Prediction

k4fe8js1d1rm00cjnxt94vz9tr

Status

Succeeded

Source

Web

Hardware

H100

Total duration

11.5s

Created

10 months ago

Input

model: dev
height: 256
prompt: ANAT0MY, medical textbook schematics, vertigo, headaches, and anxiety
lora_scale: 1.1
num_outputs: 1
aspect_ratio: 21:9
output_format: webp
guidance_scale: 3.5
output_quality: 90
prompt_strength: 0.8
extra_lora_scale: 1
num_inference_steps: 28

{
  "model": "dev",
  "height": 256,
  "prompt": "ANAT0MY, medical textbook schematics, vertigo, headaches, and anxiety",
  "lora_scale": 1.1,
  "num_outputs": 1,
  "aspect_ratio": "21:9",
  "output_format": "webp",
  "guidance_scale": 3.5,
  "output_quality": 90,
  "prompt_strength": 0.8,
  "extra_lora_scale": 1,
  "num_inference_steps": 28
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";
import fs from "node:fs";

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

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

const output = await replicate.run(
  "sliday/anatomy:45a3dfdbbd7b724c2bc1be813cb25628e73595ca59ee38c3a4ea78a27804c2e9",
  {
    input: {
      model: "dev",
      height: 256,
      prompt: "ANAT0MY, medical textbook schematics, vertigo, headaches, and anxiety",
      lora_scale: 1.1,
      num_outputs: 1,
      aspect_ratio: "21:9",
      output_format: "webp",
      guidance_scale: 3.5,
      output_quality: 90,
      prompt_strength: 0.8,
      extra_lora_scale: 1,
      num_inference_steps: 28
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

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

pip install replicate

Import the client:

import replicate

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

output = replicate.run(
    "sliday/anatomy:45a3dfdbbd7b724c2bc1be813cb25628e73595ca59ee38c3a4ea78a27804c2e9",
    input={
        "model": "dev",
        "height": 256,
        "prompt": "ANAT0MY, medical textbook schematics, vertigo, headaches, and anxiety",
        "lora_scale": 1.1,
        "num_outputs": 1,
        "aspect_ratio": "21:9",
        "output_format": "webp",
        "guidance_scale": 3.5,
        "output_quality": 90,
        "prompt_strength": 0.8,
        "extra_lora_scale": 1,
        "num_inference_steps": 28
    }
)

# To access the file URL:
print(output[0].url())
#=> "http://example.com"

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output[0].read())

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

Run sliday/anatomy 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": "sliday/anatomy:45a3dfdbbd7b724c2bc1be813cb25628e73595ca59ee38c3a4ea78a27804c2e9",
    "input": {
      "model": "dev",
      "height": 256,
      "prompt": "ANAT0MY, medical textbook schematics, vertigo, headaches, and anxiety",
      "lora_scale": 1.1,
      "num_outputs": 1,
      "aspect_ratio": "21:9",
      "output_format": "webp",
      "guidance_scale": 3.5,
      "output_quality": 90,
      "prompt_strength": 0.8,
      "extra_lora_scale": 1,
      "num_inference_steps": 28
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-10-21T14:21:28.045677Z",
  "created_at": "2024-10-21T14:21:16.520000Z",
  "data_removed": false,
  "error": null,
  "id": "k4fe8js1d1rm00cjnxt94vz9tr",
  "input": {
    "model": "dev",
    "height": 256,
    "prompt": "ANAT0MY, medical textbook schematics, vertigo, headaches, and anxiety",
    "lora_scale": 1.1,
    "num_outputs": 1,
    "aspect_ratio": "21:9",
    "output_format": "webp",
    "guidance_scale": 3.5,
    "output_quality": 90,
    "prompt_strength": 0.8,
    "extra_lora_scale": 1,
    "num_inference_steps": 28
  },
  "logs": "Using seed: 63167\nPrompt: ANAT0MY, medical textbook schematics, vertigo, headaches, and anxiety\n[!] txt2img mode\nUsing dev model\nfree=6857580150784\nDownloading weights\n2024-10-21T14:21:16Z | INFO  | [ Initiating ] chunk_size=150M dest=/tmp/tmpedn4l8e3/weights url=https://replicate.delivery/yhqm/fmfTwYRejJTv3omNu2HA1vUckf7lUbpRj1dFgBRK3WzfYzHdC/trained_model.tar\n2024-10-21T14:21:17Z | INFO  | [ Complete ] dest=/tmp/tmpedn4l8e3/weights size=\"174 MB\" total_elapsed=1.203s url=https://replicate.delivery/yhqm/fmfTwYRejJTv3omNu2HA1vUckf7lUbpRj1dFgBRK3WzfYzHdC/trained_model.tar\nDownloaded weights in 1.26s\nLoaded LoRAs in 2.01s\n  0%|          | 0/28 [00:00<?, ?it/s]\n  4%|▎         | 1/28 [00:00<00:09,  2.96it/s]\n  7%|▋         | 2/28 [00:00<00:08,  3.23it/s]\n 11%|█         | 3/28 [00:00<00:07,  3.14it/s]\n 14%|█▍        | 4/28 [00:01<00:07,  3.10it/s]\n 18%|█▊        | 5/28 [00:01<00:07,  3.08it/s]\n 21%|██▏       | 6/28 [00:01<00:07,  3.06it/s]\n 25%|██▌       | 7/28 [00:02<00:06,  3.05it/s]\n 29%|██▊       | 8/28 [00:02<00:06,  3.05it/s]\n 32%|███▏      | 9/28 [00:02<00:06,  3.04it/s]\n 36%|███▌      | 10/28 [00:03<00:05,  3.04it/s]\n 39%|███▉      | 11/28 [00:03<00:05,  3.04it/s]\n 43%|████▎     | 12/28 [00:03<00:05,  3.04it/s]\n 46%|████▋     | 13/28 [00:04<00:04,  3.04it/s]\n 50%|█████     | 14/28 [00:04<00:04,  3.04it/s]\n 54%|█████▎    | 15/28 [00:04<00:04,  3.04it/s]\n 57%|█████▋    | 16/28 [00:05<00:03,  3.04it/s]\n 61%|██████    | 17/28 [00:05<00:03,  3.04it/s]\n 64%|██████▍   | 18/28 [00:05<00:03,  3.04it/s]\n 68%|██████▊   | 19/28 [00:06<00:02,  3.04it/s]\n 71%|███████▏  | 20/28 [00:06<00:02,  3.04it/s]\n 75%|███████▌  | 21/28 [00:06<00:02,  3.04it/s]\n 79%|███████▊  | 22/28 [00:07<00:01,  3.04it/s]\n 82%|████████▏ | 23/28 [00:07<00:01,  3.04it/s]\n 86%|████████▌ | 24/28 [00:07<00:01,  3.04it/s]\n 89%|████████▉ | 25/28 [00:08<00:00,  3.04it/s]\n 93%|█████████▎| 26/28 [00:08<00:00,  3.04it/s]\n 96%|█████████▋| 27/28 [00:08<00:00,  3.04it/s]\n100%|██████████| 28/28 [00:09<00:00,  3.04it/s]\n100%|██████████| 28/28 [00:09<00:00,  3.05it/s]",
  "metrics": {
    "predict_time": 11.516176758,
    "total_time": 11.525677
  },
  "output": [
    "https://replicate.delivery/yhqm/Ub2M1fLm6O3YCaV7eeq94huStBcQieSmhCGdKRWtnmziWFkOB/out-0.webp"
  ],
  "started_at": "2024-10-21T14:21:16.529500Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/k4fe8js1d1rm00cjnxt94vz9tr",
    "cancel": "https://api.replicate.com/v1/predictions/k4fe8js1d1rm00cjnxt94vz9tr/cancel"
  },
  "version": "45a3dfdbbd7b724c2bc1be813cb25628e73595ca59ee38c3a4ea78a27804c2e9"
}

Generated in

11.5 seconds

sliday/anatomy:45a3dfdbbd7b724c2bc1be813cb25628e73595ca59ee38c3a4ea78a27804c2e9

Using seed: 63167
Prompt: ANAT0MY, medical textbook schematics, vertigo, headaches, and anxiety
[!] txt2img mode
Using dev model
free=6857580150784
Downloading weights
2024-10-21T14:21:16Z | INFO  | [ Initiating ] chunk_size=150M dest=/tmp/tmpedn4l8e3/weights url=https://replicate.delivery/yhqm/fmfTwYRejJTv3omNu2HA1vUckf7lUbpRj1dFgBRK3WzfYzHdC/trained_model.tar
2024-10-21T14:21:17Z | INFO  | [ Complete ] dest=/tmp/tmpedn4l8e3/weights size="174 MB" total_elapsed=1.203s url=https://replicate.delivery/yhqm/fmfTwYRejJTv3omNu2HA1vUckf7lUbpRj1dFgBRK3WzfYzHdC/trained_model.tar
Downloaded weights in 1.26s
Loaded LoRAs in 2.01s
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Prediction

Model

9kh5gq5f5srm40cjnxva1kx7ar

Status

Succeeded

Source

Web

Hardware

H100

Total duration

11.5s

Created

10 months ago

Input

model: dev
height: 512
prompt: ANAT0MY, medical textbook schematics, Olanzapine
lora_scale: 1.1
num_outputs: 1
aspect_ratio: 4:5
output_format: webp
guidance_scale: 3.5
output_quality: 90
prompt_strength: 0.8
extra_lora_scale: 1
num_inference_steps: 28

{
  "model": "dev",
  "height": 512,
  "prompt": "ANAT0MY, medical textbook schematics, Olanzapine",
  "lora_scale": 1.1,
  "num_outputs": 1,
  "aspect_ratio": "4:5",
  "output_format": "webp",
  "guidance_scale": 3.5,
  "output_quality": 90,
  "prompt_strength": 0.8,
  "extra_lora_scale": 1,
  "num_inference_steps": 28
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";
import fs from "node:fs";

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

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

const output = await replicate.run(
  "sliday/anatomy:45a3dfdbbd7b724c2bc1be813cb25628e73595ca59ee38c3a4ea78a27804c2e9",
  {
    input: {
      model: "dev",
      height: 512,
      prompt: "ANAT0MY, medical textbook schematics, Olanzapine",
      lora_scale: 1.1,
      num_outputs: 1,
      aspect_ratio: "4:5",
      output_format: "webp",
      guidance_scale: 3.5,
      output_quality: 90,
      prompt_strength: 0.8,
      extra_lora_scale: 1,
      num_inference_steps: 28
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

