Readme
This model doesn't have a readme.
SDXL fine tuned on a set of paintings of Remedios Varo. Activate the fine tuning with the prefix: "In the style of RemediosVaro, ..."! Helps to include the negative prompt: "broken, disfigured, dismembered people". (Updated 1 year, 7 months ago)
Run this model in Node.js with one line of code:
npm install replicate
REPLICATE_API_TOKEN
environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
import Replicate from "replicate";
import fs from "node:fs";
const replicate = new Replicate({
auth: process.env.REPLICATE_API_TOKEN,
});
Run cyber42/remedios_varo using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"cyber42/remedios_varo:c8acb09ba51d3c1f0d87c7a69266ca4d1550761ee01a9b102ccd22df723ef5fd",
{
input: {
width: 1024,
height: 1024,
prompt: "In the style of RemediosVaro, a person looking at her image in a lake",
refine: "no_refiner",
scheduler: "K_EULER",
lora_scale: 0.6,
num_outputs: 1,
guidance_scale: 7.5,
apply_watermark: true,
high_noise_frac: 0.8,
negative_prompt: "",
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
REPLICATE_API_TOKEN
environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
import replicate
Run cyber42/remedios_varo using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"cyber42/remedios_varo:c8acb09ba51d3c1f0d87c7a69266ca4d1550761ee01a9b102ccd22df723ef5fd",
input={
"width": 1024,
"height": 1024,
"prompt": "In the style of RemediosVaro, a person looking at her image in a lake",
"refine": "no_refiner",
"scheduler": "K_EULER",
"lora_scale": 0.6,
"num_outputs": 1,
"guidance_scale": 7.5,
"apply_watermark": True,
"high_noise_frac": 0.8,
"negative_prompt": "",
"prompt_strength": 0.8,
"num_inference_steps": 50
}
)
print(output)
To learn more, take a look at the guide on getting started with Python.
REPLICATE_API_TOKEN
environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Run cyber42/remedios_varo 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": "cyber42/remedios_varo:c8acb09ba51d3c1f0d87c7a69266ca4d1550761ee01a9b102ccd22df723ef5fd",
"input": {
"width": 1024,
"height": 1024,
"prompt": "In the style of RemediosVaro, a person looking at her image in a lake",
"refine": "no_refiner",
"scheduler": "K_EULER",
"lora_scale": 0.6,
"num_outputs": 1,
"guidance_scale": 7.5,
"apply_watermark": true,
"high_noise_frac": 0.8,
"negative_prompt": "",
"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.
Add a payment method to run this model.
By signing in, you agree to our
terms of service and privacy policy
{
"completed_at": "2023-10-25T22:08:23.473634Z",
"created_at": "2023-10-25T22:07:55.696525Z",
"data_removed": false,
"error": null,
"id": "k6t2fmtbufvv2cy2jtzcwkubui",
"input": {
"width": 1024,
"height": 1024,
"prompt": "In the style of RemediosVaro, a person looking at her image in a lake",
"refine": "no_refiner",
"scheduler": "K_EULER",
"lora_scale": 0.6,
"num_outputs": 1,
"guidance_scale": 7.5,
"apply_watermark": true,
"high_noise_frac": 0.8,
"negative_prompt": "",
"prompt_strength": 0.8,
"num_inference_steps": 50
},
"logs": "Using seed: 45600\nEnsuring enough disk space...\nFree disk space: 2447670820864\nDownloading weights: https://pbxt.replicate.delivery/zWSDjZjiBeVSGautk3UsukdhKG1Fg7xn6lJuvt5cnNntF64IA/trained_model.tar\nb'Downloaded 186 MB bytes in 4.804s (39 MB/s)\\nExtracted 186 MB in 0.067s (2.8 GB/s)\\n'\nDownloaded weights in 5.175899982452393 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: In the style of RemediosVaro, a person looking at her image in a lake\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]/usr/local/lib/python3.9/site-packages/torch/nn/modules/conv.py:459: UserWarning: Applied workaround for CuDNN issue, install nvrtc.so (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:80.)