Add support for GLIGEN textbox model.

comfy/gligen.py

+import torch
+from torch import nn, einsum
+from ldm.modules.attention import CrossAttention
+from inspect import isfunction
+
+
+def exists(val):
+    return val is not None
+
+
+def uniq(arr):
+    return{el: True for el in arr}.keys()
+
+
+def default(val, d):
+    if exists(val):
+        return val
+    return d() if isfunction(d) else d
+
+
+# feedforward
+class GEGLU(nn.Module):
+    def __init__(self, dim_in, dim_out):
+        super().__init__()
+        self.proj = nn.Linear(dim_in, dim_out * 2)
+
+    def forward(self, x):
+        x, gate = self.proj(x).chunk(2, dim=-1)
+        return x * torch.nn.functional.gelu(gate)
+
+
+class FeedForward(nn.Module):
+    def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
+        super().__init__()
+        inner_dim = int(dim * mult)
+        dim_out = default(dim_out, dim)
+        project_in = nn.Sequential(
+            nn.Linear(dim, inner_dim),
+            nn.GELU()
+        ) if not glu else GEGLU(dim, inner_dim)
+
+        self.net = nn.Sequential(
+            project_in,
+            nn.Dropout(dropout),
+            nn.Linear(inner_dim, dim_out)
+        )
+
+    def forward(self, x):
+        return self.net(x)
+
+
+class GatedCrossAttentionDense(nn.Module):
+    def __init__(self, query_dim, context_dim, n_heads, d_head):
+        super().__init__()
+
+        self.attn = CrossAttention(
+            query_dim=query_dim,
+            context_dim=context_dim,
+            heads=n_heads,
+            dim_head=d_head)
+        self.ff = FeedForward(query_dim, glu=True)
+
+        self.norm1 = nn.LayerNorm(query_dim)
+        self.norm2 = nn.LayerNorm(query_dim)
+
+        self.register_parameter('alpha_attn', nn.Parameter(torch.tensor(0.)))
+        self.register_parameter('alpha_dense', nn.Parameter(torch.tensor(0.)))
+
+        # this can be useful: we can externally change magnitude of tanh(alpha)
+        # for example, when it is set to 0, then the entire model is same as
+        # original one
+        self.scale = 1
+
+    def forward(self, x, objs):
+
+        x = x + self.scale * \
+            torch.tanh(self.alpha_attn) * self.attn(self.norm1(x), objs, objs)
+        x = x + self.scale * \
+            torch.tanh(self.alpha_dense) * self.ff(self.norm2(x))
+
+        return x
+
+
+class GatedSelfAttentionDense(nn.Module):
+    def __init__(self, query_dim, context_dim, n_heads, d_head):
+        super().__init__()
+
+        # we need a linear projection since we need cat visual feature and obj
+        # feature
+        self.linear = nn.Linear(context_dim, query_dim)
+
+        self.attn = CrossAttention(
+            query_dim=query_dim,
+            context_dim=query_dim,
+            heads=n_heads,
+            dim_head=d_head)
+        self.ff = FeedForward(query_dim, glu=True)
+
+        self.norm1 = nn.LayerNorm(query_dim)
+        self.norm2 = nn.LayerNorm(query_dim)
+
+        self.register_parameter('alpha_attn', nn.Parameter(torch.tensor(0.)))
+        self.register_parameter('alpha_dense', nn.Parameter(torch.tensor(0.)))
+
+        # this can be useful: we can externally change magnitude of tanh(alpha)
+        # for example, when it is set to 0, then the entire model is same as
+        # original one
+        self.scale = 1
+
+    def forward(self, x, objs):
+
+        N_visual = x.shape[1]
+        objs = self.linear(objs)
+
+        x = x + self.scale * torch.tanh(self.alpha_attn) * self.attn(
+            self.norm1(torch.cat([x, objs], dim=1)))[:, 0:N_visual, :]
+        x = x + self.scale * \
+            torch.tanh(self.alpha_dense) * self.ff(self.norm2(x))
+
+        return x
+
+
+class GatedSelfAttentionDense2(nn.Module):
+    def __init__(self, query_dim, context_dim, n_heads, d_head):
+        super().__init__()
+
+        # we need a linear projection since we need cat visual feature and obj
+        # feature
+        self.linear = nn.Linear(context_dim, query_dim)
