Merge branch 'master_old'

.github/workflows/windows_release_cu118_dependencies_2.yml

+name: "Windows Release cu118 dependencies 2"
+
+on:
+  workflow_dispatch:
+#  push:
+#    branches:
+#      - master
+
+jobs:
+  build_dependencies:
+    runs-on: windows-latest
+    steps:
+        - uses: actions/checkout@v3
+        - uses: actions/setup-python@v4
+          with:
+            python-version: '3.10.9'
+
+        - shell: bash
+          run: |
+            python -m pip wheel --no-cache-dir torch torchvision torchaudio xformers==0.0.19.dev516 --extra-index-url https://download.pytorch.org/whl/cu118 -r requirements.txt pygit2 -w ./temp_wheel_dir
+            python -m pip install --no-cache-dir ./temp_wheel_dir/*
+            echo installed basic
+            ls -lah temp_wheel_dir
+            mv temp_wheel_dir cu118_python_deps
+            tar cf cu118_python_deps.tar cu118_python_deps
+
+        - uses: actions/cache/save@v3
+          with:
+            path: cu118_python_deps.tar
+            key: ${{ runner.os }}-build-cu118

README.md

@@ -32,14 +32,28 @@ This ui will let you design and execute advanced stable diffusion pipelines usin
 Workflow examples can be found on the [Examples page](https://comfyanonymous.github.io/ComfyUI_examples/)
 
 ## Shortcuts
-- **Ctrl + A** select all nodes
-- **Ctrl + M** mute/unmute selected nodes
-- **Delete** or **Backspace** delete selected nodes
-- **Space** Holding space key while moving the cursor moves the canvas around. It works when holding the mouse button down so it is easier to connect different nodes when the canvas gets too large.
-- **Ctrl/Shift + Click** Add clicked node to selection.
-- **Ctrl + C/Ctrl + V** - Copy and paste selected nodes, without maintaining the connection to the outputs of unselected nodes.
-- **Ctrl + C/Ctrl + Shift + V** - Copy and paste selected nodes, and maintaining the connection from the outputs of unselected nodes to the inputs of the newly pasted nodes.
-- Holding **Shift** and drag selected nodes - Move multiple selected nodes at the same time.
+
+| Keybind | Explanation |
+| - | - |
+| Ctrl + Enter | Queue up current graph for generation |
+| Ctrl + Shift + Enter | Queue up current graph as first for generation |
+| Ctrl + S | Save workflow |
+| Ctrl + O | Load workflow |
+| Ctrl + A | Select all nodes |
+| Ctrl + M | Mute/unmute selected nodes |
+| Delete/Backspace | Delete selected nodes |
+| Ctrl + Delete/Backspace | Delete the current graph |
+| Space | Move the canvas around when held and moving the cursor |
+| Ctrl/Shift + Click | Add clicked node to selection |
+| Ctrl + C/Ctrl + V | Copy and paste selected nodes (without maintaining connections to outputs of unselected nodes) |
+| Ctrl + C/Ctrl + Shift + V| Copy and paste selected nodes (maintaining connections from outputs of unselected nodes to inputs of pasted nodes) |
+| Shift + Drag | Move multiple selected nodes at the same time |
+| Ctrl + D | Load default graph |
+| Q | Toggle visibility of the queue |
+| H | Toggle visibility of history |
+| R | Refresh graph |
+
+Ctrl can also be replaced with Cmd instead for MacOS users
 
 # Installing
 
@@ -69,7 +83,7 @@ Put your VAE in: models/vae
 
 At the time of writing this pytorch has issues with python versions higher than 3.10 so make sure your python/pip versions are 3.10.
