How to use unsloth/FLUX.2-dev-FP8 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("unsloth/FLUX.2-dev-FP8", dtype=torch.bfloat16, device_map="cuda") prompt = "cute sloth typing on a computer (INT8)" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0]
How to use unsloth/FLUX.2-dev-FP8 with Diffusion Single File:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
How to use unsloth/FLUX.2-dev-FP8 with Unsloth Studio:
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for unsloth/FLUX.2-dev-FP8 to start chatting
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for unsloth/FLUX.2-dev-FP8 to start chatting
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for unsloth/FLUX.2-dev-FP8 to start chatting
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="unsloth/FLUX.2-dev-FP8", max_seq_length=2048, )
