Modelscope t2v

Here's a fun bit of context that doesn't show up anywhere in the node itself: the author of this pack, cerspense, is Spencer Sterling, the actual creator of Zeroscope - the open text-to-video fine-tune this node loads by default. So this isn't some third party wrapping someone else's model; it's the model's own author wiring it directly into ComfyUI via diffusers, downloading straight from Hugging Face rather than expecting you to manage a checkpoint file by hand.

The honest take

Zeroscope had its moment - a real one. It landed in mid-2023 as one of the first genuinely usable open-source text-to-video setups, and r/StableDiffusion lit up over it for a solid stretch that summer. That moment has passed. Wan, LTX, and Hunyuan-class video models all beat it comfortably on resolution, motion coherence, and prompt adherence today, and community chatter around Zeroscope has been essentially flat since 2024 - the rare mention since then has been nostalgia or a meetup shoutout, not people actually generating with it. Reach for this node if you want something small and fast to sanity-check a pipeline, or if the grainy, low-res 2023 look is specifically the aesthetic you're after. For serious text-to-video work today, this isn't where you want to be spending compute.

How it works

Unlike most ComfyUI video nodes, this one isn't wired into ComfyUI's own sampler/VAE machinery at all - it loads a complete diffusers DiffusionPipeline from model_path at run time, swaps in a DPMSolverMultistepScheduler, and enables CPU offload automatically (not optional, always on) to help it fit in more modest VRAM. That means the first time you run it against a given model_path, it downloads several gigabytes straight from Hugging Face - there's no manual model file to place in a ComfyUI models folder.

The inputs and outputs that matter

• prompt / negative_prompt - plain text, same idea as any text-to-image node.
• model_path - a Hugging Face repo id, defaulting to cerspense/zeroscope_v2_576w. Swap this to point at a different Zeroscope variant, or any other Modelscope-architecture checkpoint you want to try.
• width / height / num_frames - default 576×320 at 24 frames, which is the model's native comfort zone. Pushing well past this fights both the model's training resolution and your VRAM at the same time.
• num_inference_steps, guidance_scale, seed - the standard diffusion knobs, defaulting to 25 steps, 9.0 guidance, seed 42.

Output: a single IMAGE batch - the generated frames, ready to hand to any video-combine/save node the same way you'd handle any other frame sequence.

Installing it

Search cspnodes in ComfyUI Manager, or clone it directly:

cd ComfyUI/custom_nodes
git clone https://github.com/cerspense/ComfyUI_cspnodes

Restart after. This is one of the two nodes in the pack that actually needs its hard dependencies for real - diffusers is required just to run a generation, not only to load the node. Budget time and disk for that first-run download too: several gigabytes pulled straight from the Hugging Face repo named in model_path, with no offline fallback unless you've already got that repo cached locally.

Where people get tripped up

The first run against a new model_path will sit there looking stuck while it downloads the model - that's normal, and it's happening in your terminal output, not ComfyUI's progress bar, so check there before assuming it's hung.

VRAM is a real concern with this pipeline even at modest settings, and the community has reported the sibling XL checkpoint (used by this node's video-to-video counterpart) running out of memory even on 24GB cards despite documentation suggesting it should fit - so if you're on a tighter card, start at the defaults and only push resolution or frame count up once you've confirmed you have headroom, rather than assuming the stated VRAM numbers are a hard ceiling.