89 lines
3.1 KiB
Markdown
89 lines
3.1 KiB
Markdown
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# Documentation
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- Class name: InstructPixToPixConditioningAdvanced
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- Category: conditioning/instructpix2pix
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- Output node: False
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- Repo Ref: https://github.com/comfyanonymous/ComfyUI
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InstructPixToPixConditioningAdvanced节点是为Pix2Pix图像转换任务中的高级条件设置而设计的。它接收正面和负面的条件输入，以及新的和原始的潜在表示，以生成修改后的条件和潜在输出。该节点旨在通过允许将额外的潜在信息整合到条件设置过程中，来促进复杂的图像操作任务。
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# Input types
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## Required
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- positive
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- 代表正面条件输入，用于引导图像转换过程朝着所需方向发展。
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- Comfy dtype: CONDITIONING
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- Python dtype: Tuple[CONDITIONING]
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- negative
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- 代表负面条件输入，用于引导图像转换远离不希望的特征。
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- Comfy dtype: CONDITIONING
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- Python dtype: Tuple[CONDITIONING]
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- new
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- 代表要整合到条件设置过程中的新潜在表示。
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- Comfy dtype: LATENT
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- Python dtype: Dict with key 'samples' and value as a tensor
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- original
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- 代表原始潜在表示，用于与新表示进行形状一致性比较，并整合到条件设置中。
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- Comfy dtype: LATENT
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- Python dtype: Dict with key 'samples' and value as a tensor
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# Output types
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- cond1
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- 修改后的正面条件输出。
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- Comfy dtype: CONDITIONING
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- Python dtype: CONDITIONING
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- cond2
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- 修改后的负面条件输出。
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- Comfy dtype: CONDITIONING
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- Python dtype: CONDITIONING
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- negative
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- 未修改的负面条件输入，为保持一致性而传递。
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- Comfy dtype: CONDITIONING
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- Python dtype: CONDITIONING
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- latent
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- 新的潜在表示，其中的样本被整合到条件输出中。
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- Comfy dtype: LATENT
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- Python dtype: Dict with key 'samples' and value as a tensor
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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 InstructPixToPixConditioningAdvanced:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {"positive": ("CONDITIONING", ),
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"negative": ("CONDITIONING", ),
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"new": ("LATENT", ),
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"original": ("LATENT", ),
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}}
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RETURN_TYPES = ("CONDITIONING","CONDITIONING","CONDITIONING","LATENT")
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RETURN_NAMES = ("cond1", "cond2", "negative", "latent")
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FUNCTION = "encode"
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CATEGORY = "conditioning/instructpix2pix"
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def encode(self, positive, negative, new, original):
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new_shape, orig_shape = new["samples"].shape, original["samples"].shape
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if new_shape != orig_shape:
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raise Exception(f"Latent shape mismatch: {new_shape} and {orig_shape}")
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out_latent = {}
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out_latent["samples"] = new["samples"]
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out = []
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for conditioning in [positive, negative]:
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c = []
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for t in conditioning:
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d = t[1].copy()
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d["concat_latent_image"] = original["samples"]
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n = [t[0], d]
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c.append(n)
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out.append(c)
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return (out[0], out[1], negative, out_latent)
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```
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