Create app.py

app.py

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+# -*- coding: utf-8 -*-
+"""ImagePromtGenerator.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/14IVhWCKpCLQnMrb4wuAqYRM4a6j14Dyt
+
+# CLIP Interrogator 2.2 by [@pharmapsychotic](https://twitter.com/pharmapsychotic) 
+
+Want to figure out what a good prompt might be to create new images like an existing one? The CLIP Interrogator is here to get you answers!
+
+<br>
+
+For Stable Diffusion 1.X choose the **ViT-L** model and for Stable Diffusion 2.0+ choose the **ViT-H** CLIP Model.
+
+This version is specialized for producing nice prompts for use with Stable Diffusion and achieves higher alignment between generated text prompt and source image. You can try out the old [version 1](https://colab.research.google.com/github/pharmapsychotic/clip-interrogator/blob/v1/clip_interrogator.ipynb) to see how different CLIP models ranks terms. 
+
+You can also run this on HuggingFace and Replicate<br>
+[![Generic badge](https://img.shields.io/badge/🤗-Open%20in%20Spaces-blue.svg)](https://huggingface.co/spaces/pharma/CLIP-Interrogator) [![Replicate](https://replicate.com/pharmapsychotic/clip-interrogator/badge)](https://replicate.com/pharmapsychotic/clip-interrogator)
+
+<br>
+
+If this notebook is helpful to you please consider buying me a coffee via [ko-fi](https://ko-fi.com/pharmapsychotic) or following me on [twitter](https://twitter.com/pharmapsychotic) for more cool Ai stuff. 🙂
+
+And if you're looking for more Ai art tools check out my [Ai generative art tools list](https://pharmapsychotic.com/tools.html).
+"""
+
+
+#@title Setup
+import os, subprocess
+
+def setup():
+    install_cmds = [
+        ['pip', 'install', 'gradio'],
+        ['pip', 'install', 'open_clip_torch'],
+        ['pip', 'install', 'clip-interrogator'],
+        ['pip', 'install', 'git+https://github.com/pharmapsychotic/BLIP.git'],
+    ]
+    for cmd in install_cmds:
+        print(subprocess.run(cmd, stdout=subprocess.PIPE).stdout.decode('utf-8'))
+
+setup()
+
+
+clip_model_name = 'ViT-L-14/openai' #@param ["ViT-L-14/openai", "ViT-H-14/laion2b_s32b_b79k"]
+
+
+print("Download preprocessed cache files...")
+CACHE_URLS = [
+    'https://huggingface.co/pharma/ci-preprocess/resolve/main/ViT-L-14_openai_artists.pkl',
+    'https://huggingface.co/pharma/ci-preprocess/resolve/main/ViT-L-14_openai_flavors.pkl',
+    'https://huggingface.co/pharma/ci-preprocess/resolve/main/ViT-L-14_openai_mediums.pkl',
+    'https://huggingface.co/pharma/ci-preprocess/resolve/main/ViT-L-14_openai_movements.pkl',
+    'https://huggingface.co/pharma/ci-preprocess/resolve/main/ViT-L-14_openai_trendings.pkl',
+] if clip_model_name == 'ViT-L-14/openai' else [
+    'https://huggingface.co/pharma/ci-preprocess/resolve/main/ViT-H-14_laion2b_s32b_b79k_artists.pkl',
+    'https://huggingface.co/pharma/ci-preprocess/resolve/main/ViT-H-14_laion2b_s32b_b79k_flavors.pkl',
+    'https://huggingface.co/pharma/ci-preprocess/resolve/main/ViT-H-14_laion2b_s32b_b79k_mediums.pkl',
+    'https://huggingface.co/pharma/ci-preprocess/resolve/main/ViT-H-14_laion2b_s32b_b79k_movements.pkl',
+    'https://huggingface.co/pharma/ci-preprocess/resolve/main/ViT-H-14_laion2b_s32b_b79k_trendings.pkl',
+]
+os.makedirs('cache', exist_ok=True)
+for url in CACHE_URLS:
+    print(subprocess.run(['wget', url, '-P', 'cache'], stdout=subprocess.PIPE).stdout.decode('utf-8'))
+
+
+import gradio as gr
+from clip_interrogator import Config, Interrogator
+
+config = Config()
+config.blip_num_beams = 64
+config.blip_offload = False
+config.clip_model_name = clip_model_name
+ci = Interrogator(config)
+
+def inference(image, mode, best_max_flavors=32):
+    ci.config.chunk_size = 2048 if ci.config.clip_model_name == "ViT-L-14/openai" else 1024
+    ci.config.flavor_intermediate_count = 2048 if ci.config.clip_model_name == "ViT-L-14/openai" else 1024
+    image = image.convert('RGB')
+    if mode == 'best':
+        return ci.interrogate(image, max_flavors=int(best_max_flavors))
+    elif mode == 'classic':
+        return ci.interrogate_classic(image)
+    else:
+        return ci.interrogate_fast(image)
+
+#@title Image to prompt! 🖼️ -> 📝
+   
+inputs = [
+    gr.inputs.Image(type='pil'),
+    gr.Radio(['best', 'fast'], label='', value='best'),
+    gr.Number(value=16, label='best mode max flavors'),
+]
+outputs = [
+    gr.outputs.Textbox(label="Output"),
+]
+
+io = gr.Interface(
+    inference, 
+    inputs, 
+    outputs, 
+    allow_flagging=False,
+)
+io.launch(debug=False, share=True)
+
+#@title Batch process a folder of images 📁 -> 📝
+
+#@markdown This will generate prompts for every image in a folder and either save results 
+#@markdown to a desc.csv file in the same folder or rename the files to contain their prompts.
