Update README.md

README.md

@@ -1,3 +1,70 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- en
+library_name: diffusers
+pipeline_tag: text-to-image
+---
+
+# Target-Driven Distillation
+
+<div align="center">
+
+[**Project Page**](https://tdd.github.io/tdd) **|** [**Paper**](https://arxiv.org/abs) **|** [**Code**](https://github.com/RedAIGC/Target-Driven-Distillation) **|** [🤗 **Gradio demo**](https://huggingface.co/spaces)
+
+
+</div>
+
+## Introduction
+
+Target-Driven Distillation: Consistency Distillation with Target Timestep Selection and Decoupled Guidance
+
+<div  align="center">
+<img src='teaser.jpg'>
+</div>
+
+## Update
+[2024.08.22]:Upload the TDD LoRA weights of Stable Diffusion XL, YamerMIX and RealVisXL-V4.0, fast text-to-image generation.
+- sdxl_tdd_lora_weights.safetensors
+- yamermix_tdd_lora_weights.safetensors
+- realvis_tdd_sdxl_lora_weights.safetensors
+
+Thanks to [Yamer](https://civitai.com/user/Yamer) and [SG_161222](https://civitai.com/user/SG_161222) for developing [YamerMIX](https://civitai.com/models/84040?modelVersionId=395107) and [RealVisXL V4.0](https://civitai.com/models/139562/realvisxl-v40) respectively.
+## Usage
+
+You can directly download the model in this repository.
+You also can download the model in python script:
+
+```python
+from huggingface_hub import hf_hub_download
+hf_hub_download(repo_id="RedAIGC/TDD", filename="sdxl_tdd_lora_weights.safetensors", local_dir="./tdd_lora")
+```
+
+```python
+# !pip install opencv-python transformers accelerate 
+import torch
+import diffusers
+from diffusers import StableDiffusionXLPipeline
+from tdd_scheduler import TDDScheduler
+
+device = "cuda"
+tdd_lora_path = "tdd_lora/sdxl_tdd_lora_weights.safetensors"
+
+pipe = StableDiffusionXLPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16").to(device)
+
+pipe.scheduler = TDDSchedulerPlus.from_config(pipe.scheduler.config)
+pipe.load_lora_weights(tdd_lora_path, adapter_name="accelerate")
+pipe.fuse_lora()
+
+prompt = "A photo of a cat made of water."
+
+image = pipe(
+    prompt=prompt,
+    num_inference_steps=4,
+    guidance_scale=1.7,
+    eta=0.2, 
+    generator=torch.Generator(device=device).manual_seed(546237),
+).images[0]
+
+image.save("tdd.png")
+```