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
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Download README.md from seb2oo/seb_realistic_avatar_lora: direct link, hf CLI and curl.
- Browser
- Download file 704 Bytes
-
https://huggingface.co/seb2oo/seb_realistic_avatar_lora/resolve/main/README.md
- Command line
-
hf download hf://seb2oo/seb_realistic_avatar_lora/README.md
-
curl -L -o README.md https://huggingface.co/seb2oo/seb_realistic_avatar_lora/resolve/main/README.md
704 Bytes
metadata
base_model: SG161222/Realistic_Vision_V6.0_B1_noVAE
library_name: diffusers
pipeline_tag: text-to-image
tags:
- lora
- diffusers
- text-to-image
- template:diffusion-lora
license: openrail
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")