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  1. .gitattributes +60 -0
  2. README.md +91 -0
  3. diffusion/cond/ideogram4-Q4_K.gguf +3 -0
  4. diffusion/cond/ideogram4-Q5_0.gguf +3 -0
  5. diffusion/cond/ideogram4-Q6_K.gguf +3 -0
  6. diffusion/cond/ideogram4-Q8_0.gguf +3 -0
  7. diffusion/cond/ideogram4_Q4_0.gguf +3 -0
  8. diffusion/cond/ideogram4_Q4_1.gguf +3 -0
  9. diffusion/cond/ideogram4_Q5_1.gguf +3 -0
  10. diffusion/cond/ideogram4_Q5_K.gguf +3 -0
  11. diffusion/uncond/ideogram4_unconditional-Q8_0.gguf +3 -0
  12. diffusion/uncond/ideogram4_unconditional_Q4_0.gguf +3 -0
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  14. diffusion/uncond/ideogram4_unconditional_Q4_K.gguf +3 -0
  15. diffusion/uncond/ideogram4_unconditional_Q5_0.gguf +3 -0
  16. diffusion/uncond/ideogram4_unconditional_Q5_1.gguf +3 -0
  17. diffusion/uncond/ideogram4_unconditional_Q5_K.gguf +3 -0
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  28. text_encoder/Qwen3-VL-8B-Q4_0.gguf +3 -0
  29. text_encoder/Qwen3-VL-8B-Q4_1.gguf +3 -0
  30. text_encoder/Qwen3-VL-8B-Q4_K_M.gguf +3 -0
  31. text_encoder/Qwen3-VL-8B-Q4_K_S.gguf +3 -0
  32. text_encoder/Qwen3-VL-8B-Q5_K_M.gguf +3 -0
  33. text_encoder/Qwen3-VL-8B-Q5_K_S.gguf +3 -0
  34. text_encoder/Qwen3-VL-8B-Q6_K.gguf +3 -0
  35. text_encoder/Qwen3-VL-8B-Q8_0.gguf +3 -0
  36. vae/flux2-hdr-vae.safetensors +3 -0
  37. vae/flux2-vae.safetensors +3 -0
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+ Qwen3-VL-8B-Q6_K.gguf filter=lfs diff=lfs merge=lfs -text
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+ Qwen3-VL-8B-Q8_0.gguf filter=lfs diff=lfs merge=lfs -text
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README.md ADDED
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+ ---
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+ language:
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+ - en
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+ - ru
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+ - zh
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+ base_model:
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+ - ideogram-ai/ideogram-4-fp8
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+ - ideogram-ai/ideogram-4-nf4
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+ tags:
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+ - gguf
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+ - text-to-image
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+ - image-generation
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+ - comfyui
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+ - stable-diffusion.cpp
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+ pipeline_tag: text-to-image
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+ license: other
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+ license_name: ideogram-non-commercial-model-agreement
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+ license_link: https://huggingface.co/ideogram-ai/ideogram-4-fp8/blob/main/LICENSE.md
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+ ---
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+
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+ # Ideogram4 GGUF quantized files
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+
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+ ```tree
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+ .
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+ ├── diffusion/
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+ │ ├── cond/
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+ │ │ ├── ideogram4_Q4_0.gguf
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+ │ │ ├── ideogram4_Q4_1.gguf
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+ │ │ ├── ideogram4-Q4_K.gguf
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+ │ │ ├── ideogram4-Q5_0.gguf
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+ │ │ ├── ideogram4_Q5_1.gguf
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+ │ │ ├── ideogram4_Q5_K.gguf
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+ │ │ ├── ideogram4-Q6_K.gguf
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+ │ │ └── ideogram4-Q8_0.gguf
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+ │ └── uncond/
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+ │ ├── ideogram4_unconditional_Q4_0.gguf
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+ │ ├── ideogram4_unconditional_Q4_1.gguf
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+ │ ├── ideogram4_unconditional_Q4_K.gguf
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+ │ ├── ideogram4_unconditional_Q5_0.gguf
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+ │ ├── ideogram4_unconditional_Q5_1.gguf
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+ │ ├── ideogram4_unconditional_Q5_K.gguf
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+ │ ├── ideogram4_unconditional_Q6_K.gguf
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+ │ └── ideogram4_unconditional-Q8_0.gguf
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+ ├── text_encoder/
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+ │ ├── Qwen3-VL-8B-Q4_0.gguf
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+ │ ├── Qwen3-VL-8B-Q4_1.gguf
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+ │ ├── Qwen3-VL-8B-Q4_K_S.gguf
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+ │ ├── Qwen3-VL-8B-Q4_K_M.gguf
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+ │ ├── Qwen3-VL-8B-Q5_K_S.gguf
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+ │ ├── Qwen3-VL-8B-Q5_K_M.gguf
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+ │ ├── Qwen3-VL-8B-Q6_K.gguf
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+ │ └── Qwen3-VL-8B-Q8_0.gguf
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+ └── vae/
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+ │ ├── flux2-vae.safetensors
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+ │ └── flux2-hdr-vae.safetensors
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+ └── lora/
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+ ├── realism_engine_v3.safetensors
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+ ├── big_boobs.safetensors
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+ ├── cum.safetensors
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+ ├── innie_vulva_x.safetensors
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+ ├── vintage_beauties_womans.safetensors
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+ ├── missionary_sex.safetensors
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+ ├── 80s_anime.safetensors
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+ ├── penis.safetensors
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+ └── penix.safetensors
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+ ```
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+
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+ ### Model Selection & Quantization Guide
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+
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+ To balance generation quality, memory usage, and inference speed, we recommend the following quantization choices for each component:
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+
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+ #### 1. Conditional Diffusion Model (`diffusion/cond/`)
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+ * **Recommended:** `Q6_K` or `Q8_0`
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+ * Since this model handles the main conditional generation pass, keeping a higher quantization level is key to preserving detail and prompt adherence.
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+
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+ #### 2. Unconditional Diffusion Model (`diffusion/uncond/`)
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+ * **Recommended:** `Q4_K` or `Q5_K`
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+ * **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.
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+
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+ #### 3. Text Encoder (`text_encoder/`)
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+ * **Recommended:** `Q5_K_M` or `Q4_K_M`
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+ * 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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+
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+ ---
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+
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+ ### General Recommendations for Quantization Types
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+
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+ 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:
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+
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+ * **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`).
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+ * **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.
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