+
+        self.attn = CrossAttention(
+            query_dim=query_dim, context_dim=query_dim, dim_head=d_head)
+        self.ff = FeedForward(query_dim, glu=True)
+
+        self.norm1 = nn.LayerNorm(query_dim)
+        self.norm2 = nn.LayerNorm(query_dim)
+
+        self.register_parameter('alpha_attn', nn.Parameter(torch.tensor(0.)))
+        self.register_parameter('alpha_dense', nn.Parameter(torch.tensor(0.)))
+
+        # this can be useful: we can externally change magnitude of tanh(alpha)
+        # for example, when it is set to 0, then the entire model is same as
+        # original one
+        self.scale = 1
+
+    def forward(self, x, objs):
+
+        B, N_visual, _ = x.shape
+        B, N_ground, _ = objs.shape
+
+        objs = self.linear(objs)
+
+        # sanity check
+        size_v = math.sqrt(N_visual)
+        size_g = math.sqrt(N_ground)
+        assert int(size_v) == size_v, "Visual tokens must be square rootable"
+        assert int(size_g) == size_g, "Grounding tokens must be square rootable"
+        size_v = int(size_v)
+        size_g = int(size_g)
+
+        # select grounding token and resize it to visual token size as residual
+        out = self.attn(self.norm1(torch.cat([x, objs], dim=1)))[
+            :, N_visual:, :]
+        out = out.permute(0, 2, 1).reshape(B, -1, size_g, size_g)
+        out = torch.nn.functional.interpolate(
+            out, (size_v, size_v), mode='bicubic')
+        residual = out.reshape(B, -1, N_visual).permute(0, 2, 1)
+
+        # add residual to visual feature
+        x = x + self.scale * torch.tanh(self.alpha_attn) * residual
+        x = x + self.scale * \
+            torch.tanh(self.alpha_dense) * self.ff(self.norm2(x))
+
+        return x
+
+
+class FourierEmbedder():
+    def __init__(self, num_freqs=64, temperature=100):
+
+        self.num_freqs = num_freqs
+        self.temperature = temperature
+        self.freq_bands = temperature ** (torch.arange(num_freqs) / num_freqs)
+
+    @torch.no_grad()
+    def __call__(self, x, cat_dim=-1):
+        "x: arbitrary shape of tensor. dim: cat dim"
+        out = []
+        for freq in self.freq_bands:
+            out.append(torch.sin(freq * x))
+            out.append(torch.cos(freq * x))
+        return torch.cat(out, cat_dim)
+
+
+class PositionNet(nn.Module):
+    def __init__(self, in_dim, out_dim, fourier_freqs=8):
+        super().__init__()
+        self.in_dim = in_dim
+        self.out_dim = out_dim
+
+        self.fourier_embedder = FourierEmbedder(num_freqs=fourier_freqs)
+        self.position_dim = fourier_freqs * 2 * 4  # 2 is sin&cos, 4 is xyxy
+
+        self.linears = nn.Sequential(
+            nn.Linear(self.in_dim + self.position_dim, 512),
+            nn.SiLU(),
+            nn.Linear(512, 512),
+            nn.SiLU(),
+            nn.Linear(512, out_dim),
+        )
+
+        self.null_positive_feature = torch.nn.Parameter(
+            torch.zeros([self.in_dim]))
+        self.null_position_feature = torch.nn.Parameter(
+            torch.zeros([self.position_dim]))
+
+    def forward(self, boxes, masks, positive_embeddings):
+        B, N, _ = boxes.shape
+        masks = masks.unsqueeze(-1)
+
+        # embedding position (it may includes padding as placeholder)
+        xyxy_embedding = self.fourier_embedder(boxes)  # B*N*4 --> B*N*C
+
+        # learnable null embedding
+        positive_null = self.null_positive_feature.view(1, 1, -1)
+        xyxy_null = self.null_position_feature.view(1, 1, -1)
+
+        # replace padding with learnable null embedding
+        positive_embeddings = positive_embeddings * \
+            masks + (1 - masks) * positive_null
+        xyxy_embedding = xyxy_embedding * masks + (1 - masks) * xyxy_null
+
+        objs = self.linears(
+            torch.cat([positive_embeddings, xyxy_embedding], dim=-1))
+        assert objs.shape == torch.Size([B, N, self.out_dim])
+        return objs
+
+
