Instructions to use tensorart/stable-diffusion-3.5-medium-turbo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use tensorart/stable-diffusion-3.5-medium-turbo with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("tensorart/stable-diffusion-3.5-medium-turbo", 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
Img2Img Optimizations
Thanks for building this!
I'm curious if you've tested img2img with this, and what sort of config is optimal. I've gotten it working, but required higher amount of steps (20 vs expected 8). I had to change the shift on the scheduler to 1-2 for it to not completely overwrite the image. Lower shift fixes output mostly, but just wondering about the performance side if there's any speedups to be had. I get about 10-15 seconds for 2 imgs at 768x768 on NVIDIA L4 AWS instance. If I could get down to half that it'd be golden, but the step count is obviously hurting and not sure how to work around it.
I mostly work with illustrations, not photo realistic, for context.
pipe = StableDiffusion3Img2ImgPipeline.from_pretrained(
"tensorart/stable-diffusion-3.5-medium-turbo",
torch_dtype=torch.float16,
cache_dir="weights",
)
pipe.scheduler = FlowMatchEulerDiscreteScheduler(
num_train_timesteps=1000,
shift=2.0,
use_dynamic_shifting=False,
)
pipe = _pipe.to("cuda")
Our sd3.5m controlnet will come soon, it may meet your need.