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qwen3-4b-dflash
Qwen3-4B paired with its DFlash block-diffusion drafter for speculative decoding on the llama.cpp backend. This is the canonical DFlash pairing documented upstream (`z-lab/Qwen3-4B-DFlash` + `Qwen/Qwen3-4B`).
DFlash produces a whole block of draft tokens in a single forward pass and injects the target model's hidden states into the drafter's attention, which keeps the drafter tiny while making drafting GPU-friendly. The Q4_K_M file carries the full Qwen3-4B target; the ~0.5 GB Q8_0 drafter (`draft-dflash`) accelerates generation without changing the target's outputs. The drafter is not a standalone chat model: it only runs paired with the target, which is why both are bundled here.
Flash attention is required for DFlash and is enabled in this config. A GPU is recommended.
License: Apache 2.0 (Qwen3-4B target) / MIT (z-lab DFlash drafter).
Repository: localaiLicense: apache-2.0
qwen3.5-4b-dflash
Qwen3.5-4B paired with its DFlash block-diffusion drafter for speculative decoding on the llama.cpp backend.
DFlash produces a whole block of draft tokens in a single forward pass and injects the target model's hidden states into the drafter's attention, which keeps the drafter tiny while making drafting GPU-friendly. The Q4_K_M file carries the full Qwen3.5-4B target; the ~0.6 GB Q8_0 drafter (`draft-dflash`) accelerates generation without changing the target's outputs. The drafter is not a standalone chat model: it only runs paired with the target, which is why both are bundled here.
Flash attention is required for DFlash and is enabled in this config. A GPU is recommended.
License: Apache 2.0 (Qwen3.5-4B target) / MIT (z-lab DFlash drafter).
Repository: localaiLicense: apache-2.0
qwen3.5-9b-dflash
Qwen3.5-9B paired with its DFlash block-diffusion drafter for speculative decoding on the llama.cpp backend.
DFlash produces a whole block of draft tokens in a single forward pass and injects the target model's hidden states into the drafter's attention, which keeps the drafter tiny while making drafting GPU-friendly. The Q4_K_M file carries the full Qwen3.5-9B target; the ~1 GB Q8_0 drafter (`draft-dflash`) accelerates generation without changing the target's outputs. The drafter is not a standalone chat model: it only runs paired with the target, which is why both are bundled here.
Flash attention is required for DFlash and is enabled in this config. A GPU is recommended.
License: Apache 2.0 (Qwen3.5-9B target) / MIT (z-lab DFlash drafter).
Repository: localaiLicense: apache-2.0
qwen3.6-27b-dflash
Qwen3.6-27B (dense) paired with its DFlash block-diffusion drafter for speculative decoding on the llama.cpp backend. DFlash gives its largest speedups on dense targets like this one.
DFlash produces a whole block of draft tokens in a single forward pass and injects the target model's hidden states into the drafter's attention, which keeps the drafter tiny while making drafting GPU-friendly. The Q4_K_M file carries the full Qwen3.6-27B target; the ~1.8 GB Q8_0 drafter (`draft-dflash`) accelerates generation without changing the target's outputs. The drafter is not a standalone chat model: it only runs paired with the target, which is why both are bundled here.
Flash attention is required for DFlash and is enabled in this config. A GPU is recommended.
License: Apache 2.0.
Repository: localaiLicense: apache-2.0
qwen3.6-35b-a3b-dflash
Qwen3.6-35B-A3B (Mixture-of-Experts, ~3B active per token) paired with its DFlash block-diffusion drafter for speculative decoding on the llama.cpp backend. DFlash speedups on MoE targets are smaller than on dense models, but still useful.
DFlash produces a whole block of draft tokens in a single forward pass and injects the target model's hidden states into the drafter's attention, which keeps the drafter tiny while making drafting GPU-friendly. The UD-Q4_K_M file carries the full Qwen3.6-35B-A3B target; the ~0.4 GB Q8_0 drafter (`draft-dflash`) accelerates generation without changing the target's outputs. The drafter is not a standalone chat model: it only runs paired with the target, which is why both are bundled here.
Flash attention is required for DFlash and is enabled in this config. A GPU is recommended.
License: Apache 2.0.
Repository: localaiLicense: apache-2.0
vllm-omni-wan2.2-t2v
Wan2.2-T2V-A14B via vLLM-Omni - Text-to-video generation model from Wan-AI. Generates high-quality videos from text prompts using a 14B parameter diffusion model.
Repository: localaiLicense: apache-2.0
vllm-omni-wan2.2-i2v
Wan2.2-I2V-A14B via vLLM-Omni - Image-to-video generation model from Wan-AI. Generates high-quality videos from images using a 14B parameter diffusion model.
Repository: localaiLicense: apache-2.0
dream-org_dream-v0-instruct-7b
This is the instruct model of Dream 7B, which is an open diffusion large language model with top-tier performance.
Repository: localaiLicense: apache-2.0
dreamshaper
A text-to-image model that uses Stable Diffusion 1.5 to generate images from text prompts. This model is DreamShaper model by Lykon.
Repository: localaiLicense: other
stable-diffusion-3-medium
Stable Diffusion 3 Medium is a Multimodal Diffusion Transformer (MMDiT) text-to-image model that features greatly improved performance in image quality, typography, complex prompt understanding, and resource-efficiency.
