Readme
This model doesn't have a readme.
stephendmalloy
/
gowaterbottle
- Public
- 179 runs
-
L40S
- SDXL fine-tune
Input
Run this model in Node.js with one line of code:
npx create-replicate --model=stephendmalloy/gowaterbottle
or set up a project from scratch
npm install replicateSet the
REPLICATE_API_TOKEN environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the client
import Replicate from "replicate";
const replicate = new Replicate({
auth: process.env.REPLICATE_API_TOKEN,
});Run stephendmalloy/gowaterbottle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"stephendmalloy/gowaterbottle:7f8acdd90e9b74cb10d00dd0aa25e8e1a84df18b8600bf886f9e897687747711",
{
input: {
width: 1024,
height: 1024,
prompt: "A photo of TOK water bottle, in a busy coffee shop",
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
}
}
);
console.log(output);To learn more, take a look at the guide on getting started with Node.js.
pip install replicateSet the
REPLICATE_API_TOKEN environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client
import replicateRun stephendmalloy/gowaterbottle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"stephendmalloy/gowaterbottle:7f8acdd90e9b74cb10d00dd0aa25e8e1a84df18b8600bf886f9e897687747711",
input={
"width": 1024,
"height": 1024,
"prompt": "A photo of TOK water bottle, in a busy coffee shop",
"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.
Set the
REPLICATE_API_TOKEN environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run stephendmalloy/gowaterbottle 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": "7f8acdd90e9b74cb10d00dd0aa25e8e1a84df18b8600bf886f9e897687747711",
"input": {
"width": 1024,
"height": 1024,
"prompt": "A photo of TOK water bottle, in a busy coffee shop",
"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/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Install Cog
brew install cogIf you don’t have Homebrew, there are other installation options available.
Pull and run stephendmalloy/gowaterbottle using Cog (this will download the full model and run it in your local environment):
cog predict r8.im/stephendmalloy/gowaterbottle@sha256:7f8acdd90e9b74cb10d00dd0aa25e8e1a84df18b8600bf886f9e897687747711 \
-i 'width=1024' \
-i 'height=1024' \
-i 'prompt="A photo of TOK water bottle, in a busy coffee shop"' \
-i 'refine="no_refiner"' \
-i 'scheduler="K_EULER"' \
-i 'lora_scale=0.6' \
-i 'num_outputs=1' \
-i 'guidance_scale=7.5' \
-i 'apply_watermark=true' \
-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 stephendmalloy/gowaterbottle 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/stephendmalloy/gowaterbottle@sha256:7f8acdd90e9b74cb10d00dd0aa25e8e1a84df18b8600bf886f9e897687747711curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 1024, "height": 1024, "prompt": "A photo of TOK water bottle, in a busy coffee shop", "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 } }' \ http://localhost:5000/predictions
Add a payment method to run this model.
Each run costs approximately $0.019. Alternatively, try out our featured models for free.
Sign in with GitHub
By signing in, you agree to our
terms of service and privacy policy
Output
{
"completed_at": "2024-02-09T23:03:46.935666Z",
"created_at": "2024-02-09T23:03:27.560884Z",
"data_removed": false,
"error": null,
"id": "mniwkqlbj3wg5pz2ipohso36sm",
"input": {
"width": 1024,
"height": 1024,
"prompt": "A photo of TOK water bottle, in a busy coffee shop",
"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: 56162\nEnsuring enough disk space...\nFree disk space: 1777420779520\nDownloading weights: https://replicate.delivery/pbxt/8cySTK1wq9JHApNR5A3veUPFlcZ76nUsL51sJhPIIfNNXCVSA/trained_model.tar\n2024-02-09T23:03:29Z | INFO | [ Initiating ] dest=/src/weights-cache/a87263fc3cb61291 minimum_chunk_size=150M url=https://replicate.delivery/pbxt/8cySTK1wq9JHApNR5A3veUPFlcZ76nUsL51sJhPIIfNNXCVSA/trained_model.tar\n2024-02-09T23:03:30Z | INFO | [ Complete ] dest=/src/weights-cache/a87263fc3cb61291 size=\"186 MB\" total_elapsed=0.629s url=https://replicate.delivery/pbxt/8cySTK1wq9JHApNR5A3veUPFlcZ76nUsL51sJhPIIfNNXCVSA/trained_model.tar\nb''\nDownloaded weights in 0.7754557132720947 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: A photo of <s0><s1> water bottle, in a busy coffee shop\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.69it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.68it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.67it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.66it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.65it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.65it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.65it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.65it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.64it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.64it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.64it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.64it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.64it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.64it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.62it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.63it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.64it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.64it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.65it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.65it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.65it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.65it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.65it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.64it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.64it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.64it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.63it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.63it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.63it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.63it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.64it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.64it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.64it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.65it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.65it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.65it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.65it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.65it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.65it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.65it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.65it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.65it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.65it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.65it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.65it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.65it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.65it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.65it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.65it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.65it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.65it/s]",
"metrics": {
"predict_time": 17.318059,
"total_time": 19.374782
},
"output": [
"https://replicate.delivery/pbxt/c1EqfHfTPYgo5kr29Zc6sIsHBRWgMwNU4TMFPOdijJfjKMqkA/out-0.png"
],
"started_at": "2024-02-09T23:03:29.617607Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/mniwkqlbj3wg5pz2ipohso36sm",
"cancel": "https://api.replicate.com/v1/predictions/mniwkqlbj3wg5pz2ipohso36sm/cancel"
},
"version": "7f8acdd90e9b74cb10d00dd0aa25e8e1a84df18b8600bf886f9e897687747711"
}Generated in
Using seed: 56162
Ensuring enough disk space...
