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adirik /masactrl-anything-v4-0:9db49bf5
Input
Run this model in Node.js with one line of code:
npx create-replicate --model=adirik/masactrl-anything-v4-0
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 adirik/masactrl-anything-v4-0 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"adirik/masactrl-anything-v4-0:9db49bf5fcedfb9279471e89b1daa326308ddaf633179d52f8695c47cfbe0800",
{
input: {
source_prompt: "1boy, casual, outdoors, sitting",
target_prompt: "1boy, casual, outdoors, standing",
guidance_scale: 7.5,
masactrl_start_step: 4,
num_inference_steps: 50,
masactrl_start_layer: 10
}
}
);
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 adirik/masactrl-anything-v4-0 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"adirik/masactrl-anything-v4-0:9db49bf5fcedfb9279471e89b1daa326308ddaf633179d52f8695c47cfbe0800",
input={
"source_prompt": "1boy, casual, outdoors, sitting",
"target_prompt": "1boy, casual, outdoors, standing",
"guidance_scale": 7.5,
"masactrl_start_step": 4,
"num_inference_steps": 50,
"masactrl_start_layer": 10
}
)
print(output)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 adirik/masactrl-anything-v4-0 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": "9db49bf5fcedfb9279471e89b1daa326308ddaf633179d52f8695c47cfbe0800",
"input": {
"source_prompt": "1boy, casual, outdoors, sitting",
"target_prompt": "1boy, casual, outdoors, standing",
"guidance_scale": 7.5,
"masactrl_start_step": 4,
"num_inference_steps": 50,
"masactrl_start_layer": 10
}
}' \
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/adirik/masactrl-anything-v4-0@sha256:9db49bf5fcedfb9279471e89b1daa326308ddaf633179d52f8695c47cfbe0800 \
-i 'source_prompt="1boy, casual, outdoors, sitting"' \
-i 'target_prompt="1boy, casual, outdoors, standing"' \
-i 'guidance_scale=7.5' \
-i 'masactrl_start_step=4' \
-i 'num_inference_steps=50' \
-i 'masactrl_start_layer=10'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/adirik/masactrl-anything-v4-0@sha256:9db49bf5fcedfb9279471e89b1daa326308ddaf633179d52f8695c47cfbe0800curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "source_prompt": "1boy, casual, outdoors, sitting", "target_prompt": "1boy, casual, outdoors, standing", "guidance_scale": 7.5, "masactrl_start_step": 4, "num_inference_steps": 50, "masactrl_start_layer": 10 } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Add a payment method to run this model.
Each run costs approximately $0.12. Alternatively, try out our featured models for free.
Sign in with GitHub
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Output
{
"completed_at": "2023-12-05T10:13:45.252553Z",
"created_at": "2023-12-05T10:13:23.700571Z",
"data_removed": false,
"error": null,
"id": "puk7yk3b7aqhegw24tbbb7i6ry",
"input": {
"source_prompt": "1boy, casual, outdoors, sitting",
"target_prompt": "1boy, casual, outdoors, standing",
"guidance_scale": 7.5,
"masactrl_start_step": 4,
"num_inference_steps": 50,
"masactrl_start_layer": 10
},
"logs": "/root/.pyenv/versions/3.11.6/lib/python3.11/site-packages/lightning_fabric/utilities/seed.py:40: No seed found, seed set to 3047472308\nSeed set to 3047472308\nMasaCtrl at denoising steps: [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49]\nMasaCtrl at U-Net layers: [10, 11, 12, 13, 14, 15]\ninput text embeddings : torch.Size([2, 77, 768])\n/src/masactrl/diffuser_utils.py:139: FutureWarning: Accessing config attribute `in_channels` directly via 'UNet2DConditionModel' object attribute is deprecated. Please access 'in_channels' over 'UNet2DConditionModel's config object instead, e.g. 'unet.config.in_channels'.