Prediction

Model

pnyompen/sdxl-controlnet-lora-small:0a0d2136

9e7sq1d8ndrgj0cg8eqsa578s4

Status

Succeeded

Source

Web

Hardware

A40

Total duration

20.1s

Created

over 1 year ago

Webhook

–

Input

prompt: shot in the style of sksfer, a woman in alaska
image: https://replicate.delivery/pbxt/JiOTMCHj4oGrTTf8Pg2r7vyI8YdXc5jL2IDyC2SfhuggjYe6/out-0%20%281%29.png
img2img: false
auto_generate_caption: false
condition_scale: 0.5
strength: 0.8
negative_prompt: Empty string
num_inference_steps: 40
num_outputs: 1
scheduler: K_EULER
guidance_scale: 7.5
lora_scale: 0.95
lora_weights: https://pbxt.replicate.delivery/mwN3AFyYZyouOB03Uhw8ubKW9rpqMgdtL9zYV9GF2WGDiwbE/trained_model.tar

{
  "auto_generate_caption": false,
  "condition_scale": 0.5,
  "guidance_scale": 7.5,
  "image": "https://replicate.delivery/pbxt/JiOTMCHj4oGrTTf8Pg2r7vyI8YdXc5jL2IDyC2SfhuggjYe6/out-0%20%281%29.png",
  "img2img": false,
  "lora_scale": 0.95,
  "lora_weights": "https://pbxt.replicate.delivery/mwN3AFyYZyouOB03Uhw8ubKW9rpqMgdtL9zYV9GF2WGDiwbE/trained_model.tar",
  "negative_prompt": "",
  "num_inference_steps": 40,
  "num_outputs": 1,
  "prompt": "shot in the style of sksfer, a woman in alaska",
  "scheduler": "K_EULER",
  "strength": 0.8
}

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=r8_F9U**********************************

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 pnyompen/sdxl-controlnet-lora-small using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "pnyompen/sdxl-controlnet-lora-small:0a0d21366b464cc0d6618f0a8da2ecbd22290423faa800a97e733be748a68616",
  {
    input: {
      auto_generate_caption: false,
      condition_scale: 0.5,
      guidance_scale: 7.5,
      image: "https://replicate.delivery/pbxt/JiOTMCHj4oGrTTf8Pg2r7vyI8YdXc5jL2IDyC2SfhuggjYe6/out-0%20%281%29.png",
      img2img: false,
      lora_scale: 0.95,
      lora_weights: "https://pbxt.replicate.delivery/mwN3AFyYZyouOB03Uhw8ubKW9rpqMgdtL9zYV9GF2WGDiwbE/trained_model.tar",
      negative_prompt: "",
      num_inference_steps: 40,
      num_outputs: 1,
      prompt: "shot in the style of sksfer, a woman in alaska",
      scheduler: "K_EULER",
      strength: 0.8
    }
  }
);

// 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_F9U**********************************

This is your API token. Keep it to yourself.

Import the client:

import replicate

Run pnyompen/sdxl-controlnet-lora-small using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "pnyompen/sdxl-controlnet-lora-small:0a0d21366b464cc0d6618f0a8da2ecbd22290423faa800a97e733be748a68616",
    input={
        "auto_generate_caption": False,
        "condition_scale": 0.5,
        "guidance_scale": 7.5,
        "image": "https://replicate.delivery/pbxt/JiOTMCHj4oGrTTf8Pg2r7vyI8YdXc5jL2IDyC2SfhuggjYe6/out-0%20%281%29.png",
        "img2img": False,
        "lora_scale": 0.95,
        "lora_weights": "https://pbxt.replicate.delivery/mwN3AFyYZyouOB03Uhw8ubKW9rpqMgdtL9zYV9GF2WGDiwbE/trained_model.tar",
        "negative_prompt": "",
        "num_inference_steps": 40,
        "num_outputs": 1,
        "prompt": "shot in the style of sksfer, a woman in alaska",
        "scheduler": "K_EULER",
        "strength": 0.8
    }
)

# 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_F9U**********************************

This is your API token. Keep it to yourself.

