CavinHuang-comfyui-nodes-docs/docs/easy fullLoader.md

Documentation

• Class name: fullLoader
• Category: EasyUse/Loaders
• Output node: False
• Repo Ref: https://github.com/yolain/ComfyUI-Easy-Use.git

fullLoader类封装了加载和准备管道所需的各种模型和组件的功能。它简化了集成检查点、VAE、CLIP和条件数据的过程，为模型初始化和设置提供了一个连贯的接口。

Input types

Required

• ckpt_name
  • 检查点名称对于识别要加载和在管道中使用的特定模型至关重要。它指导加载器找到模型的正确保存状态，这对于后续处理步骤中的一致性和准确性至关重要。
  • Comfy dtype: FILE
  • Python dtype: List[str]
• config_name
  • 配置名称在确定模型的设置和超参数方面起着关键作用。它确保模型在期望的参数内运行，影响整体性能和输出质量。
  • Comfy dtype: ENUM
  • Python dtype: Enum
• vae_name
  • VAE名称对于选择适当的变分自编码器模型至关重要。它影响管道的生成能力和潜在空间表示的质量。
  • Comfy dtype: FILE
  • Python dtype: List[str]
• clip_skip
  • clip_skip参数对于控制在管道中应用CLIP模型的层级具有重要意义。它影响文本条件的粒度和模型对文本提示的响应性。
  • Comfy dtype: INT
  • Python dtype: int

Output types

• pipe
  • pipe输出代表了fullLoader工作的成果，提供了包含模型、VAE、CLIP和条件数据的综合管道设置。它是生成过程的支柱，确保所有组件的无缝集成。
  • Comfy dtype: PIPE_LINE
  • Python dtype: Dict

Usage tips

• Infra type: CPU

Source code

class fullLoader:

    @classmethod
    def INPUT_TYPES(cls):
        resolution_strings = [f'{width} x {height}' for (width, height) in BASE_RESOLUTIONS]
        a1111_prompt_style_default = False
        return {'required': {'ckpt_name': (folder_paths.get_filename_list('checkpoints'),), 'config_name': (['Default'] + folder_paths.get_filename_list('configs'), {'default': 'Default'}), 'vae_name': (['Baked VAE'] + folder_paths.get_filename_list('vae'),), 'clip_skip': ('INT', {'default': -1, 'min': -24, 'max': 0, 'step': 1}), 'lora_name': (['None'] + folder_paths.get_filename_list('loras'),), 'lora_model_strength': ('FLOAT', {'default': 1.0, 'min': -10.0, 'max': 10.0, 'step': 0.01}), 'lora_clip_strength': ('FLOAT', {'default': 1.0, 'min': -10.0, 'max': 10.0, 'step': 0.01}), 'resolution': (resolution_strings,), 'empty_latent_width': ('INT', {'default': 512, 'min': 64, 'max': MAX_RESOLUTION, 'step': 8}), 'empty_latent_height': ('INT', {'default': 512, 'min': 64, 'max': MAX_RESOLUTION, 'step': 8}), 'positive': ('STRING', {'default': 'Positive', 'multiline': True}), 'positive_token_normalization': (['none', 'mean', 'length', 'length+mean'],), 'positive_weight_interpretation': (['comfy', 'A1111', 'comfy++', 'compel', 'fixed attention'],), 'negative': ('STRING', {'default': 'Negative', 'multiline': True}), 'negative_token_normalization': (['none', 'mean', 'length', 'length+mean'],), 'negative_weight_interpretation': (['comfy', 'A1111', 'comfy++', 'compel', 'fixed attention'],), 'batch_size': ('INT', {'default': 1, 'min': 1, 'max': 64})}, 'optional': {'model_override': ('MODEL',), 'clip_override': ('CLIP',), 'vae_override': ('VAE',), 'optional_lora_stack': ('LORA_STACK',), 'a1111_prompt_style': ('BOOLEAN', {'default': a1111_prompt_style_default})}, 'hidden': {'prompt': 'PROMPT', 'my_unique_id': 'UNIQUE_ID'}}
    RETURN_TYPES = ('PIPE_LINE', 'MODEL', 'VAE', 'CLIP', 'CONDITIONING', 'CONDITIONING', 'LATENT')
    RETURN_NAMES = ('pipe', 'model', 'vae', 'clip', 'positive', 'negative', 'latent')
    FUNCTION = 'adv_pipeloader'
    CATEGORY = 'EasyUse/Loaders'