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

pip install replicate

Import the client:

import replicate

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

output = replicate.run(
    "sliday/anatomy:45a3dfdbbd7b724c2bc1be813cb25628e73595ca59ee38c3a4ea78a27804c2e9",
    input={
        "model": "dev",
        "height": 512,
        "prompt": "ANAT0MY, medical textbook schematics, Olanzapine",
        "lora_scale": 1.1,
        "num_outputs": 1,
        "aspect_ratio": "4:5",
        "output_format": "webp",
        "guidance_scale": 3.5,
        "output_quality": 90,
        "prompt_strength": 0.8,
        "extra_lora_scale": 1,
        "num_inference_steps": 28
    }
)

# To access the file URL:
print(output[0].url())
#=> "http://example.com"

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output[0].read())

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

Run sliday/anatomy 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": "sliday/anatomy:45a3dfdbbd7b724c2bc1be813cb25628e73595ca59ee38c3a4ea78a27804c2e9",
    "input": {
      "model": "dev",
      "height": 512,
      "prompt": "ANAT0MY, medical textbook schematics, Olanzapine",
      "lora_scale": 1.1,
      "num_outputs": 1,
      "aspect_ratio": "4:5",
      "output_format": "webp",
      "guidance_scale": 3.5,
      "output_quality": 90,
      "prompt_strength": 0.8,
      "extra_lora_scale": 1,
      "num_inference_steps": 28
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-10-21T14:24:15.416834Z",
  "created_at": "2024-10-21T14:24:03.886000Z",
  "data_removed": false,
  "error": null,
  "id": "9kh5gq5f5srm40cjnxva1kx7ar",
  "input": {
    "model": "dev",
    "height": 512,
    "prompt": "ANAT0MY, medical textbook schematics, Olanzapine",
    "lora_scale": 1.1,
    "num_outputs": 1,
    "aspect_ratio": "4:5",
    "output_format": "webp",
    "guidance_scale": 3.5,
    "output_quality": 90,
    "prompt_strength": 0.8,
    "extra_lora_scale": 1,
    "num_inference_steps": 28
  },
  "logs": "Using seed: 43284\nPrompt: ANAT0MY, medical textbook schematics, Olanzapine\n[!] txt2img mode\nUsing dev model\nfree=8913213825024\nDownloading weights\n2024-10-21T14:24:03Z | INFO  | [ Initiating ] chunk_size=150M dest=/tmp/tmpwgu4l5yh/weights url=https://replicate.delivery/yhqm/fmfTwYRejJTv3omNu2HA1vUckf7lUbpRj1dFgBRK3WzfYzHdC/trained_model.tar\n2024-10-21T14:24:05Z | INFO  | [ Complete ] dest=/tmp/tmpwgu4l5yh/weights size=\"174 MB\" total_elapsed=1.098s url=https://replicate.delivery/yhqm/fmfTwYRejJTv3omNu2HA1vUckf7lUbpRj1dFgBRK3WzfYzHdC/trained_model.tar\nDownloaded weights in 1.22s\nLoaded LoRAs in 1.94s\n  0%|          | 0/28 [00:00<?, ?it/s]\n  4%|▎         | 1/28 [00:00<00:09,  3.00it/s]\n  7%|▋         | 2/28 [00:00<00:07,  3.29it/s]\n 11%|█         | 3/28 [00:00<00:07,  3.18it/s]\n 14%|█▍        | 4/28 [00:01<00:07,  3.14it/s]\n 18%|█▊        | 5/28 [00:01<00:07,  3.11it/s]\n 21%|██▏       | 6/28 [00:01<00:07,  3.09it/s]\n 25%|██▌       | 7/28 [00:02<00:06,  3.09it/s]\n 29%|██▊       | 8/28 [00:02<00:06,  3.08it/s]\n 32%|███▏      | 9/28 [00:02<00:06,  3.08it/s]\n 36%|███▌      | 10/28 [00:03<00:05,  3.07it/s]\n 39%|███▉      | 11/28 [00:03<00:05,  3.07it/s]\n 43%|████▎     | 12/28 [00:03<00:05,  3.07it/s]\n 46%|████▋     | 13/28 [00:04<00:04,  3.07it/s]\n 50%|█████     | 14/28 [00:04<00:04,  3.07it/s]\n 54%|█████▎    | 15/28 [00:04<00:04,  3.07it/s]\n 57%|█████▋    | 16/28 [00:05<00:03,  3.07it/s]\n 61%|██████    | 17/28 [00:05<00:03,  3.07it/s]\n 64%|██████▍   | 18/28 [00:05<00:03,  3.06it/s]\n 68%|██████▊   | 19/28 [00:06<00:02,  3.06it/s]\n 71%|███████▏  | 20/28 [00:06<00:02,  3.07it/s]\n 75%|███████▌  | 21/28 [00:06<00:02,  3.07it/s]\n 79%|███████▊  | 22/28 [00:07<00:01,  3.06it/s]\n 82%|████████▏ | 23/28 [00:07<00:01,  3.06it/s]\n 86%|████████▌ | 24/28 [00:07<00:01,  3.07it/s]\n 89%|████████▉ | 25/28 [00:08<00:00,  3.07it/s]\n 93%|█████████▎| 26/28 [00:08<00:00,  3.07it/s]\n 96%|█████████▋| 27/28 [00:08<00:00,  3.07it/s]\n100%|██████████| 28/28 [00:09<00:00,  3.07it/s]\n100%|██████████| 28/28 [00:09<00:00,  3.08it/s]",
  "metrics": {
    "predict_time": 11.520803416,
    "total_time": 11.530834
  },
  "output": [
    "https://replicate.delivery/yhqm/QWA5F8Qp5oJPA5aoi7peguiXrfhcCLvfSGBw9pOzL4CegFkOB/out-0.webp"
  ],
  "started_at": "2024-10-21T14:24:03.896031Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/9kh5gq5f5srm40cjnxva1kx7ar",
    "cancel": "https://api.replicate.com/v1/predictions/9kh5gq5f5srm40cjnxva1kx7ar/cancel"
  },
  "version": "45a3dfdbbd7b724c2bc1be813cb25628e73595ca59ee38c3a4ea78a27804c2e9"
}

Generated in

11.5 seconds

sliday/anatomy:45a3dfdbbd7b724c2bc1be813cb25628e73595ca59ee38c3a4ea78a27804c2e9

Using seed: 43284
Prompt: ANAT0MY, medical textbook schematics, Olanzapine
[!] txt2img mode
Using dev model
free=8913213825024
Downloading weights
2024-10-21T14:24:03Z | INFO  | [ Initiating ] chunk_size=150M dest=/tmp/tmpwgu4l5yh/weights url=https://replicate.delivery/yhqm/fmfTwYRejJTv3omNu2HA1vUckf7lUbpRj1dFgBRK3WzfYzHdC/trained_model.tar
2024-10-21T14:24:05Z | INFO  | [ Complete ] dest=/tmp/tmpwgu4l5yh/weights size="174 MB" total_elapsed=1.098s url=https://replicate.delivery/yhqm/fmfTwYRejJTv3omNu2HA1vUckf7lUbpRj1dFgBRK3WzfYzHdC/trained_model.tar
Downloaded weights in 1.22s
Loaded LoRAs in 1.94s
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Prediction

Model

wh38nr7n6xrm00cjnxyrk2jmj4

Status

Succeeded

Source

Web

Hardware

H100

Total duration

11.0s

Created

10 months ago

Input

seed: 37103
model: dev
prompt: ANAT0MY, simplified medical textbook schematics, ear
lora_scale: 1
num_outputs: 1
aspect_ratio: 21:9
output_format: webp
guidance_scale: 3.5
output_quality: 90
prompt_strength: 0.8
extra_lora_scale: 1
num_inference_steps: 28

{
  "seed": 37103,
  "model": "dev",
  "prompt": "ANAT0MY, simplified medical textbook schematics, ear",
  "lora_scale": 1,
  "num_outputs": 1,
  "aspect_ratio": "21:9",
  "output_format": "webp",
  "guidance_scale": 3.5,
  "output_quality": 90,
  "prompt_strength": 0.8,
  "extra_lora_scale": 1,
  "num_inference_steps": 28
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";
import fs from "node:fs";

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

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

const output = await replicate.run(
  "sliday/anatomy:45a3dfdbbd7b724c2bc1be813cb25628e73595ca59ee38c3a4ea78a27804c2e9",
  {
    input: {
      seed: 37103,
      model: "dev",
      prompt: "ANAT0MY, simplified medical textbook schematics, ear",
      lora_scale: 1,
      num_outputs: 1,
      aspect_ratio: "21:9",
      output_format: "webp",
      guidance_scale: 3.5,
      output_quality: 90,
      prompt_strength: 0.8,
      extra_lora_scale: 1,
      num_inference_steps: 28
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

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

pip install replicate

Import the client:

import replicate

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

output = replicate.run(
    "sliday/anatomy:45a3dfdbbd7b724c2bc1be813cb25628e73595ca59ee38c3a4ea78a27804c2e9",
    input={
        "seed": 37103,
        "model": "dev",
        "prompt": "ANAT0MY, simplified medical textbook schematics, ear",
        "lora_scale": 1,
        "num_outputs": 1,
        "aspect_ratio": "21:9",
        "output_format": "webp",
        "guidance_scale": 3.5,
        "output_quality": 90,
        "prompt_strength": 0.8,
        "extra_lora_scale": 1,
        "num_inference_steps": 28
    }
)

# To access the file URL:
print(output[0].url())
#=> "http://example.com"

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output[0].read())

