Move controlnet code to comfy/controlnet.py

comfy/controlnet.py

@@ -0,0 +1,483 @@
+import torch
+import math
+import comfy.utils
+import comfy.sd
+import comfy.model_management
+import comfy.model_detection
+
+import comfy.cldm.cldm
+import comfy.t2i_adapter.adapter
+
+
+def broadcast_image_to(tensor, target_batch_size, batched_number):
+    current_batch_size = tensor.shape[0]
+    #print(current_batch_size, target_batch_size)
+    if current_batch_size == 1:
+        return tensor
+
+    per_batch = target_batch_size // batched_number
+    tensor = tensor[:per_batch]
+
+    if per_batch > tensor.shape[0]:
+        tensor = torch.cat([tensor] * (per_batch // tensor.shape[0]) + [tensor[:(per_batch % tensor.shape[0])]], dim=0)
+
+    current_batch_size = tensor.shape[0]
+    if current_batch_size == target_batch_size:
+        return tensor
+    else:
+        return torch.cat([tensor] * batched_number, dim=0)
+
+class ControlBase:
+    def __init__(self, device=None):
+        self.cond_hint_original = None
+        self.cond_hint = None
+        self.strength = 1.0
+        self.timestep_percent_range = (1.0, 0.0)
+        self.timestep_range = None
+
+        if device is None:
+            device = comfy.model_management.get_torch_device()
+        self.device = device
+        self.previous_controlnet = None
+        self.global_average_pooling = False
+
+    def set_cond_hint(self, cond_hint, strength=1.0, timestep_percent_range=(1.0, 0.0)):
+        self.cond_hint_original = cond_hint
+        self.strength = strength
+        self.timestep_percent_range = timestep_percent_range
+        return self
+
+    def pre_run(self, model, percent_to_timestep_function):
+        self.timestep_range = (percent_to_timestep_function(self.timestep_percent_range[0]), percent_to_timestep_function(self.timestep_percent_range[1]))
+        if self.previous_controlnet is not None:
+            self.previous_controlnet.pre_run(model, percent_to_timestep_function)
+
+    def set_previous_controlnet(self, controlnet):
+        self.previous_controlnet = controlnet
+        return self
+
+    def cleanup(self):
+        if self.previous_controlnet is not None:
+            self.previous_controlnet.cleanup()
+        if self.cond_hint is not None:
+            del self.cond_hint
+            self.cond_hint = None
+        self.timestep_range = None
+
+    def get_models(self):
+        out = []
+        if self.previous_controlnet is not None:
+            out += self.previous_controlnet.get_models()
+        return out
+
+    def copy_to(self, c):
+        c.cond_hint_original = self.cond_hint_original
+        c.strength = self.strength
+        c.timestep_percent_range = self.timestep_percent_range
+
+    def inference_memory_requirements(self, dtype):
+        if self.previous_controlnet is not None:
+            return self.previous_controlnet.inference_memory_requirements(dtype)
+        return 0
+
+    def control_merge(self, control_input, control_output, control_prev, output_dtype):
+        out = {'input':[], 'middle':[], 'output': []}
+
+        if control_input is not None:
+            for i in range(len(control_input)):
+                key = 'input'
+                x = control_input[i]
+                if x is not None:
+                    x *= self.strength
+                    if x.dtype != output_dtype:
+                        x = x.to(output_dtype)
+                out[key].insert(0, x)
+
+        if control_output is not None:
+            for i in range(len(control_output)):
+                if i == (len(control_output) - 1):
+                    key = 'middle'
+                    index = 0
+                else:
+                    key = 'output'
+                    index = i
+                x = control_output[i]
+                if x is not None:
+                    if self.global_average_pooling:
+                        x = torch.mean(x, dim=(2, 3), keepdim=True).repeat(1, 1, x.shape[2], x.shape[3])
+
+                    x *= self.strength
+                    if x.dtype != output_dtype:
+                        x = x.to(output_dtype)
+
+                out[key].append(x)
+        if control_prev is not None:
+            for x in ['input', 'middle', 'output']:
+                o = out[x]
+                for i in range(len(control_prev[x])):
+                    prev_val = control_prev[x][i]
+                    if i >= len(o):
+                        o.append(prev_val)
+                    elif prev_val is not None:
+                        if o[i] is None:
+                            o[i] = prev_val
+                        else:
+                            o[i] += prev_val
+        return out
+
+class ControlNet(ControlBase):
+    def __init__(self, control_model, global_average_pooling=False, device=None):
+        super().__init__(device)
+        self.control_model = control_model
+        self.control_model_wrapped = comfy.sd.ModelPatcher(self.control_model, load_device=comfy.model_management.get_torch_device(), offload_device=comfy.model_management.unet_offload_device())
+        self.global_average_pooling = global_average_pooling
+
+    def get_control(self, x_noisy, t, cond, batched_number):
+        control_prev = None
+        if self.previous_controlnet is not None:
+            control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number)
+
+        if self.timestep_range is not None:
+            if t[0] > self.timestep_range[0] or t[0] < self.timestep_range[1]:
+                if control_prev is not None:
+                    return control_prev
+                else:
+                    return {}
+
+        output_dtype = x_noisy.dtype
+        if self.cond_hint is None or x_noisy.shape[2] * 8 != self.cond_hint.shape[2] or x_noisy.shape[3] * 8 != self.cond_hint.shape[3]:
+            if self.cond_hint is not None:
+                del self.cond_hint
+            self.cond_hint = None
+            self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * 8, x_noisy.shape[2] * 8, 'nearest-exact', "center").to(self.control_model.dtype).to(self.device)
+        if x_noisy.shape[0] != self.cond_hint.shape[0]:
+            self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number)
+
+
+        context = torch.cat(cond['c_crossattn'], 1)
+        y = cond.get('c_adm', None)
+        if y is not None:
+            y = y.to(self.control_model.dtype)
+        control = self.control_model(x=x_noisy.to(self.control_model.dtype), hint=self.cond_hint, timesteps=t, context=context.to(self.control_model.dtype), y=y)
+        return self.control_merge(None, control, control_prev, output_dtype)
+
+    def copy(self):
+        c = ControlNet(self.control_model, global_average_pooling=self.global_average_pooling)
