metadata

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:

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