Prediction

ID

apavgstbdz3d7hmafw2sx3pqw4

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

62.3s

Created

over 1 year ago

Input

seed: 1789
width: 1024
height: 1024
prompt: SHRMI dog sitting on a first class seat in 747 airplane
refine: no_refiner
scheduler: K_EULER
lora_scale: 0.75
num_outputs: 4
guidance_scale: 7.5
apply_watermark
high_noise_frac: 0.8
negative_prompt: dog harness, bad anatomy, dog leash
prompt_strength: 0.8
num_inference_steps: 50

{
  "seed": 1789,
  "width": 1024,
  "height": 1024,
  "prompt": "SHRMI dog sitting on a first class seat in 747 airplane",
  "refine": "no_refiner",
  "scheduler": "K_EULER",
  "lora_scale": 0.75,
  "num_outputs": 4,
  "guidance_scale": 7.5,
  "apply_watermark": true,
  "high_noise_frac": 0.8,
  "negative_prompt": "dog harness, bad anatomy, dog leash",
  "prompt_strength": 0.8,
  "num_inference_steps": 50
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

Run sakemin/shroomie-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "sakemin/shroomie-diffusion:2c6eec71acf4ab33faaab1d72effbb41d5811efa7b27ab505df0dbe1efcdeee6",
  {
    input: {
      seed: 1789,
      width: 1024,
      height: 1024,
      prompt: "SHRMI dog sitting on a first class seat in 747 airplane",
      refine: "no_refiner",
      scheduler: "K_EULER",
      lora_scale: 0.75,
      num_outputs: 4,
      guidance_scale: 7.5,
      apply_watermark: true,
      high_noise_frac: 0.8,
      negative_prompt: "dog harness, bad anatomy, dog leash",
      prompt_strength: 0.8,
      num_inference_steps: 50
    }
  }
);

// 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 sakemin/shroomie-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "sakemin/shroomie-diffusion:2c6eec71acf4ab33faaab1d72effbb41d5811efa7b27ab505df0dbe1efcdeee6",
    input={
        "seed": 1789,
        "width": 1024,
        "height": 1024,
        "prompt": "SHRMI dog sitting on a first class seat in 747 airplane",
        "refine": "no_refiner",
        "scheduler": "K_EULER",
        "lora_scale": 0.75,
        "num_outputs": 4,
        "guidance_scale": 7.5,
        "apply_watermark": True,
        "high_noise_frac": 0.8,
        "negative_prompt": "dog harness, bad anatomy, dog leash",
        "prompt_strength": 0.8,
        "num_inference_steps": 50
    }
)
print(output)