\nreturn F.conv2d(input, weight, bias, self.stride,\n 2%|▏ | 1/50 [00:00<00:46, 1.06it/s]\n 4%|▍ | 2/50 [00:01<00:26, 1.82it/s]\n 6%|▌ | 3/50 [00:01<00:19, 2.36it/s]\n 8%|▊ | 4/50 [00:01<00:16, 2.74it/s]\n 10%|█ | 5/50 [00:02<00:14, 3.01it/s]\n 12%|█▏ | 6/50 [00:02<00:13, 3.20it/s]\n 14%|█▍ | 7/50 [00:02<00:12, 3.33it/s]\n 16%|█▌ | 8/50 [00:02<00:12, 3.43it/s]\n 18%|█▊ | 9/50 [00:03<00:11, 3.50it/s]\n 20%|██ | 10/50 [00:03<00:11, 3.54it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.57it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.59it/s]\n 26%|██▌ | 13/50 [00:04<00:10, 3.61it/s]\n 28%|██▊ | 14/50 [00:04<00:09, 3.62it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.63it/s]\n 32%|███▏ | 16/50 [00:05<00:09, 3.64it/s]\n 34%|███▍ | 17/50 [00:05<00:09, 3.65it/s]\n 36%|███▌ | 18/50 [00:05<00:08, 3.66it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.66it/s]\n 40%|████ | 20/50 [00:06<00:08, 3.66it/s]\n 42%|████▏ | 21/50 [00:06<00:07, 3.67it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.67it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.67it/s]\n 48%|████▊ | 24/50 [00:07<00:07, 3.67it/s]\n 50%|█████ | 25/50 [00:07<00:06, 3.67it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.67it/s]\n 54%|█████▍ | 27/50 [00:08<00:06, 3.67it/s]\n 56%|█████▌ | 28/50 [00:08<00:06, 3.66it/s]\n 58%|█████▊ | 29/50 [00:08<00:05, 3.67it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.66it/s]\n 62%|██████▏ | 31/50 [00:09<00:05, 3.66it/s]\n 64%|██████▍ | 32/50 [00:09<00:04, 3.66it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.66it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.66it/s]\n 70%|███████ | 35/50 [00:10<00:04, 3.66it/s]\n 72%|███████▏ | 36/50 [00:10<00:03, 3.66it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.67it/s]\n 76%|███████▌ | 38/50 [00:11<00:03, 3.66it/s]\n 78%|███████▊ | 39/50 [00:11<00:03, 3.66it/s]\n 80%|████████ | 40/50 [00:11<00:02, 3.66it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.66it/s]\n 84%|████████▍ | 42/50 [00:12<00:02, 3.66it/s]\n 86%|████████▌ | 43/50 [00:12<00:01, 3.66it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.66it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.66it/s]\n 92%|█████████▏| 46/50 [00:13<00:01, 3.66it/s]\n 94%|█████████▍| 47/50 [00:13<00:00, 3.65it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.66it/s]\n 98%|█████████▊| 49/50 [00:14<00:00, 3.65it/s]\n100%|██████████| 50/50 [00:14<00:00, 3.65it/s]\n100%|██████████| 50/50 [00:14<00:00, 3.49it/s]",
"metrics": {
"predict_time": 22.889806,
"total_time": 27.777109
},
"output": [
"https://replicate.delivery/pbxt/fUcnTL4bOKVSAad6UvrfC8EJeVSHMLedrNMxXfssQ1K06hOOC/out-0.png"
],
"started_at": "2023-10-25T22:08:00.583828Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/k6t2fmtbufvv2cy2jtzcwkubui",
"cancel": "https://api.replicate.com/v1/predictions/k6t2fmtbufvv2cy2jtzcwkubui/cancel"
},
"version": "c8acb09ba51d3c1f0d87c7a69266ca4d1550761ee01a9b102ccd22df723ef5fd"
}
Using seed: 45600
Ensuring enough disk space...
Free disk space: 2447670820864
Downloading weights: https://pbxt.replicate.delivery/zWSDjZjiBeVSGautk3UsukdhKG1Fg7xn6lJuvt5cnNntF64IA/trained_model.tar
b'Downloaded 186 MB bytes in 4.804s (39 MB/s)\nExtracted 186 MB in 0.067s (2.8 GB/s)\n'
Downloaded weights in 5.175899982452393 seconds
Loading fine-tuned model
Does not have Unet. assume we are using LoRA
Loading Unet LoRA
Prompt: In the style of RemediosVaro, a person looking at her image in a lake
txt2img mode
0%| | 0/50 [00:00<?, ?it/s]/usr/local/lib/python3.9/site-packages/torch/nn/modules/conv.py:459: UserWarning: Applied workaround for CuDNN issue, install nvrtc.so (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:80.)
