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import gradio as gr
import torch
from transformers import AutoProcessor, AutoModelForZeroShotImageClassification
from diffusers import DiffusionPipeline
import requests
from PIL import Image
from io import BytesIO
import onnxruntime as ort
from huggingface_hub import hf_hub_download
# Initialize models
anime_model_path = hf_hub_download("SmilingWolf/wd-convnext-tagger-v3", "model.onnx")
anime_model = ort.InferenceSession(anime_model_path)
photo_model = AutoModelForCausalLM.from_pretrained("microsoft/Florence-2-base", trust_remote_code=True)
processor = AutoProcessor.from_pretrained("microsoft/Florence-2-base", trust_remote_code=True)
# Load labels for the anime model
labels_path = hf_hub_download("SmilingWolf/wd-convnext-tagger-v3", "selected_tags.csv")
with open(labels_path, 'r') as f:
anime_labels = [line.strip().split(',')[0] for line in f.readlines()[1:]] # Skip header
def preprocess_image(image):
image = image.convert('RGB')
image = image.resize((448, 448), Image.LANCZOS)
image = np.array(image).astype(np.float32)
image = image[:, :, ::-1] # RGB -> BGR
image = np.transpose(image, (2, 0, 1)) # HWC -> CHW
image = image / 255.0
return image[np.newaxis, ...]
def transcribe_image(image, image_type, transcriber, booru_tags=None):
if image_type == "Anime":
input_image = preprocess_image(image)
input_name = anime_model.get_inputs()[0].name
output_name = anime_model.get_outputs()[0].name
probs = anime_model.run([output_name], {input_name: input_image})[0]
# Get top 50 tags
top_indices = probs[0].argsort()[-50:][::-1]
tags = [anime_labels[i] for i in top_indices]
else:
prompt = "<MORE_DETAILED_CAPTION>"
inputs = processor(text=prompt, images=image, return_tensors="pt")
generated_ids = photo_model.generate(
input_ids=inputs["input_ids"],
pixel_values=inputs["pixel_values"],
max_new_tokens=1024,
do_sample=False,
num_beams=3,
)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
parsed_answer = processor.post_process_generation(generated_text, task="<OD>", image_size=(image.width, image.height))
# Extract tags from parsed_answer
tags = [obj['class'] for obj in parsed_answer]
if booru_tags:
tags = list(set(tags + booru_tags))
return ", ".join(tags)
def get_booru_image(booru, image_id):
if booru == "Gelbooru":
url = f"https://gelbooru.com/index.php?page=dapi&s=post&q=index&json=1&id={image_id}"
elif booru == "Danbooru":
url = f"https://danbooru.donmai.us/posts/{image_id}.json"
elif booru == "rule34.xxx":
url = f"https://api.rule34.xxx/index.php?page=dapi&s=post&q=index&json=1&id={image_id}"
else:
raise ValueError("Unsupported booru")
response = requests.get(url)
data = response.json()
# The exact structure of the response will vary depending on the booru
# You'll need to adjust this part based on each booru's API
image_url = data[0]['file_url'] if isinstance(data, list) else data['file_url']
tags = data[0]['tags'].split() if isinstance(data, list) else data['tags'].split()
img_response = requests.get(image_url)
img = Image.open(BytesIO(img_response.content))
return img, tags
def update_image(image_type, booru, image_id, uploaded_image):
if image_type == "Anime" and booru != "Upload":
image, booru_tags = get_booru_image(booru, image_id)
return image, gr.update(visible=True), booru_tags
elif uploaded_image is not None:
return uploaded_image, gr.update(visible=True), None
else:
return None, gr.update(visible=False), None
def on_image_type_change(image_type):
if image_type == "Anime":
return gr.update(visible=True), gr.update(visible=True), gr.update(choices=["Anime", "Photo/Other"])
else:
return gr.update(visible=False), gr.update(visible=True), gr.update(choices=["Photo/Other", "Anime"])
with gr.Blocks() as app:
gr.Markdown("# Image Transcription App")
with gr.Tab("Step 1: Image"):
image_type = gr.Dropdown(["Anime", "Photo/Other"], label="Image type")
with gr.Column(visible=False) as anime_options:
booru = gr.Dropdown(["Gelbooru", "Danbooru", "Upload"], label="Boorus")
image_id = gr.Textbox(label="Image ID")
get_image_btn = gr.Button("Get image")
upload_btn = gr.UploadButton("Upload Image", visible=False)
image_display = gr.Image(label="Image to transcribe", visible=False)
booru_tags = gr.State(None)
transcribe_btn = gr.Button("Transcribe", visible=False)
transcribe_with_tags_btn = gr.Button("Transcribe with booru tags", visible=False)
with gr.Tab("Step 2: Transcribe"):
transcriber = gr.Dropdown(["Anime", "Photo/Other"], label="Transcriber")
transcribe_image_display = gr.Image(label="Image to transcribe")
transcribe_btn_final = gr.Button("Transcribe")
tags_output = gr.Textbox(label="Transcribed tags")
image_type.change(on_image_type_change, inputs=[image_type],
outputs=[anime_options, upload_btn, transcriber])
get_image_btn.click(update_image,
inputs=[image_type, booru, image_id, upload_btn],
outputs=[image_display, transcribe_btn, booru_tags])
upload_btn.upload(update_image,
inputs=[image_type, booru, image_id, upload_btn],
outputs=[image_display, transcribe_btn, booru_tags])
def transcribe_and_update(image, image_type, transcriber, booru_tags):
tags = transcribe_image(image, image_type, transcriber, booru_tags)
return image, tags
transcribe_btn.click(transcribe_and_update,
inputs=[image_display, image_type, transcriber, booru_tags],
outputs=[transcribe_image_display, tags_output])
transcribe_with_tags_btn.click(transcribe_and_update,
inputs=[image_display, image_type, transcriber, booru_tags],
outputs=[transcribe_image_display, tags_output])
transcribe_btn_final.click(transcribe_image,
inputs=[transcribe_image_display, image_type, transcriber],
outputs=[tags_output])
app.launch() |