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

Run sakemin/shroomie-diffusion 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": "sakemin/shroomie-diffusion:2c6eec71acf4ab33faaab1d72effbb41d5811efa7b27ab505df0dbe1efcdeee6",
    "input": {
      "seed": 1789,
      "width": 1024,
      "height": 1024,
      "prompt": "SHRMI dog sitting on a first class seat in 747 airplane",
      "refine": "no_refiner",
      "scheduler": "K_EULER",
      "lora_scale": 0.75,
      "num_outputs": 4,
      "guidance_scale": 7.5,
      "apply_watermark": true,
      "high_noise_frac": 0.8,
      "negative_prompt": "dog harness, bad anatomy, dog leash",
      "prompt_strength": 0.8,
      "num_inference_steps": 50
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2023-11-26T08:39:40.588537Z",
  "created_at": "2023-11-26T08:38:38.319503Z",
  "data_removed": false,
  "error": null,
  "id": "apavgstbdz3d7hmafw2sx3pqw4",
  "input": {
    "seed": 1789,
    "width": 1024,
    "height": 1024,
    "prompt": "SHRMI dog sitting on a first class seat in 747 airplane",
    "refine": "no_refiner",
    "scheduler": "K_EULER",
    "lora_scale": 0.75,
    "num_outputs": 4,
    "guidance_scale": 7.5,
    "apply_watermark": true,
    "high_noise_frac": 0.8,
    "negative_prompt": "dog harness, bad anatomy, dog leash",
    "prompt_strength": 0.8,
    "num_inference_steps": 50
  },
  "logs": "Using seed: 1789\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: <s0><s1> dog sitting on a first class seat in 747 airplane\ntxt2img mode\n  0%|          | 0/50 [00:00<?, ?it/s]\n  2%|▏         | 1/50 [00:01<00:51,  1.06s/it]\n  4%|▍         | 2/50 [00:02<00:51,  1.06s/it]\n  6%|▌         | 3/50 [00:03<00:49,  1.06s/it]\n  8%|▊         | 4/50 [00:04<00:48,  1.06s/it]\n 10%|█         | 5/50 [00:05<00:47,  1.06s/it]\n 12%|█▏        | 6/50 [00:06<00:46,  1.07s/it]\n 14%|█▍        | 7/50 [00:07<00:45,  1.07s/it]\n 16%|█▌        | 8/50 [00:08<00:44,  1.06s/it]\n 18%|█▊        | 9/50 [00:09<00:43,  1.07s/it]\n 20%|██        | 10/50 [00:10<00:42,  1.07s/it]\n 22%|██▏       | 11/50 [00:11<00:41,  1.07s/it]\n 24%|██▍       | 12/50 [00:12<00:40,  1.06s/it]\n 26%|██▌       | 13/50 [00:13<00:39,  1.06s/it]\n 28%|██▊       | 14/50 [00:14<00:38,  1.06s/it]\n 30%|███       | 15/50 [00:15<00:37,  1.06s/it]\n 32%|███▏      | 16/50 [00:17<00:36,  1.06s/it]\n 34%|███▍      | 17/50 [00:18<00:35,  1.07s/it]\n 36%|███▌      | 18/50 [00:19<00:34,  1.07s/it]\n 38%|███▊      | 19/50 [00:20<00:33,  1.07s/it]\n 40%|████      | 20/50 [00:21<00:32,  1.07s/it]\n 42%|████▏     | 21/50 [00:22<00:30,  1.07s/it]\n 44%|████▍     | 22/50 [00:23<00:29,  1.07s/it]\n 46%|████▌     | 23/50 [00:24<00:28,  1.07s/it]\n 48%|████▊     | 24/50 [00:25<00:27,  1.07s/it]\n 50%|█████     | 25/50 [00:26<00:26,  1.07s/it]\n 52%|█████▏    | 26/50 [00:27<00:25,  1.07s/it]\n 54%|█████▍    | 27/50 [00:28<00:24,  1.07s/it]\n 56%|█████▌    | 28/50 [00:29<00:23,  1.07s/it]\n 58%|█████▊    | 29/50 [00:30<00:22,  1.07s/it]\n 60%|██████    | 30/50 [00:32<00:21,  1.07s/it]\n 62%|██████▏   | 31/50 [00:33<00:20,  1.07s/it]\n 64%|██████▍   | 32/50 [00:34<00:19,  1.07s/it]\n 66%|██████▌   | 33/50 [00:35<00:18,  1.07s/it]\n 68%|██████▊   | 34/50 [00:36<00:17,  1.07s/it]\n 70%|███████   | 35/50 [00:37<00:16,  1.07s/it]\n 72%|███████▏  | 36/50 [00:38<00:14,  1.07s/it]\n 74%|███████▍  | 37/50 [00:39<00:13,  1.07s/it]\n 76%|███████▌  | 38/50 [00:40<00:12,  1.07s/it]\n 78%|███████▊  | 39/50 [00:41<00:11,  1.07s/it]\n 80%|████████  | 40/50 [00:42<00:10,  1.07s/it]\n 82%|████████▏ | 41/50 [00:43<00:09,  1.07s/it]\n 84%|████████▍ | 42/50 [00:44<00:08,  1.07s/it]\n 86%|████████▌ | 43/50 [00:45<00:07,  1.07s/it]\n 88%|████████▊ | 44/50 [00:47<00:06,  1.07s/it]\n 90%|█████████ | 45/50 [00:48<00:05,  1.07s/it]\n 92%|█████████▏| 46/50 [00:49<00:04,  1.07s/it]\n 94%|█████████▍| 47/50 [00:50<00:03,  1.07s/it]\n 96%|█████████▌| 48/50 [00:51<00:02,  1.07s/it]\n 98%|█████████▊| 49/50 [00:52<00:01,  1.07s/it]\n100%|██████████| 50/50 [00:53<00:00,  1.07s/it]\n100%|██████████| 50/50 [00:53<00:00,  1.07s/it]",
  "metrics": {
    "predict_time": 59.86537,
    "total_time": 62.269034
  },
  "output": [
    "https://replicate.delivery/pbxt/9dkNwn7dMiKSL1EU6aiviSXXSzrqywCUkqJfh04IU3olsFeRA/out-0.png",
    "https://replicate.delivery/pbxt/UPQufr2OKGR8UitWyG40YomaV7i22eB9byZ6WaLiq7ALZL8RA/out-1.png",
    "https://replicate.delivery/pbxt/YwBIt2ZBm457Oxpk4exUW1q7lpDse9DRXHhjJTybzKhMZL8RA/out-2.png",
    "https://replicate.delivery/pbxt/sn2suhhOfExiLydYwIU6bffvlqs8dkAQuBO7XLbLanKYyW4jA/out-3.png"
  ],
  "started_at": "2023-11-26T08:38:40.723167Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/apavgstbdz3d7hmafw2sx3pqw4",
    "cancel": "https://api.replicate.com/v1/predictions/apavgstbdz3d7hmafw2sx3pqw4/cancel"
  },
  "version": "2c6eec71acf4ab33faaab1d72effbb41d5811efa7b27ab505df0dbe1efcdeee6"
}

Generated in

59.9 seconds

sakemin/shroomie-diffusion:2c6eec71acf4ab33faaab1d72effbb41d5811efa7b27ab505df0dbe1efcdeee6

