Upload cldm.py

cldm.py

@@ -0,0 +1,456 @@
+import os
+import einops
+from omegaconf import OmegaConf
+import torch
+import torch as th
+import torch.nn as nn
+from modules import devices, lowvram, shared
+
+from ldm.modules.diffusionmodules.util import (
+    conv_nd,
+    linear,
+    zero_module,
+    timestep_embedding,
+)
+
+from ldm.modules.attention import SpatialTransformer
+from ldm.modules.diffusionmodules.openaimodel import UNetModel, TimestepEmbedSequential, ResBlock, Downsample, AttentionBlock
+from ldm.util import exists
+
+
+def load_state_dict(ckpt_path, location='cpu'):
+    _, extension = os.path.splitext(ckpt_path)
+    if extension.lower() == ".safetensors":
+        import safetensors.torch
+        state_dict = safetensors.torch.load_file(ckpt_path, device=location)
+    else:
+        state_dict = get_state_dict(torch.load(
+            ckpt_path, map_location=torch.device(location)))
+    state_dict = get_state_dict(state_dict)
+    print(f'Loaded state_dict from [{ckpt_path}]')
+    return state_dict
+
+
+def get_state_dict(d):
+    return d.get('state_dict', d)
+
+
+def align(hint, size):
+    b, c, h1, w1 = hint.shape
+    h, w = size
+    if h != h1 or w != w1:
+         hint = torch.nn.functional.interpolate(hint, size=size, mode="nearest")
+    return hint
+
+
+def get_node_name(name, parent_name):
+    if len(name) <= len(parent_name):
+        return False, ''
+    p = name[:len(parent_name)]
+    if p != parent_name:
+        return False, ''
+    return True, name[len(parent_name):]
+
+
+class PlugableControlModel(nn.Module):
+    def __init__(self, model_path, config_path, weight=1.0, lowvram=False, base_model=None) -> None:
+        super().__init__()
+        # temp code
+        config_path = "/mnt/workspace/stable-diffusion-webui/extensions/sd-webui-controlnet/models/cldm_v15.yaml"
+        print(config_path)
+        config = OmegaConf.load(config_path)
+        
+        self.control_model = ControlNet(**config.model.params.control_stage_config.params)
+        state_dict = load_state_dict(model_path)
+        
+        if any([k.startswith("control_model.") for k, v in state_dict.items()]):
+            
+            is_diff_model = 'difference' in state_dict
+            transfer_ctrl_opt = shared.opts.data.get("control_net_control_transfer", False) and \
+                any([k.startswith("model.diffusion_model.") for k, v in state_dict.items()])
+                
+            if (is_diff_model or transfer_ctrl_opt) and base_model is not None:
+                # apply transfer control - https://github.com/lllyasviel/ControlNet/blob/main/tool_transfer_control.py
+                
+                unet_state_dict = base_model.state_dict()
+                unet_state_dict_keys = unet_state_dict.keys()
+                final_state_dict = {}
+                counter = 0
+                for key in state_dict.keys():
+                    if not key.startswith("control_model."):
+                        continue
+                    
+                    p = state_dict[key]
+                    is_control, node_name = get_node_name(key, 'control_')
+                    key_name = node_name.replace("model.", "") if is_control else key
+
+                    if key_name in unet_state_dict_keys:
+                        if is_diff_model:
+                            # transfer control by make difference in advance
+                            p_new = p + unet_state_dict[key_name].clone().cpu()
+                        else:
+                            # transfer control by calculate offsets from (delta = p + current_unet_encoder - frozen_unet_encoder)
+                            p_new = p + unet_state_dict[key_name].clone().cpu() - state_dict["model.diffusion_model."+key_name]
+                        counter += 1
+                    else:
+                        p_new = p
+                    final_state_dict[key] = p_new
+                    
+                print(f'Offset cloned: {counter} values')
+                state_dict = final_state_dict
+                
+            state_dict = {k.replace("control_model.", ""): v for k, v in state_dict.items() if k.startswith("control_model.")}
+        else:
+            # assume that model is done by user
+            pass
+            
+        self.control_model.load_state_dict(state_dict)
+        self.lowvram = lowvram            
+        self.weight = weight
+        self.only_mid_control = False
+        self.control = None
+        self.hint_cond = None
+        
+        if not self.lowvram:
+            self.control_model.to(devices.get_device_for("controlnet"))
+
+    def hook(self, model, parent_model):
+        outer = self
+
+        def forward(self, x, timesteps=None, context=None, **kwargs):
+            only_mid_control = outer.only_mid_control
+            
+            # hires stuffs
+            # note that this method may not works if hr_scale < 1.1
+            if abs(x.shape[-1] - outer.hint_cond.shape[-1] // 8) > 8:
+                only_mid_control = shared.opts.data.get("control_net_only_midctrl_hires", True)
+                # If you want to completely disable control net, uncomment this.