 
-### AMD (Linux only)
+### AMD GPUs (Linux only)
 AMD users can install rocm and pytorch with pip if you don't have it already installed, this is the command to install the stable version:
 
 ```pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/rocm5.4.2```

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

@@ -9,7 +9,7 @@ from typing import Optional, Any
 from ldm.modules.diffusionmodules.util import checkpoint
 from .sub_quadratic_attention import efficient_dot_product_attention
 
-import model_management
+from comfy import model_management
 
 from . import tomesd
 
@@ -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/model.py

@@ -7,7 +7,7 @@ from einops import rearrange
 from typing import Optional, Any
 
 from ldm.modules.attention import MemoryEfficientCrossAttention
-import model_management
+from comfy import model_management
 
 if model_management.xformers_enabled_vae():
     import xformers

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/ldm/modules/sub_quadratic_attention.py

@@ -24,7 +24,7 @@ except ImportError:
 from torch import Tensor
 from typing import List
 
-import model_management
+from comfy import model_management
 
 def dynamic_slice(
     x: Tensor,

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

@@ -3,7 +3,7 @@ from .k_diffusion import external as k_diffusion_external
 from .extra_samplers import uni_pc
 import torch
 import contextlib
-import model_management
+from comfy import model_management
 from .ldm.models.diffusion.ddim import DDIMSampler
 from .ldm.modules.diffusionmodules.util import make_ddim_timesteps
 
@@ -36,8 +36,8 @@ def sampling_function(model_function, x, timestep, uncond, cond, cond_scale, con
                 strength = cond[1]['strength']
 
             adm_cond = None
-            if 'adm' in cond[1]:
-                adm_cond = cond[1]['adm']
+            if 'adm_encoded' in cond[1]:
+                adm_cond = cond[1]['adm_encoded']
 
             input_x = x_in[:,:,area[2]:area[0] + area[2],area[3]:area[1] + area[3]]
             mult = torch.ones_like(input_x) * strength
@@ -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
@@ -211,7 +242,10 @@ def sampling_function(model_function, x, timestep, uncond, cond, cond_scale, con
 
         max_total_area = model_management.maximum_batch_area()
         cond, uncond = calc_cond_uncond_batch(model_function, cond, uncond, x, timestep, max_total_area, cond_concat, model_options)
-        return uncond + (cond - uncond) * cond_scale
+        if "sampler_cfg_function" in model_options:
+            return model_options["sampler_cfg_function"](cond, uncond, cond_scale)
+        else:
+            return uncond + (cond - uncond) * cond_scale
 
 
 class CompVisVDenoiser(k_diffusion_external.DiscreteVDDPMDenoiser):
@@ -306,8 +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 = []
@@ -315,15 +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))
 
@@ -333,15 +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]
@@ -371,10 +405,11 @@ def encode_adm(noise_augmentor, conds, batch_size, device):
         else:
             adm_out = torch.zeros((1, noise_augmentor.time_embed.dim * 2), device=device)
         x[1] = x[1].copy()
-        x[1]["adm"] = torch.cat([adm_out] * batch_size)
+        x[1]["adm_encoded"] = torch.cat([adm_out] * batch_size)
 
     return conds
 
+
 class KSampler:
     SCHEDULERS = ["karras", "normal", "simple", "ddim_uniform"]
     SAMPLERS = ["euler", "euler_ancestral", "heun", "dpm_2", "dpm_2_ancestral",
@@ -463,7 +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

@@ -4,7 +4,7 @@ import copy
 
 import sd1_clip
 import sd2_clip
-import model_management
+from comfy import model_management
 from .ldm.util import instantiate_from_config
 from .ldm.models.autoencoder import AutoencoderKL
 import yaml
@@ -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)
@@ -250,6 +251,9 @@ class ModelPatcher:
     def set_model_tomesd(self, ratio):
         self.model_options["transformer_options"]["tomesd"] = {"ratio": ratio}
 
+    def set_model_sampler_cfg_function(self, sampler_cfg_function):
+        self.model_options["sampler_cfg_function"] = sampler_cfg_function
+
     def model_dtype(self):
         return self.model.diffusion_model.dtype
 
@@ -372,10 +376,12 @@ class CLIP:
     def clip_layer(self, layer_idx):
         self.layer_idx = layer_idx
 
-    def encode(self, text):
+    def tokenize(self, text, return_word_ids=False):
+        return self.tokenizer.tokenize_with_weights(text, return_word_ids)
+
+    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)
-        tokens = self.tokenizer.tokenize_with_weights(text)
         try:
             self.patcher.patch_model()
             cond = self.cond_stage_model.encode_token_weights(tokens)
@@ -383,8 +389,16 @@ 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):
+        tokens = self.tokenize(text)
+        return self.encode_from_tokens(tokens)
+
 class VAE:
     def __init__(self, ckpt_path=None, scale_factor=0.18215, device=None, config=None):
         if config is None:
@@ -555,10 +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
 
@@ -728,10 +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):
@@ -778,6 +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)

comfy/sd1_clip.py

@@ -260,60 +260,97 @@ class SD1Tokenizer:
         self.inv_vocab = {v: k for k, v in vocab.items()}
         self.embedding_directory = embedding_directory
         self.max_word_length = 8
+        self.embedding_identifier = "embedding:"
+
+    def _try_get_embedding(self, embedding_name:str):
+        '''
+        Takes a potential embedding name and tries to retrieve it.
