metadata
license: other
base_model: stabilityai/stable-diffusion-3.5-large
tags:
- sd3
- sd3-diffusers
- text-to-image
- diffusers
- simpletuner
- safe-for-work
- lora
- template:sd-lora
- standard
inference: true
widget:
- text: grey shirt with a small logo of a bunny painted in the alebrijeros style
output:
url: images/example_wr1vsmbx0.png
- text: hoodie with flowers painted in the alebrijeros style
output:
url: images/example_8srfnn37z.png
sd3-lora-alebrijeros-final
Este es un LoRA derivado del modelo m谩s reciente de stable diffusion: stabilityai/stable-diffusion-3.5-large.
El prompt usado durante el entrenamiento:
sweatshirt painted in the alebrijeros style
You can find some example images in the following gallery:
- Prompt
- grey shirt with a small logo of a bunny painted in the alebrijeros style
- Prompt
- hoodie with flowers painted in the alebrijeros style
Configuraci贸n del entrene
- Training epochs: 4
- Training steps: 2600
- Learning rate: 5e-05
- Max grad norm: 0.01
- Effective batch size: 1
- Micro-batch size: 1
- Gradient accumulation steps: 1
- Number of GPUs: 1
- Prediction type: flow-matching
- Rescaled betas zero SNR: False
- Optimizer: adamw_bf16
- Precision: Pure BF16
- Quantised: Yes: int8-quanto
- Xformers: Not used
- LoRA Rank: 64
- LoRA Alpha: None
- LoRA Dropout: 0.1
- LoRA initialisation style: default
Datasets
Im谩genes variadas obtenidas de google que incluyen desde un mercedes pintado por alebrijeros hasta miss universo que fue con un vestido estilo alebrijero
Inference
import torch
from diffusers import DiffusionPipeline
model_id = 'stabilityai/stable-diffusion-3.5-large'
adapter_id = 'CarlosRiverMe/sd3-lora-alebrijeros-final'
pipeline = DiffusionPipeline.from_pretrained(model_id)
pipeline.load_lora_weights(adapter_id)
prompt = "sweatshirt painted in the alebrijeros style"
negative_prompt = 'blurry, cropped, ugly'
pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu')
image = pipeline(
prompt=prompt,
negative_prompt=negative_prompt,
num_inference_steps=20,
generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(1641421826),
width=512,
height=512,
guidance_scale=5.0,
).images[0]
image.save("output.png", format="PNG")