import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("alphaduriendur/avatar-weights-01")
prompt = "a photo of alphaduriendur"
image = pipe(prompt).images[0]Flux.1-dev LoRA — Avatar
LoRA fine-tuned on Flux.1-dev for personalized avatar generation.
Training details
- Base model:
black-forest-labs/FLUX.1-dev - Instance prompt:
a photo of alphaduriendur - LoRA rank: 16
- Training steps: 1000
- Learning rate: 0.0004
- Resolution: 1024
Usage
from diffusers import FluxPipeline
import torch
pipe = FluxPipeline.from_pretrained(
"black-forest-labs/FLUX.1-dev",
torch_dtype=torch.bfloat16
)
pipe.load_lora_weights("alphaduriendur/avatar-weights-01")
pipe = pipe.to("cuda")
image = pipe(
prompt="a photo of alphaduriendur, professional headshot, studio lighting, 8k",
num_inference_steps=28,
guidance_scale=3.5
).images[0]
image.save("avatar.png")
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Model tree for alphaduriendur/avatar-weights-01
Base model
black-forest-labs/FLUX.1-dev