Hand Refiner 512x512
Run this model in Node.js with one line of code:
npm install replicateREPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
import Replicate from "replicate";
import fs from "node:fs";
const replicate = new Replicate({
auth: process.env.REPLICATE_API_TOKEN,
});Run camenduru/hand-refiner using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"camenduru/hand-refiner:a1314c7fb83fec0d6ef991dfd97ae671198ea3c793dc42a01f59d92e6ce472d7",
{
input: {
seed: 34343,
prompt: "a person facing the camera, making a hand gesture, indoor",
strength: 0.6,
input_image: "https://replicate.delivery/pbxt/KMZkbywX0JCSzdTmeUl3VsQucok0tFXrLeTXrnASvn4Tkp65/1.jpg"
}
}
);
// To access the file URL:
console.log(output.url()); //=> "http://example.com"
// To write the file to disk:
fs.writeFile("my-image.png", output);To learn more, take a look at the guide on getting started with Node.js.
pip install replicateREPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
import replicateRun camenduru/hand-refiner using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"camenduru/hand-refiner:a1314c7fb83fec0d6ef991dfd97ae671198ea3c793dc42a01f59d92e6ce472d7",
input={
"seed": 34343,
"prompt": "a person facing the camera, making a hand gesture, indoor",
"strength": 0.6,
"input_image": "https://replicate.delivery/pbxt/KMZkbywX0JCSzdTmeUl3VsQucok0tFXrLeTXrnASvn4Tkp65/1.jpg"
}
)
print(output)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 camenduru/hand-refiner 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": "camenduru/hand-refiner:a1314c7fb83fec0d6ef991dfd97ae671198ea3c793dc42a01f59d92e6ce472d7",
"input": {
"seed": 34343,
"prompt": "a person facing the camera, making a hand gesture, indoor",
"strength": 0.6,
"input_image": "https://replicate.delivery/pbxt/KMZkbywX0JCSzdTmeUl3VsQucok0tFXrLeTXrnASvn4Tkp65/1.jpg"
}
}' \
https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
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/camenduru/hand-refiner@sha256:a1314c7fb83fec0d6ef991dfd97ae671198ea3c793dc42a01f59d92e6ce472d7 \
-i 'seed=34343' \
-i 'prompt="a person facing the camera, making a hand gesture, indoor"' \
-i 'strength=0.6' \
-i 'input_image="https://replicate.delivery/pbxt/KMZkbywX0JCSzdTmeUl3VsQucok0tFXrLeTXrnASvn4Tkp65/1.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 r8.im/camenduru/hand-refiner@sha256:a1314c7fb83fec0d6ef991dfd97ae671198ea3c793dc42a01f59d92e6ce472d7curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "seed": 34343, "prompt": "a person facing the camera, making a hand gesture, indoor", "strength": 0.6, "input_image": "https://replicate.delivery/pbxt/KMZkbywX0JCSzdTmeUl3VsQucok0tFXrLeTXrnASvn4Tkp65/1.jpg" } }' \ 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 $0.16. Alternatively, try out our featured models for free.
By signing in, you agree to our
terms of service and privacy policy
{
"completed_at": "2024-02-10T06:25:39.624561Z",
"created_at": "2024-02-10T06:21:55.035785Z",
"data_removed": false,
"error": null,
"id": "2p4yme3b6ho4crtw5o6y2uwyeq",
"input": {
"seed": 34343,
"prompt": "a person facing the camera, making a hand gesture, indoor",
"strength": 0.6,
"input_image": "https://replicate.delivery/pbxt/KMZkbywX0JCSzdTmeUl3VsQucok0tFXrLeTXrnASvn4Tkp65/1.jpg"
},
"logs": "set os.environ[OMP_NUM_THREADS] to 4\n/content/HandRefiner/MeshGraphormer/manopth/manopth/manolayer.py:72: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:206.)\ntorch.Tensor(smpl_data['betas'].r).unsqueeze(0))\n/content/HandRefiner/MeshGraphormer/src/modeling/_mano.py:94: UserWarning: torch.sparse.SparseTensor(indices, values, shape, *, device=) is deprecated. Please use torch.sparse_coo_tensor(indices, values, shape, dtype=, device=). (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:605.)\nptU.append(torch.sparse.FloatTensor(i, v, u.shape))\n/usr/local/lib/python3.10/site-packages/scipy/sparse/_index.py:102: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.