sdxl-longcliponly

177
4
1.0B
BF16
—
by
opendiffusionai
Image Model
OTHER
1.0B params
New
177 downloads
Early-stage
Try on Hugging FaceAdd to Compare
Edge AI:
Mobile
Laptop
Server
3GB+ RAM
Mobile
Laptop
Server
Quick Summary

BUGFIXES!!! (Latest update 2025/05/21) Please note that the initial release had a bug in the tokenizer config. Additionally.. I padded out tokenizer2 and texte...

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
1GB+ RAM

Code Examples

Sample python codetextpytorch
import torch
from diffusers import StableDiffusionXLPipeline
import argparse

parser = argparse.ArgumentParser()
parser.add_argument("-p", "--prompt", default="sad girl in snow")
parser.add_argument("-m", "--model", default="opendiffusionai/sdxl-longcliponly")

args = parser.parse_args()

pipe = StableDiffusionXLPipeline.from_pretrained(args.model)
device = torch.device("cuda")
pipe = pipe.to(device)

result = pipe(args.prompt, guidance_scale=7.5, num_inference_steps=30)

image = result.images[0]
image.save("testimg.png")
Sample python codetextpytorch
import torch
from diffusers import StableDiffusionXLPipeline
import argparse

parser = argparse.ArgumentParser()
parser.add_argument("-p", "--prompt", default="sad girl in snow")
parser.add_argument("-m", "--model", default="opendiffusionai/sdxl-longcliponly")

args = parser.parse_args()

pipe = StableDiffusionXLPipeline.from_pretrained(args.model)
device = torch.device("cuda")
pipe = pipe.to(device)

result = pipe(args.prompt, guidance_scale=7.5, num_inference_steps=30)

image = result.images[0]
image.save("testimg.png")
Sample python codetextpytorch
import torch
from diffusers import StableDiffusionXLPipeline
import argparse

parser = argparse.ArgumentParser()
parser.add_argument("-p", "--prompt", default="sad girl in snow")
parser.add_argument("-m", "--model", default="opendiffusionai/sdxl-longcliponly")

args = parser.parse_args()

pipe = StableDiffusionXLPipeline.from_pretrained(args.model)
device = torch.device("cuda")
pipe = pipe.to(device)

result = pipe(args.prompt, guidance_scale=7.5, num_inference_steps=30)

image = result.images[0]
image.save("testimg.png")
Sample python codetextpytorch
import torch
from diffusers import StableDiffusionXLPipeline
import argparse

parser = argparse.ArgumentParser()
parser.add_argument("-p", "--prompt", default="sad girl in snow")
parser.add_argument("-m", "--model", default="opendiffusionai/sdxl-longcliponly")

args = parser.parse_args()

pipe = StableDiffusionXLPipeline.from_pretrained(args.model)
device = torch.device("cuda")
pipe = pipe.to(device)

result = pipe(args.prompt, guidance_scale=7.5, num_inference_steps=30)

image = result.images[0]
image.save("testimg.png")
Sample python codetextpytorch
import torch
from diffusers import StableDiffusionXLPipeline
import argparse

parser = argparse.ArgumentParser()
parser.add_argument("-p", "--prompt", default="sad girl in snow")
parser.add_argument("-m", "--model", default="opendiffusionai/sdxl-longcliponly")

args = parser.parse_args()

pipe = StableDiffusionXLPipeline.from_pretrained(args.model)
device = torch.device("cuda")
pipe = pipe.to(device)

result = pipe(args.prompt, guidance_scale=7.5, num_inference_steps=30)

image = result.images[0]
image.save("testimg.png")
Sample python codetextpytorch
import torch
from diffusers import StableDiffusionXLPipeline
import argparse

parser = argparse.ArgumentParser()
parser.add_argument("-p", "--prompt", default="sad girl in snow")
parser.add_argument("-m", "--model", default="opendiffusionai/sdxl-longcliponly")

args = parser.parse_args()

pipe = StableDiffusionXLPipeline.from_pretrained(args.model)
device = torch.device("cuda")
pipe = pipe.to(device)

result = pipe(args.prompt, guidance_scale=7.5, num_inference_steps=30)

image = result.images[0]
image.save("testimg.png")
Sample python codetextpytorch
import torch
from diffusers import StableDiffusionXLPipeline
import argparse