Run pnyompen/sdxl-controlnet-lora-small 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": "pnyompen/sdxl-controlnet-lora-small:0a0d21366b464cc0d6618f0a8da2ecbd22290423faa800a97e733be748a68616",
    "input": {
      "auto_generate_caption": false,
      "condition_scale": 0.5,
      "guidance_scale": 7.5,
      "image": "https://replicate.delivery/pbxt/JiOTMCHj4oGrTTf8Pg2r7vyI8YdXc5jL2IDyC2SfhuggjYe6/out-0%20%281%29.png",
      "img2img": false,
      "lora_scale": 0.95,
      "lora_weights": "https://pbxt.replicate.delivery/mwN3AFyYZyouOB03Uhw8ubKW9rpqMgdtL9zYV9GF2WGDiwbE/trained_model.tar",
      "negative_prompt": "",
      "num_inference_steps": 40,
      "num_outputs": 1,
      "prompt": "shot in the style of sksfer, a woman in alaska",
      "scheduler": "K_EULER",
      "strength": 0.8
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "id": "9e7sq1d8ndrgj0cg8eqsa578s4",
  "model": "pnyompen/sdxl-controlnet-lora-small",
  "version": "0a0d21366b464cc0d6618f0a8da2ecbd22290423faa800a97e733be748a68616",
  "input": {
    "auto_generate_caption": false,
    "condition_scale": 0.5,
    "guidance_scale": 7.5,
    "image": "https://replicate.delivery/pbxt/JiOTMCHj4oGrTTf8Pg2r7vyI8YdXc5jL2IDyC2SfhuggjYe6/out-0%20%281%29.png",
    "img2img": false,
    "lora_scale": 0.95,
    "lora_weights": "https://pbxt.replicate.delivery/mwN3AFyYZyouOB03Uhw8ubKW9rpqMgdtL9zYV9GF2WGDiwbE/trained_model.tar",
    "negative_prompt": "",
    "num_inference_steps": 40,
    "num_outputs": 1,
    "prompt": "shot in the style of sksfer, a woman in alaska",
    "scheduler": "K_EULER",
    "strength": 0.8
  },
  "logs": "Using seed: 47765\nloading custom weights\nweights not in cache\nEnsuring enough disk space...\nFree disk space: 1634558648320\nDownloading weights: https://pbxt.replicate.delivery/mwN3AFyYZyouOB03Uhw8ubKW9rpqMgdtL9zYV9GF2WGDiwbE/trained_model.tar\ndownloading https://pbxt.replicate.delivery/mwN3AFyYZyouOB03Uhw8ubKW9rpqMgdtL9zYV9GF2WGDiwbE/trained_model.tar\n2024-06-23T06:01:44Z | INFO  | [ Initiating ] chunk_size=150M dest=./weights-cache/931be18428e37365 url=https://pbxt.replicate.delivery/mwN3AFyYZyouOB03Uhw8ubKW9rpqMgdtL9zYV9GF2WGDiwbE/trained_model.tar\n2024-06-23T06:01:46Z | INFO  | [ Complete ] dest=./weights-cache/931be18428e37365 size=\"186 MB\" total_elapsed=2.434s url=https://pbxt.replicate.delivery/mwN3AFyYZyouOB03Uhw8ubKW9rpqMgdtL9zYV9GF2WGDiwbE/trained_model.tar\nb''\nDownloaded weights in 2.5596249103546143 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nOriginal width:1024, height:1024\nAspect Ratio: 1.00\nnew_width:1024, new_height:1024\nPrompt: shot in the style of <s0><s1>, a woman in alaska\ntext2img mode\n  0%|          | 0/40 [00:00<?, ?it/s]/root/.pyenv/versions/3.9.19/lib/python3.9/site-packages/diffusers/models/attention_processor.py:1468: FutureWarning: `LoRAAttnProcessor2_0` is deprecated and will be removed in version 0.26.0. Make sure use AttnProcessor2_0 instead by settingLoRA layers to `self.