Prediction

Model

emaph/outpaint-controlnet-union:4d11bfa6

sbx0zj2vmnrgm0cgz5pt274q5g

Status

Succeeded

Source

Web

Hardware

A40

Total duration

2m 7s

Created

over 1 year ago

Webhook

–

Input

prompt: ice dragon, wings wide open
negative_prompt: ugly
image: https://i.postimg.cc/vmY5BZP3/2024-04-16-21-30-59-9956.png
left: 400
right: 400
top: 0
bottom: 0
output_format: webp
output_quality: 80

{
  "bottom": 0,
  "image": "https://i.postimg.cc/vmY5BZP3/2024-04-16-21-30-59-9956.png",
  "left": 400,
  "negative_prompt": "ugly",
  "output_format": "webp",
  "output_quality": 80,
  "prompt": "ice dragon, wings wide open",
  "right": 400,
  "top": 0
}

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=r8_ebV**********************************

This is your API token. Keep it to yourself.

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 emaph/outpaint-controlnet-union using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "emaph/outpaint-controlnet-union:4d11bfa652f267e27f480471dd8d435cd92c2b99750644a0b38303d13946aae5",
  {
    input: {
      bottom: 0,
      image: "https://i.postimg.cc/vmY5BZP3/2024-04-16-21-30-59-9956.png",
      left: 400,
      negative_prompt: "ugly",
      output_format: "webp",
      output_quality: 80,
      prompt: "ice dragon, wings wide open",
      right: 400,
      top: 0
    }
  }
);

// 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.

Install Replicate’s Python client library:

pip install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=r8_ebV**********************************

This is your API token. Keep it to yourself.

Import the client:

import replicate

Run emaph/outpaint-controlnet-union using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "emaph/outpaint-controlnet-union:4d11bfa652f267e27f480471dd8d435cd92c2b99750644a0b38303d13946aae5",
    input={
        "bottom": 0,
        "image": "https://i.postimg.cc/vmY5BZP3/2024-04-16-21-30-59-9956.png",
        "left": 400,
        "negative_prompt": "ugly",
        "output_format": "webp",
        "output_quality": 80,
        "prompt": "ice dragon, wings wide open",
        "right": 400,
        "top": 0
    }
)

# 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.

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=r8_ebV**********************************

This is your API token. Keep it to yourself.