Using seed: 1789
Loading fine-tuned model
Does not have Unet. assume we are using LoRA
Loading Unet LoRA
Prompt: <s0><s1> dog sitting on a first class seat in 747 airplane
txt2img mode
  0%|          | 0/50 [00:00<?, ?it/s]
  2%|▏         | 1/50 [00:01<00:51,  1.06s/it]
  4%|▍         | 2/50 [00:02<00:51,  1.06s/it]
  6%|▌         | 3/50 [00:03<00:49,  1.06s/it]
  8%|▊         | 4/50 [00:04<00:48,  1.06s/it]
 10%|█         | 5/50 [00:05<00:47,  1.06s/it]
 12%|█▏        | 6/50 [00:06<00:46,  1.07s/it]
 14%|█▍        | 7/50 [00:07<00:45,  1.07s/it]
 16%|█▌        | 8/50 [00:08<00:44,  1.06s/it]
 18%|█▊        | 9/50 [00:09<00:43,  1.07s/it]
 20%|██        | 10/50 [00:10<00:42,  1.07s/it]
 22%|██▏       | 11/50 [00:11<00:41,  1.07s/it]
 24%|██▍       | 12/50 [00:12<00:40,  1.06s/it]
 26%|██▌       | 13/50 [00:13<00:39,  1.06s/it]
 28%|██▊       | 14/50 [00:14<00:38,  1.06s/it]
 30%|███       | 15/50 [00:15<00:37,  1.06s/it]
 32%|███▏      | 16/50 [00:17<00:36,  1.06s/it]
 34%|███▍      | 17/50 [00:18<00:35,  1.07s/it]
 36%|███▌      | 18/50 [00:19<00:34,  1.07s/it]
 38%|███▊      | 19/50 [00:20<00:33,  1.07s/it]
 40%|████      | 20/50 [00:21<00:32,  1.07s/it]
 42%|████▏     | 21/50 [00:22<00:30,  1.07s/it]
 44%|████▍     | 22/50 [00:23<00:29,  1.07s/it]
 46%|████▌     | 23/50 [00:24<00:28,  1.07s/it]
 48%|████▊     | 24/50 [00:25<00:27,  1.07s/it]
 50%|█████     | 25/50 [00:26<00:26,  1.07s/it]
 52%|█████▏    | 26/50 [00:27<00:25,  1.07s/it]
 54%|█████▍    | 27/50 [00:28<00:24,  1.07s/it]
 56%|█████▌    | 28/50 [00:29<00:23,  1.07s/it]
 58%|█████▊    | 29/50 [00:30<00:22,  1.07s/it]
 60%|██████    | 30/50 [00:32<00:21,  1.07s/it]
 62%|██████▏   | 31/50 [00:33<00:20,  1.07s/it]
 64%|██████▍   | 32/50 [00:34<00:19,  1.07s/it]
 66%|██████▌   | 33/50 [00:35<00:18,  1.07s/it]
 68%|██████▊   | 34/50 [00:36<00:17,  1.07s/it]
 70%|███████   | 35/50 [00:37<00:16,  1.07s/it]
 72%|███████▏  | 36/50 [00:38<00:14,  1.07s/it]
 74%|███████▍  | 37/50 [00:39<00:13,  1.07s/it]
 76%|███████▌  | 38/50 [00:40<00:12,  1.07s/it]
 78%|███████▊  | 39/50 [00:41<00:11,  1.07s/it]
 80%|████████  | 40/50 [00:42<00:10,  1.07s/it]
 82%|████████▏ | 41/50 [00:43<00:09,  1.07s/it]
 84%|████████▍ | 42/50 [00:44<00:08,  1.07s/it]
 86%|████████▌ | 43/50 [00:45<00:07,  1.07s/it]
 88%|████████▊ | 44/50 [00:47<00:06,  1.07s/it]
 90%|█████████ | 45/50 [00:48<00:05,  1.07s/it]
 92%|█████████▏| 46/50 [00:49<00:04,  1.07s/it]
 94%|█████████▍| 47/50 [00:50<00:03,  1.07s/it]
 96%|█████████▌| 48/50 [00:51<00:02,  1.07s/it]
 98%|█████████▊| 49/50 [00:52<00:01,  1.07s/it]
100%|██████████| 50/50 [00:53<00:00,  1.07s/it]
100%|██████████| 50/50 [00:53<00:00,  1.07s/it]

Prediction

Model

ID

7457delbog5rcoh33ndd75bdre

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

64.6s

Created

over 1 year ago

Input

width: 1024
height: 1024
prompt: SHRMI dog sitting on a first class seat in 747 airplane, eating airplane food, wearing neck pillow++, sunset outside windows
refine: no_refiner
scheduler: K_EULER
lora_scale: 0.75
num_outputs: 4
guidance_scale: 7.5
apply_watermark
high_noise_frac: 0.8
negative_prompt: dog harness, bad anatomy, dog leash, dog collar, poorly generated limbs
prompt_strength: 0.8
num_inference_steps: 50

{
  "width": 1024,
  "height": 1024,
  "prompt": "SHRMI dog sitting on a first class seat in 747 airplane, eating airplane food, wearing neck pillow++, sunset outside windows",
  "refine": "no_refiner",
  "scheduler": "K_EULER",
  "lora_scale": 0.75,
  "num_outputs": 4,
  "guidance_scale": 7.5,
  "apply_watermark": true,
  "high_noise_frac": 0.8,
  "negative_prompt": "dog harness, bad anatomy, dog leash, dog collar, poorly generated limbs",
  "prompt_strength": 0.8,
  "num_inference_steps": 50
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

Run sakemin/shroomie-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "sakemin/shroomie-diffusion:2c6eec71acf4ab33faaab1d72effbb41d5811efa7b27ab505df0dbe1efcdeee6",
  {
    input: {
      width: 1024,
      height: 1024,
      prompt: "SHRMI dog sitting on a first class seat in 747 airplane, eating airplane food, wearing neck pillow++, sunset outside windows",
      refine: "no_refiner",
      scheduler: "K_EULER",
      lora_scale: 0.75,
      num_outputs: 4,
      guidance_scale: 7.5,
      apply_watermark: true,
      high_noise_frac: 0.8,
      negative_prompt: "dog harness, bad anatomy, dog leash, dog collar, poorly generated limbs",
      prompt_strength: 0.8,
      num_inference_steps: 50
    }
  }
);

// 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 sakemin/shroomie-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "sakemin/shroomie-diffusion:2c6eec71acf4ab33faaab1d72effbb41d5811efa7b27ab505df0dbe1efcdeee6",
    input={
        "width": 1024,
        "height": 1024,
        "prompt": "SHRMI dog sitting on a first class seat in 747 airplane, eating airplane food, wearing neck pillow++, sunset outside windows",
        "refine": "no_refiner",
        "scheduler": "K_EULER",
        "lora_scale": 0.75,
        "num_outputs": 4,
        "guidance_scale": 7.5,
        "apply_watermark": True,
        "high_noise_frac": 0.8,
        "negative_prompt": "dog harness, bad anatomy, dog leash, dog collar, poorly generated limbs",
        "prompt_strength": 0.8,
        "num_inference_steps": 50
    }
)
print(output)

