Instructions to use Viggle/Meridian with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Viggle/Meridian with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Viggle/Meridian", 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: 3,576 Bytes
9f57754 | 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 | {
"take": "videos-all/meridian_longtake_l150_nba3_apex_right14_175",
"media": {
"source": {
"path": "videos-all/meridian_longtake_l150_nba3_apex_right14_175/source.mp4",
"sha256": "642f992fdc516c57fbaeabd9c4a6aa773c76fb9f1fd342412e20baa27b3dbb46",
"samples": [
{
"frame": 40,
"jpeg_sha256": "993b86a03df949e23a31b6ba4b64c0fd896516851d633cf279b9dad8faf4c19c"
},
{
"frame": 95,
"jpeg_sha256": "638ad3ceee19eaf238979fa5deb302394c3e750cba67d989405d303f80afd522"
},
{
"frame": 150,
"jpeg_sha256": "dd976c5db5a7861dbbdf174a79e3a233e98ef67bebf4e156203a2f749049720d"
}
]
},
"render": {
"path": "videos-all/meridian_longtake_l150_nba3_apex_right14_175/render.mp4",
"sha256": "e4f130524c2e09351203ca6dd410b3505031e72cdb4411e3d231787dba62bd23",
"samples": [
{
"frame": 40,
"jpeg_sha256": "b83be569064e26cefdf9bba2c5c89e3e63cd0c35beca4eeb57a8bda181406d2d"
},
{
"frame": 95,
"jpeg_sha256": "36347035ab0051f8a93fd1f1bb2bd31420de3a5be21b942a76f78e3742ce4171"
},
{
"frame": 150,
"jpeg_sha256": "1383ab0dd947894b3c05986e3f5b7b901061eca752f263e6e69aa8d4b4dff730"
}
]
},
"out": {
"path": "videos-all/meridian_longtake_l150_nba3_apex_right14_175/out.mp4",
"sha256": "49d33d29dca587f252dc43171e6b98513348770332ca44a8eb3a8b46b4300fb0",
"samples": [
{
"frame": 40,
"jpeg_sha256": "8ab722c84d3f21c5984cc9f294de6a208d2fd02dfcba71851660ed125b239792"
},
{
"frame": 95,
"jpeg_sha256": "dbd0138d1f7e03a002aefcceed8e3faededaade3319bc6940b0d0fb0f13876cd"
},
{
"frame": 150,
"jpeg_sha256": "7c6ed5f9f3b2003665d4e0322c0c4fa3ab9a79edcbd627eae4879a1e222d83b3"
}
]
}
},
"front_frame": 95,
"back_frames": [
40,
150
],
"matched_frames": [
{
"output_frame": 40,
"input_frame": 40,
"original_frame": 48,
"original_seconds": 1.6016,
"held": false
},
{
"output_frame": 95,
"input_frame": 70,
"original_frame": 85,
"original_seconds": 2.8361666666666667,
"held": true
},
{
"output_frame": 150,
"input_frame": 99,
"original_frame": 120,
"original_seconds": 4.004,
"held": false
}
],
"video_display": "Actual decoded frames, JPEG downsampling, fit-only; overlapping cards expose parts of back frames. The front frame is uncropped. No generative replacement, retouching or repair.",
"geometry_display": "Procedural illustrative point cloud, camera frustums, and path; not actual VGGT-Omega output or recorded camera poses. Fixed seed 17. No reconstruction or service call.",
"implementation": [
"recam/geometry.py: reconstruct, unproject, warp",
"recam/path.py: plan_path",
"service/app.py: geo, do_render",
"recam/h3.py: pack, denoise, decode_video"
],
"scope": "Architecture illustration, not measured reconstruction quality or a globally consistent world. Source time selects supplied moments. Model completion is generated, not recovered.",
"credit": "User-supplied NBA footage for local research. Public promotional permission and endorsement are not established.",
"source_map": {
"path": "videos-all/longtake_edit/nba_study_data.json",
"sha256": "d596edd2904fc3c7d1b5d5a0248ea9da05db1a43feafb5b0224acbc8f7f2d27f"
}
}
|