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nightmareai /disco-diffusion:3db33992
Input
Run this model in Node.js with one line of code:
npx create-replicate --model=nightmareai/disco-diffusion
or set up a project from scratch
Install Replicate’s Node.js client library:
npm install replicateSet the
REPLICATE_API_TOKEN environment variable:export 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 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:3db339922ea7421159874c71c20123079fa9c696849fb7a84da283ca5a9d5904",
{
input: {
RN50: false,
RN101: true,
steps: 250,
width: 1280,
RN50x4: false,
ViTB16: true,
ViTB32: true,
ViTL14: false,
height: 768,
prompt: "a submarine sandwich lost at sea",
RN50x16: false,
RN50x64: false,
RN50x101: false,
tv_scale: 0,
sat_scale: 0,
skip_augs: false,
ViTL14_336: false,
init_scale: 1000,
skip_steps: 10,
range_scale: 150,
cutn_batches: 4,
display_rate: 20,
target_scale: 20000,
diffusion_model: "512x512_diffusion_uncond_finetune_008100",
clip_guidance_scale: 5000,
use_secondary_model: true,
diffusion_sampling_mode: "ddim"
}
}
);
console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:
pip install replicateSet the
REPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:
import replicateRun 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:3db339922ea7421159874c71c20123079fa9c696849fb7a84da283ca5a9d5904",
input={
"RN50": False,
"RN101": True,
"steps": 250,
"width": 1280,
"RN50x4": False,
"ViTB16": True,
"ViTB32": True,
"ViTL14": False,
"height": 768,
"prompt": "a submarine sandwich lost at sea",
"RN50x16": False,
"RN50x64": False,
"RN50x101": False,
"tv_scale": 0,
"sat_scale": 0,
"skip_augs": False,
"ViTL14_336": False,
"init_scale": 1000,
"skip_steps": 10,
"range_scale": 150,
"cutn_batches": 4,
"display_rate": 20,
"target_scale": 20000,
"diffusion_model": "512x512_diffusion_uncond_finetune_008100",
"clip_guidance_scale": 5000,
"use_secondary_model": True,
"diffusion_sampling_mode": "ddim"
}
)
# 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.
Set the
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": "3db339922ea7421159874c71c20123079fa9c696849fb7a84da283ca5a9d5904",
"input": {
"RN50": false,
"RN101": true,
"steps": 250,
"width": 1280,
"RN50x4": false,
"ViTB16": true,
"ViTB32": true,
"ViTL14": false,
"height": 768,
"prompt": "a submarine sandwich lost at sea",
"RN50x16": false,
"RN50x64": false,
"RN50x101": false,
"tv_scale": 0,
"sat_scale": 0,
"skip_augs": false,
"ViTL14_336": false,
"init_scale": 1000,
"skip_steps": 10,
"range_scale": 150,
"cutn_batches": 4,
"display_rate": 20,
"target_scale": 20000,
"diffusion_model": "512x512_diffusion_uncond_finetune_008100",
"clip_guidance_scale": 5000,
"use_secondary_model": true,
"diffusion_sampling_mode": "ddim"
}
}' \
https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:
brew install cogIf 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@sha256:3db339922ea7421159874c71c20123079fa9c696849fb7a84da283ca5a9d5904 \
-i 'RN50=false' \
-i 'RN101=true' \
-i 'steps=250' \
-i 'width=1280' \
-i 'RN50x4=false' \
-i 'ViTB16=true' \
-i 'ViTB32=true' \
-i 'ViTL14=false' \
-i 'height=768' \
-i 'prompt="a submarine sandwich lost at sea"' \
-i 'RN50x16=false' \
-i 'RN50x64=false' \
-i 'RN50x101=false' \
-i 'tv_scale=0' \
-i 'sat_scale=0' \
-i 'skip_augs=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 'diffusion_model="512x512_diffusion_uncond_finetune_008100"' \
-i 'clip_guidance_scale=5000' \
-i 'use_secondary_model=true' \
-i 'diffusion_sampling_mode="ddim"'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@sha256:3db339922ea7421159874c71c20123079fa9c696849fb7a84da283ca5a9d5904curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "RN50": false, "RN101": true, "steps": 250, "width": 1280, "RN50x4": false, "ViTB16": true, "ViTB32": true, "ViTL14": false, "height": 768, "prompt": "a submarine sandwich lost at sea", "RN50x16": false, "RN50x64": false, "RN50x101": false, "tv_scale": 0, "sat_scale": 0, "skip_augs": false, "ViTL14_336": false, "init_scale": 1000, "skip_steps": 10, "range_scale": 150, "cutn_batches": 4, "display_rate": 20, "target_scale": 20000, "diffusion_model": "512x512_diffusion_uncond_finetune_008100", "clip_guidance_scale": 5000, "use_secondary_model": true, "diffusion_sampling_mode": "ddim" } }' \ 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": "2022-07-11T15:05:37.539941Z",