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

Run sakemin/shroomie-diffusion 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": "sakemin/shroomie-diffusion:2c6eec71acf4ab33faaab1d72effbb41d5811efa7b27ab505df0dbe1efcdeee6",
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "SHRMI dog sitting on a first class seat in 747 airplane, eating airplane food, wearing neck pillow++, sunset outside windows",
      "refine": "no_refiner",
      "scheduler": "K_EULER",
      "lora_scale": 0.75,
      "num_outputs": 4,
      "guidance_scale": 7.5,
      "apply_watermark": true,
      "high_noise_frac": 0.8,
      "negative_prompt": "dog harness, bad anatomy, dog leash, dog collar, poorly generated limbs",
      "prompt_strength": 0.8,
      "num_inference_steps": 50
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2023-11-26T08:52:53.348569Z",
  "created_at": "2023-11-26T08:51:48.724462Z",
  "data_removed": false,
  "error": null,
  "id": "7457delbog5rcoh33ndd75bdre",
  "input": {
    "width": 1024,
    "height": 1024,
    "prompt": "SHRMI dog sitting on a first class seat in 747 airplane, eating airplane food, wearing neck pillow++, sunset outside windows",
    "refine": "no_refiner",
    "scheduler": "K_EULER",
    "lora_scale": 0.75,
    "num_outputs": 4,
    "guidance_scale": 7.5,
    "apply_watermark": true,
    "high_noise_frac": 0.8,
    "negative_prompt": "dog harness, bad anatomy, dog leash, dog collar, poorly generated limbs",
    "prompt_strength": 0.8,
    "num_inference_steps": 50
  },
  "logs": "Using seed: 41209\nEnsuring enough disk space...\nFree disk space: 1636803436544\nDownloading weights: https://pbxt.replicate.delivery/i2HfjZWkJC1yIqHPDaPHOx9eJsqFqZ2tnWMgn5FFd9evSGWjA/trained_model.tar\nb'Downloaded 186 MB bytes in 0.126s (1.5 GB/s)\\nExtracted 186 MB in 0.064s (2.9 GB/s)\\n'\nDownloaded weights in 0.34587955474853516 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: <s0><s1> dog sitting on a first class seat in 747 airplane, eating airplane food, wearing neck pillow++, sunset outside windows\ntxt2img mode\n  0%|          | 0/50 [00:00<?, ?it/s]\n  2%|▏         | 1/50 [00:01<00:52,  1.07s/it]\n  4%|▍         | 2/50 [00:02<00:50,  1.06s/it]\n  6%|▌         | 3/50 [00:03<00:49,  1.06s/it]\n  8%|▊         | 4/50 [00:04<00:48,  1.06s/it]\n 10%|█         | 5/50 [00:05<00:47,  1.06s/it]\n 12%|█▏        | 6/50 [00:06<00:46,  1.06s/it]\n 14%|█▍        | 7/50 [00:07<00:45,  1.06s/it]\n 16%|█▌        | 8/50 [00:08<00:44,  1.06s/it]\n 18%|█▊        | 9/50 [00:09<00:43,  1.06s/it]\n 20%|██        | 10/50 [00:10<00:42,  1.06s/it]\n 22%|██▏       | 11/50 [00:11<00:41,  1.06s/it]\n 24%|██▍       | 12/50 [00:12<00:40,  1.06s/it]\n 26%|██▌       | 13/50 [00:13<00:39,  1.06s/it]\n 28%|██▊       | 14/50 [00:14<00:38,  1.06s/it]\n 30%|███       | 15/50 [00:15<00:37,  1.06s/it]\n 32%|███▏      | 16/50 [00:16<00:36,  1.06s/it]\n 34%|███▍      | 17/50 [00:18<00:35,  1.06s/it]\n 36%|███▌      | 18/50 [00:19<00:34,  1.06s/it]\n 38%|███▊      | 19/50 [00:20<00:32,  1.06s/it]\n 40%|████      | 20/50 [00:21<00:31,  1.06s/it]\n 42%|████▏     | 21/50 [00:22<00:30,  1.06s/it]\n 44%|████▍     | 22/50 [00:23<00:29,  1.06s/it]\n 46%|████▌     | 23/50 [00:24<00:28,  1.06s/it]\n 48%|████▊     | 24/50 [00:25<00:27,  1.06s/it]\n 50%|█████     | 25/50 [00:26<00:26,  1.06s/it]\n 52%|█████▏    | 26/50 [00:27<00:25,  1.06s/it]\n 54%|█████▍    | 27/50 [00:28<00:24,  1.07s/it]\n 56%|█████▌    | 28/50 [00:29<00:23,  1.07s/it]\n 58%|█████▊    | 29/50 [00:30<00:22,  1.07s/it]\n 60%|██████    | 30/50 [00:31<00:21,  1.07s/it]\n 62%|██████▏   | 31/50 [00:32<00:20,  1.07s/it]\n 64%|██████▍   | 32/50 [00:34<00:19,  1.07s/it]\n 66%|██████▌   | 33/50 [00:35<00:18,  1.07s/it]\n 68%|██████▊   | 34/50 [00:36<00:17,  1.07s/it]\n 70%|███████   | 35/50 [00:37<00:16,  1.07s/it]\n 72%|███████▏  | 36/50 [00:38<00:14,  1.07s/it]\n 74%|███████▍  | 37/50 [00:39<00:13,  1.07s/it]\n 76%|███████▌  | 38/50 [00:40<00:12,  1.07s/it]\n 78%|███████▊  | 39/50 [00:41<00:11,  1.07s/it]\n 80%|████████  | 40/50 [00:42<00:10,  1.07s/it]\n 82%|████████▏ | 41/50 [00:43<00:09,  1.07s/it]\n 84%|████████▍ | 42/50 [00:44<00:08,  1.07s/it]\n 86%|████████▌ | 43/50 [00:45<00:07,  1.07s/it]\n 88%|████████▊ | 44/50 [00:46<00:06,  1.07s/it]\n 90%|█████████ | 45/50 [00:47<00:05,  1.07s/it]\n 92%|█████████▏| 46/50 [00:48<00:04,  1.07s/it]\n 94%|█████████▍| 47/50 [00:50<00:03,  1.07s/it]\n 96%|█████████▌| 48/50 [00:51<00:02,  1.07s/it]\n 98%|█████████▊| 49/50 [00:52<00:01,  1.07s/it]\n100%|██████████| 50/50 [00:53<00:00,  1.07s/it]\n100%|██████████| 50/50 [00:53<00:00,  1.07s/it]",
  "metrics": {
    "predict_time": 61.304232,
    "total_time": 64.624107
  },
  "output": [
    "https://replicate.delivery/pbxt/hXZpN5zovR6KFlfCmaspZE6f2nAg4bIjj6N40Lk3f1DFLX4jA/out-0.png",
    "https://replicate.delivery/pbxt/Rdla5lHQeUx6DK5sLS1e9MKFamWU6yGtnGXyzjukslFklL8RA/out-1.png",
    "https://replicate.delivery/pbxt/MKDDtrhulZ5jIhOnB5cerH9vBBDAhAdYiONJ27fWnRfJLX4jA/out-2.png",
    "https://replicate.delivery/pbxt/GgkIi0HlipK7B9jjtn8YsjKKnrpJ96bAOt4zZvUfvcmyyFeRA/out-3.png"
  ],
  "started_at": "2023-11-26T08:51:52.044337Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/7457delbog5rcoh33ndd75bdre",
    "cancel": "https://api.replicate.com/v1/predictions/7457delbog5rcoh33ndd75bdre/cancel"
  },
  "version": "2c6eec71acf4ab33faaab1d72effbb41d5811efa7b27ab505df0dbe1efcdeee6"
}

