# Documentation - Class name: RegionalSampler - Category: ImpactPack/Regional - Output node: False - Repo Ref: https://github.com/ltdrdata/ComfyUI-Impact-Pack.git RegionalSampler节点旨在对图像的特定区域执行高级采样技术，允许对生成过程进行细粒度控制。它使用户能够对图像的不同区域应用不同的采样策略，提高生成输出的整体质量和一致性。 # Input types ## Required - seed - 种子参数对于随机数生成过程的初始化至关重要，确保了采样结果的可复现性。它在确定采样算法的起始点中起着重要作用，从而影响最终输出。 - Comfy dtype: INT - Python dtype: int - steps - 步骤参数定义了采样过程将经历的迭代次数。它是控制生成图像的收敛和细节级别的关键因素，更多的步骤通常会导致更精细的结果。 - Comfy dtype: INT - Python dtype: int - regional_prompts - 区域提示对于指导图像特定区域内的采样过程至关重要。它们作为掩码来隔离不同的区域，允许有针对性的采样，并增强最终输出的区域细节。 - Comfy dtype: REGIONAL_PROMPTS - Python dtype: List[RegionalPrompt] # Output types - latent - 潜在参数代表采样过程后生成图像的编码形式。它很重要，因为它捕获了图像的底层结构和信息，可以进一步处理或用于其他任务。 - Comfy dtype: LATENT - Python dtype: torch.Tensor # Usage tips - Infra type: GPU # Source code ``` class RegionalSampler: @classmethod def INPUT_TYPES(s): return {'required': {'seed': ('INT', {'default': 0, 'min': 0, 'max': 18446744073709551615}), 'seed_2nd': ('INT', {'default': 0, 'min': 0, 'max': 18446744073709551615}), 'seed_2nd_mode': (['ignore', 'fixed', 'seed+seed_2nd', 'seed-seed_2nd', 'increment', 'decrement', 'randomize'],), 'steps': ('INT', {'default': 20, 'min': 1, 'max': 10000}), 'base_only_steps': ('INT', {'default': 2, 'min': 0, 'max': 10000}), 'denoise': ('FLOAT', {'default': 1.0, 'min': 0.0, 'max': 1.0, 'step': 0.01}), 'samples': ('LATENT',), 'base_sampler': ('KSAMPLER_ADVANCED',), 'regional_prompts': ('REGIONAL_PROMPTS',), 'overlap_factor': ('INT', {'default': 10, 'min': 0, 'max': 10000}), 'restore_latent': ('BOOLEAN', {'default': True, 'label_on': 'enabled', 'label_off': 'disabled'}), 'additional_mode': (['DISABLE', 'ratio additional', 'ratio between'], {'default': 'ratio between'}), 'additional_sampler': (['AUTO', 'euler', 'heun', 'heunpp2', 'dpm_2', 'dpm_fast', 'dpmpp_2m', 'ddpm'],), 'additional_sigma_ratio': ('FLOAT', {'default': 0.3, 'min': 0.0, 'max': 1.0, 'step': 0.01})}, 'hidden': {'unique_id': 'UNIQUE_ID'}} RETURN_TYPES = ('LATENT',) FUNCTION = 'doit' CATEGORY = 'ImpactPack/Regional' @staticmethod def mask_erosion(samples, mask, grow_mask_by): mask = mask.clone() w = samples['samples'].shape[3] h = samples['samples'].shape[2] mask2 = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(w, h), mode='bilinear') if grow_mask_by == 0: mask_erosion = mask2 else: kernel_tensor = torch.ones((1, 1, grow_mask_by, grow_mask_by)) padding = math.ceil((grow_mask_by - 1) / 2) mask_erosion = torch.clamp(torch.nn.functional.conv2d(mask2.round(), kernel_tensor, padding=padding), 0, 1) return mask_erosion[:, :, :w, :h].round() def doit(self, seed, seed_2nd, seed_2nd_mode, steps, base_only_steps, denoise, samples, base_sampler, regional_prompts, overlap_factor, restore_latent, additional_mode, additional_sampler, additional_sigma_ratio, unique_id=None): if restore_latent: latent_compositor = nodes.NODE_CLASS_MAPPINGS['LatentCompositeMasked']() else: latent_compositor = None masks = [regional_prompt.mask.numpy() for regional_prompt in regional_prompts] masks = [np.ceil(mask).astype(np.int32) for mask in masks] combined_mask = torch.from_numpy(np.bitwise_or.reduce(masks)) inv_mask = torch.where(combined_mask == 0, torch.tensor(1.0), torch.tensor(0.0)) adv_steps = int(steps / denoise) start_at_step = adv_steps - steps region_len = len(regional_prompts) total = steps * region_len leftover_noise = False if base_only_steps > 0: if seed_2nd_mode == 'ignore': leftover_noise = True samples = base_sampler.sample_advanced(True, seed, adv_steps, samples, start_at_step, start_at_step + base_only_steps, leftover_noise, recovery_mode='DISABLE') if seed_2nd_mode == 'seed+seed_2nd': seed += seed_2nd if seed > 1125899906842624: seed = seed - 1125899906842624 elif seed_2nd_mode == 'seed-seed_2nd': seed -= seed_2nd if seed < 0: seed += 1125899906842624 elif seed_2nd_mode != 'ignore': seed = seed_2nd new_latent_image = samples.copy() base_latent_image = None if not leftover_noise: add_noise = True else: add_noise = False for i in range(start_at_step + base_only_steps, adv_steps): core.update_node_status(unique_id, f'{i}/{steps} steps | ', (i - start_at_step) * region_len / total) new_latent_image['noise_mask'] = inv_mask new_latent_image = base_sampler.sample_advanced(add_noise, seed, adv_steps, new_latent_image, i, i + 1, True, recovery_mode=additional_mode, recovery_sampler=additional_sampler, recovery_sigma_ratio=additional_sigma_ratio) if restore_latent: if 'noise_mask' in new_latent_image: del new_latent_image['noise_mask'] base_latent_image = new_latent_image.copy() j = 1 for regional_prompt in regional_prompts: if restore_latent: new_latent_image = base_latent_image.copy() core.update_node_status(unique_id, f'{i}/{steps} steps | {j}/{region_len}', ((i - start_at_step) * region_len + j) / total) region_mask = regional_prompt.get_mask_erosion(overlap_factor).squeeze(0).squeeze(0) new_latent_image['noise_mask'] = region_mask new_latent_image = regional_prompt.sampler.sample_advanced(False, seed, adv_steps, new_latent_image, i, i + 1, True, recovery_mode=additional_mode, recovery_sampler=additional_sampler, recovery_sigma_ratio=additional_sigma_ratio) if restore_latent: del new_latent_image['noise_mask'] base_latent_image = latent_compositor.composite(base_latent_image, new_latent_image, 0, 0, False, region_mask)[0] new_latent_image = base_latent_image j += 1 add_noise = False core.update_node_status(unique_id, f'finalize') if base_latent_image is not None: new_latent_image = base_latent_image else: base_latent_image = new_latent_image new_latent_image['noise_mask'] = inv_mask new_latent_image = base_sampler.sample_advanced(False, seed, adv_steps, new_latent_image, adv_steps, adv_steps + 1, False, recovery_mode=additional_mode, recovery_sampler=additional_sampler, recovery_sigma_ratio=additional_sigma_ratio) core.update_node_status(unique_id, f'{steps}/{steps} steps', total) core.update_node_status(unique_id, '', None) if restore_latent: new_latent_image = base_latent_image if 'noise_mask' in new_latent_image: del new_latent_image['noise_mask'] return (new_latent_image,) ```