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AI 모델 84759
- control_v11p_sd15_normalbae↗
lllyasviel
**Controlnet v1.1** is the successor model of Controlnet v1.0 and was released in lllyasviel/ControlNet-v1-1 by Lvmin Zhang. This checkpoint is a conversion of the original checkpoint into `diffusers` format. It can be used in combination with **Stable Diffusion**, such as runwayml/stable-diffusion-v1-5. @misc{zhang2023adding, title={Adding Conditional Control to Text-to-Image Diffusion Models}, author={Lvmin Zhang and Maneesh Agrawala}, year={2023}, eprint={2302.05543}, archivePrefix={arXiv}, primaryClass={cs.CV} }
- Qwen2.5-Math-1.5B-Instruct-GGUF↗
lmstudio-community
- unik3d-vitl↗
lpiccinelli
This model has been pushed to the Hub using the PytorchModelHubMixin integration: - Library: https://github.com/lpiccinelli-eth/UniK3D - Docs: [More Information Needed]
- acestep-v15-xl-turbo-bf16↗
marcorez8
- MOSS-Image-SFT-DeBand-int8↗
MATLOWAI
MOSS-Image **SFT** with its **vertical color stripes ("banding") fixed**, as single files you load like any other model: no LoRAs, no custom nodes. Each file is 5.4 GB int8 (the same format as Comfy-Org's `int8_convrot` files) and loads with the stock **Load Diffusion Model** node. These are the merged, ready-to-run versions of MATLOWAI/MOSS-Image-SFT-DeBand. That repo has the LoRAs, the full results and the write-up. 1. Put the file in `ComfyUI/models/diffusion_models/`. 2. You also need the MOSS-Image text encoder and VAE from Comfy-Org/MOSS-Image: - `qwen3.5_9b_int8_convrot.safetensors` → `models/text_encoders` - `flux2-vae.safetensors` → `models/vae` 3. Drag `workflows/moss_deband_int8.json` into ComfyUI. Pick your file in **Model** and the same **Version** at the top; the version sets the steps.
- Qwen3.8-2.4T-A95B-UD-IQ4_XS-layers↗
meshllm
- Dayhoff-3b-GR-HM-21000↗
Microsoft
- skala-1.1↗
Microsoft
In pursuit of the universal functional for density functional theory (DFT), the OneDFT team from Microsoft Research AI for Science has developed the Skala-1.1 exchange-correlation functional, as introduced in Accurate and scalable exchange-correlation with deep learning, Luise et al. 2025. This approach departs from the traditional route of incorporating increasingly expensive hand-designed non-local features from Jacob\'s ladder into functional forms to improve their accuracy. Instead, we employ a deep learning approach with a scalable neural network that uses only inexpensive input features to learn the necessary non-local representations. The functional is based on a neural network architecture that takes as input features on a 3D grid describing the electron density and derived meta-generalized-gradient (meta-GGA) quantities. The architecture performs scalable non-local message-passing on the integration grid via a second, coarser grid, combined with shared local layers that enable representation learning of both local and non-local features. These representations are then used to predict the exchange-correlation energy in an end-to-end data-driven manner. To facilitate this learning, the model is trained on a dataset of unprecedented size, containing highly accurate energy labels from coupled cluster theory. The largest subset focuses on atomization energies and was generated in collaboration with the University of New England. This subset is released as part of the Microsoft Research Accurate Chemistry Collection (MSR-ACC, Accurate Chemistry Collection: Coupled cluster atomization energies for broad chemical space, Ehlert et al. 2025). To broaden coverage of other types of chemistry, the training dataset is further complemented with in-house generated datasets covering conformers, ionization potentials, electron affinities, proton affinities, noncovalent interactions, distorted equilibrium geometries, and elementary reactions, as well as a small amount of publicly available high-accuracy data.
- phixtral-2x2_8↗
mlabonne
- Mistral-7B-Instruct-v0.3-q3f16_1-MLC↗
mlc-ai