Flux Lora, use "jellycat toy" to trigger style like jellycat toy.
{
"model": "dev",
"prompt": "jellycat toy, a monkey ride bicycle in park",
"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 replicateimport Replicate from "replicate";
import fs from "node:fs";
const replicate = new Replicate({
auth: process.env.REPLICATE_API_TOKEN,
});Run cleexiang/jellycat using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"cleexiang/jellycat:8f049c12e6286f69b4d3282dafe328e4af895aa4e7e9a31fbc115ba9293961b1",
{
input: {
model: "dev",
prompt: "jellycat toy, a monkey ride bicycle in park",
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 replicateimport replicateRun cleexiang/jellycat using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"cleexiang/jellycat:8f049c12e6286f69b4d3282dafe328e4af895aa4e7e9a31fbc115ba9293961b1",
input={
"model": "dev",
"prompt": "jellycat toy, a monkey ride bicycle in park",
"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 cleexiang/jellycat 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": "cleexiang/jellycat:8f049c12e6286f69b4d3282dafe328e4af895aa4e7e9a31fbc115ba9293961b1",
"input": {
"model": "dev",
"prompt": "jellycat toy, a monkey ride bicycle in park",
"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/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
{
"completed_at": "2024-09-06T06:53:52.514901Z",
"created_at": "2024-09-06T06:53:33.829000Z",
"data_removed": false,
"error": null,
"id": "5ca3010h0nrm60chrreahhy4fg",
"input": {
"model": "dev",
"prompt": "jellycat toy, a monkey ride bicycle in park",
"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: 9913\nPrompt: jellycat toy, a monkey ride bicycle in park\n[!] txt2img mode\nUsing dev model\nfree=8407098720256\nDownloading weights\n2024-09-06T06:53:33Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpl7c__xph/weights url=https://replicate.delivery/yhqm/a7WfUnyEvlRzXCZTpSZDEGrLBUiugJcIc9WqdP5LrtDoqCtJA/trained_model.tar\n2024-09-06T06:53:36Z | INFO | [ Complete ] dest=/tmp/tmpl7c__xph/weights size=\"172 MB\" total_elapsed=2.825s url=https://replicate.delivery/yhqm/a7WfUnyEvlRzXCZTpSZDEGrLBUiugJcIc9WqdP5LrtDoqCtJA/trained_model.tar\nDownloaded weights in 2.86s\nLoaded LoRAs in 10.10s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.47it/s]\n 7%|▋ | 2/28 [00:00<00:06, 3.93it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.71it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.61it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.56it/s]\n 21%|██▏ | 6/28 [00:01<00:06, 3.53it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.51it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.50it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.49it/s]\n 36%|███▌ | 10/28 [00:02<00:05, 3.49it/s]\n 39%|███▉ | 11/28 [00:03<00:04, 3.48it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.48it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.47it/s]\n 50%|█████ | 14/28 [00:03<00:04, 3.47it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.47it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.47it/s]\n 61%|██████ | 17/28 [00:04<00:03, 3.47it/s]\n 64%|██████▍ | 18/28 [00:05<00:02, 3.47it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.47it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.47it/s]\n 75%|███████▌ | 21/28 [00:05<00:02, 3.47it/s]\n 79%|███████▊ | 22/28 [00:06<00:01, 3.47it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.47it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.47it/s]\n 89%|████████▉ | 25/28 [00:07<00:00, 3.47it/s]\n 93%|█████████▎| 26/28 [00:07<00:00, 3.47it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.47it/s]\n100%|██████████| 28/28 [00:08<00:00, 3.47it/s]\n100%|██████████| 28/28 [00:08<00:00, 3.50it/s]",
"metrics": {
"predict_time": 18.677529999,
"total_time": 18.685901
},
"output": [
"https://replicate.delivery/yhqm/zhTkrkoSQa5vKZQNEV5wQFkjsnNwut89qHfoEffGwjpBIL0mA/out-0.webp"
],
"started_at": "2024-09-06T06:53:33.837371Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/5ca3010h0nrm60chrreahhy4fg",
"cancel": "https://api.replicate.com/v1/predictions/5ca3010h0nrm60chrreahhy4fg/cancel"
},
"version": "8f049c12e6286f69b4d3282dafe328e4af895aa4e7e9a31fbc115ba9293961b1"
}Using seed: 9913
Prompt: jellycat toy, a monkey ride bicycle in park
[!] txt2img mode
Using dev model
free=8407098720256
Downloading weights
2024-09-06T06:53:33Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpl7c__xph/weights url=https://replicate.delivery/yhqm/a7WfUnyEvlRzXCZTpSZDEGrLBUiugJcIc9WqdP5LrtDoqCtJA/trained_model.tar
2024-09-06T06:53:36Z | INFO | [ Complete ] dest=/tmp/tmpl7c__xph/weights size="172 MB" total_elapsed=2.825s url=https://replicate.delivery/yhqm/a7WfUnyEvlRzXCZTpSZDEGrLBUiugJcIc9WqdP5LrtDoqCtJA/trained_model.tar
Downloaded weights in 2.86s
Loaded LoRAs in 10.10s
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100%|██████████| 28/28 [00:08<00:00, 3.50it/s]Want to make some of these yourself?
Run this modelThis model is warm. You'll get a fast response if the model is warm and already running, and a slower response if the model is cold and starting up.
This model runs on H100. View more.
Using seed: 9913
Prompt: jellycat toy, a monkey ride bicycle in park
[!] txt2img mode
Using dev model
free=8407098720256
Downloading weights
2024-09-06T06:53:33Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpl7c__xph/weights url=https://replicate.delivery/yhqm/a7WfUnyEvlRzXCZTpSZDEGrLBUiugJcIc9WqdP5LrtDoqCtJA/trained_model.tar
2024-09-06T06:53:36Z | INFO | [ Complete ] dest=/tmp/tmpl7c__xph/weights size="172 MB" total_elapsed=2.825s url=https://replicate.delivery/yhqm/a7WfUnyEvlRzXCZTpSZDEGrLBUiugJcIc9WqdP5LrtDoqCtJA/trained_model.tar
Downloaded weights in 2.86s
Loaded LoRAs in 10.10s
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