parser = argparse.ArgumentParser()
parser.add_argument("-p", "--prompt", default="sad girl in snow")
parser.add_argument("-m", "--model", default="opendiffusionai/sdxl-longcliponly")

args = parser.parse_args()

pipe = StableDiffusionXLPipeline.from_pretrained(args.model)
device = torch.device("cuda")
pipe = pipe.to(device)

result = pipe(args.prompt, guidance_scale=7.5, num_inference_steps=30)

image = result.images[0]
image.save("testimg.png")
Sample python codetextpytorch
import torch
from diffusers import StableDiffusionXLPipeline
import argparse

parser = argparse.ArgumentParser()
parser.add_argument("-p", "--prompt", default="sad girl in snow")
parser.add_argument("-m", "--model", default="opendiffusionai/sdxl-longcliponly")

args = parser.parse_args()

pipe = StableDiffusionXLPipeline.from_pretrained(args.model)
device = torch.device("cuda")
pipe = pipe.to(device)

result = pipe(args.prompt, guidance_scale=7.5, num_inference_steps=30)

image = result.images[0]
image.save("testimg.png")
Sample python codetextpytorch
import torch
from diffusers import StableDiffusionXLPipeline
import argparse

parser = argparse.ArgumentParser()
parser.add_argument("-p", "--prompt", default="sad girl in snow")
parser.add_argument("-m", "--model", default="opendiffusionai/sdxl-longcliponly")

args = parser.parse_args()

pipe = StableDiffusionXLPipeline.from_pretrained(args.model)
device = torch.device("cuda")
pipe = pipe.to(device)

result = pipe(args.prompt, guidance_scale=7.5, num_inference_steps=30)

image = result.images[0]
image.save("testimg.png")
Sample python codetextpytorch
import torch
from diffusers import StableDiffusionXLPipeline
import argparse

parser = argparse.ArgumentParser()
parser.add_argument("-p", "--prompt", default="sad girl in snow")
parser.add_argument("-m", "--model", default="opendiffusionai/sdxl-longcliponly")

args = parser.parse_args()

pipe = StableDiffusionXLPipeline.from_pretrained(args.model)
device = torch.device("cuda")
pipe = pipe.to(device)

result = pipe(args.prompt, guidance_scale=7.5, num_inference_steps=30)

image = result.images[0]
image.save("testimg.png")
Sample python codetextpytorch
import torch
from diffusers import StableDiffusionXLPipeline
import argparse

parser = argparse.ArgumentParser()
parser.add_argument("-p", "--prompt", default="sad girl in snow")
parser.add_argument("-m", "--model", default="opendiffusionai/sdxl-longcliponly")

args = parser.parse_args()

pipe = StableDiffusionXLPipeline.from_pretrained(args.model)
device = torch.device("cuda")
pipe = pipe.to(device)

result = pipe(args.prompt, guidance_scale=7.5, num_inference_steps=30)

image = result.images[0]
image.save("testimg.png")
Sample python codetextpytorch
import torch
from diffusers import StableDiffusionXLPipeline
import argparse

parser = argparse.ArgumentParser()
parser.add_argument("-p", "--prompt", default="sad girl in snow")
parser.add_argument("-m", "--model", default="opendiffusionai/sdxl-longcliponly")

args = parser.parse_args()

pipe = StableDiffusionXLPipeline.from_pretrained(args.model)
device = torch.device("cuda")
pipe = pipe.to(device)

result = pipe(args.prompt, guidance_scale=7.5, num_inference_steps=30)

image = result.images[0]
image.save("testimg.png")
Sample python codetextpytorch
import torch
from diffusers import StableDiffusionXLPipeline
import argparse

parser = argparse.ArgumentParser()
parser.add_argument("-p", "--prompt", default="sad girl in snow")
parser.add_argument("-m", "--model", default="opendiffusionai/sdxl-longcliponly")

args = parser.parse_args()

pipe = StableDiffusionXLPipeline.from_pretrained(args.model)
device = torch.device("cuda")
pipe = pipe.to(device)

result = pipe(args.prompt, guidance_scale=7.5, num_inference_steps=30)

image = result.images[0]
image.save("testimg.png")
Sample python codetextpytorch
import torch
from diffusers import StableDiffusionXLPipeline
import argparse

parser = argparse.ArgumentParser()
parser.add_argument("-p", "--prompt", default="sad girl in snow")
parser.add_argument("-m", "--model", default="opendiffusionai/sdxl-longcliponly")

args = parser.parse_args()

