Instructions to use Pedro21613/PS-IMAGE-1.3-LEVE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Pedro21613/PS-IMAGE-1.3-LEVE with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Pedro21613/PS-IMAGE-1.3-LEVE") 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
PS-IMAGE 1.3 LEVE โ pessoas + automoveis + texturas (compacta e rapida)
LoRA r16 em fp16 (11,5 MB) derivado de Pedro21613/PS-IMAGE-1.3 (23 MB fp32). Pesos identicos, metade do tamanho.
Uso rapido (12 steps, ~5x mais rapido que o original de 30 steps)
import torch
from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler, AutoencoderTiny
from peft import PeftModel
BASE = "stable-diffusion-v1-5/stable-diffusion-v1-5"
pipe = StableDiffusionPipeline.from_pretrained(BASE, torch_dtype=torch.float16, safety_checker=None)
pipe.unet = PeftModel.from_pretrained(pipe.unet, "Pedro21613/PS-IMAGE-1.3-LEVE")
pipe.unet = pipe.unet.merge_and_unload()
pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taesd", torch_dtype=torch.float16)
pipe.scheduler = DPMSolverMultistepScheduler.from_pretrained(BASE, subfolder="scheduler", use_karras_sigmas=True)
pipe.to("cuda")
img = pipe("ultra realistic photo of a man's face, weathered skin, visible pores, "
"detailed skin texture, natural light, portrait, high quality",
num_inference_steps=12, guidance_scale=6.0, height=512, width=512).images[0]
Troque Pedro21613/PS-IMAGE-1.3-LEVE pelo id deste repo. Teste interativo: gradio_colab.py (Gradio, pronto p/ Colab).
Script local: usar_leve.py.
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