Image-to-Image
PyTorch
ONNX
TensorRT
English
super-resolution
image-restoration
sisr
real-world-restoration
spandrel
chainner
transformer
attention
Instructions to use Phips/HEART with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- TensorRT
How to use Phips/HEART with TensorRT:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
File size: 965 Bytes
b415c47 | 1 2 3 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 | #!/usr/bin/env python3
"""Minimal memory ONNX export. No validation, no onnxsim, tiny dummy shape.
If this fits in RAM, great. Otherwise the export must run on another machine."""
import gc, os, sys
import torch
from safetensors.torch import load_file
from traiNNer.archs.heart_arch import heart
ckpt, out, scale = sys.argv[1], sys.argv[2], int(sys.argv[3])
torch.set_num_threads(2)
torch.set_grad_enabled(False)
sd = load_file(ckpt, device="cpu")
m = heart(scale=scale, ape=False, use_checkpoint=False).eval()
m.load_state_dict(sd, strict=True)
del sd; gc.collect()
# tiny dummy to minimize activation memory during trace
x = torch.randn(1, 3, 64, 64)
torch.onnx.export(
m, x, out,
input_names=["input"], output_names=["output"],
dynamic_axes={"input": {0:"batch",2:"height",3:"width"}, "output": {0:"batch",2:"height_out",3:"width_out"}},
opset_version=17, do_constant_folding=True, dynamo=False,
)
del m, x; gc.collect()
print(f"OK -> {out}")
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