378 lines
17 KiB
Markdown
378 lines
17 KiB
Markdown
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# Documentation
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- Class name: ttN pipeKSampler
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- Category: ttN/legacy
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- Output node: True
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- Repo Ref: https://github.com/comfyanonymous/ComfyUI
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ttN_pipeKSampler节点旨在简化高级采样技术的应用，用于图像生成。它整合了模型输入、采样配置和图像处理参数，为用户提供了一个简洁的界面，以生成具有特定属性或修改的图像。该节点通过抽象化复杂的采样过程配置和执行，大大简化了用户的操作流程。
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# Input types
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## Required
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- pipe
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- 包含采样过程所需的各种配置设置和模型参数的结构化输入。
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- Comfy dtype: PIPE_LINE
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- Python dtype: dict
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- lora_name
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- 指定要使用的LoRA模型，影响生成模型的适应性和微调效果。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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- lora_model_strength
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- 决定LoRA模型对生成模型的影响强度，调整适应程度。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- lora_clip_strength
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- 控制LoRA模型对CLIP模型的影响强度，调整语义理解能力。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- upscale_method
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- 定义用于放大生成图像的方法，影响图像分辨率和质量。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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- factor
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- 图像放大的倍数，直接影响最终图像大小。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- crop
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- 指定生成图像的裁剪参数，影响最终图像构图。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: dict
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- sampler_state
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- 图像生成过程中使用的采样器状态，影响采样行为。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: dict
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- steps
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- 运行采样过程的步骤数，影响生成图像的细节和质量。
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- Comfy dtype: INT
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- Python dtype: int
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- cfg
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- 配置引导因子，影响生成图像的连贯性和质量。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- sampler_name
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- 使用的采样器算法名称，决定采样技术。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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- scheduler
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- 指定用于控制采样过程的调度器，影响图像生成的进程。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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- denoise
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- 图像生成过程中应用的去噪因子，影响图像清晰度和噪声水平。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- image_output
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- 生成图像的输出路径，决定图像保存位置。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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- save_prefix
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- 添加到保存图像名称前的前缀，辅助图像组织和识别。
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- Comfy dtype: STRING
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- Python dtype: str
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## Optional
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- seed
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- 用于采样过程的随机种子，确保生成图像的可重复性。
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- Comfy dtype: INT
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- Python dtype: int
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- optional_model
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- 可选的模型参数，允许指定替代生成模型。
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- Comfy dtype: MODEL
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- Python dtype: str
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- optional_positive
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- 可选的正面提示，引导图像生成朝向特定主题或元素。
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- Comfy dtype: CONDITIONING
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- Python dtype: List[str]
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- optional_negative
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- 可选的负面提示，引导图像生成远离特定主题或元素。
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- Comfy dtype: CONDITIONING
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- Python dtype: List[str]
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- optional_latent
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- 可选的初始潜在空间输入，作为图像生成过程的起点。
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- Comfy dtype: LATENT
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- Python dtype: torch.Tensor
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- optional_vae
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- 可选的变分自编码器，用于增强图像生成过程。
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- Comfy dtype: VAE
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- Python dtype: torch.nn.Module
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- optional_clip
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- 可选的CLIP模型，用于图像和文本的语义理解，帮助生成的图像与提供的提示保持一致。
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- Comfy dtype: CLIP
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- Python dtype: torch.nn.Module
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- xyPlot
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- 指定生成XY图的参数，可能用于可视化采样过程的某些方面。
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- Comfy dtype: XYPLOT
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- Python dtype: dict
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# Output types
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- pipe
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- 包含采样过程结果的更新后的pipe结构，包括配置和生成的图像。
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- Comfy dtype: PIPE_LINE
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- Python dtype: dict
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- model
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- 采样过程中使用的生成模型，可能经过LoRA或其他参数的修改。
