# A model I can use with video data? **URL:** \ **Category:** Models\ **Created:** [June 20, 2025, 2:44pm UTC](https://discuss.huggingface.co/t/a-model-i-can-use-with-video-data/160097 "2025-06-20T14:44:14Z")\ **Posts on this page:** 3\ **Page:** 1
**Author:** ![OmerKuru](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/omerkuru/32/46084_2.png) [@OmerKuru](https://discuss.huggingface.co/u/OmerKuru)\ **Post date:** [June 20, 2025, 2:44pm UTC](https://discuss.huggingface.co/t/a-model-i-can-use-with-video-data/160097/1 "2025-06-20T14:44:14Z")
Hello everyone. I’ve been exploring and researching VLM models for a while now. However, there are so many purpose-built and specialized models that I’m a bit confused, so I decided to write here. I built a model for quality control of a material. However, this model is a CNN model composed of images. My goal is to use VLM to control the same concept with video input. Our objective is to create a model that can detect defects in the coating surface of a microscope image that has been magnified multiple times. Is there a VLM model you could recommend for this purpose? ---
**Author:** ![John6666](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/john6666/32/27664_2.png) [@John6666](https://discuss.huggingface.co/u/John6666)\ **Post date:** [June 20, 2025, 2:55pm UTC](https://discuss.huggingface.co/t/a-model-i-can-use-with-video-data/160097/2 "2025-06-20T14:55:34Z")
For video interpretation, these models may be easier to fine-tune. For still images, there are countless other options. In addition, super-resolution and upscaling might be used for pre-processing restored enlarged photos, but there is a risk of generating information that does not originally exist in the image during the interpolation process. > **[SmolVLM2: Bringing Video Understanding to Every Device](https://huggingface.co/blog/smolvlm2)** > > We’re on a journey to advance and democratize artificial intelligence through open source and open science. > **[Qwen/Qwen2.5-VL-3B-Instruct · Hugging Face](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct)** > > We’re on a journey to advance and democratize artificial intelligence through open source and open science. > **[Models - Hugging Face](https://huggingface.co/models?pipeline_tag=video-text-to-text&sort=trending)** > > We’re on a journey to advance and democratize artificial intelligence through open source and open science. > > > \*\*Is your feature request related to a problem? Please describe.\*\* > Currently, d…iffusers library lacks advanced tiled upscaling capabilities that are available in other Stable Diffusion implementations. While the library supports basic img2img and upscaling, there's no built-in solution for handling large images through intelligent tiling and seam fixing. This makes it difficult to process high-resolution images while maintaining quality and managing memory efficiently. > > \*\*Describe the solution you'd like\*\* > Implement Ultimate SD Upscale functionality (similar to Automatic1111's WebUI extension) as a pipeline in diffusers. Key features should include: > > 1. Progressive upscaling with intelligent scale factor determination > 2. Multiple tiling modes: > - Linear processing > - Chess pattern processing > 3. Advanced seam fixing options: > - Band pass mode > - Half tile offset > - Half tile with intersections > 4. Configurable parameters: > - Tile sizes > - Padding > - Mask blur > - Denoise strength for seam fixing > > The implementation should integrate smoothly with existing diffusers pipelines and maintain the library's user-friendly API style. > > \*\*Describe alternatives you've considered\*\* > \- Using basic tiling without seam fixing (leads to visible artifacts) > \- Running multiple separate upscale passes (inefficient and lower quality) > \- Implementing as a separate package (loses benefits of diffusers' optimization and integration) > \- Using other libraries like PIL or cv2 for tiling (lacks SD-specific optimizations) > > \*\*Additional context\*\* > \- Reference implementation: https://github.com/Coyote-A/ultimate-upscale-for-automatic1111 > \- This feature would be particularly valuable for: > - Professional image upscaling > - Batch processing of large images > - Creating high-resolution outputs while managing VRAM > - Maintaining image quality in tiled processing > \- Could potentially be implemented as either a standalone pipeline or an enhancement to existing img2img pipelines > \- Would complement existing super-resolution models in the library ---
**Author:** ![OmerKuru](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/omerkuru/32/46084_2.png) [@OmerKuru](https://discuss.huggingface.co/u/OmerKuru)\ **Post date:** [June 22, 2025, 5:12pm UTC](https://discuss.huggingface.co/t/a-model-i-can-use-with-video-data/160097/3 "2025-06-22T17:12:52Z")
hey John, thanks a lot for your answers. I will check theese models.