Text-to-Image
Diffusers
PyTorch
StableDiffusionPipeline
stable-diffusion
diffusion-models-class
dreambooth-hackathon
landscape
Instructions to use CCMat/fforiver-river-mdj with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use CCMat/fforiver-river-mdj with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("CCMat/fforiver-river-mdj", dtype=torch.bfloat16, device_map="cuda") prompt = "Fallout concept of fforiver river in front of the Great Pyramid of Giza" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
DreamBooth model for the fforiver concept trained on the CCMat/forest-river dataset.
This is a Stable Diffusion model fine-tuned on the fforiver concept with DreamBooth. It can be used by modifying the instance_prompt: a photo of fforiver river
This model was created as part of the DreamBooth Hackathon 🔥. Visit the organisation page for instructions on how to take part!
Description
This is a Stable Diffusion model fine-tuned on river images for the landscape theme.
Pretrained Model: prompthero/openjourney
Usage
from diffusers import StableDiffusionPipeline
pipeline = StableDiffusionPipeline.from_pretrained('CCMat/fforiver-river-mdj')
image = pipeline().images[0]
image
Samples
Prompt: "high quality photo of fforiver river along the Colosseum in Rome"
Prompt: "Fallout concept of fforiver river in front of Chichén Itzá in Mexico, sun rays, unreal engine 5"
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