+        self.copy_to(c)
+        return c
+
+    def get_models(self):
+        out = super().get_models()
+        out.append(self.control_model_wrapped)
+        return out
+
+class ControlLoraOps:
+    class Linear(torch.nn.Module):
+        def __init__(self, in_features: int, out_features: int, bias: bool = True,
+                    device=None, dtype=None) -> None:
+            factory_kwargs = {'device': device, 'dtype': dtype}
+            super().__init__()
+            self.in_features = in_features
+            self.out_features = out_features
+            self.weight = None
+            self.up = None
+            self.down = None
+            self.bias = None
+
+        def forward(self, input):
+            if self.up is not None:
+                return torch.nn.functional.linear(input, self.weight.to(input.device) + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), self.bias)
+            else:
+                return torch.nn.functional.linear(input, self.weight.to(input.device), self.bias)
+
+    class Conv2d(torch.nn.Module):
+        def __init__(
+            self,
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=1,
+            padding=0,
+            dilation=1,
+            groups=1,
+            bias=True,
+            padding_mode='zeros',
+            device=None,
+            dtype=None
+        ):
+            super().__init__()
+            self.in_channels = in_channels
+            self.out_channels = out_channels
+            self.kernel_size = kernel_size
+            self.stride = stride
+            self.padding = padding
+            self.dilation = dilation
+            self.transposed = False
+            self.output_padding = 0
+            self.groups = groups
+            self.padding_mode = padding_mode
+
+            self.weight = None
+            self.bias = None
+            self.up = None
+            self.down = None
+
+
+        def forward(self, input):
+            if self.up is not None:
+                return torch.nn.functional.conv2d(input, self.weight.to(input.device) + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), self.bias, self.stride, self.padding, self.dilation, self.groups)
+            else:
+                return torch.nn.functional.conv2d(input, self.weight.to(input.device), self.bias, self.stride, self.padding, self.dilation, self.groups)
+
+    def conv_nd(self, dims, *args, **kwargs):
+        if dims == 2:
+            return self.Conv2d(*args, **kwargs)
+        else:
+            raise ValueError(f"unsupported dimensions: {dims}")
+
+
+class ControlLora(ControlNet):
+    def __init__(self, control_weights, global_average_pooling=False, device=None):
+        ControlBase.__init__(self, device)
+        self.control_weights = control_weights
+        self.global_average_pooling = global_average_pooling
+
+    def pre_run(self, model, percent_to_timestep_function):
+        super().pre_run(model, percent_to_timestep_function)
+        controlnet_config = model.model_config.unet_config.copy()
+        controlnet_config.pop("out_channels")
+        controlnet_config["hint_channels"] = self.control_weights["input_hint_block.0.weight"].shape[1]
+        controlnet_config["operations"] = ControlLoraOps()
+        self.control_model = comfy.cldm.cldm.ControlNet(**controlnet_config)
+        dtype = model.get_dtype()
+        self.control_model.to(dtype)
+        self.control_model.to(comfy.model_management.get_torch_device())
+        diffusion_model = model.diffusion_model
+        sd = diffusion_model.state_dict()
+        cm = self.control_model.state_dict()
+
+        for k in sd:
+            weight = sd[k]
+            if weight.device == torch.device("meta"): #lowvram NOTE: this depends on the inner working of the accelerate library so it might break.
+                key_split = k.split('.')              # I have no idea why they don't just leave the weight there instead of using the meta device.
+                op = comfy.utils.get_attr(diffusion_model, '.'.join(key_split[:-1]))
+                weight = op._hf_hook.weights_map[key_split[-1]]
+
+            try:
+                comfy.utils.set_attr(self.control_model, k, weight)
+            except:
+                pass
+
+        for k in self.control_weights:
+            if k not in {"lora_controlnet"}:
+                comfy.utils.set_attr(self.control_model, k, self.control_weights[k].to(dtype).to(comfy.model_management.get_torch_device()))
+
+    def copy(self):
+        c = ControlLora(self.control_weights, global_average_pooling=self.global_average_pooling)
+        self.copy_to(c)
+        return c
+
+    def cleanup(self):
+        del self.control_model
+        self.control_model = None
+        super().cleanup()
+
+    def get_models(self):
+        out = ControlBase.get_models(self)
+        return out
+
+    def inference_memory_requirements(self, dtype):
+        return comfy.utils.calculate_parameters(self.control_weights) * comfy.model_management.dtype_size(dtype) + ControlBase.inference_memory_requirements(self, dtype)
+
+def load_controlnet(ckpt_path, model=None):
+    controlnet_data = comfy.utils.load_torch_file(ckpt_path, safe_load=True)
+    if "lora_controlnet" in controlnet_data:
+        return ControlLora(controlnet_data)
+
+    controlnet_config = None
+    if "controlnet_cond_embedding.conv_in.weight" in controlnet_data: #diffusers format
+        use_fp16 = comfy.model_management.should_use_fp16()
+        controlnet_config = comfy.model_detection.unet_config_from_diffusers_unet(controlnet_data, use_fp16)
+        diffusers_keys = comfy.utils.unet_to_diffusers(controlnet_config)
+        diffusers_keys["controlnet_mid_block.weight"] = "middle_block_out.0.weight"
+        diffusers_keys["controlnet_mid_block.bias"] = "middle_block_out.0.bias"
+
+        count = 0
+        loop = True
+        while loop:
+            suffix = [".weight", ".bias"]
+            for s in suffix:
+                k_in = "controlnet_down_blocks.{}{}".format(count, s)
+                k_out = "zero_convs.{}.0{}".format(count, s)
+                if k_in not in controlnet_data:
+                    loop = False
+                    break
+                diffusers_keys[k_in] = k_out
+            count += 1
+
+        count = 0
+        loop = True
+        while loop:
+            suffix = [".weight", ".bias"]
+            for s in suffix:
+                if count == 0:
+                    k_in = "controlnet_cond_embedding.conv_in{}".format(s)
+                else:
+                    k_in = "controlnet_cond_embedding.blocks.{}{}".format(count - 1, s)
+                k_out = "input_hint_block.{}{}".format(count * 2, s)
+                if k_in not in controlnet_data:
