Hunyuan Video Text to Video
Generate videos from text prompts using Hunyuan model.
About
This ComfyUI workflow turns short text prompts into coherent, multi‑frame videos using Tencent’s Hunyuan Video model. It starts by loading the model stack in Step1 - Load models: DualCLIPLoader provides the text encoders, CLIPTextEncode converts your positive and negative prompts into embeddings, and UNETLoader brings in the Hunyuan Video UNet weights. ModelSamplingSD3 configures the sampler for Hunyuan’s SD3‑style noise schedule, while FluxGuidance applies classifier‑free guidance during denoising.
What you provide
- Reading the workflow
Runs in ComfyUI. This workflow can't run on this page. Open it in Comfy Cloud with everything loaded, or download it for your own ComfyUI.
Try in CloudQuestions & answers
Use VAEDecodeTiled (default) and reduce tile_size while increasing overlap slightly to hide seams. In UNETLoader, choose fp8 weight_dtype if supported by your GPU. Lower resolution, shorten duration (fewer frames), or reduce steps in BasicScheduler.
Keep width/height as multiples of 16 and start with 20–35 steps. Set a fixed seed in RandomNoise. Use FluxGuidance with a moderate guidance scale; too high can cause flicker, too low weakens prompt adherence. Refine prompts to be specific but not overly long.
Use VAEDecodeTiled by default—it’s memory‑efficient and recommended for most GPUs. Switch to VAEDecode only if you have sufficient VRAM and want faster decoding without tiling. If you see seams with tiled decode, increase overlap or lower tile_size.
Keep ModelSamplingSD3 as provided for Hunyuan. Start with dpmpp_2m or euler in KSamplerSelect and 20–35 steps in BasicScheduler. If details smear, try more steps; if results are noisy, reduce steps or lower guidance in FluxGuidance.