Update app.py

app.py

@@ -187,26 +187,49 @@ def infer(prompt, negative_prompt,
         use_anchor=use_anchor,
     )
 
-    # CLIP-L
+    # --- STEP 0: build shift config -----------------------------------------
+    cfg_shift = ShiftConfig(
+        prompt           = prompt,
+        seed             = seed,
+        strength         = strength,
+        delta_scale      = delta_scale,
+        sigma_scale      = sigma_scale,
+        gate_probability = gate_prob,
+        noise_injection  = noise,
+        use_anchor       = use_anchor,
+        guidance_scale   = gpred_scale,
+    )
+    
+    # --- STEP 1: encoder embeddings -----------------------------------------
+    t5_seq = ConditioningShifter.extract_encoder_embeddings(
+        {"tokenizer": _t5_tok, "model": _t5_mod, "config": {"config": {}}},
+        device, cfg_shift
+    )
+    
+    # --- STEP 2: run adapters -----------------------------------------------
+    outputs = []
     if adapter_l_file and adapter_l_file != "None":
-        cfg_l = conf_l.copy(); cfg_l.update(cfg_common)
-        if "booru" in adapter_l_file: cfg_l["heads"] = 4
-        adapter_l = load_adapter(repo_l, adapter_l_file, conf_l, device)
-        clip_l_mod, delta_l, gate_l, g_pred_l, tau_l = adapter_forward(
-            adapter_l, t5_seq, embeds["clip_l"], cfg_l)
-    else:
-        clip_l_mod = embeds["clip_l"]; delta_l = torch.zeros_like(clip_l_mod)
-        gate_l = torch.zeros_like(clip_l_mod[..., :1]); g_pred_l = tau_l = torch.tensor(0.)
-
-    # CLIP-G
+        ada_l = load_adapter(repo_l, adapter_l_file, conf_l, device)
+        outputs.append(ConditioningShifter.run_adapter(
+            ada_l, t5_seq, embeds["clip_l"],
+            cfg_shift.guidance_scale, "clip_l", (0, 768)))
+    
     if adapter_g_file and adapter_g_file != "None":
-        cfg_g = conf_g.copy(); cfg_g.update(cfg_common)
-        adapter_g = load_adapter(repo_g, adapter_g_file, conf_g, device)
-        clip_g_mod, delta_g, gate_g, g_pred_g, tau_g = adapter_forward(
-            adapter_g, t5_seq, embeds["clip_g"], cfg_g)
-    else:
-        clip_g_mod = embeds["clip_g"]; delta_g = torch.zeros_like(clip_g_mod)
-        gate_g = torch.zeros_like(clip_g_mod[..., :1]); g_pred_g = tau_g = torch.tensor(0.)
+        ada_g = load_adapter(repo_g, adapter_g_file, conf_g, device)
+        outputs.append(ConditioningShifter.run_adapter(
+            ada_g, t5_seq, embeds["clip_g"],
+            cfg_shift.guidance_scale, "clip_g", (768, 2048)))
+    
+    # --- STEP 3: apply mods --------------------------------------------------
+    clip_l_mod, clip_g_mod = embeds["clip_l"], embeds["clip_g"]
+    for out in outputs:
+        tgt = clip_l_mod if out.adapter_type == "clip_l" else clip_g_mod
+        mod = ConditioningShifter.apply_modifications(tgt, [out], cfg_shift)
+        if out.adapter_type == "clip_l":
+            clip_l_mod = mod
+        else:
+            clip_g_mod = mod
+
 
     # concatenate for SDXL
     prompt_embeds = torch.cat([clip_l_mod, clip_g_mod], dim=-1)