+                    k_in = "controlnet_cond_embedding.conv_out{}".format(s)
+                    loop = False
+                diffusers_keys[k_in] = k_out
+            count += 1
+
+        new_sd = {}
+        for k in diffusers_keys:
+            if k in controlnet_data:
+                new_sd[diffusers_keys[k]] = controlnet_data.pop(k)
+
+        leftover_keys = controlnet_data.keys()
+        if len(leftover_keys) > 0:
+            print("leftover keys:", leftover_keys)
+        controlnet_data = new_sd
+
+    pth_key = 'control_model.zero_convs.0.0.weight'
+    pth = False
+    key = 'zero_convs.0.0.weight'
+    if pth_key in controlnet_data:
+        pth = True
+        key = pth_key
+        prefix = "control_model."
+    elif key in controlnet_data:
+        prefix = ""
+    else:
+        net = load_t2i_adapter(controlnet_data)
+        if net is None:
+            print("error checkpoint does not contain controlnet or t2i adapter data", ckpt_path)
+        return net
+
+    if controlnet_config is None:
+        use_fp16 = comfy.model_management.should_use_fp16()
+        controlnet_config = comfy.model_detection.model_config_from_unet(controlnet_data, prefix, use_fp16).unet_config
+    controlnet_config.pop("out_channels")
+    controlnet_config["hint_channels"] = controlnet_data["{}input_hint_block.0.weight".format(prefix)].shape[1]
+    control_model = comfy.cldm.cldm.ControlNet(**controlnet_config)
+
+    if pth:
+        if 'difference' in controlnet_data:
+            if model is not None:
+                comfy.model_management.load_models_gpu([model])
+                model_sd = model.model_state_dict()
+                for x in controlnet_data:
+                    c_m = "control_model."
+                    if x.startswith(c_m):
+                        sd_key = "diffusion_model.{}".format(x[len(c_m):])
+                        if sd_key in model_sd:
+                            cd = controlnet_data[x]
+                            cd += model_sd[sd_key].type(cd.dtype).to(cd.device)
+            else:
+                print("WARNING: Loaded a diff controlnet without a model. It will very likely not work.")
+
+        class WeightsLoader(torch.nn.Module):
+            pass
+        w = WeightsLoader()
+        w.control_model = control_model
+        missing, unexpected = w.load_state_dict(controlnet_data, strict=False)
+    else:
+        missing, unexpected = control_model.load_state_dict(controlnet_data, strict=False)
+    print(missing, unexpected)
+
+    if use_fp16:
+        control_model = control_model.half()
+
+    global_average_pooling = False
+    if ckpt_path.endswith("_shuffle.pth") or ckpt_path.endswith("_shuffle.safetensors") or ckpt_path.endswith("_shuffle_fp16.safetensors"): #TODO: smarter way of enabling global_average_pooling
+        global_average_pooling = True
+
+    control = ControlNet(control_model, global_average_pooling=global_average_pooling)
+    return control
+
+class T2IAdapter(ControlBase):
+    def __init__(self, t2i_model, channels_in, device=None):
+        super().__init__(device)
+        self.t2i_model = t2i_model
+        self.channels_in = channels_in
+        self.control_input = None
+
+    def scale_image_to(self, width, height):
+        unshuffle_amount = self.t2i_model.unshuffle_amount
+        width = math.ceil(width / unshuffle_amount) * unshuffle_amount
+        height = math.ceil(height / unshuffle_amount) * unshuffle_amount
+        return width, height
+
+    def get_control(self, x_noisy, t, cond, batched_number):
+        control_prev = None
+        if self.previous_controlnet is not None:
+            control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number)
+
+        if self.timestep_range is not None:
+            if t[0] > self.timestep_range[0] or t[0] < self.timestep_range[1]:
+                if control_prev is not None:
+                    return control_prev
+                else:
+                    return {}
+
+        if self.cond_hint is None or x_noisy.shape[2] * 8 != self.cond_hint.shape[2] or x_noisy.shape[3] * 8 != self.cond_hint.shape[3]:
+            if self.cond_hint is not None:
+                del self.cond_hint
+            self.control_input = None
+            self.cond_hint = None
+            width, height = self.scale_image_to(x_noisy.shape[3] * 8, x_noisy.shape[2] * 8)
+            self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, width, height, 'nearest-exact', "center").float().to(self.device)
+            if self.channels_in == 1 and self.cond_hint.shape[1] > 1:
+                self.cond_hint = torch.mean(self.cond_hint, 1, keepdim=True)
+        if x_noisy.shape[0] != self.cond_hint.shape[0]:
+            self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number)
+        if self.control_input is None:
+            self.t2i_model.to(x_noisy.dtype)
+            self.t2i_model.to(self.device)
+            self.control_input = self.t2i_model(self.cond_hint.to(x_noisy.dtype))
+            self.t2i_model.cpu()
+
+        control_input = list(map(lambda a: None if a is None else a.clone(), self.control_input))
+        mid = None
+        if self.t2i_model.xl == True:
+            mid = control_input[-1:]
+            control_input = control_input[:-1]
+        return self.control_merge(control_input, mid, control_prev, x_noisy.dtype)
+
+    def copy(self):
+        c = T2IAdapter(self.t2i_model, self.channels_in)
+        self.copy_to(c)
+        return c
+
+def load_t2i_adapter(t2i_data):
+    keys = t2i_data.keys()
+    if 'adapter' in keys:
+        t2i_data = t2i_data['adapter']
+        keys = t2i_data.keys()
+    if "body.0.in_conv.weight" in keys:
+        cin = t2i_data['body.0.in_conv.weight'].shape[1]
+        model_ad = comfy.t2i_adapter.adapter.Adapter_light(cin=cin, channels=[320, 640, 1280, 1280], nums_rb=4)
+    elif 'conv_in.weight' in keys:
+        cin = t2i_data['conv_in.weight'].shape[1]
+        channel = t2i_data['conv_in.weight'].shape[0]
+        ksize = t2i_data['body.0.block2.weight'].shape[2]
+        use_conv = False
+        down_opts = list(filter(lambda a: a.endswith("down_opt.op.weight"), keys))
+        if len(down_opts) > 0:
+            use_conv = True
+        xl = False
+        if cin == 256:
+            xl = True
+        model_ad = comfy.t2i_adapter.adapter.Adapter(cin=cin, channels=[channel, channel*2, channel*4, channel*4][:4], nums_rb=2, ksize=ksize, sk=True, use_conv=use_conv, xl=xl)
+    else:
+        return None
+    missing, unexpected = model_ad.load_state_dict(t2i_data)
+    if len(missing) > 0:
+        print("t2i missing", missing)
+
+    if len(unexpected) > 0:
+        print("t2i unexpected", unexpected)
+
+    return T2IAdapter(model_ad, model_ad.input_channels)