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

Run sliday/anatomy 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": "sliday/anatomy:45a3dfdbbd7b724c2bc1be813cb25628e73595ca59ee38c3a4ea78a27804c2e9",
    "input": {
      "seed": 37103,
      "model": "dev",
      "prompt": "ANAT0MY, simplified medical textbook schematics, ear",
      "lora_scale": 1,
      "num_outputs": 1,
      "aspect_ratio": "21:9",
      "output_format": "webp",
      "guidance_scale": 3.5,
      "output_quality": 90,
      "prompt_strength": 0.8,
      "extra_lora_scale": 1,
      "num_inference_steps": 28
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-10-21T14:32:11.553977Z",
  "created_at": "2024-10-21T14:32:00.567000Z",
  "data_removed": false,
  "error": null,
  "id": "wh38nr7n6xrm00cjnxyrk2jmj4",
  "input": {
    "seed": 37103,
    "model": "dev",
    "prompt": "ANAT0MY, simplified medical textbook schematics, ear",
    "lora_scale": 1,
    "num_outputs": 1,
    "aspect_ratio": "21:9",
    "output_format": "webp",
    "guidance_scale": 3.5,
    "output_quality": 90,
    "prompt_strength": 0.8,
    "extra_lora_scale": 1,
    "num_inference_steps": 28
  },
  "logs": "Using seed: 37103\nPrompt: ANAT0MY, simplified medical textbook schematics, ear\n[!] txt2img mode\nUsing dev model\nLoaded LoRAs in 0.59s\n  0%|          | 0/28 [00:00<?, ?it/s]\n  4%|▎         | 1/28 [00:00<00:08,  3.05it/s]\n  7%|▋         | 2/28 [00:00<00:07,  3.40it/s]\n 11%|█         | 3/28 [00:00<00:07,  3.23it/s]\n 14%|█▍        | 4/28 [00:01<00:07,  3.15it/s]\n 18%|█▊        | 5/28 [00:01<00:07,  3.12it/s]\n 21%|██▏       | 6/28 [00:01<00:07,  3.09it/s]\n 25%|██▌       | 7/28 [00:02<00:06,  3.08it/s]\n 29%|██▊       | 8/28 [00:02<00:06,  3.07it/s]\n 32%|███▏      | 9/28 [00:02<00:06,  3.06it/s]\n 36%|███▌      | 10/28 [00:03<00:05,  3.06it/s]\n 39%|███▉      | 11/28 [00:03<00:05,  3.05it/s]\n 43%|████▎     | 12/28 [00:03<00:05,  3.05it/s]\n 46%|████▋     | 13/28 [00:04<00:04,  3.05it/s]\n 50%|█████     | 14/28 [00:04<00:04,  3.05it/s]\n 54%|█████▎    | 15/28 [00:04<00:04,  3.05it/s]\n 57%|█████▋    | 16/28 [00:05<00:03,  3.05it/s]\n 61%|██████    | 17/28 [00:05<00:03,  3.05it/s]\n 64%|██████▍   | 18/28 [00:05<00:03,  3.05it/s]\n 68%|██████▊   | 19/28 [00:06<00:02,  3.05it/s]\n 71%|███████▏  | 20/28 [00:06<00:02,  3.05it/s]\n 75%|███████▌  | 21/28 [00:06<00:02,  3.05it/s]\n 79%|███████▊  | 22/28 [00:07<00:01,  3.05it/s]\n 82%|████████▏ | 23/28 [00:07<00:01,  3.05it/s]\n 86%|████████▌ | 24/28 [00:07<00:01,  3.05it/s]\n 89%|████████▉ | 25/28 [00:08<00:00,  3.05it/s]\n 93%|█████████▎| 26/28 [00:08<00:00,  3.04it/s]\n 96%|█████████▋| 27/28 [00:08<00:00,  3.04it/s]\n100%|██████████| 28/28 [00:09<00:00,  3.04it/s]\n100%|██████████| 28/28 [00:09<00:00,  3.07it/s]",
  "metrics": {
    "predict_time": 10.584360285,
    "total_time": 10.986977
  },
  "output": [
    "https://replicate.delivery/yhqm/AbnVM5pVWdbfeUZ7meOWQjDD0eeKfxdu5ffENQC1kyMqrfCSnA/out-0.webp"
  ],
  "started_at": "2024-10-21T14:32:00.969617Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/wh38nr7n6xrm00cjnxyrk2jmj4",
    "cancel": "https://api.replicate.com/v1/predictions/wh38nr7n6xrm00cjnxyrk2jmj4/cancel"
  },
  "version": "45a3dfdbbd7b724c2bc1be813cb25628e73595ca59ee38c3a4ea78a27804c2e9"
}

Generated in

10.6 seconds

sliday/anatomy:45a3dfdbbd7b724c2bc1be813cb25628e73595ca59ee38c3a4ea78a27804c2e9

Prediction

Model

ewqeb3j8s1rm40cjnxzbc2a8er

Status

Succeeded

Source

Web

Hardware

H100

Total duration

10.0s

Created

10 months ago

Input

seed: 37103
model: dev
prompt: ANAT0MY, simplified medical textbook schematics, ear
lora_scale: 1
num_outputs: 1
aspect_ratio: 1:1
output_format: webp
guidance_scale: 3.5
output_quality: 90
prompt_strength: 0.8
extra_lora_scale: 1
num_inference_steps: 28

{
  "seed": 37103,
  "model": "dev",
  "prompt": "ANAT0MY, simplified medical textbook schematics, ear",
  "lora_scale": 1,
  "num_outputs": 1,
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "guidance_scale": 3.5,
  "output_quality": 90,
  "prompt_strength": 0.8,
  "extra_lora_scale": 1,
  "num_inference_steps": 28
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";
import fs from "node:fs";

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

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

const output = await replicate.run(
  "sliday/anatomy:45a3dfdbbd7b724c2bc1be813cb25628e73595ca59ee38c3a4ea78a27804c2e9",
  {
    input: {
      seed: 37103,
      model: "dev",
      prompt: "ANAT0MY, simplified medical textbook schematics, ear",
      lora_scale: 1,
      num_outputs: 1,
      aspect_ratio: "1:1",
      output_format: "webp",
      guidance_scale: 3.5,
      output_quality: 90,
      prompt_strength: 0.8,
      extra_lora_scale: 1,
      num_inference_steps: 28
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

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

pip install replicate

Import the client:

import replicate

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

output = replicate.run(
    "sliday/anatomy:45a3dfdbbd7b724c2bc1be813cb25628e73595ca59ee38c3a4ea78a27804c2e9",
    input={
        "seed": 37103,
        "model": "dev",
        "prompt": "ANAT0MY, simplified medical textbook schematics, ear",
        "lora_scale": 1,
        "num_outputs": 1,
        "aspect_ratio": "1:1",
        "output_format": "webp",
        "guidance_scale": 3.5,
        "output_quality": 90,
        "prompt_strength": 0.8,
        "extra_lora_scale": 1,
        "num_inference_steps": 28
    }
)

# To access the file URL:
print(output[0].url())
#=> "http://example.com"

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output[0].read())

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

Run sliday/anatomy 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": "sliday/anatomy:45a3dfdbbd7b724c2bc1be813cb25628e73595ca59ee38c3a4ea78a27804c2e9",
    "input": {
      "seed": 37103,
      "model": "dev",
      "prompt": "ANAT0MY, simplified medical textbook schematics, ear",
      "lora_scale": 1,
      "num_outputs": 1,
      "aspect_ratio": "1:1",
      "output_format": "webp",
      "guidance_scale": 3.5,
      "output_quality": 90,
      "prompt_strength": 0.8,
      "extra_lora_scale": 1,
      "num_inference_steps": 28
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-10-21T14:32:31.986488Z",
  "created_at": "2024-10-21T14:32:21.960000Z",
  "data_removed": false,
  "error": null,
  "id": "ewqeb3j8s1rm40cjnxzbc2a8er",
  "input": {
    "seed": 37103,
    "model": "dev",
    "prompt": "ANAT0MY, simplified medical textbook schematics, ear",
    "lora_scale": 1,
    "num_outputs": 1,
    "aspect_ratio": "1:1",
    "output_format": "webp",
    "guidance_scale": 3.5,
    "output_quality": 90,
    "prompt_strength": 0.8,
    "extra_lora_scale": 1,
    "num_inference_steps": 28
  },
  "logs": "Using seed: 37103\nPrompt: ANAT0MY, simplified medical textbook schematics, ear\n[!] txt2img mode\nUsing dev model\nWeights already loaded\nLoaded LoRAs in 0.03s\n  0%|          | 0/28 [00:00<?, ?it/s]\n  4%|▎         | 1/28 [00:00<00:09,  2.88it/s]\n  7%|▋         | 2/28 [00:00<00:08,  3.21it/s]\n 11%|█         | 3/28 [00:00<00:08,  3.05it/s]\n 14%|█▍        | 4/28 [00:01<00:08,  2.98it/s]\n 18%|█▊        | 5/28 [00:01<00:07,  2.94it/s]\n 21%|██▏       | 6/28 [00:02<00:07,  2.92it/s]\n 25%|██▌       | 7/28 [00:02<00:07,  2.90it/s]\n 29%|██▊       | 8/28 [00:02<00:06,  2.90it/s]\n 32%|███▏      | 9/28 [00:03<00:06,  2.89it/s]\n 36%|███▌      | 10/28 [00:03<00:06,  2.88it/s]\n 39%|███▉      | 11/28 [00:03<00:05,  2.88it/s]\n 43%|████▎     | 12/28 [00:04<00:05,  2.88it/s]\n 46%|████▋     | 13/28 [00:04<00:05,  2.88it/s]\n 50%|█████     | 14/28 [00:04<00:04,  2.88it/s]\n 54%|█████▎    | 15/28 [00:05<00:04,  2.88it/s]\n 57%|█████▋    | 16/28 [00:05<00:04,  2.88it/s]\n 61%|██████    | 17/28 [00:05<00:03,  2.88it/s]\n 64%|██████▍   | 18/28 [00:06<00:03,  2.88it/s]\n 68%|██████▊   | 19/28 [00:06<00:03,  2.88it/s]\n 71%|███████▏  | 20/28 [00:06<00:02,  2.88it/s]\n 75%|███████▌  | 21/28 [00:07<00:02,  2.88it/s]\n 79%|███████▊  | 22/28 [00:07<00:02,  2.88it/s]\n 82%|████████▏ | 23/28 [00:07<00:01,  2.87it/s]\n 86%|████████▌ | 24/28 [00:08<00:01,  2.87it/s]\n 89%|████████▉ | 25/28 [00:08<00:01,  2.87it/s]\n 93%|█████████▎| 26/28 [00:08<00:00,  2.87it/s]\n 96%|█████████▋| 27/28 [00:09<00:00,  2.87it/s]\n100%|██████████| 28/28 [00:09<00:00,  2.87it/s]\n100%|██████████| 28/28 [00:09<00:00,  2.89it/s]",
  "metrics": {
    "predict_time": 10.018659144,
    "total_time": 10.026488
  },
  "output": [
    "https://replicate.delivery/yhqm/F3o1d3VTkzZeZaOW5OGuwem8AUeqSNcsQCZBzCig1ESffLIdC/out-0.webp"
  ],
  "started_at": "2024-10-21T14:32:21.967829Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/ewqeb3j8s1rm40cjnxzbc2a8er",
    "cancel": "https://api.replicate.com/v1/predictions/ewqeb3j8s1rm40cjnxzbc2a8er/cancel"
  },
  "version": "45a3dfdbbd7b724c2bc1be813cb25628e73595ca59ee38c3a4ea78a27804c2e9"
}

