DPM++ 2M Sharp for Qwen Image 2.1

DPM++ 2M Sharp

A better sampler for Qwen Image 2.1, with adjustable sharpening of the denoised history used by DPM++ 2M.

The sampler progressively scales the previous denoised prediction before the next multistep update. It preserves the original DPM++ 2M step count and model-call count. Results depend on the prompt, model, and settings; this repository does not include a comparative benchmark.

Install

Search for DPM++ 2M Sharp for Qwen Image 2.1 in ComfyUI Manager, or install from the registry:

comfy node install dpmpp-2m-sharp

For a manual install, run this from your ComfyUI directory:

git clone https://github.com/envy-ai/ComfyUI-DPMpp-2M-Sharp custom_nodes/ComfyUI-DPMpp-2M-Sharp

Restart ComfyUI after installation. The package uses ComfyUI's V3 node API and its existing PyTorch and tqdm dependencies; no additional packages or core modifications are required.

Use

1. Add Sampler DPM++ 2M Sharp from model/sampling/samplers.
2. Connect its SAMPLER output to SamplerCustom or SamplerCustomAdvanced.
3. Use your existing model, conditioning, noise, latent, and sigma schedule.
4. Start with sharpness = 0.15. Set it to 0.0 for ordinary DPM++ 2M behavior. Larger values strengthen the history adjustment and may introduce artifacts.

The node ID is SamplerDPMPP_2M_Sharp, matching the original local node. This package exposes a sampler node; it does not add an entry to the standard KSampler dropdown.

License and credits

GPL-3.0; see LICENSE. The sampler is adapted from ComfyUI's DPM++ 2M implementation, based on k-diffusion. The k-diffusion MIT notice is included in LICENSE.k-diffusion.