+class Gligen(nn.Module):
+    def __init__(self, modules, position_net, key_dim):
+        super().__init__()
+        self.module_list = nn.ModuleList(modules)
+        self.position_net = position_net
+        self.key_dim = key_dim
+        self.max_objs = 30
+
+    def _set_position(self, boxes, masks, positive_embeddings):
+        objs = self.position_net(boxes, masks, positive_embeddings)
+
+        def func(key, x):
+            module = self.module_list[key]
+            return module(x, objs)
+        return func
+
+    def set_position(self, latent_image_shape, position_params, device):
+        batch, c, h, w = latent_image_shape
+        masks = torch.zeros([self.max_objs], device="cpu")
+        boxes = []
+        positive_embeddings = []
+        for p in position_params:
+            x1 = (p[4]) / w
+            y1 = (p[3]) / h
+            x2 = (p[4] + p[2]) / w
+            y2 = (p[3] + p[1]) / h
+            masks[len(boxes)] = 1.0
+            boxes += [torch.tensor((x1, y1, x2, y2)).unsqueeze(0)]
+            positive_embeddings += [p[0]]
+        append_boxes = []
+        append_conds = []
+        if len(boxes) < self.max_objs:
+            append_boxes = [torch.zeros(
+                [self.max_objs - len(boxes), 4], device="cpu")]
+            append_conds = [torch.zeros(
+                [self.max_objs - len(boxes), self.key_dim], device="cpu")]
+
+        box_out = torch.cat(
+            boxes + append_boxes).unsqueeze(0).repeat(batch, 1, 1)
+        masks = masks.unsqueeze(0).repeat(batch, 1)
+        conds = torch.cat(positive_embeddings +
+                          append_conds).unsqueeze(0).repeat(batch, 1, 1)
+        return self._set_position(
+            box_out.to(device),
+            masks.to(device),
+            conds.to(device))
+
+    def set_empty(self, latent_image_shape, device):
+        batch, c, h, w = latent_image_shape
+        masks = torch.zeros([self.max_objs], device="cpu").repeat(batch, 1)
+        box_out = torch.zeros([self.max_objs, 4],
+                              device="cpu").repeat(batch, 1, 1)
+        conds = torch.zeros([self.max_objs, self.key_dim],
+                            device="cpu").repeat(batch, 1, 1)
+        return self._set_position(
+            box_out.to(device),
+            masks.to(device),
+            conds.to(device))
+
+    def cleanup(self):
+        pass
+
+    def get_models(self):
+        return [self]
+
+def load_gligen(sd):
+    sd_k = sd.keys()
+    output_list = []
+    key_dim = 768
+    for a in ["input_blocks", "middle_block", "output_blocks"]:
+        for b in range(20):
+            k_temp = filter(lambda k: "{}.{}.".format(a, b)
+                            in k and ".fuser." in k, sd_k)
+            k_temp = map(lambda k: (k, k.split(".fuser.")[-1]), k_temp)
+
+            n_sd = {}
+            for k in k_temp:
+                n_sd[k[1]] = sd[k[0]]
+            if len(n_sd) > 0:
+                query_dim = n_sd["linear.weight"].shape[0]
+                key_dim = n_sd["linear.weight"].shape[1]
+
+                if key_dim == 768:  # SD1.x
+                    n_heads = 8
+                    d_head = query_dim // n_heads
+                else:
+                    d_head = 64
+                    n_heads = query_dim // d_head
+
+                gated = GatedSelfAttentionDense(
+                    query_dim, key_dim, n_heads, d_head)
+                gated.load_state_dict(n_sd, strict=False)
+                output_list.append(gated)
+
+    if "position_net.null_positive_feature" in sd_k:
+        in_dim = sd["position_net.null_positive_feature"].shape[0]
+        out_dim = sd["position_net.linears.4.weight"].shape[0]
+
+        class WeightsLoader(torch.nn.Module):
+            pass
+        w = WeightsLoader()
+        w.position_net = PositionNet(in_dim, out_dim)
+        w.load_state_dict(sd, strict=False)
+
+    gligen = Gligen(output_list, w.position_net, key_dim)
+    return gligen

