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--- |
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license: cc |
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tags: |
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- text-to-image |
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- lora |
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- diffusers |
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- template:sd-lora |
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base_model: |
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- black-forest-labs/FLUX.1-dev |
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--- |
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**fake geoguessr locations lora for flux-dev** |
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trained for 3500 steps on over 200 labeled locations. trigger word ("geoguessr") not necessary, just name a location |
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known model biases: |
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- v1 of this model leans heavily towards rural locations due to dataset bias, will be fixed in v2 as I collect more locations |
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- it managed to generalize to locations not available on geoguessr, like china, although it drifts towards generic locs |
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- its trained on lowercase country names, and flux is case sensitive. results may vary |
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run this with diffusers: |
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```py |
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import torch |
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from diffusers import FluxPipeline |
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import time |
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import random |
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# initialize pipeline and lora |
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pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to("cuda") |
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lora_weight = 0.8 |
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pipe.load_lora_weights('/workspace/geoguessr_v1_000003500.safetensors', weight_name='geoguessr_v1_000003500.safetensors', adapter_name='geoguessr_v1') |
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pipe.set_adapters('geoguessr_v1', adapter_weights=[lora_weight]) |
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# set params and generate |
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seed = -1 |
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seed = seed if seed != -1 else random.randint(0, 2**32) |
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print(seed) |
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prompt = "sweden, snow" |
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out = pipe( |
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prompt=prompt, |
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guidance_scale=4, |
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height=624, |
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width=960, |
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num_inference_steps=40, |
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generator=torch.Generator("cuda").manual_seed(seed), |
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).images[0] |
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# save and display output |
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filename=f"{time.time()}.png" |
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out.save(filename) |
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from IPython.display import Image, display |
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display(Image(filename=filename)) |
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``` |
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geoguessr_v2 with a much larger dataset and less location bias will be out eventually. |
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this model is a part of my much larger desterilizer project- a bit more here https://x.com/_lyraaaa_/status/1824003678086590646 |