+        Returns a Tuple consisting of the embedding and any leftover string, embedding can be None.
+        '''
+        embed = load_embed(embedding_name, self.embedding_directory)
+        if embed is None:
+            stripped = embedding_name.strip(',')
+            if len(stripped) < len(embedding_name):
+                embed = load_embed(stripped, self.embedding_directory)
+                return (embed, embedding_name[len(stripped):])
+        return (embed, "")
+
+
+    def tokenize_with_weights(self, text:str, return_word_ids=False):
+        '''
+        Takes a prompt and converts it to a list of (token, weight, word id) elements.
+        Tokens can both be integer tokens and pre computed CLIP tensors.
+        Word id values are unique per word and embedding, where the id 0 is reserved for non word tokens.
+        Returned list has the dimensions NxM where M is the input size of CLIP
+        '''
+        if self.pad_with_end:
+            pad_token = self.end_token
+        else:
+            pad_token = 0
 
-    def tokenize_with_weights(self, text):
         text = escape_important(text)
         parsed_weights = token_weights(text, 1.0)
 
+        #tokenize words
         tokens = []
-        for t in parsed_weights:
-            to_tokenize = unescape_important(t[0]).replace("\n", " ").split(' ')
-            while len(to_tokenize) > 0:
-                word = to_tokenize.pop(0)
-                temp_tokens = []
-                embedding_identifier = "embedding:"
-                if word.startswith(embedding_identifier) and self.embedding_directory is not None:
-                    embedding_name = word[len(embedding_identifier):].strip('\n')
-                    embed = load_embed(embedding_name, self.embedding_directory)
+        for weighted_segment, weight in parsed_weights:
+            to_tokenize = unescape_important(weighted_segment).replace("\n", " ").split(' ')
+            to_tokenize = [x for x in to_tokenize if x != ""]
+            for word in to_tokenize:
+                #if we find an embedding, deal with the embedding
+                if word.startswith(self.embedding_identifier) and self.embedding_directory is not None:
+                    embedding_name = word[len(self.embedding_identifier):].strip('\n')
+                    embed, leftover = self._try_get_embedding(embedding_name)
                     if embed is None:
-                        stripped = embedding_name.strip(',')
-                        if len(stripped) < len(embedding_name):
-                            embed = load_embed(stripped, self.embedding_directory)
-                            if embed is not None:
-                                to_tokenize.insert(0, embedding_name[len(stripped):])
-
-                    if embed is not None:
+                        print(f"warning, embedding:{embedding_name} does not exist, ignoring")
+                    else:
                         if len(embed.shape) == 1:
-                            temp_tokens += [(embed, t[1])]
+                            tokens.append([(embed, weight)])
                         else:
-                            for x in range(embed.shape[0]):
-                                temp_tokens += [(embed[x], t[1])]
+                            tokens.append([(embed[x], weight) for x in range(embed.shape[0])])
+                    #if we accidentally have leftover text, continue parsing using leftover, else move on to next word
+                    if leftover != "":
+                        word = leftover
                     else:
-                        print("warning, embedding:{} does not exist, ignoring".format(embedding_name))
-                elif len(word) > 0:
-                    tt = self.tokenizer(word)["input_ids"][1:-1]
-                    for x in tt:
-                        temp_tokens += [(x, t[1])]
-                tokens_left = self.max_tokens_per_section - (len(tokens) % self.max_tokens_per_section)
-
-                #try not to split words in different sections
-                if tokens_left < len(temp_tokens) and len(temp_tokens) < (self.max_word_length):
-                    for x in range(tokens_left):
-                        tokens += [(self.end_token, 1.0)]
-                tokens += temp_tokens
