Add model card and comparison assets

.gitattributes

@@ -34,3 +34,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 tokenizer/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+assets/flux2-sdnq-uint4-static-comparison.webp filter=lfs diff=lfs merge=lfs -text

LICENSE.md

@@ -0,0 +1,53 @@
+FLUX Non-Commercial License v2.1
+
+Black Forest Labs Inc. (“we” or “our” or “Company”) is pleased to make the weights, parameters, and inference code for the FLUX Model (as defined below) freely available for your non-commercial and non-production use as set forth in this FLUX Non-Commercial License (“License”).  “FLUX Model” includes, individually and collectively, the models denoted as FLUX.x [dev], where “.x” denotes the FLUX Model version number, and models made available under the License, as indicated by a license notice that is included in or attached to the work, and their elements which includes algorithms, software, checkpoints, parameters, source code (inference code, evaluation code, and if applicable, fine-tuning code) and any other materials associated with the FLUX AI models made available by Company under this License, including if any, the technical documentation, manuals, and instructions for the use and operation thereof. Note that we may also make available certain elements of what is included in the definition of “FLUX Model” under a separate license, such as the inference code, and nothing in this License will be deemed to restrict or limit any other licenses granted by us in such elements.
+
+By downloading, accessing, using, Distributing (as defined below), or creating a Derivative (as defined below) of the FLUX Model, you agree to the terms of this License. If you do not agree to this License, then you do not have any rights to access, use, Distribute or create a Derivative of the FLUX Model and you must immediately cease using the FLUX Model. If you are agreeing to be bound by the terms of this License on behalf of your employer or other entity, you represent and warrant to us that you have full legal authority to bind your employer or such entity to this License. If you do not have the requisite authority, you may not accept the License or access the FLUX Model on behalf of your employer or other entity.
+
+ 1. Definitions.
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README.md

