tstramer/material-diffusion 🖼️🔢❓📝 → 🖼️
About
Stable diffusion fork for generating tileable outputs using v1.5 model

Example Output
Prompt:
"Mossy Runic Bricks seamless texture, trending on artstation, stone, moss, base color, albedo, 4k"
Output

Performance Metrics
9.27s
Prediction Time
254.16s
Total Time
All Input Parameters
{ "width": 512, "height": 512, "prompt": "Mossy Runic Bricks seamless texture, trending on artstation, stone, moss, base color, albedo, 4k", "scheduler": "K-LMS", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 }
Input Parameters
- mask
- Black and white image to use as mask for inpainting over init_image. Black pixels are inpainted and white pixels are preserved. Tends to work better with prompt strength of 0.5-0.7. Consider using https://replicate.com/andreasjansson/stable-diffusion-inpainting instead.
- seed
- Random seed. Leave blank to randomize the seed
- width
- Width of output image. Maximum size is 1024x768 or 768x1024 because of memory limits
- height
- Height of output image. Maximum size is 1024x768 or 768x1024 because of memory limits
- prompt
- Input prompt
- scheduler
- Choose a scheduler. If you use an init image, PNDM will be used
- init_image
- Inital image to generate variations of. Will be resized to the specified width and height
- num_outputs
- Number of images to output. If the NSFW filter is triggered, you may get fewer outputs than this.
- guidance_scale
- Scale for classifier-free guidance
- prompt_strength
- Prompt strength when using init image. 1.0 corresponds to full destruction of information in init image
- num_inference_steps
- Number of denoising steps
Output Schema
Output
Example Execution Logs
Using seed: 52336 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:03<02:57, 3.63s/it] 6%|▌ | 3/50 [00:03<00:47, 1.00s/it] 10%|█ | 5/50 [00:03<00:23, 1.90it/s] 14%|█▍ | 7/50 [00:04<00:14, 2.98it/s] 18%|█▊ | 9/50 [00:04<00:09, 4.22it/s] 22%|██▏ | 11/50 [00:04<00:07, 5.54it/s] 26%|██▌ | 13/50 [00:04<00:05, 6.88it/s] 30%|███ | 15/50 [00:04<00:04, 8.07it/s] 34%|███▍ | 17/50 [00:04<00:03, 9.02it/s] 38%|███▊ | 19/50 [00:05<00:03, 10.04it/s] 42%|████▏ | 21/50 [00:05<00:02, 10.78it/s] 46%|████▌ | 23/50 [00:05<00:02, 11.48it/s] 50%|█████ | 25/50 [00:05<00:02, 12.04it/s] 54%|█████▍ | 27/50 [00:05<00:01, 12.43it/s] 58%|█████▊ | 29/50 [00:05<00:01, 12.67it/s] 62%|██████▏ | 31/50 [00:05<00:01, 12.72it/s] 66%|██████▌ | 33/50 [00:06<00:01, 12.94it/s] 70%|███████ | 35/50 [00:06<00:01, 13.11it/s] 74%|███████▍ | 37/50 [00:06<00:00, 13.23it/s] 78%|███████▊ | 39/50 [00:06<00:00, 13.30it/s] 82%|████████▏ | 41/50 [00:06<00:00, 13.38it/s] 86%|████████▌ | 43/50 [00:06<00:00, 13.29it/s] 90%|█████████ | 45/50 [00:06<00:00, 13.07it/s] 94%|█████████▍| 47/50 [00:07<00:00, 13.14it/s] 98%|█████████▊| 49/50 [00:07<00:00, 13.14it/s] 100%|██████████| 50/50 [00:07<00:00, 6.79it/s]
Version Details
- Version ID
a42692c54c0f407f803a0a8a9066160976baedb77c91171a01730f9b0d7beeff
- Version Created
- November 13, 2022