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nightmareai /disco-diffusion:ed429a3a
Input
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 nightmareai/disco-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"nightmareai/disco-diffusion:ed429a3a5b9b9f40c772cc8e0d2eab060c5e1d004ea576e32909ae1dfd220b33",
{
input: {
RN50: false,
RN101: true,
steps: 250,
width: 512,
RN50x4: false,
ViTB16: false,
ViTB32: true,
ViTL14: true,
height: 512,
prompt: "a bed of roses",
RN50x16: false,
RN50x64: false,
RN50x101: false,
tv_scale: 0,
sat_scale: 0,
skip_augs: false,
RN50_cc12m: false,
ViTL14_336: false,
init_scale: 1000,
skip_steps: 10,
range_scale: 150,
cutn_batches: 4,
display_rate: 20,
target_scale: 20000,
RN101_yfcc15m: false,
RN50_yffcc15m: false,
diffusion_model: "floraldiffusion",
ViTB32_laion2b_e16: false,
clip_guidance_scale: 5000,
use_secondary_model: true,
RN50_quickgelu_cc12m: false,
ViTB16_laion400m_e31: false,
ViTB16_laion400m_e32: false,
ViTB32_laion400m_e31: false,
ViTB32_laion400m_e32: false,
RN50_quickgelu_yfcc15m: false,
RN101_quickgelu_yfcc15m: false,
diffusion_sampling_mode: "ddim",
ViTB32quickgelu_laion400m_e31: false,
ViTB32quickgelu_laion400m_e32: false
}
}
);
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 variable:export REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
import replicate
Run nightmareai/disco-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"nightmareai/disco-diffusion:ed429a3a5b9b9f40c772cc8e0d2eab060c5e1d004ea576e32909ae1dfd220b33",
input={
"RN50": False,
"RN101": True,
"steps": 250,
"width": 512,
"RN50x4": False,
"ViTB16": False,
"ViTB32": True,
"ViTL14": True,
"height": 512,
"prompt": "a bed of roses",
"RN50x16": False,
"RN50x64": False,
"RN50x101": False,
"tv_scale": 0,
"sat_scale": 0,
"skip_augs": False,
"RN50_cc12m": False,
"ViTL14_336": False,
"init_scale": 1000,
"skip_steps": 10,
"range_scale": 150,
"cutn_batches": 4,
"display_rate": 20,
"target_scale": 20000,
"RN101_yfcc15m": False,
"RN50_yffcc15m": False,
"diffusion_model": "floraldiffusion",
"ViTB32_laion2b_e16": False,
"clip_guidance_scale": 5000,
"use_secondary_model": True,
"RN50_quickgelu_cc12m": False,
"ViTB16_laion400m_e31": False,
"ViTB16_laion400m_e32": False,
"ViTB32_laion400m_e31": False,
"ViTB32_laion400m_e32": False,
"RN50_quickgelu_yfcc15m": False,
"RN101_quickgelu_yfcc15m": False,
"diffusion_sampling_mode": "ddim",
"ViTB32quickgelu_laion400m_e31": False,
"ViTB32quickgelu_laion400m_e32": False
}
)
# The nightmareai/disco-diffusion model can stream output as it's running.
# The predict method returns an iterator, and you can iterate over that output.
for item in output:
# https://replicate.com/nightmareai/disco-diffusion/api#output-schema
print(item)
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 nightmareai/disco-diffusion 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": "ed429a3a5b9b9f40c772cc8e0d2eab060c5e1d004ea576e32909ae1dfd220b33",
"input": {
"RN50": false,
"RN101": true,
"steps": 250,
"width": 512,
"RN50x4": false,
"ViTB16": false,
"ViTB32": true,
"ViTL14": true,
"height": 512,
"prompt": "a bed of roses",
"RN50x16": false,
"RN50x64": false,
"RN50x101": false,
"tv_scale": 0,
"sat_scale": 0,
"skip_augs": false,
"RN50_cc12m": false,
"ViTL14_336": false,
"init_scale": 1000,
"skip_steps": 10,
"range_scale": 150,
"cutn_batches": 4,
"display_rate": 20,
"target_scale": 20000,
"RN101_yfcc15m": false,
"RN50_yffcc15m": false,
"diffusion_model": "floraldiffusion",
"ViTB32_laion2b_e16": false,
"clip_guidance_scale": 5000,
"use_secondary_model": true,
"RN50_quickgelu_cc12m": false,
"ViTB16_laion400m_e31": false,
"ViTB16_laion400m_e32": false,
"ViTB32_laion400m_e31": false,
"ViTB32_laion400m_e32": false,
"RN50_quickgelu_yfcc15m": false,
"RN101_quickgelu_yfcc15m": false,
"diffusion_sampling_mode": "ddim",
"ViTB32quickgelu_laion400m_e31": false,
"ViTB32quickgelu_laion400m_e32": false
}
}' \
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.
