stable cascade en license:cc-by-nc-4.0 region:us
---
license: cc-by-nc-4.0
language:
- en
tags:
- stable cascade
---

# Stable-Cascade FP16 fix

**A modified version of [Stable-Cascade](https://huggingface.co/stabilityai/stable-cascade) which is compatibile with fp16 inference**

**In theory, you don't need to actually download this model file. It is possible to do onfly modification. This model is for experiments.**

## Demo
| FP16| BF16|
| - | - |
|![image/png](https://cdn-uploads.huggingface.co/production/uploads/630593e2fca1d8d92b81d2a1/fkWNY15JQbfh5pe1SY7wS.png)|![image/png](https://cdn-uploads.huggingface.co/production/uploads/630593e2fca1d8d92b81d2a1/XpfqkimqJTeDjggTaV4Mt.png)|

LPIPS difference: 0.088


| FP16 | BF16|
| - | - |
|![image/png](https://cdn-uploads.huggingface.co/production/uploads/630593e2fca1d8d92b81d2a1/muOkoNjVK6CFv2rs6QyBr.png)|![image/png](https://cdn-uploads.huggingface.co/production/uploads/630593e2fca1d8d92b81d2a1/rrgb8yMuJDyjJu6wd366j.png)|

LPIPS difference: 0.012

## How
After doing some check to the L1 norm of each hidden state. I found the last block group(8, 24, 24, 8 <- this one) make the hiddens states become bigger and bigger.

So I just apply some transformation on the TimestepBlock to directly modify the scale of hidden state. (Since it is not a residual block, so this is possible)

How the transformation be done is written in the modified "stable_cascade.py", you can put the file into kohya-ss/sd-scripts' stable-cascade branch and uncomment things to check weights or doing the conversion by yourselve.


### FP8
Some people may know the FP8 quant for inference SDXL with lowvram cards. The technique can be applied to this model too.<br>
But since the last block group is basically ruined, so it is recommend to ignore the last block group:<br>
```python
for name, module in generator_c.named_modules():
    if "up_blocks.1" in name: continue
    if isinstance(module, torch.nn.Linear):
        module.to(torch.float8_e5m2)
    elif isinstance(module, torch.nn.Conv2d):
        module.to(torch.float8_e5m2)
    elif isinstance(module, torch.nn.MultiheadAttention):
        module.to(torch.float8_e5m2)
```

This sample code should transform 70% of weight into fp8. (Use FP8 weight with scale is better solution, it is recommended to implement that)

I have tried different transform settings which is more friendly for FP8 but the differences between original model is more significant.

FP8 Demo (Same Seed):
![image/png](https://cdn-uploads.huggingface.co/production/uploads/630593e2fca1d8d92b81d2a1/wPoZeWGGhcPMck45--y_X.png)


## Notice
The modified version of model will not be compatibile with the lora/lycoris trained on original weight. <br> 
(actually it can, just do the same transformation, I'm considering to rewrite a version to use key name to determine what to do.)

Also the ControlNets will not be compatible too. Unless you also apply the needed transformation to them.

I don't want to do all of these by myself so hope some others will do that.

## License
Stable-Cascade is published with a non-commercial lisence so I use CC-BY-NC 4.0 to publish this model.
**The source code to make this model is published with apache-2.0 license**