Instructions to use Instinct-AI/InstinctiveDiffuse with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Instinct-AI/InstinctiveDiffuse with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Instinct-AI/InstinctiveDiffuse", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
| library_name: diffusers | |
| # InstinctiveDiffuse | |
| InstinctiveDiffuse is fine-tuned from `StableDiffusionXL` base model that carefully trained on 100,000 free-copyright images, and carefully DPO tuned. | |
| the base model is `stabilityai/stable-diffusion-xl-base-1.0` and the model has improved on being sharper in image generation with prompt tag of ~sharp~. | |
| ## Usage | |
| the usage of the model is available via github of [InstinctiveDiffuse](https://github.com/erfanzar/InstinctiveDiffuse). | |
| please also consider using these keep words to improve your prompts: best quality, ocean render, HD, --version 8. | |
| ## Direct Use | |
| ```python | |
| import torch | |
| from diffusers import StableDiffusionXLPipeline, EulerDiscreteScheduler | |
| base = "Instinct-AI/InstinctiveDiffuse" | |
| pipe = StableDiffusionXLPipeline.from_pretrained(base, torch_dtype=torch.float16) | |
| pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing") | |
| pipe.enable_model_cpu_offload() | |
| pipe( | |
| "An astronaut riding a yellow horse on mars", | |
| num_inference_steps=8, | |
| guidance_scale=1 | |
| ).images[0] | |
| ``` |