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import faiss |
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import numpy as np |
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from autofaiss import build_index |
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import open_clip |
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import torch |
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import pandas as pd |
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import pickle |
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import numpy as np |
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from PIL import Image |
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import glob |
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import os |
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df = pd.read_parquet("laioncocoknn367.parquet") |
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print(df) |
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texts = df['caption'].tolist() |
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model, _, transform = open_clip.create_model_and_transforms("hf-hub:laion/CLIP-ViT-B-32-256x256-DataComp-s34B-b86K") |
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def normalized(a, axis=-1, order=2): |
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import numpy as np |
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l2 = np.atleast_1d(np.linalg.norm(a, order, axis)) |
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l2[l2 == 0] = 1 |
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return a / np.expand_dims(l2, axis) |
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index = faiss.read_index("laioncocoknn367.index", faiss.IO_FLAG_MMAP | faiss.IO_FLAG_READ_ONLY) |
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files =glob.glob("/images/*.jpg") |
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mediatype = ["movie still HQ depth-of-field", "painting","drawing","realistic photo, photograph","CGI - computer graphics - 3D", "powerpoint slide - text - ebook", "pixelart, pixelated retro video game, low resolution", "ASCII", "cartoon", "stockphoto", "beautiful anime still, no text", "meme", "selfie", "beautiful artwork" , "wallpaper, HD, 4k"] |
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tokenizer = open_clip.get_tokenizer('ViT-B-32') |
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with torch.no_grad(), torch.cuda.amp.autocast(): |
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text_features = normalized(model.encode_text(tokenizer(mediatype)).cpu().detach().numpy()) |
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for im in files: |
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image = Image.open(im) |
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tensor_image = transform(image).unsqueeze(0) |
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with torch.no_grad(): |
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image_features = normalized(model.encode_image(tensor_image).cpu().detach().numpy()) |
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mediatypepredictions = np.matmul(image_features, text_features.T) |
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mediatypepredictions = image_features @ text_features.T |
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max_index = np.argmax(mediatypepredictions) |
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query_vector = image_features |
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k =1 |
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distances, indices = index.search(query_vector, k) |
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results = list(zip(distances[0], indices[0])) |
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text="" |
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for r in results: |
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text +=texts[r[1]].replace("_"," ")+" , " |
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text += mediatype[max_index] |
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print(im, text) |
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try: |
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image.save(f"./output/{text}.jpg") |
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except: |
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pass |