Generated in

61.3 seconds

sakemin/shroomie-diffusion:2c6eec71acf4ab33faaab1d72effbb41d5811efa7b27ab505df0dbe1efcdeee6

Using seed: 41209
Ensuring enough disk space...
Free disk space: 1636803436544
Downloading weights: https://pbxt.replicate.delivery/i2HfjZWkJC1yIqHPDaPHOx9eJsqFqZ2tnWMgn5FFd9evSGWjA/trained_model.tar
b'Downloaded 186 MB bytes in 0.126s (1.5 GB/s)\nExtracted 186 MB in 0.064s (2.9 GB/s)\n'
Downloaded weights in 0.34587955474853516 seconds
Loading fine-tuned model
Does not have Unet. assume we are using LoRA
Loading Unet LoRA
Prompt: <s0><s1> dog sitting on a first class seat in 747 airplane, eating airplane food, wearing neck pillow++, sunset outside windows
txt2img mode
  0%|          | 0/50 [00:00<?, ?it/s]
  2%|▏         | 1/50 [00:01<00:52,  1.07s/it]
  4%|▍         | 2/50 [00:02<00:50,  1.06s/it]
  6%|▌         | 3/50 [00:03<00:49,  1.06s/it]
  8%|▊         | 4/50 [00:04<00:48,  1.06s/it]
 10%|█         | 5/50 [00:05<00:47,  1.06s/it]
 12%|█▏        | 6/50 [00:06<00:46,  1.06s/it]
 14%|█▍        | 7/50 [00:07<00:45,  1.06s/it]
 16%|█▌        | 8/50 [00:08<00:44,  1.06s/it]
 18%|█▊        | 9/50 [00:09<00:43,  1.06s/it]
 20%|██        | 10/50 [00:10<00:42,  1.06s/it]
 22%|██▏       | 11/50 [00:11<00:41,  1.06s/it]
 24%|██▍       | 12/50 [00:12<00:40,  1.06s/it]
 26%|██▌       | 13/50 [00:13<00:39,  1.06s/it]
 28%|██▊       | 14/50 [00:14<00:38,  1.06s/it]
 30%|███       | 15/50 [00:15<00:37,  1.06s/it]
 32%|███▏      | 16/50 [00:16<00:36,  1.06s/it]
 34%|███▍      | 17/50 [00:18<00:35,  1.06s/it]
 36%|███▌      | 18/50 [00:19<00:34,  1.06s/it]
 38%|███▊      | 19/50 [00:20<00:32,  1.06s/it]
 40%|████      | 20/50 [00:21<00:31,  1.06s/it]
 42%|████▏     | 21/50 [00:22<00:30,  1.06s/it]
 44%|████▍     | 22/50 [00:23<00:29,  1.06s/it]
 46%|████▌     | 23/50 [00:24<00:28,  1.06s/it]
 48%|████▊     | 24/50 [00:25<00:27,  1.06s/it]
 50%|█████     | 25/50 [00:26<00:26,  1.06s/it]
 52%|█████▏    | 26/50 [00:27<00:25,  1.06s/it]
 54%|█████▍    | 27/50 [00:28<00:24,  1.07s/it]
 56%|█████▌    | 28/50 [00:29<00:23,  1.07s/it]
 58%|█████▊    | 29/50 [00:30<00:22,  1.07s/it]
 60%|██████    | 30/50 [00:31<00:21,  1.07s/it]
 62%|██████▏   | 31/50 [00:32<00:20,  1.07s/it]
 64%|██████▍   | 32/50 [00:34<00:19,  1.07s/it]
 66%|██████▌   | 33/50 [00:35<00:18,  1.07s/it]
 68%|██████▊   | 34/50 [00:36<00:17,  1.07s/it]
 70%|███████   | 35/50 [00:37<00:16,  1.07s/it]
 72%|███████▏  | 36/50 [00:38<00:14,  1.07s/it]
 74%|███████▍  | 37/50 [00:39<00:13,  1.07s/it]
 76%|███████▌  | 38/50 [00:40<00:12,  1.07s/it]
 78%|███████▊  | 39/50 [00:41<00:11,  1.07s/it]
 80%|████████  | 40/50 [00:42<00:10,  1.07s/it]
 82%|████████▏ | 41/50 [00:43<00:09,  1.07s/it]
 84%|████████▍ | 42/50 [00:44<00:08,  1.07s/it]
 86%|████████▌ | 43/50 [00:45<00:07,  1.07s/it]
 88%|████████▊ | 44/50 [00:46<00:06,  1.07s/it]
 90%|█████████ | 45/50 [00:47<00:05,  1.07s/it]
 92%|█████████▏| 46/50 [00:48<00:04,  1.07s/it]
 94%|█████████▍| 47/50 [00:50<00:03,  1.07s/it]
 96%|█████████▌| 48/50 [00:51<00:02,  1.07s/it]
 98%|█████████▊| 49/50 [00:52<00:01,  1.07s/it]
100%|██████████| 50/50 [00:53<00:00,  1.07s/it]
100%|██████████| 50/50 [00:53<00:00,  1.07s/it]