Free disk space: 1777420779520
Downloading weights: https://replicate.delivery/pbxt/8cySTK1wq9JHApNR5A3veUPFlcZ76nUsL51sJhPIIfNNXCVSA/trained_model.tar
2024-02-09T23:03:29Z | INFO | [ Initiating ] dest=/src/weights-cache/a87263fc3cb61291 minimum_chunk_size=150M url=https://replicate.delivery/pbxt/8cySTK1wq9JHApNR5A3veUPFlcZ76nUsL51sJhPIIfNNXCVSA/trained_model.tar
2024-02-09T23:03:30Z | INFO | [ Complete ] dest=/src/weights-cache/a87263fc3cb61291 size="186 MB" total_elapsed=0.629s url=https://replicate.delivery/pbxt/8cySTK1wq9JHApNR5A3veUPFlcZ76nUsL51sJhPIIfNNXCVSA/trained_model.tar
b''
Downloaded weights in 0.7754557132720947 seconds
Loading fine-tuned model
Does not have Unet. assume we are using LoRA
Loading Unet LoRA
Prompt: A photo of <s0><s1> water bottle, in a busy coffee shop
txt2img mode
0%| | 0/50 [00:00<?, ?it/s]
2%|▏ | 1/50 [00:00<00:13, 3.69it/s]
4%|▍ | 2/50 [00:00<00:13, 3.68it/s]
6%|▌ | 3/50 [00:00<00:12, 3.67it/s]
8%|▊ | 4/50 [00:01<00:12, 3.66it/s]
10%|█ | 5/50 [00:01<00:12, 3.65it/s]
12%|█▏ | 6/50 [00:01<00:12, 3.65it/s]
14%|█▍ | 7/50 [00:01<00:11, 3.65it/s]
16%|█▌ | 8/50 [00:02<00:11, 3.65it/s]
18%|█▊ | 9/50 [00:02<00:11, 3.64it/s]
20%|██ | 10/50 [00:02<00:10, 3.64it/s]
22%|██▏ | 11/50 [00:03<00:10, 3.64it/s]
24%|██▍ | 12/50 [00:03<00:10, 3.64it/s]
26%|██▌ | 13/50 [00:03<00:10, 3.64it/s]
28%|██▊ | 14/50 [00:03<00:09, 3.64it/s]
30%|███ | 15/50 [00:04<00:09, 3.62it/s]
32%|███▏ | 16/50 [00:04<00:09, 3.63it/s]
34%|███▍ | 17/50 [00:04<00:09, 3.64it/s]
36%|███▌ | 18/50 [00:04<00:08, 3.64it/s]
38%|███▊ | 19/50 [00:05<00:08, 3.65it/s]
40%|████ | 20/50 [00:05<00:08, 3.65it/s]
42%|████▏ | 21/50 [00:05<00:07, 3.65it/s]
44%|████▍ | 22/50 [00:06<00:07, 3.65it/s]
46%|████▌ | 23/50 [00:06<00:07, 3.65it/s]
48%|████▊ | 24/50 [00:06<00:07, 3.64it/s]
50%|█████ | 25/50 [00:06<00:06, 3.64it/s]
52%|█████▏ | 26/50 [00:07<00:06, 3.64it/s]
54%|█████▍ | 27/50 [00:07<00:06, 3.63it/s]
56%|█████▌ | 28/50 [00:07<00:06, 3.63it/s]
58%|█████▊ | 29/50 [00:07<00:05, 3.63it/s]
60%|██████ | 30/50 [00:08<00:05, 3.63it/s]
62%|██████▏ | 31/50 [00:08<00:05, 3.64it/s]
64%|██████▍ | 32/50 [00:08<00:04, 3.64it/s]
66%|██████▌ | 33/50 [00:09<00:04, 3.64it/s]
68%|██████▊ | 34/50 [00:09<00:04, 3.65it/s]
70%|███████ | 35/50 [00:09<00:04, 3.65it/s]
72%|███████▏ | 36/50 [00:09<00:03, 3.65it/s]
74%|███████▍ | 37/50 [00:10<00:03, 3.65it/s]
76%|███████▌ | 38/50 [00:10<00:03, 3.65it/s]
78%|███████▊ | 39/50 [00:10<00:03, 3.65it/s]
80%|████████ | 40/50 [00:10<00:02, 3.65it/s]
82%|████████▏ | 41/50 [00:11<00:02, 3.65it/s]
84%|████████▍ | 42/50 [00:11<00:02, 3.65it/s]
86%|████████▌ | 43/50 [00:11<00:01, 3.65it/s]
88%|████████▊ | 44/50 [00:12<00:01, 3.65it/s]
90%|█████████ | 45/50 [00:12<00:01, 3.65it/s]
92%|█████████▏| 46/50 [00:12<00:01, 3.65it/s]
94%|█████████▍| 47/50 [00:12<00:00, 3.65it/s]
96%|█████████▌| 48/50 [00:13<00:00, 3.65it/s]
98%|█████████▊| 49/50 [00:13<00:00, 3.65it/s]
100%|██████████| 50/50 [00:13<00:00, 3.65it/s]
100%|██████████| 50/50 [00:13<00:00, 3.65it/s]Examples
Run time and cost
This model costs approximately $0.019 to run on Replicate, or 52 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 20 seconds.