Repository: localaiLicense: stabilityai-ai-community
wan-2.1-t2v-1.3b-ggml
Wan 2.1 T2V 1.3B — text-to-video diffusion model, GGUF-quantized for the
stable-diffusion.cpp backend. Generates short (33-frame) 832x480 clips
from a text prompt. Cheapest Wan variant, suitable for CPU-offloaded
inference with ~10 GB of usable RAM.
Repository: localaiLicense: apache-2.0
wan-2.1-i2v-14b-480p-ggml
Wan 2.1 I2V 14B 480P — image-to-video diffusion, GGUF Q4 quantization.
Animates a reference image into a 33-frame 480p clip. Requires more
RAM than the 1.3B T2V variant; CPU offload enabled by default.
Repository: localaiLicense: apache-2.0
wan-2.1-flf2v-14b-720p-ggml
Wan 2.1 FLF2V 14B 720P — first-last-frame-to-video diffusion, GGUF Q4_K_M.
Takes a start and end reference image and interpolates a 33-frame clip
between them. Unlike the plain I2V variant this model feeds the end
frame through clip_vision as well, so it conditions semantically (not
just in pixel-space) on both endpoints. That makes it the right choice
for seamless loops (start_image == end_image) and clean narrative cuts.
Native 720p but accepts 480p resolutions; shares the same VAE, t5xxl
text encoder, and clip_vision_h as I2V 14B.
Repository: localaiLicense: apache-2.0
wan-2.1-i2v-14b-720p-ggml
Wan 2.1 I2V 14B 720P — image-to-video diffusion, GGUF Q4_K_M.
Native 720p sibling of the 480p I2V model: animates a single
reference image into a 33-frame clip at up to 1280x720. Trained
purely as image-to-video (no first-last-frame interpolation path),
so motion is freer and better-suited to single-anchor animation
than repurposing the FLF2V 720P variant for i2v. Shares the same
VAE, umt5_xxl text encoder, and clip_vision_h as the I2V 14B 480P
and FLF2V 14B 720P entries.
Repository: localaiLicense: apache-2.0
sd-1.5-ggml
Stable Diffusion 1.5
Repository: localaiLicense: creativeml-openrail-m
sd-3.5-medium-ggml
Stable Diffusion 3.5 Medium is a Multimodal Diffusion Transformer (MMDiT) text-to-image model that features improved performance in image quality, typography, complex prompt understanding, and resource-efficiency.
Repository: localaiLicense: stabilityai-ai-community
sd-3.5-large-ggml
Stable Diffusion 3.5 Large is a Multimodal Diffusion Transformer (MMDiT) text-to-image model that features improved performance in image quality, typography, complex prompt understanding, and resource-efficiency.
Repository: localaiLicense: stabilityai-ai-community
flux.1-schnell
FLUX.1 [schnell] is a 12 billion parameter rectified flow transformer capable of generating images from text descriptions. For more information, please read our blog post.
Key Features
Cutting-edge output quality and competitive prompt following, matching the performance of closed source alternatives.
Trained using latent adversarial diffusion distillation, FLUX.1 [schnell] can generate high-quality images in only 1 to 4 steps.
Released under the apache-2.0 licence, the model can be used for personal, scientific, and commercial purposes.
Repository: localaiLicense: apache-2.0
flux.1-kontext-dev
FLUX.1 Kontext [dev] is a 12 billion parameter rectified flow transformer capable of editing images based on text instructions. For more information, please read our blog post and our technical report. You can find information about the [pro] version in here.
Key Features
Change existing images based on an edit instruction.
Have character, style and object reference without any finetuning.
Robust consistency allows users to refine an image through multiple successive edits with minimal visual drift.
Trained using guidance distillation, making FLUX.1 Kontext [dev] more efficient.
Open weights to drive new scientific research, and empower artists to develop innovative workflows.
Generated outputs can be used for personal, scientific, and commercial purposes, as described in the FLUX.1 [dev] Non-Commercial License.
Repository: localaiLicense: flux-1-dev-non-commercial-license
flux.2-klein-4b
The FLUX.2 [klein] model family are our fastest image models to date. FLUX.2 [klein] unifies generation and editing in a single compact architecture, delivering state-of-the-art quality with end-to-end inference in as low as under a second. Built for applications that require real-time image generation without sacrificing quality, and runs on consumer hardware, with as little as 13GB VRAM.
FLUX.2 [klein] 4B is a 4 billion parameter rectified flow transformer capable of generating images from text descriptions and supports multi-reference editing capabilities.
Repository: localaiLicense: apache-2.0
Z-Image-Turbo
Z-Image is a powerful and highly efficient image generation model with 6B parameters. Currently there are three variants of which this is the Turbo edition.
🚀 Z-Image-Turbo – A distilled version of Z-Image that matches or exceeds leading competitors with only 8 NFEs (Number of Function Evaluations). It offers ⚡️sub-second inference latency⚡️ on enterprise-grade H800 GPUs and fits comfortably within 16G VRAM consumer devices. It excels in photorealistic image generation, bilingual text rendering (English & Chinese), and robust instruction adherence.
Repository: localaiLicense: apache-2.0
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