Run emaph/outpaint-controlnet-union 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": "emaph/outpaint-controlnet-union:4d11bfa652f267e27f480471dd8d435cd92c2b99750644a0b38303d13946aae5",
    "input": {
      "bottom": 0,
      "image": "https://i.postimg.cc/vmY5BZP3/2024-04-16-21-30-59-9956.png",
      "left": 400,
      "negative_prompt": "ugly",
      "output_format": "webp",
      "output_quality": 80,
      "prompt": "ice dragon, wings wide open",
      "right": 400,
      "top": 0
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "id": "sbx0zj2vmnrgm0cgz5pt274q5g",
  "model": "emaph/outpaint-controlnet-union",
  "version": "4d11bfa652f267e27f480471dd8d435cd92c2b99750644a0b38303d13946aae5",
  "input": {
    "bottom": 0,
    "image": "https://i.postimg.cc/vmY5BZP3/2024-04-16-21-30-59-9956.png",
    "left": 400,
    "negative_prompt": "ugly",
    "output_format": "webp",
    "output_quality": 80,
    "prompt": "ice dragon, wings wide open",
    "right": 400,
    "top": 0
  },
  "logs": "Random seed set to: 2036446667\nChecking inputs\nDownloading https://i.postimg.cc/vmY5BZP3/2024-04-16-21-30-59-9956.png to /tmp/inputs/2024-04-16-21-30-59-9956.png\n✅ /tmp/inputs/2024-04-16-21-30-59-9956.png\n====================================\nChecking weights\n✅ diffusion_pytorch_model_promax.safetensors exists in ComfyUI/models/controlnet\n⏳ Downloading juggernautXL_v8Rundiffusion.safetensors to ComfyUI/models/checkpoints\n✅ juggernautXL_v8Rundiffusion.safetensors downloaded to ComfyUI/models/checkpoints in 16.15s, size: 6776.19MB\n====================================\nRunning workflow\ngot prompt\nExecuting node 4, title: Load Checkpoint, class type: CheckpointLoaderSimple\nmodel_type EPS\nUsing pytorch attention in VAE\nUsing pytorch attention in VAE\nloaded straight to GPU\nRequested to load SDXL\nLoading 1 new model\nExecuting node 6, title: Positive, class type: CLIPTextEncode\nRequested to load SDXLClipModel\nLoading 1 new model\nExecuting node 7, title: Negetive, class type: CLIPTextEncode\nExecuting node 16, title: Load ControlNet Model, class type: ControlNetLoader\nExecuting node 17, title: SetUnionControlNetType, class type: SetUnionControlNetType\nExecuting node 11, title: Load Image, class type: LoadImage\nExecuting node 10, title: Pad Image for Outpainting, class type: ImagePadForOutpaint\nExecuting node 48, title: InvertMask, class type: InvertMask\nExecuting node 47, title: Convert Mask to Image, class type: MaskToImage\nExecuting node 49, title: ImageCompositeMasked, class type: ImageCompositeMasked\nExecuting node 15, title: Apply ControlNet (Advanced), class type: ControlNetApplyAdvanced\nExecuting node 39, title: VAE Encode (for Inpainting), class type: VAEEncodeForInpaint\nRequested to load AutoencoderKL\nLoading 1 new model\nExecuting node 3, title: KSampler, class type: KSampler\nRequested to load ControlNet\nLoading 1 new model\n  0%|          | 0/21 [00:00<?, ?it/s]\n  5%|▍         | 1/21 [00:00<00:08,  2.48it/s]\n 10%|▉         | 2/21 [00:00<00:06,  2.80it/s]\n 14%|█▍        | 3/21 [00:01<00:06,  2.93it/s]\n 19%|█▉        | 4/21 [00:01<00:05,  3.00it/s]\n 24%|██▍       | 5/21 [00:01<00:05,  3.03it/s]\n 29%|██▊       | 6/21 [00:02<00:04,  3.05it/s]\n 33%|███▎      | 7/21 [00:02<00:04,  3.07it/s]\n 38%|███▊      | 8/21 [00:02<00:04,  3.08it/s]\n 43%|████▎     | 9/21 [00:02<00:03,  3.08it/s]\n 48%|████▊     | 10/21 [00:03<00:03,  3.09it/s]\n 52%|█████▏    | 11/21 [00:03<00:03,  3.10it/s]\n 57%|█████▋    | 12/21 [00:03<00:02,  3.10it/s]\n 62%|██████▏   | 13/21 [00:04<00:02,  3.11it/s]\n 67%|██████▋   | 14/21 [00:04<00:02,  3.11it/s]\n 71%|███████▏  | 15/21 [00:04<00:01,  3.11it/s]\n 76%|███████▌  | 16/21 [00:05<00:01,  3.11it/s]\n 81%|████████  | 17/21 [00:05<00:01,  3.11it/s]\n 86%|████████▌ | 18/21 [00:05<00:00,  3.11it/s]\n 90%|█████████ | 19/21 [00:06<00:00,  3.11it/s]\n 95%|█████████▌| 20/21 [00:06<00:00,  3.11it/s]\n100%|██████████| 21/21 [00:06<00:00,  3.11it/s]\n100%|██████████| 21/21 [00:06<00:00,  3.07it/s]\nExecuting node 8, title: VAE Decode, class type: VAEDecode\nExecuting node 60, title: Save Image, class type: SaveImage\nPrompt executed in 13.26 seconds\noutputs:  {'60': {'images': [{'filename': 'outpainted_00001_.png', 'subfolder': '', 'type': 'output'}]}}\n====================================\noutpainted_00001_.png",
  "output": [
    "https://replicate.delivery/pbxt/krjseJnglBxuaqpAv6G7j7J8xZbhzDHs6d2Gf0MQym2aNfZmA/outpainted_00001_.webp"
  ],
  "data_removed": false,
  "error": null,
  "source": "web",
  "status": "succeeded",
  "created_at": "2024-07-28T13:00:12.069Z",
  "started_at": "2024-07-28T13:01:47.793943Z",
  "completed_at": "2024-07-28T13:02:19.473227Z",
  "urls": {
    "cancel": "https://api.replicate.com/v1/predictions/sbx0zj2vmnrgm0cgz5pt274q5g/cancel",
    "get": "https://api.replicate.com/v1/predictions/sbx0zj2vmnrgm0cgz5pt274q5g",
    "web": "https://replicate.com/p/sbx0zj2vmnrgm0cgz5pt274q5g"
  },
  "metrics": {
    "predict_time": 31.679283715,
    "total_time": 127.404227
  }
}

