1.6 KiB
1.6 KiB
Documentation
- Class name: SAMEmbeddingToImage
- Category: Art Venture/Segmentation
- Output node: False
SAMEmbeddingToImage节点用于将SAM嵌入转换为可视化表示,从而实现对嵌入数据的直观解释和操作。它将嵌入中包含的复杂信息抽象为更易于理解的图像格式。
Input types
Required
- embedding
- embedding参数代表需要转换为图像的SAM嵌入。这个嵌入至关重要,因为它包含了将被可视化为图像的编码信息。
- Comfy dtype: SAM_EMBEDDING
- Python dtype: np.ndarray
Output types
- image
- 输出是从SAM嵌入生成的图像,允许对编码信息进行可视化和进一步处理。
- Comfy dtype: IMAGE
- Python dtype: Tuple[torch.Tensor]
Usage tips
- Infra type:
CPU - Common nodes: unknown
Source code
class SAMEmbeddingToImage:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"embedding": ("SAM_EMBEDDING",),
},
}
RETURN_TYPES = ("IMAGE",)
CATEGORY = "Art Venture/Segmentation"
FUNCTION = "sam_embedding_to_noise_image"
def sam_embedding_to_noise_image(self, embedding: np.ndarray):
# Flatten the array to a 1D array
flat_arr = embedding.flatten()
# Convert the 1D array to bytes
bytes_arr = flat_arr.astype(np.float32).tobytes()
# Convert bytes to RGBA PIL Image
size = (embedding.shape[1] * 4, int(embedding.shape[2] * embedding.shape[3] / 4))
img = Image.frombytes("RGBA", size, bytes_arr)
return (pil2tensor(img),)