metadata
license: cc
tags:
- text-to-image
- lora
- diffusers
- template:sd-lora
base_model:
- black-forest-labs/FLUX.1-dev
fake geoguessr locations lora for flux-dev
trained for 3500 steps on over 200 labeled locations. trigger word ("geoguessr") not necessary, just name a location
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 locations
- it managed to generalize to locations not available on geoguessr, like china, although it drifts towards generic locs
- its trained on lowercase country names, and flux is case sensitive. results may vary
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', weight_name='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))
geoguessr_v2 with a much larger dataset and less location bias will be out eventually.
this model is a part of my much larger desterilizer project- a bit more here https://x.com/_lyraaaa_/status/1824003678086590646