{to_q,to_k,to_v,to_out[0]}.lora_layer` respectively. This will be done automatically when using `LoraLoaderMixin.load_lora_weights`\ndeprecate(\n  2%|▎         | 1/40 [00:00<00:14,  2.74it/s]\n  5%|▌         | 2/40 [00:00<00:13,  2.73it/s]\n  8%|▊         | 3/40 [00:01<00:13,  2.73it/s]\n 10%|█         | 4/40 [00:01<00:13,  2.73it/s]\n 12%|█▎        | 5/40 [00:01<00:12,  2.73it/s]\n 15%|█▌        | 6/40 [00:02<00:12,  2.74it/s]\n 18%|█▊        | 7/40 [00:02<00:12,  2.74it/s]\n 20%|██        | 8/40 [00:02<00:11,  2.74it/s]\n 22%|██▎       | 9/40 [00:03<00:11,  2.74it/s]\n 25%|██▌       | 10/40 [00:03<00:10,  2.74it/s]\n 28%|██▊       | 11/40 [00:04<00:10,  2.73it/s]\n 30%|███       | 12/40 [00:04<00:10,  2.73it/s]\n 32%|███▎      | 13/40 [00:04<00:09,  2.73it/s]\n 35%|███▌      | 14/40 [00:05<00:09,  2.73it/s]\n 38%|███▊      | 15/40 [00:05<00:09,  2.73it/s]\n 40%|████      | 16/40 [00:05<00:08,  2.73it/s]\n 42%|████▎     | 17/40 [00:06<00:08,  2.73it/s]\n 45%|████▌     | 18/40 [00:06<00:08,  2.73it/s]\n 48%|████▊     | 19/40 [00:06<00:07,  2.73it/s]\n 50%|█████     | 20/40 [00:07<00:07,  2.73it/s]\n 52%|█████▎    | 21/40 [00:07<00:06,  2.73it/s]\n 55%|█████▌    | 22/40 [00:08<00:06,  2.73it/s]\n 57%|█████▊    | 23/40 [00:08<00:06,  2.73it/s]\n 60%|██████    | 24/40 [00:08<00:05,  2.73it/s]\n 62%|██████▎   | 25/40 [00:09<00:05,  2.73it/s]\n 65%|██████▌   | 26/40 [00:09<00:05,  2.73it/s]\n 68%|██████▊   | 27/40 [00:09<00:04,  2.73it/s]\n 70%|███████   | 28/40 [00:10<00:04,  2.73it/s]\n 72%|███████▎  | 29/40 [00:10<00:04,  2.73it/s]\n 75%|███████▌  | 30/40 [00:10<00:03,  2.73it/s]\n 78%|███████▊  | 31/40 [00:11<00:03,  2.72it/s]\n 80%|████████  | 32/40 [00:11<00:02,  2.72it/s]\n 82%|████████▎ | 33/40 [00:12<00:02,  2.72it/s]\n 85%|████████▌ | 34/40 [00:12<00:02,  2.72it/s]\n 88%|████████▊ | 35/40 [00:12<00:01,  2.72it/s]\n 90%|█████████ | 36/40 [00:13<00:01,  2.72it/s]\n 92%|█████████▎| 37/40 [00:13<00:01,  2.72it/s]\n 95%|█████████▌| 38/40 [00:13<00:00,  2.72it/s]\n 98%|█████████▊| 39/40 [00:14<00:00,  2.72it/s]\n100%|██████████| 40/40 [00:14<00:00,  2.72it/s]\n100%|██████████| 40/40 [00:14<00:00,  2.73it/s]",
  "output": [
    "https://replicate.delivery/pbxt/PtfoygW38dw4dqfWutkOcYUDelpveL0oVb6BKoFvDVioFbFMB/out-0.png"
  ],
  "data_removed": false,
  "error": null,
  "source": "web",
  "status": "succeeded",
  "created_at": "2024-06-23T06:01:43.851Z",
  "started_at": "2024-06-23T06:01:43.896686Z",
  "completed_at": "2024-06-23T06:02:03.981923Z",
  "urls": {
    "cancel": "https://api.replicate.com/v1/predictions/9e7sq1d8ndrgj0cg8eqsa578s4/cancel",
    "get": "https://api.replicate.com/v1/predictions/9e7sq1d8ndrgj0cg8eqsa578s4",
    "web": "https://replicate.com/p/9e7sq1d8ndrgj0cg8eqsa578s4"
  },
  "metrics": {
    "predict_time": 20.08523615,
    "total_time": 20.130923
  }
}

