metadata
license: openrail
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- midjourney
- midjourney V7
- 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: man on the yeacht, particles, photoreal style
output:
url: images/yacht.png
- text: A professional photo of a woman in blue particles
output:
url: images/woman.png
Midjourney V7
Midjourney is most realistic and powerful ai image generator in the world. Here is is the first ever release of version 7
Examples
![](https://huggingface.co/Kvikontent/midjourney-v7/resolve/main/images/ed.png)
- Prompt
- ed sheeran made of thnderstorm clouds, lights, thunder, rain, particles
![](https://huggingface.co/Kvikontent/midjourney-v7/resolve/main/images/yacht.png)
- Prompt
- man on the yeacht, particles, photoreal style
![](https://huggingface.co/Kvikontent/midjourney-v7/resolve/main/images/woman.png)
- Prompt
- A professional photo of a woman in blue particles
Usage
You can use this model using huggingface Interface API:
import requests
API_URL = "https://api-inference.huggingface.co/models/Kvikontent/midjourney-v7"
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))