+                        continue
+                #parse word
+                tokens.append([(t, weight) for t in self.tokenizer(word)["input_ids"][1:-1]])
+
+        #reshape token array to CLIP input size
+        batched_tokens = []
+        batch = [(self.start_token, 1.0, 0)]
+        batched_tokens.append(batch)
+        for i, t_group in enumerate(tokens):
+            #determine if we're going to try and keep the tokens in a single batch
+            is_large = len(t_group) >= self.max_word_length
+
+            while len(t_group) > 0:
+                if len(t_group) + len(batch) > self.max_length - 1:
+                    remaining_length = self.max_length - len(batch) - 1
+                    #break word in two and add end token
+                    if is_large:
+                        batch.extend([(t,w,i+1) for t,w in t_group[:remaining_length]])
+                        batch.append((self.end_token, 1.0, 0))
+                        t_group = t_group[remaining_length:]
+                    #add end token and pad
+                    else:
+                        batch.append((self.end_token, 1.0, 0))
+                        batch.extend([(pad_token, 1.0, 0)] * (remaining_length))
+                    #start new batch
+                    batch = [(self.start_token, 1.0, 0)]
+                    batched_tokens.append(batch)
+                else:
+                    batch.extend([(t,w,i+1) for t,w in t_group])
+                    t_group = []
 
-        out_tokens = []
-        for x in range(0, len(tokens), self.max_tokens_per_section):
-            o_token = [(self.start_token, 1.0)] + tokens[x:min(self.max_tokens_per_section + x, len(tokens))]
-            o_token += [(self.end_token, 1.0)]
-            if self.pad_with_end:
-                o_token +=[(self.end_token, 1.0)] * (self.max_length - len(o_token))
-            else:
-                o_token +=[(0, 1.0)] * (self.max_length - len(o_token))
+        #fill last batch
+        batch.extend([(self.end_token, 1.0, 0)] + [(pad_token, 1.0, 0)] * (self.max_length - len(batch) - 1))
 
-            out_tokens += [o_token]
+        if not return_word_ids:
+            batched_tokens = [[(t, w) for t, w,_ in x] for x in batched_tokens]
+
+        return batched_tokens
 
-        return out_tokens
 
     def untokenize(self, token_weight_pair):
         return list(map(lambda a: (a, self.inv_vocab[a[0]]), token_weight_pair))

comfy/sd2_clip.py

-import sd1_clip
+from comfy import sd1_clip
 import torch
 import os
 

comfy_extras/nodes_upscale_model.py

 import os
 from comfy_extras.chainner_models import model_loading
-import model_management
+from comfy import model_management
 import torch
 import comfy.utils
 import folder_paths

extra_model_paths.yaml.example

@@ -18,6 +18,7 @@ a111:
 #other_ui:
 #    base_path: path/to/ui
 #    checkpoints: models/checkpoints
-
+#    gligen: models/gligen
+#    custom_nodes: path/custom_nodes
 
 

folder_paths.py

@@ -12,8 +12,8 @@ except:
 
 folder_names_and_paths = {}
 
-
-models_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "models")
+base_path = os.path.dirname(os.path.realpath(__file__))
+models_dir = os.path.join(base_path, "models")
 folder_names_and_paths["checkpoints"] = ([os.path.join(models_dir, "checkpoints")], supported_ckpt_extensions)
 folder_names_and_paths["configs"] = ([os.path.join(models_dir, "configs")], [".yaml"])
 
@@ -26,8 +26,13 @@ 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")], [])
+
+
 output_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), "output")
 temp_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), "temp")
 input_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), "input")

main.py

@@ -81,6 +81,14 @@ if __name__ == "__main__":
     server = server.PromptServer(loop)
     q = execution.PromptQueue(server)
 
+    extra_model_paths_config_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "extra_model_paths.yaml")
+    if os.path.isfile(extra_model_paths_config_path):
+        load_extra_path_config(extra_model_paths_config_path)
+
+    if args.extra_model_paths_config:
+        for config_path in itertools.chain(*args.extra_model_paths_config):
+            load_extra_path_config(config_path)
+
     init_custom_nodes()
     server.add_routes()
