ | 24/256 [00:01<00:14, 16.53it/s] 10%|█ | 26/256 [00:01<00:13, 16.51it/s] 11%|█ | 28/256 [00:01<00:13, 16.35it/s] 12%|█▏ | 30/256 [00:01<00:13, 16.29it/s] 12%|█▎ | 32/256 [00:01<00:13, 16.41it/s] 13%|█▎ | 34/256 [00:02<00:13, 16.49it/s] 14%|█▍ | 36/256 [00:02<00:13, 16.20it/s] 15%|█▍ | 38/256 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256/256 [00:15<00:00, 15.79it/s] 100%|██████████| 256/256 [00:15<00:00, 16.13it/s] torch.Size([16, 16, 16]) torch.Size([16, 3, 256, 256]) tensor([[[0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], ..., [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.]], [[0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], ..., [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.]], [[0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], ..., [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.]]]) torch.Size([3, 1124, 1064])
Version Details
- Version ID
f74d40125d71ddd5885020201e638b6b270347bb606a6afbc61edc7b077bfb7b- Version Created
- November 23, 2022