Here is a prompt to generate an image similar to the one provided: --- A calm and serene illustration of a woman practicing yoga in the Butterfly Pose. She is seated on the floor with her legs bent, the soles of her feet pressed together, and her knees gently dropping to the sides. Her hands are resting on her feet, fingers lightly clasping them. She has a straight back, with a relaxed posture, eyes gently closed, and a peaceful expression on her face. She is wearing a black sports bra and gray leggings, with her dark hair pulled back into a neat style. The background is plain and softly lit, creating a clean, minimalistic look that focuses on the yoga pose. The lighting is even and soft, giving a relaxed and meditative atmosphere to the scene.

---
license: openrail
language:
- en
- ru
library_name: diffusers
pipeline_tag: text-to-image
tags:
- midjourney
- midjourney V6
- v6
- MJ
- mj
- kviai
- kvikontent
- text-to-image
- lora
base_model: runwayml/stable-diffusion-v1-5
widget:
- text: ed sheeran made of thnderstorm clouds, lights, thunder, rain, particles
  output:
    url: images/ed.png
- text: silhouette of a dog in the thunderstorm clouds, thumder, photo, bad weather,
  output:
    url: images/dog.png
- text: >-
    A professional photo of a beautiful night waterfall in the jungle with a
    touch of blue and a few fireflies flying around
  output:
    url: images/water.png
---

# Midjourney V6

Midjourney is most realistic and powerful ai image generator in the world. Here is is Stable Diffusion LoRA model trained on 100k+ midjourney V6 images.

## Examples

<Gallery />

## Usage

You can use this model using huggingface Interface API:
```Python
import requests

API_URL = "https://api-inference.huggingface.co/models/Kvikontent/midjourney-v6"
headers = {"Authorization": "Bearer HUGGINGFACE_API_TOKEN"}

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))
```