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 variableexport 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 daanelson/training-2 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"daanelson/training-2:f0cd4cbf0e8f8fa2975f5222cd879bb1a12a3265bfc12c64814bc6ca168cad1a",
{
input: {
width: 1024,
height: 1024,
prompt: "An <s0> riding a rainbow unicorn",
refine: "expert_ensemble_refiner",
scheduler: "DDIM",
num_outputs: 1,
guidance_scale: 7.5,
high_noise_frac: 0.8,
negative_prompt: "",
prompt_strength: 0.8,
num_inference_steps: 50
}
}
);
console.log(output);
To learn more, take a look at the guide on getting started with Node.js.
pip install replicate
REPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
import replicate
Run daanelson/training-2 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"daanelson/training-2:f0cd4cbf0e8f8fa2975f5222cd879bb1a12a3265bfc12c64814bc6ca168cad1a",
input={
"width": 1024,
"height": 1024,
"prompt": "An <s0> riding a rainbow unicorn",
"refine": "expert_ensemble_refiner",
"scheduler": "DDIM",
"num_outputs": 1,
"guidance_scale": 7.5,
"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 variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Run daanelson/training-2 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": "f0cd4cbf0e8f8fa2975f5222cd879bb1a12a3265bfc12c64814bc6ca168cad1a",
"input": {
"width": 1024,
"height": 1024,
"prompt": "An <s0> riding a rainbow unicorn",
"refine": "expert_ensemble_refiner",
"scheduler": "DDIM",
"num_outputs": 1,
"guidance_scale": 7.5,
"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.
brew install cog
If you don’t have Homebrew, there are other installation options available.
Pull and run daanelson/training-2 using Cog (this will download the full model and run it in your local environment):
cog predict r8.im/daanelson/training-2@sha256:f0cd4cbf0e8f8fa2975f5222cd879bb1a12a3265bfc12c64814bc6ca168cad1a \
-i 'width=1024' \
-i 'height=1024' \
-i 'prompt="An <s0> riding a rainbow unicorn"' \
-i 'refine="expert_ensemble_refiner"' \
-i 'scheduler="DDIM"' \
-i 'num_outputs=1' \
-i 'guidance_scale=7.5' \
-i 'high_noise_frac=0.8' \
-i 'negative_prompt=""' \
-i 'prompt_strength=0.8' \
-i 'num_inference_steps=50'
To learn more, take a look at the Cog documentation.
Pull and run daanelson/training-2 using Docker (this will download the full model and run it in your local environment):
docker run -d -p 5000:5000 --gpus=all r8.im/daanelson/training-2@sha256:f0cd4cbf0e8f8fa2975f5222cd879bb1a12a3265bfc12c64814bc6ca168cad1a
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 1024, "height": 1024, "prompt": "An <s0> riding a rainbow unicorn", "refine": "expert_ensemble_refiner", "scheduler": "DDIM", "num_outputs": 1, "guidance_scale": 7.5, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ http://localhost:5000/predictions
Add a payment method to run this model.
Each run costs approximately $0.011. Alternatively, try out our featured models for free.
