---
license: openrail++
---

This Repo contains a diffusers format version of the PixArt-Sigma Repos 
PixArt-alpha/pixart_sigma_sdxlvae_T5_diffusers
PixArt-alpha/PixArt-Sigma-XL-2-2K-MS
with the models loaded and saved in fp16 and bf16 formats, roughly halfing their sizes.
It can be used where download bandwith, memory or diskspace are relatively low, a T4 Colab instance for example.

To use in a diffusers script you currently(15/04/2024) need to use a Source distribution of Diffusers
and an extra 'patch' from the PixArt-Alpha's teams Sigma Github repo

**NOTE: This Model has been converted but not successfully tested, during the memory effecient attention
it generates 16Gb buffer, this appears to be a MPS limitation, but it may also mean if requires more than 16Gb even
with the 16 bit model**

The diffusers script below assumes those with more memory on none MPS GPU's have more luck running it!

a Diffusers script looks like this, **currently (25th April 2024) you need will to install diffusers from source**.


```py
import random
import sys
import torch
from diffusers from PixArtSigmaPipeline

device = 'mps'
weight_dtype = torch.bfloat16

pipe = PixArtSigmaPipeline.from_pretrained(
    "Vargol/PixArt-Sigma_2k_16bit",
    torch_dtype=weight_dtype,
    variant="fp16",
    use_safetensors=True,
)

# Enable memory optimizations.
# pipe.enable_model_cpu_offload()
pipe.to(device)

prompt = "Cinematic science fiction film still.A cybernetic demon awaits her friend in a bar selling flaming oil drinks.  The barman is a huge tree being, towering over the demon"

for i in range(4):

    seed = random.randint(0, sys.maxsize)
    generator =  torch.Generator("mps").manual_seed(seed);

    image = pipe(prompt, generator=generator, num_iferencenum_inference_steps=40).images[0]
    image.save(f"pas_{seed}.png")a
~~

```