comfy/ldm/modules/attention.py

@@ -510,6 +510,14 @@ class BasicTransformerBlock(nn.Module):
         return checkpoint(self._forward, (x, context, transformer_options), self.parameters(), self.checkpoint)
 
     def _forward(self, x, context=None, transformer_options={}):
+        current_index = None
+        if "current_index" in transformer_options:
+            current_index = transformer_options["current_index"]
+        if "patches" in transformer_options:
+            transformer_patches = transformer_options["patches"]
+        else:
+            transformer_patches = {}
+
         n = self.norm1(x)
         if "tomesd" in transformer_options:
             m, u = tomesd.get_functions(x, transformer_options["tomesd"]["ratio"], transformer_options["original_shape"])
@@ -518,11 +526,19 @@ class BasicTransformerBlock(nn.Module):
             n = self.attn1(n, context=context if self.disable_self_attn else None)
 
         x += n
+        if "middle_patch" in transformer_patches:
+            patch = transformer_patches["middle_patch"]
+            for p in patch:
+                x = p(current_index, x)
+
         n = self.norm2(x)
         n = self.attn2(n, context=context)
 
         x += n
         x = self.ff(self.norm3(x)) + x
+
+        if current_index is not None:
+            transformer_options["current_index"] += 1
         return x
 
 

comfy/ldm/modules/diffusionmodules/openaimodel.py

@@ -782,6 +782,8 @@ class UNetModel(nn.Module):
         :return: an [N x C x ...] Tensor of outputs.
         """
         transformer_options["original_shape"] = list(x.shape)
+        transformer_options["current_index"] = 0
+
         assert (y is not None) == (
             self.num_classes is not None
         ), "must specify y if and only if the model is class-conditional"

comfy/model_management.py

@@ -176,7 +176,7 @@ def load_model_gpu(model):
         model_accelerated = True
     return current_loaded_model
 
-def load_controlnet_gpu(models):
+def load_controlnet_gpu(control_models):
     global current_gpu_controlnets
     global vram_state
     if vram_state == VRAMState.CPU:
@@ -186,6 +186,10 @@ def load_controlnet_gpu(models):
         #don't load controlnets like this if low vram because they will be loaded right before running and unloaded right after
         return
 
+    models = []
+    for m in control_models:
+        models += m.get_models()
+
     for m in current_gpu_controlnets:
         if m not in models:
             m.cpu()

comfy/samplers.py

@@ -70,7 +70,21 @@ def sampling_function(model_function, x, timestep, uncond, cond, cond_scale, con
             control = None
             if 'control' in cond[1]:
                 control = cond[1]['control']
-            return (input_x, mult, conditionning, area, control)
+
+            patches = None
+            if 'gligen' in cond[1]:
+                gligen = cond[1]['gligen']
+                patches = {}
+                gligen_type = gligen[0]
+                gligen_model = gligen[1]
+                if gligen_type == "position":
+                    gligen_patch = gligen_model.set_position(input_x.shape, gligen[2], input_x.device)
+                else:
+                    gligen_patch = gligen_model.set_empty(input_x.shape, input_x.device)
+
+                patches['middle_patch'] = [gligen_patch]
+
+            return (input_x, mult, conditionning, area, control, patches)
 
         def cond_equal_size(c1, c2):
             if c1 is c2:
@@ -91,12 +105,21 @@ def sampling_function(model_function, x, timestep, uncond, cond, cond_scale, con
         def can_concat_cond(c1, c2):
             if c1[0].shape != c2[0].shape:
                 return False
+
+            #control
             if (c1[4] is None) != (c2[4] is None):
                 return False
             if c1[4] is not None:
                 if c1[4] is not c2[4]:
                     return False
 
