--- license: cc tags: - text-to-image - lora - diffusers - template:sd-lora base_model: - black-forest-labs/FLUX.1-dev widget: - text: brazil output: url: images/brazil.png - text: canada, geoguessr output: url: images/canada.png - text: mongolia output: url: images/mongolia.png - text: serbia village output: url: images/serbia.png - text: thailand output: url: images/thailand.png --- # fake geoguessr locations lora for flux-dev https://x.com/_lyraaaa_/status/1841762752404369745 rank 32, trained for 3500 steps on over 200 labeled locations. trigger word ("geoguessr") not always necessary, just name a location **run this with diffusers:** ```py import torch from diffusers import FluxPipeline import time import random # initialize pipeline and lora pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to("cuda") lora_weight = 0.8 pipe.load_lora_weights( '/workspace/geoguessr_v1_000003500.safetensors', adapter_name='geoguessr_v1' ) pipe.set_adapters('geoguessr_v1', adapter_weights=[lora_weight]) # set params and generate seed = -1 seed = seed if seed != -1 else random.randint(0, 2**32) print(seed) prompt = "sweden, snow" out = pipe( prompt=prompt, guidance_scale=4, height=624, width=960, num_inference_steps=40, generator=torch.Generator("cuda").manual_seed(seed), ).images[0] # save and display output filename=f"{time.time()}.png" out.save(filename) from IPython.display import Image, display display(Image(filename=filename)) ``` **known model biases:** - v1 of this model leans heavily towards rural locations due to dataset bias, will be fixed in v2 as i collect more data - it managed to generalize to locations not available on geoguessr, like china, although it drifts towards generic locations - its trained on lowercase country names, and flux is case sensitive. results may vary - it LOVES orange/red dirt colors. this will be fixed in v2 also geoguessr_v2 with a much larger dataset and less location bias will be out eventually. since i do not own the data for this model, i can't really claim ownership of the model itself either. have fun! trained with https://github.com/ostris/ai-toolkit/blob/main/notebooks/FLUX_1_dev_LoRA_Training.ipynb this model is a part of my much larger desterilizer project- a bit more here https://x.com/_lyraaaa_/status/1824003678086590646