     hijack_progress(server)
@@ -91,13 +99,6 @@ if __name__ == "__main__":
 
     dont_print = args.dont_print_server
 
-    extra_model_paths_config_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "extra_model_paths.yaml")
-    if os.path.isfile(extra_model_paths_config_path):
-        load_extra_path_config(extra_model_paths_config_path)
-
-    if args.extra_model_paths_config:
-        for config_path in itertools.chain(*args.extra_model_paths_config):
-            load_extra_path_config(config_path)
 
     if args.output_directory:
         output_dir = os.path.abspath(args.output_directory)

nodes.py

@@ -21,16 +21,16 @@ import comfy.utils
 
 import comfy.clip_vision
 
-import model_management
+import comfy.model_management
 import importlib
 
 import folder_paths
 
 def before_node_execution():
-    model_management.throw_exception_if_processing_interrupted()
+    comfy.model_management.throw_exception_if_processing_interrupted()
 
 def interrupt_processing(value=True):
-    model_management.interrupt_current_processing(value)
+    comfy.model_management.interrupt_current_processing(value)
 
 MAX_RESOLUTION=8192
 
@@ -241,7 +241,7 @@ class DiffusersLoader:
                     model_path = os.path.join(search_path, model_path)
                     break
 
-        return comfy.diffusers_convert.load_diffusers(model_path, fp16=model_management.should_use_fp16(), output_vae=output_vae, output_clip=output_clip, embedding_directory=folder_paths.get_folder_paths("embeddings"))
+        return comfy.diffusers_convert.load_diffusers(model_path, fp16=comfy.model_management.should_use_fp16(), output_vae=output_vae, output_clip=output_clip, embedding_directory=folder_paths.get_folder_paths("embeddings"))
 
 
 class unCLIPCheckpointLoader:
@@ -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"):
@@ -510,6 +555,24 @@ class EmptyLatentImage:
         return ({"samples":latent}, )
 
 
+class LatentFromBatch:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "samples": ("LATENT",),
+                              "batch_index": ("INT", {"default": 0, "min": 0, "max": 63}),
+                              }}
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "rotate"
+
+    CATEGORY = "latent"
+
+    def rotate(self, samples, batch_index):
+        s = samples.copy()
+        s_in = samples["samples"]
+        batch_index = min(s_in.shape[0] - 1, batch_index)
+        s["samples"] = s_in[batch_index:batch_index + 1].clone()
+        s["batch_index"] = batch_index
+        return (s,)
 
 class LatentUpscale:
     upscale_methods = ["nearest-exact", "bilinear", "area"]
@@ -680,12 +743,19 @@ class SetLatentNoiseMask:
 def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False):
     latent_image = latent["samples"]
     noise_mask = None
-    device = model_management.get_torch_device()
+    device = comfy.model_management.get_torch_device()
 
     if disable_noise:
         noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
     else:
-        noise = torch.randn(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, generator=torch.manual_seed(seed), device="cpu")
+        batch_index = 0
+        if "batch_index" in latent:
+            batch_index = latent["batch_index"]
+
+        generator = torch.manual_seed(seed)
+        for i in range(batch_index):
+            noise = torch.randn([1] + list(latent_image.size())[1:], dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu")
+        noise = torch.randn(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu")
 
     if "noise_mask" in latent:
         noise_mask = latent['noise_mask']
@@ -696,7 +766,7 @@ def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive,
         noise_mask = noise_mask.to(device)
 
     real_model = None
-    model_management.load_model_gpu(model)
+    comfy.model_management.load_model_gpu(model)
     real_model = model.model
 
     noise = noise.to(device)
@@ -706,27 +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()
-    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)
@@ -736,8 +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
@@ -1073,6 +1146,7 @@ NODE_CLASS_MAPPINGS = {
     "VAELoader": VAELoader,
     "EmptyLatentImage": EmptyLatentImage,
     "LatentUpscale": LatentUpscale,
+    "LatentFromBatch": LatentFromBatch,
