This basic Hunyuan workflow is designed to be as simple as possible while prioritizing quality. It has been tested on short, high-quality video generations and performs exceptionally well. The workflow includes settings and values that, based on my tests, currently bring out the best in the Hunyuan model. While I have yet to experiment further, I am fairly satisfied with the results so far. The workflow is centered around the Fast model, though you are welcome to switch to another model and adjust the steps accordingly. By default, the Fast LoRA is also loaded but set to a negative value.
I recommend Ultra GPU if you have the resource, but it works with even Large and Large Pro GPU's but takes more time. I strongly suggest you test all three and based on your need choose the best GPU to run this workflow. Enjoy and have fun :)
HUNYUAN ALL IN ONE BASIC - FAST WITH UPSCALE T2V, I2V & V2V uses native Comfy nodes and offers three operation methods:
- T2V (Text-to-Video)
- I2V (Image-to-Video) In this method, an image is multiplied into x frames and sent to latent space with a balanced denoising level to preserve the structure, composition, and colors of the original image. This approach is highly effective as it reduces inference time and provides better guidance toward the desired outcome. However, it comes at the cost of general motionâlowering the denoising level too much can result in a static final output with minimal movement. The denoise threshold can be adjusted based on your requirements. While there are other methods for achieving a more accurate image-to-video process, they tend to be slower. For this reason, a negative prompt wasn't included in the workflow, as it would double the waiting time.
- V2V (Video-to-Video) This method operates on the same principle as I2V.
- You can even use Hunyuan Lora with this workflow ;)
An improved and much better version of Hunyuan FAST workflow is also available, check that out !
This workflow was inspired and optimized to run on MimicPC from the original workflow shared by Latent Dream from civitai, big thanks to him for sharing his knowledge.
You can visit his profile here : https://civitai.com/user/LatentDream