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- Comfy dtype: MODEL
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- Python dtype: str
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- positive
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- 用于引导图像生成过程的正面提示列表。
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- Comfy dtype: CONDITIONING
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- Python dtype: List[str]
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- negative
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- 用于细化图像生成过程的负面提示列表。
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- Comfy dtype: CONDITIONING
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- Python dtype: List[str]
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- latent
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- 采样过程产生的潜在空间表示。
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- Comfy dtype: LATENT
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- Python dtype: torch.Tensor
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- vae
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- 图像生成过程中涉及的变分自编码器。
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- Comfy dtype: VAE
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- Python dtype: torch.nn.Module
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- clip
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- 采样过程中用于语义理解的CLIP模型。
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- Comfy dtype: CLIP
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- Python dtype: torch.nn.Module
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- image
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- 采样过程最终生成的图像或图像集。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- seed
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- 采样过程中使用的种子值，影响结果的随机性和可重复性。
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- Comfy dtype: INT
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- Python dtype: int
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## Usage tips
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- Infra type: `GPU`
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- Common nodes:
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- [ImageUpscaleWithModel](../../Comfy/Nodes/ImageUpscaleWithModel.md)
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## Source code
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```python
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class ttN_TSC_pipeKSampler:
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version = '1.0.5'
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upscale_methods = ["None", "nearest-exact", "bilinear", "area", "bicubic", "lanczos", "bislerp"]
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crop_methods = ["disabled", "center"]
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(cls):
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return {"required":
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{"pipe": ("PIPE_LINE",),
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"lora_name": (["None"] + folder_paths.get_filename_list("loras"),),
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"lora_model_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
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"lora_clip_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
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"upscale_method": (cls.upscale_methods,),
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"factor": ("FLOAT", {"default": 2, "min": 0.0, "max": 10.0, "step": 0.25}),
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"crop": (cls.crop_methods,),
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"sampler_state": (["Sample", "Hold"], ),
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"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
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"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
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"sampler_name": (comfy.samplers.KSampler.SAMPLERS,),
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"scheduler": (comfy.samplers.KSampler.SCHEDULERS,),
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"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
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"image_output": (["Hide", "Preview", "Save", "Hide/Save"],),
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"save_prefix": ("STRING", {"default": "ComfyUI"})
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},
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"optional":
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{"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
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"optional_model": ("MODEL",),
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"optional_positive": ("CONDITIONING",),
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"optional_negative": ("CONDITIONING",),
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"optional_latent": ("LATENT",),
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"optional_vae": ("VAE",),
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"optional_clip": ("CLIP",),
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"xyPlot": ("XYPLOT",),
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},
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"hidden":
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{"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO", "my_unique_id": "UNIQUE_ID",
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"embeddingsList": (folder_paths.get_filename_list("embeddings"),),
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"ttNnodeVersion": ttN_TSC_pipeKSampler.version},
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}
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RETURN_TYPES = ("PIPE_LINE", "MODEL", "CONDITIONING", "CONDITIONING", "LATENT", "VAE", "CLIP", "IMAGE", "INT",)
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RETURN_NAMES = ("pipe", "model", "positive", "negative", "latent","vae", "clip", "image", "seed", )
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OUTPUT_NODE = True
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FUNCTION = "sample"
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CATEGORY = "ttN/legacy"
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def sample(self, pipe, lora_name, lora_model_strength, lora_clip_strength, sampler_state, steps, cfg, sampler_name, scheduler, image_output, save_prefix, denoise=1.0,
