Instructions to use phi-lab-rice/GRADE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use phi-lab-rice/GRADE with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("phi-lab-rice/GRADE", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
File size: 3,770 Bytes
f348660 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 | VOID_INTERMEDIATE = {
"dpt_beit_large_512" : {
"void_150" : {
"mean" : {"int_depth" : 0.730, "int_scales" : 0.380},
"std" : {"int_depth" : 0.226, "int_scales" : 0.102},
},
"void_500" : {
"mean" : {"int_depth" : 0.736, "int_scales" : 0.366},
"std" : {"int_depth" : 0.232, "int_scales" : 0.099},
},
"void_1500" : {
"mean" : {"int_depth" : 0.730, "int_scales" : 0.355},
"std" : {"int_depth" : 0.232, "int_scales" : 0.096},
},
},
"dpt_swin2_large_384" : {
"void_150" : {
"mean" : {"int_depth" : 0.730, "int_scales" : 0.402},
"std" : {"int_depth" : 0.219, "int_scales" : 0.107},
},
"void_500" : {
"mean" : {"int_depth" : 0.736, "int_scales" : 0.389},
"std" : {"int_depth" : 0.224, "int_scales" : 0.106},
},
"void_1500" : {
"mean" : {"int_depth" : 0.730, "int_scales" : 0.377},
"std" : {"int_depth" : 0.226, "int_scales" : 0.103},
},
},
"dpt_large" : {
"void_150" : {
"mean" : {"int_depth" : 0.729, "int_scales" : 0.403},
"std" : {"int_depth" : 0.213, "int_scales" : 0.116},
},
"void_500" : {
"mean" : {"int_depth" : 0.735, "int_scales" : 0.390},
"std" : {"int_depth" : 0.219, "int_scales" : 0.116},
},
"void_1500" : {
"mean" : {"int_depth" : 0.730, "int_scales" : 0.380},
"std" : {"int_depth" : 0.221, "int_scales" : 0.116},
},
},
"dpt_hybrid": {
"void_150" : {
"mean" : {"int_depth" : 0.729, "int_scales" : 0.404},
"std" : {"int_depth" : 0.210, "int_scales" : 0.117},
},
"void_500" : {
"mean" : {"int_depth" : 0.735, "int_scales" : 0.392},
"std" : {"int_depth" : 0.215, "int_scales" : 0.118},
},
"void_1500" : {
"mean" : {"int_depth" : 0.730, "int_scales" : 0.381},
"std" : {"int_depth" : 0.218, "int_scales" : 0.117},
},
},
"dpt_swin2_tiny_256" : {
"void_150" : {
"mean" : {"int_depth" : 0.735, "int_scales" : 0.419},
"std" : {"int_depth" : 0.207, "int_scales" : 0.122},
},
"void_500" : {
"mean" : {"int_depth" : 0.741, "int_scales" : 0.406},
"std" : {"int_depth" : 0.212, "int_scales" : 0.124},
},
"void_1500" : {
"mean" : {"int_depth" : 0.733, "int_scales" : 0.396},
"std" : {"int_depth" : 0.213, "int_scales" : 0.125},
},
},
"dpt_levit_224" : {
"void_150" : {
"mean" : {"int_depth" : 0.734, "int_scales" : 0.421},
"std" : {"int_depth" : 0.198, "int_scales" : 0.129},
},
"void_500" : {
"mean" : {"int_depth" : 0.740, "int_scales" : 0.410},
"std" : {"int_depth" : 0.202, "int_scales" : 0.134},
},
"void_1500" : {
"mean" : {"int_depth" : 0.734, "int_scales" : 0.400},
"std" : {"int_depth" : 0.204, "int_scales" : 0.137},
},
},
"midas_small" : {
"void_150" : {
"mean" : {"int_depth" : 0.723, "int_scales" : 0.402},
"std" : {"int_depth" : 0.190, "int_scales" : 0.132},
},
"void_500" : {
"mean" : {"int_depth" : 0.731, "int_scales" : 0.393},
"std" : {"int_depth" : 0.196, "int_scales" : 0.136},
},
"void_1500" : {
"mean" : {"int_depth" : 0.728, "int_scales" : 0.385},
"std" : {"int_depth" : 0.199, "int_scales" : 0.140},
},
},
}
|