Before you rush to download this 28GB+ file, let's talk about the elephant in the room:

The most common way to use this model is via ComfyUI, a node-based GUI for Stable Diffusion and related models.

: Generally exceeds the capacity of standard consumer GPUs (like the RTX 4090/5090) when used alongside high-resolution text encoders and VAEs in a single workflow. Recommendation : Many users opt for FP8 or GGUF (quantized) versions to fit the model into 24GB VRAM. Performance

Running a 14-billion parameter video model locally requires substantial computational power. Because this specific file is in unquantized precision, its storage and VRAM footprint are high. Hardware Tiers Requirement Minimum (Quantized/Shared) Recommended (Native FP16) GPU VRAM 16 GB - 24 GB (using GGUF/NF4 weights) 24 GB - 48 GB (RTX 4090, RTX 6000 Ada, A600) System RAM 32 GB DDR4/DDR5 64 GB+ DDR5 Storage Space ~50 GB free space 100 GB+ Solid State Drive (NVMe SSD)

# Load your source anchor image init_image = load_image("path_to_your_input_image.png") # Define prompt directing the motion prompt = "Cinematic slow motion, waves crashing against the rocks, detailed water droplets, dramatic lighting, 8k resolution" negative_prompt = "static, low quality, distorted anatomy, fast cuts, text, watermark" # Generate video frames video_frames = pipeline( prompt=prompt, negative_prompt=negative_prompt, image=init_image, num_frames=81, # Standard length for Wan2.1 video clips height=720, width=1280, guidance_scale=6.0, num_inference_steps=50, generator=torch.manual_seed(42) ).frames Use code with caution. Optimization Strategies for Peak Quality