    def adv_pipeloader(self, ckpt_name, config_name, vae_name, clip_skip, lora_name, lora_model_strength, lora_clip_strength, resolution, empty_latent_width, empty_latent_height, positive, positive_token_normalization, positive_weight_interpretation, negative, negative_token_normalization, negative_weight_interpretation, batch_size, model_override=None, clip_override=None, vae_override=None, optional_lora_stack=None, a1111_prompt_style=False, prompt=None, my_unique_id=None):
        model: ModelPatcher | None = None
        clip: CLIP | None = None
        vae: VAE | None = None
        can_load_lora = True
        pipe_lora_stack = []
        if resolution != '自定义 x 自定义':
            try:
                (width, height) = map(int, resolution.split(' x '))
                empty_latent_width = width
                empty_latent_height = height
            except ValueError:
                raise ValueError('Invalid base_resolution format.')
        latent = torch.zeros([batch_size, 4, empty_latent_height // 8, empty_latent_width // 8]).cpu()
        samples = {'samples': latent}
        easyCache.update_loaded_objects(prompt)
        log_node_warn('正在处理模型...')
        xy_model_id = next((x for x in prompt if str(prompt[x]['class_type']) in ['easy XYInputs: ModelMergeBlocks', 'easy XYInputs: Checkpoint']), None)
        xy_lora_id = next((x for x in prompt if str(prompt[x]['class_type']) == 'easy XYInputs: Lora'), None)
        if xy_lora_id is not None:
            can_load_lora = False
        if xy_model_id is not None:
            node = prompt[xy_model_id]
            if 'ckpt_name_1' in node['inputs']:
                ckpt_name_1 = node['inputs']['ckpt_name_1']
                (model, clip, vae, clip_vision) = easyCache.load_checkpoint(ckpt_name_1)
                can_load_lora = False
        elif model_override is not None and clip_override is not None and (vae_override is not None):
            model = model_override
            clip = clip_override
            vae = vae_override
        elif model_override is not None:
            raise Exception(f'[ERROR] clip or vae is missing')
        elif vae_override is not None:
            raise Exception(f'[ERROR] model or clip is missing')
        elif clip_override is not None:
            raise Exception(f'[ERROR] model or vae is missing')
        else:
            (model, clip, vae, clip_vision) = easyCache.load_checkpoint(ckpt_name, config_name)
        if optional_lora_stack is not None and can_load_lora:
            for lora in optional_lora_stack:
                lora = {'lora_name': lora[0], 'model': model, 'clip': clip, 'model_strength': lora[1], 'clip_strength': lora[2]}
                (model, clip) = easyCache.load_lora(lora)
                lora['model'] = model
                lora['clip'] = clip
                pipe_lora_stack.append(lora)
        if lora_name != 'None' and can_load_lora:
            lora = {'lora_name': lora_name, 'model': model, 'clip': clip, 'model_strength': lora_model_strength, 'clip_strength': lora_clip_strength}
            (model, clip) = easyCache.load_lora(lora)
            pipe_lora_stack.append(lora)
        if vae_name not in ['Baked VAE', 'Baked-VAE']:
            vae = easyCache.load_vae(vae_name)
        if not clip:
            raise Exception('No CLIP found')
        (positive_embeddings_final, positive_wildcard_prompt, model, clip) = prompt_to_cond('positive', model, clip, clip_skip, pipe_lora_stack, positive, positive_token_normalization, positive_weight_interpretation, a1111_prompt_style, my_unique_id, prompt, easyCache)
        (negative_embeddings_final, negative_wildcard_prompt, model, clip) = prompt_to_cond('negative', model, clip, clip_skip, pipe_lora_stack, negative, negative_token_normalization, negative_weight_interpretation, a1111_prompt_style, my_unique_id, prompt, easyCache)
        image = easySampler.pil2tensor(Image.new('RGB', (1, 1), (0, 0, 0)))
        log_node_warn('处理结束...')
        pipe = {'model': model, 'positive': positive_embeddings_final, 'negative': negative_embeddings_final, 'vae': vae, 'clip': clip, 'samples': samples, 'images': image, 'seed': 0, 'loader_settings': {'ckpt_name': ckpt_name, 'vae_name': vae_name, 'lora_name': lora_name, 'lora_model_strength': lora_model_strength, 'lora_clip_strength': lora_clip_strength, 'lora_stack': pipe_lora_stack, 'refiner_ckpt_name': None, 'refiner_vae_name': None, 'refiner_lora_name': None, 'refiner_lora_model_strength': None, 'refiner_lora_clip_strength': None, 'clip_skip': clip_skip, 'a1111_prompt_style': a1111_prompt_style, 'positive': positive, 'positive_l': None, 'positive_g': None, 'positive_token_normalization': positive_token_normalization, 'positive_weight_interpretation': positive_weight_interpretation, 'positive_balance': None, 'negative': negative, 'negative_l': None, 'negative_g': None, 'negative_token_normalization': negative_token_normalization, 'negative_weight_interpretation': negative_weight_interpretation, 'negative_balance': None, 'empty_latent_width': empty_latent_width, 'empty_latent_height': empty_latent_height, 'batch_size': batch_size, 'seed': 0, 'empty_samples': samples}}
        return {'ui': {'positive': positive_wildcard_prompt, 'negative': negative_wildcard_prompt}, 'result': (pipe, model, vae, clip, positive_embeddings_final, negative_embeddings_final, samples)}