remove old fun
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cb3cffe044
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@ -6,4 +6,5 @@ Use two AI models to generate tags for images. The tags will be saved in a `json
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* Actually make interchangeable objects that gives tags per file
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* handle videos: (idea: split using ffmpeg - tag those and somehow combine into taglist)
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* ui
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* ui
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* progress (collect files and print progress)
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@ -16,6 +16,14 @@ CUTOFF = 0.035
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model_clip = CLIPModel.from_pretrained("openai/clip-vit-base-patch32").to(DEVICE)
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processor_clip = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
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def print_cuda_ava():
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print(f"CUDA available: {torch.cuda.is_available()}")
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print(f"Current device: {torch.cuda.current_device()}")
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print(f"Device name: {torch.cuda.get_device_name(torch.cuda.current_device())}")
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print(f"Allocated memory: {torch.cuda.memory_allocated()} bytes")
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print(f"Reserved memory: {torch.cuda.memory_reserved()} bytes")
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processor_blib = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
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model_blib = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base").to(DEVICE)
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@ -68,18 +76,6 @@ def add_tags_to_path(tags, json_path):
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with open(json_path, "w") as json_file:
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json.dump(new_json_data, json_file, indent=4)
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def process_folder_clip(directory):
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for root, _, files in os.walk(directory):
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for fname in files:
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if fname.lower().endswith((".jpg", ".png", ".jpeg", ".webp", ".bmp")):
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full_path = os.path.join(root, fname)
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json_path = os.path.join(root, f"{fname}.json")
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tags = get_clip_tags(full_path, candidate_tags)
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filtered_tags = [ tag for tag, score in tags if score > CUTOFF ]
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add_tags_to_path(filtered_tags, json_path)
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def process_image_folder(directory, clip, blib):
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for root, _, files in os.walk(directory):
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for fname in files:
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