+                # return self._original_forward(x, timesteps=timesteps, context=context, **kwargs)
+            
+            control = outer.control_model(x=x, hint=outer.hint_cond, timesteps=timesteps, context=context)
+            assert timesteps is not None, ValueError(f"insufficient timestep: {timesteps}")
+            hs = []
+            with torch.no_grad():
+                t_emb = timestep_embedding(
+                    timesteps, self.model_channels, repeat_only=False)
+                emb = self.time_embed(t_emb)
+                h = x.type(self.dtype)
+                for module in self.input_blocks:
+                    h = module(h, emb, context)
+                    hs.append(h)
+                h = self.middle_block(h, emb, context)
+
+            h += control.pop()
+
+            for i, module in enumerate(self.output_blocks):
+                if only_mid_control:
+                    h = torch.cat([h, hs.pop()], dim=1)
+                else:
+                    hs_input, control_input = hs.pop(), control.pop()
+                    h = align(h, hs_input.shape[-2:])
+                    h = torch.cat([h, hs_input + control_input * outer.weight], dim=1)
+                h = module(h, emb, context)
+
+            h = h.type(x.dtype)
+            return self.out(h)
+
+        def forward2(*args, **kwargs):
+            # webui will handle other compoments 
+            try:
+                if shared.cmd_opts.lowvram:
+                    lowvram.send_everything_to_cpu()
+                if self.lowvram:
+                    self.control_model.to(devices.get_device_for("controlnet"))
+                return forward(*args, **kwargs)
+            finally:
+                if self.lowvram:
+                    self.control_model.cpu()
+        
+        model._original_forward = model.forward
+        model.forward = forward2.__get__(model, UNetModel)
+    
+    def notify(self, cond_like, weight):
+        self.hint_cond = cond_like
+        self.weight = weight
+        # print(self.hint_cond.shape)
+
+    def restore(self, model):
+        if not hasattr(model, "_original_forward"):
+            # no such handle, ignore
+            return
+        
+        model.forward = model._original_forward
+        del model._original_forward
+
+
+class ControlNet(nn.Module):
+    def __init__(
+        self,
+        image_size,
+        in_channels,
+        model_channels,
+        hint_channels,
+        num_res_blocks,
+        attention_resolutions,
+        dropout=0,
+        channel_mult=(1, 2, 4, 8),
+        conv_resample=True,
+        dims=2,
+        use_checkpoint=False,
+        use_fp16=False,
+        num_heads=-1,
+        num_head_channels=-1,
+        num_heads_upsample=-1,
+        use_scale_shift_norm=False,
+        resblock_updown=False,
+        use_new_attention_order=False,
+        use_spatial_transformer=False,    # custom transformer support
+        transformer_depth=1,              # custom transformer support
+        context_dim=None,                 # custom transformer support
+        # custom support for prediction of discrete ids into codebook of first stage vq model
+        n_embed=None,
+        legacy=True,
+        disable_self_attentions=None,
+        num_attention_blocks=None,
+        disable_middle_self_attn=False,
+        use_linear_in_transformer=False,
+    ):
+        super().__init__()
+        if use_spatial_transformer:
+            assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'
+
+        if context_dim is not None:
+            assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
+            from omegaconf.listconfig import ListConfig
+            if type(context_dim) == ListConfig:
+                context_dim = list(context_dim)
+
+        if num_heads_upsample == -1:
+            num_heads_upsample = num_heads
+
+        if num_heads == -1:
+            assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'
+
+        if num_head_channels == -1:
+            assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'
+
+        self.dims = dims
+        self.image_size = image_size
+        self.in_channels = in_channels
+        self.model_channels = model_channels
+        if isinstance(num_res_blocks, int):
+            self.num_res_blocks = len(channel_mult) * [num_res_blocks]
+        else:
+            if len(num_res_blocks) != len(channel_mult):
+                raise ValueError("provide num_res_blocks either as an int (globally constant) or "
+                                 "as a list/tuple (per-level) with the same length as channel_mult")
+            self.num_res_blocks = num_res_blocks
+        if disable_self_attentions is not None:
+            # should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not
+            assert len(disable_self_attentions) == len(channel_mult)
+        if num_attention_blocks is not None:
+            assert len(num_attention_blocks) == len(self.num_res_blocks)
+            assert all(map(lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], range(
+                len(num_attention_blocks))))
+            print(f"Constructor of UNetModel received num_attention_blocks={num_attention_blocks}. "
+                  f"This option has LESS priority than attention_resolutions {attention_resolutions}, "
+                  f"i.e., in cases where num_attention_blocks[i] > 0 but 2**i not in attention_resolutions, "
+                  f"attention will still not be set.")
