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---
name: diffusion_sampler_proxy
description: Run a tiny reverse-diffusion-like sampler that exercises classifier-free guided score calls at each step.
---
# Diffusion Sampler Proxy
Use this skill for bounded recovery of classifier-free guidance when full image diffusion models are unavailable. It consumes conditional and unconditional predictions and calls the guided score formula every step.
## Inputs
- Initial scalar noise values.
- Condition/class target prototype.
- Guidance strength list and step count.
- Callable guided-score implementation.
## Outputs
- Generated scalar proxy samples.
- Per-step trace containing conditional prediction, unconditional prediction, and guided prediction.
## Workflow
1. Initialize samples from deterministic noise.
2. At every step estimate conditional and unconditional residuals toward class and global means.
3. Combine estimates with classifier-free guidance.
4. Update the sample and retain trace records.
## Validation
Tests verify that stronger guidance moves samples closer to the class prototype and that trace records include both denoiser evaluations.
## Limitations
This is a reduced mechanism check, not a photographic diffusion sampler.
