Hunyuan Video Text to Video
Generate videos from text prompts using Hunyuan model.
简介
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.
你需要提供
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在 ComfyUI 中运行。 此工作流无法在本页面运行。你可以在 Comfy Cloud 中打开（已预先加载所需内容），或下载后在自己的 ComfyUI 中使用。
在 Cloud 中试用常见问答
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.