234 lines
11 KiB
Markdown
234 lines
11 KiB
Markdown
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# Documentation
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- Class name: HighRes-Fix Script
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- Category: Efficiency Nodes/Scripts
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- Output node: False
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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。
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# Input types
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## Required
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- upscale_type
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- 指定要使用的放大方法，影响高分辨率输出的质量和方法。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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- hires_ckpt_name
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- 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。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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- latent_upscaler
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- 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。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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- pixel_upscaler
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- 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。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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- upscale_by
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- 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。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- use_same_seed
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- 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。
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- Comfy dtype: BOOLEAN
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- Python dtype: bool
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- seed
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- 随机过程的初始种子值，为可重复性和变化控制提供起点。
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- Comfy dtype: INT
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- Python dtype: int
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- hires_steps
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- 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。
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- Comfy dtype: INT
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- Python dtype: int
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- denoise
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- 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。
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- Comfy dtype: FLOAT
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- Python dtype: bool
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- iterations
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- 运行放大过程的迭代次数，允许多次细化。
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- Comfy dtype: INT
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- Python dtype: int
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- use_controlnet
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- 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。
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- Comfy dtype: BOOLEAN
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- Python dtype: bool
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- control_net_name
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- 要使用的控制网络的名称，指定用于引导放大的模型。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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- strength
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- 控制网络对放大过程影响的强度，调整引导的程度。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- preprocessor
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- 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。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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- preprocessor_imgs
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- 准备好进行放大的预处理图像，作为增强过程的输入。
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- Comfy dtype: BOOLEAN
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- Python dtype: list
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## Optional
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- script
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- 包含放大过程的附加参数或配置的可选脚本。
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- Comfy dtype: SCRIPT
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- Python dtype: dict
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# Output types
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- script
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- 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。
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- Comfy dtype: SCRIPT
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- Python dtype: dict
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## Usage tips
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- Infra type: `GPU`
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- Common nodes:
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- [KSampler SDXL (Eff.)](../../efficiency-nodes-comfyui/Nodes/KSampler SDXL (Eff.).md)
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## Source code
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```python
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class TSC_HighRes_Fix:
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default_latent_upscalers = LatentUpscaleBy.INPUT_TYPES()["required"]["upscale_method"][0]
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city96_upscale_methods =\
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["city96." + ver for ver in city96_latent_upscaler.LatentUpscaler.INPUT_TYPES()["required"]["latent_ver"][0]]
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city96_scalings_raw = city96_latent_upscaler.LatentUpscaler.INPUT_TYPES()["required"]["scale_factor"][0]
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city96_scalings_float = [float(scale) for scale in city96_scalings_raw]
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ttl_nn_upscale_methods = \
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["ttl_nn." + ver for ver in
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ttl_nn_latent_upscaler.NNLatentUpscale.INPUT_TYPES()["required"]["version"][0]]
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latent_upscalers = default_latent_upscalers + city96_upscale_methods + ttl_nn_upscale_methods
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pixel_upscalers = folder_paths.get_filename_list("upscale_models")
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@classmethod
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def INPUT_TYPES(cls):
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return {"required": {"upscale_type": (["latent","pixel","both"],),
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"hires_ckpt_name": (["(use same)"] + folder_paths.get_filename_list("checkpoints"),),
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"latent_upscaler": (cls.latent_upscalers,),
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"pixel_upscaler": (cls.pixel_upscalers,),
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"upscale_by": ("FLOAT", {"default": 1.25, "min": 0.01, "max": 8.0, "step": 0.05}),
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"use_same_seed": ("BOOLEAN", {"default": True}),
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"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
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"hires_steps": ("INT", {"default": 12, "min": 1, "max": 10000}),
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"denoise": ("FLOAT", {"default": .56, "min": 0.00, "max": 1.00, "step": 0.01}),
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"iterations": ("INT", {"default": 1, "min": 0, "max": 5, "step": 1}),
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"use_controlnet": use_controlnet_widget,
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"control_net_name": (folder_paths.get_filename_list("controlnet"),),
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"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