return F.conv2d(input, weight, bias, self.stride,
2%|▏ | 1/50 [00:00<00:46, 1.06it/s]
4%|▍ | 2/50 [00:01<00:26, 1.82it/s]
6%|▌ | 3/50 [00:01<00:19, 2.36it/s]
8%|▊ | 4/50 [00:01<00:16, 2.74it/s]
10%|█ | 5/50 [00:02<00:14, 3.01it/s]
12%|█▏ | 6/50 [00:02<00:13, 3.20it/s]
14%|█▍ | 7/50 [00:02<00:12, 3.33it/s]
16%|█▌ | 8/50 [00:02<00:12, 3.43it/s]
18%|█▊ | 9/50 [00:03<00:11, 3.50it/s]
20%|██ | 10/50 [00:03<00:11, 3.54it/s]
22%|██▏ | 11/50 [00:03<00:10, 3.57it/s]
24%|██▍ | 12/50 [00:03<00:10, 3.59it/s]
26%|██▌ | 13/50 [00:04<00:10, 3.61it/s]
28%|██▊ | 14/50 [00:04<00:09, 3.62it/s]
30%|███ | 15/50 [00:04<00:09, 3.63it/s]
32%|███▏ | 16/50 [00:05<00:09, 3.64it/s]
34%|███▍ | 17/50 [00:05<00:09, 3.65it/s]
36%|███▌ | 18/50 [00:05<00:08, 3.66it/s]
38%|███▊ | 19/50 [00:05<00:08, 3.66it/s]
40%|████ | 20/50 [00:06<00:08, 3.66it/s]
42%|████▏ | 21/50 [00:06<00:07, 3.67it/s]
44%|████▍ | 22/50 [00:06<00:07, 3.67it/s]
46%|████▌ | 23/50 [00:06<00:07, 3.67it/s]
48%|████▊ | 24/50 [00:07<00:07, 3.67it/s]
50%|█████ | 25/50 [00:07<00:06, 3.67it/s]
52%|█████▏ | 26/50 [00:07<00:06, 3.67it/s]
54%|█████▍ | 27/50 [00:08<00:06, 3.67it/s]
56%|█████▌ | 28/50 [00:08<00:06, 3.66it/s]
58%|█████▊ | 29/50 [00:08<00:05, 3.67it/s]
60%|██████ | 30/50 [00:08<00:05, 3.66it/s]
62%|██████▏ | 31/50 [00:09<00:05, 3.66it/s]
64%|██████▍ | 32/50 [00:09<00:04, 3.66it/s]
66%|██████▌ | 33/50 [00:09<00:04, 3.66it/s]
68%|██████▊ | 34/50 [00:09<00:04, 3.66it/s]
70%|███████ | 35/50 [00:10<00:04, 3.66it/s]
72%|███████▏ | 36/50 [00:10<00:03, 3.66it/s]
74%|███████▍ | 37/50 [00:10<00:03, 3.67it/s]
76%|███████▌ | 38/50 [00:11<00:03, 3.66it/s]
78%|███████▊ | 39/50 [00:11<00:03, 3.66it/s]
80%|████████ | 40/50 [00:11<00:02, 3.66it/s]
82%|████████▏ | 41/50 [00:11<00:02, 3.66it/s]
84%|████████▍ | 42/50 [00:12<00:02, 3.66it/s]
86%|████████▌ | 43/50 [00:12<00:01, 3.66it/s]
88%|████████▊ | 44/50 [00:12<00:01, 3.66it/s]
90%|█████████ | 45/50 [00:12<00:01, 3.66it/s]
92%|█████████▏| 46/50 [00:13<00:01, 3.66it/s]
94%|█████████▍| 47/50 [00:13<00:00, 3.65it/s]
96%|█████████▌| 48/50 [00:13<00:00, 3.66it/s]
98%|█████████▊| 49/50 [00:14<00:00, 3.65it/s]
100%|██████████| 50/50 [00:14<00:00, 3.65it/s]
100%|██████████| 50/50 [00:14<00:00, 3.49it/s]
This model costs approximately $0.022 to run on Replicate, or 45 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.
This model runs on Nvidia L40S GPU hardware. Predictions typically complete within 23 seconds.
This model doesn't have a readme.
This 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.
Choose a file from your machine
Hint: you can also drag files onto the input
Choose a file from your machine
Hint: you can also drag files onto the input
Using seed: 45600
Ensuring enough disk space...
