Add diffusion README

auto_round/compressors/diffusion/README.md

@@ -0,0 +1,88 @@
+# AutoRound for Diffusion Models (Experimental)
+
+This feature is experimental and may be subject to changes, including potential bug fixes, API modifications, or adjustments to default parameters.
+
+## Quantization
+
+### API Usage (CPU/GPU) Recommended
+
+By default, AutoRoundDiffusion only quantizes the transformer module of diffusion models and uses `COCO2014 captions` for calibration.
+
+```python
+import torch
+from auto_round import AutoRound
+from diffusers import AutoPipelineForText2Image
+
+# Load the model
+model_name = "black-forest-labs/FLUX.1-dev"
+pipe = AutoPipelineForText2Image.from_pretrained(model_name, torch_dtype=torch.bfloat16)
+
+# Quantize the model
+autoround = AutoRound(
+    pipe,
+    scheme="MXFP8",
+    dataset="coco2014",
+    num_inference_steps=10,
+    guidance_scale=7.5,
+    generator_seed=None,
+    batch_size=1,
+)
+autoround.quantize()
+
+# Save the quantized model
+output_dir = "./tmp_autoround"
+# Currently loading the quantized diffusion model is not supported, so use fake format
+autoround.save_quantized(output_dir, format="fake", inplace=True)
+```
+
+- `dataset`: the dataset for quantization training. Currently only support coco2014 and user customized .tsv file.
+
+- `num_inference_steps`: The reference number of denoising steps.
+
+- `guidance_scale`: Control how much the image generation process follows the text prompt. The more it is, the more closely it follows the prompt.
+
+- `generator_seed`: A seed that controls the initial noise from which an image is generated.
+
+for more hyperparameters introduction, please refer [Homepage Detailed Hyperparameters](../../README.md#api-usage-gaudi2cpugpu)
+
+### CLI Usage
+
+A user guide detailing the full list of supported arguments is provided by calling ```auto-round -h``` on the
+terminal.
+
+```bash
+auto-round \
+    --model black-forest-labs/FLUX.1-dev \
+    --scheme MXFP8 \
+    --format fake \
+    --batch_size 1 \
+    --output_dir ./tmp_autoround
+```
+
+### Diffusion Support Matrix
+
+For diffusion models, currently we only validate quantizaion on the FLUX.1-dev, which involves quantizing the transformer component of the pipeline.
+
+| Model     | calibration dataset |
+|--------------|--------------|
+| black-forest-labs/FLUX.1-dev | COCO2014      |
+
+
+
+<details>
+<summary style="font-size:17px;">Calibration Dataset</summary>
+
+For diffusion models, we used [**coco2014**]("https://github.com/mlcommons/inference/raw/refs/heads/master/text_to_image/coco2014/captions/captions_source.tsv") calibration dataset as our default.
+
+If users want to use their own dataset, please build the dataset file in ".tsv" format following below structure and use it through argument --dataset (tsv file):
+```
+id      caption
+0       YOUR_PROMPT
+1       YOUR_PROMPT
+...     ...
+```
+- `id`: The id used to map generated images and prompts.
+- `caption`: The text prompt used to generate the images.
+
+
+</details>

docs/step_by_step.md

@@ -1,7 +1,7 @@
 Step-by-Step
 ============
 
-This document presents step-by-step instructions for auto-round llm quantization. For vlms quantization, please refer to [vlms user guide](../auto_round/mllm/README.md)
+This document presents step-by-step instructions for auto-round llm quantization. You can refer to [vlms user guide](../auto_round/compressors/mllm/README.md) for vlms quantization and [diffusions user guide](../auto_round/compressors/diffusion/README.md) for diffusions quantization.
 
 * [1 Prerequisite](#1-prerequisite)
 * [2 Prepare Calibration Dataset](#2-prepare-calibration-dataset)