\nself._set_intXint(row, col, x.flat[0])\n=> loading pretrained model MeshGraphormer/models/hrnet/hrnetv2_w64_imagenet_pretrained.pth\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nI0000 00:00:1707546322.029188 119 gl_context_egl.cc:85] Successfully initialized EGL. Major : 1 Minor: 5\nI0000 00:00:1707546322.051412 331 gl_context.cc:344] GL version: 3.2 (OpenGL ES 3.2 NVIDIA 525.147.05), renderer: NVIDIA A40/PCIe/SSE2\nINFO: Created TensorFlow Lite XNNPACK delegate for CPU.\nSeed set to 34343\nSeed set to 34343\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<00:12, 3.81it/s]\nDDIM Sampler: 4%|▍ | 2/50 [00:00<00:10, 4.69it/s]\nDDIM Sampler: 6%|▌ | 3/50 [00:00<00:09, 5.06it/s]\nDDIM Sampler: 8%|▊ | 4/50 [00:00<00:08, 5.26it/s]\nDDIM Sampler: 10%|█ | 5/50 [00:00<00:08, 5.37it/s]\nDDIM Sampler: 12%|█▏ | 6/50 [00:01<00:08, 5.44it/s]\nDDIM Sampler: 14%|█▍ | 7/50 [00:01<00:07, 5.49it/s]\nDDIM Sampler: 16%|█▌ | 8/50 [00:01<00:07, 5.52it/s]\nDDIM Sampler: 18%|█▊ | 9/50 [00:01<00:07, 5.54it/s]\nDDIM Sampler: 20%|██ | 10/50 [00:01<00:07, 5.54it/s]\nDDIM Sampler: 22%|██▏ | 11/50 [00:02<00:07, 5.55it/s]\nDDIM Sampler: 24%|██▍ | 12/50 [00:02<00:06, 5.56it/s]\nDDIM Sampler: 26%|██▌ | 13/50 [00:02<00:06, 5.56it/s]\nDDIM Sampler: 28%|██▊ | 14/50 [00:02<00:06, 5.57it/s]\nDDIM Sampler: 30%|███ | 15/50 [00:02<00:06, 5.57it/s]\nDDIM Sampler: 32%|███▏ | 16/50 [00:02<00:06, 5.57it/s]\nDDIM Sampler: 34%|███▍ | 17/50 [00:03<00:05, 5.57it/s]\nDDIM Sampler: 36%|███▌ | 18/50 [00:03<00:05, 5.57it/s]\nDDIM Sampler: 38%|███▊ | 19/50 [00:03<00:05, 5.56it/s]\nDDIM Sampler: 40%|████ | 20/50 [00:03<00:05, 5.57it/s]\nDDIM Sampler: 42%|████▏ | 21/50 [00:03<00:05, 5.56it/s]\nDDIM Sampler: 44%|████▍ | 22/50 [00:04<00:05, 5.57it/s]\nDDIM Sampler: 46%|████▌ | 23/50 [00:04<00:04, 5.57it/s]\nDDIM Sampler: 48%|████▊ | 24/50 [00:04<00:04, 5.57it/s]\nDDIM Sampler: 50%|█████ | 25/50 [00:04<00:04, 5.56it/s]\nDDIM Sampler: 52%|█████▏ | 26/50 [00:04<00:04, 5.57it/s]\nDDIM Sampler: 54%|█████▍ | 27/50 [00:04<00:04, 5.57it/s]\nDDIM Sampler: 56%|█████▌ | 28/50 [00:05<00:03, 5.57it/s]\nDDIM Sampler: 58%|█████▊ | 29/50 [00:05<00:03, 5.57it/s]\nDDIM Sampler: 60%|██████ | 30/50 [00:05<00:03, 5.57it/s]\nDDIM Sampler: 62%|██████▏ | 31/50 [00:05<00:03, 5.57it/s]\nDDIM Sampler: 64%|██████▍ | 32/50 [00:05<00:03, 5.57it/s]\nDDIM Sampler: 66%|██████▌ | 33/50 [00:06<00:03, 5.57it/s]\nDDIM Sampler: 68%|██████▊ | 34/50 [00:06<00:02, 5.57it/s]\nDDIM Sampler: 70%|███████ | 35/50 [00:06<00:02, 5.57it/s]\nDDIM Sampler: 72%|███████▏ | 36/50 [00:06<00:02, 5.57it/s]\nDDIM Sampler: 74%|███████▍ | 37/50 [00:06<00:02, 5.57it/s]\nDDIM Sampler: 76%|███████▌ | 38/50 [00:06<00:02, 5.56it/s]\nDDIM Sampler: 78%|███████▊ | 39/50 [00:07<00:01, 5.56it/s]\nDDIM Sampler: 80%|████████ | 40/50 [00:07<00:01, 5.56it/s]\nDDIM Sampler: 82%|████████▏ | 41/50 [00:07<00:01, 5.56it/s]\nDDIM Sampler: 84%|████████▍ | 42/50 [00:07<00:01, 5.56it/s]\nDDIM Sampler: 86%|████████▌ | 43/50 [00:07<00:01, 5.56it/s]\nDDIM Sampler: 88%|████████▊ | 44/50 [00:07<00:01, 5.57it/s]\nDDIM Sampler: 90%|█████████ | 45/50 [00:08<00:00, 5.57it/s]\nDDIM Sampler: 92%|█████████▏| 46/50 [00:08<00:00, 5.57it/s]\nDDIM Sampler: 94%|█████████▍| 47/50 [00:08<00:00, 5.57it/s]\nDDIM Sampler: 96%|█████████▌| 48/50 [00:08<00:00, 5.57it/s]\nDDIM Sampler: 98%|█████████▊| 49/50 [00:08<00:00, 5.56it/s]\nDDIM Sampler: 100%|██████████| 50/50 [00:09<00:00, 5.56it/s]\nDDIM Sampler: 100%|██████████| 50/50 [00:09<00:00, 5.52it/s]",
"metrics": {
"predict_time": 21.150054,
"total_time": 224.588776
},
"output": "https://replicate.delivery/pbxt/AbTkJ6S7bNKOHhySnAlJ7wbQd3LATPe2SKHqEZhpbMrxRmKJA/output_image.jpg",
"started_at": "2024-02-10T06:25:18.474507Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/2p4yme3b6ho4crtw5o6y2uwyeq",
"cancel": "https://api.replicate.com/v1/predictions/2p4yme3b6ho4crtw5o6y2uwyeq/cancel"
},
"version": "a1314c7fb83fec0d6ef991dfd97ae671198ea3c793dc42a01f59d92e6ce472d7"
}set os.environ[OMP_NUM_THREADS] to 4
/content/HandRefiner/MeshGraphormer/manopth/manopth/manolayer.py:72: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:206.)
