Minimax H3 prompt adherence really varies depending on the resolution
Hey guys,
I'm running the official MiniMax H3 model from Hugging Face on 4xB300, and I'm seeing a pretty significant difference in prompt adherence depending on the generation resolution.
With the same REF2VA prompt:
At 352p / 416p, the model generally follows the requested shot structure, camera angles, character placement, and opening-frame conditioning correctly.
At 768p, prompt adherence becomes dramatically worse. It may ignore requested camera angles, change character placement, ignoring duplicate interdiction...
Increasing the number of inference steps at 768p seems to improve/refine visual coherence somewhat, but doesn't really fix the structural guidance or composition.
I understand that changing resolution changes the latent dimensions, so I don't expect the same seed to produce an identical video across resolutions. But what surprises me is the systematic difference in instruction adherence, not just the resulting pixels/composition.
Has anyone else observed H3 following REF2VA / keyframe / shot guidance significantly better around 352p–416p than at 768p?
Is 768p native inference expected to behave differently, or are there resolution-specific settings / scheduler / RoPE / positional encoding / preprocessing parameters that need to be adjusted when scaling up the resolution?
I'm running that with a low step count, but increasing the steps with same seed and prompt will just make it a bit better looking, it will not remove undesired characters or problems...
I suspect that its due to higher resolutions making it so there are more tokens for the transformer backbone to process on a single pass,
More steps at a fixed lower res is a linear increase of compute, more res is quadratic increase of tokens (doesnt mean its quadratic in compute, but it still means that the context is bigger and bigger)
Hey guys,
I'm running the official MiniMax H3 model from Hugging Face on 4xB300, and I'm seeing a pretty significant difference in prompt adherence depending on the generation resolution.
With the same REF2VA prompt:
At 352p / 416p, the model generally follows the requested shot structure, camera angles, character placement, and opening-frame conditioning correctly.
At 768p, prompt adherence becomes dramatically worse. It may ignore requested camera angles, change character placement, ignoring duplicate interdiction...
Increasing the number of inference steps at 768p seems to improve/refine visual coherence somewhat, but doesn't really fix the structural guidance or composition.
I understand that changing resolution changes the latent dimensions, so I don't expect the same seed to produce an identical video across resolutions. But what surprises me is the systematic difference in instruction adherence, not just the resulting pixels/composition.
Has anyone else observed H3 following REF2VA / keyframe / shot guidance significantly better around 352p–416p than at 768p?
Is 768p native inference expected to behave differently, or are there resolution-specific settings / scheduler / RoPE / positional encoding / preprocessing parameters that need to be adjusted when scaling up the resolution?
I'm running that with a low step count, but increasing the steps with same seed and prompt will just make it a bit better looking, it will not remove undesired characters or problems...
Yes, I have definitely observed too that prompt adherence is almost spot on at low resolutions than high resolution. Almost like it skips some of the request being made in the prompt. I also found that strange. But even so this is still overall a big leap for open source video models. They did a great job with prompt understanding of this model.