LTX2 gemma3 enhanced prompt support

[zwukong]

Can you add support for enhanced prompt. I found it can get better result with enhanced prompt. And it is really very special,only useful in gemma3, other LLM not that good. PS, I2V need the mmproj file 😄

  def _enhance(
        self,
        messages: list[dict[str, str]],
        image: torch.Tensor | None = None,
        max_new_tokens: int = 512,
        seed: int = 42,
    ) -> str:
        if self.processor is None:
            self._init_image_processor()
        text = self.processor.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

        model_inputs = self.processor(
            text=text,
            images=image,
            return_tensors="pt",
        ).to(self.model.device)
        pad_token_id = self.processor.tokenizer.pad_token_id if self.processor.tokenizer.pad_token_id is not None else 0
        model_inputs = _pad_inputs_for_attention_alignment(model_inputs, pad_token_id=pad_token_id)

        with torch.inference_mode(), torch.random.fork_rng(devices=[self.model.device]):
            torch.manual_seed(seed)
            outputs = self.model.generate(
                **model_inputs,
                max_new_tokens=max_new_tokens,
                do_sample=True,
                temperature=0.7,
            )
            generated_ids = outputs[0][len(model_inputs.input_ids[0]) :]
            enhanced_prompt = self.processor.tokenizer.decode(generated_ids, skip_special_tokens=True)

        return enhanced_prompt

    def enhance_t2v(
        self,
        prompt: str,
        max_new_tokens: int = 512,
        system_prompt: str | None = None,
        seed: int = 42,
    ) -> str:
        """Enhance a text prompt for T2V generation."""

        system_prompt = system_prompt or self.default_gemma_t2v_system_prompt

        messages = [
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": f"user prompt: {prompt}"},
        ]

        return self._enhance(messages, max_new_tokens=max_new_tokens, seed=seed)

    def enhance_i2v(
        self,
        prompt: str,
        image: torch.Tensor,
        max_new_tokens: int = 512,
        system_prompt: str | None = None,
        seed: int = 42,
    ) -> str:
        """Enhance a text prompt for I2V generation using a reference image."""
        system_prompt = system_prompt or self.default_gemma_i2v_system_prompt
        messages = [
            {"role": "system", "content": system_prompt},
            {
                "role": "user",
                "content": [
                    {"type": "image"},
                    {"type": "text", "text": f"User Raw Input Prompt: {prompt}."},
                ],
            },
        ]
        return self._enhance(messages, image=image, max_new_tokens=max_new_tokens, seed=seed)

    @functools.cached_property
    def default_gemma_i2v_system_prompt(self) -> str:
        return _load_system_prompt("gemma_i2v_system_prompt.txt")

    @functools.cached_property
    def default_gemma_t2v_system_prompt(self) -> str:
        return _load_system_prompt("gemma_t2v_system_prompt.txt")

    def forward(self, text: str, padding_side: str = "left") -> tuple[torch.Tensor, torch.Tensor]:
        raise NotImplementedError("This method is not implemented for the base class")