@@ -0,0 +1,278 @@
+---
+language:
+- en
+license: other
+license_name: flux-non-commercial-license
+license_link: LICENSE.md
+base_model:
+- black-forest-labs/FLUX.2-klein-9B
+base_model_relation: quantized
+library_name: diffusers
+pipeline_tag: text-to-image
+tags:
+- image-generation
+- image-editing
+- flux
+- flux2
+- Flux2KleinPipeline
+- sdnq
+- 4-bit
+- uint4
+- quantized
+- diffusers
+---
+
+# FLUX.2 Klein 9B SDNQ UINT4 Static
+
+Static UINT4 SDNQ quantization of
+[black-forest-labs/FLUX.2-klein-9B](https://huggingface.co/black-forest-labs/FLUX.2-klein-9B).
+
+This checkpoint was selected as a practical deployment-oriented variant because
+it was the fastest option in the A40 benchmark and used substantially less VRAM
+than the original BF16 pipeline, while visual quality differences were minor in
+the prompt-following stress comparison.
+
+![Full resolution comparison canvas](assets/flux2-sdnq-uint4-static-comparison.webp)
+
+The image above is a compressed WebP version of a 1:1 comparison canvas. It
+contains the original FLUX.2 Klein 9B, the previous SDNQ baseline, this
+`uint4-static` checkpoint, and a quality-oriented dynamic SVD candidate across
+text-heavy prompts including an additional Russian-only chalkboard prompt.
+
+## Why This Variant
+
+We compared broad SDNQ 4-bit recipes across speed, VRAM, and visual quality.
+This `uint4-static` recipe was chosen because it gives the best deployment
+tradeoff:
+
+- Lowest latency among the final candidates in the corrected single-process
+  top-5 rebenchmark.
+- Low runtime VRAM in a 1024x1024, 4-step image-generation pipeline.
+- Much smaller full-pipeline checkpoint footprint than the original BF16
+  FLUX.2 Klein 9B checkpoint in the measured setup.
+- Visual differences versus the baseline and the original model were small in
+  the stress set, including long text, signs, labels, small details, and a
+  Russian chalkboard prompt.
+
+## Benchmark Setup
+
+All numbers below are from a single NVIDIA A40 test host and were measured with
+a consistent `Flux2KleinPipeline` inference harness.
+
+- GPU: NVIDIA A40 46 GB
+- Resolution: 1024x1024
+- Steps: 4
+- Guidance scale: 0.0
+- Torch dtype: bfloat16
+- Quantized matmul: enabled for SDNQ inference comparisons
+- Batch/concurrency: single process for the corrected VRAM numbers
+
+These are deployment-oriented measurements for one hardware/software setup, not
+universal minimum requirements.
+
+## Corrected Top-5 Rebenchmark
+
+The first speed screen was run in parallel with two processes per A40, which
+made `nvidia-smi memory.used` peaks look too high. The table below uses the
+corrected single-process rebenchmark.
+
+| Variant | Warm avg | GPU peak | CUDA allocated |
+| --- | ---: | ---: | ---: |
+| `uint4-static` | 3.826 s | 14.8 GB | 14.1 GB |
+| `int4-dynamic-th0p1-svd-r16-s32-g128` | 4.020 s | 14.3 GB | 13.5 GB |
+| `uint4-static-svd-r32-s32` | 4.070 s | 14.7 GB | 13.9 GB |
+| `float4_e4m0fnu-dynamic-th0p1-svd-r16-s32` | 4.116 s | 16.0 GB | 15.3 GB |
+| `float4_e4m0fnu-dynamic-th0p01-svd-r128-s32` | 4.185 s | 17.2 GB | 16.5 GB |
+
+## Text And Detail Stress Comparison
+
+This stress set contains 9 prompts with signs, chalkboards, posters, labels,
+timetables, small props, and a Russian-only chalkboard prompt. Each row was run
+twice; the table reports the warm run average.
+
+| Model | Warm avg | GPU peak | CUDA allocated | Prompt count |
+| --- | ---: | ---: | ---: | ---: |
+| Original `FLUX.2-klein-9B` BF16 pipeline | 4.244 s | 36.3 GB | 35.6 GB | 9 |
+| Previous SDNQ baseline | 4.079 s | 15.2 GB | 14.5 GB | 9 |
+| This `uint4-static` checkpoint | 3.866 s | 14.8 GB | 14.1 GB | 9 |
+| Dynamic SVD r128 quality candidate | 4.182 s | 17.2 GB | 16.5 GB | 9 |
+
+The full-resolution HTML table and original JPEG canvas were used for visual
+review. The model-card image is a WebP copy optimized from that canvas:
+
+| WebP quality | Size | RGB PSNR | Luma SSIM-like score |
+| ---: | ---: | ---: | ---: |
+| 85 | 5.72 MB | 46.93 dB | 0.999977 |
+
+The source JPEG canvas was about 13 MB, so the card image is much smaller while
+remaining visually very close to the original artifact.
+
+## Model Size
+
+Approximate full-pipeline folder sizes in the measured setup:
+
+| Checkpoint | Folder size |
+| --- | ---: |
+| Original `black-forest-labs/FLUX.2-klein-9B` | 52.9 GB |
+| Previous SDNQ baseline | 12.6 GB |
+| This `uint4-static` checkpoint | 12.2 GB |