Generated in

10.0 seconds

sliday/anatomy:45a3dfdbbd7b724c2bc1be813cb25628e73595ca59ee38c3a4ea78a27804c2e9

Prediction

Model

vkj9c9phnsrm20cjnxzace4k5c

Status

Succeeded

Source

Web

Hardware

H100

Total duration

11.0s

Created

10 months ago

Input

seed: 37103
model: dev
prompt: ANAT0MY, anatomically correct medical textbook schematics, nose
lora_scale: 1
num_outputs: 1
aspect_ratio: 1:1
output_format: webp
guidance_scale: 3.5
output_quality: 90
prompt_strength: 0.8
extra_lora_scale: 1
num_inference_steps: 28

{
  "seed": 37103,
  "model": "dev",
  "prompt": "ANAT0MY, anatomically correct medical textbook schematics, nose",
  "lora_scale": 1,
  "num_outputs": 1,
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "guidance_scale": 3.5,
  "output_quality": 90,
  "prompt_strength": 0.8,
  "extra_lora_scale": 1,
  "num_inference_steps": 28
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";
import fs from "node:fs";

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

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

const output = await replicate.run(
  "sliday/anatomy:45a3dfdbbd7b724c2bc1be813cb25628e73595ca59ee38c3a4ea78a27804c2e9",
  {
    input: {
      seed: 37103,
      model: "dev",
      prompt: "ANAT0MY, anatomically correct medical textbook schematics, nose",
      lora_scale: 1,
      num_outputs: 1,
      aspect_ratio: "1:1",
      output_format: "webp",
      guidance_scale: 3.5,
      output_quality: 90,
      prompt_strength: 0.8,
      extra_lora_scale: 1,
      num_inference_steps: 28
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

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

pip install replicate

Import the client:

import replicate

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

output = replicate.run(
    "sliday/anatomy:45a3dfdbbd7b724c2bc1be813cb25628e73595ca59ee38c3a4ea78a27804c2e9",
    input={
        "seed": 37103,
        "model": "dev",
        "prompt": "ANAT0MY, anatomically correct medical textbook schematics, nose",
        "lora_scale": 1,
        "num_outputs": 1,
        "aspect_ratio": "1:1",
        "output_format": "webp",
        "guidance_scale": 3.5,
        "output_quality": 90,
        "prompt_strength": 0.8,
        "extra_lora_scale": 1,
        "num_inference_steps": 28
    }
)

# To access the file URL:
print(output[0].url())
#=> "http://example.com"

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output[0].read())

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

Run sliday/anatomy 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": "sliday/anatomy:45a3dfdbbd7b724c2bc1be813cb25628e73595ca59ee38c3a4ea78a27804c2e9",
    "input": {
      "seed": 37103,
      "model": "dev",
      "prompt": "ANAT0MY, anatomically correct medical textbook schematics, nose",
      "lora_scale": 1,
      "num_outputs": 1,
      "aspect_ratio": "1:1",
      "output_format": "webp",
      "guidance_scale": 3.5,
      "output_quality": 90,
      "prompt_strength": 0.8,
      "extra_lora_scale": 1,
      "num_inference_steps": 28
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-10-21T14:33:07.999555Z",
  "created_at": "2024-10-21T14:32:57.006000Z",
  "data_removed": false,
  "error": null,
  "id": "vkj9c9phnsrm20cjnxzace4k5c",
  "input": {
    "seed": 37103,
    "model": "dev",
    "prompt": "ANAT0MY, anatomically correct medical textbook schematics, nose",
    "lora_scale": 1,
    "num_outputs": 1,
    "aspect_ratio": "1:1",
    "output_format": "webp",
    "guidance_scale": 3.5,
    "output_quality": 90,
    "prompt_strength": 0.8,
    "extra_lora_scale": 1,
    "num_inference_steps": 28
  },
  "logs": "Using seed: 37103\nPrompt: ANAT0MY, anatomically correct medical textbook schematics, nose\n[!] txt2img mode\nUsing dev model\nLoaded LoRAs in 0.61s\n  0%|          | 0/28 [00:00<?, ?it/s]\n  4%|▎         | 1/28 [00:00<00:09,  2.89it/s]\n  7%|▋         | 2/28 [00:00<00:08,  3.22it/s]\n 11%|█         | 3/28 [00:00<00:08,  3.06it/s]\n 14%|█▍        | 4/28 [00:01<00:08,  2.99it/s]\n 18%|█▊        | 5/28 [00:01<00:07,  2.95it/s]\n 21%|██▏       | 6/28 [00:02<00:07,  2.94it/s]\n 25%|██▌       | 7/28 [00:02<00:07,  2.92it/s]\n 29%|██▊       | 8/28 [00:02<00:06,  2.92it/s]\n 32%|███▏      | 9/28 [00:03<00:06,  2.91it/s]\n 36%|███▌      | 10/28 [00:03<00:06,  2.91it/s]\n 39%|███▉      | 11/28 [00:03<00:05,  2.90it/s]\n 43%|████▎     | 12/28 [00:04<00:05,  2.90it/s]\n 46%|████▋     | 13/28 [00:04<00:05,  2.90it/s]\n 50%|█████     | 14/28 [00:04<00:04,  2.90it/s]\n 54%|█████▎    | 15/28 [00:05<00:04,  2.90it/s]\n 57%|█████▋    | 16/28 [00:05<00:04,  2.90it/s]\n 61%|██████    | 17/28 [00:05<00:03,  2.90it/s]\n 64%|██████▍   | 18/28 [00:06<00:03,  2.90it/s]\n 68%|██████▊   | 19/28 [00:06<00:03,  2.87it/s]\n 71%|███████▏  | 20/28 [00:06<00:02,  2.88it/s]\n 75%|███████▌  | 21/28 [00:07<00:02,  2.88it/s]\n 79%|███████▊  | 22/28 [00:07<00:02,  2.89it/s]\n 82%|████████▏ | 23/28 [00:07<00:01,  2.89it/s]\n 86%|████████▌ | 24/28 [00:08<00:01,  2.89it/s]\n 89%|████████▉ | 25/28 [00:08<00:01,  2.89it/s]\n 93%|█████████▎| 26/28 [00:08<00:00,  2.89it/s]\n 96%|█████████▋| 27/28 [00:09<00:00,  2.89it/s]\n100%|██████████| 28/28 [00:09<00:00,  2.89it/s]\n100%|██████████| 28/28 [00:09<00:00,  2.91it/s]",
  "metrics": {
    "predict_time": 10.557712278,
    "total_time": 10.993555
  },
  "output": [
    "https://replicate.delivery/yhqm/evRWeb9evDSFyIuDMG2evznqHfqdeKGxDoJsvilabMm0IYQ6E/out-0.webp"
  ],
  "started_at": "2024-10-21T14:32:57.441843Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/vkj9c9phnsrm20cjnxzace4k5c",
    "cancel": "https://api.replicate.com/v1/predictions/vkj9c9phnsrm20cjnxzace4k5c/cancel"
  },
  "version": "45a3dfdbbd7b724c2bc1be813cb25628e73595ca59ee38c3a4ea78a27804c2e9"
}

Generated in

10.6 seconds

sliday/anatomy:45a3dfdbbd7b724c2bc1be813cb25628e73595ca59ee38c3a4ea78a27804c2e9

Prediction

Model

vnqar22j0xrm00cjnxzrjkht88

Status

Succeeded

Source

Web

Hardware

H100

Total duration

10.0s

Created

10 months ago

Input

seed: 37103
model: dev
prompt: ANAT0MY, anatomically correct medical textbook schematics, arm
lora_scale: 1
num_outputs: 1
aspect_ratio: 1:1
output_format: webp
guidance_scale: 3.5
output_quality: 90
prompt_strength: 0.8
extra_lora_scale: 1
num_inference_steps: 28

{
  "seed": 37103,
  "model": "dev",
  "prompt": "ANAT0MY, anatomically correct medical textbook schematics, arm",
  "lora_scale": 1,
  "num_outputs": 1,
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "guidance_scale": 3.5,
  "output_quality": 90,
  "prompt_strength": 0.8,
  "extra_lora_scale": 1,
  "num_inference_steps": 28
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";
import fs from "node:fs";

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

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

const output = await replicate.run(
  "sliday/anatomy:45a3dfdbbd7b724c2bc1be813cb25628e73595ca59ee38c3a4ea78a27804c2e9",
  {
    input: {
      seed: 37103,
      model: "dev",
      prompt: "ANAT0MY, anatomically correct medical textbook schematics, arm",
      lora_scale: 1,
      num_outputs: 1,
      aspect_ratio: "1:1",
      output_format: "webp",
      guidance_scale: 3.5,
      output_quality: 90,
      prompt_strength: 0.8,
      extra_lora_scale: 1,
      num_inference_steps: 28
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

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

pip install replicate

Import the client:

import replicate

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

output = replicate.run(
    "sliday/anatomy:45a3dfdbbd7b724c2bc1be813cb25628e73595ca59ee38c3a4ea78a27804c2e9",
    input={
        "seed": 37103,
        "model": "dev",
        "prompt": "ANAT0MY, anatomically correct medical textbook schematics, arm",
        "lora_scale": 1,
        "num_outputs": 1,
        "aspect_ratio": "1:1",
        "output_format": "webp",
        "guidance_scale": 3.5,
        "output_quality": 90,
        "prompt_strength": 0.8,
        "extra_lora_scale": 1,
        "num_inference_steps": 28
    }
)

# To access the file URL:
print(output[0].url())
#=> "http://example.com"

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output[0].read())

