import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("eramth/realism-sdxl", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]A realism portrait SDXL model with a memory-efficient SDXL VAE that saves about 3GB of RAM with almost no loss of image quality during VAE decoding.
Recommended arguments
step: 20-30, CFG: 2-4
Usage
from diffusers import StableDiffusionXLPipeline
import torch
pipeline = StableDiffusionXLPipeline.from_pretrained("eramth/realism-sdxl",torch_dtype=torch.float16).to("cuda")
# This allows you to generate higher resolution images without much extra VRAM usage.
pipeline.vae.enable_tiling()
image = pipeline(prompt="a beautiful woman",num_inference_steps=25,guidance_scale=2.5).images[0]
image
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stabilityai/stable-diffusion-xl-base-1.0