134 lines
5.0 KiB
Markdown
134 lines
5.0 KiB
Markdown
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# Documentation
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- Class name: KSamplerProgress __Inspire
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- Category: InspirePack/analysis
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- Output node: False
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- Repo Ref: https://github.com/ZHO-ZHO-ZHO/ComfyUI-Inspire-Pack
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KSamplerProgress __Inspire 节点设计用于对潜在图像进行迭代采样和优化，过程分为多个步骤，实现采样过程的渐进式可视化。它利用先进的采样技术来提升生成图像的质量和多样性，适用于需要详细图像进展分析的应用场景。
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# Input types
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## Required
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- model
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- 指定用于采样的模型，是决定生成图像特征和质量的核心。
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- Comfy dtype: MODEL
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- Python dtype: torch.nn.Module
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- seed
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- 定义噪声生成的种子，确保采样结果的可重复性和一致性。
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- Comfy dtype: INT
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- Python dtype: int
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- steps
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- 确定采样过程的步骤数，直接影响潜在图像的优化和进展。
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- Comfy dtype: INT
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- Python dtype: int
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- cfg
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- 采样过程的配置设置，用于调整采样器的行为和参数。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- sampler_name
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- 标识要使用的特定采样器，影响采样策略和结果图像特征。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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- scheduler
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- 指定控制采样过程的调度器，影响图像生成的进展和质量。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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- positive
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- 正面提示，引导采样向期望的图像属性发展，提高相关性和特异性。
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- Comfy dtype: CONDITIONING
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- Python dtype: str
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- negative
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- 负面提示，用于引导采样远离不希望的图像属性，提升输出质量。
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- Comfy dtype: CONDITIONING
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- Python dtype: str
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- latent_image
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- 初始潜在图像，作为进展的起点，通过采样过程进行优化。
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- Comfy dtype: LATENT
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- Python dtype: dict
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- denoise
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- 指定采样过程中应用的去噪因子，影响生成图像的清晰度和细节。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- noise_mode
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- 采样过程中噪声应用的模式，影响生成图像的纹理和细节。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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- interval
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- 捕获和返回潜在图像的时间间隔，便于渐进式可视化。
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- Comfy dtype: INT
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- Python dtype: int
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- omit_start_latent
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- 指示是否从结果中排除起始潜在图像，为进展输出提供灵活性。
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- Comfy dtype: BOOLEAN
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- Python dtype: bool
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# Output types
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- latent
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- 采样过程完成后的最终潜在图像，代表渐进优化的最终结果。
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- Comfy dtype: LATENT
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- Python dtype: dict
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- progress_latent
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- 在采样过程中按指定间隔捕获的潜在图像集合，展示图像优化的进展过程。
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- Comfy dtype: LATENT
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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: unknown
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## Source code
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```python
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class KSampler_progress(a1111_compat.KSampler_inspire):
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@classmethod
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def INPUT_TYPES(s):
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return {"required":
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{"model": ("MODEL",),
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"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
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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": (common.SCHEDULERS, ),
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"positive": ("CONDITIONING", ),
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"negative": ("CONDITIONING", ),
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"latent_image": ("LATENT", ),
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"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
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"noise_mode": (["GPU(=A1111)", "CPU"],),
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"interval": ("INT", {"default": 1, "min": 1, "max": 10000}),
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"omit_start_latent": ("BOOLEAN", {"default": True, "label_on": "True", "label_off": "False"}),
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}
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}
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CATEGORY = "InspirePack/analysis"
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RETURN_TYPES = ("LATENT", "LATENT")
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RETURN_NAMES = ("latent", "progress_latent")
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def sample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise, noise_mode, interval, omit_start_latent):
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adv_steps = int(steps / denoise)
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sampler = a1111_compat.KSamplerAdvanced_inspire()
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if omit_start_latent:
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result = []
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else:
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result = [latent_image['samples']]
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for i in range(0, adv_steps+1):
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add_noise = i == 0
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return_with_leftover_noise = i != adv_steps
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latent_image = sampler.sample(model, add_noise, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, i, i+1, noise_mode, return_with_leftover_noise)[0]
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if i % interval == 0 or i == adv_steps:
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result.append(latent_image['samples'])
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if len(result) > 0:
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result = torch.cat(result)
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result = {'samples': result}
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else:
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result = latent_image
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return (latent_image, result)
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```
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