Create README.md

README.md

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+---
+license: apache-2.0
+base_model: stabilityai/stable-diffusion-xl-base-1.0
+tags:
+  - art
+  - t2i-adapter
+  - stable-diffusion
+  - image-to-image
+---
+
+# T2I-Adapter-SDXL - Sketch
+
+T2I Adapter is a network providing additional conditioning to stable diffusion. Each t2i checkpoint takes a different type of conditioning as input and is used with a specific base stable diffusion checkpoint.
+
+This checkpoint provides conditioning on canny for the StableDiffusionXL checkpoint.
+
+## Model Details
+- **Developed by:** T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models
+- **Model type:** Diffusion-based text-to-image generation model
+- **Language(s):** English
+- **License:** Apache 2.0
+- **Resources for more information:** [GitHub Repository](https://github.com/TencentARC/T2I-Adapter), [Paper](https://arxiv.org/abs/2302.08453).
+- **Cite as:**
+
+  @misc{
+    title={T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models}, 
+    author={Chong Mou, Xintao Wang, Liangbin Xie, Yanze Wu, Jian Zhang, Zhongang Qi, Ying Shan, Xiaohu Qie},
+    year={2023},
+    eprint={2302.08453},
+    archivePrefix={arXiv},
+    primaryClass={cs.CV}
+  }
+
+### Checkpoints
+
+| Model Name | Control Image Overview| Control Image Example | Generated Image Example |
+|---|---|---|---|
+|[Adapter/t2iadapter_canny_sdxlv1](https://huggingface.co/Adapter/t2iadapter_canny_sdxlv1)<br/> *Trained with canny edge detection* | A monochrome image with white edges on a black background.|<a href=""><img width="64" style="margin:0;padding:0;" src=""/></a>|<a href=""><img width="64" src=""/></a>|
+|[Adapter/t2iadapter_sketch_sdxlv1](https://huggingface.co/Adapter/t2iadapter_sketch_sdxlv1)<br/> *Trained with [PidiNet](https://github.com/zhuoinoulu/pidinet) edge detection* | A hand-drawn monochrome image with white outlines on a black background.|<a href=""><img width="64" style="margin:0;padding:0;" src=""/></a>|<a href=""><img width="64" src=""/></a>|
+|[Adapter/t2iadapter_depth_sdxlv1](https://huggingface.co/Adapter/t2iadapter_depth_sdxlv1)<br/> *Trained with Midas depth estimation*  | A grayscale image with black representing deep areas and white representing shallow areas.|<a href=""><img width="64" src=""/></a>|<a href=""><img width="64" src=""/></a>|
+|[Adapter/t2iadapter_openpose_sdxlv1](https://huggingface.co/Adapter/t2iadapter_openpose_sdxlv1)<br/> *Trained with OpenPose bone image*  | A [OpenPose bone](https://github.com/CMU-Perceptual-Computing-Lab/openpose) image.|<a href=""><img width="64" src=""/></a>|<a href=""><img width="64" src=""/></a>|
+
+
+## Example
+
+To get started, first install the required dependencies:
+
+```bash
+pip install git+https://github.com/huggingface/diffusers.git@t2iadapterxl # for now
+pip install git+https://github.com/patrickvonplaten/controlnet_aux.git # for conditioning models and detectors  
+pip install transformers accelerate safetensors
+```
+
+1. Images are first downloaded into the appropriate *control image* format.
+ 2. The *control image* and *prompt* are passed to the [`StableDiffusionXLAdapterPipeline`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.py#L125).
+
+Let's have a look at a simple example using the [Canny Adapter](https://huggingface.co/Adapter/t2iadapter_canny_sdxlv1).
+
+```py
+from diffusers import StableDiffusionXLAdapterPipeline, T2IAdapter, EulerAncestralDiscreteScheduler
+from diffusers.utils import load_image, make_image_grid
+from controlnet_aux.pidi import PidiNetDetector
+
+# load adapter
+adapter = T2IAdapter.from_pretrained(
+  "Adapter/t2i-adapter-sketch-sdxl-1.0", torch_dtype=torch.float16, varient="fp16"
+).to("cuda")
+
+# load euler_a scheduler
+model_id = 'stabilityai/stable-diffusion-xl-base-1.0'
+euler_a = EulerAncestralDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler")
+vae= AutoencoderKL.from_pretrained(
+  "madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16
+)
+pipe = StableDiffusionXLAdapterPipeline.from_pretrained(
+  model_id, vae=vae, adapter=adapter, scheduler=euler_a, torch_dtype=torch.float16, variant="fp16", 
+).to("cuda")
+pipe.enable_xformers_memory_efficient_attention()
+
+# Load PidiNet
+pidinet = PidiNetDetector.from_pretrained("lllyasviel/Annotators").to("cuda")
+
+url = "https://raw.githubusercontent.com/lllyasviel/ControlNet/main/test_imgs/cyber.png"
+image = load_image(url)
+image = pidinet(
+  image, detect_resolution=512, image_resolution=1024, apply_filter=True
+).resize((896, 1152))
+
+prompt = "a robot, mount fuji in the background, 4k photo, highly detailed"
+negative_prompt = "anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured"
+
+gen_images = pipe(
+  prompt=prompt,
+  negative_prompt=negative_prompt,
+  image=image,
+  num_inference_steps=30,
+  adapter_conditioning_scale=1, 
+  cond_tau=1
+).images
+gen_images[0]
+```