lllyasviel / lllyasviel/FramePack
The generated video lacks sufficient clarity
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 17.3k
- Forks
- 1.7k
- PR merge metrics
- No merged PRs in 30d
Description
Even when using high-quality input images to generate the video, the output lacks sufficient clarity.
After reviewing the underlying bucket_tools.py code, I noticed that the input images are resized, and the available width/height options are quite limited. This results in processed images losing clarity, which in turn makes the generated video less sharp.
I'd like to ask: Are we restricted to only these preset options? Is it possible to adjust the parameters to allow higher-resolution video generation?
bucket_options = {
640: [
(416, 960),
(448, 864),
(480, 832),
(512, 768),
(544, 704),
(576, 672),
(608, 640),
(640, 608),
(672, 576),
(704, 544),
(768, 512),
(832, 480),
(864, 448),
(960, 416),
],
}
def find_nearest_bucket(h, w, resolution=640):
min_metric = float('inf')
best_bucket = None
for (bucket_h, bucket_w) in bucket_options[resolution]:
metric = abs(h * bucket_w - w * bucket_h)
if metric <= min_metric:
min_metric = metric
best_bucket = (bucket_h, bucket_w)
return best_bucket
Contributor guide
No contributing guide indexed for this repository
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reading bucket_tools.py, focusing on bucket_options and find_nearest_bucket. Trace where the selected resolution and buckets are used in video generation, then determine whether higher-resolution options are supported by the surrounding pipeline. Done means the supported resolution limits and any required changes are clearly established and covered by relevant validation.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100