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from datasets import load_dataset | |
from collections import Counter, defaultdict | |
import pandas as pd | |
from huggingface_hub import list_datasets | |
import os | |
import gradio as gr | |
parti_prompt_results = [] | |
ORG = "diffusers-parti-prompts" | |
SUBMISSIONS = { | |
"sd-v1-5": None, | |
"sd-v2-1": None, | |
"if-v1-0": None, | |
"karlo": None, | |
} | |
LINKS = { | |
"sd-v1-5": "https://huggingface.co/runwayml/stable-diffusion-v1-5", | |
"sd-v2-1": "https://huggingface.co/stabilityai/stable-diffusion-2-1", | |
"if-v1-0": "https://huggingface.co/DeepFloyd/IF-I-XL-v1.0", | |
"karlo": "https://huggingface.co/kakaobrain/karlo-v1-alpha", | |
} | |
MODEL_KEYS = "-".join(SUBMISSIONS.keys()) | |
SUBMISSION_ORG = f"results-{MODEL_KEYS}" | |
submission_names = list(SUBMISSIONS.keys()) | |
parti_prompt_categories = load_dataset(os.path.join(ORG, "sd-v1-5"))["train"]["Category"] | |
parti_prompt_challenge = load_dataset(os.path.join(ORG, "sd-v1-5"))["train"]["Challenge"] | |
def load_submissions(): | |
all_datasets = list_datasets(author=SUBMISSION_ORG) | |
relevant_ids = [d.id for d in all_datasets] | |
ids = defaultdict(list) | |
challenges = defaultdict(list) | |
categories = defaultdict(list) | |
for _id in relevant_ids: | |
ds = load_dataset(_id)["train"] | |
for result, image_id in zip(ds["result"], ds["id"]): | |
ids[result].append(image_id) | |
challenges[parti_prompt_challenge[image_id]].append(result) | |
categories[parti_prompt_categories[image_id]].append(result) | |
all_values = sum(len(v) for v in ids.values()) | |
main_dict = {k: '{:.2%}'.format(len(v)/all_values) for k, v in ids.items()} | |
challenges = {k: Counter(v) for k, v in challenges.items()} | |
categories = {k: Counter(v) for k, v in categories.items()} | |
return main_dict, challenges, categories | |
def sort_by_highest_percentage(df): | |
# Convert percentage values to numeric format | |
df = df[df.loc[0].sort_values(ascending=False).index] | |
return df | |
def get_dataframe_all(): | |
main, challenges, categories = load_submissions() | |
main_frame = pd.DataFrame([main]) | |
challenges_frame = pd.DataFrame.from_dict(challenges).fillna(0).T | |
challenges_frame = challenges_frame.div(challenges_frame.sum(axis=1), axis=0) | |
challenges_frame = challenges_frame.applymap(lambda x: '{:.2%}'.format(x)) | |
categories_frame = pd.DataFrame.from_dict(categories).fillna(0).T | |
categories_frame = categories_frame.div(categories_frame.sum(axis=1), axis=0) | |
categories_frame = categories_frame.applymap(lambda x: '{:.2%}'.format(x)) | |
main_frame = sort_by_highest_percentage(main_frame) | |
categories_frame = categories_frame.reindex(columns=main_frame.columns.to_list()) | |
challenges_frame = challenges_frame.reindex(columns=main_frame.columns.to_list()) | |
categories_frame = categories_frame.reset_index().rename(columns={'index': 'Category'}) | |
challenges_frame = challenges_frame.reset_index().rename(columns={'index': 'Challenge'}) | |
return main_frame, challenges_frame, categories_frame | |
TITLE = "# Open Parti Prompts Leaderboard" | |
DESCRIPTION = """ | |
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 | |
Text-to-image models are notoriously difficult to evaluate. [FID](https://en.wikipedia.org/wiki/Fr%C3%A9chet_inception_distance) and | |
[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 | |
**generate "good" images**. "Good" is extremely difficult to put into numbers. \n\n | |
Instead, the **Open Parti Prompts Leaderboard** uses human feedback from the community to compare images from different text-to-image models to each other. | |
\n\n | |
β€οΈ ***Please take 3 minutes to contribute to the benchmark.*** \n | |
π ***Play one round of [Open Parti Prompts Game](https://huggingface.co/spaces/OpenGenAI/open-parti-prompts) to contribute 10 answers.*** π€ | |
""" | |
EXPLANATION = """\n\n | |
## How the is data collected π \n\n | |
In more detail, the [Open Parti Prompts Game](https://huggingface.co/spaces/OpenGenAI/open-parti-prompts) collects human preferences that state which generated image | |
best fits a given prompt from the [Parti Prompts](https://huggingface.co/datasets/nateraw/parti-prompts) dataset. Parti Prompts has been designed to challenge | |
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. | |
For more information of how the images were created, please refer to [Open Parti Prompts](https://huggingface.co/spaces/OpenGenAI/open-parti-prompts). | |
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 | |
Currently the leaderboard includes the following models: | |
- [sd-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) | |
- [sd-v2-1](https://huggingface.co/stabilityai/stable-diffusion-2-1) | |
- [if-v1-0](https://huggingface.co/DeepFloyd/IF-I-XL-v1.0) | |
- [karlo](https://huggingface.co/kakaobrain/karlo-v1-alpha) \n\n | |
In the following you can see three result tables. The first shows the overall comparison of the 4 models. The score states, | |
**the percentage at which images generated from the corresponding model are preferred over the image from all other models**. The second and third tables | |
show you a breakdown analysis per category and per type of challenge as defined by [Parti Prompts](https://huggingface.co/datasets/nateraw/parti-prompts). | |
""" | |
GALLERY_COLUMN_NUM = len(SUBMISSIONS) | |
def refresh(): | |
return get_dataframe_all() | |
with gr.Blocks() as demo: | |
with gr.Column(visible=True) as intro_view: | |
gr.Markdown(TITLE) | |
gr.Markdown(DESCRIPTION) | |
gr.Markdown(EXPLANATION) | |
headers = list(SUBMISSIONS.keys()) | |
datatype = "str" | |
main_df, challenge_df, category_df = get_dataframe_all() | |
with gr.Column(): | |
gr.Markdown("# Open Parti Prompts") | |
main_dataframe = gr.Dataframe( | |
value=main_df, | |
headers=main_df.columns.to_list(), | |
datatype="str", | |
row_count=main_df.shape[0], | |
col_count=main_df.shape[1], | |
interactive=False, | |
) | |
with gr.Column(): | |
gr.Markdown("## per category") | |
cat_dataframe = gr.Dataframe( | |
value=category_df, | |
headers=category_df.columns.to_list(), | |
datatype="str", | |
row_count=category_df.shape[0], | |
col_count=category_df.shape[1], | |
interactive=False, | |
) | |
with gr.Column(): | |
gr.Markdown("## per challenge") | |
chal_dataframe = gr.Dataframe( | |
value=challenge_df, | |
headers=challenge_df.columns.to_list(), | |
datatype="str", | |
row_count=challenge_df.shape[0], | |
col_count=challenge_df.shape[1], | |
interactive=False, | |
) | |
with gr.Row(): | |
refresh_button = gr.Button("Refresh") | |
refresh_button.click(refresh, inputs=[], outputs=[main_dataframe, cat_dataframe, chal_dataframe]) | |
demo.launch() | |