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Input
Run this model in Node.js with one line of code:
npx create-replicate --model=ali-vilab/anydoor
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 ali-vilab/anydoor using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"ali-vilab/anydoor:542c963129c4661ab53a875b1b9a84b2102ca784cf872ef2752a468721c0eb2a",
{
input: {
steps: 50,
bg_mask_path: "https://replicate.delivery/pbxt/KAr3ayPx9LV5sJ66yNbNE6Ge1fg3KE8B7RU999MoMywBMsef/woman-mask.png",
bg_image_path: "https://replicate.delivery/pbxt/KAr3aFKWbmoWfqIZGqxNnMCtCh6LSqS4BnmsDx1sRrgmOI3N/woman.jpg",
guidance_scale: 4.5,
control_strength: 1,
enable_shape_control: false,
reference_image_mask: "https://replicate.delivery/pbxt/KAr3avL7HCXeu0o4QxZyxg7qNXUsjxwoZqKKIBIuyvs6CyeF/blue-polo-mask.png",
reference_image_path: "https://replicate.delivery/pbxt/KAr3b5G84hHCXnwJNPOybjHP7oNUlXYOJUTUkBQVxc3D52zo/blue-polo.jpg"
}
}
);
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 ali-vilab/anydoor using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"ali-vilab/anydoor:542c963129c4661ab53a875b1b9a84b2102ca784cf872ef2752a468721c0eb2a",
input={
"steps": 50,
"bg_mask_path": "https://replicate.delivery/pbxt/KAr3ayPx9LV5sJ66yNbNE6Ge1fg3KE8B7RU999MoMywBMsef/woman-mask.png",
"bg_image_path": "https://replicate.delivery/pbxt/KAr3aFKWbmoWfqIZGqxNnMCtCh6LSqS4BnmsDx1sRrgmOI3N/woman.jpg",
"guidance_scale": 4.5,
"control_strength": 1,
"enable_shape_control": False,
"reference_image_mask": "https://replicate.delivery/pbxt/KAr3avL7HCXeu0o4QxZyxg7qNXUsjxwoZqKKIBIuyvs6CyeF/blue-polo-mask.png",
"reference_image_path": "https://replicate.delivery/pbxt/KAr3b5G84hHCXnwJNPOybjHP7oNUlXYOJUTUkBQVxc3D52zo/blue-polo.jpg"
}
)
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 ali-vilab/anydoor 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": "542c963129c4661ab53a875b1b9a84b2102ca784cf872ef2752a468721c0eb2a",
"input": {
"steps": 50,
"bg_mask_path": "https://replicate.delivery/pbxt/KAr3ayPx9LV5sJ66yNbNE6Ge1fg3KE8B7RU999MoMywBMsef/woman-mask.png",
"bg_image_path": "https://replicate.delivery/pbxt/KAr3aFKWbmoWfqIZGqxNnMCtCh6LSqS4BnmsDx1sRrgmOI3N/woman.jpg",
"guidance_scale": 4.5,
"control_strength": 1,
"enable_shape_control": false,
"reference_image_mask": "https://replicate.delivery/pbxt/KAr3avL7HCXeu0o4QxZyxg7qNXUsjxwoZqKKIBIuyvs6CyeF/blue-polo-mask.png",
"reference_image_path": "https://replicate.delivery/pbxt/KAr3b5G84hHCXnwJNPOybjHP7oNUlXYOJUTUkBQVxc3D52zo/blue-polo.jpg"
}
}' \
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/ali-vilab/anydoor@sha256:542c963129c4661ab53a875b1b9a84b2102ca784cf872ef2752a468721c0eb2a \
-i 'steps=50' \
-i 'bg_mask_path="https://replicate.delivery/pbxt/KAr3ayPx9LV5sJ66yNbNE6Ge1fg3KE8B7RU999MoMywBMsef/woman-mask.png"' \
-i 'bg_image_path="https://replicate.delivery/pbxt/KAr3aFKWbmoWfqIZGqxNnMCtCh6LSqS4BnmsDx1sRrgmOI3N/woman.jpg"' \
-i 'guidance_scale=4.5' \
-i 'control_strength=1' \
-i 'enable_shape_control=false' \
-i 'reference_image_mask="https://replicate.delivery/pbxt/KAr3avL7HCXeu0o4QxZyxg7qNXUsjxwoZqKKIBIuyvs6CyeF/blue-polo-mask.png"' \
