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+# ComfyUI-AutoGuidance
 
+A ComfyUI custom node implementing **AutoGuidance** from the paper:
+
+> **Guiding a Diffusion Model with a Bad Version of Itself** (Karras et al., 2024)  
+> https://arxiv.org/abs/2406.02507
+
+Repository: https://github.com/xmarre/ComfyUI-AutoGuidance
+
+## What this does
+
+Classic CFG steers generation by contrasting conditional and unconditional predictions.  
+**AutoGuidance** adds a second model path (“bad model”) and guides relative to that weaker reference.
+
+In practice, this gives you another control axis for balancing:
+- quality/faithfulness,
+- collapse/overcooking risk,
+- structure vs detail emphasis (via ramping).
+
+## Included nodes
+
+This extension registers two nodes:
+
+- **AutoGuidance CFG Guider (good+bad)** (`AutoGuidanceCFGGuider`)  
+  Produces a `GUIDER` for use with `SamplerCustomAdvanced`.
+- **AutoGuidance Detailer Hook (Impact Pack)** (`AutoGuidanceImpactDetailerHookProvider`)  
+  Produces a `DETAILER_HOOK` for Impact Pack detailer workflows (including FaceDetailer).
+
+## Installation
+
+Clone into your ComfyUI custom nodes directory and restart ComfyUI:
+
+```bash
+git clone https://github.com/xmarre/ComfyUI-AutoGuidance
+```
+
+No extra dependencies.
+
+## Basic wiring (SamplerCustomAdvanced)
+
+1. Load two models:
+   - `good_model`
+   - `bad_model`
+2. Build conditioning normally:
+   - `positive`
+   - `negative`
+3. Add **AutoGuidance CFG Guider (good+bad)**.
+4. Connect its `GUIDER` output to **SamplerCustomAdvanced** `guider` input.
+
+## Impact Pack / FaceDetailer integration
+
+Use **AutoGuidance Detailer Hook (Impact Pack)** when your detailer nodes accept a `DETAILER_HOOK`.
+
+This injects AutoGuidance into detailer sampling passes without editing Impact Pack source files.
+
+## Important: dual-model mode must use truly distinct model instances
+
+If you use:
+
+- `swap_mode = dual_models_2x_vram`
+
+then ensure ComfyUI does **not** dedupe the two model loads into one shared instance.
+
+### Recommended setup
+
+Make a real file copy of your checkpoint (same bytes, different filename), for example:
+
+- `SDXL_base.safetensors`
+- `SDXL_base_BADCOPY.safetensors`
+
+Then:
+
+- Loader A (file 1) → `good_model`
+- Loader B (file 2) → `bad_model`
+
+If both loaders point to the exact same path, ComfyUI may share/collapse model state and dual-mode behavior/performance can be incorrect.
+
+## Parameters (AutoGuidance CFG Guider)
+
+### Required
+
+- `cfg`
+- `w_autoguide` (effect is effectively off at `1.0`; stronger above `1.0`)
+- `swap_mode`
+  - `shared_safe_low_vram` (safest/slowest)
+  - `shared_fast_extra_vram` (faster shared swap, extra VRAM)
+  - `dual_models_2x_vram` (fastest, highest VRAM, requires distinct instances)
+
+### Optional core controls
+
+- `ag_delta_mode`
+  - `bad_conditional` (default, common starting point)
+  - `raw_delta`
+  - `project_cfg`
+  - `reject_cfg`
+- `ag_max_ratio` (caps AutoGuidance push relative to CFG update magnitude)
+- `ag_allow_negative`
+- `ag_ramp_mode`
+  - `flat`
+  - `detail_late`
+  - `compose_early`
+  - `mid_peak`
+- `ag_ramp_power`
+- `ag_ramp_floor`
+- `ag_post_cfg_mode`
+  - `keep`
+  - `apply_after`
+  - `skip`
+
+### Swap/debug controls
+
+- `safe_force_clean_swap`
+- `uuid_only_noop`
+- `debug_swap`
+- `debug_metrics`
+
+## Example setup (one working recipe)
+
+> This is one practical baseline, not a rule.
+
+### Models
+
+- **Good side**:
+  - Base checkpoint + more fully-trained/specialized stack (e.g., 40-epoch character LoRA + DMD2/LCM, etc.)
+- **Bad side** options:
+  - Base checkpoint + earlier/weaker checkpoint/LoRA (e.g., 10-epoch) with intentionally poor weighting
+  - Base checkpoint + fewer adaptation modules
+  - Base checkpoint only
+
+Core idea: bad side should be meaningfully weaker/less specialized than good side.
+
+### Node settings example
+
+- `cfg: 1.1`
+- `w_autoguide: 3.00`
+- `swap_mode: dual_models_2x_vram`
+- `ag_delta_mode: reject_cfg`
+- `ag_max_ratio: 0.75`
+- `ag_allow_negative: true`
+- `ag_ramp_mode: compose_early`
+- `ag_ramp_power: 2.0`
+- `ag_ramp_floor: 0.00`
+- `ag_post_cfg_mode: skip`
+- `safe_force_clean_swap: true`
+- `uuid_only_noop: false`
+- `debug_swap: true`
+- `debug_metrics: true`
+
+## Practical tuning notes
+
+- Increase `w_autoguide` above `1.0` to strengthen effect.
+- Use `ag_max_ratio` to prevent runaway/cooked outputs.
+- `compose_early` tends to affect composition/structure earlier in denoise.
+- Try `detail_late` for a more late-step/detail-leaning influence.
+
+## VRAM and speed
+
+AutoGuidance adds extra forward work versus plain CFG.
+
+- `dual_models_2x_vram`: fastest but highest VRAM and strict dual-instance requirement.
+- Shared modes: lower VRAM, slower due to swapping.
+
+## Suggested A/B evaluation
+
+At fixed seed/steps, compare:
+
+- CFG-only vs CFG + AutoGuidance
+- different `ag_ramp_mode`
+- different `ag_max_ratio` caps
+- different `ag_delta_mode`
+
+## Feedback wanted
+
+Useful community feedback includes:
+
+- what “bad model” definitions work best in real SD pipelines,
+- parameter combos that outperform standard CFG or NAG,
+- reproducible A/B examples with fixed seed + settings.
+
+---
+
+If this node helps, share workflows and side-by-side comparisons in issues/discussions.