pipe = StableDiffusionXLPipeline.from_pretrained(args.model)
device = torch.device("cuda")
pipe = pipe.to(device)

result = pipe(args.prompt, guidance_scale=7.5, num_inference_steps=30)

image = result.images[0]
image.save("testimg.png")
Sample python codetextpytorch
import torch
from diffusers import StableDiffusionXLPipeline
import argparse

parser = argparse.ArgumentParser()
parser.add_argument("-p", "--prompt", default="sad girl in snow")
parser.add_argument("-m", "--model", default="opendiffusionai/sdxl-longcliponly")

args = parser.parse_args()

pipe = StableDiffusionXLPipeline.from_pretrained(args.model)
device = torch.device("cuda")
pipe = pipe.to(device)

result = pipe(args.prompt, guidance_scale=7.5, num_inference_steps=30)

image = result.images[0]
image.save("testimg.png")
Sample python codetextpytorch
import torch
from diffusers import StableDiffusionXLPipeline
import argparse

parser = argparse.ArgumentParser()
parser.add_argument("-p", "--prompt", default="sad girl in snow")
parser.add_argument("-m", "--model", default="opendiffusionai/sdxl-longcliponly")

args = parser.parse_args()

pipe = StableDiffusionXLPipeline.from_pretrained(args.model)
device = torch.device("cuda")
pipe = pipe.to(device)

result = pipe(args.prompt, guidance_scale=7.5, num_inference_steps=30)

image = result.images[0]
image.save("testimg.png")
Sample python codetextpytorch
import torch
from diffusers import StableDiffusionXLPipeline
import argparse

parser = argparse.ArgumentParser()
parser.add_argument("-p", "--prompt", default="sad girl in snow")
parser.add_argument("-m", "--model", default="opendiffusionai/sdxl-longcliponly")

args = parser.parse_args()

pipe = StableDiffusionXLPipeline.from_pretrained(args.model)
device = torch.device("cuda")
pipe = pipe.to(device)

result = pipe(args.prompt, guidance_scale=7.5, num_inference_steps=30)

image = result.images[0]
image.save("testimg.png")
Sample python codetextpytorch
import torch
from diffusers import StableDiffusionXLPipeline
import argparse

parser = argparse.ArgumentParser()
parser.add_argument("-p", "--prompt", default="sad girl in snow")
parser.add_argument("-m", "--model", default="opendiffusionai/sdxl-longcliponly")

args = parser.parse_args()

pipe = StableDiffusionXLPipeline.from_pretrained(args.model)
device = torch.device("cuda")
pipe = pipe.to(device)

result = pipe(args.prompt, guidance_scale=7.5, num_inference_steps=30)

image = result.images[0]
image.save("testimg.png")
Sample python codetextpytorch
import torch
from diffusers import StableDiffusionXLPipeline
import argparse

parser = argparse.ArgumentParser()
parser.add_argument("-p", "--prompt", default="sad girl in snow")
parser.add_argument("-m", "--model", default="opendiffusionai/sdxl-longcliponly")

args = parser.parse_args()

pipe = StableDiffusionXLPipeline.from_pretrained(args.model)
device = torch.device("cuda")
pipe = pipe.to(device)

result = pipe(args.prompt, guidance_scale=7.5, num_inference_steps=30)

image = result.images[0]
image.save("testimg.png")
Sample python codetextpytorch
import torch
from diffusers import StableDiffusionXLPipeline
import argparse

parser = argparse.ArgumentParser()
parser.add_argument("-p", "--prompt", default="sad girl in snow")
parser.add_argument("-m", "--model", default="opendiffusionai/sdxl-longcliponly")

args = parser.parse_args()

pipe = StableDiffusionXLPipeline.from_pretrained(args.model)
device = torch.device("cuda")
pipe = pipe.to(device)

result = pipe(args.prompt, guidance_scale=7.5, num_inference_steps=30)

image = result.images[0]
image.save("testimg.png")
Sample python codetextpytorch
import torch
from diffusers import StableDiffusionXLPipeline
import argparse

parser = argparse.ArgumentParser()
parser.add_argument("-p", "--prompt", default="sad girl in snow")
parser.add_argument("-m", "--model", default="opendiffusionai/sdxl-longcliponly")

args = parser.parse_args()

pipe = StableDiffusionXLPipeline.from_pretrained(args.model)
device = torch.device("cuda")
pipe = pipe.to(device)