Generated in

20.1 seconds

Tweak it Report

Using seed: 47765 loading custom weights weights not in cache Ensuring enough disk space... Free disk space: 1634558648320 Downloading weights: https://pbxt.replicate.delivery/mwN3AFyYZyouOB03Uhw8ubKW9rpqMgdtL9zYV9GF2WGDiwbE/trained_model.tar downloading https://pbxt.replicate.delivery/mwN3AFyYZyouOB03Uhw8ubKW9rpqMgdtL9zYV9GF2WGDiwbE/trained_model.tar 2024-06-23T06:01:44Z | INFO | [ Initiating ] chunk_size=150M dest=./weights-cache/931be18428e37365 url=https://pbxt.replicate.delivery/mwN3AFyYZyouOB03Uhw8ubKW9rpqMgdtL9zYV9GF2WGDiwbE/trained_model.tar 2024-06-23T06:01:46Z | INFO | [ Complete ] dest=./weights-cache/931be18428e37365 size="186 MB" total_elapsed=2.434s url=https://pbxt.replicate.delivery/mwN3AFyYZyouOB03Uhw8ubKW9rpqMgdtL9zYV9GF2WGDiwbE/trained_model.tar b'' Downloaded weights in 2.5596249103546143 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Original width:1024, height:1024 Aspect Ratio: 1.00 new_width:1024, new_height:1024 Prompt: shot in the style of <s0><s1>, a woman in alaska text2img mode 0%| | 0/40 [00:00<?, ?it/s]/root/.pyenv/versions/3.9.19/lib/python3.9/site-packages/diffusers/models/attention_processor.py:1468: FutureWarning: `LoRAAttnProcessor2_0` is deprecated and will be removed in version 0.26.0. Make sure use AttnProcessor2_0 instead by settingLoRA layers to `self.{to_q,to_k,to_v,to_out[0]}.lora_layer` respectively. This will be done automatically when using `LoraLoaderMixin.load_lora_weights` deprecate( 2%|▎ | 1/40 [00:00<00:14, 2.74it/s] 5%|▌ | 2/40 [00:00<00:13, 2.73it/s] 8%|▊ | 3/40 [00:01<00:13, 2.73it/s] 10%|█ | 4/40 [00:01<00:13, 2.73it/s] 12%|█▎ | 5/40 [00:01<00:12, 2.73it/s] 15%|█▌ | 6/40 [00:02<00:12, 2.74it/s] 18%|█▊ | 7/40 [00:02<00:12, 2.74it/s] 20%|██ | 8/40 [00:02<00:11, 2.74it/s] 22%|██▎ | 9/40 [00:03<00:11, 2.74it/s] 25%|██▌ | 10/40 [00:03<00:10, 2.74it/s] 28%|██▊ | 11/40 [00:04<00:10, 2.73it/s] 30%|███ | 12/40 [00:04<00:10, 2.73it/s] 32%|███▎ | 13/40 [00:04<00:09, 2.73it/s] 35%|███▌ | 14/40 [00:05<00:09, 2.73it/s] 38%|███▊ | 15/40 [00:05<00:09, 2.73it/s] 40%|████ | 16/40 [00:05<00:08, 2.73it/s] 42%|████▎ | 17/40 [00:06<00:08, 2.73it/s] 45%|████▌ | 18/40 [00:06<00:08, 2.73it/s] 48%|████▊ | 19/40 [00:06<00:07, 2.73it/s] 50%|█████ | 20/40 [00:07<00:07, 2.73it/s] 52%|█████▎ | 21/40 [00:07<00:06, 2.73it/s] 55%|█████▌ | 22/40 [00:08<00:06, 2.73it/s] 57%|█████▊ | 23/40 [00:08<00:06, 2.73it/s] 60%|██████ | 24/40 [00:08<00:05, 2.73it/s] 62%|██████▎ | 25/40 [00:09<00:05, 2.73it/s] 65%|██████▌ | 26/40 [00:09<00:05, 2.73it/s] 68%|██████▊ | 27/40 [00:09<00:04, 2.73it/s] 70%|███████ | 28/40 [00:10<00:04, 2.73it/s] 72%|███████▎ | 29/40 [00:10<00:04, 2.73it/s] 75%|███████▌ | 30/40 [00:10<00:03, 2.73it/s] 78%|███████▊ | 31/40 [00:11<00:03, 2.72it/s] 80%|████████ | 32/40 [00:11<00:02, 2.72it/s] 82%|████████▎ | 33/40 [00:12<00:02, 2.72it/s] 85%|████████▌ | 34/40 [00:12<00:02, 2.72it/s] 88%|████████▊ | 35/40 [00:12<00:01, 2.72it/s] 90%|█████████ | 36/40 [00:13<00:01, 2.72it/s] 92%|█████████▎| 37/40 [00:13<00:01, 2.72it/s] 95%|█████████▌| 38/40 [00:13<00:00, 2.72it/s] 98%|█████████▊| 39/40 [00:14<00:00, 2.72it/s] 100%|██████████| 40/40 [00:14<00:00, 2.72it/s] 100%|██████████| 40/40 [00:14<00:00, 2.73it/s]