By signing in, you agree to our
terms of service and privacy policy
{
"completed_at": "2023-07-27T07:43:49.764310Z",
"created_at": "2023-07-27T07:41:36.123862Z",
"data_removed": false,
"error": null,
"id": "vyjsz5zbrgx4qqqkcvo6htgyse",
"input": {
"width": 1024,
"height": 1024,
"prompt": "An <s0> riding a rainbow unicorn",
"refine": "expert_ensemble_refiner",
"scheduler": "DDIM",
"num_outputs": 1,
"guidance_scale": 7.5,
"high_noise_frac": 0.8,
"prompt_strength": 0.8,
"num_inference_steps": 50
},
"logs": "Using seed: 53975\ntxt2img mode\n 0%| | 0/40 [00:00<?, ?it/s]\n 2%|▎ | 1/40 [00:01<00:58, 1.50s/it]\n 5%|▌ | 2/40 [00:01<00:26, 1.44it/s]\n 8%|▊ | 3/40 [00:01<00:15, 2.33it/s]\n 10%|█ | 4/40 [00:01<00:11, 3.27it/s]\n 12%|█▎ | 5/40 [00:01<00:08, 4.20it/s]\n 15%|█▌ | 6/40 [00:02<00:06, 4.99it/s]\n 18%|█▊ | 7/40 [00:02<00:05, 5.76it/s]\n 20%|██ | 8/40 [00:02<00:05, 6.40it/s]\n 22%|██▎ | 9/40 [00:02<00:04, 6.92it/s]\n 25%|██▌ | 10/40 [00:02<00:04, 7.34it/s]\n 28%|██▊ | 11/40 [00:02<00:03, 7.67it/s]\n 30%|███ | 12/40 [00:02<00:03, 7.90it/s]\n 32%|███▎ | 13/40 [00:02<00:03, 8.10it/s]\n 35%|███▌ | 14/40 [00:03<00:03, 8.22it/s]\n 38%|███▊ | 15/40 [00:03<00:03, 8.33it/s]\n 40%|████ | 16/40 [00:03<00:02, 8.39it/s]\n 42%|████▎ | 17/40 [00:03<00:02, 8.25it/s]\n 45%|████▌ | 18/40 [00:03<00:02, 8.35it/s]\n 48%|████▊ | 19/40 [00:03<00:02, 8.41it/s]\n 50%|█████ | 20/40 [00:03<00:02, 8.45it/s]\n 52%|█████▎ | 21/40 [00:03<00:02, 8.48it/s]\n 55%|█████▌ | 22/40 [00:03<00:02, 8.51it/s]\n 57%|█████▊ | 23/40 [00:04<00:02, 8.37it/s]\n 60%|██████ | 24/40 [00:04<00:01, 8.36it/s]\n 62%|██████▎ | 25/40 [00:04<00:01, 8.30it/s]\n 65%|██████▌ | 26/40 [00:04<00:01, 8.38it/s]\n 68%|██████▊ | 27/40 [00:04<00:01, 8.45it/s]\n 70%|███████ | 28/40 [00:04<00:01, 8.50it/s]\n 72%|███████▎ | 29/40 [00:04<00:01, 8.50it/s]\n 75%|███████▌ | 30/40 [00:04<00:01, 8.45it/s]\n 78%|███████▊ | 31/40 [00:05<00:01, 8.47it/s]\n 80%|████████ | 32/40 [00:05<00:00, 8.23it/s]\n 82%|████████▎ | 33/40 [00:05<00:00, 8.33it/s]\n 85%|████████▌ | 34/40 [00:05<00:00, 8.39it/s]\n 88%|████████▊ | 35/40 [00:05<00:00, 8.37it/s]\n 90%|█████████ | 36/40 [00:05<00:00, 8.39it/s]\n 92%|█████████▎| 37/40 [00:05<00:00, 8.31it/s]\n 95%|█████████▌| 38/40 [00:05<00:00, 8.39it/s]\n 98%|█████████▊| 39/40 [00:06<00:00, 8.45it/s]\n100%|██████████| 40/40 [00:06<00:00, 8.33it/s]\n100%|██████████| 40/40 [00:06<00:00, 6.51it/s]\n 0%| | 0/10 [00:00<?, ?it/s]\n 10%|█ | 1/10 [00:00<00:02, 4.12it/s]\n 20%|██ | 2/10 [00:00<00:01, 5.68it/s]\n 30%|███ | 3/10 [00:00<00:01, 6.46it/s]\n 40%|████ | 4/10 [00:00<00:00, 6.91it/s]\n 50%|█████ | 5/10 [00:00<00:00, 7.18it/s]\n 60%|██████ | 6/10 [00:00<00:00, 7.34it/s]\n 70%|███████ | 7/10 [00:01<00:00, 7.23it/s]\n 80%|████████ | 8/10 [00:01<00:00, 7.38it/s]\n 90%|█████████ | 9/10 [00:01<00:00, 7.48it/s]\n100%|██████████| 10/10 [00:01<00:00, 7.55it/s]\n100%|██████████| 10/10 [00:01<00:00, 7.03it/s]",