result = pipe(args.prompt, guidance_scale=7.5, num_inference_steps=30)

image = result.images[0]
image.save("testimg.png")
Sample python codetextpytorch
import torch
from diffusers import StableDiffusionXLPipeline
import argparse

parser = argparse.ArgumentParser()
parser.add_argument("-p", "--prompt", default="sad girl in snow")
parser.add_argument("-m", "--model", default="opendiffusionai/sdxl-longcliponly")

args = parser.parse_args()

pipe = StableDiffusionXLPipeline.from_pretrained(args.model)
device = torch.device("cuda")
pipe = pipe.to(device)

result = pipe(args.prompt, guidance_scale=7.5, num_inference_steps=30)

image = result.images[0]
image.save("testimg.png")
Sample python codetextpytorch
import torch
from diffusers import StableDiffusionXLPipeline
import argparse

parser = argparse.ArgumentParser()
parser.add_argument("-p", "--prompt", default="sad girl in snow")
parser.add_argument("-m", "--model", default="opendiffusionai/sdxl-longcliponly")

args = parser.parse_args()

pipe = StableDiffusionXLPipeline.from_pretrained(args.model)
device = torch.device("cuda")
pipe = pipe.to(device)

result = pipe(args.prompt, guidance_scale=7.5, num_inference_steps=30)

image = result.images[0]
image.save("testimg.png")
Sample python codetextpytorch
import torch
from diffusers import StableDiffusionXLPipeline
import argparse

parser = argparse.ArgumentParser()
parser.add_argument("-p", "--prompt", default="sad girl in snow")
parser.add_argument("-m", "--model", default="opendiffusionai/sdxl-longcliponly")

args = parser.parse_args()

pipe = StableDiffusionXLPipeline.from_pretrained(args.model)
device = torch.device("cuda")
pipe = pipe.to(device)

result = pipe(args.prompt, guidance_scale=7.5, num_inference_steps=30)

image = result.images[0]
image.save("testimg.png")
Sample python codetextpytorch
import torch
from diffusers import StableDiffusionXLPipeline
import argparse

parser = argparse.ArgumentParser()
parser.add_argument("-p", "--prompt", default="sad girl in snow")
parser.add_argument("-m", "--model", default="opendiffusionai/sdxl-longcliponly")

args = parser.parse_args()

pipe = StableDiffusionXLPipeline.from_pretrained(args.model)
device = torch.device("cuda")
pipe = pipe.to(device)

result = pipe(args.prompt, guidance_scale=7.5, num_inference_steps=30)

image = result.images[0]
image.save("testimg.png")
Sample python codetextpytorch
import torch
from diffusers import StableDiffusionXLPipeline
import argparse

parser = argparse.ArgumentParser()
parser.add_argument("-p", "--prompt", default="sad girl in snow")
parser.add_argument("-m", "--model", default="opendiffusionai/sdxl-longcliponly")

args = parser.parse_args()

pipe = StableDiffusionXLPipeline.from_pretrained(args.model)
device = torch.device("cuda")
pipe = pipe.to(device)

result = pipe(args.prompt, guidance_scale=7.5, num_inference_steps=30)

image = result.images[0]
image.save("testimg.png")
Sample python codetextpytorch
import torch
from diffusers import StableDiffusionXLPipeline
import argparse

parser = argparse.ArgumentParser()
parser.add_argument("-p", "--prompt", default="sad girl in snow")
parser.add_argument("-m", "--model", default="opendiffusionai/sdxl-longcliponly")

args = parser.parse_args()

pipe = StableDiffusionXLPipeline.from_pretrained(args.model)
device = torch.device("cuda")
pipe = pipe.to(device)

result = pipe(args.prompt, guidance_scale=7.5, num_inference_steps=30)

image = result.images[0]
image.save("testimg.png")
Sample python codetextpytorch
import torch
from diffusers import StableDiffusionXLPipeline
import argparse

parser = argparse.ArgumentParser()
parser.add_argument("-p", "--prompt", default="sad girl in snow")
parser.add_argument("-m", "--model", default="opendiffusionai/sdxl-longcliponly")

args = parser.parse_args()

pipe = StableDiffusionXLPipeline.from_pretrained(args.model)
device = torch.device("cuda")
pipe = pipe.to(device)

result = pipe(args.prompt, guidance_scale=7.5, num_inference_steps=30)

