This PR adds support for the timestep-shift technique required for inference with NitroFusion models and Diff-Instruct* and other one-step models. It also adds support for two schedulers: SGM Uniform and Simple, because the existing scheduler, for a mysterious reason, fails at step 2 calculation and produces an output similar to step 1.

NitroFusion is one of the best models for single-step inference, making it useful for inference on compute-constrained devices like mobile phones or CPUs.

Example command:

./bin/sd -m nitrosd-realism_f16.gguf -v -p "cute cat" --cfg-scale 1 --steps 1 --timestep-shift 250 -H 1024 -W 1024 --seed 2024 --schedule sgm_uniform

Step	NitroSD-Realism (Timestep-shift 250)	NitroSD-Vibrant (Timestep-shift 500)	Diff-Instruct* (Timestep-shift 400)
Step 1
Step 2			Not well supported
Step 3			Not well supported
Step 4			Not well supported

The recommended timestep-shift values by the authors are 250 for NitroSD-Realism and 500 for NitroSD-Vibrant and 400 for Diff-Instruct*.

I created GGUF versions of NitroFusion that already include the fixed SDXL VAE, available for download here.

The authors mentioned it's possible to extract LoRA weights from these models and apply them to other checkpoints. I’ll try to do that in the future.

EDIT : Just add Diff-Instruct* GGUF
References:

Timestep-shift implementation: node.py
SGM Uniform: sd_schedulers
Simple: samplers.py