# Documentation - Class name: HighRes-Fix Script - Category: Efficiency Nodes/Scripts - Output node: False HighRes-Fix Script节点旨在以可定制的方式提升图像分辨率和质量。它利用各种放大技术、控制网络和预处理步骤来优化图像，提供种子一致性、去噪和迭代细化的选项。该节点与动画和控制网络脚本集成，适应特定的高分辨率增强需求，同时保持与不同放大模型和预处理器配合使用的灵活性。 # Input types ## Required - upscale_type - 指定要使用的放大方法，影响高分辨率输出的质量和方法。 - Comfy dtype: COMBO[STRING] - Python dtype: str - hires_ckpt_name - 高分辨率模型的检查点名称，表示用于放大的特定模型版本或配置。 - Comfy dtype: COMBO[STRING] - Python dtype: str - latent_upscaler - 标识潜在空间放大方法，在进行像素级调整之前在基础层面上增强图像细节。 - Comfy dtype: COMBO[STRING] - Python dtype: str - pixel_upscaler - 定义像素级放大方法，在潜在空间增强后进一步细化图像细节。 - Comfy dtype: COMBO[STRING] - Python dtype: str - upscale_by - 确定放大过程的缩放因子，直接影响最终图像的大小和细节。 - Comfy dtype: FLOAT - Python dtype: float - use_same_seed - 控制是否在整个放大过程中使用相同的种子，确保随机元素的一致性。 - Comfy dtype: BOOLEAN - Python dtype: bool - seed - 随机过程的初始种子值，为可重复性和变化控制提供起点。 - Comfy dtype: INT - Python dtype: int - hires_steps - 指定在高分辨率增强过程中要执行的步骤数，影响细化的深度。 - Comfy dtype: INT - Python dtype: int - denoise - 表示在放大过程中是否应用去噪，通过减少噪声来改善图像清晰度。 - Comfy dtype: FLOAT - Python dtype: bool - iterations - 运行放大过程的迭代次数，允许多次细化。 - Comfy dtype: INT - Python dtype: int - use_controlnet - 确定是否使用控制网络进行引导放大，影响增强的方向和质量。 - Comfy dtype: BOOLEAN - Python dtype: bool - control_net_name - 要使用的控制网络的名称，指定用于引导放大的模型。 - Comfy dtype: COMBO[STRING] - Python dtype: str - strength - 控制网络对放大过程影响的强度，调整引导的程度。 - Comfy dtype: FLOAT - Python dtype: float - preprocessor - 用于准备图像进行放大的预处理方法，影响初始质量和兼容性。 - Comfy dtype: COMBO[STRING] - Python dtype: str - preprocessor_imgs - 准备好进行放大的预处理图像，作为增强过程的输入。 - Comfy dtype: BOOLEAN - Python dtype: list ## Optional - script - 包含放大过程的附加参数或配置的可选脚本。 - Comfy dtype: SCRIPT - Python dtype: dict # Output types - script - 应用高分辨率修复后产生的脚本配置，封装了过程中指定的所有参数和进行的调整。 - Comfy dtype: SCRIPT - Python dtype: dict ## Usage tips - Infra type: `GPU` - Common nodes: - [KSampler SDXL (Eff.)](../../efficiency-nodes-comfyui/Nodes/KSampler SDXL (Eff.).md) ## Source code ```python class TSC_HighRes_Fix: default_latent_upscalers = LatentUpscaleBy.INPUT_TYPES()["required"]["upscale_method"][0] city96_upscale_methods =\ ["city96." + ver for ver in city96_latent_upscaler.LatentUpscaler.INPUT_TYPES()["required"]["latent_ver"][0]] city96_scalings_raw = city96_latent_upscaler.LatentUpscaler.INPUT_TYPES()["required"]["scale_factor"][0] city96_scalings_float = [float(scale) for scale in city96_scalings_raw] ttl_nn_upscale_methods = \ ["ttl_nn." + ver for ver in ttl_nn_latent_upscaler.NNLatentUpscale.INPUT_TYPES()["required"]["version"][0]] latent_upscalers = default_latent_upscalers + city96_upscale_methods + ttl_nn_upscale_methods pixel_upscalers = folder_paths.get_filename_list("upscale_models") @classmethod def INPUT_TYPES(cls): return {"required": {"upscale_type": (["latent","pixel","both"],), "hires_ckpt_name": (["(use same)"] + folder_paths.get_filename_list("checkpoints"),), "latent_upscaler": (cls.latent_upscalers,), "pixel_upscaler": (cls.pixel_upscalers,), "upscale_by": ("FLOAT", {"default": 1.25, "min": 0.01, "max": 8.0, "step": 0.05}), "use_same_seed": ("BOOLEAN", {"default": True}), "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), "hires_steps": ("INT", {"default": 12, "min": 1, "max": 10000}), "denoise": ("FLOAT", {"default": .56, "min": 0.00, "max": 1.00, "step": 0.01}), "iterations": ("INT", {"default": 1, "min": 0, "max": 5, "step": 1}), "use_controlnet": use_controlnet_widget, "control_net_name": (folder_paths.get_filename_list("controlnet"),), "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), "preprocessor": preprocessor_widget, "preprocessor_imgs": ("BOOLEAN", {"default": False}) }, "optional": {"script": ("SCRIPT",)}, "hidden": {"my_unique_id": "UNIQUE_ID"} } RETURN_TYPES = ("SCRIPT",) FUNCTION = "hires_fix_script" CATEGORY = "Efficiency Nodes/Scripts" def hires_fix_script(self, upscale_type, hires_ckpt_name, latent_upscaler, pixel_upscaler, upscale_by, use_same_seed, seed, hires_steps, denoise, iterations, use_controlnet, control_net_name, strength, preprocessor, preprocessor_imgs, script=None, my_unique_id=None): latent_upscale_function = None latent_upscale_model = None pixel_upscale_model = None def float_to_string(num): if num == int(num): return "{:.1f}".format(num) else: return str(num) if iterations > 0 and upscale_by > 0: if upscale_type == "latent": # For latent methods from city96 if latent_upscaler in self.city96_upscale_methods: # Remove extra characters added latent_upscaler = latent_upscaler.replace("city96.", "") # Set function to city96_latent_upscaler.LatentUpscaler latent_upscale_function = city96_latent_upscaler.LatentUpscaler # Find the nearest valid scaling in city96_scalings_float nearest_scaling = min(self.city96_scalings_float, key=lambda x: abs(x - upscale_by)) # Retrieve the index of the nearest scaling nearest_scaling_index = self.city96_scalings_float.index(nearest_scaling) # Use the index to get the raw string representation nearest_scaling_raw = self.city96_scalings_raw[nearest_scaling_index] upscale_by = float_to_string(upscale_by) # Check if the input upscale_by value was different from the nearest valid value if upscale_by != nearest_scaling_raw: print(f"{warning('HighRes-Fix Warning:')} " f"When using 'city96.{latent_upscaler}', 'upscale_by' must be one of {self.city96_scalings_raw}.\n" f"Rounding to the nearest valid value ({nearest_scaling_raw}).\033[0m") upscale_by = nearest_scaling_raw # For ttl upscale methods elif latent_upscaler in self.ttl_nn_upscale_methods: # Remove extra characters added latent_upscaler = latent_upscaler.replace("ttl_nn.", "") # Bound to min/max limits upscale_by_clamped = min(max(upscale_by, 1), 2) if upscale_by != upscale_by_clamped: print(f"{warning('HighRes-Fix Warning:')} " f"When using 'ttl_nn.{latent_upscaler}', 'upscale_by' must be between 1 and 2.\n" f"Rounding to the nearest valid value ({upscale_by_clamped}).\033[0m") upscale_by = upscale_by_clamped latent_upscale_function = ttl_nn_latent_upscaler.NNLatentUpscale # For default upscale methods elif latent_upscaler in self.default_latent_upscalers: latent_upscale_function = LatentUpscaleBy else: # Default latent_upscale_function = LatentUpscaleBy latent_upscaler = self.default_latent_upscalers[0] print(f"{warning('HiResFix Script Warning:')} Chosen latent upscale method not found! " f"defaulting to '{latent_upscaler}'.\n") # Load Checkpoint if defined if hires_ckpt_name == "(use same)": clear_cache(my_unique_id, 0, "ckpt") else: latent_upscale_model, _, _ = \ load_checkpoint(hires_ckpt_name, my_unique_id, output_vae=False, cache=1, cache_overwrite=True) elif upscale_type == "pixel": pixel_upscale_model = UpscaleModelLoader().load_model(pixel_upscaler)[0] elif upscale_type == "both": latent_upscale_function = LatentUpscaleBy latent_upscaler = self.default_latent_upscalers[0] pixel_upscale_model = UpscaleModelLoader().load_model(pixel_upscaler)[0] if hires_ckpt_name == "(use same)": clear_cache(my_unique_id, 0, "ckpt") else: latent_upscale_model, _, _ = \ load_checkpoint(hires_ckpt_name, my_unique_id, output_vae=False, cache=1, cache_overwrite=True) control_net = ControlNetLoader().load_controlnet(control_net_name)[0] if use_controlnet is True else None # Construct the script output script = script or {} script["hiresfix"] = (upscale_type, latent_upscaler, upscale_by, use_same_seed, seed, hires_steps, denoise, iterations, control_net, strength, preprocessor, preprocessor_imgs, latent_upscale_function, latent_upscale_model, pixel_upscale_model) return (script,) ```