"created_at": "2022-07-11T14:46:54.122608Z",
"data_removed": false,
"error": null,
"id": "g3zigqtpjjffxbx6u3ayjmulg4",
"input": {
"RN50": false,
"RN101": true,
"steps": 250,
"width": 1280,
"ViTB16": true,
"ViTB32": true,
"ViTL14": false,
"height": 768,
"prompt": "a submarine sandwich lost at sea",
"ViTL14_336": false,
"init_scale": 1000,
"skip_steps": 10,
"range_scale": 150,
"cutn_batches": 4,
"display_rate": 20,
"target_scale": 20000,
"diffusion_model": "512x512_diffusion_uncond_finetune_008100",
"clip_guidance_scale": 5000,
"use_secondary_model": true,
"diffusion_sampling_mode": "ddim"
},
"logs": "2022-07-11 14:57:30.637 | INFO | dd:start_run:2224 - 💼 1 jobs found.\n2022-07-11 14:57:30.638 | INFO | dd:start_run:2236 - ⚒️ Session 11c90c0a-f2d8-4aa4-acd8-60fe77c69493 started...\n2022-07-11 14:57:30.638 | INFO | dd:start_run:2239 - 💼 Processing job 1 of 1...\n2022-07-11 14:57:30.638 | INFO | dd:start_run:2242 - 🌲 Setting TORCH_HOME environment variable to /root/.cache/disco-diffusion...\n2022-07-11 14:57:30.639 | INFO | dd:processBatch:2329 - 🌱 Randomly using seed: 662228741\n2022-07-11 14:57:30.639 | INFO | dd:free_mem:71 - Clearing CUDA cache on cuda:0...\n2022-07-11 14:57:30.639 | INFO | dd:do_run:1190 - 💻 Starting Run: f79cddc5-2ed9-4497-a3e4-fe01c89eb2c1(0) at frame 0\n2022-07-11 14:57:30.640 | INFO | dd:clipLoad:1182 - 🤖 Loading model 'ViT-B/32'...\n2022-07-11 14:57:35.640 | INFO | dd:clipLoad:1182 - 🤖 Loading model 'ViT-B/16'...\n2022-07-11 14:57:39.823 | INFO | dd:clipLoad:1182 - 🤖 Loading model 'RN101'...\n2022-07-11 14:57:43.869 | INFO | dd:do_run:1216 - 🤖 Loading secondary model...\n2022-07-11 14:57:44.018 | INFO | dd:do_run:1222 - 🤖 Loading LPIPS...\n/root/.pyenv/versions/3.8.13/lib/python3.8/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and will be removed in 0.15, please use 'weights' instead.\n warnings.warn(\n/root/.pyenv/versions/3.8.13/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.\n warnings.warn(msg)\n\u001b[2K\n2022-07-11 14:57:45.803 | INFO | dd:disco:1502 - timestep_respacing : ddim250 | diffusion_steps: 1000\n2022-07-11 14:57:45.803 | INFO | dd:prepModels:1054 - Prepping models...\n2022-07-11 14:57:53.329 | INFO | dd:disco:1532 - Running job '857b2d27-d54e-4ec7-8496-39cf3c4b6fee'...\n2022-07-11 14:57:53.329 | INFO | dd:disco:1545 - 🌱 Seed used: 662228741\n2022-07-11 14:57:53.332 | INFO | dd:disco:1571 - Frame 0 📝 Prompt: ['a submarine sandwich lost at sea']\n\u001b[2K\n\u001b[2K\n\u001b[2K\nBatches: 0%| | 0/1 [00:00<?, ?it/s]\nOutput()\n2022-07-11 14:57:53.398 | INFO | dd:free_mem:71 - Clearing CUDA cache on cuda:0...\n\n\n 0%| | 0/240 [00:00<?, ?it/s]\u001b[A\n\n\n\u001b[A\nBatch 0, step 0, output 0:\n\n\u001b[2K\n\u001b[2K\n<PIL.Image.Image image mode=RGB size=1280x768 at 0x7FBDA37CF460>\n 0%| | 0/240 [00:01<?, ?it/s]\u001b[A\n\n 0%| | 1/240 [00:02<08:06, 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[07:38<00:00, 1.91s/it]\n2022-07-11 15:05:32.314 | INFO | dd:free_mem:71 - Clearing CUDA cache on cuda:0...\n2022-07-11 15:05:32.315 | SUCCESS | dd:start_run:2245 - ✅ Session 11c90c0a-f2d8-4aa4-acd8-60fe77c69493 finished by user.\n2022-07-11 15:05:32.315 | DEBUG | dd:sendSMS:2530 - Not sending SMS\n2022-07-11 15:05:32.315 | INFO | dd:free_mem:71 - Clearing CUDA cache on cuda:0...",
"metrics": {
"predict_time": 486.934334,
"total_time": 1123.417333
},
"output": [
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"https://replicate.delivery/mgxm/58a7966b-0d9f-4ec5-bef4-65f91cf3f4e0/progress.png",
"https://replicate.delivery/mgxm/2d153c46-d3df-4ba0-8a12-8b532d6c505c/progress.png",
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"status": "succeeded",
"urls": {
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}Generated in
2022-07-11 14:57:30.637 | INFO | dd:start_run:2224 - 💼 1 jobs found.
