153 lines
6.0 KiB
Markdown
153 lines
6.0 KiB
Markdown
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# Documentation
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- Class name: StableCascade_CheckpointLoader __Inspire
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- Category: InspirePack/Backend
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- Output node: False
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- Repo Ref: https://github.com/ltdrdata/ComfyUI-Inspire-Pack
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本节点旨在高效管理和加载模型检查点，采用级联方式优化流程，利用缓存机制避免重复加载。它支持基于缓存可用性和特定需求的条件加载，从而在模型部署场景中提高性能和资源利用率。
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# Input types
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## Required
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- stage_b
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- 指定'b'阶段模型的检查点。该参数对于识别和加载正确的模型至关重要，并可选择利用缓存以提高效率。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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- key_opt_b
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- 'b'阶段检查点的可选键，用于在缓存中唯一标识它，实现精确的缓存管理和检索。
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- Comfy dtype: STRING
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- Python dtype: str
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- stage_c
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- 指定'c'阶段模型的检查点。与'stage_b'类似，它对于加载适当的模型至关重要，并可利用缓存进行优化。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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- key_opt_c
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- 作为'c'阶段检查点的可选键，支持特定的缓存操作并确保高效的模型管理。
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- Comfy dtype: STRING
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- Python dtype: str
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- cache_mode
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- 确定缓存策略（如none、stage_b、stage_c、all），影响检查点如何存储或从缓存中检索以优化加载时间。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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# Output types
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- b_model
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- 从'b'阶段检查点加载的模型，可供使用。
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- Comfy dtype: MODEL
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- Python dtype: torch.nn.Module
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- b_vae
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- 与'b'阶段模型相关的VAE组件。
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- Comfy dtype: VAE
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- Python dtype: torch.nn.Module
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- c_model
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- 从'c'阶段检查点加载的模型，可供使用。
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- Comfy dtype: MODEL
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- Python dtype: torch.nn.Module
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- c_vae
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- 与'c'阶段模型相关的VAE组件。
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- Comfy dtype: VAE
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- Python dtype: torch.nn.Module
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- c_clip_vision
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- 与'c'阶段模型一起加载的CLIP视觉组件，便于多模态操作。
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- Comfy dtype: CLIP_VISION
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- Python dtype: torch.nn.Module
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- clip
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- CLIP模型组件，支持包括文本和图像理解在内的各种任务。
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- Comfy dtype: CLIP
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- Python dtype: torch.nn.Module
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- key_b
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- 与'b'阶段模型关联的缓存键，反映其缓存状态或标识符。
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- Comfy dtype: STRING
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- Python dtype: str
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- key_c
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- 与'c'阶段模型关联的缓存键，反映其缓存状态或标识符。
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- Comfy dtype: STRING
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- Python dtype: str
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## Usage tips
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- Infra type: `CPU`
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- Common nodes: unknown
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## Source code
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```python
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class StableCascade_CheckpointLoader:
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@classmethod
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def INPUT_TYPES(s):
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ckpts = folder_paths.get_filename_list("checkpoints")
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default_stage_b = ''
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default_stage_c = ''
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sc_ckpts = [x for x in ckpts if 'cascade' in x.lower()]
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sc_b_ckpts = [x for x in sc_ckpts if 'stage_b' in x.lower()]
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sc_c_ckpts = [x for x in sc_ckpts if 'stage_c' in x.lower()]
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if len(sc_b_ckpts) == 0:
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sc_b_ckpts = [x for x in ckpts if 'stage_b' in x.lower()]
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if len(sc_c_ckpts) == 0:
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sc_c_ckpts = [x for x in ckpts if 'stage_c' in x.lower()]
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if len(sc_b_ckpts) == 0:
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sc_b_ckpts = ckpts
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if len(sc_c_ckpts) == 0:
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sc_c_ckpts = ckpts
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if len(sc_b_ckpts) > 0:
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default_stage_b = sc_b_ckpts[0]
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if len(sc_c_ckpts) > 0:
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default_stage_c = sc_c_ckpts[0]
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return {"required": {
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"stage_b": (ckpts, {'default': default_stage_b}),
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"key_opt_b": ("STRING", {"multiline": False, "placeholder": "If empty, use 'stage_b' as the key."}),
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"stage_c": (ckpts, {'default': default_stage_c}),
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"key_opt_c": ("STRING", {"multiline": False, "placeholder": "If empty, use 'stage_c' as the key."}),
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"cache_mode": (["none", "stage_b", "stage_c", "all"], {"default": "none"}),
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}}
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RETURN_TYPES = ("MODEL", "VAE", "MODEL", "VAE", "CLIP_VISION", "CLIP", "STRING", "STRING")
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RETURN_NAMES = ("b_model", "b_vae", "c_model", "c_vae", "c_clip_vision", "clip", "key_b", "key_c")
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FUNCTION = "doit"
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CATEGORY = "InspirePack/Backend"
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def doit(self, stage_b, key_opt_b, stage_c, key_opt_c, cache_mode):
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if key_opt_b.strip() == '':
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key_b = stage_b
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else:
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key_b = key_opt_b.strip()
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if key_opt_c.strip() == '':
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key_c = stage_c
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else:
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key_c = key_opt_c.strip()
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if cache_mode in ['stage_b', "all"]:
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if key_b not in cache:
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res_b = nodes.CheckpointLoaderSimple().load_checkpoint(ckpt_name=stage_b)
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update_cache(key_b, "ckpt", (False, res_b))
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print(f"[Inspire Pack] StableCascade_CheckpointLoader: Ckpt '{stage_b}' is cached to '{key_b}'.")
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else:
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_, (_, res_b) = cache[key_b]
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print(f"[Inspire Pack] StableCascade_CheckpointLoader: Cached ckpt '{key_b}' is loaded. (Loading skip)")
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b_model, clip, b_vae = res_b
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else:
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b_model, clip, b_vae = nodes.CheckpointLoaderSimple().load_checkpoint(ckpt_name=stage_b)
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if cache_mode in ['stage_c', "all"]:
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if key_c not in cache:
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res_c = nodes.unCLIPCheckpointLoader().load_checkpoint(ckpt_name=stage_c)
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update_cache(key_c, "unclip_ckpt", (False, res_c))
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print(f"[Inspire Pack] StableCascade_CheckpointLoader: Ckpt '{stage_c}' is cached to '{key_c}'.")
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else:
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_, (_, res_c) = cache[key_c]
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print(f"[Inspire Pack] StableCascade_CheckpointLoader: Cached ckpt '{key_c}' is loaded. (Loading skip)")
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c_model, _, c_vae, clip_vision = res_c
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else:
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c_model, _, c_vae, clip_vision = nodes.unCLIPCheckpointLoader().load_checkpoint(ckpt_name=stage_c)
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return b_model, b_vae, c_model, c_vae, clip_vision, clip, key_b, key_c
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```
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