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optional_model=None, optional_positive=None, optional_negative=None, optional_latent=None, optional_vae=None, optional_clip=None, seed=None, xyPlot=None, upscale_method=None, factor=None, crop=None, prompt=None, extra_pnginfo=None, my_unique_id=None, start_step=None, last_step=None, force_full_denoise=False, disable_noise=False):
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# Clean Loader Models from Global
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loader.update_loaded_objects(prompt)
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my_unique_id = int(my_unique_id)
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ttN_save = ttNsave(my_unique_id, prompt, extra_pnginfo)
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samp_model = optional_model if optional_model is not None else pipe["model"]
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samp_positive = optional_positive if optional_positive is not None else pipe["positive"]
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samp_negative = optional_negative if optional_negative is not None else pipe["negative"]
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samp_samples = optional_latent if optional_latent is not None else pipe["samples"]
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samp_vae = optional_vae if optional_vae is not None else pipe["vae"]
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samp_clip = optional_clip if optional_clip is not None else pipe["clip"]
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if seed in (None, 'undefined'):
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samp_seed = pipe["seed"]
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else:
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samp_seed = seed
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def process_sample_state(pipe, samp_model, samp_clip, samp_samples, samp_vae, samp_seed, samp_positive, samp_negative, lora_name, lora_model_strength, lora_clip_strength,
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steps, cfg, sampler_name, scheduler, denoise,
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image_output, save_prefix, prompt, extra_pnginfo, my_unique_id, preview_latent, disable_noise=disable_noise):
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# Load Lora
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if lora_name not in (None, "None"):
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samp_model, samp_clip = loader.load_lora(lora_name, samp_model, samp_clip, lora_model_strength, lora_clip_strength)
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# Upscale samples if enabled
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samp_samples = sampler.handle_upscale(samp_samples, upscale_method, factor, crop)
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samp_samples = sampler.common_ksampler(samp_model, samp_seed, steps, cfg, sampler_name, scheduler, samp_positive, samp_negative, samp_samples, denoise=denoise, preview_latent=preview_latent, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, disable_noise=disable_noise)
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latent = samp_samples["samples"]
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samp_images = samp_vae.decode(latent)
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results = ttN_save.images(samp_images, save_prefix, image_output)
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sampler.update_value_by_id("results", my_unique_id, results)
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# Clean loaded_objects
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loader.update_loaded_objects(prompt)
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new_pipe = {
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"model": samp_model,
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"positive": samp_positive,
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"negative": samp_negative,
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"vae": samp_vae,
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"clip": samp_clip,
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"samples": samp_samples,
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"images": samp_images,
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"seed": samp_seed,
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"loader_settings": pipe["loader_settings"],
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}
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sampler.update_value_by_id("pipe_line", my_unique_id, new_pipe)
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del pipe
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if image_output in ("Hide", "Hide/Save"):
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return sampler.get_output(new_pipe)
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return {"ui": {"images": results},
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"result": sampler.get_output(new_pipe)}
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def process_hold_state(pipe, image_output, my_unique_id):
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last_pipe = sampler.init_state(my_unique_id, "pipe_line", pipe)
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last_results = sampler.init_state(my_unique_id, "results", list())
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if image_output in ("Hide", "Hide/Save"):
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return sampler.get_output(last_pipe)
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return {"ui": {"images": last_results}, "result": sampler.get_output(last_pipe)}
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def process_xyPlot(pipe, samp_model, samp_clip, samp_samples, samp_vae, samp_seed, samp_positive, samp_negative, lora_name, lora_model_strength, lora_clip_strength,
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steps, cfg, sampler_name, scheduler, denoise,
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image_output, save_prefix, prompt, extra_pnginfo, my_unique_id, preview_latent, xyPlot):
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random.seed(seed)
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sampleXYplot = ttNxyPlot(xyPlot, save_prefix, image_output, prompt, extra_pnginfo, my_unique_id)
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if not sampleXYplot.validate_xy_plot():
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return process_sample_state(pipe, lora_name, lora_model_strength, lora_clip_strength, steps, cfg, sampler_name, scheduler, denoise, image_output, save_prefix, prompt, extra_pnginfo, my_unique_id, preview_latent)
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plot_image_vars = {