image = result.images[0]
image.save("testimg.png")
Sample python codetextpytorch
import torch
from diffusers import StableDiffusionXLPipeline
import argparse

parser = argparse.ArgumentParser()
parser.add_argument("-p", "--prompt", default="sad girl in snow")
parser.add_argument("-m", "--model", default="opendiffusionai/sdxl-longcliponly")

args = parser.parse_args()

pipe = StableDiffusionXLPipeline.from_pretrained(args.model)
device = torch.device("cuda")
pipe = pipe.to(device)

result = pipe(args.prompt, guidance_scale=7.5, num_inference_steps=30)

image = result.images[0]
image.save("testimg.png")
Sample python codetextpytorch
import torch
from diffusers import StableDiffusionXLPipeline
import argparse

parser = argparse.ArgumentParser()
parser.add_argument("-p", "--prompt", default="sad girl in snow")
parser.add_argument("-m", "--model", default="opendiffusionai/sdxl-longcliponly")

args = parser.parse_args()

pipe = StableDiffusionXLPipeline.from_pretrained(args.model)
device = torch.device("cuda")
pipe = pipe.to(device)

result = pipe(args.prompt, guidance_scale=7.5, num_inference_steps=30)

image = result.images[0]
image.save("testimg.png")
Sample python codetextpytorch
import torch
from diffusers import StableDiffusionXLPipeline
import argparse

parser = argparse.ArgumentParser()
parser.add_argument("-p", "--prompt", default="sad girl in snow")
parser.add_argument("-m", "--model", default="opendiffusionai/sdxl-longcliponly")

args = parser.parse_args()

pipe = StableDiffusionXLPipeline.from_pretrained(args.model)
device = torch.device("cuda")
pipe = pipe.to(device)

result = pipe(args.prompt, guidance_scale=7.5, num_inference_steps=30)

image = result.images[0]
image.save("testimg.png")
Sample python codetextpytorch
import torch
from diffusers import StableDiffusionXLPipeline
import argparse

parser = argparse.ArgumentParser()
parser.add_argument("-p", "--prompt", default="sad girl in snow")
parser.add_argument("-m", "--model", default="opendiffusionai/sdxl-longcliponly")

args = parser.parse_args()

pipe = StableDiffusionXLPipeline.from_pretrained(args.model)
device = torch.device("cuda")
pipe = pipe.to(device)

result = pipe(args.prompt, guidance_scale=7.5, num_inference_steps=30)

image = result.images[0]
image.save("testimg.png")
Sample python codetextpytorch
import torch
from diffusers import StableDiffusionXLPipeline
import argparse

parser = argparse.ArgumentParser()
parser.add_argument("-p", "--prompt", default="sad girl in snow")
parser.add_argument("-m", "--model", default="opendiffusionai/sdxl-longcliponly")

args = parser.parse_args()

pipe = StableDiffusionXLPipeline.from_pretrained(args.model)
device = torch.device("cuda")
pipe = pipe.to(device)

result = pipe(args.prompt, guidance_scale=7.5, num_inference_steps=30)

image = result.images[0]
image.save("testimg.png")
Sample python codetextpytorch
import torch
from diffusers import StableDiffusionXLPipeline
import argparse

parser = argparse.ArgumentParser()
parser.add_argument("-p", "--prompt", default="sad girl in snow")
parser.add_argument("-m", "--model", default="opendiffusionai/sdxl-longcliponly")

args = parser.parse_args()

pipe = StableDiffusionXLPipeline.from_pretrained(args.model)
device = torch.device("cuda")
pipe = pipe.to(device)

result = pipe(args.prompt, guidance_scale=7.5, num_inference_steps=30)

image = result.images[0]
image.save("testimg.png")
Sample python codetextpytorch
import torch
from diffusers import StableDiffusionXLPipeline
import argparse

parser = argparse.ArgumentParser()
parser.add_argument("-p", "--prompt", default="sad girl in snow")
parser.add_argument("-m", "--model", default="opendiffusionai/sdxl-longcliponly")

args = parser.parse_args()

pipe = StableDiffusionXLPipeline.from_pretrained(args.model)
device = torch.device("cuda")
pipe = pipe.to(device)

result = pipe(args.prompt, guidance_scale=7.5, num_inference_steps=30)

image = result.images[0]
image.save("testimg.png")
Sample python codetextpytorch
import torch
from diffusers import StableDiffusionXLPipeline
import argparse

