Openai/6912b0ee-f888-8005-bad4-23801314352b

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gpt-5

User: explain

explain

           # 2. 运动幅度增强（亮度保护核心算法）
           if motion_amplitude > 1.0:
               base_latent = concat_latent_image[:, :, 0:1]      # 首帧
               gray_latent = concat_latent_image[:, :, 1:]       # 灰帧
               
               diff = gray_latent - base_latent
               diff_mean = diff.mean(dim=(1, 3, 4), keepdim=True)
               diff_centered = diff - diff_mean
               scaled_latent = base_latent + diff_centered * motion_amplitude + diff_mean
               
               # Clamp & 组合
               scaled_latent = torch.clamp(scaled_latent, -6, 6)
               concat_latent_image = torch.cat([base_latent, scaled_latent], dim=2)

Assistant: This block amplifies motion in a latent video representation while preserving brightness.

This block amplifies motion in a latent video representation while preserving brightness. Step-by-step:

1. Condition if motion_amplitude > 1.0: → Only applies enhancement if requested amplitude exceeds normal motion.
2. Frame separation ``python base_latent = concat_latent_image[:, :, 0:1] # first frame gray_latent = concat_latent_image[:, :, 1:] # subsequent frames ` The latent tensor holds multiple frames; first frame acts as reference.
3. Difference extraction `python diff = gray_latent - base_latent diff_mean = diff.mean(dim=(1, 3, 4), keepdim=True) diff_centered = diff - diff_mean ` Computes motion signal (diff) between frames, then removes its mean over spatial dimensions to isolate motion (without brightness bias).
4. Scaling motion `python scaled_latent = base_latent + diff_centered * motion_amplitude + diff_mean ` Multiplies the motion component by motion_amplitude, keeping the mean brightness unchanged (+ diff_mean restores global luminance).
5. Clamp and recombine `python scaled_latent = torch.clamp(scaled_latent, -6, 6) concat_latent_image = torch.cat([base_latent, scaled_latent], dim=2) `` Limits latent values to avoid over-saturation, then reassembles the frames.

Effect: Motion contrast (changes between frames) is intensified, but brightness consistency (mean value) is preserved — that’s the “亮度保护核心算法” (brightness protection core algorithm).