Instructions to use data-archetype/moss_image_base_dmd2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use data-archetype/moss_image_base_dmd2 with PEFT:
Task type is invalid.
- Diffusers
How to use data-archetype/moss_image_base_dmd2 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("OpenMOSS-Team/MOSS-Image-Base", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("data-archetype/moss_image_base_dmd2") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Awesome turbo LoRA!
awesome turbo LoRA, hands down. It also fixed the blur issue that was present in 8-steps-dmd2-lora.safetensors by Lodestone. Thanks!
This model is indeed very good; it works exceptionally well for image-to-image tasks, such as style transfer. However, when it comes to text-to-image generation, the model tends not to follow the prompts very closely.
This model is indeed very good; it works exceptionally well for image-to-image tasks, such as style transfer. However, when it comes to text-to-image generation, the model tends not to follow the prompts very closely.
can you post an example that reproduces the prompt following issue so I can take a look ?