temporal update

app.py

@@ -9,6 +9,8 @@ from datasets import load_dataset, Dataset, load_from_disk
 from huggingface_hub import login
 import os
 import datasets
+import requests
+from bs4 import BeautifulSoup
 
 
 class GalleryApp:
@@ -26,12 +28,12 @@ class GalleryApp:
                 st.image(image,
                          use_column_width=True,
                 )
-                with st.expander('Similarity Info'):
-                    tab1, tab2 = st.tabs(['Most Similar', 'Least Similar'])
-                    with tab1:
-                        st.image(image, use_column_width=True)
-                    with tab2:
-                        st.image(image, use_column_width=True)
+                # with st.expander('Similarity Info'):
+                #     tab1, tab2 = st.tabs(['Most Similar', 'Least Similar'])
+                #     with tab1:
+                #         st.image(image, use_column_width=True)
+                #     with tab2:
+                #         st.image(image, use_column_width=True)
                 for key in info:
                     st.write(f"**{key}**: {items.iloc[idx][key]}")
 
@@ -61,11 +63,14 @@ class GalleryApp:
             original_prompts = np.sort(items['prompt'].unique())[::-1]
 
             # remove the first four items in the prompt, which are mostly the same
-            prompts = [', '.join(x.split(', ')[4:]) for x in original_prompts]
-            prompt = st.selectbox('Select prompt', prompts)
+            if tag != 'abstract':
+                prompts = [', '.join(x.split(', ')[4:]) for x in original_prompts]
+                prompt = st.selectbox('Select prompt', prompts)
 
-            idx = prompts.index(prompt)
-            prompt_full = ', '.join(original_prompts[idx].split(', ')[:4]) + ', ' + prompt
+                idx = prompts.index(prompt)
+                prompt_full = ', '.join(original_prompts[idx].split(', ')[:4]) + ', ' + prompt
+            else:
+                prompt_full = st.selectbox('Select prompt', original_prompts)
 
             prompt_id = items[items['prompt'] == prompt_full]['prompt_id'].unique()[0]
             items = items[items['prompt_id'] == prompt_id].reset_index(drop=True)
@@ -85,11 +90,23 @@ class GalleryApp:
             st.write('**Seed**')
             st.caption(f"{items['seed'][0]}")
 
+            # for tag as civitai, add civitai reference
+            if tag == 'civitai':
+                st.write('**Reference**')
+
+                res = requests.get(f'https://civitai.com/images', params={'post_id': prompt_id})
+                st.write(res)
+                # image_url = res.json()['items'][0]['url']
+                # st.image(image_url, use_column_width=True)
+
         # with images:
         selecters = st.columns([1, 1, 2])
 
         with selecters[0]:
-            sort_by = st.selectbox('Sort by', items.columns[11: -1])
+            # sort_by = st.selectbox('Sort by', items.columns[11: -1])
+            sort_by = st.selectbox('Sort by', ['model_download_count', 'model_name', 'model_id',
+                                   'modelVersion_name', 'modelVersion_id'])
+            print(items.columns)
 
         with selecters[1]:
             order = st.selectbox('Order', ['Ascending', 'Descending'], index=1 if sort_by == 'clip_score' or sort_by == 'model_download_count' else 0)
@@ -101,7 +118,7 @@ class GalleryApp:
         items = items.sort_values(by=[sort_by], ascending=order).reset_index(drop=True)
         with selecters[2]:
             info = st.multiselect('Show Info',
-                                  ['brisque_score', 'clip_score', 'model_download_count', 'model_name', 'model_id',
+                                  ['model_download_count', 'model_name', 'model_id',
                                    'modelVersion_name', 'modelVersion_id'],
                                   default=sort_by)
 
@@ -123,7 +140,7 @@ if __name__ == '__main__':
         print('loading promptBook')
 
         st.session_state.promptBook = pd.DataFrame(load_dataset('NYUSHPRP/ModelCofferMetadata', split='train'))
-        st.session_state.images = load_from_disk(os.path.join(os.getcwd(), 'data', 'promptbook'))['train']
+        st.session_state.images = load_from_disk(os.path.join(os.getcwd(), 'data', 'promptbook'))
         print(st.session_state.images)
         print('images loaded')
         # st.session_state.promptBook = pd.DataFrame(load_dataset('NYUSHPRP/ModelCofferPromptBook', split='train'))

data/download_script.py

@@ -1,10 +1,8 @@
 from datasets import load_dataset, Dataset, load_from_disk
-from diffusers import DPMSolverMultistepScheduler
 
-DPMSolverMultistepScheduler.from_config()
 
 def main():
-    promptbook = load_dataset('NYUSHPRP/ModelCofferPromptBook')
+    promptbook = load_dataset('NYUSHPRP/ModelCofferPromptBook', split='train')
     print(promptbook)
     promptbook.save_to_disk('./promptbook')
 

data/promptbook/{train/data-00003-of-00010.arrow → data-00000-of-00021.arrow}

@@ -1,3 +1,3 @@
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+size 503087760

data/promptbook/{train/data-00000-of-00010.arrow → data-00001-of-00021.arrow}

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data/promptbook/{train/data-00001-of-00010.arrow → data-00002-of-00021.arrow}

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data/promptbook/{train/data-00002-of-00010.arrow → data-00003-of-00021.arrow}

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data/promptbook/dataset_dict.json

@@ -1 +0,0 @@
-{"splits": ["train", "validation"]}

data/promptbook/{train/dataset_info.json → dataset_info.json}

@@ -1,54 +1,94 @@
 {
   "citation": "",
-  "dataset_size": 5164402699,
+  "dataset_size": 10415010218,
   "description": "",
   "download_checksums": {
-    "https://huggingface.co/datasets/NYUSHPRP/ModelCofferPromptBook/resolve/9c76b44a3c3319f788ee6fea27d3575618ff3e9c/data/train-00000-of-00010-2156f01bb59ccb9c.parquet": {
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   },
-  "download_size": 5162233951,
+  "download_size": 10408931967,
   "features": {
     "image": {
       "_type": "Image"
@@ -90,44 +130,37 @@
       "_type": "Value"
     },
     "cfgScale": {
-      "dtype": "int64",
+      "dtype": "float64",
       "_type": "Value"
     },
     "sampler": {
       "dtype": "string",
       "_type": "Value"
-    },
-    "brisque_score": {
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-      "_type": "Value"
-    },
-    "clip_score": {
-      "dtype": "float64",
-      "_type": "Value"
     }
   },
   "homepage": "",
   "license": "",
-  "size_in_bytes": 10326636650,
+  "size_in_bytes": 20823942185,
   "splits": {
     "train": {
       "name": "train",
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-      "num_examples": 7935,
+      "num_bytes": 10415010218,
+      "num_examples": 16000,
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-      "num_examples": 560,
-      "dataset_name": "parquet"
     }
   }
 }

data/promptbook/state.json

@@ -0,0 +1,73 @@
+{
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