#!/usr/bin/env python3 """ Genesis quick start — super-resolution + outpainting on a live Esri tile. Downloads the main JiT-B/16 checkpoints from https://huggingface.co/MVRL/genesis, fetches one real Esri World Imagery tile (Sydney Opera House / Circular Quay, zoom 16), then: 1. SR: the 256x256 parent tile (z16) -> 512x512 mosaic of its four z17 children in a single forward pass -> genesis_sr_out.png 2. OP: the tile viewed as a 2x2 quadrant mosaic (each quadrant is the footprint of one real z17 child); keep the upper-left quadrant real, outpaint the other three -> genesis_op_out.png Run from the root of a https://github.com/mvrl/genesis checkout, with its environment active (`uv sync && source .venv/bin/activate`): python example_sr_op.py Sampling uses the paper eval setting: 50 steps, cfg = 1.0 (the loader defaults). EMA weights are loaded automatically. Imagery: Esri World Imagery — Source: Esri, Maxar, Earthstar Geographics, and the GIS User Community. """ import os import sys # Repo root: this file at the genesis repo root, or run from a checkout. REPO = os.path.dirname(os.path.abspath(__file__)) if not os.path.isdir(os.path.join(REPO, "src")): REPO = os.getcwd() assert os.path.isdir(os.path.join(REPO, "src")), \ "Run this script from the root of a genesis checkout (github.com/mvrl/genesis)." # Same sys.path recipe as demos/*.py — demos/ goes LAST (highest priority) # so its `utils/` package is not shadowed by src/*/utils.py. sys.path.insert(0, os.path.join(REPO, "src")) sys.path.insert(0, os.path.join(REPO, "demos")) import torch from huggingface_hub import hf_hub_download from PIL import Image from utils.genesis_common import ( # demos/utils/genesis_common.py fetch_tile_at_zoom, inference_device, load_op_denoiser, load_sr_denoiser, outpaint_context_white_holes_preview, run_outpainting, run_superresolution, ) HF_REPO = "MVRL/genesis" ARCH = "JiT-B/16" # main B/16 checkpoints (H/16 SR alone is 23 GB) # Fixed scenic spot: Sydney Opera House / Circular Quay waterfront. LAT, LON, ZOOM = -33.8568, 151.2153, 16 # training zoom levels are 10..19 def main() -> None: device = inference_device() # cuda if available, else cpu print(f"Device: {device}") # 1. Checkpoints from the HF Hub (cached under ~/.cache/huggingface). # The main SR model is DINOv3-conditioned; the main OP model is no-DINO. sr_ckpt = hf_hub_download(HF_REPO, "main/superresolution/main_B16/sr-full-tile-stage3-step0800000.ckpt") op_ckpt = hf_hub_download(HF_REPO, "main/outpainting/main_B16/op-new-stage3-step0800000.ckpt") dino = hf_hub_download(HF_REPO, "dinov3_vitl16_pretrain_sat493m-eadcf0ff.pth") # 2. One real 256x256 Esri World Imagery tile as the input. tile = fetch_tile_at_zoom(LON, LAT, ZOOM) # PIL RGB, 256x256 tile.save("genesis_sr_in.png") # 3. Super-resolution: z16 parent -> 512x512 mosaic of its 4 z17 children. # target_zoom is the CHILD zoom level (feeds the model's GSD embedder). sr_model, _ = load_sr_denoiser(sr_ckpt, ARCH, dino_weights=dino, device=device) sr_out = run_superresolution(sr_model, tile, device, target_zoom=ZOOM + 1) sr_out.save("genesis_sr_out.png") print("Saved genesis_sr_in.png (256 parent) and genesis_sr_out.png (512 SR mosaic)") del sr_model # free memory before loading the OP model if device.type == "cuda": torch.cuda.empty_cache() # 4. Outpainting: 2x2 quadrant mosaic — 1 real quadrant, 3 masked holes. # Mask convention: white (255) = hole to generate, black (0) = known. mask = Image.new("L", (256, 256), 255) # everything is a hole ... mask.paste(0, (0, 0, 128, 128)) # ... except the upper-left quadrant op_model, _ = load_op_denoiser(op_ckpt, ARCH, dino_weights="", device=device) outpaint_context_white_holes_preview(tile, mask).save("genesis_op_in.png") op_out = run_outpainting(op_model, tile, mask, device, tile_zoom=ZOOM) op_out.save("genesis_op_out.png") print("Saved genesis_op_in.png (masked input) and genesis_op_out.png (outpainted)") if __name__ == "__main__": main()