Free disk space: 2447670820864
Downloading weights: https://pbxt.replicate.delivery/zWSDjZjiBeVSGautk3UsukdhKG1Fg7xn6lJuvt5cnNntF64IA/trained_model.tar
b'Downloaded 186 MB bytes in 4.804s (39 MB/s)\nExtracted 186 MB in 0.067s (2.8 GB/s)\n'
Downloaded weights in 5.175899982452393 seconds
Loading fine-tuned model
Does not have Unet. assume we are using LoRA
Loading Unet LoRA
Prompt: In the style of RemediosVaro, a person looking at her image in a lake
txt2img mode
0%| | 0/50 [00:00<?, ?it/s]/usr/local/lib/python3.9/site-packages/torch/nn/modules/conv.py:459: UserWarning: Applied workaround for CuDNN issue, install nvrtc.so (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:80.)
return F.conv2d(input, weight, bias, self.stride,
2%|▏ | 1/50 [00:00<00:46, 1.06it/s]
4%|▍ | 2/50 [00:01<00:26, 1.82it/s]
6%|▌ | 3/50 [00:01<00:19, 2.36it/s]
8%|▊ | 4/50 [00:01<00:16, 2.74it/s]
10%|█ | 5/50 [00:02<00:14, 3.01it/s]
12%|█▏ | 6/50 [00:02<00:13, 3.20it/s]
14%|█▍ | 7/50 [00:02<00:12, 3.33it/s]
16%|█▌ | 8/50 [00:02<00:12, 3.43it/s]
18%|█▊ | 9/50 [00:03<00:11, 3.50it/s]
20%|██ | 10/50 [00:03<00:11, 3.54it/s]
22%|██▏ | 11/50 [00:03<00:10, 3.57it/s]
24%|██▍ | 12/50 [00:03<00:10, 3.59it/s]
26%|██▌ | 13/50 [00:04<00:10, 3.61it/s]
28%|██▊ | 14/50 [00:04<00:09, 3.62it/s]
30%|███ | 15/50 [00:04<00:09, 3.63it/s]
32%|███▏ | 16/50 [00:05<00:09, 3.64it/s]
34%|███▍ | 17/50 [00:05<00:09, 3.65it/s]
36%|███▌ | 18/50 [00:05<00:08, 3.66it/s]
38%|███▊ | 19/50 [00:05<00:08, 3.66it/s]
40%|████ | 20/50 [00:06<00:08, 3.66it/s]
42%|████▏ | 21/50 [00:06<00:07, 3.67it/s]
44%|████▍ | 22/50 [00:06<00:07, 3.67it/s]
46%|████▌ | 23/50 [00:06<00:07, 3.67it/s]
48%|████▊ | 24/50 [00:07<00:07, 3.67it/s]
50%|█████ | 25/50 [00:07<00:06, 3.67it/s]
52%|█████▏ | 26/50 [00:07<00:06, 3.67it/s]
54%|█████▍ | 27/50 [00:08<00:06, 3.67it/s]
56%|█████▌ | 28/50 [00:08<00:06, 3.66it/s]
58%|█████▊ | 29/50 [00:08<00:05, 3.67it/s]
60%|██████ | 30/50 [00:08<00:05, 3.66it/s]
62%|██████▏ | 31/50 [00:09<00:05, 3.66it/s]
64%|██████▍ | 32/50 [00:09<00:04, 3.66it/s]
66%|██████▌ | 33/50 [00:09<00:04, 3.66it/s]
68%|██████▊ | 34/50 [00:09<00:04, 3.66it/s]
70%|███████ | 35/50 [00:10<00:04, 3.66it/s]
72%|███████▏ | 36/50 [00:10<00:03, 3.66it/s]
74%|███████▍ | 37/50 [00:10<00:03, 3.67it/s]
76%|███████▌ | 38/50 [00:11<00:03, 3.66it/s]
78%|███████▊ | 39/50 [00:11<00:03, 3.66it/s]
80%|████████ | 40/50 [00:11<00:02, 3.66it/s]
82%|████████▏ | 41/50 [00:11<00:02, 3.66it/s]
84%|████████▍ | 42/50 [00:12<00:02, 3.66it/s]
86%|████████▌ | 43/50 [00:12<00:01, 3.66it/s]
88%|████████▊ | 44/50 [00:12<00:01, 3.66it/s]
90%|█████████ | 45/50 [00:12<00:01, 3.66it/s]
92%|█████████▏| 46/50 [00:13<00:01, 3.66it/s]
94%|█████████▍| 47/50 [00:13<00:00, 3.65it/s]
96%|█████████▌| 48/50 [00:13<00:00, 3.66it/s]
98%|█████████▊| 49/50 [00:14<00:00, 3.65it/s]
100%|██████████| 50/50 [00:14<00:00, 3.65it/s]
100%|██████████| 50/50 [00:14<00:00, 3.49it/s]