comfy/sd.py

@@ -8,10 +8,9 @@ from comfy import model_management
 from .ldm.util import instantiate_from_config
 from .ldm.models.autoencoder import AutoencoderKL
 import yaml
-from .cldm import cldm
-from .t2i_adapter import adapter
 
-from . import utils
+import comfy.utils
+
 from . import clip_vision
 from . import gligen
 from . import diffusers_convert
@@ -23,6 +22,7 @@ from . import sd2_clip
 from . import sdxl_clip
 
 import comfy.lora
+import comfy.t2i_adapter.adapter
 
 def load_model_weights(model, sd):
     m, u = model.load_state_dict(sd, strict=False)
@@ -50,26 +50,9 @@ def load_clip_weights(model, sd):
         if ids.dtype == torch.float32:
             sd['cond_stage_model.transformer.text_model.embeddings.position_ids'] = ids.round()
 
-    sd = utils.transformers_convert(sd, "cond_stage_model.model.", "cond_stage_model.transformer.text_model.", 24)
+    sd = comfy.utils.transformers_convert(sd, "cond_stage_model.model.", "cond_stage_model.transformer.text_model.", 24)
     return load_model_weights(model, sd)
 
-
-
-def set_attr(obj, attr, value):
-    attrs = attr.split(".")
-    for name in attrs[:-1]:
-        obj = getattr(obj, name)
-    prev = getattr(obj, attrs[-1])
-    setattr(obj, attrs[-1], torch.nn.Parameter(value))
-    del prev
-
-def get_attr(obj, attr):
-    attrs = attr.split(".")
-    for name in attrs:
-        obj = getattr(obj, name)
-    return obj
-
-
 class ModelPatcher:
     def __init__(self, model, load_device, offload_device, size=0, current_device=None):
         self.size = size
@@ -224,7 +207,7 @@ class ModelPatcher:
             else:
                 temp_weight = weight.to(torch.float32, copy=True)
             out_weight = self.calculate_weight(self.patches[key], temp_weight, key).to(weight.dtype)
-            set_attr(self.model, key, out_weight)
+            comfy.utils.set_attr(self.model, key, out_weight)
             del temp_weight
 
         if device_to is not None:
@@ -327,7 +310,7 @@ class ModelPatcher:
         keys = list(self.backup.keys())
 
         for k in keys:
-            set_attr(self.model, k, self.backup[k])
+            comfy.utils.set_attr(self.model, k, self.backup[k])
 
         self.backup = {}
 
@@ -431,7 +414,7 @@ class VAE:
             self.first_stage_model = AutoencoderKL(**(config['params']))
         self.first_stage_model = self.first_stage_model.eval()
         if ckpt_path is not None:
-            sd = utils.load_torch_file(ckpt_path)
+            sd = comfy.utils.load_torch_file(ckpt_path)
             if 'decoder.up_blocks.0.resnets.0.norm1.weight' in sd.keys(): #diffusers format
                 sd = diffusers_convert.convert_vae_state_dict(sd)
             self.first_stage_model.load_state_dict(sd, strict=False)
@@ -444,29 +427,29 @@ class VAE:
         self.first_stage_model.to(self.vae_dtype)
 
     def decode_tiled_(self, samples, tile_x=64, tile_y=64, overlap = 16):
-        steps = samples.shape[0] * utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x, tile_y, overlap)
+        steps = samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x, tile_y, overlap)
-        steps += samples.shape[0] * utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x // 2, tile_y * 2, overlap)
+        steps += samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x // 2, tile_y * 2, overlap)
-        steps += samples.shape[0] * utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x * 2, tile_y // 2, overlap)
+        steps += samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x * 2, tile_y // 2, overlap)
-        pbar = utils.ProgressBar(steps)
+        pbar = comfy.utils.ProgressBar(steps)
 
         decode_fn = lambda a: (self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)) + 1.0).float()
         output = torch.clamp((
-            (utils.tiled_scale(samples, decode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = 8, pbar = pbar) +
+            (comfy.utils.tiled_scale(samples, decode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = 8, pbar = pbar) +
-            utils.tiled_scale(samples, decode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = 8, pbar = pbar) +
+            comfy.utils.tiled_scale(samples, decode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = 8, pbar = pbar) +
-             utils.tiled_scale(samples, decode_fn, tile_x, tile_y, overlap, upscale_amount = 8, pbar = pbar))
+             comfy.utils.tiled_scale(samples, decode_fn, tile_x, tile_y, overlap, upscale_amount = 8, pbar = pbar))
             / 3.0) / 2.0, min=0.0, max=1.0)
         return output
 
     def encode_tiled_(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64):
-        steps = pixel_samples.shape[0] * utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x, tile_y, overlap)
+        steps = pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x, tile_y, overlap)
-        steps += pixel_samples.shape[0] * utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x // 2, tile_y * 2, overlap)
+        steps += pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x // 2, tile_y * 2, overlap)
-        steps += pixel_samples.shape[0] * utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x * 2, tile_y // 2, overlap)
+        steps += pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x * 2, tile_y // 2, overlap)
-        pbar = utils.ProgressBar(steps)
+        pbar = comfy.utils.ProgressBar(steps)
 
         encode_fn = lambda a: self.first_stage_model.encode(2. * a.to(self.vae_dtype).to(self.device) - 1.).sample().float()
-        samples = utils.tiled_scale(pixel_samples, encode_fn, tile_x, tile_y, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar)
+        samples = comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x, tile_y, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar)
-        samples += utils.tiled_scale(pixel_samples, encode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar)
+        samples += comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar)
-        samples += utils.tiled_scale(pixel_samples, encode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar)
+        samples += comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar)
         samples /= 3.0
         return samples
 