+| Dynamic SVD r128 candidate | 14.7 GB |
+
+## Usage
+
+Install current Diffusers and SDNQ:
+
+```bash
+pip install git+https://github.com/huggingface/diffusers.git
+pip install sdnq
+```
+
+Run with `Flux2KleinPipeline`:
+
+```python
+import torch
+from diffusers import Flux2KleinPipeline
+from sdnq import SDNQConfig  # registers SDNQ support in diffusers/transformers
+from sdnq.common import use_torch_compile as triton_is_available
+from sdnq.loader import apply_sdnq_options_to_model
+
+repo_id = "WaveCut/FLUX.2-klein-9B-SDNQ-uint4-static"
+device = "cuda"
+
+pipe = Flux2KleinPipeline.from_pretrained(
+    repo_id,
+    torch_dtype=torch.bfloat16,
+)
+
+if triton_is_available and torch.cuda.is_available():
+    pipe.transformer = apply_sdnq_options_to_model(
+        pipe.transformer,
+        use_quantized_matmul=True,
+    )
+    pipe.text_encoder = apply_sdnq_options_to_model(
+        pipe.text_encoder,
+        use_quantized_matmul=True,
+    )
+
+pipe.to(device)
+
+prompt = "A clean editorial poster with large readable text: OPEN SOURCE IMAGE MODEL"
+image = pipe(
+    prompt=prompt,
+    height=1024,
+    width=1024,
+    num_inference_steps=4,
+    guidance_scale=0.0,
+    generator=torch.Generator(device=device).manual_seed(0),
+).images[0]
+
+image.save("flux2-klein-sdnq-uint4-static.png")
+```
+
+The same pipeline also supports image editing:
+
+```python
+from diffusers.utils import load_image
+
+input_image = load_image("input.png")
+image = pipe(
+    image=input_image,
+    prompt="Turn the handwritten sign into a clean printed sign while preserving the scene",
+    height=1024,
+    width=1024,
+    num_inference_steps=4,
+    guidance_scale=0.0,
+    generator=torch.Generator(device=device).manual_seed(1),
+).images[0]
+image.save("flux2-klein-sdnq-uint4-static-edit.png")
+```
+
+If your GPU has less VRAM, replace `pipe.to(device)` with
+`pipe.enable_model_cpu_offload()`.
+
+## Quantization Recipe
+
+This checkpoint was produced with SDNQ post-load quantization over the
+`transformer` and `text_encoder` components of FLUX.2 Klein 9B.
+
+Recipe:
+
+```python
+variant = {
+    "weights_dtype": "uint4",
+    "use_dynamic_quantization": False,
+    "dynamic_loss_threshold": None,
+    "use_svd": False,
+    "svd_rank": 32,   # unused because use_svd is False
+    "svd_steps": 8,   # unused because use_svd is False
+    "group_size": 0,
+    "dequantize_fp32": False,
+    "quantized_matmul_dtype": None,
+    "use_quantized_matmul": False,
+    "use_stochastic_rounding": False,
+}
+```
+
+Minimal quantization sketch:
+
+```python
+import torch
+from diffusers import Flux2KleinPipeline
+from sdnq import sdnq_post_load_quant
+from sdnq.loader import save_sdnq_model
+
+base_model = "black-forest-labs/FLUX.2-klein-9B"
+pipe = Flux2KleinPipeline.from_pretrained(
+    base_model,
+    torch_dtype=torch.bfloat16,
+)
+
+common_kwargs = dict(
+    weights_dtype="uint4",
+    torch_dtype=torch.bfloat16,
+    group_size=0,
+    svd_rank=32,
+    svd_steps=8,
+    dynamic_loss_threshold=None,
+    use_svd=False,
+    quant_conv=False,
+    quant_embedding=False,
+    use_quantized_matmul=False,
+    use_quantized_matmul_conv=False,
+    use_dynamic_quantization=False,
+    use_stochastic_rounding=False,
+    dequantize_fp32=False,
+    non_blocking=True,
+    add_skip_keys=True,
+    quantization_device="cuda",
+    return_device="cuda",
+)
+
+pipe.transformer = sdnq_post_load_quant(pipe.transformer, **common_kwargs)
+pipe.text_encoder = sdnq_post_load_quant(pipe.text_encoder, **common_kwargs)
+
+save_sdnq_model(
+    pipe,
+    "FLUX.2-klein-9B-SDNQ-uint4-static",
+    max_shard_size="5GB",
+    is_pipeline=True,
+)
+```
+
+## Limitations
+
+- This is a quantized derivative of FLUX.2 Klein 9B; it inherits the base
+  model's limitations and acceptable-use requirements.
+- Text rendering can still be inaccurate, especially for long strings or small
+  background text.
+- The quality comparison here is visual prompt-following review, not a
+  large-scale human preference or FID benchmark.
+- Benchmarks were run on an A40 test host and should be validated again for
+  your exact serving stack.
+
+## License
+
+This model is a quantized derivative of
+[black-forest-labs/FLUX.2-klein-9B](https://huggingface.co/black-forest-labs/FLUX.2-klein-9B)
+and follows the FLUX Non-Commercial License. Please review `LICENSE.md` and the
+Black Forest Labs acceptable-use policy before use.