parser = argparse.ArgumentParser()
parser.add_argument("-p", "--prompt", default="sad girl in snow")
parser.add_argument("-m", "--model", default="opendiffusionai/sdxl-longcliponly")

args = parser.parse_args()

pipe = StableDiffusionXLPipeline.from_pretrained(args.model)
device = torch.device("cuda")
pipe = pipe.to(device)

result = pipe(args.prompt, guidance_scale=7.5, num_inference_steps=30)

image = result.images[0]
image.save("testimg.png")
Sample python codetextpytorch
import torch
from diffusers import StableDiffusionXLPipeline
import argparse

parser = argparse.ArgumentParser()
parser.add_argument("-p", "--prompt", default="sad girl in snow")
parser.add_argument("-m", "--model", default="opendiffusionai/sdxl-longcliponly")

args = parser.parse_args()

pipe = StableDiffusionXLPipeline.from_pretrained(args.model)
device = torch.device("cuda")
pipe = pipe.to(device)

result = pipe(args.prompt, guidance_scale=7.5, num_inference_steps=30)

image = result.images[0]
image.save("testimg.png")
Sample python codetextpytorch
import torch
from diffusers import StableDiffusionXLPipeline
import argparse

parser = argparse.ArgumentParser()
parser.add_argument("-p", "--prompt", default="sad girl in snow")
parser.add_argument("-m", "--model", default="opendiffusionai/sdxl-longcliponly")

args = parser.parse_args()

pipe = StableDiffusionXLPipeline.from_pretrained(args.model)
device = torch.device("cuda")
pipe = pipe.to(device)

result = pipe(args.prompt, guidance_scale=7.5, num_inference_steps=30)

image = result.images[0]
image.save("testimg.png")
Sample python codetextpytorch
import torch
from diffusers import StableDiffusionXLPipeline
import argparse

parser = argparse.ArgumentParser()
parser.add_argument("-p", "--prompt", default="sad girl in snow")
parser.add_argument("-m", "--model", default="opendiffusionai/sdxl-longcliponly")

args = parser.parse_args()

pipe = StableDiffusionXLPipeline.from_pretrained(args.model)
device = torch.device("cuda")
pipe = pipe.to(device)

result = pipe(args.prompt, guidance_scale=7.5, num_inference_steps=30)

image = result.images[0]
image.save("testimg.png")
Sample python codetextpytorch
import torch
from diffusers import StableDiffusionXLPipeline
import argparse

parser = argparse.ArgumentParser()
parser.add_argument("-p", "--prompt", default="sad girl in snow")
parser.add_argument("-m", "--model", default="opendiffusionai/sdxl-longcliponly")

args = parser.parse_args()

pipe = StableDiffusionXLPipeline.from_pretrained(args.model)
device = torch.device("cuda")
pipe = pipe.to(device)

result = pipe(args.prompt, guidance_scale=7.5, num_inference_steps=30)

image = result.images[0]
image.save("testimg.png")
Sample python codetextpytorch
import torch
from diffusers import StableDiffusionXLPipeline
import argparse

parser = argparse.ArgumentParser()
parser.add_argument("-p", "--prompt", default="sad girl in snow")
parser.add_argument("-m", "--model", default="opendiffusionai/sdxl-longcliponly")

args = parser.parse_args()

pipe = StableDiffusionXLPipeline.from_pretrained(args.model)
device = torch.device("cuda")
pipe = pipe.to(device)

result = pipe(args.prompt, guidance_scale=7.5, num_inference_steps=30)

image = result.images[0]
image.save("testimg.png")
Sample python codetextpytorch
import torch
from diffusers import StableDiffusionXLPipeline
import argparse

parser = argparse.ArgumentParser()
parser.add_argument("-p", "--prompt", default="sad girl in snow")
parser.add_argument("-m", "--model", default="opendiffusionai/sdxl-longcliponly")

args = parser.parse_args()

pipe = StableDiffusionXLPipeline.from_pretrained(args.model)
device = torch.device("cuda")
pipe = pipe.to(device)

result = pipe(args.prompt, guidance_scale=7.5, num_inference_steps=30)

image = result.images[0]
image.save("testimg.png")
Sample python codetextpytorch
import torch
from diffusers import StableDiffusionXLPipeline
import argparse

parser = argparse.ArgumentParser()
parser.add_argument("-p", "--prompt", default="sad girl in snow")
parser.add_argument("-m", "--model", default="opendiffusionai/sdxl-longcliponly")