"metrics": {
"predict_time": 9.663728,
"total_time": 133.640448
},
"output": [
"https://replicate.delivery/pbxt/8Eap6aw4nYKiAxTLSfoSEHuwrU0VC80pCdSSwqECKqtakeTRA/out-0.png"
],
"started_at": "2023-07-27T07:43:40.100582Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/vyjsz5zbrgx4qqqkcvo6htgyse",
"cancel": "https://api.replicate.com/v1/predictions/vyjsz5zbrgx4qqqkcvo6htgyse/cancel"
},
"version": "0019e28f3fd28dc2dce2e4c8f425f63285f157a83acb646e17336dcb16fc57aa"
}
Using seed: 53975
txt2img mode
0%| | 0/40 [00:00<?, ?it/s]
2%|▎ | 1/40 [00:01<00:58, 1.50s/it]
5%|▌ | 2/40 [00:01<00:26, 1.44it/s]
8%|▊ | 3/40 [00:01<00:15, 2.33it/s]
10%|█ | 4/40 [00:01<00:11, 3.27it/s]
12%|█▎ | 5/40 [00:01<00:08, 4.20it/s]
15%|█▌ | 6/40 [00:02<00:06, 4.99it/s]
18%|█▊ | 7/40 [00:02<00:05, 5.76it/s]
20%|██ | 8/40 [00:02<00:05, 6.40it/s]
22%|██▎ | 9/40 [00:02<00:04, 6.92it/s]
25%|██▌ | 10/40 [00:02<00:04, 7.34it/s]
28%|██▊ | 11/40 [00:02<00:03, 7.67it/s]
30%|███ | 12/40 [00:02<00:03, 7.90it/s]
32%|███▎ | 13/40 [00:02<00:03, 8.10it/s]
35%|███▌ | 14/40 [00:03<00:03, 8.22it/s]
38%|███▊ | 15/40 [00:03<00:03, 8.33it/s]
40%|████ | 16/40 [00:03<00:02, 8.39it/s]
42%|████▎ | 17/40 [00:03<00:02, 8.25it/s]
45%|████▌ | 18/40 [00:03<00:02, 8.35it/s]
48%|████▊ | 19/40 [00:03<00:02, 8.41it/s]
50%|█████ | 20/40 [00:03<00:02, 8.45it/s]
52%|█████▎ | 21/40 [00:03<00:02, 8.48it/s]
55%|█████▌ | 22/40 [00:03<00:02, 8.51it/s]
57%|█████▊ | 23/40 [00:04<00:02, 8.37it/s]
60%|██████ | 24/40 [00:04<00:01, 8.36it/s]
62%|██████▎ | 25/40 [00:04<00:01, 8.30it/s]
65%|██████▌ | 26/40 [00:04<00:01, 8.38it/s]
68%|██████▊ | 27/40 [00:04<00:01, 8.45it/s]
70%|███████ | 28/40 [00:04<00:01, 8.50it/s]
72%|███████▎ | 29/40 [00:04<00:01, 8.50it/s]
75%|███████▌ | 30/40 [00:04<00:01, 8.45it/s]
78%|███████▊ | 31/40 [00:05<00:01, 8.47it/s]
80%|████████ | 32/40 [00:05<00:00, 8.23it/s]
82%|████████▎ | 33/40 [00:05<00:00, 8.33it/s]
85%|████████▌ | 34/40 [00:05<00:00, 8.39it/s]
88%|████████▊ | 35/40 [00:05<00:00, 8.37it/s]
90%|█████████ | 36/40 [00:05<00:00, 8.39it/s]
92%|█████████▎| 37/40 [00:05<00:00, 8.31it/s]
95%|█████████▌| 38/40 [00:05<00:00, 8.39it/s]
98%|█████████▊| 39/40 [00:06<00:00, 8.45it/s]
100%|██████████| 40/40 [00:06<00:00, 8.33it/s]
100%|██████████| 40/40 [00:06<00:00, 6.51it/s]
0%| | 0/10 [00:00<?, ?it/s]
10%|█ | 1/10 [00:00<00:02, 4.12it/s]
20%|██ | 2/10 [00:00<00:01, 5.68it/s]
30%|███ | 3/10 [00:00<00:01, 6.46it/s]
40%|████ | 4/10 [00:00<00:00, 6.91it/s]
50%|█████ | 5/10 [00:00<00:00, 7.18it/s]
60%|██████ | 6/10 [00:00<00:00, 7.34it/s]
70%|███████ | 7/10 [00:01<00:00, 7.23it/s]
80%|████████ | 8/10 [00:01<00:00, 7.38it/s]
90%|█████████ | 9/10 [00:01<00:00, 7.48it/s]
100%|██████████| 10/10 [00:01<00:00, 7.55it/s]
100%|██████████| 10/10 [00:01<00:00, 7.03it/s]
This example was created by a different version, daanelson/training-2:0019e28f.
