Norod

from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
import torch


#////////////////////////////////////////////////////////////////
guidance_scale=7.5
steps=40
width=1024
height=1024
base_model_id_str = "sdxl_10"

Norod

Norod

Norod

from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
import torch

#////////////////////////////////////////////////////////////////
guidance_scale=8.0
steps=40
width=512
height=512

Norod

from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
import torch


#////////////////////////////////////////////////////////////////
guidance_scale=7.5
steps_base=50
steps_refiner=50
width=1024
height=1024

Norod

# Ran it with the following packages installed:
# accelerate                   0.18.0
# diffusers                    0.16.0.dev0
# torch                        2.0.0+cu118
# torchvision                  0.15.0+cu118
# transformers                 4.28.1
# xformers                     0.0.18

import torch
from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler

Norod

#### pip install diffusers==0.2.4 transformers scipy ftfy
####

from diffusers import StableDiffusionPipeline, LMSDiscreteScheduler
import torch

def main():
    seed = 1000 #1000, 42, 420
    torch.manual_seed(seed)
    generator = torch.Generator()

Norod

Norod

import torch
import textwrap
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, MarianTokenizer, MarianMTModel

def chunk_examples(txt_list, width):
    chunks = []
    for sentence in txt_list:        
        chunks += textwrap.wrap(sentence, width=width)
    return chunks

Norod