rectangleworm/ideogram-4-gguf

Ideogram4 GGUF quantized files

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├── diffusion/
│   ├── cond/
│   │   ├── ideogram4_Q4_0.gguf
│   │   ├── ideogram4_Q4_1.gguf
│   │   ├── ideogram4-Q4_K.gguf
│   │   ├── ideogram4-Q5_0.gguf
│   │   ├── ideogram4_Q5_1.gguf
│   │   ├── ideogram4_Q5_K.gguf
│   │   ├── ideogram4-Q6_K.gguf
│   │   └── ideogram4-Q8_0.gguf
│   └── uncond/
│       ├── ideogram4_unconditional_Q4_0.gguf
│       ├── ideogram4_unconditional_Q4_1.gguf
│       ├── ideogram4_unconditional_Q4_K.gguf
│       ├── ideogram4_unconditional_Q5_0.gguf
│       ├── ideogram4_unconditional_Q5_1.gguf
│       ├── ideogram4_unconditional_Q5_K.gguf
│       ├── ideogram4_unconditional_Q6_K.gguf
│       └── ideogram4_unconditional-Q8_0.gguf
├── text_encoder/
│   ├── Qwen3-VL-8B-Q4_0.gguf
│   ├── Qwen3-VL-8B-Q4_1.gguf
│   ├── Qwen3-VL-8B-Q4_K_S.gguf
│   ├── Qwen3-VL-8B-Q4_K_M.gguf
│   ├── Qwen3-VL-8B-Q5_K_S.gguf
│   ├── Qwen3-VL-8B-Q5_K_M.gguf
│   ├── Qwen3-VL-8B-Q6_K.gguf
│   └── Qwen3-VL-8B-Q8_0.gguf
└── vae/
│   ├── flux2-vae.safetensors
│   └── flux2-hdr-vae.safetensors
└── lora/
    ├── realism_engine_v3.safetensors
    ├── big_boobs.safetensors
    ├── cum.safetensors
    ├── innie_vulva_x.safetensors
    ├── vintage_beauties_womans.safetensors
    ├── missionary_sex.safetensors
    ├── 80s_anime.safetensors
    ├── penis.safetensors
    └── penix.safetensors

Model Selection & Quantization Guide

To balance generation quality, memory usage, and inference speed, we recommend the following quantization choices for each component:

1. Conditional Diffusion Model (diffusion/cond/)

• Recommended: Q6_K or Q8_0
• Since this model handles the main conditional generation pass, keeping a higher quantization level is key to preserving detail and prompt adherence.

2. Unconditional Diffusion Model (diffusion/uncond/)

• Recommended: Q4_K or Q5_K
• Note: Using Q6_K or Q8_0 for the unconditional model is generally unnecessary (overkill) and may slow down generation without providing a noticeable improvement in quality.

3. Text Encoder (text_encoder/)

• Recommended: Q5_K_M or Q4_K_M
• These medium-sized "K-measure" quants offer a good trade-off, retaining the text encoder's comprehension capabilities while fitting within reasonable memory limits.

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General Recommendations for Quantization Types

If you are optimizing for inference speed or trying to fit a specific model entirely into VRAM/RAM, keep these rules of thumb in mind:

• Prefer _K variants over _0 and _1: When choosing between Q4 or Q5 options, always prefer the _K variants (e.g., Q4_K_M, Q5_K_M, or standard _K).
• Avoid _0 and _1 if possible: The older _0 and _1 quants (like Q4_0 or Q4_1) perform worse in terms of quality loss. While they are marginally smaller, the minor size reduction rarely justifies the drop in generation quality compared to _K equivalents.