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import gradio as gr
import torch
from PIL import Image
from transformers import BlipProcessor, BlipForConditionalGeneration, CLIPProcessor, CLIPModel
from torchvision import transforms
# Geräteeinstellung
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# BLIP-Modelle
blip_processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
blip_model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base").to(device)
# CLIP-Modelle
clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32").to(device)
clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
# Platzhalter für DeepDanbooru
def danbooru_tagging(image):
return "1girl, bodysuit, sitting, wooden floor, solo"
def generate_blip_caption(image):
raw_image = Image.open(image).convert("RGB")
inputs = blip_processor(raw_image, return_tensors="pt").to(device)
out = blip_model.generate(**inputs)
caption = blip_processor.decode(out[0], skip_special_tokens=True)
return caption
def generate_clip_prompt(image, detail_level):
raw_image = Image.open(image).convert("RGB")
inputs = clip_processor(images=raw_image, return_tensors="pt").to(device)
outputs = clip_model.get_image_features(**inputs)
base_prompt = "a woman in a bodysuit on wooden floor"
if detail_level >= 4:
return base_prompt + ", cinematic lighting, ultra detailed, HDR"
elif detail_level == 3:
return base_prompt + ", moody atmosphere"
elif detail_level == 2:
return base_prompt + ", minimal shadows"
else:
return base_prompt
def get_output(image, output_type, style, detail_level, tags, model_choice):
if model_choice == "BLIP":
if output_type == "Detailed Description":
return generate_blip_caption(image)
elif output_type == "Short Caption":
return generate_blip_caption(image).split(",")[0]
elif output_type == "Model Training Data":
return generate_blip_caption(image).lower().replace(" ", "_")
elif output_type == "AI Prompt":
return generate_clip_prompt(image, detail_level)
elif model_choice == "CLIP":
return generate_clip_prompt(image, detail_level)
elif model_choice == "DeepDanbooru":
return danbooru_tagging(image)
elif model_choice == "NSFW Detector":
return "(Simulierter NSFW-Klassifikator: Ergebnis nicht implementiert)"
return "[Keine gültige Auswahl getroffen]"
with gr.Blocks(css="style.css") as app:
gr.Markdown("# NSFW Image to Text Generator ✨")
with gr.Row():
with gr.Column():
img = gr.Image(type="filepath", label="Upload Image")
output_type = gr.Dropdown([
"Detailed Description", "Short Caption", "Tag List", "AI Prompt", "Model Training Data"],
label="Output Type", value="Detailed Description")
style = gr.Dropdown([
"Neutral", "Erotic", "Artistic", "Technical", "Literary", "BDSM", "Fetish"],
label="Style", value="Neutral")
detail = gr.Slider(1, 5, step=1, value=3, label="Detail Level")
tags = gr.Textbox(label="Custom Tags (comma separated)")
model_choice = gr.Radio([
"CLIP", "BLIP", "DeepDanbooru", "NSFW Detector"],
label="AI Model", value="BLIP")
btn_generate = gr.Button("Generate Text")
with gr.Column():
output = gr.Textbox(label="Generated Output", lines=8)
gr.Button("Enhance")
gr.Button("Shorten")
gr.Button("Rewrite")
btn_generate.click(get_output,
inputs=[img, output_type, style, detail, tags, model_choice],
outputs=output)
with gr.Tab("Prompt Tools"):
prompt_input = gr.Textbox(label="Prompt Builder")
btn_optimize = gr.Button("Optimize Prompt")
btn_random = gr.Button("Randomize")
optimized_output = gr.Textbox(label="Optimized Prompt")
btn_optimize.click(lambda p: p + ", ultra detailed", inputs=prompt_input, outputs=optimized_output)
btn_random.click(lambda: "a cyberpunk alley at night", outputs=optimized_output)
with gr.Tab("Training Data"):
btn_tags = gr.Button("Generate Tags")
tags_out = gr.Textbox(label="Training Tags")
btn_tags.click(lambda: "1girl, solo, black bodysuit, sitting", outputs=tags_out)
caption_mode = gr.Dropdown([
"Basic Caption", "Detailed Description", "Booru Style", "Natural Language"],
label="Caption Generation")
btn_caption = gr.Button("Generate Caption")
caption_out = gr.Textbox(label="Training Caption")
btn_caption.click(lambda mode: {
"Basic Caption": "A woman posing for a photo",
"Detailed Description": "A woman in a futuristic city wearing a sleek bodysuit.",
"Booru Style": "1girl, bodysuit, city, night",
"Natural Language": "She stands still beneath neon lights, calm yet focused."
}.get(mode, ""), inputs=caption_mode, outputs=caption_out)
trigger_word = gr.Textbox(label="Trigger Word")
trigger_class = gr.Textbox(label="Class")
btn_lora = gr.Button("Prepare LoRA Training Data")
lora_out = gr.Textbox(label="LoRA Output")
btn_lora.click(lambda t, c: f"LoRA: {t}, class: {c}", inputs=[trigger_word, trigger_class], outputs=lora_out)
app.launch()
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