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

Run sliday/anatomy 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": "sliday/anatomy:45a3dfdbbd7b724c2bc1be813cb25628e73595ca59ee38c3a4ea78a27804c2e9",
    "input": {
      "seed": 37103,
      "model": "dev",
      "prompt": "ANAT0MY, anatomically correct medical textbook schematics, arm",
      "lora_scale": 1,
      "num_outputs": 1,
      "aspect_ratio": "1:1",
      "output_format": "webp",
      "guidance_scale": 3.5,
      "output_quality": 90,
      "prompt_strength": 0.8,
      "extra_lora_scale": 1,
      "num_inference_steps": 28
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-10-21T14:33:39.841886Z",
  "created_at": "2024-10-21T14:33:29.863000Z",
  "data_removed": false,
  "error": null,
  "id": "vnqar22j0xrm00cjnxzrjkht88",
  "input": {
    "seed": 37103,
    "model": "dev",
    "prompt": "ANAT0MY, anatomically correct medical textbook schematics, arm",
    "lora_scale": 1,
    "num_outputs": 1,
    "aspect_ratio": "1:1",
    "output_format": "webp",
    "guidance_scale": 3.5,
    "output_quality": 90,
    "prompt_strength": 0.8,
    "extra_lora_scale": 1,
    "num_inference_steps": 28
  },
  "logs": "Using seed: 37103\nPrompt: ANAT0MY, anatomically correct medical textbook schematics, arm\n[!] txt2img mode\nUsing dev model\nWeights already loaded\nLoaded LoRAs in 0.03s\n  0%|          | 0/28 [00:00<?, ?it/s]\n  4%|▎         | 1/28 [00:00<00:09,  2.89it/s]\n  7%|▋         | 2/28 [00:00<00:08,  3.22it/s]\n 11%|█         | 3/28 [00:00<00:08,  3.06it/s]\n 14%|█▍        | 4/28 [00:01<00:08,  2.99it/s]\n 18%|█▊        | 5/28 [00:01<00:07,  2.96it/s]\n 21%|██▏       | 6/28 [00:02<00:07,  2.94it/s]\n 25%|██▌       | 7/28 [00:02<00:07,  2.92it/s]\n 29%|██▊       | 8/28 [00:02<00:06,  2.91it/s]\n 32%|███▏      | 9/28 [00:03<00:06,  2.91it/s]\n 36%|███▌      | 10/28 [00:03<00:06,  2.90it/s]\n 39%|███▉      | 11/28 [00:03<00:05,  2.90it/s]\n 43%|████▎     | 12/28 [00:04<00:05,  2.90it/s]\n 46%|████▋     | 13/28 [00:04<00:05,  2.90it/s]\n 50%|█████     | 14/28 [00:04<00:04,  2.90it/s]\n 54%|█████▎    | 15/28 [00:05<00:04,  2.90it/s]\n 57%|█████▋    | 16/28 [00:05<00:04,  2.90it/s]\n 61%|██████    | 17/28 [00:05<00:03,  2.90it/s]\n 64%|██████▍   | 18/28 [00:06<00:03,  2.90it/s]\n 68%|██████▊   | 19/28 [00:06<00:03,  2.90it/s]\n 71%|███████▏  | 20/28 [00:06<00:02,  2.90it/s]\n 75%|███████▌  | 21/28 [00:07<00:02,  2.90it/s]\n 79%|███████▊  | 22/28 [00:07<00:02,  2.90it/s]\n 82%|████████▏ | 23/28 [00:07<00:01,  2.90it/s]\n 86%|████████▌ | 24/28 [00:08<00:01,  2.90it/s]\n 89%|████████▉ | 25/28 [00:08<00:01,  2.90it/s]\n 93%|█████████▎| 26/28 [00:08<00:00,  2.90it/s]\n 96%|█████████▋| 27/28 [00:09<00:00,  2.90it/s]\n100%|██████████| 28/28 [00:09<00:00,  2.90it/s]\n100%|██████████| 28/28 [00:09<00:00,  2.91it/s]",
  "metrics": {
    "predict_time": 9.969217458,
    "total_time": 9.978886
  },
  "output": [
    "https://replicate.delivery/yhqm/oyakbljIf8XlGapybOJW5BYjVcdAuT5yPwxaxI1Y2Twhwg0JA/out-0.webp"
  ],
  "started_at": "2024-10-21T14:33:29.872668Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/vnqar22j0xrm00cjnxzrjkht88",
    "cancel": "https://api.replicate.com/v1/predictions/vnqar22j0xrm00cjnxzrjkht88/cancel"
  },
  "version": "45a3dfdbbd7b724c2bc1be813cb25628e73595ca59ee38c3a4ea78a27804c2e9"
}

Generated in

10.0 seconds

sliday/anatomy:45a3dfdbbd7b724c2bc1be813cb25628e73595ca59ee38c3a4ea78a27804c2e9

Prediction

Model

e981vxp2x9rm00cjnxzr9w8g04

Status

Succeeded

Source

Web

Hardware

H100

Total duration

10.0s

Created

10 months ago

Input

seed: 37103
model: dev
prompt: ANAT0MY, anatomically correct medical textbook schematics, neck
lora_scale: 1
num_outputs: 1
aspect_ratio: 1:1
output_format: webp
guidance_scale: 3.5
output_quality: 90
prompt_strength: 0.8
extra_lora_scale: 1
num_inference_steps: 28

{
  "seed": 37103,
  "model": "dev",
  "prompt": "ANAT0MY, anatomically correct medical textbook schematics, neck",
  "lora_scale": 1,
  "num_outputs": 1,
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "guidance_scale": 3.5,
  "output_quality": 90,
  "prompt_strength": 0.8,
  "extra_lora_scale": 1,
  "num_inference_steps": 28
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";
import fs from "node:fs";

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

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

const output = await replicate.run(
  "sliday/anatomy:45a3dfdbbd7b724c2bc1be813cb25628e73595ca59ee38c3a4ea78a27804c2e9",
  {
    input: {
      seed: 37103,
      model: "dev",
      prompt: "ANAT0MY, anatomically correct medical textbook schematics, neck",
      lora_scale: 1,
      num_outputs: 1,
      aspect_ratio: "1:1",
      output_format: "webp",
      guidance_scale: 3.5,
      output_quality: 90,
      prompt_strength: 0.8,
      extra_lora_scale: 1,
      num_inference_steps: 28
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

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

pip install replicate

Import the client:

import replicate

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

output = replicate.run(
    "sliday/anatomy:45a3dfdbbd7b724c2bc1be813cb25628e73595ca59ee38c3a4ea78a27804c2e9",
    input={
        "seed": 37103,
        "model": "dev",
        "prompt": "ANAT0MY, anatomically correct medical textbook schematics, neck",
        "lora_scale": 1,
        "num_outputs": 1,
        "aspect_ratio": "1:1",
        "output_format": "webp",
        "guidance_scale": 3.5,
        "output_quality": 90,
        "prompt_strength": 0.8,
        "extra_lora_scale": 1,
        "num_inference_steps": 28
    }
)

# To access the file URL:
print(output[0].url())
#=> "http://example.com"

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output[0].read())

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

Run sliday/anatomy 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": "sliday/anatomy:45a3dfdbbd7b724c2bc1be813cb25628e73595ca59ee38c3a4ea78a27804c2e9",
    "input": {
      "seed": 37103,
      "model": "dev",
      "prompt": "ANAT0MY, anatomically correct medical textbook schematics, neck",
      "lora_scale": 1,
      "num_outputs": 1,
      "aspect_ratio": "1:1",
      "output_format": "webp",
      "guidance_scale": 3.5,
      "output_quality": 90,
      "prompt_strength": 0.8,
      "extra_lora_scale": 1,
      "num_inference_steps": 28
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-10-21T14:34:08.735039Z",
  "created_at": "2024-10-21T14:33:58.762000Z",
  "data_removed": false,
  "error": null,
  "id": "e981vxp2x9rm00cjnxzr9w8g04",
  "input": {
    "seed": 37103,
    "model": "dev",
    "prompt": "ANAT0MY, anatomically correct medical textbook schematics, neck",
    "lora_scale": 1,
    "num_outputs": 1,
    "aspect_ratio": "1:1",
    "output_format": "webp",
    "guidance_scale": 3.5,
    "output_quality": 90,
    "prompt_strength": 0.8,
    "extra_lora_scale": 1,
    "num_inference_steps": 28
  },
  "logs": "Using seed: 37103\nPrompt: ANAT0MY, anatomically correct medical textbook schematics, neck\n[!] txt2img mode\nUsing dev model\nWeights already loaded\nLoaded LoRAs in 0.03s\n  0%|          | 0/28 [00:00<?, ?it/s]\n  4%|▎         | 1/28 [00:00<00:09,  2.89it/s]\n  7%|▋         | 2/28 [00:00<00:08,  3.22it/s]\n 11%|█         | 3/28 [00:00<00:08,  3.06it/s]\n 14%|█▍        | 4/28 [00:01<00:08,  2.99it/s]\n 18%|█▊        | 5/28 [00:01<00:07,  2.95it/s]\n 21%|██▏       | 6/28 [00:02<00:07,  2.93it/s]\n 25%|██▌       | 7/28 [00:02<00:07,  2.92it/s]\n 29%|██▊       | 8/28 [00:02<00:06,  2.91it/s]\n 32%|███▏      | 9/28 [00:03<00:06,  2.91it/s]\n 36%|███▌      | 10/28 [00:03<00:06,  2.90it/s]\n 39%|███▉      | 11/28 [00:03<00:05,  2.90it/s]\n 43%|████▎     | 12/28 [00:04<00:05,  2.90it/s]\n 46%|████▋     | 13/28 [00:04<00:05,  2.90it/s]\n 50%|█████     | 14/28 [00:04<00:04,  2.90it/s]\n 54%|█████▎    | 15/28 [00:05<00:04,  2.90it/s]\n 57%|█████▋    | 16/28 [00:05<00:04,  2.90it/s]\n 61%|██████    | 17/28 [00:05<00:03,  2.90it/s]\n 64%|██████▍   | 18/28 [00:06<00:03,  2.90it/s]\n 68%|██████▊   | 19/28 [00:06<00:03,  2.90it/s]\n 71%|███████▏  | 20/28 [00:06<00:02,  2.90it/s]\n 75%|███████▌  | 21/28 [00:07<00:02,  2.90it/s]\n 79%|███████▊  | 22/28 [00:07<00:02,  2.90it/s]\n 82%|████████▏ | 23/28 [00:07<00:01,  2.90it/s]\n 86%|████████▌ | 24/28 [00:08<00:01,  2.90it/s]\n 89%|████████▉ | 25/28 [00:08<00:01,  2.90it/s]\n 93%|█████████▎| 26/28 [00:08<00:00,  2.90it/s]\n 96%|█████████▋| 27/28 [00:09<00:00,  2.90it/s]\n100%|██████████| 28/28 [00:09<00:00,  2.90it/s]\n100%|██████████| 28/28 [00:09<00:00,  2.91it/s]",
  "metrics": {
    "predict_time": 9.956953844000001,
    "total_time": 9.973039
  },
  "output": [
    "https://replicate.delivery/yhqm/tzkM2illOfT0TSzitxAgpnhn2kTpYZjPIdiG0kmXIndwwg0JA/out-0.webp"
  ],
  "started_at": "2024-10-21T14:33:58.778085Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/e981vxp2x9rm00cjnxzr9w8g04",
    "cancel": "https://api.replicate.com/v1/predictions/e981vxp2x9rm00cjnxzr9w8g04/cancel"
  },
  "version": "45a3dfdbbd7b724c2bc1be813cb25628e73595ca59ee38c3a4ea78a27804c2e9"
}

