patrickvonplaten commited on
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914f06c
1 Parent(s): 6d4e0ea

Update app.py

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  1. app.py +13 -5
app.py CHANGED
@@ -79,15 +79,22 @@ def get_dataframe_all():
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  TITLE = "# Open Parti Prompts Leaderboard"
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  DESCRIPTION = """
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- *This leaderboard is retrieved from answers of [Community Evaluations on Parti Prompts](https://huggingface.co/spaces/OpenGenAI/open-parti-prompts)*
 
 
 
 
 
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  """
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  EXPLANATION = """\n\n
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  ## How the is data collected 📊 \n\n
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- In the [Community Parti Prompts](https://huggingface.co/spaces/OpenGenAI/open-parti-prompts), community members select for every prompt
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- of [Parti Prompts](https://huggingface.co/datasets/nateraw/parti-prompts) which open-source image generation model has generated the best image.
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- The community's answers are then stored and used in this space to give a human evaluation of the different models. \n\n
 
 
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  Currently the leaderboard includes the following models:
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  - [sd-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5)
@@ -95,7 +102,8 @@ Currently the leaderboard includes the following models:
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  - [if-v1-0](https://huggingface.co/DeepFloyd/IF-I-XL-v1.0)
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  - [karlo](https://huggingface.co/kakaobrain/karlo-v1-alpha) \n\n
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- In the following you can see three result tables. The first shows you the overall preferences across all prompts. The second and third tables
 
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  show you a breakdown analysis per category and per type of challenge as defined by [Parti Prompts](https://huggingface.co/datasets/nateraw/parti-prompts).
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  """
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  TITLE = "# Open Parti Prompts Leaderboard"
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  DESCRIPTION = """
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+ The *Open Parti Prompts Leaderboard* compares state-of-the-art, open-source text-to-image models to each other according to **human preferences**. \n\n
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+ Text-to-image models are notoriously difficult to evaluate. [FID](https://en.wikipedia.org/wiki/Fr%C3%A9chet_inception_distance) and
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+ [CLIP Score](https://en.wikipedia.org/wiki/Fr%C3%A9chet_inception_distance) are not enough to accurately state whether a text-to-image model can
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+ **generate "good" images**. "Good" is extremely difficult to put into numbers. \n\n
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+ Instead, the **Open Parti Prompts Leaderboard** uses human feedback from the community to compare images from different text-to-image models to each other.
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+
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  """
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  EXPLANATION = """\n\n
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  ## How the is data collected 📊 \n\n
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+ In more detail, the [Open Parti Prompts Game](https://huggingface.co/spaces/OpenGenAI/open-parti-prompts) collects human preferences that state which generated image
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+ best fits a given prompt from the [Parti Prompts](https://huggingface.co/datasets/nateraw/parti-prompts) dataset. Parti Prompts has been designed to challenge
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+ text-to-image models on prompts of varying categories and difficulty. The images have been pre-generated from the models that are compared in this space.
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+ For more information of how the images were created, plesae refer to [Open Parti Prompts](https://huggingface.co/spaces/OpenGenAI/open-parti-prompts).
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+ The community's answers are then stored and used in this space to give a human-preference-based comparison of the different models. \n\n
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  Currently the leaderboard includes the following models:
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  - [sd-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5)
 
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  - [if-v1-0](https://huggingface.co/DeepFloyd/IF-I-XL-v1.0)
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  - [karlo](https://huggingface.co/kakaobrain/karlo-v1-alpha) \n\n
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+ In the following you can see three result tables. The first shows the overall comparison of the 4 models. The score states,
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+ **the percentage at which images generated from the corresponding model are preferred over the image from all other models**. The second and third tables
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  show you a breakdown analysis per category and per type of challenge as defined by [Parti Prompts](https://huggingface.co/datasets/nateraw/parti-prompts).
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  """
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