Our great sponsors
-
Real-ESRGAN
Real-ESRGAN aims at developing Practical Algorithms for General Image/Video Restoration.
-
InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
The code I shared is just modifying the input/output and could be applied to any model. Although xinntao/Real-ESRGAN needed to be modified to load as a PIL image and then into the input tensors. There's probably a better way to modify the model and add circular padding, but my attempts failed so I fell back to blending.
this is sick, thanks for sharing. I've also made a script to avoid the seams upscaling usually makes, check it out here: https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/3590
Using junyanz/pytorch-CycleGAN-and-pix2pix as a basis for pix2pix, I applied the same blending method to fix seams. It essentially takes an input image and generates an output. The results depend on the paired training data. In this case, each map (height, roughness, etc.) is a separate checkpoint and had to be trained on paired training data with the diffuse as the input and the respective map as the output.