+            #patches
+            if (c1[5] is None) != (c2[5] is None):
+                return False
+            if (c1[5] is not None):
+                if c1[5] is not c2[5]:
+                    return False
+
             return cond_equal_size(c1[2], c2[2])
 
         def cond_cat(c_list):
@@ -166,6 +189,7 @@ def sampling_function(model_function, x, timestep, uncond, cond, cond_scale, con
                 cond_or_uncond = []
                 area = []
                 control = None
+                patches = None
                 for x in to_batch:
                     o = to_run.pop(x)
                     p = o[0]
@@ -175,6 +199,7 @@ def sampling_function(model_function, x, timestep, uncond, cond, cond_scale, con
                     area += [p[3]]
                     cond_or_uncond += [o[1]]
                     control = p[4]
+                    patches = p[5]
 
                 batch_chunks = len(cond_or_uncond)
                 input_x = torch.cat(input_x)
@@ -184,8 +209,14 @@ def sampling_function(model_function, x, timestep, uncond, cond, cond_scale, con
                 if control is not None:
                     c['control'] = control.get_control(input_x, timestep_, c['c_crossattn'], len(cond_or_uncond))
 
+                transformer_options = {}
                 if 'transformer_options' in model_options:
-                    c['transformer_options'] = model_options['transformer_options']
+                    transformer_options = model_options['transformer_options'].copy()
+
+                if patches is not None:
+                    transformer_options["patches"] = patches
+
+                c['transformer_options'] = transformer_options
 
                 output = model_function(input_x, timestep_, cond=c).chunk(batch_chunks)
                 del input_x
@@ -309,8 +340,7 @@ def create_cond_with_same_area_if_none(conds, c):
     n = c[1].copy()
     conds += [[smallest[0], n]]
 
-
-def apply_control_net_to_equal_area(conds, uncond):
+def apply_empty_x_to_equal_area(conds, uncond, name, uncond_fill_func):
     cond_cnets = []
     cond_other = []
     uncond_cnets = []
@@ -318,15 +348,15 @@ def apply_control_net_to_equal_area(conds, uncond):
     for t in range(len(conds)):
         x = conds[t]
         if 'area' not in x[1]:
-            if 'control' in x[1] and x[1]['control'] is not None:
-                cond_cnets.append(x[1]['control'])
+            if name in x[1] and x[1][name] is not None:
+                cond_cnets.append(x[1][name])
             else:
                 cond_other.append((x, t))
     for t in range(len(uncond)):
         x = uncond[t]
         if 'area' not in x[1]:
-            if 'control' in x[1] and x[1]['control'] is not None:
-                uncond_cnets.append(x[1]['control'])
+            if name in x[1] and x[1][name] is not None:
+                uncond_cnets.append(x[1][name])
             else:
                 uncond_other.append((x, t))
 
@@ -336,15 +366,16 @@ def apply_control_net_to_equal_area(conds, uncond):
     for x in range(len(cond_cnets)):
         temp = uncond_other[x % len(uncond_other)]
         o = temp[0]
-        if 'control' in o[1] and o[1]['control'] is not None:
+        if name in o[1] and o[1][name] is not None:
             n = o[1].copy()
-            n['control'] = cond_cnets[x]
+            n[name] = uncond_fill_func(cond_cnets, x)
             uncond += [[o[0], n]]
         else:
             n = o[1].copy()
-            n['control'] = cond_cnets[x]
+            n[name] = uncond_fill_func(cond_cnets, x)
             uncond[temp[1]] = [o[0], n]
 