@@ -528,481 +511,6 @@ class VAE:
     def get_sd(self):
         return self.first_stage_model.state_dict()
 
-
-def broadcast_image_to(tensor, target_batch_size, batched_number):
-    current_batch_size = tensor.shape[0]
-    #print(current_batch_size, target_batch_size)
-    if current_batch_size == 1:
-        return tensor
-
-    per_batch = target_batch_size // batched_number
-    tensor = tensor[:per_batch]
-
-    if per_batch > tensor.shape[0]:
-        tensor = torch.cat([tensor] * (per_batch // tensor.shape[0]) + [tensor[:(per_batch % tensor.shape[0])]], dim=0)
-
-    current_batch_size = tensor.shape[0]
-    if current_batch_size == target_batch_size:
-        return tensor
-    else:
-        return torch.cat([tensor] * batched_number, dim=0)
-
-class ControlBase:
-    def __init__(self, device=None):
-        self.cond_hint_original = None
-        self.cond_hint = None
-        self.strength = 1.0
-        self.timestep_percent_range = (1.0, 0.0)
-        self.timestep_range = None
-
-        if device is None:
-            device = model_management.get_torch_device()
-        self.device = device
-        self.previous_controlnet = None
-        self.global_average_pooling = False
-
-    def set_cond_hint(self, cond_hint, strength=1.0, timestep_percent_range=(1.0, 0.0)):
-        self.cond_hint_original = cond_hint
-        self.strength = strength
-        self.timestep_percent_range = timestep_percent_range
-        return self
-
-    def pre_run(self, model, percent_to_timestep_function):
-        self.timestep_range = (percent_to_timestep_function(self.timestep_percent_range[0]), percent_to_timestep_function(self.timestep_percent_range[1]))
-        if self.previous_controlnet is not None:
-            self.previous_controlnet.pre_run(model, percent_to_timestep_function)
-
-    def set_previous_controlnet(self, controlnet):
-        self.previous_controlnet = controlnet
-        return self
-
-    def cleanup(self):
-        if self.previous_controlnet is not None:
-            self.previous_controlnet.cleanup()
-        if self.cond_hint is not None:
-            del self.cond_hint
-            self.cond_hint = None
-        self.timestep_range = None
-
-    def get_models(self):
-        out = []
-        if self.previous_controlnet is not None:
-            out += self.previous_controlnet.get_models()
-        return out
-
-    def copy_to(self, c):
-        c.cond_hint_original = self.cond_hint_original
-        c.strength = self.strength
-        c.timestep_percent_range = self.timestep_percent_range
-
-    def inference_memory_requirements(self, dtype):
-        if self.previous_controlnet is not None:
-            return self.previous_controlnet.inference_memory_requirements(dtype)
-        return 0
-
-    def control_merge(self, control_input, control_output, control_prev, output_dtype):
-        out = {'input':[], 'middle':[], 'output': []}
-
-        if control_input is not None:
-            for i in range(len(control_input)):
-                key = 'input'
-                x = control_input[i]
-                if x is not None:
-                    x *= self.strength
-                    if x.dtype != output_dtype:
-                        x = x.to(output_dtype)
-                out[key].insert(0, x)
-
-        if control_output is not None:
-            for i in range(len(control_output)):
-                if i == (len(control_output) - 1):
-                    key = 'middle'
-                    index = 0
-                else:
-                    key = 'output'
-                    index = i
-                x = control_output[i]
-                if x is not None:
-                    if self.global_average_pooling:
-                        x = torch.mean(x, dim=(2, 3), keepdim=True).repeat(1, 1, x.shape[2], x.shape[3])
-
-                    x *= self.strength
-                    if x.dtype != output_dtype:
-                        x = x.to(output_dtype)
-
-                out[key].append(x)
-        if control_prev is not None:
-            for x in ['input', 'middle', 'output']:
-                o = out[x]
-                for i in range(len(control_prev[x])):
-                    prev_val = control_prev[x][i]
-                    if i >= len(o):
-                        o.append(prev_val)
-                    elif prev_val is not None:
-                        if o[i] is None:
-                            o[i] = prev_val
-                        else:
-                            o[i] += prev_val
-        return out
-
-class ControlNet(ControlBase):
-    def __init__(self, control_model, global_average_pooling=False, device=None):
-        super().__init__(device)
-        self.control_model = control_model
-        self.control_model_wrapped = ModelPatcher(self.control_model, load_device=model_management.get_torch_device(), offload_device=model_management.unet_offload_device())
-        self.global_average_pooling = global_average_pooling
-
-    def get_control(self, x_noisy, t, cond, batched_number):
-        control_prev = None
-        if self.previous_controlnet is not None:
-            control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number)
-
-        if self.timestep_range is not None:
-            if t[0] > self.timestep_range[0] or t[0] < self.timestep_range[1]:
-                if control_prev is not None:
-                    return control_prev
-                else:
-                    return {}
-
-        output_dtype = x_noisy.dtype
-        if self.cond_hint is None or x_noisy.shape[2] * 8 != self.cond_hint.shape[2] or x_noisy.shape[3] * 8 != self.cond_hint.shape[3]:
-            if self.cond_hint is not None:
-                del self.cond_hint
-            self.cond_hint = None
-            self.cond_hint = utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * 8, x_noisy.shape[2] * 8, 'nearest-exact', "center").to(self.control_model.dtype).to(self.device)
-        if x_noisy.shape[0] != self.cond_hint.shape[0]:
-            self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number)
-
-
-        context = torch.cat(cond['c_crossattn'], 1)
-        y = cond.get('c_adm', None)
-        if y is not None:
-            y = y.to(self.control_model.dtype)
-        control = self.control_model(x=x_noisy.to(self.control_model.dtype), hint=self.cond_hint, timesteps=t, context=context.to(self.control_model.dtype), y=y)
-        return self.control_merge(None, control, control_prev, output_dtype)
-
-    def copy(self):
-        c = ControlNet(self.control_model, global_average_pooling=self.global_average_pooling)
-        self.copy_to(c)
-        return c
-
-    def get_models(self):
-        out = super().get_models()
