Text-to-Image
Diffusers
TensorBoard
stable-diffusion
stable-diffusion-diffusers
diffusers-training
lora
Instructions to use lamble-lambe/atelie with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use lamble-lambe/atelie with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("fill-in-base-model", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("lamble-lambe/atelie") 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
File size: 1,649 Bytes
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license: mit
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
library_name: diffusers
---
# Ateliê Generativo — Estilo Lambe-Lambe
O modelo foi treinado sobre a base do **Stable Diffusion v1.5** no estilo lambe-lambe / retrato vintage, por meio de fine-tuning LoRA (Low-Rank Adaptation), integrado a um pipeline multimodal completo (texto → imagem → áudio).
## 🌟 Informações Gerais
- **Disciplina:** Inteligência Artificial Generativa e Modelos Multimodais — UniCEUB
- **Professor:** Prof. Romes Heriberto
- **Desenvolvido por:** Diego Nunes de Morais, Eduardo Deodoro de Moraes Florindo, Higo Soares do Lago, Lucio Flavio Vilar de Azevedo, Paulo Victor Torres Martins
- **Dataset:** Imagens coletadas de acervos com proveniência verificável (Wikimedia Commons, The Met, Flickr), sob licenças de domínio público, CC0, CC-BY ou CC-BY-SA.
## Hiperparâmetros
- **rank:** 4
- **learning_rate:** 5e-5
- **max_train_steps:** 1000
- **lr_warmup_steps:** 100
- **lr_scheduler:** cosine
## 🚀 Como Usar
```python
from diffusers import StableDiffusionPipeline
import torch
pipe = StableDiffusionPipeline.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16
).to("cuda")
# revision fixa uma versão específica (ver "Versionamento" abaixo); v1.4.0 = produção
pipe.load_lora_weights("lamble-lambe/atelie", revision="v1.4.0")
img = pipe(
"estilo_lambelambe, a vintage portrait of an elderly man with a hat, detailed face, black and white",
cross_attention_kwargs={"scale": 0.7},
).images[0]
img.save("saida.png")
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