# Documentation - Class name: samplerCascadeFull - Category: EasyUse/Sampler - Output node: True - Repo Ref: https://github.com/yolain/ComfyUI-Easy-Use.git samplerCascadeFull节点旨在执行生成模型框架中的抽样过程。它协调编码、抽样和解码阶段，以产生一系列图像或潜在表示。该节点对于基于提供的种子和模型生成新图像内容至关重要，为用户提供了利用高级抽样技术的无缝接口。 # Input types ## Required - pipe - ‘pipe’参数是一个关键输入，它携带了抽样过程所需的管道设置和数据。它包括加载器设置、模型引用和其他指导节点操作的必要信息。 - Comfy dtype: PIPE_LINE - Python dtype: Dict[str, Any] - encode_vae_name - ‘encode_vae_name’参数指定用于将输入图像编码到潜在空间的VAE模型的名称。它是一个可选参数，允许对编码过程进行定制。 - Comfy dtype: STRING - Python dtype: Union[str, None] - decode_vae_name - ‘decode_vae_name’参数确定用于将潜在表示解码回图像的VAE模型。它为用户提供了根据他们的需求选择解码模型的灵活性。 - Comfy dtype: STRING - Python dtype: Union[str, None] - steps - ‘steps’参数定义了抽样过程中要执行的抽样步骤数。它直接影响生成图像的质量和细节。 - Comfy dtype: INT - Python dtype: int # Output types - pipe - ‘pipe’输出包含抽样过程后更新的管道设置和结果。它是一个全面的输出，封装了节点执行后的状态。 - Comfy dtype: PIPE_LINE - Python dtype: Dict[str, Any] - model_b - ‘model_b’输出代表了抽样过程中解码阶段使用模型。对于需要引用其操作中使用的具体模型的用户来说，它是重要的。 - Comfy dtype: MODEL - Python dtype: torch.nn.Module - latent_b - ‘latent_b’输出提供了由抽样过程生成的潜在表示。这些潜在编码对于希望进一步操作或分析生成内容的用户至关重要。 - Comfy dtype: LATENT - Python dtype: torch.Tensor # Usage tips - Infra type: GPU # Source code ``` class samplerCascadeFull: def __init__(self): pass @classmethod def INPUT_TYPES(cls): return {'required': {'pipe': ('PIPE_LINE',), 'encode_vae_name': (['None'] + folder_paths.get_filename_list('vae'),), 'decode_vae_name': (['None'] + folder_paths.get_filename_list('vae'),), 'steps': ('INT', {'default': 20, 'min': 1, 'max': 10000}), 'cfg': ('FLOAT', {'default': 4.0, 'min': 0.0, 'max': 100.0}), 'sampler_name': (comfy.samplers.KSampler.SAMPLERS, {'default': 'euler_ancestral'}), 'scheduler': (comfy.samplers.KSampler.SCHEDULERS, {'default': 'simple'}), 'denoise': ('FLOAT', {'default': 1.0, 'min': 0.0, 'max': 1.0, 'step': 0.01}), 'image_output': (['Hide', 'Preview', 'Save', 'Hide/Save', 'Sender', 'Sender/Save'],), 'link_id': ('INT', {'default': 0, 'min': 0, 'max': sys.maxsize, 'step': 1}), 'save_prefix': ('STRING', {'default': 'ComfyUI'}), 'seed': ('INT', {'default': 0, 'min': 0, 'max': MAX_SEED_NUM})}, 'optional': {'image_to_latent_c': ('IMAGE',), 'latent_c': ('LATENT',), 'model_c': ('MODEL',)}, 'hidden': {'tile_size': 'INT', 'prompt': 'PROMPT', 'extra_pnginfo': 'EXTRA_PNGINFO', 'my_unique_id': 'UNIQUE_ID', 'embeddingsList': (folder_paths.get_filename_list('embeddings'),)}} RETURN_TYPES = ('PIPE_LINE', 'MODEL', 'LATENT') RETURN_NAMES = ('pipe', 'model_b', 'latent_b') OUTPUT_NODE = True FUNCTION = 'run' CATEGORY = 'EasyUse/Sampler' def run(self, pipe, encode_vae_name, decode_vae_name, steps, cfg, sampler_name, scheduler, denoise, image_output, link_id, save_prefix, seed, image_to_latent_c=None, latent_c=None, model_c=None, tile_size=None, prompt=None, extra_pnginfo=None, my_unique_id=None, force_full_denoise=False, disable_noise=False): encode_vae_name = encode_vae_name if encode_vae_name is not None else pipe['loader_settings']['encode_vae_name'] decode_vae_name = decode_vae_name if decode_vae_name is not None else pipe['loader_settings']['decode_vae_name'] batch_size = pipe['loader_settings']['batch_size'] if 'batch_size' in pipe['loader_settings'] else 1 if image_to_latent_c is not None: if encode_vae_name != 'None': encode_vae = easyCache.load_vae(encode_vae_name) else: encode_vae = pipe['vae'][0] if 'compression' not in pipe['loader_settings']: raise Exception('compression is not found') compression = pipe['loader_settings']['compression'] width = image_to_latent_c.shape[-2] height = image_to_latent_c.shape[-3] out_width = width // compression * encode_vae.downscale_ratio out_height = height // compression * encode_vae.downscale_ratio s = comfy.utils.common_upscale(image_to_latent_c.movedim(-1, 1), out_width, out_height, 'bicubic', 'center').movedim(1, -1) latent_c = encode_vae.encode(s[:, :, :, :3]) latent_b = torch.zeros([latent_c.shape[0], 4, height // 4, width // 4]) samples_c = {'samples': latent_c} samples_c = RepeatLatentBatch().repeat(samples_c, batch_size)[0] samples_b = {'samples': latent_b} samples_b = RepeatLatentBatch().repeat(samples_b, batch_size)[0] images = image_to_latent_c elif latent_c is not None: samples_c = latent_c samples_b = pipe['samples'][1] images = pipe['images'] else: samples_c = pipe['samples'][0] samples_b = pipe['samples'][1] images = pipe['images'] easyCache.update_loaded_objects(prompt) samp_model = model_c if model_c else pipe['model'][0] samp_positive = pipe['positive'] samp_negative = pipe['negative'] samp_samples = samples_c samp_seed = seed if seed is not None else pipe['seed'] steps = steps if steps is not None else pipe['loader_settings']['steps'] start_step = pipe['loader_settings']['start_step'] if 'start_step' in pipe['loader_settings'] else 0 last_step = pipe['loader_settings']['last_step'] if 'last_step' in pipe['loader_settings'] else 10000 cfg = cfg if cfg is not None else pipe['loader_settings']['cfg'] sampler_name = sampler_name if sampler_name is not None else pipe['loader_settings']['sampler_name'] scheduler = scheduler if scheduler is not None else pipe['loader_settings']['scheduler'] denoise = denoise if denoise is not None else pipe['loader_settings']['denoise'] start_time = int(time.time() * 1000) samp_samples = sampler.common_ksampler(samp_model, samp_seed, steps, cfg, sampler_name, scheduler, samp_positive, samp_negative, samp_samples, denoise=denoise, preview_latent=False, start_step=start_step, last_step=last_step, force_full_denoise=False, disable_noise=False) end_time = int(time.time() * 1000) stage_c = samp_samples['samples'] results = None if image_output not in ['Hide', 'Hide/Save']: if decode_vae_name != 'None': decode_vae = easyCache.load_vae(decode_vae_name) else: decode_vae = pipe['vae'][0] samp_images = decode_vae.decode(stage_c).cpu() results = easySave(samp_images, save_prefix, image_output, prompt, extra_pnginfo) sampler.update_value_by_id('results', my_unique_id, results) end_decode_time = int(time.time() * 1000) spent_time = '扩散:' + str((end_time - start_time) / 1000) + '秒, 解码:' + str((end_decode_time - end_time) / 1000) + '秒' easyCache.update_loaded_objects(prompt) c1 = [] for t in samp_positive: d = t[1].copy() if 'pooled_output' in d: d['pooled_output'] = torch.zeros_like(d['pooled_output']) n = [torch.zeros_like(t[0]), d] c1.append(n) c2 = [] for t in c1: d = t[1].copy() d['stable_cascade_prior'] = stage_c n = [t[0], d] c2.append(n) new_pipe = {'model': pipe['model'][1], 'positive': c2, 'negative': c1, 'vae': pipe['vae'][1], 'clip': pipe['clip'], 'samples': samples_b, 'images': images, 'seed': seed, 'loader_settings': {**pipe['loader_settings'], 'spent_time': spent_time}} sampler.update_value_by_id('pipe_line', my_unique_id, new_pipe) del pipe if image_output in ('Hide', 'Hide/Save'): return {'ui': {}, 'result': sampler.get_output(new_pipe)} if image_output in ('Sender', 'Sender/Save') and results is not None: PromptServer.instance.send_sync('img-send', {'link_id': link_id, 'images': results}) return {'ui': {'images': results}, 'result': (new_pipe, new_pipe['model'], new_pipe['samples'])} ```