import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("eolecvk/model-test", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]Mini Stable Diffusion (miniSD)
MiniSD is a latent text-to-image diffusion model that has been conditionned on 256x256 images through finetuning.
Examples
WIP
Usage
!pip install diffusers==0.3.0
!pip install transformers scipy ftfy
import torch
from diffusers import StableDiffusionPipeline
from torch import autocast
# TODO: change model_id to "lambdalabs/miniSD"
pipe = StableDiffusionPipeline.from_pretrained("eolecvk/model-test", torch_dtype=torch.float16)
pipe = pipe.to("cuda")
prompt = "Yoda"
scale = 10
n_samples = 4
# Sometimes the nsfw checker is confused, you can disable it at your own risk here
disable_safety = False
if disable_safety:
def null_safety(images, **kwargs):
return images, False
pipe.safety_checker = null_safety
with autocast("cuda"):
images = pipe(n_samples*[prompt], guidance_scale=scale).images
for idx, im in enumerate(images):
im.save(f"{idx:06}.png")
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