+#@markdown The renamed files work well for [DreamBooth extension](https://github.com/d8ahazard/sd_dreambooth_extension)
+#@markdown in the [Stable Diffusion Web UI](https://github.com/AUTOMATIC1111/stable-diffusion-webui).
+#@markdown You can use the generated csv in the [Stable Diffusion Finetuning](https://colab.research.google.com/drive/1vrh_MUSaAMaC5tsLWDxkFILKJ790Z4Bl?usp=sharing)
+
+import csv
+import os
+from IPython.display import clear_output, display
+from PIL import Image
+from tqdm import tqdm
+
+folder_path = "/content/my_images" #@param {type:"string"}
+prompt_mode = 'best' #@param ["best","fast"]
+output_mode = 'rename' #@param ["desc.csv","rename"]
+max_filename_len = 128 #@param {type:"integer"}
+best_max_flavors = 16 #@param {type:"integer"}
+
+
+def sanitize_for_filename(prompt: str, max_len: int) -> str:
+    name = "".join(c for c in prompt if (c.isalnum() or c in ",._-! "))
+    name = name.strip()[:(max_len-4)] # extra space for extension
+    return name
+
+ci.config.quiet = True
+
+files = [f for f in os.listdir(folder_path) if f.endswith('.jpg') or f.endswith('.png')] if os.path.exists(folder_path) else []
+prompts = []
+for idx, file in enumerate(tqdm(files, desc='Generating prompts')):
+    if idx > 0 and idx % 100 == 0:
+        clear_output(wait=True)
+
+    image = Image.open(os.path.join(folder_path, file)).convert('RGB')
+    prompt = inference(image, prompt_mode, best_max_flavors=best_max_flavors)
+    prompts.append(prompt)
+
+    print(prompt)
+    thumb = image.copy()
+    thumb.thumbnail([256, 256])
+    display(thumb)
+
+    if output_mode == 'rename':
+        name = sanitize_for_filename(prompt, max_filename_len)
+        ext = os.path.splitext(file)[1]
+        filename = name + ext
+        idx = 1
+        while os.path.exists(os.path.join(folder_path, filename)):
+            print(f'File {filename} already exists, trying {idx+1}...')
+            filename = f"{name}_{idx}{ext}"
+            idx += 1
+        os.rename(os.path.join(folder_path, file), os.path.join(folder_path, filename))
+
+if len(prompts):
+    if output_mode == 'desc.csv':
+        csv_path = os.path.join(folder_path, 'desc.csv')
+        with open(csv_path, 'w', encoding='utf-8', newline='') as f:
+            w = csv.writer(f, quoting=csv.QUOTE_MINIMAL)
+            w.writerow(['image', 'prompt'])
+            for file, prompt in zip(files, prompts):
+                w.writerow([file, prompt])
+
+        print(f"\n\n\n\nGenerated {len(prompts)} prompts and saved to {csv_path}, enjoy!")
+    else:
+        print(f"\n\n\n\nGenerated {len(prompts)} prompts and renamed your files, enjoy!")
+else:
+    print(f"Sorry, I couldn't find any images in {folder_path}")