Generated in

10.0 seconds

sliday/anatomy:45a3dfdbbd7b724c2bc1be813cb25628e73595ca59ee38c3a4ea78a27804c2e9

Prediction

Model

aa92wzg9khrm20cjny0by1qx0r

Status

Succeeded

Source

Web

Hardware

H100

Total duration

12.2s

Created

10 months ago

Input

seed: 37103
model: dev
prompt: ANAT0MY, anatomically correct medical textbook schematics, cell
lora_scale: 1
num_outputs: 1
aspect_ratio: 1:1
output_format: webp
guidance_scale: 3.5
output_quality: 90
prompt_strength: 0.8
extra_lora_scale: 1
num_inference_steps: 28

{
  "seed": 37103,
  "model": "dev",
  "prompt": "ANAT0MY, anatomically correct medical textbook schematics, cell",
  "lora_scale": 1,
  "num_outputs": 1,
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "guidance_scale": 3.5,
  "output_quality": 90,
  "prompt_strength": 0.8,
  "extra_lora_scale": 1,
  "num_inference_steps": 28
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";
import fs from "node:fs";

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

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

const output = await replicate.run(
  "sliday/anatomy:45a3dfdbbd7b724c2bc1be813cb25628e73595ca59ee38c3a4ea78a27804c2e9",
  {
    input: {
      seed: 37103,
      model: "dev",
      prompt: "ANAT0MY, anatomically correct medical textbook schematics, cell",
      lora_scale: 1,
      num_outputs: 1,
      aspect_ratio: "1:1",
      output_format: "webp",
      guidance_scale: 3.5,
      output_quality: 90,
      prompt_strength: 0.8,
      extra_lora_scale: 1,
      num_inference_steps: 28
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

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

pip install replicate

Import the client:

import replicate

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

output = replicate.run(
    "sliday/anatomy:45a3dfdbbd7b724c2bc1be813cb25628e73595ca59ee38c3a4ea78a27804c2e9",
    input={
        "seed": 37103,
        "model": "dev",
        "prompt": "ANAT0MY, anatomically correct medical textbook schematics, cell",
        "lora_scale": 1,
        "num_outputs": 1,
        "aspect_ratio": "1:1",
        "output_format": "webp",
        "guidance_scale": 3.5,
        "output_quality": 90,
        "prompt_strength": 0.8,
        "extra_lora_scale": 1,
        "num_inference_steps": 28
    }
)

# To access the file URL:
print(output[0].url())
#=> "http://example.com"

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output[0].read())

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

Run sliday/anatomy 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": "sliday/anatomy:45a3dfdbbd7b724c2bc1be813cb25628e73595ca59ee38c3a4ea78a27804c2e9",
    "input": {
      "seed": 37103,
      "model": "dev",
      "prompt": "ANAT0MY, anatomically correct medical textbook schematics, cell",
      "lora_scale": 1,
      "num_outputs": 1,
      "aspect_ratio": "1:1",
      "output_format": "webp",
      "guidance_scale": 3.5,
      "output_quality": 90,
      "prompt_strength": 0.8,
      "extra_lora_scale": 1,
      "num_inference_steps": 28
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-10-21T14:34:29.004298Z",
  "created_at": "2024-10-21T14:34:16.860000Z",
  "data_removed": false,
  "error": null,
  "id": "aa92wzg9khrm20cjny0by1qx0r",
  "input": {
    "seed": 37103,
    "model": "dev",
    "prompt": "ANAT0MY, anatomically correct medical textbook schematics, cell",
    "lora_scale": 1,
    "num_outputs": 1,
    "aspect_ratio": "1:1",
    "output_format": "webp",
    "guidance_scale": 3.5,
    "output_quality": 90,
    "prompt_strength": 0.8,
    "extra_lora_scale": 1,
    "num_inference_steps": 28
  },
  "logs": "Using seed: 37103\nPrompt: ANAT0MY, anatomically correct medical textbook schematics, cell\n[!] txt2img mode\nUsing dev model\nfree=7114631766016\nDownloading weights\n2024-10-21T14:34:17Z | INFO  | [ Initiating ] chunk_size=150M dest=/tmp/tmpzp35bygw/weights url=https://replicate.delivery/yhqm/fmfTwYRejJTv3omNu2HA1vUckf7lUbpRj1dFgBRK3WzfYzHdC/trained_model.tar\n2024-10-21T14:34:18Z | INFO  | [ Complete ] dest=/tmp/tmpzp35bygw/weights size=\"174 MB\" total_elapsed=1.303s url=https://replicate.delivery/yhqm/fmfTwYRejJTv3omNu2HA1vUckf7lUbpRj1dFgBRK3WzfYzHdC/trained_model.tar\nDownloaded weights in 1.37s\nLoaded LoRAs in 1.96s\n  0%|          | 0/28 [00:00<?, ?it/s]\n  4%|▎         | 1/28 [00:00<00:09,  2.88it/s]\n  7%|▋         | 2/28 [00:00<00:08,  3.21it/s]\n 11%|█         | 3/28 [00:00<00:08,  3.05it/s]\n 14%|█▍        | 4/28 [00:01<00:08,  2.98it/s]\n 18%|█▊        | 5/28 [00:01<00:07,  2.94it/s]\n 21%|██▏       | 6/28 [00:02<00:07,  2.92it/s]\n 25%|██▌       | 7/28 [00:02<00:07,  2.90it/s]\n 29%|██▊       | 8/28 [00:02<00:06,  2.89it/s]\n 32%|███▏      | 9/28 [00:03<00:06,  2.89it/s]\n 36%|███▌      | 10/28 [00:03<00:06,  2.88it/s]\n 39%|███▉      | 11/28 [00:03<00:05,  2.88it/s]\n 43%|████▎     | 12/28 [00:04<00:05,  2.88it/s]\n 46%|████▋     | 13/28 [00:04<00:05,  2.88it/s]\n 50%|█████     | 14/28 [00:04<00:04,  2.88it/s]\n 54%|█████▎    | 15/28 [00:05<00:04,  2.87it/s]\n 57%|█████▋    | 16/28 [00:05<00:04,  2.87it/s]\n 61%|██████    | 17/28 [00:05<00:03,  2.87it/s]\n 64%|██████▍   | 18/28 [00:06<00:03,  2.87it/s]\n 68%|██████▊   | 19/28 [00:06<00:03,  2.87it/s]\n 71%|███████▏  | 20/28 [00:06<00:02,  2.87it/s]\n 75%|███████▌  | 21/28 [00:07<00:02,  2.88it/s]\n 79%|███████▊  | 22/28 [00:07<00:02,  2.88it/s]\n 82%|████████▏ | 23/28 [00:07<00:01,  2.88it/s]\n 86%|████████▌ | 24/28 [00:08<00:01,  2.88it/s]\n 89%|████████▉ | 25/28 [00:08<00:01,  2.88it/s]\n 93%|█████████▎| 26/28 [00:08<00:00,  2.88it/s]\n 96%|█████████▋| 27/28 [00:09<00:00,  2.88it/s]\n100%|██████████| 28/28 [00:09<00:00,  2.88it/s]\n100%|██████████| 28/28 [00:09<00:00,  2.89it/s]",
  "metrics": {
    "predict_time": 11.965978191,
    "total_time": 12.144298
  },
  "output": [
    "https://replicate.delivery/yhqm/clXi4JAJTg4tOdS0qASKLebL1oB76aAIWuJwyNWKQUF6wg0JA/out-0.webp"
  ],
  "started_at": "2024-10-21T14:34:17.038320Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/aa92wzg9khrm20cjny0by1qx0r",
    "cancel": "https://api.replicate.com/v1/predictions/aa92wzg9khrm20cjny0by1qx0r/cancel"
  },
  "version": "45a3dfdbbd7b724c2bc1be813cb25628e73595ca59ee38c3a4ea78a27804c2e9"
}

Generated in

12.0 seconds

sliday/anatomy:45a3dfdbbd7b724c2bc1be813cb25628e73595ca59ee38c3a4ea78a27804c2e9

Using seed: 37103
Prompt: ANAT0MY, anatomically correct medical textbook schematics, cell
[!] txt2img mode
Using dev model
free=7114631766016
Downloading weights
2024-10-21T14:34:17Z | INFO  | [ Initiating ] chunk_size=150M dest=/tmp/tmpzp35bygw/weights url=https://replicate.delivery/yhqm/fmfTwYRejJTv3omNu2HA1vUckf7lUbpRj1dFgBRK3WzfYzHdC/trained_model.tar
2024-10-21T14:34:18Z | INFO  | [ Complete ] dest=/tmp/tmpzp35bygw/weights size="174 MB" total_elapsed=1.303s url=https://replicate.delivery/yhqm/fmfTwYRejJTv3omNu2HA1vUckf7lUbpRj1dFgBRK3WzfYzHdC/trained_model.tar
Downloaded weights in 1.37s
Loaded LoRAs in 1.96s
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Prediction