-        out.append(self.control_model_wrapped)
-        return out
-
-class ControlLoraOps:
-    class Linear(torch.nn.Module):
-        def __init__(self, in_features: int, out_features: int, bias: bool = True,
-                    device=None, dtype=None) -> None:
-            factory_kwargs = {'device': device, 'dtype': dtype}
-            super().__init__()
-            self.in_features = in_features
-            self.out_features = out_features
-            self.weight = None
-            self.up = None
-            self.down = None
-            self.bias = None
-
-        def forward(self, input):
-            if self.up is not None:
-                return torch.nn.functional.linear(input, self.weight.to(input.device) + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), self.bias)
-            else:
-                return torch.nn.functional.linear(input, self.weight.to(input.device), self.bias)
-
-    class Conv2d(torch.nn.Module):
-        def __init__(
-            self,
-            in_channels,
-            out_channels,
-            kernel_size,
-            stride=1,
-            padding=0,
-            dilation=1,
-            groups=1,
-            bias=True,
-            padding_mode='zeros',
-            device=None,
-            dtype=None
-        ):
-            super().__init__()
-            self.in_channels = in_channels
-            self.out_channels = out_channels
-            self.kernel_size = kernel_size
-            self.stride = stride
-            self.padding = padding
-            self.dilation = dilation
-            self.transposed = False
-            self.output_padding = 0
-            self.groups = groups
-            self.padding_mode = padding_mode
-
-            self.weight = None
-            self.bias = None
-            self.up = None
-            self.down = None
-
-
-        def forward(self, input):
-            if self.up is not None:
-                return torch.nn.functional.conv2d(input, self.weight.to(input.device) + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), self.bias, self.stride, self.padding, self.dilation, self.groups)
-            else:
-                return torch.nn.functional.conv2d(input, self.weight.to(input.device), self.bias, self.stride, self.padding, self.dilation, self.groups)
-
-    def conv_nd(self, dims, *args, **kwargs):
-        if dims == 2:
-            return self.Conv2d(*args, **kwargs)
-        else:
-            raise ValueError(f"unsupported dimensions: {dims}")
-
-
-class ControlLora(ControlNet):
-    def __init__(self, control_weights, global_average_pooling=False, device=None):
-        ControlBase.__init__(self, device)
-        self.control_weights = control_weights
-        self.global_average_pooling = global_average_pooling
-
-    def pre_run(self, model, percent_to_timestep_function):
-        super().pre_run(model, percent_to_timestep_function)
-        controlnet_config = model.model_config.unet_config.copy()
-        controlnet_config.pop("out_channels")
-        controlnet_config["hint_channels"] = self.control_weights["input_hint_block.0.weight"].shape[1]
-        controlnet_config["operations"] = ControlLoraOps()
-        self.control_model = cldm.ControlNet(**controlnet_config)
-        dtype = model.get_dtype()
-        self.control_model.to(dtype)
-        self.control_model.to(model_management.get_torch_device())
-        diffusion_model = model.diffusion_model
-        sd = diffusion_model.state_dict()
-        cm = self.control_model.state_dict()
-
-        for k in sd:
-            weight = sd[k]
-            if weight.device == torch.device("meta"): #lowvram NOTE: this depends on the inner working of the accelerate library so it might break.
-                key_split = k.split('.')              # I have no idea why they don't just leave the weight there instead of using the meta device.
-                op = get_attr(diffusion_model, '.'.join(key_split[:-1]))
-                weight = op._hf_hook.weights_map[key_split[-1]]
-
-            try:
-                set_attr(self.control_model, k, weight)
-            except:
-                pass
-
-        for k in self.control_weights:
-            if k not in {"lora_controlnet"}:
-                set_attr(self.control_model, k, self.control_weights[k].to(dtype).to(model_management.get_torch_device()))
-
-    def copy(self):
-        c = ControlLora(self.control_weights, global_average_pooling=self.global_average_pooling)
-        self.copy_to(c)
-        return c
-
-    def cleanup(self):
-        del self.control_model
-        self.control_model = None
-        super().cleanup()
-
-    def get_models(self):
-        out = ControlBase.get_models(self)
-        return out
-
-    def inference_memory_requirements(self, dtype):
-        return utils.calculate_parameters(self.control_weights) * model_management.dtype_size(dtype) + ControlBase.inference_memory_requirements(self, dtype)
-
-def load_controlnet(ckpt_path, model=None):
-    controlnet_data = utils.load_torch_file(ckpt_path, safe_load=True)
-    if "lora_controlnet" in controlnet_data:
-        return ControlLora(controlnet_data)
-
-    controlnet_config = None
-    if "controlnet_cond_embedding.conv_in.weight" in controlnet_data: #diffusers format
-        use_fp16 = model_management.should_use_fp16()
-        controlnet_config = model_detection.unet_config_from_diffusers_unet(controlnet_data, use_fp16)
-        diffusers_keys = utils.unet_to_diffusers(controlnet_config)
-        diffusers_keys["controlnet_mid_block.weight"] = "middle_block_out.0.weight"
-        diffusers_keys["controlnet_mid_block.bias"] = "middle_block_out.0.bias"
-
-        count = 0
-        loop = True
-        while loop:
-            suffix = [".weight", ".bias"]
-            for s in suffix:
-                k_in = "controlnet_down_blocks.{}{}".format(count, s)
-                k_out = "zero_convs.{}.0{}".format(count, s)
-                if k_in not in controlnet_data:
-                    loop = False
-                    break
-                diffusers_keys[k_in] = k_out
-            count += 1
-
-        count = 0
-        loop = True
-        while loop:
-            suffix = [".weight", ".bias"]
-            for s in suffix:
-                if count == 0:
-                    k_in = "controlnet_cond_embedding.conv_in{}".format(s)
-                else:
-                    k_in = "controlnet_cond_embedding.blocks.{}{}".format(count - 1, s)
-                k_out = "input_hint_block.{}{}".format(count * 2, s)
-                if k_in not in controlnet_data:
-                    k_in = "controlnet_cond_embedding.conv_out{}".format(s)
-                    loop = False
-                diffusers_keys[k_in] = k_out
