Refactor calc_cond_uncond_batch into calc_cond_batch.

calc_cond_batch can take an arbitrary amount of cond inputs.

Added a calc_cond_uncond_batch wrapper with a warning so custom nodes
won't break.

comfy/samplers.py

@@ -127,30 +127,23 @@ def cond_cat(c_list):
 
     return out
 
-def calc_cond_uncond_batch(model, cond, uncond, x_in, timestep, model_options):
-    out_cond = torch.zeros_like(x_in)
-    out_count = torch.ones_like(x_in) * 1e-37
-
-    out_uncond = torch.zeros_like(x_in)
-    out_uncond_count = torch.ones_like(x_in) * 1e-37
-
-    COND = 0
-    UNCOND = 1
-
+def calc_cond_batch(model, conds, x_in, timestep, model_options):
+    out_conds = []
+    out_counts = []
     to_run = []
-    for x in cond:
-        p = get_area_and_mult(x, x_in, timestep)
-        if p is None:
-            continue
 
-        to_run += [(p, COND)]
-    if uncond is not None:
-        for x in uncond:
-            p = get_area_and_mult(x, x_in, timestep)
-            if p is None:
-                continue
+    for i in range(len(conds)):
+        out_conds.append(torch.zeros_like(x_in))
+        out_counts.append(torch.ones_like(x_in) * 1e-37)
 
-            to_run += [(p, UNCOND)]
+        cond = conds[i]
+        if cond is not None:
+            for x in cond:
+                p = get_area_and_mult(x, x_in, timestep)
+                if p is None:
+                    continue
+
+                to_run += [(p, i)]
 
     while len(to_run) > 0:
         first = to_run[0]
@@ -222,22 +215,20 @@ def calc_cond_uncond_batch(model, cond, uncond, x_in, timestep, model_options):
             output = model_options['model_function_wrapper'](model.apply_model, {"input": input_x, "timestep": timestep_, "c": c, "cond_or_uncond": cond_or_uncond}).chunk(batch_chunks)
         else:
             output = model.apply_model(input_x, timestep_, **c).chunk(batch_chunks)
-        del input_x
 
         for o in range(batch_chunks):
-            if cond_or_uncond[o] == COND:
-                out_cond[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += output[o] * mult[o]
-                out_count[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += mult[o]
-            else:
-                out_uncond[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += output[o] * mult[o]
-                out_uncond_count[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += mult[o]
-        del mult
+            cond_index = cond_or_uncond[o]
+            out_conds[cond_index][:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += output[o] * mult[o]
+            out_counts[cond_index][:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += mult[o]
 
-    out_cond /= out_count
-    del out_count
-    out_uncond /= out_uncond_count
-    del out_uncond_count
-    return out_cond, out_uncond
+    for i in range(len(out_conds)):
+        out_conds[i] /= out_counts[i]
+
+    return out_conds
+
+def calc_cond_uncond_batch(model, cond, uncond, x_in, timestep, model_options): #TODO: remove
+    logging.warning("WARNING: The comfy.samplers.calc_cond_uncond_batch function is deprecated please use the calc_cond_batch one instead.")
+    return tuple(calc_cond_batch(model, [cond, uncond], x_in, timestep, model_options))
 
 #The main sampling function shared by all the samplers
 #Returns denoised
@@ -247,7 +238,13 @@ def sampling_function(model, x, timestep, uncond, cond, cond_scale, model_option
         else:
             uncond_ = uncond
 
-        cond_pred, uncond_pred = calc_cond_uncond_batch(model, cond, uncond_, x, timestep, model_options)
+
+        conds = [cond, uncond_]
+
+        out = calc_cond_batch(model, conds, x, timestep, model_options)
+        cond_pred = out[0]
+        uncond_pred = out[1]
+
         if "sampler_cfg_function" in model_options:
             args = {"cond": x - cond_pred, "uncond": x - uncond_pred, "cond_scale": cond_scale, "timestep": timestep, "input": x, "sigma": timestep,
                     "cond_denoised": cond_pred, "uncond_denoised": uncond_pred, "model": model, "model_options": model_options}

comfy_extras/nodes_perpneg.py

@@ -31,7 +31,7 @@ class PerpNeg:
             model_options = args["model_options"]
             nocond_processed = comfy.samplers.encode_model_conds(model.extra_conds, nocond, x, x.device, "negative")
 
-            (noise_pred_nocond, _) = comfy.samplers.calc_cond_uncond_batch(model, nocond_processed, None, x, sigma, model_options)
+            (noise_pred_nocond,) = comfy.samplers.calc_cond_batch(model, [nocond_processed], x, sigma, model_options)
 
             pos = noise_pred_pos - noise_pred_nocond
             neg = noise_pred_neg - noise_pred_nocond

comfy_extras/nodes_sag.py

@@ -150,7 +150,7 @@ class SelfAttentionGuidance:
             degraded = create_blur_map(uncond_pred, uncond_attn, sag_sigma, sag_threshold)
             degraded_noised = degraded + x - uncond_pred
             # call into the UNet
-            (sag, _) = comfy.samplers.calc_cond_uncond_batch(model, uncond, None, degraded_noised, sigma, model_options)
+            (sag,) = comfy.samplers.calc_cond_batch(model, [uncond], degraded_noised, sigma, model_options)
             return cfg_result + (degraded - sag) * sag_scale
 
         m.set_model_sampler_post_cfg_function(post_cfg_function, disable_cfg1_optimization=True)