tonera
/

Nepotism_xii-Nunchaku

@@ -1,93 +1,109 @@
 ---
-language:
-- en
-license: other
-license_name: flux-1-dev-non-commercial-license
-license_link: LICENSE.md
-extra_gated_prompt: By clicking "Agree", you agree to the [FluxDev Non-Commercial License Agreement](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md)
-  and acknowledge the [Acceptable Use Policy](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/POLICY.md).
 tags:
-- text-to-image
-- image-generation
-- flux
 ---
-![FLUX.1 [dev] Grid](./dev_grid.jpg)
-`FLUX.1 [dev]` is a 12 billion parameter rectified flow transformer capable of generating images from text descriptions.
-For more information, please read our [blog post](https://blackforestlabs.ai/announcing-black-forest-labs/).
-# Key Features
-1. Cutting-edge output quality, second only to our state-of-the-art model `FLUX.1 [pro]`.
-2. Competitive prompt following, matching the performance of closed source alternatives .
-3. Trained using guidance distillation, making `FLUX.1 [dev]` more efficient.
-4. Open weights to drive new scientific research, and empower artists to develop innovative workflows.
-5. Generated outputs can be used for personal, scientific, and commercial purposes as described in the [`FLUX.1 [dev]` Non-Commercial License](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).
-# Usage
-We provide a reference implementation of `FLUX.1 [dev]`, as well as sampling code, in a dedicated [github repository](https://github.com/black-forest-labs/flux).
-Developers and creatives looking to build on top of `FLUX.1 [dev]` are encouraged to use this as a starting point.
-## API Endpoints
-The FLUX.1 models are also available via API from the following sources
-- [bfl.ml](https://docs.bfl.ml/) (currently `FLUX.1 [pro]`)
-- [replicate.com](https://replicate.com/collections/flux)
-- [fal.ai](https://fal.ai/models/fal-ai/flux/dev)
-- [mystic.ai](https://www.mystic.ai/black-forest-labs/flux1-dev)
-## ComfyUI
-`FLUX.1 [dev]` is also available in [Comfy UI](https://github.com/comfyanonymous/ComfyUI) for local inference with a node-based workflow.
-## Diffusers
-To use `FLUX.1 [dev]` with the 🧨 diffusers python library, first install or upgrade diffusers
-```shell
-pip install -U diffusers
 ```
-Then you can use `FluxPipeline` to run the model
 ```python
 import torch
 from diffusers import FluxPipeline
-pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16)
-pipe.enable_model_cpu_offload() #save some VRAM by offloading the model to CPU. Remove this if you have enough GPU power
-prompt = "A cat holding a sign that says hello world"
-image = pipe(
-    prompt,
-    height=1024,
-    width=1024,
-    guidance_scale=3.5,
-    num_inference_steps=50,
-    max_sequence_length=512,
-    generator=torch.Generator("cpu").manual_seed(0)
-).images[0]
-image.save("flux-dev.png")
 ```
-To learn more check out the [diffusers](https://huggingface.co/docs/diffusers/main/en/api/pipelines/flux) documentation
----
-# Limitations
-- This model is not intended or able to provide factual information.
-- As a statistical model this checkpoint might amplify existing societal biases.
-- The model may fail to generate output that matches the prompts.
-- Prompt following is heavily influenced by the prompting-style.
-# Out-of-Scope Use
-The model and its derivatives may not be used
-- In any way that violates any applicable national, federal, state, local or international law or regulation.
-- For the purpose of exploiting, harming or attempting to exploit or harm minors in any way; including but not limited to the solicitation, creation, acquisition, or dissemination of child exploitative content.
-- To generate or disseminate verifiably false information and/or content with the purpose of harming others.
-- To generate or disseminate personal identifiable information that can be used to harm an individual.
-- To harass, abuse, threaten, stalk, or bully individuals or groups of individuals.
-- To create non-consensual nudity or illegal pornographic content.
-- For fully automated decision making that adversely impacts an individual's legal rights or otherwise creates or modifies a binding, enforceable obligation.
-- Generating or facilitating large-scale disinformation campaigns.
-# License
-This model falls under the [`FLUX.1 [dev]` Non-Commercial License](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).

 ---
+pipeline_tag: text-to-image
+library_name: diffusers
 tags:
+  - Flux
+  - text-to-image
+  - quantization
+  - svdquant
+  - nunchaku
+  - fp4
+  - int4
+base_model: black-forest-labs/FLUX.1-dev
+base_model_relation: finetune
+license: other
 ---
+# Model Card (SVDQuant · Nepotism_xii)
+![Nepotism_xii sample output](Attached_image.png)
+> **Language**: English | [中文](README_CN.md)
+## Model name
+- **Model repo**: `tonera/Nepotism_xii-Nunchaku`
+- **Source checkpoint (full-precision)**: [Nepotism on Civitai](https://civitai.com/models/618792/nepotism) — this quantization is derived from the **XII** (and Flux.1 D family) release; licensing and usage are also subject to upstream terms and Civitai’s policies.