Model

4pat8bth5srm40cjny087z2z20

Status

Succeeded

Source

Web

Hardware

H100

Total duration

10.1s

Created

10 months ago

Input

seed: 37103
model: dev
prompt: ANAT0MY, anatomically correct medical textbook schematics, brain cell
lora_scale: 1
num_outputs: 1
aspect_ratio: 1:1
output_format: webp
guidance_scale: 3.5
output_quality: 90
prompt_strength: 0.8
extra_lora_scale: 1
num_inference_steps: 28

{
  "seed": 37103,
  "model": "dev",
  "prompt": "ANAT0MY, anatomically correct medical textbook schematics, brain cell",
  "lora_scale": 1,
  "num_outputs": 1,
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "guidance_scale": 3.5,
  "output_quality": 90,
  "prompt_strength": 0.8,
  "extra_lora_scale": 1,
  "num_inference_steps": 28
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";
import fs from "node:fs";

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

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

const output = await replicate.run(
  "sliday/anatomy:45a3dfdbbd7b724c2bc1be813cb25628e73595ca59ee38c3a4ea78a27804c2e9",
  {
    input: {
      seed: 37103,
      model: "dev",
      prompt: "ANAT0MY, anatomically correct medical textbook schematics, brain cell",
      lora_scale: 1,
      num_outputs: 1,
      aspect_ratio: "1:1",
      output_format: "webp",
      guidance_scale: 3.5,
      output_quality: 90,
      prompt_strength: 0.8,
      extra_lora_scale: 1,
      num_inference_steps: 28
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

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

pip install replicate

Import the client:

import replicate

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

output = replicate.run(
    "sliday/anatomy:45a3dfdbbd7b724c2bc1be813cb25628e73595ca59ee38c3a4ea78a27804c2e9",
    input={
        "seed": 37103,
        "model": "dev",
        "prompt": "ANAT0MY, anatomically correct medical textbook schematics, brain cell",
        "lora_scale": 1,
        "num_outputs": 1,
        "aspect_ratio": "1:1",
        "output_format": "webp",
        "guidance_scale": 3.5,
        "output_quality": 90,
        "prompt_strength": 0.8,
        "extra_lora_scale": 1,
        "num_inference_steps": 28
    }
)

# To access the file URL:
print(output[0].url())
#=> "http://example.com"

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output[0].read())

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

Run sliday/anatomy 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": "sliday/anatomy:45a3dfdbbd7b724c2bc1be813cb25628e73595ca59ee38c3a4ea78a27804c2e9",
    "input": {
      "seed": 37103,
      "model": "dev",
      "prompt": "ANAT0MY, anatomically correct medical textbook schematics, brain cell",
      "lora_scale": 1,
      "num_outputs": 1,
      "aspect_ratio": "1:1",
      "output_format": "webp",
      "guidance_scale": 3.5,
      "output_quality": 90,
      "prompt_strength": 0.8,
      "extra_lora_scale": 1,
      "num_inference_steps": 28
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-10-21T14:34:45.226839Z",
  "created_at": "2024-10-21T14:34:35.182000Z",
  "data_removed": false,
  "error": null,
  "id": "4pat8bth5srm40cjny087z2z20",
  "input": {
    "seed": 37103,
    "model": "dev",
    "prompt": "ANAT0MY, anatomically correct medical textbook schematics, brain cell",
    "lora_scale": 1,
    "num_outputs": 1,
    "aspect_ratio": "1:1",
    "output_format": "webp",
    "guidance_scale": 3.5,
    "output_quality": 90,
    "prompt_strength": 0.8,
    "extra_lora_scale": 1,
    "num_inference_steps": 28
  },
  "logs": "Using seed: 37103\nPrompt: ANAT0MY, anatomically correct medical textbook schematics, brain cell\n[!] txt2img mode\nUsing dev model\nWeights already loaded\nLoaded LoRAs in 0.03s\n  0%|          | 0/28 [00:00<?, ?it/s]\n  4%|▎         | 1/28 [00:00<00:09,  2.87it/s]\n  7%|▋         | 2/28 [00:00<00:08,  3.21it/s]\n 11%|█         | 3/28 [00:00<00:08,  3.05it/s]\n 14%|█▍        | 4/28 [00:01<00:08,  2.98it/s]\n 18%|█▊        | 5/28 [00:01<00:07,  2.94it/s]\n 21%|██▏       | 6/28 [00:02<00:07,  2.92it/s]\n 25%|██▌       | 7/28 [00:02<00:07,  2.91it/s]\n 29%|██▊       | 8/28 [00:02<00:06,  2.90it/s]\n 32%|███▏      | 9/28 [00:03<00:06,  2.89it/s]\n 36%|███▌      | 10/28 [00:03<00:06,  2.89it/s]\n 39%|███▉      | 11/28 [00:03<00:05,  2.88it/s]\n 43%|████▎     | 12/28 [00:04<00:05,  2.88it/s]\n 46%|████▋     | 13/28 [00:04<00:05,  2.88it/s]\n 50%|█████     | 14/28 [00:04<00:04,  2.88it/s]\n 54%|█████▎    | 15/28 [00:05<00:04,  2.88it/s]\n 57%|█████▋    | 16/28 [00:05<00:04,  2.88it/s]\n 61%|██████    | 17/28 [00:05<00:03,  2.88it/s]\n 64%|██████▍   | 18/28 [00:06<00:03,  2.88it/s]\n 68%|██████▊   | 19/28 [00:06<00:03,  2.88it/s]\n 71%|███████▏  | 20/28 [00:06<00:02,  2.88it/s]\n 75%|███████▌  | 21/28 [00:07<00:02,  2.88it/s]\n 79%|███████▊  | 22/28 [00:07<00:02,  2.88it/s]\n 82%|████████▏ | 23/28 [00:07<00:01,  2.88it/s]\n 86%|████████▌ | 24/28 [00:08<00:01,  2.88it/s]\n 89%|████████▉ | 25/28 [00:08<00:01,  2.88it/s]\n 93%|█████████▎| 26/28 [00:08<00:00,  2.88it/s]\n 96%|█████████▋| 27/28 [00:09<00:00,  2.88it/s]\n100%|██████████| 28/28 [00:09<00:00,  2.88it/s]\n100%|██████████| 28/28 [00:09<00:00,  2.90it/s]",
  "metrics": {
    "predict_time": 10.036395489,
    "total_time": 10.044839
  },
  "output": [
    "https://replicate.delivery/yhqm/TBjzpVSfsFSuY65euSEZUOraeJvXSPpEnOKCKvnKeDSWIGkOB/out-0.webp"
  ],
  "started_at": "2024-10-21T14:34:35.190444Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/4pat8bth5srm40cjny087z2z20",
    "cancel": "https://api.replicate.com/v1/predictions/4pat8bth5srm40cjny087z2z20/cancel"
  },
  "version": "45a3dfdbbd7b724c2bc1be813cb25628e73595ca59ee38c3a4ea78a27804c2e9"
}

Generated in

10.0 seconds

sliday/anatomy:45a3dfdbbd7b724c2bc1be813cb25628e73595ca59ee38c3a4ea78a27804c2e9

Prediction

Model

bw1ymp0exnrm60cjny0sbvfgpg

Status

Succeeded

Source

Web

Hardware

H100

Total duration

10.9s

Created

10 months ago

Input

model: dev
prompt: ANAT0MY, medical illustration of a bone cell
lora_scale: 1
num_outputs: 1
aspect_ratio: 1:1
output_format: webp
guidance_scale: 3.5
output_quality: 90
prompt_strength: 0.8
extra_lora_scale: 1
num_inference_steps: 28

{
  "model": "dev",
  "prompt": "ANAT0MY, medical illustration of a bone cell",
  "lora_scale": 1,
  "num_outputs": 1,
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "guidance_scale": 3.5,
  "output_quality": 90,
  "prompt_strength": 0.8,
  "extra_lora_scale": 1,
  "num_inference_steps": 28
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";
import fs from "node:fs";

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

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

const output = await replicate.run(
  "sliday/anatomy:45a3dfdbbd7b724c2bc1be813cb25628e73595ca59ee38c3a4ea78a27804c2e9",
  {
    input: {
      model: "dev",
      prompt: "ANAT0MY, medical illustration of a bone cell",
      lora_scale: 1,
      num_outputs: 1,
      aspect_ratio: "1:1",
      output_format: "webp",
      guidance_scale: 3.5,
      output_quality: 90,
      prompt_strength: 0.8,
      extra_lora_scale: 1,
      num_inference_steps: 28
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

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

pip install replicate

Import the client:

import replicate

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

output = replicate.run(
    "sliday/anatomy:45a3dfdbbd7b724c2bc1be813cb25628e73595ca59ee38c3a4ea78a27804c2e9",
    input={
        "model": "dev",
        "prompt": "ANAT0MY, medical illustration of a bone cell",
        "lora_scale": 1,
        "num_outputs": 1,
        "aspect_ratio": "1:1",
        "output_format": "webp",
        "guidance_scale": 3.5,
        "output_quality": 90,
        "prompt_strength": 0.8,
        "extra_lora_scale": 1,
        "num_inference_steps": 28
    }
)

# To access the file URL:
print(output[0].url())
#=> "http://example.com"

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output[0].read())

