Instructions to use linoyts/trained-hidream-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use linoyts/trained-hidream-lora with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("HiDream-ai/HiDream-I1-Dev", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("linoyts/trained-hidream-lora") prompt = "a photo of sks dog" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
HiDream Image DreamBooth LoRA - linoyts/trained-hidream-lora
Model description
These are linoyts/trained-hidream-lora DreamBooth LoRA weights for HiDream-ai/HiDream-I1-Dev.
The weights were trained using DreamBooth with the HiDream Image diffusers trainer.
Trigger words
You should use a photo of sks dog to trigger the image generation.
Download model
Download the *.safetensors LoRA in the Files & versions tab.
Use it with the 🧨 diffusers library
TODO
For more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers
Intended uses & limitations
How to use
# TODO: add an example code snippet for running this diffusion pipeline
Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
Training details
[TODO: describe the data used to train the model]
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Model tree for linoyts/trained-hidream-lora
Base model
HiDream-ai/HiDream-I1-Dev