Run this to download the model and run it in your local environment:
cog predict r8.im/nightmareai/disco-diffusion@sha256:ed429a3a5b9b9f40c772cc8e0d2eab060c5e1d004ea576e32909ae1dfd220b33 \
-i 'RN50=false' \
-i 'RN101=true' \
-i 'steps=250' \
-i 'width=512' \
-i 'RN50x4=false' \
-i 'ViTB16=false' \
-i 'ViTB32=true' \
-i 'ViTL14=true' \
-i 'height=512' \
-i 'prompt="a bed of roses"' \
-i 'RN50x16=false' \
-i 'RN50x64=false' \
-i 'RN50x101=false' \
-i 'tv_scale=0' \
-i 'sat_scale=0' \
-i 'skip_augs=false' \
-i 'RN50_cc12m=false' \
-i 'ViTL14_336=false' \
-i 'init_scale=1000' \
-i 'skip_steps=10' \
-i 'range_scale=150' \
-i 'cutn_batches=4' \
-i 'display_rate=20' \
-i 'target_scale=20000' \
-i 'RN101_yfcc15m=false' \
-i 'RN50_yffcc15m=false' \
-i 'diffusion_model="floraldiffusion"' \
-i 'ViTB32_laion2b_e16=false' \
-i 'clip_guidance_scale=5000' \
-i 'use_secondary_model=true' \
-i 'RN50_quickgelu_cc12m=false' \
-i 'ViTB16_laion400m_e31=false' \
-i 'ViTB16_laion400m_e32=false' \
-i 'ViTB32_laion400m_e31=false' \
-i 'ViTB32_laion400m_e32=false' \
-i 'RN50_quickgelu_yfcc15m=false' \
-i 'RN101_quickgelu_yfcc15m=false' \
-i 'diffusion_sampling_mode="ddim"' \
-i 'ViTB32quickgelu_laion400m_e31=false' \
-i 'ViTB32quickgelu_laion400m_e32=false'
To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/nightmareai/disco-diffusion@sha256:ed429a3a5b9b9f40c772cc8e0d2eab060c5e1d004ea576e32909ae1dfd220b33
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "RN50": false, "RN101": true, "steps": 250, "width": 512, "RN50x4": false, "ViTB16": false, "ViTB32": true, "ViTL14": true, "height": 512, "prompt": "a bed of roses", "RN50x16": false, "RN50x64": false, "RN50x101": false, "tv_scale": 0, "sat_scale": 0, "skip_augs": false, "RN50_cc12m": false, "ViTL14_336": false, "init_scale": 1000, "skip_steps": 10, "range_scale": 150, "cutn_batches": 4, "display_rate": 20, "target_scale": 20000, "RN101_yfcc15m": false, "RN50_yffcc15m": false, "diffusion_model": "floraldiffusion", "ViTB32_laion2b_e16": false, "clip_guidance_scale": 5000, "use_secondary_model": true, "RN50_quickgelu_cc12m": false, "ViTB16_laion400m_e31": false, "ViTB16_laion400m_e32": false, "ViTB32_laion400m_e31": false, "ViTB32_laion400m_e32": false, "RN50_quickgelu_yfcc15m": false, "RN101_quickgelu_yfcc15m": false, "diffusion_sampling_mode": "ddim", "ViTB32quickgelu_laion400m_e31": false, "ViTB32quickgelu_laion400m_e32": false } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Add a payment method to run this model.
