fire/trellis:ef5f7aac – Run with an API on Replicate

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fire /trellis:ef5f7aac

Playground API

Input

images

*file[]

Add multiple files

List of input images to generate 3D asset from

seed

integer

Random seed for generation

Default: 0

randomize_seed

boolean

Randomize seed

Default: true

generate_color

boolean

Generate color video render

Default: true

generate_normal

boolean

Generate normal video render

Default: false

generate_model

boolean

Generate 3D model file (GLB)

Default: false

save_gaussian_ply

boolean

Save Gaussian point cloud as PLY file

Default: false

return_no_background

boolean

Return the preprocessed images without background

Default: false

ss_guidance_strength

number

(minimum: 0, maximum: 10)

Stage 1: Sparse Structure Generation - Guidance Strength

Default: 7.5

ss_sampling_steps

integer

(minimum: 1, maximum: 50)

Stage 1: Sparse Structure Generation - Sampling Steps

Default: 12

slat_guidance_strength

number

(minimum: 0, maximum: 10)

Stage 2: Structured Latent Generation - Guidance Strength

Default: 3

slat_sampling_steps

integer

(minimum: 1, maximum: 50)

Stage 2: Structured Latent Generation - Sampling Steps

Default: 12

mesh_simplify

number

(minimum: 0, maximum: 0.98)

GLB Extraction - Mesh Simplification (only used if generate_model=True)

Default: 0.65

texture_size

integer

(minimum: 512, maximum: 2048)

GLB Extraction - Texture Size (only used if generate_model=True)

Default: 512

Run this model in Node.js with one line of code:

npx create-replicate --model=fire/trellis

or set up a project from scratch

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import and set up the client:

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run fire/trellis using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "fire/trellis:ef5f7aacc4779906d75e891763af9db45f061ea382e263081e2b1ed38a5d72da",
  {
    input: {
      seed: 0,
      texture_size: 2048,
      mesh_simplify: 0,
      generate_color: false,
      generate_model: true,
      randomize_seed: true,
      generate_normal: false,
      save_gaussian_ply: false,
      ss_sampling_steps: 12,
      slat_sampling_steps: 12,
      return_no_background: false,
      ss_guidance_strength: 7.5,
      slat_guidance_strength: 3
    }
  }
);
console.log(output);

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import the client:

import replicate

Run fire/trellis using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "fire/trellis:ef5f7aacc4779906d75e891763af9db45f061ea382e263081e2b1ed38a5d72da",
    input={
        "seed": 0,
        "texture_size": 2048,
        "mesh_simplify": 0,
        "generate_color": False,
        "generate_model": True,
        "randomize_seed": True,
        "generate_normal": False,
        "save_gaussian_ply": False,
        "ss_sampling_steps": 12,
        "slat_sampling_steps": 12,
        "return_no_background": False,
        "ss_guidance_strength": 7.5,
        "slat_guidance_strength": 3
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Run fire/trellis using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

curl -s -X POST \
  -H "Authorization: Bearer $REPLICATE_API_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Prefer: wait" \
  -d $'{
    "version": "ef5f7aacc4779906d75e891763af9db45f061ea382e263081e2b1ed38a5d72da",
    "input": {
      "seed": 0,
      "texture_size": 2048,
      "mesh_simplify": 0,
      "generate_color": false,
      "generate_model": true,
      "randomize_seed": true,
      "generate_normal": false,
      "save_gaussian_ply": false,
      "ss_sampling_steps": 12,
      "slat_sampling_steps": 12,
      "return_no_background": false,
      "ss_guidance_strength": 7.5,
      "slat_guidance_strength": 3
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

You can run this model locally using Cog. First, install Cog:

brew install cog

If you don’t have Homebrew, there are other installation options available.

Run this to download the model and run it in your local environment:

cog predict r8.im/fire/trellis@sha256:ef5f7aacc4779906d75e891763af9db45f061ea382e263081e2b1ed38a5d72da \
  -i 'seed=0' \
  -i 'texture_size=2048' \
  -i 'mesh_simplify=0' \
  -i 'generate_color=false' \
  -i 'generate_model=true' \
  -i 'randomize_seed=true' \
  -i 'generate_normal=false' \
  -i 'save_gaussian_ply=false' \
  -i 'ss_sampling_steps=12' \
  -i 'slat_sampling_steps=12' \
  -i 'return_no_background=false' \
  -i 'ss_guidance_strength=7.5' \
  -i 'slat_guidance_strength=3'

To learn more, take a look at the Cog documentation.

Run this to download the model and run it in your local environment:

docker run -d -p 5000:5000 --gpus=all r8.im/fire/trellis@sha256:ef5f7aacc4779906d75e891763af9db45f061ea382e263081e2b1ed38a5d72da
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "seed": 0,
      "texture_size": 2048,
      "mesh_simplify": 0,
      "generate_color": false,
      "generate_model": true,
      "randomize_seed": true,
      "generate_normal": false,
      "save_gaussian_ply": false,
      "ss_sampling_steps": 12,
      "slat_sampling_steps": 12,
      "return_no_background": false,
      "ss_guidance_strength": 7.5,
      "slat_guidance_strength": 3
    }
  }' \
  http://localhost:5000/predictions

To learn more, take a look at the Cog documentation.

