Add support for GLIGEN textbox model.

nodes.py

@@ -490,6 +490,51 @@ class unCLIPConditioning:
             c.append(n)
         return (c, )
 
+class GLIGENLoader:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "gligen_name": (folder_paths.get_filename_list("gligen"), )}}
+
+    RETURN_TYPES = ("GLIGEN",)
+    FUNCTION = "load_gligen"
+
+    CATEGORY = "_for_testing/gligen"
+
+    def load_gligen(self, gligen_name):
+        gligen_path = folder_paths.get_full_path("gligen", gligen_name)
+        gligen = comfy.sd.load_gligen(gligen_path)
+        return (gligen,)
+
+class GLIGENTextBoxApply:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"conditioning_to": ("CONDITIONING", ),
+                              "clip": ("CLIP", ),
+                              "gligen_textbox_model": ("GLIGEN", ),
+                              "text": ("STRING", {"multiline": True}),
+                              "width": ("INT", {"default": 64, "min": 8, "max": MAX_RESOLUTION, "step": 8}),
+                              "height": ("INT", {"default": 64, "min": 8, "max": MAX_RESOLUTION, "step": 8}),
+                              "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
+                              "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
+                             }}
+    RETURN_TYPES = ("CONDITIONING",)
+    FUNCTION = "append"
+
+    CATEGORY = "_for_testing/gligen"
+
+    def append(self, conditioning_to, clip, gligen_textbox_model, text, width, height, x, y):
+        c = []
+        cond, cond_pooled = clip.encode_from_tokens(clip.tokenize(text), return_pooled=True)
+        for t in conditioning_to:
+            n = [t[0], t[1].copy()]
+            position_params = [(cond_pooled, height // 8, width // 8, y // 8, x // 8)]
+            prev = []
+            if "gligen" in n[1]:
+                prev = n[1]['gligen'][2]
+
+            n[1]['gligen'] = ("position", gligen_textbox_model, prev + position_params)
+            c.append(n)
+        return (c, )
 
 class EmptyLatentImage:
     def __init__(self, device="cpu"):
@@ -731,27 +776,30 @@ def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive,
     negative_copy = []
 
     control_nets = []
+    def get_models(cond):
+        models = []
+        for c in cond:
+            if 'control' in c[1]:
+                models += [c[1]['control']]
+            if 'gligen' in c[1]:
+                models += [c[1]['gligen'][1]]
+        return models
+
     for p in positive:
         t = p[0]
         if t.shape[0] < noise.shape[0]:
             t = torch.cat([t] * noise.shape[0])
         t = t.to(device)
-        if 'control' in p[1]:
-            control_nets += [p[1]['control']]
         positive_copy += [[t] + p[1:]]
     for n in negative:
         t = n[0]
         if t.shape[0] < noise.shape[0]:
             t = torch.cat([t] * noise.shape[0])
         t = t.to(device)
-        if 'control' in n[1]:
-            control_nets += [n[1]['control']]
         negative_copy += [[t] + n[1:]]
 
-    control_net_models = []
-    for x in control_nets:
-        control_net_models += x.get_control_models()
-    comfy.model_management.load_controlnet_gpu(control_net_models)
+    models = get_models(positive) + get_models(negative)
+    comfy.model_management.load_controlnet_gpu(models)
 
     if sampler_name in comfy.samplers.KSampler.SAMPLERS:
         sampler = comfy.samplers.KSampler(real_model, steps=steps, device=device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options)
@@ -761,8 +809,8 @@ def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive,
 
     samples = sampler.sample(noise, positive_copy, negative_copy, cfg=cfg, latent_image=latent_image, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, denoise_mask=noise_mask)
     samples = samples.cpu()
-    for c in control_nets:
-        c.cleanup()
+    for m in models:
+        m.cleanup()
 
     out = latent.copy()
     out["samples"] = samples
@@ -1128,6 +1176,9 @@ NODE_CLASS_MAPPINGS = {
     "VAEEncodeTiled": VAEEncodeTiled,
     "TomePatchModel": TomePatchModel,
     "unCLIPCheckpointLoader": unCLIPCheckpointLoader,
+    "GLIGENLoader": GLIGENLoader,
+    "GLIGENTextBoxApply": GLIGENTextBoxApply,
+
     "CheckpointLoader": CheckpointLoader,
     "DiffusersLoader": DiffusersLoader,
 }