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@@ -7,7 +7,7 @@ pipeline_tag: text-to-image
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  tags:
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  - stable-diffusion
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  ---
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- # 🧩 TokenCompose SD14 Model Card
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  [TokenCompose_SD21_A](https://mlpc-ucsd.github.io/TokenCompose/) is a [latent text-to-image diffusion model](https://arxiv.org/abs/2112.10752) finetuned from the [**Stable-Diffusion-v2-1**](https://huggingface.co/stabilityai/stable-diffusion-2-1) checkpoint at resolution 768x768 on the [VSR](https://github.com/cambridgeltl/visual-spatial-reasoning) split of [COCO image-caption pairs](https://cocodataset.org/#download) for 32,000 steps with a learning rate of 5e-6. The training objective involves token-level grounding terms in addition to denoising loss for enhanced multi-category instance composition and photorealism. The "_A/B" postfix indicates different finetuning runs of the model using the same above configurations.
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@@ -19,7 +19,7 @@ We strongly recommend using the [🤗Diffuser](https://github.com/huggingface/di
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  import torch
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  from diffusers import StableDiffusionPipeline
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- model_id = "mlpc-lab/TokenCompose_SD14_A"
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  device = "cuda"
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  pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
@@ -31,72 +31,12 @@ image = pipe(prompt).images[0]
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  image.save("cat_and_wine_glass.png")
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  ```
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- # ⬆️Improvements over SD14
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- <table>
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-
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- <tr>
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- <th rowspan="3" align="center">Method</th>
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- <th colspan="9" align="center">Multi-category Instance Composition</th>
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- <th colspan="2" align="center">Photorealism</th>
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- <th colspan="1" align="center">Efficiency</th>
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- </tr>
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-
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- <tr>
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- <!-- <th align="center">&nbsp;</th> -->
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- <th rowspan="2" align="center">Object Accuracy</th>
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- <th colspan="4" align="center">COCO</th>
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- <th colspan="4" align="center">ADE20K</th>
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- <th rowspan="2" align="center">FID (COCO)</th>
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- <th rowspan="2" align="center">FID (Flickr30K)</th>
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- <th rowspan="2" align="center">Latency</th>
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- </tr>
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-
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- <tr>
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- <!-- <th align="center">&nbsp;</th> -->
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- <th align="center">MG2</th>
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- <th align="center">MG3</th>
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- <th align="center">MG4</th>
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- <th align="center">MG5</th>
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- <th align="center">MG2</th>
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- <th align="center">MG3</th>
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- <th align="center">MG4</th>
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- <th align="center">MG5</th>
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- </tr>
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-
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- <tr>
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- <td align="center"><a href="https://huggingface.co/CompVis/stable-diffusion-v1-4">SD 1.4</a></td>
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- <td align="center">29.86</td>
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- <td align="center">90.72<sub>1.33</sub></td>
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- <td align="center">50.74<sub>0.89</sub></td>
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- <td align="center">11.68<sub>0.45</sub></td>
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- <td align="center">0.88<sub>0.21</sub></td>
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- <td align="center">89.81<sub>0.40</sub></td>
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- <td align="center">53.96<sub>1.14</sub></td>
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- <td align="center">16.52<sub>1.13</sub></td>
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- <td align="center">1.89<sub>0.34</sub></td>
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- <td align="center"><u>20.88</u></td>
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- <td align="center"><u>71.46</u></td>
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- <td align="center"><b>7.54</b><sub>0.17</sub></td>
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- </tr>
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-
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- <tr>
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- <td align="center"><a href="https://github.com/mlpc-ucsd/TokenCompose"><strong>TokenCompose (Ours)</strong></a></td>
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- <td align="center"><b>52.15</b></td>
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- <td align="center"><b>98.08</b><sub>0.40</sub></td>
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- <td align="center"><b>76.16</b><sub>1.04</sub></td>
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- <td align="center"><b>28.81</b><sub>0.95</sub></td>
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- <td align="center"><u>3.28</u><sub>0.48</sub></td>
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- <td align="center"><b>97.75</b><sub>0.34</sub></td>
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- <td align="center"><b>76.93</b><sub>1.09</sub></td>
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- <td align="center"><b>33.92</b><sub>1.47</sub></td>
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- <td align="center"><b>6.21</b><sub>0.62</sub></td>
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- <td align="center"><b>20.19</b></td>
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- <td align="center"><b>71.13</b></td>
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- <td align="center"><b>7.56</b><sub>0.14</sub></td>
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- </tr>
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-
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- </table>
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  # 📰 Citation
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  Coming soon!
 
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  tags:
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  - stable-diffusion
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  ---
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+ # 🧩 TokenCompose SD21 Model Card
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  [TokenCompose_SD21_A](https://mlpc-ucsd.github.io/TokenCompose/) is a [latent text-to-image diffusion model](https://arxiv.org/abs/2112.10752) finetuned from the [**Stable-Diffusion-v2-1**](https://huggingface.co/stabilityai/stable-diffusion-2-1) checkpoint at resolution 768x768 on the [VSR](https://github.com/cambridgeltl/visual-spatial-reasoning) split of [COCO image-caption pairs](https://cocodataset.org/#download) for 32,000 steps with a learning rate of 5e-6. The training objective involves token-level grounding terms in addition to denoising loss for enhanced multi-category instance composition and photorealism. The "_A/B" postfix indicates different finetuning runs of the model using the same above configurations.
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  import torch
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  from diffusers import StableDiffusionPipeline
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+ model_id = "mlpc-lab/TokenCompose_SD21_A"
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  device = "cuda"
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  pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
 
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  image.save("cat_and_wine_glass.png")
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  ```
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+ # ⬆️Improvements over SD21
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+ | Model | Object Accuracy | MG3 COCO | MG4 COCO | MG5 COCO | MG3 ADE20K | MG4 ADE20K | MG5 ADE20K | FID COCO |
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+ |---------------------|-----------------|----------|----------|----------|------------|------------|------------|----------|
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+ | SD21 | 47.82 | 70.14 | 25.57 | 3.27 | 75.13 | 35.07 | 7.16 | 19.59 |
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+ | TokenCompose (SD21) | 60.10 | 80.48 | 36.69 | 5.71 | 79.51 | 39.59 | 8.13 | 19.15 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  # 📰 Citation
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  Coming soon!