Readme
This model doesn't have a readme.
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";
const replicate = new Replicate({
auth: process.env.REPLICATE_API_TOKEN,
});
Run farbodmehr/fcm4 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"farbodmehr/fcm4:673f5e044305a69891315b0b13c043a43442350674ac71769c027dacd2ed0c3c",
{
input: {
width: 1024,
height: 1024,
prompt: "image of TOK Soccer player in style of TOK",
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 farbodmehr/fcm4 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"farbodmehr/fcm4:673f5e044305a69891315b0b13c043a43442350674ac71769c027dacd2ed0c3c",
input={
"width": 1024,
"height": 1024,
"prompt": "image of TOK Soccer player in style of TOK",
"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 farbodmehr/fcm4 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": "farbodmehr/fcm4:673f5e044305a69891315b0b13c043a43442350674ac71769c027dacd2ed0c3c",
"input": {
"width": 1024,
"height": 1024,
"prompt": "image of TOK Soccer player in style of TOK",
"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": "2024-01-11T23:57:09.131521Z",
"created_at": "2024-01-11T23:56:51.182293Z",
"data_removed": false,
"error": null,
"id": "l5blv23bkhyr2ctrleabfvowee",
"input": {
"width": 1024,
"height": 1024,
"prompt": "image of TOK Soccer player in style of TOK",
"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: 30533\nEnsuring enough disk space...\nFree disk space: 2237604446208\nDownloading weights: https://replicate.delivery/pbxt/2DKPJAQRBq6gGdyO7HYqDfcP4bP7eFCrf9TWNC5ACHXpHFXkA/trained_model.tar\n2024-01-11T23:56:53Z | INFO | [ Initiating ] dest=/src/weights-cache/ed926043c65355df minimum_chunk_size=150M url=https://replicate.delivery/pbxt/2DKPJAQRBq6gGdyO7HYqDfcP4bP7eFCrf9TWNC5ACHXpHFXkA/trained_model.tar\n2024-01-11T23:56:53Z | INFO | [ Complete ] dest=/src/weights-cache/ed926043c65355df size=\"186 MB\" total_elapsed=0.353s url=https://replicate.delivery/pbxt/2DKPJAQRBq6gGdyO7HYqDfcP4bP7eFCrf9TWNC5ACHXpHFXkA/trained_model.tar\nb''\nDownloaded weights in 0.5125992298126221 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: image of <s0><s1> Soccer player in style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.68it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.67it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.67it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.67it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.67it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.67it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.67it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.67it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.67it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.67it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.66it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.67it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.67it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.67it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.67it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.66it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.66it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.66it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.66it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.66it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.66it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.66it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.66it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.66it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.66it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.65it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.66it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.65it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.65it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.65it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.65it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.65it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.65it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.64it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.65it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.64it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.64it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.64it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.64it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.64it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.64it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.64it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.64it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.64it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.64it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.64it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.64it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.64it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.64it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.64it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.65it/s]",
"metrics": {
"predict_time": 15.914212,
"total_time": 17.949228
},
"output": [
"https://replicate.delivery/pbxt/ar3iWiK5eC0ZQiQCP4oWsKudOj344z3hqXQfjEdOBMDUJjLSA/out-0.png"
],
"started_at": "2024-01-11T23:56:53.217309Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/l5blv23bkhyr2ctrleabfvowee",
"cancel": "https://api.replicate.com/v1/predictions/l5blv23bkhyr2ctrleabfvowee/cancel"
},
"version": "673f5e044305a69891315b0b13c043a43442350674ac71769c027dacd2ed0c3c"
}
Using seed: 30533
Ensuring enough disk space...
Free disk space: 2237604446208
Downloading weights: https://replicate.delivery/pbxt/2DKPJAQRBq6gGdyO7HYqDfcP4bP7eFCrf9TWNC5ACHXpHFXkA/trained_model.tar
2024-01-11T23:56:53Z | INFO | [ Initiating ] dest=/src/weights-cache/ed926043c65355df minimum_chunk_size=150M url=https://replicate.delivery/pbxt/2DKPJAQRBq6gGdyO7HYqDfcP4bP7eFCrf9TWNC5ACHXpHFXkA/trained_model.tar
2024-01-11T23:56:53Z | INFO | [ Complete ] dest=/src/weights-cache/ed926043c65355df size="186 MB" total_elapsed=0.353s url=https://replicate.delivery/pbxt/2DKPJAQRBq6gGdyO7HYqDfcP4bP7eFCrf9TWNC5ACHXpHFXkA/trained_model.tar
b''
Downloaded weights in 0.5125992298126221 seconds
Loading fine-tuned model
Does not have Unet. assume we are using LoRA
Loading Unet LoRA
Prompt: image of <s0><s1> Soccer player in style of <s0><s1>
txt2img mode
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This model costs approximately $0.023 to run on Replicate, or 43 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 24 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: 30533
Ensuring enough disk space...
Free disk space: 2237604446208
Downloading weights: https://replicate.delivery/pbxt/2DKPJAQRBq6gGdyO7HYqDfcP4bP7eFCrf9TWNC5ACHXpHFXkA/trained_model.tar
2024-01-11T23:56:53Z | INFO | [ Initiating ] dest=/src/weights-cache/ed926043c65355df minimum_chunk_size=150M url=https://replicate.delivery/pbxt/2DKPJAQRBq6gGdyO7HYqDfcP4bP7eFCrf9TWNC5ACHXpHFXkA/trained_model.tar
2024-01-11T23:56:53Z | INFO | [ Complete ] dest=/src/weights-cache/ed926043c65355df size="186 MB" total_elapsed=0.353s url=https://replicate.delivery/pbxt/2DKPJAQRBq6gGdyO7HYqDfcP4bP7eFCrf9TWNC5ACHXpHFXkA/trained_model.tar
b''
Downloaded weights in 0.5125992298126221 seconds
Loading fine-tuned model
Does not have Unet. assume we are using LoRA
Loading Unet LoRA
Prompt: image of <s0><s1> Soccer player in style of <s0><s1>
txt2img mode
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