torch.Tensor(smpl_data['betas'].r).unsqueeze(0))
/content/HandRefiner/MeshGraphormer/src/modeling/_mano.py:94: UserWarning: torch.sparse.SparseTensor(indices, values, shape, *, device=) is deprecated. Please use torch.sparse_coo_tensor(indices, values, shape, dtype=, device=). (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:605.)
ptU.append(torch.sparse.FloatTensor(i, v, u.shape))
/usr/local/lib/python3.10/site-packages/scipy/sparse/_index.py:102: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.
self._set_intXint(row, col, x.flat[0])
=> loading pretrained model MeshGraphormer/models/hrnet/hrnetv2_w64_imagenet_pretrained.pth
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1707546322.029188 119 gl_context_egl.cc:85] Successfully initialized EGL. Major : 1 Minor: 5
I0000 00:00:1707546322.051412 331 gl_context.cc:344] GL version: 3.2 (OpenGL ES 3.2 NVIDIA 525.147.05), renderer: NVIDIA A40/PCIe/SSE2
INFO: Created TensorFlow Lite XNNPACK delegate for CPU.
Seed set to 34343
Seed set to 34343
Data shape for DDIM sampling is (1, 4, 64, 64), eta 0.0
Running DDIM Sampling with 50 timesteps
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DDIM Sampler: 100%|██████████| 50/50 [00:09<00:00, 5.52it/s]This model costs approximately $0.16 to run on Replicate, or 6 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 3 minutes. The predict time for this model varies significantly based on the inputs.
🐣 Please follow me for new updates https://twitter.com/camenduru
🔥 Please join our discord server https://discord.gg/k5BwmmvJJU
🥳 Please join my patreon community https://patreon.com/camenduru
https://replicate.com/camenduru/hand-refiner
https://github.com/wenquanlu/HandRefiner
https://arxiv.org/abs/2311.17957
This model is cold. You'll get a fast response if the model is warm and already running, and a slower response if the model is cold and starting up.
This model costs approximately $0.16 to run on Replicate, but this varies depending on your inputs. View more.
Choose a file from your machine
Hint: you can also drag files onto the input
set os.environ[OMP_NUM_THREADS] to 4
/content/HandRefiner/MeshGraphormer/manopth/manopth/manolayer.py:72: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:206.)
torch.Tensor(smpl_data['betas'].r).unsqueeze(0))
/content/HandRefiner/MeshGraphormer/src/modeling/_mano.py:94: UserWarning: torch.sparse.SparseTensor(indices, values, shape, *, device=) is deprecated. Please use torch.sparse_coo_tensor(indices, values, shape, dtype=, device=). (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:605.)
ptU.append(torch.sparse.FloatTensor(i, v, u.shape))
/usr/local/lib/python3.10/site-packages/scipy/sparse/_index.py:102: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.
self._set_intXint(row, col, x.flat[0])
=> loading pretrained model MeshGraphormer/models/hrnet/hrnetv2_w64_imagenet_pretrained.pth
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1707546322.029188 119 gl_context_egl.cc:85] Successfully initialized EGL. Major : 1 Minor: 5
I0000 00:00:1707546322.051412 331 gl_context.cc:344] GL version: 3.2 (OpenGL ES 3.2 NVIDIA 525.147.05), renderer: NVIDIA A40/PCIe/SSE2
INFO: Created TensorFlow Lite XNNPACK delegate for CPU.
Seed set to 34343
Seed set to 34343
Data shape for DDIM sampling is (1, 4, 64, 64), eta 0.0
Running DDIM Sampling with 50 timesteps
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