args = parser.parse_args()

pipe = StableDiffusionXLPipeline.from_pretrained(args.model)
device = torch.device("cuda")
pipe = pipe.to(device)

result = pipe(args.prompt, guidance_scale=7.5, num_inference_steps=30)

image = result.images[0]
image.save("testimg.png")
Sample python codetextpytorch
import torch
from diffusers import StableDiffusionXLPipeline
import argparse

parser = argparse.ArgumentParser()
parser.add_argument("-p", "--prompt", default="sad girl in snow")
parser.add_argument("-m", "--model", default="opendiffusionai/sdxl-longcliponly")

args = parser.parse_args()

pipe = StableDiffusionXLPipeline.from_pretrained(args.model)
device = torch.device("cuda")
pipe = pipe.to(device)

result = pipe(args.prompt, guidance_scale=7.5, num_inference_steps=30)

image = result.images[0]
image.save("testimg.png")
Sample python codetextpytorch
import torch
from diffusers import StableDiffusionXLPipeline
import argparse

parser = argparse.ArgumentParser()
parser.add_argument("-p", "--prompt", default="sad girl in snow")
parser.add_argument("-m", "--model", default="opendiffusionai/sdxl-longcliponly")

args = parser.parse_args()

pipe = StableDiffusionXLPipeline.from_pretrained(args.model)
device = torch.device("cuda")
pipe = pipe.to(device)

result = pipe(args.prompt, guidance_scale=7.5, num_inference_steps=30)

image = result.images[0]
image.save("testimg.png")
Sample python codetextpytorch
import torch
from diffusers import StableDiffusionXLPipeline
import argparse

parser = argparse.ArgumentParser()
parser.add_argument("-p", "--prompt", default="sad girl in snow")
parser.add_argument("-m", "--model", default="opendiffusionai/sdxl-longcliponly")

args = parser.parse_args()

pipe = StableDiffusionXLPipeline.from_pretrained(args.model)
device = torch.device("cuda")
pipe = pipe.to(device)

result = pipe(args.prompt, guidance_scale=7.5, num_inference_steps=30)

image = result.images[0]
image.save("testimg.png")
Sample python codetextpytorch
import torch
from diffusers import StableDiffusionXLPipeline
import argparse

parser = argparse.ArgumentParser()
parser.add_argument("-p", "--prompt", default="sad girl in snow")
parser.add_argument("-m", "--model", default="opendiffusionai/sdxl-longcliponly")

args = parser.parse_args()

pipe = StableDiffusionXLPipeline.from_pretrained(args.model)
device = torch.device("cuda")
pipe = pipe.to(device)

result = pipe(args.prompt, guidance_scale=7.5, num_inference_steps=30)

image = result.images[0]
image.save("testimg.png")
Sample python codetextpytorch
import torch
from diffusers import StableDiffusionXLPipeline
import argparse

parser = argparse.ArgumentParser()
parser.add_argument("-p", "--prompt", default="sad girl in snow")
parser.add_argument("-m", "--model", default="opendiffusionai/sdxl-longcliponly")

args = parser.parse_args()

pipe = StableDiffusionXLPipeline.from_pretrained(args.model)
device = torch.device("cuda")
pipe = pipe.to(device)

result = pipe(args.prompt, guidance_scale=7.5, num_inference_steps=30)

image = result.images[0]
image.save("testimg.png")
Sample python codetextpytorch
import torch
from diffusers import StableDiffusionXLPipeline
import argparse

parser = argparse.ArgumentParser()
parser.add_argument("-p", "--prompt", default="sad girl in snow")
parser.add_argument("-m", "--model", default="opendiffusionai/sdxl-longcliponly")

args = parser.parse_args()

pipe = StableDiffusionXLPipeline.from_pretrained(args.model)
device = torch.device("cuda")
pipe = pipe.to(device)

result = pipe(args.prompt, guidance_scale=7.5, num_inference_steps=30)

image = result.images[0]
image.save("testimg.png")

Deploy This Model

Production-ready deployment in minutes

Together.ai

Instant API access to this model

Fastest API

Production-ready inference API. Start free, scale to millions.

Try Free API

Replicate

One-click model deployment

Easiest Setup

Run models in the cloud with simple API. No DevOps required.

Deploy Now

Disclosure: We may earn a commission from these partners. This helps keep LLMYourWay free.