afiaka87/retrieval-augmented-diffusion 🔢📝🖼️✓❓ → ❓
About
Generate 768px images from text using CompVis `retrieval-augmented-diffusion`
Example Output
Output
[object Object]
Performance Metrics
5.76s
Prediction Time
5.94s
Total Time
All Input Parameters
{
"seed": -1,
"scale": 5,
"steps": 50,
"width": 768,
"height": 768,
"prompts": "an astronaut riding a horse in photorealistic style, digital art",
"use_database": true,
"database_name": "laion-aesthetic",
"num_database_results": "20"
}
Input Parameters
- seed
- Seed for the random number generator. Set to -1 to use a random seed.
- scale
- Classifier-free unconditional scale for the sampler.
- steps
- How many steps to run the model for. Using more will make generation take longer. 50 tends to work well.
- width
- Desired width of generated images. Values beside 768 are not supported, likely to cause artifacts.
- height
- Desired width of generated images. Values beside 768 are likely to cause zooming issues.
- prompts
- (batched) Use up to 8 prompts by separating with a `|` character.
- ddim_eta
- The eta parameter for ddim sampling.
- image_prompt
- (overrides `prompts`) Use an image as the prompt to generate variations of an existing image.
- use_database
- Whether to use the database for the semantic search.
- database_name
- Which database to use for the semantic search. Different databases have different capabilities.
- ddim_sampling
- Use ddim sampling instead of the faster plms sampling.
- negative_prompt
- (experimental) Use this prompt as a negative prompt for the sampler.
- num_database_results
- The number of search results from the retrieval backend to guide the generation with.
- number_of_variations
- Number of variations to generate when using only one `text_prompt`, or an `image_prompt`.
Output Schema
Output
Example Execution Logs
Prompts: ['an astronaut riding a horse in photorealistic style, digital art'] Using random seed 4267824009 Seed: -1 CLIP Text Embed: torch.Size([1, 1, 768]) Using plms sampling Data shape for PLMS sampling is (1, 16, 48, 48) Running PLMS Sampling with 50 timesteps PLMS Sampler: 0%| | 0/50 [00:00<?, ?it/s] PLMS Sampler: 2%|▏ | 1/50 [00:00<00:08, 6.06it/s] PLMS Sampler: 6%|▌ | 3/50 [00:00<00:04, 9.92it/s] PLMS Sampler: 10%|█ | 5/50 [00:00<00:03, 11.42it/s] PLMS Sampler: 14%|█▍ | 7/50 [00:00<00:03, 12.13it/s] PLMS Sampler: 18%|█▊ | 9/50 [00:00<00:03, 12.59it/s] PLMS Sampler: 22%|██▏ | 11/50 [00:00<00:03, 12.90it/s] PLMS Sampler: 26%|██▌ | 13/50 [00:01<00:02, 12.42it/s] PLMS Sampler: 30%|███ | 15/50 [00:01<00:02, 12.21it/s] PLMS Sampler: 34%|███▍ | 17/50 [00:01<00:02, 12.24it/s] PLMS Sampler: 38%|███▊ | 19/50 [00:01<00:02, 11.95it/s] PLMS Sampler: 42%|████▏ | 21/50 [00:01<00:02, 11.68it/s] PLMS Sampler: 46%|████▌ | 23/50 [00:01<00:02, 11.48it/s] PLMS Sampler: 50%|█████ | 25/50 [00:02<00:02, 11.36it/s] PLMS Sampler: 54%|█████▍ | 27/50 [00:02<00:02, 11.37it/s] PLMS Sampler: 58%|█████▊ | 29/50 [00:02<00:01, 11.60it/s] PLMS Sampler: 62%|██████▏ | 31/50 [00:02<00:01, 11.38it/s] PLMS Sampler: 66%|██████▌ | 33/50 [00:02<00:01, 11.67it/s] PLMS Sampler: 70%|███████ | 35/50 [00:02<00:01, 11.85it/s] PLMS Sampler: 74%|███████▍ | 37/50 [00:03<00:01, 12.09it/s] PLMS Sampler: 78%|███████▊ | 39/50 [00:03<00:00, 12.41it/s] PLMS Sampler: 82%|████████▏ | 41/50 [00:03<00:00, 12.70it/s] PLMS Sampler: 86%|████████▌ | 43/50 [00:03<00:00, 12.52it/s] PLMS Sampler: 90%|█████████ | 45/50 [00:03<00:00, 12.65it/s] PLMS Sampler: 94%|█████████▍| 47/50 [00:03<00:00, 12.83it/s] PLMS Sampler: 98%|█████████▊| 49/50 [00:04<00:00, 12.99it/s] PLMS Sampler: 100%|██████████| 50/50 [00:04<00:00, 12.07it/s]
Version Details
- Version ID
ac7878dfd12cb445115fb250a79faf3c3028e6cfa6c94788a7c9c53b0ce5898e- Version Created
- August 20, 2022