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.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
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+*.model filter=lfs diff=lfs merge=lfs -text
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+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
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+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+unsplash-dataset/features.npy filter=lfs diff=lfs merge=lfs -text
+unsplash-dataset filter=lfs diff=lfs merge=lfs -text
+styles filter=lfs diff=lfs merge=lfs -text
+styles/starry.jpeg filter=lfs diff=lfs merge=lfs -text
+styles/mona1.jpeg filter=lfs diff=lfs merge=lfs -text
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.gitignore

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+gradio_queue.db

README.md

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+---
+title: FLAVA Neural Style Transfer
+emoji: 🔥
+colorFrom: pink
+colorTo: yellow
+sdk: gradio
+app_file: app.py
+pinned: true
+---
+
+# Configuration
+
+`title`: _string_  
+Display title for the Space
+
+`emoji`: _string_  
+Space emoji (emoji-only character allowed)
+
+`colorFrom`: _string_  
+Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
+
+`colorTo`: _string_  
+Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
+
+`sdk`: _string_  
+Can be either `gradio` or `streamlit`
+
+`sdk_version` : _string_  
+Only applicable for `streamlit` SDK.  
+See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
+
+`app_file`: _string_  
+Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
+Path is relative to the root of the repository.
+
+`pinned`: _boolean_  
+Whether the Space stays on top of your list.

app.py

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+import os
+from io import BytesIO
+import requests
+from datetime import datetime
+import random
+
+# Interface utilities
+import gradio as gr
+
+# Data utilities
+import numpy as np
+import pandas as pd
+
+# Image utilities
+from PIL import Image
+import cv2 
+
+# FLAVA Model
+import torch
+from transformers import BertTokenizer, FlavaModel
+
+# Style Transfer Model
+import paddlehub as hub
+
+
+
+os.system("hub install stylepro_artistic==1.0.1")
+stylepro_artistic = hub.Module(name="stylepro_artistic")
+
+
+
+# FLAVA Model
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model = FlavaModel.from_pretrained("facebook/flava-full")
+tokenizer = BertTokenizer.from_pretrained("facebook/flava-full")
+model = model.to(device)
+
+# Load Data
+photo_features = np.load("unsplash-dataset/features.npy")
+photo_data = pd.read_csv("unsplash-dataset/photos.csv")
+
+def image_from_text(text_input):
+    start=datetime.now()
+
+    ## Inference
+    with torch.no_grad():
+        inputs = tokenizer([text_input],  padding=True, return_tensors="pt").to(device)
+        text_features = model.get_text_features(**inputs)[:, 0, :].cpu().numpy()
+    
+    ## Find similarity
+    similarities = list((text_features @ photo_features.T).squeeze(0))
+    
+    ## Return best image :)
+    idx = sorted(zip(similarities, range(photo_features.shape[0])), key=lambda x: x[0], reverse=True)[0][1]
+    photo = photo_data.iloc[idx]
+
+    print(f"Time spent at FLAVA: {datetime.now()-start}")
+
+    start=datetime.now()
+    # Downlaod image
+    response = requests.get(photo["path"])
+    pil_image = Image.open(BytesIO(response.content)).convert("RGB") 
+    open_cv_image = np.array(pil_image) 
+    # Convert RGB to BGR 
+    open_cv_image = open_cv_image[:, :, ::-1].copy() 
+
+    print(f"Time spent at Image request: {datetime.now()-start}")
+
+    return open_cv_image
+
+def inference(content, style):
+    content_image = image_from_text(content)
+    start=datetime.now()
+
+    result = stylepro_artistic.style_transfer(
+        images=[{
+            "content": content_image,
+            "styles": [cv2.imread(style.name)]
+        }])
+
+    print(f"Time spent at Style Transfer: {datetime.now()-start}")
+    return Image.fromarray(np.uint8(result[0]["data"])[:,:,::-1]).convert("RGB")
+
+if __name__ == "__main__": 
+    title = "FLAVA Neural Style Transfer"
+    description = "Gradio demo for Neural Style Transfer. Inspired from <a href='https://huggingface.co/spaces/WaterKnight/neural-style-transfer'>this demo for CLIP</a>. To use it, simply enter the text for image content and upload style image. Read more at the links below."
+    article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2003.07694'target='_blank'>Parameter-Free Style Projection for Arbitrary Style Transfer</a> | <a href='https://github.com/PaddlePaddle/PaddleHub' target='_blank'>Github Repo</a></br><a href='https://arxiv.org/abs/2112.04482' target='_blank'>FLAVA paper</a> | <a href='https://huggingface.co/transformers/model_doc/flava.html' target='_blank'>Hugging Face FLAVA Implementation</a></p>"
+    examples=[
+            ["a cute kangaroo", "styles/starry.jpeg"],
+            ["man holding beer", "styles/mona1.jpeg"],
+        ]
+    demo = gr.Interface(inference, 
+        inputs=[
+            gr.inputs.Textbox(lines=1, placeholder="Describe the content of the image", default="a cute kangaroo", label="Describe the image to which the style will be applied"),
+            gr.inputs.Image(type="file", label="Style to be applied"),
+        ], 
+        outputs=gr.outputs.Image(type="pil"),
+        enable_queue=True,
+        title=title,
+        description=description,
+        article=article,
+        examples=examples
+    )
+    demo.launch(share=True)

packages.txt

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+ffmpeg 
+libsm6 
+libxext6

requirements.txt

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+paddlepaddle
+paddlehub
+transformers
+torch

unsplash-dataset/features.npy

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+size 153354368

unsplash-dataset/photos.csv

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