-i 'reference_image_path="https://replicate.delivery/pbxt/KAr3b5G84hHCXnwJNPOybjHP7oNUlXYOJUTUkBQVxc3D52zo/blue-polo.jpg"'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/ali-vilab/anydoor@sha256:542c963129c4661ab53a875b1b9a84b2102ca784cf872ef2752a468721c0eb2acurl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "steps": 50, "bg_mask_path": "https://replicate.delivery/pbxt/KAr3ayPx9LV5sJ66yNbNE6Ge1fg3KE8B7RU999MoMywBMsef/woman-mask.png", "bg_image_path": "https://replicate.delivery/pbxt/KAr3aFKWbmoWfqIZGqxNnMCtCh6LSqS4BnmsDx1sRrgmOI3N/woman.jpg", "guidance_scale": 4.5, "control_strength": 1, "enable_shape_control": false, "reference_image_mask": "https://replicate.delivery/pbxt/KAr3avL7HCXeu0o4QxZyxg7qNXUsjxwoZqKKIBIuyvs6CyeF/blue-polo-mask.png", "reference_image_path": "https://replicate.delivery/pbxt/KAr3b5G84hHCXnwJNPOybjHP7oNUlXYOJUTUkBQVxc3D52zo/blue-polo.jpg" } }' \ 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": "2024-01-05T16:13:06.110758Z",
"created_at": "2024-01-05T16:11:31.836609Z",
"data_removed": false,
"error": null,
"id": "kwb7f4tbbsbyuqo6vym44bewq4",
"input": {
"steps": 50,
"bg_mask_path": "https://replicate.delivery/pbxt/KAr3ayPx9LV5sJ66yNbNE6Ge1fg3KE8B7RU999MoMywBMsef/woman-mask.png",
"bg_image_path": "https://replicate.delivery/pbxt/KAr3aFKWbmoWfqIZGqxNnMCtCh6LSqS4BnmsDx1sRrgmOI3N/woman.jpg",
"guidance_scale": 4.5,
"control_strength": 1,
"enable_shape_control": false,
"reference_image_mask": "https://replicate.delivery/pbxt/KAr3avL7HCXeu0o4QxZyxg7qNXUsjxwoZqKKIBIuyvs6CyeF/blue-polo-mask.png",
"reference_image_path": "https://replicate.delivery/pbxt/KAr3b5G84hHCXnwJNPOybjHP7oNUlXYOJUTUkBQVxc3D52zo/blue-polo.jpg"
},
"logs": "Using seed: 562681476\n/root/.pyenv/versions/3.8.5/lib/python3.8/site-packages/xformers/ops/unbind.py:46: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\nstorage_data_ptr = tensors[0].storage().data_ptr()\n/root/.pyenv/versions/3.8.5/lib/python3.8/site-packages/xformers/ops/unbind.py:48: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\nif x.storage().data_ptr() != storage_data_ptr:\nData shape for DDIM sampling is (1, 4, 64, 64), eta 0.0\nRunning DDIM Sampling with 50 timesteps\nDDIM Sampler: 0%| | 0/50 [00:00<?, ?it/s]\nDDIM Sampler: 2%|▏ | 1/50 [00:00<00:10, 4.55it/s]\nDDIM Sampler: 4%|▍ | 2/50 [00:00<00:08, 5.45it/s]\nDDIM Sampler: 6%|▌ | 3/50 [00:00<00:08, 5.82it/s]\nDDIM Sampler: 8%|▊ | 4/50 [00:00<00:07, 6.01it/s]\nDDIM Sampler: 10%|█ | 5/50 [00:00<00:07, 6.13it/s]\nDDIM Sampler: 12%|█▏ | 6/50 [00:01<00:07, 6.19it/s]\nDDIM Sampler: 14%|█▍ | 7/50 [00:01<00:06, 6.23it/s]\nDDIM Sampler: 16%|█▌ | 8/50 [00:01<00:06, 6.25it/s]\nDDIM Sampler: 18%|█▊ | 9/50 [00:01<00:06, 6.25it/s]\nDDIM Sampler: 20%|██ | 10/50 [00:01<00:06, 6.27it/s]\nDDIM Sampler: 22%|██▏ | 11/50 [00:01<00:06, 6.27it/s]\nDDIM Sampler: 24%|██▍ | 12/50 [00:01<00:06, 6.28it/s]\nDDIM Sampler: 26%|██▌ | 13/50 [00:02<00:05, 6.28it/s]\nDDIM Sampler: 28%|██▊ | 14/50 [00:02<00:05, 6.29it/s]\nDDIM Sampler: 30%|███ | 15/50 [00:02<00:05, 6.29it/s]\nDDIM Sampler: 32%|███▏ | 16/50 [00:02<00:05, 