     "SaveImage": SaveImage,
     "PreviewImage": PreviewImage,
     "LoadImage": LoadImage,
@@ -1102,6 +1176,9 @@ NODE_CLASS_MAPPINGS = {
     "VAEEncodeTiled": VAEEncodeTiled,
     "TomePatchModel": TomePatchModel,
     "unCLIPCheckpointLoader": unCLIPCheckpointLoader,
+    "GLIGENLoader": GLIGENLoader,
+    "GLIGENTextBoxApply": GLIGENTextBoxApply,
+
     "CheckpointLoader": CheckpointLoader,
     "DiffusersLoader": DiffusersLoader,
 }
@@ -1178,15 +1255,16 @@ def load_custom_node(module_path):
         print(f"Cannot import {module_path} module for custom nodes:", e)
 
 def load_custom_nodes():
-    CUSTOM_NODE_PATH = os.path.join(os.path.dirname(os.path.realpath(__file__)), "custom_nodes")
-    possible_modules = os.listdir(CUSTOM_NODE_PATH)
-    if "__pycache__" in possible_modules:
-        possible_modules.remove("__pycache__")
-
-    for possible_module in possible_modules:
-        module_path = os.path.join(CUSTOM_NODE_PATH, possible_module)
-        if os.path.isfile(module_path) and os.path.splitext(module_path)[1] != ".py": continue
-        load_custom_node(module_path)
+    node_paths = folder_paths.get_folder_paths("custom_nodes")
+    for custom_node_path in node_paths:
+        possible_modules = os.listdir(custom_node_path)
+        if "__pycache__" in possible_modules:
+            possible_modules.remove("__pycache__")
+
+        for possible_module in possible_modules:
+            module_path = os.path.join(custom_node_path, possible_module)
+            if os.path.isfile(module_path) and os.path.splitext(module_path)[1] != ".py": continue
+            load_custom_node(module_path)
 
 def init_custom_nodes():
     load_custom_nodes()

notebooks/comfyui_colab.ipynb

@@ -138,6 +138,11 @@
         "# Controlnet Preprocessor nodes by Fannovel16\n",
         "#!cd custom_nodes && git clone https://github.com/Fannovel16/comfy_controlnet_preprocessors; cd comfy_controlnet_preprocessors && python install.py\n",
         "\n",
+        "\n",
+        "# GLIGEN\n",
+        "#!wget -c https://huggingface.co/comfyanonymous/GLIGEN_pruned_safetensors/resolve/main/gligen_sd14_textbox_pruned_fp16.safetensors -P ./models/gligen/\n",
+        "\n",
+        "\n",
         "# ESRGAN upscale model\n",
         "#!wget -c https://huggingface.co/sberbank-ai/Real-ESRGAN/resolve/main/RealESRGAN_x2.pth -P ./models/upscale_models/\n",
         "#!wget -c https://huggingface.co/sberbank-ai/Real-ESRGAN/resolve/main/RealESRGAN_x4.pth -P ./models/upscale_models/\n",

web/extensions/core/editAttention.js

+import { app } from "/scripts/app.js";
+
+// Allows you to edit the attention weight by holding ctrl (or cmd) and using the up/down arrow keys
+
+app.registerExtension({
+    name: "Comfy.EditAttention",
+    init() {
+        const editAttentionDelta = app.ui.settings.addSetting({
+            id: "Comfy.EditAttention.Delta",
+            name: "Ctrl+up/down precision",
+            type: "slider",
+            attrs: {
+                min: 0.01,
+                max: 0.5,
+                step: 0.01,
+            },
+            defaultValue: 0.05,
+        });
+
+        function incrementWeight(weight, delta) {
+            const floatWeight = parseFloat(weight);
+            if (isNaN(floatWeight)) return weight;
+            const newWeight = floatWeight + delta;
+            if (newWeight < 0) return "0";
+            return String(Number(newWeight.toFixed(10)));
+        }
+
+        function findNearestEnclosure(text, cursorPos) {
+            let start = cursorPos, end = cursorPos;
+            let openCount = 0, closeCount = 0;
+
+            // Find opening parenthesis before cursor
+            while (start >= 0) {
+                start--;
+                if (text[start] === "(" && openCount === closeCount) break;
+                if (text[start] === "(") openCount++;
+                if (text[start] === ")") closeCount++;
+            }
+            if (start < 0) return false;
+
+            openCount = 0;
+            closeCount = 0;
+
+            // Find closing parenthesis after cursor
+            while (end < text.length) {
+                if (text[end] === ")" && openCount === closeCount) break;
+                if (text[end] === "(") openCount++;
+                if (text[end] === ")") closeCount++;