Prediction

Model

ID

ebupeslbsywzcckrz74uitr4ba

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

16.8s

Created

over 1 year ago

Input

width: 1024
height: 1024
prompt: SHRMI dog eating banana
refine: no_refiner
scheduler: K_EULER
lora_scale: 0.74
num_outputs: 1
guidance_scale: 7.5
apply_watermark
high_noise_frac: 0.8
negative_prompt
prompt_strength: 0.72
num_inference_steps: 50

{
  "width": 1024,
  "height": 1024,
  "prompt": "SHRMI dog eating banana",
  "refine": "no_refiner",
  "scheduler": "K_EULER",
  "lora_scale": 0.74,
  "num_outputs": 1,
  "guidance_scale": 7.5,
  "apply_watermark": true,
  "high_noise_frac": 0.8,
  "negative_prompt": "",
  "prompt_strength": 0.72,
  "num_inference_steps": 50
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

Run sakemin/shroomie-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "sakemin/shroomie-diffusion:2c6eec71acf4ab33faaab1d72effbb41d5811efa7b27ab505df0dbe1efcdeee6",
  {
    input: {
      width: 1024,
      height: 1024,
      prompt: "SHRMI dog eating banana",
      refine: "no_refiner",
      scheduler: "K_EULER",
      lora_scale: 0.74,
      num_outputs: 1,
      guidance_scale: 7.5,
      apply_watermark: true,
      high_noise_frac: 0.8,
      negative_prompt: "",
      prompt_strength: 0.72,
      num_inference_steps: 50
    }
  }
);

// 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 sakemin/shroomie-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "sakemin/shroomie-diffusion:2c6eec71acf4ab33faaab1d72effbb41d5811efa7b27ab505df0dbe1efcdeee6",
    input={
        "width": 1024,
        "height": 1024,
        "prompt": "SHRMI dog eating banana",
        "refine": "no_refiner",
        "scheduler": "K_EULER",
        "lora_scale": 0.74,
        "num_outputs": 1,
        "guidance_scale": 7.5,
        "apply_watermark": True,
        "high_noise_frac": 0.8,
        "negative_prompt": "",
        "prompt_strength": 0.72,
        "num_inference_steps": 50
    }
)
print(output)

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

Run sakemin/shroomie-diffusion 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": "sakemin/shroomie-diffusion:2c6eec71acf4ab33faaab1d72effbb41d5811efa7b27ab505df0dbe1efcdeee6",
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "SHRMI dog eating banana",
      "refine": "no_refiner",
      "scheduler": "K_EULER",
      "lora_scale": 0.74,
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "apply_watermark": true,
      "high_noise_frac": 0.8,
      "negative_prompt": "",
      "prompt_strength": 0.72,
      "num_inference_steps": 50
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

Generated in

15.6 seconds

sakemin/shroomie-diffusion:2c6eec71acf4ab33faaab1d72effbb41d5811efa7b27ab505df0dbe1efcdeee6

Prediction

Model

ID

5omlg7dbnwy3rzkoby7zedu2ha

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

16.8s

Created

over 1 year ago

Input

width: 1024
height: 1024
prompt: SHRMI dog swimming in outer space
refine: no_refiner
scheduler: K_EULER
lora_scale: 0.75
num_outputs: 1
guidance_scale: 7.5
apply_watermark
high_noise_frac: 0.8
negative_prompt: dog harness
prompt_strength: 0.8
num_inference_steps: 50

{
  "width": 1024,
  "height": 1024,
  "prompt": "SHRMI dog swimming in outer space",
  "refine": "no_refiner",
  "scheduler": "K_EULER",
  "lora_scale": 0.75,
  "num_outputs": 1,
  "guidance_scale": 7.5,
  "apply_watermark": true,
  "high_noise_frac": 0.8,
  "negative_prompt": "dog harness",
  "prompt_strength": 0.8,
  "num_inference_steps": 50
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

Run sakemin/shroomie-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "sakemin/shroomie-diffusion:2c6eec71acf4ab33faaab1d72effbb41d5811efa7b27ab505df0dbe1efcdeee6",
  {
    input: {
      width: 1024,
      height: 1024,
      prompt: "SHRMI dog swimming in outer space",
      refine: "no_refiner",
      scheduler: "K_EULER",
      lora_scale: 0.75,
      num_outputs: 1,
      guidance_scale: 7.5,
      apply_watermark: true,
      high_noise_frac: 0.8,
      negative_prompt: "dog harness",
      prompt_strength: 0.8,
      num_inference_steps: 50
    }
  }
);

