Update README.md

README.md

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+## Introduction
+
+The Stable Diffusion XL model is finetuned on comtemporatory Chinese ink paintings.
+
+## Usage
+Our inference process is speed up using [**LCM-LORA**](https://huggingface.co/latent-consistency/lcm-lora-sdxl), please make sure all the necessary libraries are up to date.
+```Python
+pip install --upgrade pip
+pip install --upgrade diffusers transformers accelerate peft
+```
+# Text to Image
+
+Text-to-Image
+
+Here, we should load two adapters, **LCM-LORA** for sample accleration and **Chinese_Ink_LORA** for styled rendering with it's base model stabilityai/stable-diffusion-xl-base-1.0. 
+Next, the scheduler needs to be changed to LCMScheduler and we can reduce the number of inference steps to just 2 to 8 steps(8 used in my experiment). 
+
+```Python
+import torch
+from diffusers import DiffusionPipeline, LCMScheduler
+
+pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0",
+                                         variant="fp16",
+                                         torch_dtype=torch.float16
+                                         ).to("cuda")
+# set scheduler
+pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
+
+# load LoRAs
+pipe.load_lora_weights("latent-consistency/lcm-lora-sdxl", adapter_name="lcm")
+pipe.load_lora_weights("ming-yang/sdxl_chinese_ink_lora", adapter_name="Chinese Ink")
+
+# Combine LoRAs
+pipe.set_adapters(["lcm", "Chinese Ink"], adapter_weights=[1.0, 0.8])
+
+prompts = ["Chinese Ink, mona lisa picture, 8k", "mona lisa, 8k"]
+generator = torch.manual_seed(1)
+images = [pipe(prompt, num_inference_steps=8, guidance_scale=1, generator=generator).images[0] for prompt in prompts]
+
+fig, axs = plt.subplots(1, 2, figsize=(40, 20))
+
+axs[0].imshow(images[0])
+axs[0].axis('off')  # 不显示坐标轴
+
+axs[1].imshow(images[1])
+axs[1].axis('off')
+plt.show()
+```
+!(images/comparison.png)
+
 ---
 tags:
 - text-to-image