| """Minimal inference with the EditPPT vision-validator LoRA on Qwen3.5-9B. |
| |
| Input order matters: [image BEFORE the edit, image AFTER the edit, prompt]. Render slides at 960x540 |
| (1 slide pt = 1 px for a standard 16:9 deck). Fill prompt_template.txt with str.replace, not str.format |
| (the template contains literal JSON braces). |
| """ |
| import json |
|
|
| import torch |
| from peft import PeftModel |
| from PIL import Image |
| from transformers import AutoModelForImageTextToText, AutoProcessor |
|
|
| BASE, ADAPTER = "Qwen/Qwen3.5-9B", "EditPPT/editppt-vision-validator-lora" |
| PIXELS = 960 * 540 |
|
|
| processor = AutoProcessor.from_pretrained(BASE, min_pixels=PIXELS, max_pixels=PIXELS) |
| model = AutoModelForImageTextToText.from_pretrained(BASE, dtype=torch.bfloat16, device_map="cuda:0") |
| model = PeftModel.from_pretrained(model, ADAPTER).eval() |
|
|
|
|
| def validate(before_png, after_png, instruction, tools_used, slide_json): |
| prompt = (open("prompt_template.txt").read() |
| .replace("{instruction}", instruction) |
| .replace("{tools_used}", str(tools_used)) |
| .replace("{slide_json}", str(slide_json))) |
| msgs = [{"role": "user", "content": [{"type": "image"}, {"type": "image"}, {"type": "text", "text": prompt}]}] |
| text = processor.apply_chat_template(msgs, add_generation_prompt=True, tokenize=False) |
| imgs = [Image.open(p).convert("RGB") for p in (before_png, after_png)] |
| enc = processor(text=[text], images=imgs, return_tensors="pt").to(model.device) |
| with torch.no_grad(): |
| out = model.generate(**enc, max_new_tokens=1024, do_sample=False) |
| raw = processor.tokenizer.decode(out[0, enc["input_ids"].shape[1]:], skip_special_tokens=True) |
| return json.loads(raw[raw.index("{"): raw.rindex("}") + 1]) |
|
|
