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--- |
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license: cc-by-nc-4.0 |
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language: |
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- en |
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tags: |
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- stable cascade |
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--- |
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# Stable-Cascade FP16 fix |
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**A modified version of [Stable-Cascade](https://huggingface.co./stabilityai/stable-cascade) which is compatibile with fp16 inference** |
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**In theory, you don't need to actually download this model file. It is possible to do onfly modification. This model is for experiments.** |
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## Demo |
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| FP16| BF16| |
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| - | - | |
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LPIPS difference: 0.088 |
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| FP16 | BF16| |
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| - | - | |
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LPIPS difference: 0.012 |
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## How |
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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. |
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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) |
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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. |
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### FP8 |
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Some people may know the FP8 quant for inference SDXL with lowvram cards. The technique can be applied to this model too.<br> |
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But since the last block group is basically ruined, so it is recommend to ignore the last block group:<br> |
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```python |
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for name, module in generator_c.named_modules(): |
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if "up_blocks.1" in name: continue |
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if isinstance(module, torch.nn.Linear): |
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module.to(torch.float8_e5m2) |
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elif isinstance(module, torch.nn.Conv2d): |
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module.to(torch.float8_e5m2) |
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elif isinstance(module, torch.nn.MultiheadAttention): |
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module.to(torch.float8_e5m2) |
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``` |
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This sample code should transform 70% of weight into fp8. (Use FP8 weight with scale is better solution, it is recommended to implement that) |
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I have tried different transform settings which is more friendly for FP8 but the differences between original model is more significant. |
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FP8 Demo (Same Seed): |
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## Notice |
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The modified version of model will not be compatibile with the lora/lycoris trained on original weight. <br> |
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(actually it can, just do the same transformation, I'm considering to rewrite a version to use key name to determine what to do.) |
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Also the ControlNets will not be compatible too. Unless you also apply the needed transformation to them. |
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I don't want to do all of these by myself so hope some others will do that. |
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## License |
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Stable-Cascade is published with a non-commercial lisence so I use CC-BY-NC 4.0 to publish this model. |
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**The source code to make this model is published with apache-2.0 license** |
