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--- |
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license: apache-2.0 |
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base_model: runwayml/stable-diffusion-v1-5 |
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tags: |
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- art |
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- t2i-adapter |
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- controlnet |
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- stable-diffusion |
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- image-to-image |
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--- |
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# T2I Adapter - Zoedepth |
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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. |
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This checkpoint provides conditioning on zoedepth depth estimation for the stable diffusion 1.5 checkpoint. |
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## Model Details |
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- **Developed by:** T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models |
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- **Model type:** Diffusion-based text-to-image generation model |
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- **Language(s):** English |
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- **License:** Apache 2.0 |
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- **Resources for more information:** [GitHub Repository](https://github.com/TencentARC/T2I-Adapter), [Paper](https://arxiv.org/abs/2302.08453). |
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- **Cite as:** |
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@misc{ |
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title={T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models}, |
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author={Chong Mou, Xintao Wang, Liangbin Xie, Yanze Wu, Jian Zhang, Zhongang Qi, Ying Shan, Xiaohu Qie}, |
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year={2023}, |
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eprint={2302.08453}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV} |
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} |
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### Checkpoints |
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| Model Name | Control Image Overview| Control Image Example | Generated Image Example | |
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|[TencentARC/t2iadapter_color_sd14v1](https://huggingface.co/TencentARC/t2iadapter_color_sd14v1)<br/> *Trained with spatial color palette* | A image with 8x8 color palette.|<a href="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/color_sample_input.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/color_sample_input.png"/></a>|<a href="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/color_sample_output.png"><img width="64" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/color_sample_output.png"/></a>| |
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|[TencentARC/t2iadapter_canny_sd14v1](https://huggingface.co/TencentARC/t2iadapter_canny_sd14v1)<br/> *Trained with canny edge detection* | A monochrome image with white edges on a black background.|<a href="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/canny_sample_input.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/canny_sample_input.png"/></a>|<a href="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/canny_sample_output.png"><img width="64" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/canny_sample_output.png"/></a>| |
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|[TencentARC/t2iadapter_sketch_sd14v1](https://huggingface.co/TencentARC/t2iadapter_sketch_sd14v1)<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="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/sketch_sample_input.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/sketch_sample_input.png"/></a>|<a href="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/sketch_sample_output.png"><img width="64" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/sketch_sample_output.png"/></a>| |
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|[TencentARC/t2iadapter_depth_sd14v1](https://huggingface.co/TencentARC/t2iadapter_depth_sd14v1)<br/> *Trained with Midas depth estimation* | A grayscale image with black representing deep areas and white representing shallow areas.|<a href="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/depth_sample_input.png"><img width="64" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/depth_sample_input.png"/></a>|<a href="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/depth_sample_output.png"><img width="64" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/depth_sample_output.png"/></a>| |
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|[TencentARC/t2iadapter_openpose_sd14v1](https://huggingface.co/TencentARC/t2iadapter_openpose_sd14v1)<br/> *Trained with OpenPose bone image* | A [OpenPose bone](https://github.com/CMU-Perceptual-Computing-Lab/openpose) image.|<a href="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/openpose_sample_input.png"><img width="64" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/openpose_sample_input.png"/></a>|<a href="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/openpose_sample_output.png"><img width="64" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/openpose_sample_output.png"/></a>| |
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|[TencentARC/t2iadapter_keypose_sd14v1](https://huggingface.co/TencentARC/t2iadapter_keypose_sd14v1)<br/> *Trained with mmpose skeleton image* | A [mmpose skeleton](https://github.com/open-mmlab/mmpose) image.|<a href="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/keypose_sample_input.png"><img width="64" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/keypose_sample_input.png"/></a>|<a href="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/keypose_sample_output.png"><img width="64" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/keypose_sample_output.png"/></a>| |
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|[TencentARC/t2iadapter_seg_sd14v1](https://huggingface.co/TencentARC/t2iadapter_seg_sd14v1)<br/>*Trained with semantic segmentation* | An [custom](https://github.com/TencentARC/T2I-Adapter/discussions/25) segmentation protocol image.|<a href="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/seg_sample_input.png"><img width="64" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/seg_sample_input.png"/></a>|<a href="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/seg_sample_output.png"><img width="64" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/seg_sample_output.png"/></a> | |
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|[TencentARC/t2iadapter_canny_sd15v2](https://huggingface.co/TencentARC/t2iadapter_canny_sd15v2)|| |
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|[TencentARC/t2iadapter_depth_sd15v2](https://huggingface.co/TencentARC/t2iadapter_depth_sd15v2)|| |
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|[TencentARC/t2iadapter_sketch_sd15v2](https://huggingface.co/TencentARC/t2iadapter_sketch_sd15v2)|| |
