Method process

nodes_sampler.py:80–2647

(self, model, image_embeds, shift, steps, cfg, seed, scheduler, riflex_freq_index, text_embeds=None,
        force_offload=True, samples=None, feta_args=None, denoise_strength=1.0, context_options=None,
        cache_args=None, teacache_args=None, flowedit_args=None, batched_cfg=False, slg_args=None, rope_function="default", loop_args=None,
        experimental_args=None, sigmas=None, unianimate_poses=None, fantasytalking_embeds=None, uni3c_embeds=None, multitalk_embeds=None, freeinit_args=None, start_step=0, end_step=-1, add_noise_to_samples=False)

    CATEGORY = "WanVideoWrapper"

    def process(self, model, image_embeds, shift, steps, cfg, seed, scheduler, riflex_freq_index, text_embeds=None,
        force_offload=True, samples=None, feta_args=None, denoise_strength=1.0, context_options=None,
        cache_args=None, teacache_args=None, flowedit_args=None, batched_cfg=False, slg_args=None, rope_function="default", loop_args=None,
        experimental_args=None, sigmas=None, unianimate_poses=None, fantasytalking_embeds=None, uni3c_embeds=None, multitalk_embeds=None, freeinit_args=None, start_step=0, end_step=-1, add_noise_to_samples=False):
        if flowedit_args is not None:
            raise Exception("FlowEdit support has been deprecated and removed due to lack of use and code maintainability")
        patcher = model
        model = model.model
        transformer = model.diffusion_model

        dtype = model["base_dtype"]
        weight_dtype = model["weight_dtype"]
        fp8_matmul = model["fp8_matmul"]
        gguf_reader = model["gguf_reader"]
        control_lora = model["control_lora"]

        vae = image_embeds.get("vae", None)
        tiled_vae = image_embeds.get("tiled_vae", False)

        transformer_options = copy.deepcopy(patcher.model_options.get("transformer_options", None))
        merge_loras = transformer_options["merge_loras"]

        block_swap_args = transformer_options.get("block_swap_args", None)
        if block_swap_args is not None:
            transformer.use_non_blocking = block_swap_args.get("use_non_blocking", False)
            transformer.blocks_to_swap = block_swap_args.get("blocks_to_swap", 0)
            transformer.vace_blocks_to_swap = block_swap_args.get("vace_blocks_to_swap", 0)
            transformer.prefetch_blocks = block_swap_args.get("prefetch_blocks", 0)
            transformer.block_swap_debug = block_swap_args.get("block_swap_debug", False)
            transformer.offload_img_emb = block_swap_args.get("offload_img_emb", False)
            transformer.offload_txt_emb = block_swap_args.get("offload_txt_emb", False)

        is_5b = transformer.out_dim == 48
        vae_upscale_factor = 16 if is_5b else 8

        # Load weights
        if transformer.audio_model is not None:
            for block in transformer.blocks:
                if hasattr(block, 'audio_block'):
                    block.audio_block = None

        if not transformer.patched_linear and patcher.model["sd"] is not None and len(patcher.patches) != 0 and gguf_reader is None:
            transformer = _replace_linear(transformer, dtype, patcher.model["sd"], compile_args=model["compile_args"])
            transformer.patched_linear = True
        if patcher.model["sd"] is not None and gguf_reader is None:
            load_weights(patcher.model.diffusion_model, patcher.model["sd"], weight_dtype, base_dtype=dtype, transformer_load_device=device,
                         block_swap_args=block_swap_args, compile_args=model["compile_args"])

        if gguf_reader is not None: #handle GGUF
            load_weights(transformer, patcher.model["sd"], base_dtype=dtype, transformer_load_device=device, patcher=patcher, gguf=True,
                         reader=gguf_reader, block_swap_args=block_swap_args, compile_args=model["compile_args"])
            set_lora_params_gguf(transformer, patcher.patches)
            transformer.patched_linear = True
        elif len(patcher.patches) != 0: #handle patched linear layers (unmerged loras, fp8 scaled)
            log.info(f"Using {len(patcher.patches)} LoRA weight patches for WanVideo model")
            if not merge_loras and fp8_matmul:
                raise NotImplementedError("FP8 matmul with unmerged LoRAs is not supported")
            set_lora_params(transformer, patcher.patches)