Add pipeline tag: text-to-image
#1
by
nielsr
HF Staff
- opened
README.md
CHANGED
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@@ -1,13 +1,14 @@
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---
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license: apache-2.0
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language:
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- en
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library_name: diffusers
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tags:
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- text-to-image
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- stable diffusion
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- personalization
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- msdiffusion
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---
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# Introduction
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@@ -31,4 +32,4 @@ Please refer to our [GitHub repository](https://github.com/MS-Diffusion/MS-Diffu
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- This repo only contains the trained model checkpoint without data, code, or base models. Please check the GitHub repository carefully to get detailed instructions.
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- The `scale` parameter is used to determine the extent of image control. For default, the `scale` is set to 0.6. In practice, the `scale` of 0.4 would be better if your input contains subjects needing to effect on the whole image, such as the background. **Feel free to adjust the `scale` in your applications.**
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- The model prefers to need layout inputs. You can use the default layouts in the inference script, while more accurate and realistic layouts generate better results.
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- Though MS-Diffusion beats SOTA personalized diffusion methods in both single-subject and multi-subject generation, it still suffers from the influence of background in subject images. The best practice is to use masked images since they contain no irrelevant information.
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---
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language:
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- en
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library_name: diffusers
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+
license: apache-2.0
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tags:
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- text-to-image
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- stable diffusion
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- personalization
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- msdiffusion
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pipeline_tag: text-to-image
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---
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# Introduction
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- This repo only contains the trained model checkpoint without data, code, or base models. Please check the GitHub repository carefully to get detailed instructions.
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| 33 |
- The `scale` parameter is used to determine the extent of image control. For default, the `scale` is set to 0.6. In practice, the `scale` of 0.4 would be better if your input contains subjects needing to effect on the whole image, such as the background. **Feel free to adjust the `scale` in your applications.**
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| 34 |
- The model prefers to need layout inputs. You can use the default layouts in the inference script, while more accurate and realistic layouts generate better results.
|
| 35 |
+
- Though MS-Diffusion beats SOTA personalized diffusion methods in both single-subject and multi-subject generation, it still suffers from the influence of background in subject images. The best practice is to use masked images since they contain no irrelevant information.
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