6.29it/s]\nDDIM Sampler: 34%|███▍ | 17/50 [00:02<00:05, 6.28it/s]\nDDIM Sampler: 36%|███▌ | 18/50 [00:02<00:05, 6.28it/s]\nDDIM Sampler: 38%|███▊ | 19/50 [00:03<00:04, 6.29it/s]\nDDIM Sampler: 40%|████ | 20/50 [00:03<00:04, 6.30it/s]\nDDIM Sampler: 42%|████▏ | 21/50 [00:03<00:04, 6.30it/s]\nDDIM Sampler: 44%|████▍ | 22/50 [00:03<00:04, 6.30it/s]\nDDIM Sampler: 46%|████▌ | 23/50 [00:03<00:04, 6.30it/s]\nDDIM Sampler: 48%|████▊ | 24/50 [00:03<00:04, 6.30it/s]\nDDIM Sampler: 50%|█████ | 25/50 [00:04<00:03, 6.30it/s]\nDDIM Sampler: 52%|█████▏ | 26/50 [00:04<00:03, 6.30it/s]\nDDIM Sampler: 54%|█████▍ | 27/50 [00:04<00:03, 6.30it/s]\nDDIM Sampler: 56%|█████▌ | 28/50 [00:04<00:03, 6.29it/s]\nDDIM Sampler: 58%|█████▊ | 29/50 [00:04<00:03, 6.28it/s]\nDDIM Sampler: 60%|██████ | 30/50 [00:04<00:03, 6.28it/s]\nDDIM Sampler: 62%|██████▏ | 31/50 [00:04<00:03, 6.29it/s]\nDDIM Sampler: 64%|██████▍ | 32/50 [00:05<00:02, 6.30it/s]\nDDIM Sampler: 66%|██████▌ | 33/50 [00:05<00:02, 6.31it/s]\nDDIM Sampler: 68%|██████▊ | 34/50 [00:05<00:02, 6.32it/s]\nDDIM Sampler: 70%|███████ | 35/50 [00:05<00:02, 6.32it/s]\nDDIM Sampler: 72%|███████▏ | 36/50 [00:05<00:02, 6.33it/s]\nDDIM Sampler: 74%|███████▍ | 37/50 [00:05<00:02, 6.33it/s]\nDDIM Sampler: 76%|███████▌ | 38/50 [00:06<00:01, 6.33it/s]\nDDIM Sampler: 78%|███████▊ | 39/50 [00:06<00:01, 6.33it/s]\nDDIM Sampler: 80%|████████ | 40/50 [00:06<00:01, 6.32it/s]\nDDIM Sampler: 82%|████████▏ | 41/50 [00:06<00:01, 6.32it/s]\nDDIM Sampler: 84%|████████▍ | 42/50 [00:06<00:01, 6.32it/s]\nDDIM Sampler: 86%|████████▌ | 43/50 [00:06<00:01, 6.32it/s]\nDDIM Sampler: 88%|████████▊ | 44/50 [00:07<00:00, 6.33it/s]\nDDIM Sampler: 90%|█████████ | 45/50 [00:07<00:00, 6.34it/s]\nDDIM Sampler: 92%|█████████▏| 46/50 [00:07<00:00, 6.35it/s]\nDDIM Sampler: 94%|█████████▍| 47/50 [00:07<00:00, 6.34it/s]\nDDIM Sampler: 96%|█████████▌| 48/50 [00:07<00:00, 6.33it/s]\nDDIM Sampler: 98%|█████████▊| 49/50 [00:07<00:00, 6.33it/s]\nDDIM Sampler: 100%|██████████| 50/50 [00:07<00:00, 6.33it/s]\nDDIM Sampler: 100%|██████████| 50/50 [00:07<00:00, 6.26it/s]",
"metrics": {
"predict_time": 11.687846,
"total_time": 94.274149
},
"output": "https://replicate.delivery/pbxt/jStRPc3ffzieUJFUiqePbecnQuCr87izGYTtaEULnQjMSuLRC/output.png",
"started_at": "2024-01-05T16:12:54.422912Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/kwb7f4tbbsbyuqo6vym44bewq4",
"cancel": "https://api.replicate.com/v1/predictions/kwb7f4tbbsbyuqo6vym44bewq4/cancel"
},
"version": "542c963129c4661ab53a875b1b9a84b2102ca784cf872ef2752a468721c0eb2a"
}Generated in
Using seed: 562681476
/root/.pyenv/versions/3.8.5/lib/python3.8/site-packages/xformers/ops/unbind.py:46: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
storage_data_ptr = tensors[0].storage().data_ptr()
/root/.pyenv/versions/3.8.5/lib/python3.8/site-packages/xformers/ops/unbind.py:48: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
if x.storage().data_ptr() != storage_data_ptr:
Data shape for DDIM sampling is (1, 4, 64, 64), eta 0.0
Running DDIM Sampling with 50 timesteps
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