-            count += 1
-
-        new_sd = {}
-        for k in diffusers_keys:
-            if k in controlnet_data:
-                new_sd[diffusers_keys[k]] = controlnet_data.pop(k)
-
-        leftover_keys = controlnet_data.keys()
-        if len(leftover_keys) > 0:
-            print("leftover keys:", leftover_keys)
-        controlnet_data = new_sd
-
-    pth_key = 'control_model.zero_convs.0.0.weight'
-    pth = False
-    key = 'zero_convs.0.0.weight'
-    if pth_key in controlnet_data:
-        pth = True
-        key = pth_key
-        prefix = "control_model."
-    elif key in controlnet_data:
-        prefix = ""
-    else:
-        net = load_t2i_adapter(controlnet_data)
-        if net is None:
-            print("error checkpoint does not contain controlnet or t2i adapter data", ckpt_path)
-        return net
-
-    if controlnet_config is None:
-        use_fp16 = model_management.should_use_fp16()
-        controlnet_config = model_detection.model_config_from_unet(controlnet_data, prefix, use_fp16).unet_config
-    controlnet_config.pop("out_channels")
-    controlnet_config["hint_channels"] = controlnet_data["{}input_hint_block.0.weight".format(prefix)].shape[1]
-    control_model = cldm.ControlNet(**controlnet_config)
-
-    if pth:
-        if 'difference' in controlnet_data:
-            if model is not None:
-                model_management.load_models_gpu([model])
-                model_sd = model.model_state_dict()
-                for x in controlnet_data:
-                    c_m = "control_model."
-                    if x.startswith(c_m):
-                        sd_key = "diffusion_model.{}".format(x[len(c_m):])
-                        if sd_key in model_sd:
-                            cd = controlnet_data[x]
-                            cd += model_sd[sd_key].type(cd.dtype).to(cd.device)
-            else:
-                print("WARNING: Loaded a diff controlnet without a model. It will very likely not work.")
-
-        class WeightsLoader(torch.nn.Module):
-            pass
-        w = WeightsLoader()
-        w.control_model = control_model
-        missing, unexpected = w.load_state_dict(controlnet_data, strict=False)
-    else:
-        missing, unexpected = control_model.load_state_dict(controlnet_data, strict=False)
-    print(missing, unexpected)
-
-    if use_fp16:
-        control_model = control_model.half()
-
-    global_average_pooling = False
-    if ckpt_path.endswith("_shuffle.pth") or ckpt_path.endswith("_shuffle.safetensors") or ckpt_path.endswith("_shuffle_fp16.safetensors"): #TODO: smarter way of enabling global_average_pooling
-        global_average_pooling = True
-
-    control = ControlNet(control_model, global_average_pooling=global_average_pooling)
-    return control
-
-class T2IAdapter(ControlBase):
-    def __init__(self, t2i_model, channels_in, device=None):
-        super().__init__(device)
-        self.t2i_model = t2i_model
-        self.channels_in = channels_in
-        self.control_input = None
-
-    def scale_image_to(self, width, height):
-        unshuffle_amount = self.t2i_model.unshuffle_amount
-        width = math.ceil(width / unshuffle_amount) * unshuffle_amount
-        height = math.ceil(height / unshuffle_amount) * unshuffle_amount
-        return width, height
-
-    def get_control(self, x_noisy, t, cond, batched_number):
-        control_prev = None
-        if self.previous_controlnet is not None:
-            control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number)
-
-        if self.timestep_range is not None:
-            if t[0] > self.timestep_range[0] or t[0] < self.timestep_range[1]:
-                if control_prev is not None:
-                    return control_prev
-                else:
-                    return {}
-
-        if self.cond_hint is None or x_noisy.shape[2] * 8 != self.cond_hint.shape[2] or x_noisy.shape[3] * 8 != self.cond_hint.shape[3]:
-            if self.cond_hint is not None:
-                del self.cond_hint
-            self.control_input = None
-            self.cond_hint = None
-            width, height = self.scale_image_to(x_noisy.shape[3] * 8, x_noisy.shape[2] * 8)
-            self.cond_hint = utils.common_upscale(self.cond_hint_original, width, height, 'nearest-exact', "center").float().to(self.device)
-            if self.channels_in == 1 and self.cond_hint.shape[1] > 1:
-                self.cond_hint = torch.mean(self.cond_hint, 1, keepdim=True)
-        if x_noisy.shape[0] != self.cond_hint.shape[0]:
-            self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number)
-        if self.control_input is None:
-            self.t2i_model.to(x_noisy.dtype)
-            self.t2i_model.to(self.device)
-            self.control_input = self.t2i_model(self.cond_hint.to(x_noisy.dtype))
-            self.t2i_model.cpu()
-
-        control_input = list(map(lambda a: None if a is None else a.clone(), self.control_input))
-        mid = None
-        if self.t2i_model.xl == True:
-            mid = control_input[-1:]
-            control_input = control_input[:-1]
-        return self.control_merge(control_input, mid, control_prev, x_noisy.dtype)
-
-    def copy(self):
-        c = T2IAdapter(self.t2i_model, self.channels_in)
-        self.copy_to(c)
-        return c
-
-def load_t2i_adapter(t2i_data):
-    keys = t2i_data.keys()
-    if 'adapter' in keys:
-        t2i_data = t2i_data['adapter']
-        keys = t2i_data.keys()
-    if "body.0.in_conv.weight" in keys:
-        cin = t2i_data['body.0.in_conv.weight'].shape[1]
-        model_ad = adapter.Adapter_light(cin=cin, channels=[320, 640, 1280, 1280], nums_rb=4)
-    elif 'conv_in.weight' in keys:
-        cin = t2i_data['conv_in.weight'].shape[1]
-        channel = t2i_data['conv_in.weight'].shape[0]
-        ksize = t2i_data['body.0.block2.weight'].shape[2]
-        use_conv = False
-        down_opts = list(filter(lambda a: a.endswith("down_opt.op.weight"), keys))
-        if len(down_opts) > 0:
-            use_conv = True
-        xl = False
-        if cin == 256:
-            xl = True
-        model_ad = adapter.Adapter(cin=cin, channels=[channel, channel*2, channel*4, channel*4][:4], nums_rb=2, ksize=ksize, sk=True, use_conv=use_conv, xl=xl)
-    else:
-        return None
-    missing, unexpected = model_ad.load_state_dict(t2i_data)
-    if len(missing) > 0:
-        print("t2i missing", missing)
-
-    if len(unexpected) > 0:
-        print("t2i unexpected", unexpected)
-
-    return T2IAdapter(model_ad, model_ad.input_channels)
-
-
 class StyleModel:
     def __init__(self, model, device="cpu"):
         self.model = model
@@ -1012,10 +520,10 @@ class StyleModel:
 
