Vargol/PixArt-Sigma_16bit

This Repo contains a diffusers format version of the PixArt-Sigma Repos PixArt-alpha/pixart_sigma_sdxlvae_T5_diffusers PixArt-alpha/PixArt-Sigma-XL-2-1024-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 PixArt0Alpha's teams Sigma Github repo

A simple Colab notebook can be found at https://github.com/Vargol/StableDiffusionColabs/blob/main/PixArt/PixArt_Sigma.ipynb

a Diffusers script looks like this.

import random
import sys
import torch
from diffusers import Transformer2DModel
from scripts.diffusers_patches import pixart_sigma_init_patched_inputs, PixArtSigmaPipeline

assert getattr(Transformer2DModel, '_init_patched_inputs', False), "Need to Upgrade diffusers: pip install git+https://github.com/huggingface/diffusers"
setattr(Transformer2DModel, '_init_patched_inputs', pixart_sigma_init_patched_inputs)
device = 'mps'
weight_dtype = torch.bfloat16

pipe = PixArtSigmaPipeline.from_pretrained(
    "/Vargol/PixArt-Sigma_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