// 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 sakemin/shroomie-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "sakemin/shroomie-diffusion:2c6eec71acf4ab33faaab1d72effbb41d5811efa7b27ab505df0dbe1efcdeee6",
    input={
        "width": 1024,
        "height": 1024,
        "prompt": "SHRMI dog swimming in outer space",
        "refine": "no_refiner",
        "scheduler": "K_EULER",
        "lora_scale": 0.75,
        "num_outputs": 1,
        "guidance_scale": 7.5,
        "apply_watermark": True,
        "high_noise_frac": 0.8,
        "negative_prompt": "dog harness",
        "prompt_strength": 0.8,
        "num_inference_steps": 50
    }
)
print(output)

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

Run sakemin/shroomie-diffusion 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": "sakemin/shroomie-diffusion:2c6eec71acf4ab33faaab1d72effbb41d5811efa7b27ab505df0dbe1efcdeee6",
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "SHRMI dog swimming in outer space",
      "refine": "no_refiner",
      "scheduler": "K_EULER",
      "lora_scale": 0.75,
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "apply_watermark": true,
      "high_noise_frac": 0.8,
      "negative_prompt": "dog harness",
      "prompt_strength": 0.8,
      "num_inference_steps": 50
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

Generated in

15.5 seconds

sakemin/shroomie-diffusion:2c6eec71acf4ab33faaab1d72effbb41d5811efa7b27ab505df0dbe1efcdeee6

Prediction

Model

ID

fsdtpidbcmpo25nkutxfmgvtay

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

15.0s

Created

over 1 year ago

Input

width: 1024
height: 1024
prompt: SHRMI dog in howl's moving castle
refine: no_refiner
scheduler: K_EULER
lora_scale: 0.74
num_outputs: 1
guidance_scale: 7.5
apply_watermark
high_noise_frac: 0.8
negative_prompt
prompt_strength: 0.72
num_inference_steps: 50

{
  "width": 1024,
  "height": 1024,
  "prompt": "SHRMI dog in howl's moving castle",
  "refine": "no_refiner",
  "scheduler": "K_EULER",
  "lora_scale": 0.74,
  "num_outputs": 1,
  "guidance_scale": 7.5,
  "apply_watermark": true,
  "high_noise_frac": 0.8,
  "negative_prompt": "",
  "prompt_strength": 0.72,
  "num_inference_steps": 50
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

Run sakemin/shroomie-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "sakemin/shroomie-diffusion:2c6eec71acf4ab33faaab1d72effbb41d5811efa7b27ab505df0dbe1efcdeee6",
  {
    input: {
      width: 1024,
      height: 1024,
      prompt: "SHRMI dog in howl's moving castle",
      refine: "no_refiner",
      scheduler: "K_EULER",
      lora_scale: 0.74,
      num_outputs: 1,
      guidance_scale: 7.5,
      apply_watermark: true,
      high_noise_frac: 0.8,
      negative_prompt: "",
      prompt_strength: 0.72,
      num_inference_steps: 50
    }
  }
);

// 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 sakemin/shroomie-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "sakemin/shroomie-diffusion:2c6eec71acf4ab33faaab1d72effbb41d5811efa7b27ab505df0dbe1efcdeee6",
    input={
        "width": 1024,
        "height": 1024,
        "prompt": "SHRMI dog in howl's moving castle",
        "refine": "no_refiner",
        "scheduler": "K_EULER",
        "lora_scale": 0.74,
        "num_outputs": 1,
        "guidance_scale": 7.5,
        "apply_watermark": True,
        "high_noise_frac": 0.8,
        "negative_prompt": "",
        "prompt_strength": 0.72,
        "num_inference_steps": 50
    }
)
print(output)

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

Run sakemin/shroomie-diffusion 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": "sakemin/shroomie-diffusion:2c6eec71acf4ab33faaab1d72effbb41d5811efa7b27ab505df0dbe1efcdeee6",
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "SHRMI dog in howl\'s moving castle",
      "refine": "no_refiner",
      "scheduler": "K_EULER",
      "lora_scale": 0.74,
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "apply_watermark": true,
      "high_noise_frac": 0.8,
      "negative_prompt": "",
      "prompt_strength": 0.72,
      "num_inference_steps": 50
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

Generated in

15.1 seconds

sakemin/shroomie-diffusion:2c6eec71acf4ab33faaab1d72effbb41d5811efa7b27ab505df0dbe1efcdeee6

Prediction

Model

ID

urdnjblbzak4ttxauip7mjnzyi

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

15.0s

Created

over 1 year ago

Input

width: 1024
height: 1024
prompt: SHRMI dog in breath of the wild
refine: no_refiner
scheduler: K_EULER
lora_scale: 0.74
num_outputs: 1
guidance_scale: 7.5
apply_watermark
high_noise_frac: 0.8
negative_prompt
prompt_strength: 0.72
num_inference_steps: 50

{
  "width": 1024,
  "height": 1024,
  "prompt": "SHRMI dog in breath of the wild",
  "refine": "no_refiner",
  "scheduler": "K_EULER",
  "lora_scale": 0.74,
  "num_outputs": 1,
  "guidance_scale": 7.5,
  "apply_watermark": true,
  "high_noise_frac": 0.8,
  "negative_prompt": "",
  "prompt_strength": 0.72,
  "num_inference_steps": 50
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

Run sakemin/shroomie-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "sakemin/shroomie-diffusion:2c6eec71acf4ab33faaab1d72effbb41d5811efa7b27ab505df0dbe1efcdeee6",
  {
    input: {
      width: 1024,
      height: 1024,
      prompt: "SHRMI dog in breath of the wild",
      refine: "no_refiner",
      scheduler: "K_EULER",
      lora_scale: 0.74,
      num_outputs: 1,
      guidance_scale: 7.5,
      apply_watermark: true,
      high_noise_frac: 0.8,
      negative_prompt: "",
      prompt_strength: 0.72,
      num_inference_steps: 50
    }
  }
);