+- **Full Diffusers layout** (VAE, text encoders, scheduler, etc.): `{REPO_ID}`
+- **Quantized Transformer weights** (for Nunchaku):
+  - `{REPO_ID}/svdq-fp4_r32-Nepotism_xii-Nunchaku.safetensors`
+  - `{REPO_ID}/svdq-int4_r32-Nepotism_xii-Nunchaku.safetensors`
+## Quantization / inference
+- **Inference engine**: Nunchaku (`https://github.com/nunchaku-ai/nunchaku`)
+Nunchaku targets **4-bit (FP4/INT4)** inference to reduce VRAM and latency while preserving quality. The `svdq-*_r32-Nepotism_xii-Nunchaku.safetensors` files in this repo are **SVDQuant**-quantized Flux Transformer weights and should be used with **FluxPipeline** on supported setups.
+## Install Nunchaku first
+- **Official install docs** (recommended): `https://nunchaku.tech/docs/nunchaku/installation/installation.html`
+### (Recommended) Prebuilt wheel
+- **Prerequisite**: Use a `PyTorch` version that matches the Nunchaku release notes (newer is often better).
+- **Install**: Pick a wheel for your Python, CUDA, and PyTorch from GitHub Releases / Hugging Face / ModelScope, e.g.:
+```bash
+# Example — replace with the correct wheel URL for your torch/cuda/python
+pip install https://github.com/nunchaku-ai/nunchaku/releases/download/vX.Y.Z/nunchaku-X.Y.Z+torch2.9-cp311-cp311-linux_x86_64.whl
 ```
+- **Tip (RTX 50 series)**: When supported by Nunchaku, **FP4** weights often give better compatibility and speed (see Nunchaku docs).
+## Quality reference (N=25 samples)
+Summary metrics (higher is generally closer to reference for PSNR/SSIM; **lower LPIPS is better**).
+### FP4
+| Metric | mean | p50 | p90 | best | worst |
+|--------|------|-----|-----|------|-------|
+| PSNR | 21.8159 | 21.9766 | 29.447 | 30.8016 | 13.0762 |
+| SSIM | 0.811984 | 0.835828 | 0.938092 | 0.944179 | 0.582228 |
+| LPIPS | 0.209448 | 0.178698 | 0.400699 | 0.0461679 | 0.64835 |
+### INT4
+| Metric | mean | p50 | p90 | best | worst |
+|--------|------|-----|-----|------|-------|
+| PSNR | 20.8759 | 20.8797 | 25.5093 | 30.0388 | 14.8672 |
+| SSIM | 0.78943 | 0.812346 | 0.890699 | 0.913605 | 0.557165 |
+| LPIPS | 0.243332 | 0.203449 | 0.419361 | 0.0868137 | 0.657203 |
+## Usage (Diffusers + Nunchaku Flux Transformer)
+Set `REPO_ID` to your Hugging Face repo id or local root. Load the **`svdq-{precision}_r32-Nepotism_xii-Nunchaku.safetensors`** transformer and the full pipeline from **`{REPO_ID}`** (alongside `model_index.json`, `transformer/`, `vae/`, etc.).
 ```python
 import torch
 from diffusers import FluxPipeline
+from nunchaku import NunchakuFluxTransformer2dModel
+from nunchaku.utils import get_precision
+REPO_ID = "tonera/Nepotism_xii-Nunchaku"
+MODEL_STEM = "Nepotism_xii-Nunchaku"
+if __name__ == "__main__":
+    precision = get_precision()  # 'int4' or 'fp4' from GPU
+    transformer = NunchakuFluxTransformer2dModel.from_pretrained(
+        f"{REPO_ID}/svdq-{precision}_r32-{MODEL_STEM}.safetensors"
+    )
+    pipeline = FluxPipeline.from_pretrained(
+        f"{REPO_ID}",
+        transformer=transformer,
+        torch_dtype=torch.bfloat16,
+    ).to("cuda")
+    image = pipeline(
+        "A cat holding a sign that says hello world",
+        num_inference_steps=50,
+        guidance_scale=3.5,
+    ).images[0]
+    image.save(f"nepotism_xii-{precision}.png")
 ```
+Licensing follows `LICENSE.md` in this repo and upstream model terms.
+If your Diffusers files live under a subfolder (e.g. local `diffusers/`), use `f"{REPO_ID}/diffusers"` for `FluxPipeline.from_pretrained` and prefix the transformer path the same way.