[25] proposed Fuzzy logic based histogram equalization (FHE) where they compare the proposed method with the noisy images. Hence, it is not clear that this method may be helpful in case of noisy images or not. In the same line of research, another team of the researcher [27, 41] ...
The logic of the code is as follows: # MotionLCM/mld/data/humanml/dataset.py (Text2MotionDataset) length = joints.shape[0] density = self.testing_density if density in [1, 2, 5]: choose_seq_num = density else: choose_seq_num = int(length * density / 100) 4. Evaluate the ...
Here is an example to compile a LCM of Stable Diffusion XL: [latent-consistency/lcm-sdxl](https://huggingface.co/latent-consistency/lcm-sdxl) and run inference on AWS Inferentia 2 : ### Compile LCM ***Export via Python API*** ```python from optimum.neuron import NeuronStableDiffusion...
fi # inverse the logic if we're testing no connectivity if [[ "$expected" != "True" ]]; then op="" failmsg="[Fail] Could ping server" fi # Because we've transformed this command so many times, print it # out at the end.local...