assets/final-top5-summary.csv

@@ -0,0 +1,7 @@
+variant,prompt_count,runs,first_seconds,warm_avg_seconds,warm_min_seconds,warm_max_seconds,gpu_peak_mb,cuda_max_allocated_mb,gpu_load_peak_mb,maxrss_mb
+baseline,5,2,41.33406836446375,4.076407670043409,4.061277890577912,4.090787582099438,15191.0,14463.91796875,11759.0,19721.9765625
+uint4-static,5,2,23.01403909549117,3.8258063884245024,3.8207358438521624,3.8303871555253863,14807.0,14074.35546875,11045.0,18909.65234375
+int4-dynamic-th0p1-svd-r16-s32-g128,5,2,26.136765606701374,4.019561266009179,4.0076204324141145,4.050093832425773,14267.0,13540.10546875,11357.0,17898.3671875
+uint4-static-svd-r32-s32,5,2,24.725356460548937,4.069596313561003,4.057041662745178,4.09303140360862,14677.0,13939.85546875,11391.0,18759.23828125
+float4_e4m0fnu-dynamic-th0p1-svd-r16-s32,5,2,36.09520728699863,4.116074575732152,4.103897335939109,4.134285475127399,16021.0,15291.35546875,12051.0,21359.40234375
+float4_e4m0fnu-dynamic-th0p01-svd-r128-s32,5,2,35.37363956775516,4.184757313070198,4.173255986534059,4.212017542682588,17191.0,16455.10546875,14281.0,23934.69140625

assets/quality-stress-summary.csv

@@ -0,0 +1,5 @@
+cuda_max_allocated_mb,first_seconds,gpu_load_peak_mb,gpu_peak_mb,maxrss_mb,prompt_count,runs,variant,warm_avg_seconds,warm_max_seconds,warm_min_seconds
+35556.35546875,4.715450732037425,33383.0,36293.0,10367.96875,9,2,original-flux2-klein-9b,4.244085428925852,4.37537744641304,4.189825661480427
+14463.91796875,41.665682481601834,30935.0,15191.0,20625.06640625,9,2,baseline,4.079271021402544,4.11067720502615,4.060629941523075
+14074.35546875,22.93841202184558,11897.0,14809.0,20719.39453125,9,2,uint4-static,3.8660740761293306,4.078776756301522,3.826545525342226
+16455.10546875,35.107925665564835,14155.0,17193.0,25905.3203125,9,2,float4_e4m0fnu-dynamic-th0p01-svd-r128-s32,4.181900700243811,4.191505083814263,4.1687538689002395

assets/webp-compression-sweep.csv

@@ -0,0 +1,11 @@
+quality,bytes,size_mb,psnr_rgb,ssim_luma_global,path
+60,2907602,2.7729053497314453,42.78653817938348,0.9999347077990188,results/model-card-assets/flux2-sdnq-uint4-static-comparison-q60.webp
+70,3260514,3.109468460083008,43.691074601453025,0.9999478724634654,results/model-card-assets/flux2-sdnq-uint4-static-comparison-q70.webp
+75,3503270,3.3409786224365234,44.19895846379671,0.9999546481667871,results/model-card-assets/flux2-sdnq-uint4-static-comparison-q75.webp
+80,4755716,4.535404205322266,45.47553636376561,0.9999669784137255,results/model-card-assets/flux2-sdnq-uint4-static-comparison-q80.webp
+82,5223292,4.981319427490234,46.08683415497053,0.9999711708543075,results/model-card-assets/flux2-sdnq-uint4-static-comparison-q82.webp
+85,5998938,5.721033096313477,46.925268863342005,0.999976667295406,results/model-card-assets/flux2-sdnq-uint4-static-comparison-q85.webp
+88,7324046,6.98475456237793,48.06721911479202,0.999982005064194,results/model-card-assets/flux2-sdnq-uint4-static-comparison-q88.webp
+90,8235904,7.8543701171875,48.95557436761949,0.9999847232253969,results/model-card-assets/flux2-sdnq-uint4-static-comparison-q90.webp
+92,9491614,9.051908493041992,49.57852939898273,0.9999869181369917,results/model-card-assets/flux2-sdnq-uint4-static-comparison-q92.webp
+95,12017382,11.460668563842773,51.127060893163474,0.9999896532336908,results/model-card-assets/flux2-sdnq-uint4-static-comparison-q95.webp