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Output
{
"completed_at": "2022-07-29T12:52:38.955238Z",
"created_at": "2022-07-29T12:50:03.255761Z",
"data_removed": false,
"error": null,
"id": "33sobkznq5a5ncm7awdjwbxnxy",
"input": {
"RN50": false,
"RN101": true,
"steps": 250,
"width": 512,
"ViTB16": false,
"ViTB32": true,
"ViTL14": true,
"height": 512,
"prompt": "a bed of roses",
"init_scale": 1000,
"skip_steps": 10,
"range_scale": 150,
"cutn_batches": 4,
"display_rate": 20,
"target_scale": 20000,
"diffusion_model": "floraldiffusion",
"ViTB32_laion2b_e16": false,
"clip_guidance_scale": 5000,
"use_secondary_model": true,
"ViTB16_laion400m_e32": false,
"diffusion_sampling_mode": "ddim"
},
"logs": "2022-07-29 12:50:24,447 - discoart - WARNING - floraldiffusion is recommended to have width_height [512, 448], but you are using [512, 512]. This may lead to suboptimal results.\n2022-07-29 12:50:25,947 - discoart - INFO -\n looks like you are using a custom diffusion model,\n to override default diffusion model config, you can specify `create(diffusion_model_config={...}, ...)` as well,\n\n2022-07-29 12:50:42,544 - discoart - INFO - preparing models...\nSetting up [LPIPS] perceptual loss: trunk [vgg], v[0.1], spatial [off]\nLoading model from: /root/.pyenv/versions/3.8.13/lib/python3.8/site-packages/lpips/weights/v0.1/vgg.pth\n2022-07-29 12:50:44,357 - discoart - INFO - creating artworks...\n\n 0%| | 0/240 [00:00<?, ?it/s]\n\n 0%| | 1/240 [00:00<01:17, 3.10it/s]\n 1%| | 2/240 [00:00<01:14, 3.21it/s]\n 1%|▏ | 3/240 [00:00<01:12, 3.25it/s]\n 2%|▏ | 4/240 [00:01<01:11, 3.31it/s]\n 2%|▏ | 5/240 [00:01<01:11, 3.30it/s]\n 2%|▎ | 6/240 [00:01<01:11, 3.30it/s]\n 3%|▎ | 7/240 [00:02<01:10, 3.32it/s]\n 3%|▎ | 8/240 [00:02<01:09, 3.33it/s]\n 4%|▍ | 9/240 [00:02<01:08, 3.35it/s]\n 4%|▍ | 10/240 [00:03<01:08, 3.35it/s]\n 5%|▍ | 11/240 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"metrics": {
"predict_time": 134.558951,
"total_time": 155.699477
},
"output": [
"https://replicate.delivery/mgxm/b83de569-4bc2-4c5a-9bc6-c91697d5f16c/0-step-0-0.png",
"https://replicate.delivery/mgxm/87e2ddca-f24f-4829-a356-0e61868436e9/0-step-20-0.png",
"https://replicate.delivery/mgxm/5280fc04-250b-4193-ac05-5d11900d450e/0-step-40-0.png",
"https://replicate.delivery/mgxm/9901e8c3-6c46-42e3-9bd3-d0d28c26bea8/0-step-60-0.png",
"https://replicate.delivery/mgxm/e163ce28-5588-40bc-a129-5a7cd34e808d/0-step-80-0.png",
"https://replicate.delivery/mgxm/b4a5e5e0-2447-42c1-82f4-6647fbb6090d/0-step-100-0.png",
"https://replicate.delivery/mgxm/075c99d3-6899-42bb-8345-f9ffa9d573e3/0-step-120-0.png",
"https://replicate.delivery/mgxm/29a6142c-9240-457c-a52c-7fe4a37c9b9b/0-step-140-0.png",
"https://replicate.delivery/mgxm/f47fabfe-35a6-4427-8640-b2de22c71350/0-step-160-0.png",
"https://replicate.delivery/mgxm/12a70af3-0508-4eb1-bc1c-33bf1d82dd84/0-step-180-0.png",
"https://replicate.delivery/mgxm/0e8bdeb9-f051-48d0-8e6a-726150fa7d7b/0-step-200-0.png",
"https://replicate.delivery/mgxm/e9cedfa9-c97f-4723-b8f6-509e51f84a32/0-step-220-0.png",
"https://replicate.delivery/mgxm/68db8a8d-bae8-46d3-8ffc-21b7456cc70c/discoart-result.png"
],
"started_at": "2022-07-29T12:50:24.396287Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/33sobkznq5a5ncm7awdjwbxnxy",
"cancel": "https://api.replicate.com/v1/predictions/33sobkznq5a5ncm7awdjwbxnxy/cancel"
},
"version": "ed429a3a5b9b9f40c772cc8e0d2eab060c5e1d004ea576e32909ae1dfd220b33"
}
2022-07-29 12:50:24,447 - discoart - WARNING - floraldiffusion is recommended to have width_height [512, 448], but you are using [512, 512]. This may lead to suboptimal results.
2022-07-29 12:50:25,947 - discoart - INFO -
looks like you are using a custom diffusion model,
to override default diffusion model config, you can specify `create(diffusion_model_config={...}, ...)` as well,
2022-07-29 12:50:42,544 - discoart - INFO - preparing models...
Setting up [LPIPS] perceptual loss: trunk [vgg], v[0.1], spatial [off]
Loading model from: /root/.pyenv/versions/3.8.13/lib/python3.8/site-packages/lpips/weights/v0.1/vgg.pth
2022-07-29 12:50:44,357 - discoart - INFO - creating artworks...
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2022-07-29 12:52:36,146 - discoart - INFO - done! discoart-c4152f028c6b4813cd23dfb59e186187