Method process

nodes.py:308–434

(self, positive_prompt, negative_prompt, t5=None, force_offload=True, model_to_offload=None, use_disk_cache=False, device="gpu")

    def process(self, positive_prompt, negative_prompt, t5=None, force_offload=True, model_to_offload=None, use_disk_cache=False, device="gpu"):
        if t5 is None and not use_disk_cache:
            raise ValueError("T5 encoder is required for text encoding. Please provide a valid T5 encoder or enable disk cache.")

        echoshot = True if "[1]" in positive_prompt else False

        if use_disk_cache:
            context, context_null = get_cached_text_embeds(positive_prompt, negative_prompt)
            if context is not None and context_null is not None:
                return{
                    "prompt_embeds": context,
                    "negative_prompt_embeds": context_null,
                    "echoshot": echoshot,
                },
            
        if t5 is None:
            raise ValueError("No cached text embeds found for prompts, please provide a T5 encoder.")

        if model_to_offload is not None and device == "gpu":
            try:
                log.info(f"Moving video model to {offload_device}")
                model_to_offload.model.to(offload_device)
            except Exception:
                pass

        encoder = t5["model"]
        dtype = t5["dtype"]
        
        positive_prompts = []
        all_weights = []

        # Split positive prompts and process each with weights
        if "|" in positive_prompt:
            log.info("Multiple positive prompts detected, splitting by '|'")
            positive_prompts_raw = [p.strip() for p in positive_prompt.split('|')]
        elif "[1]" in positive_prompt:
            log.info("Multiple positive prompts detected, splitting by [#] and enabling EchoShot")
            import re
            segments = re.split(r'\[\d+\]', positive_prompt)
            positive_prompts_raw = [segment.strip() for segment in segments if segment.strip()]
            assert len(positive_prompts_raw) > 1 and len(positive_prompts_raw) < 7, 'Input shot num must between 2~6 !'
        else:
            positive_prompts_raw = [positive_prompt.strip()]
            
        for p in positive_prompts_raw:
            cleaned_prompt, weights = self.parse_prompt_weights(p)
            positive_prompts.append(cleaned_prompt)
            all_weights.append(weights)

        mm.soft_empty_cache()

        if device == "gpu":
            device_to = mm.get_torch_device()
        else:
            device_to = torch.device("cpu")

        if encoder.quantization == "fp8_e4m3fn":
            cast_dtype = torch.float8_e4m3fn