Update README.md

README.md

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----
-license: cc
----
+---
+license: cc
+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:
+
+```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', 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