\nlatents_shape = (batch_size, self.unet.in_channels, height//8, width//8)\nlatents shape: torch.Size([2, 4, 64, 64])\nDDIM Sampler: 0%| | 0/50 [00:00<?, ?it/s]\nDDIM Sampler: 2%|▏ | 1/50 [00:00<00:29, 1.65it/s]\nDDIM Sampler: 4%|▍ | 2/50 [00:00<00:19, 2.50it/s]\nDDIM Sampler: 6%|▌ | 3/50 [00:01<00:15, 2.95it/s]\nDDIM Sampler: 8%|▊ | 4/50 [00:01<00:14, 3.22it/s]\nDDIM Sampler: 10%|█ | 5/50 [00:01<00:13, 3.36it/s]\nDDIM Sampler: 12%|█▏ | 6/50 [00:01<00:13, 3.25it/s]\nDDIM Sampler: 14%|█▍ | 7/50 [00:02<00:13, 3.17it/s]\nDDIM Sampler: 16%|█▌ | 8/50 [00:02<00:13, 3.13it/s]\nDDIM Sampler: 18%|█▊ | 9/50 [00:02<00:13, 3.10it/s]\nDDIM Sampler: 20%|██ | 10/50 [00:03<00:12, 3.08it/s]\nDDIM Sampler: 22%|██▏ | 11/50 [00:03<00:12, 3.06it/s]\nDDIM Sampler: 24%|██▍ | 12/50 [00:03<00:12, 3.06it/s]\nDDIM Sampler: 26%|██▌ | 13/50 [00:04<00:12, 3.05it/s]\nDDIM Sampler: 28%|██▊ | 14/50 [00:04<00:11, 3.04it/s]\nDDIM Sampler: 30%|███ | 15/50 [00:04<00:11, 3.04it/s]\nDDIM Sampler: 32%|███▏ | 16/50 [00:05<00:11, 3.04it/s]\nDDIM Sampler: 34%|███▍ | 17/50 [00:05<00:10, 3.04it/s]\nDDIM Sampler: 36%|███▌ | 18/50 [00:05<00:10, 3.04it/s]\nDDIM Sampler: 38%|███▊ | 19/50 [00:06<00:10, 3.03it/s]\nDDIM Sampler: 40%|████ | 20/50 [00:06<00:09, 3.03it/s]\nDDIM Sampler: 42%|████▏ | 21/50 [00:06<00:09, 3.03it/s]\nDDIM Sampler: 44%|████▍ | 22/50 [00:07<00:09, 3.03it/s]\nDDIM Sampler: 46%|████▌ | 23/50 [00:07<00:08, 3.03it/s]\nDDIM Sampler: 48%|████▊ | 24/50 [00:07<00:08, 3.03it/s]\nDDIM Sampler: 50%|█████ | 25/50 [00:08<00:08, 3.03it/s]\nDDIM Sampler: 52%|█████▏ | 26/50 [00:08<00:07, 3.03it/s]\nDDIM Sampler: 54%|█████▍ | 27/50 [00:08<00:07, 3.03it/s]\nDDIM Sampler: 56%|█████▌ | 28/50 [00:09<00:07, 3.03it/s]\nDDIM Sampler: 58%|█████▊ | 29/50 [00:09<00:06, 3.03it/s]\nDDIM Sampler: 60%|██████ | 30/50 [00:09<00:06, 3.03it/s]\nDDIM Sampler: 62%|██████▏ | 31/50 [00:10<00:06, 3.03it/s]\nDDIM Sampler: 64%|██████▍ | 32/50 [00:10<00:05, 3.03it/s]\nDDIM Sampler: 66%|██████▌ | 33/50 [00:10<00:05, 3.03it/s]\nDDIM Sampler: 68%|██████▊ | 34/50 [00:11<00:05, 3.03it/s]\nDDIM Sampler: 70%|███████ | 35/50 [00:11<00:04, 3.03it/s]\nDDIM Sampler: 72%|███████▏ | 36/50 [00:11<00:04, 3.03it/s]\nDDIM Sampler: 74%|███████▍ | 37/50 [00:12<00:04, 3.03it/s]\nDDIM Sampler: 76%|███████▌ | 38/50 [00:12<00:03, 3.03it/s]\nDDIM Sampler: 78%|███████▊ | 39/50 [00:12<00:03, 3.03it/s]\nDDIM Sampler: 80%|████████ | 40/50 [00:13<00:03, 3.03it/s]\nDDIM Sampler: 82%|████████▏ | 41/50 [00:13<00:02, 3.03it/s]\nDDIM Sampler: 84%|████████▍ | 42/50 [00:13<00:02, 3.04it/s]\nDDIM Sampler: 86%|████████▌ | 43/50 [00:14<00:02, 3.04it/s]\nDDIM Sampler: 88%|████████▊ | 44/50 [00:14<00:01, 3.04it/s]\nDDIM Sampler: 90%|█████████ | 45/50 [00:14<00:01, 3.04it/s]\nDDIM Sampler: 92%|█████████▏| 46/50 [00:15<00:01, 3.04it/s]\nDDIM Sampler: 94%|█████████▍| 47/50 [00:15<00:00, 3.04it/s]\nDDIM Sampler: 96%|█████████▌| 48/50 [00:15<00:00, 3.04it/s]\nDDIM Sampler: 98%|█████████▊| 49/50 [00:16<00:00, 3.04it/s]\nDDIM Sampler: 100%|██████████| 50/50 [00:16<00:00, 3.04it/s]\nDDIM Sampler: 100%|██████████| 50/50 [00:16<00:00, 3.03it/s]",
"metrics": {
"predict_time": 19.410476,
"total_time": 21.551982
},
"output": [
"https://replicate.delivery/pbxt/UuTJV6Ue3gWBcCf3zMY8yErLd7VEXuBOFDaQ1IbfTjFuOVeHB/synt_0.png",
"https://replicate.delivery/pbxt/Pgiaeeu4h9gVWEbXWo2IFYCC0zKVofM5gPD8E12SrATwOVeHB/synt_1.png"
],
"started_at": "2023-12-05T10:13:25.842077Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/puk7yk3b7aqhegw24tbbb7i6ry",
"cancel": "https://api.replicate.com/v1/predictions/puk7yk3b7aqhegw24tbbb7i6ry/cancel"
},
"version": "9db49bf5fcedfb9279471e89b1daa326308ddaf633179d52f8695c47cfbe0800"
}Generated in
/root/.pyenv/versions/3.11.6/lib/python3.11/site-packages/lightning_fabric/utilities/seed.py:40: No seed found, seed set to 3047472308
Seed set to 3047472308
MasaCtrl at denoising steps: [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49]
MasaCtrl at U-Net layers: [10, 11, 12, 13, 14, 15]
input text embeddings : torch.Size([2, 77, 768])
/src/masactrl/diffuser_utils.py:139: FutureWarning: Accessing config attribute `in_channels` directly via 'UNet2DConditionModel' object attribute is deprecated. Please access 'in_channels' over 'UNet2DConditionModel's config object instead, e.g. 'unet.config.in_channels'.
latents_shape = (batch_size, self.unet.in_channels, height//8, width//8)
latents shape: torch.Size([2, 4, 64, 64])
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