Update README.md

README.md

@@ -1,54 +1,48 @@
----
-library_name: diffusers
-pipeline_tag: text-to-image
-tags:
-  - safety
-  - classifier-guidance
-  - stable-diffusion
-  - plug-and-play
-license: apache-2.0
----
-
-# Safe Diffusion Guidance (SDG) — plug-and-play safety layer for Stable Diffusion
-
-**Safe Diffusion Guidance (SDG)** is a *classifier-guided denoising* layer that steers the sampling trajectory away from unsafe content **without retraining** the base model.  
-It works **standalone** with SD 1.4 / 1.5 / 2.1 and **composes** cleanly with ESD/UCE/SLD.
-
-- **Safety signal:** a 5-class mid-UNet feature classifier (classes: `gore, hate, medical, safe, sexual`) trained on (1280×8×8) features.
-- **Controls:** `safety_scale` (strength), `mid_fraction` (fraction of steps guided).
-- **Plug-in:** drop into any SD pipeline, or stack on top of ESD/UCE/SLD.
-- **No retraining:** small gradient nudges to latents during denoising.
-
-> **Note on metrics** (matching our paper): FID/KID are computed vs. _baseline model outputs_ rather than real images; baseline FID/KID are ≈0 by construction.
-
-## Quickstart (SD 1.5)
-
-```python
-import torch
-from diffusers import StableDiffusionPipeline
-
-# 1) Load base SD pipeline (disable default safety checker)
-base = StableDiffusionPipeline.from_pretrained(
-    "runwayml/stable-diffusion-v1-5",
-    torch_dtype=torch.float16,
-    safety_checker=None
-).to("cuda")
-
-# 2) Load SDG custom pipeline from Hub (this repo)
-sdg = StableDiffusionPipeline.from_pretrained(
-    "your-org/safe-diffusion-guidance",
-    custom_pipeline="safe_diffusion_guidance",
-    torch_dtype=torch.float16
-).to("cuda")
-
-img = sdg(
-    base_pipe=base,
-    prompt="portrait photograph, studio light, 85mm, realistic",
-    num_inference_steps=50,
-    guidance_scale=7.5,
-    safety_scale=5.0,        # strength: ~2–8 (Light→Strong)
-    mid_fraction=1.0,        # guide fraction of steps: 0.5, 0.8, 1.0
-    safe_class_index=3       # index of 'safe' in [gore,hate,medical,safe,sexual]
-).images[0]
-
-img.save("sdg_safe_output.png")
+---
+library_name: diffusers
+pipeline_tag: text-to-image
+tags:
+  - safety
+  - classifier-guidance
+  - stable-diffusion
+  - plug-and-play
+license: apache-2.0
+---
+
+# Safe Diffusion Guidance (SDG) —
+
+**Safe Diffusion Guidance (SDG)** is a *classifier-guided denoising* layer that steers the sampling trajectory away from unsafe content **without retraining** the base model.  
+It works **standalone** with SD 1.4 / 1.5 / 2.1.
+
+
+## Quickstart (SD 1.5)
+
+```python
+import torch
+from diffusers import StableDiffusionPipeline
+
+# 1) Load base SD pipeline (disable default safety checker)
+base = StableDiffusionPipeline.from_pretrained(
+    "runwayml/stable-diffusion-v1-5",
+    torch_dtype=torch.float16,
+    safety_checker=None
+).to("cuda")
+
+# 2) Load SDG custom pipeline from Hub (this repo)
+sdg = StableDiffusionPipeline.from_pretrained(
+    "basimazam/safe-diffusion-guidance",
+    custom_pipeline="safe_diffusion_guidance",
+    torch_dtype=torch.float16
+).to("cuda")
+
+img = sdg(
+    base_pipe=base,
+    prompt="portrait photograph, studio light, 85mm, realistic",
+    num_inference_steps=50,
+    guidance_scale=7.5,
+    safety_scale=5.0,        
+    mid_fraction=1.0,        
+    safe_class_index=3       
+).images[0]
+
+img.save("sdg_safe_output.png")