+                end++;
+            }
+            if (end === text.length) return false;
+
+            return { start: start + 1, end: end };
+        }
+
+        function addWeightToParentheses(text) {
+            const parenRegex = /^\((.*)\)$/;
+            const parenMatch = text.match(parenRegex);
+
+            const floatRegex = /:([+-]?(\d*\.)?\d+([eE][+-]?\d+)?)/;
+            const floatMatch = text.match(floatRegex);
+
+            if (parenMatch && !floatMatch) {
+                return `(${parenMatch[1]}:1.0)`;
+            } else {
+                return text;
+            }
+        };
+
+        function editAttention(event) {
+            const inputField = event.composedPath()[0];
+            const delta = parseFloat(editAttentionDelta.value);
+
+            if (inputField.tagName !== "TEXTAREA") return;
+            if (!(event.key === "ArrowUp" || event.key === "ArrowDown")) return;
+            if (!event.ctrlKey && !event.metaKey) return;
+
+            event.preventDefault();
+
+            let start = inputField.selectionStart;
+            let end = inputField.selectionEnd;
+            let selectedText = inputField.value.substring(start, end);
+
+            // If there is no selection, attempt to find the nearest enclosure, or select the current word
+            if (!selectedText) {
+                const nearestEnclosure = findNearestEnclosure(inputField.value, start);
+                if (nearestEnclosure) {
+                    start = nearestEnclosure.start;
+                    end = nearestEnclosure.end;
+                    selectedText = inputField.value.substring(start, end);
+                } else {
+                    // Select the current word, find the start and end of the word (first space before and after)
+                    const wordStart = inputField.value.substring(0, start).lastIndexOf(" ") + 1;
+                    const wordEnd = inputField.value.substring(end).indexOf(" ");
+                    // If there is no space after the word, select to the end of the string
+                    if (wordEnd === -1) {
+                        end = inputField.value.length;
+                    } else {
+                        end += wordEnd;
+                    }
+                    start = wordStart;
+
+                    // Remove all punctuation at the end and beginning of the word
+                    while (inputField.value[start].match(/[.,\/#!$%\^&\*;:{}=\-_`~()]/)) {
+                        start++;
+                    }
+                    while (inputField.value[end - 1].match(/[.,\/#!$%\^&\*;:{}=\-_`~()]/)) {
+                        end--;
+                    }
+                    selectedText = inputField.value.substring(start, end);
+                    if (!selectedText) return;
+                }
+            }
+
+            // If the selection ends with a space, remove it
+            if (selectedText[selectedText.length - 1] === " ") {
+                selectedText = selectedText.substring(0, selectedText.length - 1);
+                end -= 1;
+            }
+
+            // If there are parentheses left and right of the selection, select them
+            if (inputField.value[start - 1] === "(" && inputField.value[end] === ")") {
+                start -= 1;
+                end += 1;
+                selectedText = inputField.value.substring(start, end);
+            }
+
+            // If the selection is not enclosed in parentheses, add them
+            if (selectedText[0] !== "(" || selectedText[selectedText.length - 1] !== ")") {
+                selectedText = `(${selectedText})`;
+            }
+
+            // If the selection does not have a weight, add a weight of 1.0
+            selectedText = addWeightToParentheses(selectedText);
+
+            // Increment the weight
+            const weightDelta = event.key === "ArrowUp" ? delta : -delta;
+            const updatedText = selectedText.replace(/(.*:)(\d+(\.\d+)?)(.*)/, (match, prefix, weight, _, suffix) => {
+                return prefix + incrementWeight(weight, weightDelta) + suffix;
+            });
+
+            inputField.setRangeText(updatedText, start, end, "select");
+        }
+        window.addEventListener("keydown", editAttention);
+    },
+});