This model costs approximately $0.011 to run on Replicate, or 90 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 A100 (80GB) GPU hardware. Predictions typically complete within 8 seconds.
This model doesn't have a readme.
This model is cold. 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: 53975
txt2img mode
0%| | 0/40 [00:00<?, ?it/s]
2%|▎ | 1/40 [00:01<00:58, 1.50s/it]
5%|▌ | 2/40 [00:01<00:26, 1.44it/s]
8%|▊ | 3/40 [00:01<00:15, 2.33it/s]
10%|█ | 4/40 [00:01<00:11, 3.27it/s]
12%|█▎ | 5/40 [00:01<00:08, 4.20it/s]
15%|█▌ | 6/40 [00:02<00:06, 4.99it/s]
18%|█▊ | 7/40 [00:02<00:05, 5.76it/s]
20%|██ | 8/40 [00:02<00:05, 6.40it/s]
22%|██▎ | 9/40 [00:02<00:04, 6.92it/s]
25%|██▌ | 10/40 [00:02<00:04, 7.34it/s]
28%|██▊ | 11/40 [00:02<00:03, 7.67it/s]
30%|███ | 12/40 [00:02<00:03, 7.90it/s]
32%|███▎ | 13/40 [00:02<00:03, 8.10it/s]
35%|███▌ | 14/40 [00:03<00:03, 8.22it/s]
38%|███▊ | 15/40 [00:03<00:03, 8.33it/s]
40%|████ | 16/40 [00:03<00:02, 8.39it/s]
42%|████▎ | 17/40 [00:03<00:02, 8.25it/s]
45%|████▌ | 18/40 [00:03<00:02, 8.35it/s]
48%|████▊ | 19/40 [00:03<00:02, 8.41it/s]
50%|█████ | 20/40 [00:03<00:02, 8.45it/s]
52%|█████▎ | 21/40 [00:03<00:02, 8.48it/s]
55%|█████▌ | 22/40 [00:03<00:02, 8.51it/s]
57%|█████▊ | 23/40 [00:04<00:02, 8.37it/s]
60%|██████ | 24/40 [00:04<00:01, 8.36it/s]
62%|██████▎ | 25/40 [00:04<00:01, 8.30it/s]
65%|██████▌ | 26/40 [00:04<00:01, 8.38it/s]
68%|██████▊ | 27/40 [00:04<00:01, 8.45it/s]
70%|███████ | 28/40 [00:04<00:01, 8.50it/s]
72%|███████▎ | 29/40 [00:04<00:01, 8.50it/s]
75%|███████▌ | 30/40 [00:04<00:01, 8.45it/s]
78%|███████▊ | 31/40 [00:05<00:01, 8.47it/s]
80%|████████ | 32/40 [00:05<00:00, 8.23it/s]
82%|████████▎ | 33/40 [00:05<00:00, 8.33it/s]
85%|████████▌ | 34/40 [00:05<00:00, 8.39it/s]
88%|████████▊ | 35/40 [00:05<00:00, 8.37it/s]
90%|█████████ | 36/40 [00:05<00:00, 8.39it/s]
92%|█████████▎| 37/40 [00:05<00:00, 8.31it/s]
95%|█████████▌| 38/40 [00:05<00:00, 8.39it/s]
98%|█████████▊| 39/40 [00:06<00:00, 8.45it/s]
100%|██████████| 40/40 [00:06<00:00, 8.33it/s]
100%|██████████| 40/40 [00:06<00:00, 6.51it/s]
0%| | 0/10 [00:00<?, ?it/s]
10%|█ | 1/10 [00:00<00:02, 4.12it/s]
20%|██ | 2/10 [00:00<00:01, 5.68it/s]
30%|███ | 3/10 [00:00<00:01, 6.46it/s]
40%|████ | 4/10 [00:00<00:00, 6.91it/s]
50%|█████ | 5/10 [00:00<00:00, 7.18it/s]
60%|██████ | 6/10 [00:00<00:00, 7.34it/s]
70%|███████ | 7/10 [00:01<00:00, 7.23it/s]
80%|████████ | 8/10 [00:01<00:00, 7.38it/s]
90%|█████████ | 9/10 [00:01<00:00, 7.48it/s]
100%|██████████| 10/10 [00:01<00:00, 7.55it/s]
100%|██████████| 10/10 [00:01<00:00, 7.03it/s]