| --- |
| language: |
| - en |
| - ru |
| - zh |
| base_model: |
| - ideogram-ai/ideogram-4-fp8 |
| - ideogram-ai/ideogram-4-nf4 |
| tags: |
| - gguf |
| - text-to-image |
| - image-generation |
| - comfyui |
| - stable-diffusion.cpp |
| pipeline_tag: text-to-image |
| license: other |
| license_name: ideogram-non-commercial-model-agreement |
| license_link: https://huggingface.co/ideogram-ai/ideogram-4-fp8/blob/main/LICENSE.md |
| --- |
| |
| # Ideogram4 GGUF quantized files |
|
|
| ```tree |
| . |
| ├── 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. |
| |
| --- |
| |
| ### 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. |