Output

model_file

no_background_image

{
  "completed_at": "2024-12-25T19:22:52.600278Z",
  "created_at": "2024-12-25T19:18:12.737000Z",
  "data_removed": false,
  "error": null,
  "id": "nbyyj1jr05rma0ckzx0t2kjea4",
  "input": {
    "seed": 0,
    "image": "https://replicate.delivery/pbxt/MCprdYbZYopM9oV25EeGmNQdKjeYjIB6sfyei5rTFeaXEoun/Set_in_role_playing_game__A_young_woman_with_realistic_adult_proportio_S1009161289_St8_G1.png",
    "texture_size": 2048,
    "mesh_simplify": 0,
    "generate_color": false,
    "generate_model": true,
    "randomize_seed": true,
    "generate_normal": false,
    "ss_sampling_steps": 12,
    "slat_sampling_steps": 12,
    "ss_guidance_strength": 7.5,
    "slat_guidance_strength": 3
  },
  "logs": "INFO:predict:Loading and preprocessing input image...\nINFO:predict:Saved image without background\nINFO:predict:Using randomized seed: 1505015317\nINFO:predict:Running TRELLIS pipeline...\nSampling:   0%|          | 0/12 [00:00<?, ?it/s]\nSampling:  17%|█▋        | 2/12 [00:00<00:00, 13.09it/s]\nSampling:  33%|███▎      | 4/12 [00:00<00:00, 13.14it/s]\nSampling:  50%|█████     | 6/12 [00:00<00:00, 13.16it/s]\nSampling:  67%|██████▋   | 8/12 [00:00<00:00, 13.21it/s]\nSampling:  83%|████████▎ | 10/12 [00:00<00:00, 13.18it/s]\nSampling: 100%|██████████| 12/12 [00:00<00:00, 14.37it/s]\nSampling:   0%|          | 0/12 [00:00<?, ?it/s]\nSampling:   8%|▊         | 1/12 [00:00<00:01,  7.12it/s]\nSampling:  25%|██▌       | 3/12 [00:00<00:00,  9.17it/s]\nSampling:  42%|████▏     | 5/12 [00:00<00:00,  9.66it/s]\nSampling:  58%|█████▊    | 7/12 [00:00<00:00,  9.89it/s]\nSampling:  75%|███████▌  | 9/12 [00:00<00:00,  9.99it/s]\nSampling:  92%|█████████▏| 11/12 [00:01<00:00, 11.01it/s]\nSampling: 100%|██████████| 12/12 [00:01<00:00, 10.70it/s]\nINFO:predict:TRELLIS pipeline complete!\nINFO:predict:Available output formats: dict_keys(['mesh', 'gaussian'])\nINFO:predict:Generating GLB model...\nINFO- Loaded 58336 vertices and 116560 faces.\n0% done\n2% done\n5% done\n7% done\n10% done\n13% done\n15% done\n18% done\n21% done\n23% done\n26% done\n28% done\n31% done\n34% done\n36% done\n39% done\n42% done\n44% done\n47% done\n50% done\n52% done\n55% done\n57% done\n60% done\n63% done\n65% done\n68% done\n71% done\n73% done\n76% done\n78% done\n81% done\n84% done\n86% done\n89% done\n92% done\n94% done\n97% done\n100% done\nRendering: 0it [00:00, ?it/s]\nRendering: 15it [00:00, 147.37it/s]\nRendering: 34it [00:00, 168.88it/s]\nRendering: 53it [00:00, 175.60it/s]\nRendering: 72it [00:00, 180.23it/s]\nRendering: 91it [00:00, 181.93it/s]\nRendering: 100it [00:00, 177.28it/s]\nINFO:predict:GLB model generation complete!\nINFO:predict:Prediction complete! Returning results...",
  "metrics": {
    "predict_time": 57.754129884,
    "total_time": 279.863278
  },
  "output": {
    "model_file": "https://replicate.delivery/xezq/68nSlbHf2Vw5NauxdzCCkCZf02071xM5xaEolEk6kfKZsB9nA/output.glb",
    "color_video": null,
    "normal_video": null,
    "combined_video": null,
    "no_background_image": "https://replicate.delivery/xezq/cUALxDQQdsIeCK4IOZNMfJ4KJxnemnTAO7diWJreVEAzYD6PB/output_no_background.png"
  },
  "started_at": "2024-12-25T19:21:54.846148Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/nbyyj1jr05rma0ckzx0t2kjea4",
    "cancel": "https://api.replicate.com/v1/predictions/nbyyj1jr05rma0ckzx0t2kjea4/cancel"
  },
  "version": "ace6e61c91a2674b520ef558d7e0d7494df803e82d2bdf51e8398788f51214b4"
}

Generated in

57.8 seconds

Tweak it ShareReport

INFO:predict:Loading and preprocessing input image...
INFO:predict:Saved image without background
INFO:predict:Using randomized seed: 1505015317
INFO:predict:Running TRELLIS pipeline...
Sampling:   0%|          | 0/12 [00:00<?, ?it/s]
Sampling:  17%|█▋        | 2/12 [00:00<00:00, 13.09it/s]
Sampling:  33%|███▎      | 4/12 [00:00<00:00, 13.14it/s]
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Sampling: 100%|██████████| 12/12 [00:01<00:00, 10.70it/s]
INFO:predict:TRELLIS pipeline complete!
INFO:predict:Available output formats: dict_keys(['mesh', 'gaussian'])
INFO:predict:Generating GLB model...
INFO- Loaded 58336 vertices and 116560 faces.
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Rendering: 0it [00:00, ?it/s]
Rendering: 15it [00:00, 147.37it/s]
Rendering: 34it [00:00, 168.88it/s]
Rendering: 53it [00:00, 175.60it/s]
Rendering: 72it [00:00, 180.23it/s]
Rendering: 91it [00:00, 181.93it/s]
Rendering: 100it [00:00, 177.28it/s]
INFO:predict:GLB model generation complete!
INFO:predict:Prediction complete! Returning results...

This example was created by a different version, fire/trellis:ace6e61c.