| import torch |
|
|
| def simple_guidance_fn(out, cfg): |
| uncondition, condtion = out.chunk(2, dim=0) |
| out = uncondition + cfg * (condtion - uncondition) |
| return out |
|
|
| def guidance_fn_with_rescale(out, cfg, rescale_factor=0.7): |
| """ |
| 对模型的原始输出应用Classifier-Free Guidance (CFG)，并加入方差重缩放 (rescale_cfg)。 |
| |
| Args: |
| out (torch.Tensor): 模型的原始输出，包含了unconditional和conditional两部分。 |
| cfg (float): Guidance scale，即引导强度。 |
| rescale_factor (float): 重缩放因子。常用的值在0.5到0.8之间，0.7是一个很好的起始值。 |
| |
| Returns: |
| torch.Tensor: 应用了CFG和rescale_cfg之后的最终输出。 |
| """ |
| uncondition, condition = out.chunk(2, dim=0) |
|
|
| guided_out = uncondition + cfg * (condition - uncondition) |
|
|
| std_condition = torch.std(condition, dim=(1,2,3), keepdim=True) |
| std_guided = torch.std(guided_out, dim=(1,2,3), keepdim=True) |
| |
| scale = std_condition / (std_guided + 1e-6) |
| print(scale.mean()) |
| rescaled_out = guided_out * (scale * rescale_factor + 1.0 * (1.0 - rescale_factor)) |
| return rescaled_out |
|
|
| def c3_guidance_fn(out, cfg): |
| |
| uncondition, condtion = out.chunk(2, dim=0) |
| out = condtion |
| out[:, :3] = uncondition[:, :3] + cfg * (condtion[:, :3] - uncondition[:, :3]) |
| return out |
