title	emoji	colorFrom	colorTo	sdk	sdk_version	app_file	pinned	license
NGC5128 SDXL	🫓🥽	blue	purple	gradio	4.31.5	app.py	false	creativeml-openrail-m

🚀Check out the configuration reference at : https://huggingface.co/docs/hub/spaces-config-reference

🚀Huggingface space : https://huggingface.co/spaces/prithivMLmods/NGC5128-StableDiffusion-XL

🚀The GitHub Model Workspace :

# Make sure you have git-lfs installed (https://git-lfs.com)
git lfs install

git clone https://huggingface.co/spaces/prithivMLmods/NGC5128-StableDiffusion-XL

# If you want to clone without large files - just their pointers

GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/spaces/prithivMLmods/NGC5128-StableDiffusion-XL

ℹ️Generated Result in Huggingface Spaces:

🔮The Sample Prompt 1 Give to the AI Model for Generation :

🔮The Sample Prompt 2 Give to the AI Model for Generation :

Generated Image Based on the prompt given:

Prompt 1

Fashion shoot, Jacquemus and moncler collaboration: light pink translucent fur sneakers, hyper-realistic, detailed 8k, realistic.

prompt 2

a  painting of an eye with purple and orange flowers, in the style of james jean, mysterious jungle, martin ansin, 32k uhd, papua new guinea art, light blue and red, poster --ar 37:61 --stylize 750 --v 6

prompt 3

(Pirate ship sailing into a bioluminescence sea with a galaxy in the sky), epic, 4k, ultra,

prompt 4

front view, capture a urban style, Hoodie, technical materials, fabric small point label on text 'Graytheory', the design is minimal, with a raised collar, fabric is a dark grey, low angle to capture the Hoodie's form and detailing, f/5.6 to focus on the hoodie's craftsmanship, solid grey background, studio light setting

Samples generated from [ prompt 1,2,3,4 ]

Requirement.txt / PyPI

gradio
gradio-client

Use this model with theInference API (serverless)

import requests

API_URL = "https://api-inference.huggingface.co/models/prithivMLmods/NGC5128-StableDiffusion-XL-v4"
headers = {"Authorization": "Bearer xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"}

def query(payload):
	response = requests.post(API_URL, headers=headers, json=payload)
	return response.content
image_bytes = query({
	"inputs": "Astronaut riding a horse",
})
# You can access the image with PIL.Image for example
import io
from PIL import Image
image = Image.open(io.BytesIO(image_bytes))

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