2022-07-11 14:57:30.638 | INFO | dd:start_run:2236 - ⚒️ Session 11c90c0a-f2d8-4aa4-acd8-60fe77c69493 started...
2022-07-11 14:57:30.638 | INFO | dd:start_run:2239 - 💼 Processing job 1 of 1...
2022-07-11 14:57:30.638 | INFO | dd:start_run:2242 - 🌲 Setting TORCH_HOME environment variable to /root/.cache/disco-diffusion...
2022-07-11 14:57:30.639 | INFO | dd:processBatch:2329 - 🌱 Randomly using seed: 662228741
2022-07-11 14:57:30.639 | INFO | dd:free_mem:71 - Clearing CUDA cache on cuda:0...
2022-07-11 14:57:30.639 | INFO | dd:do_run:1190 - 💻 Starting Run: f79cddc5-2ed9-4497-a3e4-fe01c89eb2c1(0) at frame 0
2022-07-11 14:57:30.640 | INFO | dd:clipLoad:1182 - 🤖 Loading model 'ViT-B/32'...
2022-07-11 14:57:35.640 | INFO | dd:clipLoad:1182 - 🤖 Loading model 'ViT-B/16'...
2022-07-11 14:57:39.823 | INFO | dd:clipLoad:1182 - 🤖 Loading model 'RN101'...
2022-07-11 14:57:43.869 | INFO | dd:do_run:1216 - 🤖 Loading secondary model...
2022-07-11 14:57:44.018 | INFO | dd:do_run:1222 - 🤖 Loading LPIPS...
/root/.pyenv/versions/3.8.13/lib/python3.8/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and will be removed in 0.15, please use 'weights' instead.
warnings.warn(
/root/.pyenv/versions/3.8.13/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
warnings.warn(msg)
2022-07-11 14:57:45.803 | INFO | dd:disco:1502 - timestep_respacing : ddim250 | diffusion_steps: 1000
2022-07-11 14:57:45.803 | INFO | dd:prepModels:1054 - Prepping models...
2022-07-11 14:57:53.329 | INFO | dd:disco:1532 - Running job '857b2d27-d54e-4ec7-8496-39cf3c4b6fee'...
2022-07-11 14:57:53.329 | INFO | dd:disco:1545 - 🌱 Seed used: 662228741
2022-07-11 14:57:53.332 | INFO | dd:disco:1571 - Frame 0 📝 Prompt: ['a submarine sandwich lost at sea']
Batches: 0%| | 0/1 [00:00<?, ?it/s]
Output()
2022-07-11 14:57:53.398 | INFO | dd:free_mem:71 - Clearing CUDA cache on cuda:0...
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Batch 0, step 100, output 0:
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Batch 0, step 120, output 0:
<PIL.Image.Image image mode=RGB size=1280x768 at 0x7FBDA37CF520>
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Batch 0, step 140, output 0:
<PIL.Image.Image image mode=RGB size=1280x768 at 0x7FBDA37CF910>
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Batch 0, step 160, output 0:
<PIL.Image.Image image mode=RGB size=1280x768 at 0x7FBDA37CFA60>
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Batch 0, step 180, output 0:
<PIL.Image.Image image mode=RGB size=1280x768 at 0x7FBDA37CFFA0>
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Batch 0, step 200, output 0:
<PIL.Image.Image image mode=RGB size=1280x768 at 0x7FBDA37CF580>
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Batch 0, step 220, output 0:
<PIL.Image.Image image mode=RGB size=1280x768 at 0x7FBDA37CF3D0>
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Batch 0, step 239, output 0:
<PIL.Image.Image image mode=RGB size=1280x768 at 0x7FBDA37CFF10>
100%|█████████▉| 239/240 [07:38<00:01, 1.97s/it]2022-07-11 15:05:31.942 | INFO | dd:disco:1820 - Image render completed.
2022-07-11 15:05:32.314 | INFO | dd:disco:1840 - Image saved to '/src/images_out/f79cddc5-2ed9-4497-a3e4-fe01c89eb2c1/f79cddc5-2ed9-4497-a3e4-fe01c89eb2c1(0)_0.png'
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2022-07-11 15:05:32.314 | INFO | dd:free_mem:71 - Clearing CUDA cache on cuda:0...
2022-07-11 15:05:32.315 | SUCCESS | dd:start_run:2245 - ✅ Session 11c90c0a-f2d8-4aa4-acd8-60fe77c69493 finished by user.
2022-07-11 15:05:32.315 | DEBUG | dd:sendSMS:2530 - Not sending SMS
2022-07-11 15:05:32.315 | INFO | dd:free_mem:71 - Clearing CUDA cache on cuda:0...