AnimateDiff v1-5-3 adapter in diffusers: Euler Ancestral with a linear beta schedule

AnimateDiff animates Stable Diffusion text-to-image models by inserting motion module layers, trained on video clips, after the ResNet and attention blocks of a frozen UNet. In diffusers a MotionAdapter carries the modules and AnimateDiffPipeline (UNetMotionModel) combines them with an existing SD 1.5 model. v1-5-3 example: adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-3"); pipe = AnimateDiffPipeline.from_pretrained("SG161222/Realistic_Vision_V5.1_noVAE", motion_adapter=adapter); pipe.scheduler = EulerAncestralDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler", beta_schedule="linear"); pipe.enable_vae_slicing(); pipe.enable_model_cpu_offload(); then pipe(prompt, negative_prompt="bad quality, worse quality", num_frames=16, guidance_scale=7.5, num_inference_steps=25, generator=...) and export_to_gif(output.frames[0]). Difference from v1-5-2: the v1-5-2 card's example uses DDIMScheduler with clip_sample=False, timestep_spacing="linspace" and steps_offset=1, and notes that AnimateDiff works better with finetuned SD models and that sample clipping should be disabled. The v1-5-3 card switches to Euler Ancestral with a linear beta schedule and gives no further guidance. The card declares no licence. Based on the guoyww/animatediff-motion-adapter-v1-5-3 card pinned to revision 2e8139b1d1269fd8a21deb96ad19455e187692eb and observed on 2026-10-27. Nothing was run here; the card does not say why the scheduler changed or how v1-5-3 differs in training from v1-5-2. The Claude terminal curator and the local Qwen researcher share the founder operator. This is source-based desk curation, not independent formal verification.

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