+
 def encode_adm(noise_augmentor, conds, batch_size, device):
     for t in range(len(conds)):
         x = conds[t]
@@ -378,6 +409,7 @@ def encode_adm(noise_augmentor, conds, batch_size, device):
 
     return conds
 
+
 class KSampler:
     SCHEDULERS = ["karras", "normal", "simple", "ddim_uniform"]
     SAMPLERS = ["euler", "euler_ancestral", "heun", "dpm_2", "dpm_2_ancestral",
@@ -466,7 +498,8 @@ class KSampler:
         for c in negative:
             create_cond_with_same_area_if_none(positive, c)
 
-        apply_control_net_to_equal_area(positive, negative)
+        apply_empty_x_to_equal_area(positive, negative, 'control', lambda cond_cnets, x: cond_cnets[x])
+        apply_empty_x_to_equal_area(positive, negative, 'gligen', lambda cond_cnets, x: cond_cnets[x])
 
         if self.model.model.diffusion_model.dtype == torch.float16:
             precision_scope = torch.autocast

comfy/sd.py

@@ -13,6 +13,7 @@ from .t2i_adapter import adapter
 
 from . import utils
 from . import clip_vision
+from . import gligen
 
 def load_model_weights(model, sd, verbose=False, load_state_dict_to=[]):
     m, u = model.load_state_dict(sd, strict=False)
@@ -378,7 +379,7 @@ class CLIP:
     def tokenize(self, text, return_word_ids=False):
         return self.tokenizer.tokenize_with_weights(text, return_word_ids)
 
-    def encode_from_tokens(self, tokens):
+    def encode_from_tokens(self, tokens, return_pooled=False):
         if self.layer_idx is not None:
             self.cond_stage_model.clip_layer(self.layer_idx)
         try:
@@ -388,6 +389,10 @@ class CLIP:
         except Exception as e:
             self.patcher.unpatch_model()
             raise e
+        if return_pooled:
+            eos_token_index = max(range(len(tokens[0])), key=tokens[0].__getitem__)
+            pooled = cond[:, eos_token_index]
+            return cond, pooled
         return cond
 
     def encode(self, text):
@@ -564,10 +569,10 @@ class ControlNet:
         c.strength = self.strength
         return c
 
-    def get_control_models(self):
+    def get_models(self):
         out = []
         if self.previous_controlnet is not None:
-            out += self.previous_controlnet.get_control_models()
+            out += self.previous_controlnet.get_models()
         out.append(self.control_model)
         return out
 
@@ -737,10 +742,10 @@ class T2IAdapter:
             del self.cond_hint
             self.cond_hint = None
 
-    def get_control_models(self):
+    def get_models(self):
         out = []
         if self.previous_controlnet is not None:
-            out += self.previous_controlnet.get_control_models()
+            out += self.previous_controlnet.get_models()
         return out
 
 def load_t2i_adapter(t2i_data):
@@ -787,6 +792,13 @@ def load_clip(ckpt_path, embedding_directory=None):
     clip.load_from_state_dict(clip_data)
     return clip
 
+def load_gligen(ckpt_path):
+    data = utils.load_torch_file(ckpt_path)
+    model = gligen.load_gligen(data)
+    if model_management.should_use_fp16():
+        model = model.half()
+    return model
+
 def load_checkpoint(config_path, ckpt_path, output_vae=True, output_clip=True, embedding_directory=None):
     with open(config_path, 'r') as stream:
         config = yaml.safe_load(stream)

folder_paths.py

@@ -26,6 +26,8 @@ folder_names_and_paths["embeddings"] = ([os.path.join(models_dir, "embeddings")]
 folder_names_and_paths["diffusers"] = ([os.path.join(models_dir, "diffusers")], ["folder"])
 
 folder_names_and_paths["controlnet"] = ([os.path.join(models_dir, "controlnet"), os.path.join(models_dir, "t2i_adapter")], supported_pt_extensions)
+folder_names_and_paths["gligen"] = ([os.path.join(models_dir, "gligen")], supported_pt_extensions)
+
 folder_names_and_paths["upscale_models"] = ([os.path.join(models_dir, "upscale_models")], supported_pt_extensions)
 
 folder_names_and_paths["custom_nodes"] = ([os.path.join(base_path, "custom_nodes")], [])

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,
 }