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| import argparse | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| sys_prompt = """ | |
| Transform the short prompt into a detailed video-generation caption using this structure: | |
| Opening shot type (long/medium/close-up/extreme close-up/full shot) | |
| Primary subject(s) with vivid attributes (colors, textures, actions, interactions) | |
| Dynamic elements (movement, transitions, or changes over time, e.g., 'gradually lowers,' 'begins to climb,' 'camera moves toward...') | |
| Scene composition (background, environment, spatial relationships) | |
| Lighting/atmosphere (natural/artificial, time of day, mood) | |
| Camera motion (zooms, pans, static/handheld shots) if applicable. | |
| Pattern Summary from Examples: | |
| [Shot Type] of [Subject+Action] + [Detailed Subject Description] + [Environmental Context] + [Lighting Conditions] + [Camera Movement] | |
| One case: | |
| Short prompt: a person is playing football | |
| Long prompt: Medium shot of a young athlete in a red jersey sprinting across a muddy field, dribbling a soccer ball with precise footwork. The player glances toward the goalpost, adjusts their stance, and kicks the ball forcefully into the net. Raindrops fall lightly, creating reflections under stadium floodlights. The camera follows the ball’s trajectory in a smooth pan. | |
| Note: If the subject is stationary, incorporate camera movement to ensure the generated video remains dynamic. | |
| Now expand this short prompt: [{}]. Please only output the final long prompt in English. | |
| """ | |
| import argparse | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| import torch | |
| sys_prompt = """...""" # your existing sys_prompt string | |
| class PromptEnhancer: | |
| def __init__(self, model_name="Qwen/Qwen2.5-32B-Instruct"): | |
| # Determine device | |
| if torch.backends.mps.is_available(): | |
| device = "mps" | |
| device_map = None # No accelerate offloading, load entirely on MPS | |
| else: | |
| device = "cpu" | |
| device_map = None # Load entirely on CPU | |
| self.device = torch.device(device) | |
| # Load model without accelerate offloading to avoid conflicts | |
| self.model = AutoModelForCausalLM.from_pretrained( | |
| model_name, | |
| torch_dtype=torch.float16 if device == "mps" else torch.float32, | |
| device_map=device_map, | |
| ) | |
| # Move model to device manually (since device_map=None) | |
| self.model.to(self.device) | |
| self.tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| def __call__(self, prompt): | |
| prompt = prompt.strip() | |
| prompt = sys_prompt.format(prompt) | |
| messages = [ | |
| {"role": "system", "content": "You are a helpful assistant."}, | |
| {"role": "user", "content": prompt} | |
| ] | |
| text = self.tokenizer.apply_chat_template( | |
| messages, | |
| tokenize=False, | |
| add_generation_prompt=True | |
| ) | |
| model_inputs = self.tokenizer([text], return_tensors="pt").to(self.device) | |
| generated_ids = self.model.generate( | |
| **model_inputs, | |
| max_new_tokens=2048, | |
| ) | |
| generated_ids = [ | |
| output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) | |
| ] | |
| rewritten_prompt = self.tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] | |
| return rewritten_prompt | |
| if __name__ == '__main__': | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--prompt", type=str, default="In a still frame, a stop sign") | |
| args = parser.parse_args() | |
| prompt_enhancer = PromptEnhancer() | |
| enhanced_prompt = prompt_enhancer(args.prompt) | |
| print(f'Original prompt: {args.prompt}') | |
| print(f'Enhanced prompt: {enhanced_prompt}') | |