// 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 sakemin/shroomie-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "sakemin/shroomie-diffusion:2c6eec71acf4ab33faaab1d72effbb41d5811efa7b27ab505df0dbe1efcdeee6",
    input={
        "width": 1024,
        "height": 1024,
        "prompt": "SHRMI dog in breath of the wild",
        "refine": "no_refiner",
        "scheduler": "K_EULER",
        "lora_scale": 0.74,
        "num_outputs": 1,
        "guidance_scale": 7.5,
        "apply_watermark": True,
        "high_noise_frac": 0.8,
        "negative_prompt": "",
        "prompt_strength": 0.72,
        "num_inference_steps": 50
    }
)
print(output)

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

Run sakemin/shroomie-diffusion 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": "sakemin/shroomie-diffusion:2c6eec71acf4ab33faaab1d72effbb41d5811efa7b27ab505df0dbe1efcdeee6",
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "SHRMI dog in breath of the wild",
      "refine": "no_refiner",
      "scheduler": "K_EULER",
      "lora_scale": 0.74,
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "apply_watermark": true,
      "high_noise_frac": 0.8,
      "negative_prompt": "",
      "prompt_strength": 0.72,
      "num_inference_steps": 50
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

Generated in

15.1 seconds

sakemin/shroomie-diffusion:2c6eec71acf4ab33faaab1d72effbb41d5811efa7b27ab505df0dbe1efcdeee6

Prediction

Model

ID

iysn573bv7wmssgivwkmgo7nxa

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

15.2s

Created

over 1 year ago

Input

width: 1024
height: 1024
prompt: SHRMI bear
refine: no_refiner
scheduler: K_EULER
lora_scale: 0.74
num_outputs: 1
guidance_scale: 7.5
apply_watermark
high_noise_frac: 0.8
negative_prompt
prompt_strength: 0.72
num_inference_steps: 50

{
  "width": 1024,
  "height": 1024,
  "prompt": "SHRMI bear",
  "refine": "no_refiner",
  "scheduler": "K_EULER",
  "lora_scale": 0.74,
  "num_outputs": 1,
  "guidance_scale": 7.5,
  "apply_watermark": true,
  "high_noise_frac": 0.8,
  "negative_prompt": "",
  "prompt_strength": 0.72,
  "num_inference_steps": 50
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

Run sakemin/shroomie-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "sakemin/shroomie-diffusion:2c6eec71acf4ab33faaab1d72effbb41d5811efa7b27ab505df0dbe1efcdeee6",
  {
    input: {
      width: 1024,
      height: 1024,
      prompt: "SHRMI bear",
      refine: "no_refiner",
      scheduler: "K_EULER",
      lora_scale: 0.74,
      num_outputs: 1,
      guidance_scale: 7.5,
      apply_watermark: true,
      high_noise_frac: 0.8,
      negative_prompt: "",
      prompt_strength: 0.72,
      num_inference_steps: 50
    }
  }
);

// 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 sakemin/shroomie-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "sakemin/shroomie-diffusion:2c6eec71acf4ab33faaab1d72effbb41d5811efa7b27ab505df0dbe1efcdeee6",
    input={
        "width": 1024,
        "height": 1024,
        "prompt": "SHRMI bear",
        "refine": "no_refiner",
        "scheduler": "K_EULER",
        "lora_scale": 0.74,
        "num_outputs": 1,
        "guidance_scale": 7.5,
        "apply_watermark": True,
        "high_noise_frac": 0.8,
        "negative_prompt": "",
        "prompt_strength": 0.72,
        "num_inference_steps": 50
    }
)
print(output)

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

Run sakemin/shroomie-diffusion 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": "sakemin/shroomie-diffusion:2c6eec71acf4ab33faaab1d72effbb41d5811efa7b27ab505df0dbe1efcdeee6",
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "SHRMI bear",
      "refine": "no_refiner",
      "scheduler": "K_EULER",
      "lora_scale": 0.74,
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "apply_watermark": true,
      "high_noise_frac": 0.8,
      "negative_prompt": "",
      "prompt_strength": 0.72,
      "num_inference_steps": 50
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

Generated in

15.3 seconds

sakemin/shroomie-diffusion:2c6eec71acf4ab33faaab1d72effbb41d5811efa7b27ab505df0dbe1efcdeee6

Prediction

Model

ID

3wl5yqdb7zqcvi2ytz5s3l6o5i

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

17.4s

Created

over 1 year ago

Input

width: 1024
height: 1024
prompt: SHRMI whippet
refine: no_refiner
scheduler: K_EULER
lora_scale: 0.74
num_outputs: 1
guidance_scale: 7.5
apply_watermark
high_noise_frac: 0.8
negative_prompt
prompt_strength: 0.72
num_inference_steps: 50

{
  "width": 1024,
  "height": 1024,
  "prompt": "SHRMI whippet",
  "refine": "no_refiner",
  "scheduler": "K_EULER",
  "lora_scale": 0.74,
  "num_outputs": 1,
  "guidance_scale": 7.5,
  "apply_watermark": true,
  "high_noise_frac": 0.8,
  "negative_prompt": "",
  "prompt_strength": 0.72,
  "num_inference_steps": 50
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

Run sakemin/shroomie-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "sakemin/shroomie-diffusion:2c6eec71acf4ab33faaab1d72effbb41d5811efa7b27ab505df0dbe1efcdeee6",
  {
    input: {
      width: 1024,
      height: 1024,
      prompt: "SHRMI whippet",
      refine: "no_refiner",
      scheduler: "K_EULER",
      lora_scale: 0.74,
      num_outputs: 1,
      guidance_scale: 7.5,
      apply_watermark: true,
      high_noise_frac: 0.8,
      negative_prompt: "",
      prompt_strength: 0.72,
      num_inference_steps: 50
    }
  }
);

// 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.