 
 def load_style_model(ckpt_path):
-    model_data = utils.load_torch_file(ckpt_path, safe_load=True)
+    model_data = comfy.utils.load_torch_file(ckpt_path, safe_load=True)
     keys = model_data.keys()
     if "style_embedding" in keys:
-        model = adapter.StyleAdapter(width=1024, context_dim=768, num_head=8, n_layes=3, num_token=8)
+        model = comfy.t2i_adapter.adapter.StyleAdapter(width=1024, context_dim=768, num_head=8, n_layes=3, num_token=8)
     else:
         raise Exception("invalid style model {}".format(ckpt_path))
     model.load_state_dict(model_data)
@@ -1025,14 +533,14 @@ def load_style_model(ckpt_path):
 def load_clip(ckpt_paths, embedding_directory=None):
     clip_data = []
     for p in ckpt_paths:
-        clip_data.append(utils.load_torch_file(p, safe_load=True))
+        clip_data.append(comfy.utils.load_torch_file(p, safe_load=True))
 
     class EmptyClass:
         pass
 
     for i in range(len(clip_data)):
         if "transformer.resblocks.0.ln_1.weight" in clip_data[i]:
-            clip_data[i] = utils.transformers_convert(clip_data[i], "", "text_model.", 32)
+            clip_data[i] = comfy.utils.transformers_convert(clip_data[i], "", "text_model.", 32)
 
     clip_target = EmptyClass()
     clip_target.params = {}
@@ -1061,7 +569,7 @@ def load_clip(ckpt_paths, embedding_directory=None):
     return clip
 
 def load_gligen(ckpt_path):
-    data = utils.load_torch_file(ckpt_path, safe_load=True)
+    data = comfy.utils.load_torch_file(ckpt_path, safe_load=True)
     model = gligen.load_gligen(data)
     if model_management.should_use_fp16():
         model = model.half()
@@ -1101,7 +609,7 @@ def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_cl
         pass
 
     if state_dict is None:
-        state_dict = utils.load_torch_file(ckpt_path)
+        state_dict = comfy.utils.load_torch_file(ckpt_path)
 
     class EmptyClass:
         pass
@@ -1148,7 +656,7 @@ def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_cl
     return (ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=offload_device), clip, vae)
 
 def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None):
-    sd = utils.load_torch_file(ckpt_path)
+    sd = comfy.utils.load_torch_file(ckpt_path)
     sd_keys = sd.keys()
     clip = None
     clipvision = None
@@ -1156,7 +664,7 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
     model = None
     clip_target = None
 
-    parameters = utils.calculate_parameters(sd, "model.diffusion_model.")
+    parameters = comfy.utils.calculate_parameters(sd, "model.diffusion_model.")
     fp16 = model_management.should_use_fp16(model_params=parameters)
 
     class WeightsLoader(torch.nn.Module):
@@ -1206,8 +714,8 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
 
 
 def load_unet(unet_path): #load unet in diffusers format
-    sd = utils.load_torch_file(unet_path)
+    sd = comfy.utils.load_torch_file(unet_path)
-    parameters = utils.calculate_parameters(sd)
+    parameters = comfy.utils.calculate_parameters(sd)
     fp16 = model_management.should_use_fp16(model_params=parameters)
 
     model_config = model_detection.model_config_from_diffusers_unet(sd, fp16)
@@ -1215,7 +723,7 @@ def load_unet(unet_path): #load unet in diffusers format
         print("ERROR UNSUPPORTED UNET", unet_path)
         return None
 
-    diffusers_keys = utils.unet_to_diffusers(model_config.unet_config)
+    diffusers_keys = comfy.utils.unet_to_diffusers(model_config.unet_config)
 
     new_sd = {}
     for k in diffusers_keys:
@@ -1232,4 +740,4 @@ def load_unet(unet_path): #load unet in diffusers format
 def save_checkpoint(output_path, model, clip, vae, metadata=None):
     model_management.load_models_gpu([model, clip.load_model()])
     sd = model.model.state_dict_for_saving(clip.get_sd(), vae.get_sd())
-    utils.save_torch_file(sd, output_path, metadata=metadata)
+    comfy.utils.save_torch_file(sd, output_path, metadata=metadata)

comfy/utils.py

@@ -237,6 +237,20 @@ def safetensors_header(safetensors_path, max_size=100*1024*1024):
             return None
         return f.read(length_of_header)
 
+def set_attr(obj, attr, value):
+    attrs = attr.split(".")
+    for name in attrs[:-1]:
+        obj = getattr(obj, name)
+    prev = getattr(obj, attrs[-1])
+    setattr(obj, attrs[-1], torch.nn.Parameter(value))
+    del prev
+
+def get_attr(obj, attr):
+    attrs = attr.split(".")
+    for name in attrs:
+        obj = getattr(obj, name)
+    return obj
+
 def bislerp(samples, width, height):
     def slerp(b1, b2, r):
         '''slerps batches b1, b2 according to ratio r, batches should be flat e.g. NxC'''

nodes.py

@@ -22,6 +22,7 @@ import comfy.samplers
 import comfy.sample
 import comfy.sd
 import comfy.utils
+import comfy.controlnet
 
 import comfy.clip_vision
 
@@ -569,7 +570,7 @@ class ControlNetLoader:
 
     def load_controlnet(self, control_net_name):
         controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)
-        controlnet = comfy.sd.load_controlnet(controlnet_path)
+        controlnet = comfy.controlnet.load_controlnet(controlnet_path)
         return (controlnet,)
 
 class DiffControlNetLoader:
@@ -585,7 +586,7 @@ class DiffControlNetLoader:
 
     def load_controlnet(self, model, control_net_name):
         controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)
-        controlnet = comfy.sd.load_controlnet(controlnet_path, model)
+        controlnet = comfy.controlnet.load_controlnet(controlnet_path, model)
         return (controlnet,)