AI NEWS.ccAI is happening. The future is updating.
♡ Saved tools 0
AI NEWS · Model intelligenceExplore capabilities. Make informed choices. ↗Profiles indexed first · Details continuously enriched
THE AI MODEL DIRECTORY Start exploring models here

Find your ideal AI model.

From language to images, open weights to APIs. Understand capabilities and compare real costs.
Find the right engine for your next idea.

Popular searches
84,991 model profiles13202 providers / publishers45 capability categories465 with comparable prices · 30 benchmarked
TText generation▧Image generation▷Video generation⌘Code generation◖Speech synthesis♫Music generation⬡3D modeling▤Document analysis
DISCOVER & COMPARE

Explore AI models 2537

Discover models worth your attention

GGUF quantizations of Qwen/Qwen-Image-2.1 for local image generation using the original upstream base weights. Use the model with ComfyUI and ComfyUI-GGUF. These GGUF files contain the Qwen-Image-2.1 image transformer; the original text encoder and VAE are also required for inference.

Image generation
Input Text→ Output Image
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

> [!IMPORTANT] > The uncensored GGUFs are now available! > If you encounter any issues or would like to share your generated images, feel free to post them in the Community section. GGUF quantizations of Qwen/Qwen-Image-2.1 for local image generation using the original upstream base weights. All required companion files (GGUF transformer, text encoder, and VAE) are hosted directly in this repository.

Image generation
Input Text→ Output Image
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

This repository contains an FP8 quantized version of the Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro-2.0 model. **This is NOT a fine-tuned model** but a direct quantization of the original BFloat16 model to FP8 format for optimized inference performance. We provide an online demo. This model has been quantized from the original BFloat16 format to FP8 format using PyTorch's native FP8 support. Here are the specifics: The benefits of FP8 quantization include: - **Reduced Memory Usage**: Approximately 50% smaller model size compared to BFloat16/FP16 - **Faster Inference**: Potential speed improvements, especially on hardware with FP8 support - **Minimal Quality Loss**: Carefully calibrated quantization process to preserve output quality

Image generation
Input Text→ Output Image
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

GGUF, Safetensors, and MLX quantized formats of Qwen/Qwen-Image-2.1-Turbo for local image generation using the official turbo base weights. **Q4_K_M** is recommended for the best balance of size and quality for GGUF. **INT8 ConvRot** and **NVFP4** offer high performance on supported GPUs, while **MLX** formats are optimized for Apple Silicon. All required companion files (GGUF transformer, text encoder, and VAE) are hosted directly in this repository.

Image generation
Input Text→ Output Image
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

GGUF, Safetensors, and MLX quantized formats of Qwen/Qwen-Image-2.1 for local image generation using the original upstream base weights. **Q4_K_M** is recommended for the best balance of size and quality for GGUF. **INT8 ConvRot** and **NVFP4** offer high performance on supported GPUs, while **MLX** formats are optimized for Apple Silicon. Use the model with ComfyUI and ComfyUI-GGUF.

Image generationOpen weights
Input Text→ Output Image
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

fy

Abhijnyabhat

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

Basic profile indexed · More details to follow

Image generation
Input Text→ Output Image
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

This repository contains GGUF weights for Anima-base-v1.0, a 2B parameter diffusion model based on the **NVIDIA Cosmos** architecture. These weights have been quantized using a specialized pipeline to ensure maximum compatibility and image quality on consumer hardware. To ensure the highest possible quality and avoid the "BF16 noise bug" common in diffusion quantization, the following steps were taken: 1. **FP32 Casting:** The original BF16 `safetensors` were cast to `FP32` before conversion. 2. **Architecture Patching:** Used a patched version of `llama.cpp` (based on build `b3962`) with the `city96` diffusion patch to support the `cosmos` architecture. 3. **Precision Preservation:** Sensitive layers (normalizations and biases) were kept in `F32` to prevent color banding and generation artifacts.

Image generation
Input Text→ Output Image
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

GGUF quantizations of Black Forest Labs' FLUX 3 Image Klein (16-step distilled checkpoint) for use with ComfyUI-GGUF. A pre-configured Text-to-Image (T2I) workflow is included in this repository: * **Download Workflow:** image_flux3_klein_distilled_t2i_GGUF.json A pre-configured Image-edit (I2I) workflow is included in this repository: * **Download Workflow:** image_flux3_klein_distilled_image_edit_GGUF.json

Image generation
Input Text→ Output Image
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

This repository contains GGUF format quantizations of **Krea 2 Turbo**, a state-of-the-art 12-billion parameter Diffusion Transformer (DiT) text-to-image model developed by Krea.ai. GGUF weights allow for efficient, low-memory inference across consumer hardware, CPUs, and Apple Silicon. You can run these GGUF text-to-image files directly using compatible implementations such as `stable-diffusion.cpp`.

Image generation
Input Text→ Output Image
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗
COST EXPLORER

Great models. Clear costs.

Cost analysis

Adjust monthly token usage to compare estimated API costs.

1 M = 1 million tokens · USD
Compares base text-token rates within the same channel

Uses collected, comparable USD-per-million-token rates. Snapshots exclude cache discounts, tiers, extra reasoning output and taxes. Check current source prices.

MODEL HANDBOOK

Understand before you choose

Selection guide & FAQ
How should API prices be compared?+

Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.

Do open weights mean free commercial use?+

Not necessarily. Check each version’s license, commercial restrictions and policies. Self-hosting also incurs compute and maintenance costs.

Is the top-ranked model the best for me?+

Rankings depend on tasks, versions and methods. Compare accuracy, latency, reliability and costs on your own workload.

AI NEWS · From information to decisions

Follow trends. Find tools. Explore models.

Explore AI news ↗Discover AI tools ↗