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"x_node_type": sampleXYplot.x_node_type, "y_node_type": sampleXYplot.y_node_type,
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"lora_name": lora_name, "lora_model_strength": lora_model_strength, "lora_clip_strength": lora_clip_strength,
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"steps": steps, "cfg": cfg, "sampler_name": sampler_name, "scheduler": scheduler, "denoise": denoise, "seed": samp_seed,
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"model": samp_model, "vae": samp_vae, "clip": samp_clip, "positive_cond": samp_positive, "negative_cond": samp_negative,
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"ckpt_name": pipe['loader_settings']['ckpt_name'],
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"vae_name": pipe['loader_settings']['vae_name'],
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"clip_skip": pipe['loader_settings']['clip_skip'],
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"lora1_name": pipe['loader_settings']['lora1_name'],
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"lora1_model_strength": pipe['loader_settings']['lora1_model_strength'],
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"lora1_clip_strength": pipe['loader_settings']['lora1_clip_strength'],
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"lora2_name": pipe['loader_settings']['lora2_name'],
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"lora2_model_strength": pipe['loader_settings']['lora2_model_strength'],
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"lora2_clip_strength": pipe['loader_settings']['lora2_clip_strength'],
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"lora3_name": pipe['loader_settings']['lora3_name'],
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"lora3_model_strength": pipe['loader_settings']['lora3_model_strength'],
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"lora3_clip_strength": pipe['loader_settings']['lora3_clip_strength'],
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"positive": pipe['loader_settings']['positive'],
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"positive_token_normalization": pipe['loader_settings']['positive_token_normalization'],
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"positive_weight_interpretation": pipe['loader_settings']['positive_weight_interpretation'],
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"negative": pipe['loader_settings']['negative'],
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"negative_token_normalization": pipe['loader_settings']['negative_token_normalization'],
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"negative_weight_interpretation": pipe['loader_settings']['negative_weight_interpretation'],
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}
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latent_image = sampleXYplot.get_latent(pipe["samples"])
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latents_plot = sampleXYplot.get_labels_and_sample(plot_image_vars, latent_image, preview_latent, start_step, last_step, force_full_denoise, disable_noise)
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samp_samples = {"samples": latents_plot}
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images = sampleXYplot.plot_images_and_labels()
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if xyPlot["output_individuals"]:
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results = ttN_save.images(images, save_prefix, image_output)
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else:
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results = ttN_save.images(images[-1], save_prefix, image_output)
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sampler.update_value_by_id("results", my_unique_id, results)
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# Clean loaded_objects
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loader.update_loaded_objects(prompt)
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new_pipe = {
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"model": samp_model,
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"positive": samp_positive,
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"negative": samp_negative,
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"vae": samp_vae,
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"clip": samp_clip,
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"samples": samp_samples,
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"images": images,
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"seed": samp_seed,
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"loader_settings": pipe["loader_settings"],
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}
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sampler.update_value_by_id("pipe_line", my_unique_id, new_pipe)
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del pipe
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if image_output in ("Hide", "Hide/Save"):
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return sampler.get_output(new_pipe)
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return {"ui": {"images": results}, "result": sampler.get_output(new_pipe)}
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preview_latent = True
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if image_output in ("Hide", "Hide/Save"):
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preview_latent = False
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if sampler_state == "Sample" and xyPlot is None:
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return process_sample_state(pipe, samp_model, samp_clip, samp_samples, samp_vae, samp_seed, samp_positive, samp_negative, lora_name, lora_model_strength, lora_clip_strength,
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steps, cfg, sampler_name, scheduler, denoise, image_output, save_prefix, prompt, extra_pnginfo, my_unique_id, preview_latent)
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elif sampler_state == "Sample" and xyPlot is not None:
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return process_xyPlot(pipe, samp_model, samp_clip, samp_samples, samp_vae, samp_seed, samp_positive, samp_negative, lora_name, lora_model_strength, lora_clip_strength, steps, cfg, sampler_name, scheduler, denoise, image_output, save_prefix, prompt, extra_pnginfo, my_unique_id, preview_latent, xyPlot)
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elif sampler_state == "Hold":
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return process_hold_state(pipe, image_output, my_unique_id)
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```
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