Create README.md

README.md

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+---
+license: apache-2.0
+base_model:
+- Tongyi-MAI/Z-Image
+tags:
+- auto-round
+---
+
+## Model Details
+This model is an W4A16 model with group_size 128 and symmetric quantization of [Tongyi-MAI/Z-Image](https://huggingface.co/Tongyi-MAI/Z-Image) generated by [intel/auto-round](https://github.com/intel/auto-round). Please follow the license of the original model.
+
+
+## Infernece Example
+
+```bash
+pip install git+https://github.com/xin3he/diffusers@auto_round  # A special version for auto-round
+pip install git+https://github.com/intel/auto-round.git@refs/pull/1640/head  #  A special version for tuning and loading
+```
+
+```python
+import time
+
+import torch
+from diffusers import ZImagePipeline
+
+model_path = "INCModel/Z-Image-W4A16-AutoRound"
+
+# 1. Load the pipeline
+# Use bfloat16 for optimal performance on supported GPUs
+pipe = ZImagePipeline.from_pretrained(
+    model_path,
+    torch_dtype=torch.bfloat16,
+    device_map="cuda",   # set device_map="cuda" to leverage gptqmodel kernels
+)
+pipe.to("cuda")
+
+# [Optional] Attention Backend
+# Diffusers uses SDPA by default. Switch to Flash Attention for better efficiency if supported:
+# pipe.transformer.set_attention_backend("flash")    # Enable Flash-Attention-2
+# pipe.transformer.set_attention_backend("_flash_3") # Enable Flash-Attention-3
+
+# [Optional] Model Compilation
+# Compiling the DiT model accelerates inference, but the first run will take longer to compile.
+# pipe.transformer.compile()
+
+# [Optional] CPU Offloading
+# Enable CPU offloading for memory-constrained devices.
+# pipe.enable_model_cpu_offload()
+
+prompt = "Young Chinese woman in red Hanfu, intricate embroidery. Impeccable makeup, red floral forehead pattern. Elaborate high bun, golden phoenix headdress, red flowers, beads. Holds round folding fan with lady, trees, bird. Neon lightning-bolt lamp, bright yellow glow, above extended left palm. Soft-lit outdoor night background, silhouetted tiered pagoda (西安大雁塔), blurred colorful distant lights."
+
+# 2. Generate Image
+print("Starting generation...")
+image = pipe(
+    prompt=prompt,
+    negative_prompt="",
+    height=1280,
+    width=720,
+    cfg_normalization=False,
+    num_inference_steps=50,
+    guidance_scale=4,
+    generator=torch.Generator("cuda").manual_seed(42),
+).images[0]
+
+image.save("example.png")
+```
+
+## Generate the Model
+
+This pr is required https://github.com/intel/auto-round/pull/1640
+
+Tuning version
+
+~~~bash
+CUDA_VISIBLE_DEVICES=6,7 auto-round Tongyi-MAI/Z-Image --num_inference_steps 2  --output_dir /workspace/models/Tongyi-MAI/Z-Image-W4A16-AutoRound  --device_map 0,1  --low_gpu_mem_usage  --enable_torch_compile
+~~~
+
+
+## Ethical Considerations and Limitations
+
+The model can produce factually incorrect output, and should not be relied on to produce factually accurate information. Because of the limitations of the pretrained model and the finetuning datasets, it is possible that this model could generate lewd, biased or otherwise offensive outputs.
+
+Therefore, before deploying any applications of the model, developers should perform safety testing.
+
+## Caveats and Recommendations
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.
+
+Here are a couple of useful links to learn more about Intel's AI software:
+
+- [Intel Neural Compressor](https://github.com/intel/neural-compressor)
+
+## Disclaimer
+
+The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please consult an attorney before using this model for commercial purposes.
+
+## Cite
+
+@article{cheng2023optimize, title={Optimize weight rounding via signed gradient descent for the quantization of llms}, author={Cheng, Wenhua and Zhang, Weiwei and Shen, Haihao and Cai, Yiyang and He, Xin and Lv, Kaokao and Liu, Yi}, journal={arXiv preprint arXiv:2309.05516}, year={2023} }
+
+[arxiv](https://arxiv.org/abs/2309.05516) [github](https://github.com/intel/auto-round)