Instructions to use seb2oo/seb_realistic_avatar_lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use seb2oo/seb_realistic_avatar_lora with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("SG161222/Realistic_Vision_V6.0_B1_noVAE", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("seb2oo/seb_realistic_avatar_lora") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Seb Realistic Avatar LoRA
LoRA trained on:
SG161222/Realistic_Vision_V6.0_B1_noVAE
Files
- adapter_model.safetensors
- adapter_config.json
Example
from diffusers import StableDiffusionPipeline
import torch
pipe = StableDiffusionPipeline.from_pretrained(
"SG161222/Realistic_Vision_V6.0_B1_noVAE",
torch_dtype=torch.float16
)
pipe.load_lora_weights("seb2oo/seb_realistic_avatar_lora")
image = pipe(
"portrait photo of a person"
).images[0]
image.save("result.png")
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Model tree for seb2oo/seb_realistic_avatar_lora
Base model
SG161222/Realistic_Vision_V6.0_B1_noVAE