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

Run sliday/anatomy 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": "sliday/anatomy:45a3dfdbbd7b724c2bc1be813cb25628e73595ca59ee38c3a4ea78a27804c2e9",
    "input": {
      "model": "dev",
      "prompt": "ANAT0MY, medical illustration of a bone cell",
      "lora_scale": 1,
      "num_outputs": 1,
      "aspect_ratio": "1:1",
      "output_format": "webp",
      "guidance_scale": 3.5,
      "output_quality": 90,
      "prompt_strength": 0.8,
      "extra_lora_scale": 1,
      "num_inference_steps": 28
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-10-21T14:35:34.608145Z",
  "created_at": "2024-10-21T14:35:23.757000Z",
  "data_removed": false,
  "error": null,
  "id": "bw1ymp0exnrm60cjny0sbvfgpg",
  "input": {
    "model": "dev",
    "prompt": "ANAT0MY, medical illustration of a bone cell",
    "lora_scale": 1,
    "num_outputs": 1,
    "aspect_ratio": "1:1",
    "output_format": "webp",
    "guidance_scale": 3.5,
    "output_quality": 90,
    "prompt_strength": 0.8,
    "extra_lora_scale": 1,
    "num_inference_steps": 28
  },
  "logs": "Using seed: 37791\nPrompt: ANAT0MY, medical illustration of a bone cell\n[!] txt2img mode\nUsing dev model\nLoaded LoRAs in 0.71s\n  0%|          | 0/28 [00:00<?, ?it/s]\n  4%|▎         | 1/28 [00:00<00:09,  2.87it/s]\n  7%|▋         | 2/28 [00:00<00:08,  3.20it/s]\n 11%|█         | 3/28 [00:00<00:08,  3.04it/s]\n 14%|█▍        | 4/28 [00:01<00:08,  2.97it/s]\n 18%|█▊        | 5/28 [00:01<00:07,  2.93it/s]\n 21%|██▏       | 6/28 [00:02<00:07,  2.91it/s]\n 25%|██▌       | 7/28 [00:02<00:07,  2.89it/s]\n 29%|██▊       | 8/28 [00:02<00:06,  2.89it/s]\n 32%|███▏      | 9/28 [00:03<00:06,  2.88it/s]\n 36%|███▌      | 10/28 [00:03<00:06,  2.87it/s]\n 39%|███▉      | 11/28 [00:03<00:05,  2.87it/s]\n 43%|████▎     | 12/28 [00:04<00:05,  2.87it/s]\n 46%|████▋     | 13/28 [00:04<00:05,  2.87it/s]\n 50%|█████     | 14/28 [00:04<00:04,  2.87it/s]\n 54%|█████▎    | 15/28 [00:05<00:04,  2.87it/s]\n 57%|█████▋    | 16/28 [00:05<00:04,  2.87it/s]\n 61%|██████    | 17/28 [00:05<00:03,  2.87it/s]\n 64%|██████▍   | 18/28 [00:06<00:03,  2.87it/s]\n 68%|██████▊   | 19/28 [00:06<00:03,  2.87it/s]\n 71%|███████▏  | 20/28 [00:06<00:02,  2.87it/s]\n 75%|███████▌  | 21/28 [00:07<00:02,  2.87it/s]\n 79%|███████▊  | 22/28 [00:07<00:02,  2.87it/s]\n 82%|████████▏ | 23/28 [00:07<00:01,  2.87it/s]\n 86%|████████▌ | 24/28 [00:08<00:01,  2.87it/s]\n 89%|████████▉ | 25/28 [00:08<00:01,  2.87it/s]\n 93%|█████████▎| 26/28 [00:09<00:00,  2.87it/s]\n 96%|█████████▋| 27/28 [00:09<00:00,  2.87it/s]\n100%|██████████| 28/28 [00:09<00:00,  2.87it/s]\n100%|██████████| 28/28 [00:09<00:00,  2.88it/s]",
  "metrics": {
    "predict_time": 10.760606355,
    "total_time": 10.851145
  },
  "output": [
    "https://replicate.delivery/yhqm/bP0JQEyEx7b8J92hq4bmV9xTwAEFavp4KQquYWk394rtYQ6E/out-0.webp"
  ],
  "started_at": "2024-10-21T14:35:23.847539Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/bw1ymp0exnrm60cjny0sbvfgpg",
    "cancel": "https://api.replicate.com/v1/predictions/bw1ymp0exnrm60cjny0sbvfgpg/cancel"
  },
  "version": "45a3dfdbbd7b724c2bc1be813cb25628e73595ca59ee38c3a4ea78a27804c2e9"
}

Generated in

10.8 seconds

sliday/anatomy:45a3dfdbbd7b724c2bc1be813cb25628e73595ca59ee38c3a4ea78a27804c2e9

Prediction

Model

z8z2sncjsnrm00cjny0razscb4

Status

Succeeded

Source

Web

Hardware

H100

Total duration

10.1s

Created

10 months ago

Input

model: dev
prompt: ANAT0MY, medical illustration of mitochondria
lora_scale: 1
num_outputs: 1
aspect_ratio: 1:1
output_format: webp
guidance_scale: 3.5
output_quality: 90
prompt_strength: 0.8
extra_lora_scale: 1
num_inference_steps: 28

{
  "model": "dev",
  "prompt": "ANAT0MY, medical illustration of mitochondria",
  "lora_scale": 1,
  "num_outputs": 1,
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "guidance_scale": 3.5,
  "output_quality": 90,
  "prompt_strength": 0.8,
  "extra_lora_scale": 1,
  "num_inference_steps": 28
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";
import fs from "node:fs";

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

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

const output = await replicate.run(
  "sliday/anatomy:45a3dfdbbd7b724c2bc1be813cb25628e73595ca59ee38c3a4ea78a27804c2e9",
  {
    input: {
      model: "dev",
      prompt: "ANAT0MY, medical illustration of mitochondria",
      lora_scale: 1,
      num_outputs: 1,
      aspect_ratio: "1:1",
      output_format: "webp",
      guidance_scale: 3.5,
      output_quality: 90,
      prompt_strength: 0.8,
      extra_lora_scale: 1,
      num_inference_steps: 28
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

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

pip install replicate

Import the client:

import replicate

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

output = replicate.run(
    "sliday/anatomy:45a3dfdbbd7b724c2bc1be813cb25628e73595ca59ee38c3a4ea78a27804c2e9",
    input={
        "model": "dev",
        "prompt": "ANAT0MY, medical illustration of mitochondria",
        "lora_scale": 1,
        "num_outputs": 1,
        "aspect_ratio": "1:1",
        "output_format": "webp",
        "guidance_scale": 3.5,
        "output_quality": 90,
        "prompt_strength": 0.8,
        "extra_lora_scale": 1,
        "num_inference_steps": 28
    }
)

# To access the file URL:
print(output[0].url())
#=> "http://example.com"

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output[0].read())

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

Run sliday/anatomy 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": "sliday/anatomy:45a3dfdbbd7b724c2bc1be813cb25628e73595ca59ee38c3a4ea78a27804c2e9",
    "input": {
      "model": "dev",
      "prompt": "ANAT0MY, medical illustration of mitochondria",
      "lora_scale": 1,
      "num_outputs": 1,
      "aspect_ratio": "1:1",
      "output_format": "webp",
      "guidance_scale": 3.5,
      "output_quality": 90,
      "prompt_strength": 0.8,
      "extra_lora_scale": 1,
      "num_inference_steps": 28
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-10-21T14:36:07.587898Z",
  "created_at": "2024-10-21T14:35:57.517000Z",
  "data_removed": false,
  "error": null,
  "id": "z8z2sncjsnrm00cjny0razscb4",
  "input": {
    "model": "dev",
    "prompt": "ANAT0MY, medical illustration of mitochondria",
    "lora_scale": 1,
    "num_outputs": 1,
    "aspect_ratio": "1:1",
    "output_format": "webp",
    "guidance_scale": 3.5,
    "output_quality": 90,
    "prompt_strength": 0.8,
    "extra_lora_scale": 1,
    "num_inference_steps": 28
  },
  "logs": "Using seed: 18294\nPrompt: ANAT0MY, medical illustration of mitochondria\n[!] txt2img mode\nUsing dev model\nWeights already loaded\nLoaded LoRAs in 0.03s\n  0%|          | 0/28 [00:00<?, ?it/s]\n  4%|▎         | 1/28 [00:00<00:09,  2.87it/s]\n  7%|▋         | 2/28 [00:00<00:08,  3.20it/s]\n 11%|█         | 3/28 [00:00<00:08,  3.04it/s]\n 14%|█▍        | 4/28 [00:01<00:08,  2.97it/s]\n 18%|█▊        | 5/28 [00:01<00:07,  2.93it/s]\n 21%|██▏       | 6/28 [00:02<00:07,  2.91it/s]\n 25%|██▌       | 7/28 [00:02<00:07,  2.90it/s]\n 29%|██▊       | 8/28 [00:02<00:06,  2.89it/s]\n 32%|███▏      | 9/28 [00:03<00:06,  2.88it/s]\n 36%|███▌      | 10/28 [00:03<00:06,  2.88it/s]\n 39%|███▉      | 11/28 [00:03<00:05,  2.87it/s]\n 43%|████▎     | 12/28 [00:04<00:05,  2.87it/s]\n 46%|████▋     | 13/28 [00:04<00:05,  2.87it/s]\n 50%|█████     | 14/28 [00:04<00:04,  2.87it/s]\n 54%|█████▎    | 15/28 [00:05<00:04,  2.87it/s]\n 57%|█████▋    | 16/28 [00:05<00:04,  2.87it/s]\n 61%|██████    | 17/28 [00:05<00:03,  2.87it/s]\n 64%|██████▍   | 18/28 [00:06<00:03,  2.87it/s]\n 68%|██████▊   | 19/28 [00:06<00:03,  2.87it/s]\n 71%|███████▏  | 20/28 [00:06<00:02,  2.87it/s]\n 75%|███████▌  | 21/28 [00:07<00:02,  2.87it/s]\n 79%|███████▊  | 22/28 [00:07<00:02,  2.87it/s]\n 82%|████████▏ | 23/28 [00:07<00:01,  2.87it/s]\n 86%|████████▌ | 24/28 [00:08<00:01,  2.87it/s]\n 89%|████████▉ | 25/28 [00:08<00:01,  2.87it/s]\n 93%|█████████▎| 26/28 [00:09<00:00,  2.87it/s]\n 96%|█████████▋| 27/28 [00:09<00:00,  2.87it/s]\n100%|██████████| 28/28 [00:09<00:00,  2.87it/s]\n100%|██████████| 28/28 [00:09<00:00,  2.89it/s]",
  "metrics": {
    "predict_time": 10.062026999,
    "total_time": 10.070898
  },
  "output": [
    "https://replicate.delivery/yhqm/ACrxLR22HT68FRMR99eqslaCR4A5TvaYFez0lreHG4UuGDSnA/out-0.webp"
  ],
  "started_at": "2024-10-21T14:35:57.525871Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/z8z2sncjsnrm00cjny0razscb4",
    "cancel": "https://api.replicate.com/v1/predictions/z8z2sncjsnrm00cjny0razscb4/cancel"
  },
  "version": "45a3dfdbbd7b724c2bc1be813cb25628e73595ca59ee38c3a4ea78a27804c2e9"
}

Generated in

10.1 seconds