74 lines
2.4 KiB
Markdown
74 lines
2.4 KiB
Markdown
---
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tags:
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- VAE
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---
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# StableCascade_SuperResolutionControlnet
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## Documentation
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- Class name: `StableCascade_SuperResolutionControlnet`
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- Category: `_for_testing/stable_cascade`
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- Output node: `False`
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This node is designed for generating super-resolution control inputs for a cascading image generation process, utilizing a VAE to encode images into latent representations that are then adjusted for different stages of image resolution enhancement.
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## Input types
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### Required
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- **`image`**
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- The input image to be processed and encoded into latent representations for super-resolution enhancement.
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- Comfy dtype: `IMAGE`
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- Python dtype: `torch.Tensor`
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- **`vae`**
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- A variational autoencoder (VAE) model used to encode the input image into a latent space, facilitating the super-resolution process.
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- Comfy dtype: `VAE`
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- Python dtype: `torch.nn.Module`
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## Output types
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- **`controlnet_input`**
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- Comfy dtype: `IMAGE`
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- The encoded latent representation of the input image, prepared for the control network of the super-resolution process.
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- Python dtype: `torch.Tensor`
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- **`stage_c`**
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- Comfy dtype: `LATENT`
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- A latent representation tailored for the initial stage of the super-resolution process.
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- Python dtype: `Dict[str, torch.Tensor]`
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- **`stage_b`**
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- Comfy dtype: `LATENT`
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- A latent representation tailored for the subsequent stage of the super-resolution process.
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- Python dtype: `Dict[str, torch.Tensor]`
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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 StableCascade_SuperResolutionControlnet:
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def __init__(self, device="cpu"):
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self.device = device
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"image": ("IMAGE",),
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"vae": ("VAE", ),
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}}
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RETURN_TYPES = ("IMAGE", "LATENT", "LATENT")
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RETURN_NAMES = ("controlnet_input", "stage_c", "stage_b")
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FUNCTION = "generate"
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CATEGORY = "_for_testing/stable_cascade"
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def generate(self, image, vae):
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width = image.shape[-2]
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height = image.shape[-3]
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batch_size = image.shape[0]
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controlnet_input = vae.encode(image[:,:,:,:3]).movedim(1, -1)
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c_latent = torch.zeros([batch_size, 16, height // 16, width // 16])
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b_latent = torch.zeros([batch_size, 4, height // 2, width // 2])
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return (controlnet_input, {
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"samples": c_latent,
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}, {
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"samples": b_latent,
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})
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```
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