Generated in

31.7 seconds

Tweak it Report

Random seed set to: 2036446667 Checking inputs Downloading https://i.postimg.cc/vmY5BZP3/2024-04-16-21-30-59-9956.png to /tmp/inputs/2024-04-16-21-30-59-9956.png ✅ /tmp/inputs/2024-04-16-21-30-59-9956.png ==================================== Checking weights ✅ diffusion_pytorch_model_promax.safetensors exists in ComfyUI/models/controlnet ⏳ Downloading juggernautXL_v8Rundiffusion.safetensors to ComfyUI/models/checkpoints ✅ juggernautXL_v8Rundiffusion.safetensors downloaded to ComfyUI/models/checkpoints in 16.15s, size: 6776.19MB ==================================== Running workflow got prompt Executing node 4, title: Load Checkpoint, class type: CheckpointLoaderSimple model_type EPS Using pytorch attention in VAE Using pytorch attention in VAE loaded straight to GPU Requested to load SDXL Loading 1 new model Executing node 6, title: Positive, class type: CLIPTextEncode Requested to load SDXLClipModel Loading 1 new model Executing node 7, title: Negetive, class type: CLIPTextEncode Executing node 16, title: Load ControlNet Model, class type: ControlNetLoader Executing node 17, title: SetUnionControlNetType, class type: SetUnionControlNetType Executing node 11, title: Load Image, class type: LoadImage Executing node 10, title: Pad Image for Outpainting, class type: ImagePadForOutpaint Executing node 48, title: InvertMask, class type: InvertMask Executing node 47, title: Convert Mask to Image, class type: MaskToImage Executing node 49, title: ImageCompositeMasked, class type: ImageCompositeMasked Executing node 15, title: Apply ControlNet (Advanced), class type: ControlNetApplyAdvanced Executing node 39, title: VAE Encode (for Inpainting), class type: VAEEncodeForInpaint Requested to load AutoencoderKL Loading 1 new model Executing node 3, title: KSampler, class type: KSampler Requested to load ControlNet Loading 1 new model 0%| | 0/21 [00:00<?, ?it/s] 5%|▍ | 1/21 [00:00<00:08, 2.48it/s] 10%|▉ | 2/21 [00:00<00:06, 2.80it/s] 14%|█▍ | 3/21 [00:01<00:06, 2.93it/s] 19%|█▉ | 4/21 [00:01<00:05, 3.00it/s] 24%|██▍ | 5/21 [00:01<00:05, 3.03it/s] 29%|██▊ | 6/21 [00:02<00:04, 3.05it/s] 33%|███▎ | 7/21 [00:02<00:04, 3.07it/s] 38%|███▊ | 8/21 [00:02<00:04, 3.08it/s] 43%|████▎ | 9/21 [00:02<00:03, 3.08it/s] 48%|████▊ | 10/21 [00:03<00:03, 3.09it/s] 52%|█████▏ | 11/21 [00:03<00:03, 3.10it/s] 57%|█████▋ | 12/21 [00:03<00:02, 3.10it/s] 62%|██████▏ | 13/21 [00:04<00:02, 3.11it/s] 67%|██████▋ | 14/21 [00:04<00:02, 3.11it/s] 71%|███████▏ | 15/21 [00:04<00:01, 3.11it/s] 76%|███████▌ | 16/21 [00:05<00:01, 3.11it/s] 81%|████████ | 17/21 [00:05<00:01, 3.11it/s] 86%|████████▌ | 18/21 [00:05<00:00, 3.11it/s] 90%|█████████ | 19/21 [00:06<00:00, 3.11it/s] 95%|█████████▌| 20/21 [00:06<00:00, 3.11it/s] 100%|██████████| 21/21 [00:06<00:00, 3.11it/s] 100%|██████████| 21/21 [00:06<00:00, 3.07it/s] Executing node 8, title: VAE Decode, class type: VAEDecode Executing node 60, title: Save Image, class type: SaveImage Prompt executed in 13.26 seconds outputs: {'60': {'images': [{'filename': 'outpainted_00001_.png', 'subfolder': '', 'type': 'output'}]}} ==================================== outpainted_00001_.png