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"preprocessor": preprocessor_widget,
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"preprocessor_imgs": ("BOOLEAN", {"default": False})
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},
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"optional": {"script": ("SCRIPT",)},
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"hidden": {"my_unique_id": "UNIQUE_ID"}
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}
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RETURN_TYPES = ("SCRIPT",)
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FUNCTION = "hires_fix_script"
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CATEGORY = "Efficiency Nodes/Scripts"
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def hires_fix_script(self, upscale_type, hires_ckpt_name, latent_upscaler, pixel_upscaler, upscale_by,
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use_same_seed, seed, hires_steps, denoise, iterations, use_controlnet, control_net_name,
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strength, preprocessor, preprocessor_imgs, script=None, my_unique_id=None):
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latent_upscale_function = None
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latent_upscale_model = None
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pixel_upscale_model = None
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def float_to_string(num):
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if num == int(num):
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return "{:.1f}".format(num)
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else:
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return str(num)
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if iterations > 0 and upscale_by > 0:
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if upscale_type == "latent":
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# For latent methods from city96
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if latent_upscaler in self.city96_upscale_methods:
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# Remove extra characters added
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latent_upscaler = latent_upscaler.replace("city96.", "")
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# Set function to city96_latent_upscaler.LatentUpscaler
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latent_upscale_function = city96_latent_upscaler.LatentUpscaler
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# Find the nearest valid scaling in city96_scalings_float
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nearest_scaling = min(self.city96_scalings_float, key=lambda x: abs(x - upscale_by))
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# Retrieve the index of the nearest scaling
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nearest_scaling_index = self.city96_scalings_float.index(nearest_scaling)
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# Use the index to get the raw string representation
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nearest_scaling_raw = self.city96_scalings_raw[nearest_scaling_index]
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upscale_by = float_to_string(upscale_by)
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# Check if the input upscale_by value was different from the nearest valid value
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if upscale_by != nearest_scaling_raw:
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print(f"{warning('HighRes-Fix Warning:')} "
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f"When using 'city96.{latent_upscaler}', 'upscale_by' must be one of {self.city96_scalings_raw}.\n"
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f"Rounding to the nearest valid value ({nearest_scaling_raw}).\033[0m")
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upscale_by = nearest_scaling_raw
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# For ttl upscale methods
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elif latent_upscaler in self.ttl_nn_upscale_methods:
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# Remove extra characters added
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latent_upscaler = latent_upscaler.replace("ttl_nn.", "")
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# Bound to min/max limits
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upscale_by_clamped = min(max(upscale_by, 1), 2)
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if upscale_by != upscale_by_clamped:
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print(f"{warning('HighRes-Fix Warning:')} "
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f"When using 'ttl_nn.{latent_upscaler}', 'upscale_by' must be between 1 and 2.\n"
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f"Rounding to the nearest valid value ({upscale_by_clamped}).\033[0m")
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upscale_by = upscale_by_clamped
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latent_upscale_function = ttl_nn_latent_upscaler.NNLatentUpscale
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# For default upscale methods
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elif latent_upscaler in self.default_latent_upscalers:
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latent_upscale_function = LatentUpscaleBy
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else: # Default
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latent_upscale_function = LatentUpscaleBy
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latent_upscaler = self.default_latent_upscalers[0]
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print(f"{warning('HiResFix Script Warning:')} Chosen latent upscale method not found! "
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f"defaulting to '{latent_upscaler}'.\n")
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# Load Checkpoint if defined
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if hires_ckpt_name == "(use same)":
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clear_cache(my_unique_id, 0, "ckpt")
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else:
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latent_upscale_model, _, _ = \
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load_checkpoint(hires_ckpt_name, my_unique_id, output_vae=False, cache=1, cache_overwrite=True)
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elif upscale_type == "pixel":
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pixel_upscale_model = UpscaleModelLoader().load_model(pixel_upscaler)[0]
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elif upscale_type == "both":
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latent_upscale_function = LatentUpscaleBy
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latent_upscaler = self.default_latent_upscalers[0]
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pixel_upscale_model = UpscaleModelLoader().load_model(pixel_upscaler)[0]
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if hires_ckpt_name == "(use same)":
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clear_cache(my_unique_id, 0, "ckpt")
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else:
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latent_upscale_model, _, _ = \
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load_checkpoint(hires_ckpt_name, my_unique_id, output_vae=False, cache=1, cache_overwrite=True)
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control_net = ControlNetLoader().load_controlnet(control_net_name)[0] if use_controlnet is True else None
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# Construct the script output
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script = script or {}
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script["hiresfix"] = (upscale_type, latent_upscaler, upscale_by, use_same_seed, seed, hires_steps,
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denoise, iterations, control_net, strength, preprocessor, preprocessor_imgs,
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latent_upscale_function, latent_upscale_model, pixel_upscale_model)
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return (script,)
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```
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