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|[TencentARC/t2iadapter_zoedepth_sd15v1](https://huggingface.co/TencentARC/t2iadapter_zoedepth_sd15v1)|| |
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## Example |
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1. Dependencies |
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```sh |
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pip install diffusers transformers matplotlib |
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``` |
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2. Run code: |
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```python |
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from PIL import Image |
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import torch |
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import numpy as np |
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import matplotlib |
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from diffusers import T2IAdapter, StableDiffusionAdapterPipeline |
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def colorize(value, vmin=None, vmax=None, cmap='gray_r', invalid_val=-99, invalid_mask=None, background_color=(128, 128, 128, 255), gamma_corrected=False, value_transform=None): |
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"""Converts a depth map to a color image. |
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Args: |
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value (torch.Tensor, numpy.ndarry): Input depth map. Shape: (H, W) or (1, H, W) or (1, 1, H, W). All singular dimensions are squeezed |
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vmin (float, optional): vmin-valued entries are mapped to start color of cmap. If None, value.min() is used. Defaults to None. |
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vmax (float, optional): vmax-valued entries are mapped to end color of cmap. If None, value.max() is used. Defaults to None. |
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cmap (str, optional): matplotlib colormap to use. Defaults to 'magma_r'. |
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invalid_val (int, optional): Specifies value of invalid pixels that should be colored as 'background_color'. Defaults to -99. |
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invalid_mask (numpy.ndarray, optional): Boolean mask for invalid regions. Defaults to None. |
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background_color (tuple[int], optional): 4-tuple RGB color to give to invalid pixels. Defaults to (128, 128, 128, 255). |
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gamma_corrected (bool, optional): Apply gamma correction to colored image. Defaults to False. |
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value_transform (Callable, optional): Apply transform function to valid pixels before coloring. Defaults to None. |
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Returns: |
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numpy.ndarray, dtype - uint8: Colored depth map. Shape: (H, W, 4) |
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""" |
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if isinstance(value, torch.Tensor): |
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value = value.detach().cpu().numpy() |
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value = value.squeeze() |
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if invalid_mask is None: |
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invalid_mask = value == invalid_val |
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mask = np.logical_not(invalid_mask) |
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# normalize |
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vmin = np.percentile(value[mask],2) if vmin is None else vmin |
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vmax = np.percentile(value[mask],85) if vmax is None else vmax |
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if vmin != vmax: |
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value = (value - vmin) / (vmax - vmin) # vmin..vmax |
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else: |
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# Avoid 0-division |
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value = value * 0. |
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# squeeze last dim if it exists |
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# grey out the invalid values |
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value[invalid_mask] = np.nan |
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cmapper = matplotlib.cm.get_cmap(cmap) |
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if value_transform: |
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value = value_transform(value) |
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# value = value / value.max() |
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value = cmapper(value, bytes=True) # (nxmx4) |
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img = value[...] |
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img[invalid_mask] = background_color |
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if gamma_corrected: |
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img = img / 255 |
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img = np.power(img, 2.2) |
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img = img * 255 |
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img = img.astype(np.uint8) |
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return img |
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model = torch.hub.load("isl-org/ZoeDepth", "ZoeD_N", pretrained=True) |
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img = Image.open('./images/zoedepth_in.png') |
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out = model.infer_pil(img) |
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zoedepth_image = Image.fromarray(colorize(out)).convert('RGB') |
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zoedepth_image.save('images/zoedepth.png') |
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adapter = T2IAdapter.from_pretrained("TencentARC/t2iadapter_zoedepth_sd15v1", torch_dtype=torch.float16) |
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pipe = StableDiffusionAdapterPipeline.from_pretrained( |
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"runwayml/stable-diffusion-v1-5", adapter=adapter, safety_checker=None, torch_dtype=torch.float16, variant="fp16" |
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) |
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pipe.to('cuda') |
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zoedepth_image_out = pipe(prompt="motorcycle", image=zoedepth_image).images[0] |
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zoedepth_image_out.save('images/zoedepth_out.png') |
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``` |
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![zoedepth_in](./images/zoedepth_in.png) |
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![zoedepth](./images/zoedepth.png) |
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![zoedepth_out](./images/zoedepth_out.png) |
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