Instructions to use GamerC0der/MiniDiffusion1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use GamerC0der/MiniDiffusion1 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("GamerC0der/MiniDiffusion1") prompt = "Dog, Realistic, 4k, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
metadata
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: Dog, Realistic, 4k, 8k
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: null
license: unknown
MiniDiffusion 1
Model description
Welcome to MiniDiffusion 1! My first ever model! Try it now!
Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
Use Via Code!!!
import requests
API_URL = "https://api-inference.huggingface.co/models/GamerC0der/MiniDiffusion1"
headers = {"Authorization": "Bearer INSERTKEYHERE"}
def query(payload):
response = requests.post(API_URL, headers=headers, json=payload)
return response.content
image_bytes = query({
"inputs": prompthere,
})
# You can access the image with PIL.Image for example
import io
from PIL import Image
image = Image.open(io.BytesIO(image_bytes))