+
+        self.attention_resolutions = attention_resolutions
+        self.dropout = dropout
+        self.channel_mult = channel_mult
+        self.conv_resample = conv_resample
+        self.use_checkpoint = use_checkpoint
+        self.dtype = th.float16 if use_fp16 else th.float32
+        self.num_heads = num_heads
+        self.num_head_channels = num_head_channels
+        self.num_heads_upsample = num_heads_upsample
+        self.predict_codebook_ids = n_embed is not None
+
+        time_embed_dim = model_channels * 4
+        self.time_embed = nn.Sequential(
+            linear(model_channels, time_embed_dim),
+            nn.SiLU(),
+            linear(time_embed_dim, time_embed_dim),
+        )
+
+        self.input_blocks = nn.ModuleList(
+            [
+                TimestepEmbedSequential(
+                    conv_nd(dims, in_channels, model_channels, 3, padding=1)
+                )
+            ]
+        )
+        self.zero_convs = nn.ModuleList([self.make_zero_conv(model_channels)])
+
+        self.input_hint_block = TimestepEmbedSequential(
+            conv_nd(dims, hint_channels, 16, 3, padding=1),
+            nn.SiLU(),
+            conv_nd(dims, 16, 16, 3, padding=1),
+            nn.SiLU(),
+            conv_nd(dims, 16, 32, 3, padding=1, stride=2),
+            nn.SiLU(),
+            conv_nd(dims, 32, 32, 3, padding=1),
+            nn.SiLU(),
+            conv_nd(dims, 32, 96, 3, padding=1, stride=2),
+            nn.SiLU(),
+            conv_nd(dims, 96, 96, 3, padding=1),
+            nn.SiLU(),
+            conv_nd(dims, 96, 256, 3, padding=1, stride=2),
+            nn.SiLU(),
+            zero_module(conv_nd(dims, 256, model_channels, 3, padding=1))
+        )
+
+        self._feature_size = model_channels
+        input_block_chans = [model_channels]
+        ch = model_channels
+        ds = 1
+        for level, mult in enumerate(channel_mult):
+            for nr in range(self.num_res_blocks[level]):
+                layers = [
+                    ResBlock(
+                        ch,
+                        time_embed_dim,
+                        dropout,
+                        out_channels=mult * model_channels,
+                        dims=dims,
+                        use_checkpoint=use_checkpoint,
+                        use_scale_shift_norm=use_scale_shift_norm,
+                    )
+                ]
+                ch = mult * model_channels
+                if ds in attention_resolutions:
+                    if num_head_channels == -1:
+                        dim_head = ch // num_heads
+                    else:
+                        num_heads = ch // num_head_channels
+                        dim_head = num_head_channels
+                    if legacy:
+                        #num_heads = 1
+                        dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
+                    if exists(disable_self_attentions):
+                        disabled_sa = disable_self_attentions[level]
+                    else:
+                        disabled_sa = False
+
+                    if not exists(num_attention_blocks) or nr < num_attention_blocks[level]:
+                        layers.append(
+                            AttentionBlock(
+                                ch,
+                                use_checkpoint=use_checkpoint,
+                                num_heads=num_heads,
+                                num_head_channels=dim_head,
+                                use_new_attention_order=use_new_attention_order,
+                            ) if not use_spatial_transformer else SpatialTransformer(
+                                ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
+                                disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
+                                use_checkpoint=use_checkpoint
+                            )
+                        )
+                self.input_blocks.append(TimestepEmbedSequential(*layers))
+                self.zero_convs.append(self.make_zero_conv(ch))
+                self._feature_size += ch
+                input_block_chans.append(ch)
+            if level != len(channel_mult) - 1:
+                out_ch = ch
+                self.input_blocks.append(
+                    TimestepEmbedSequential(
+                        ResBlock(
+                            ch,
+                            time_embed_dim,
+                            dropout,
+                            out_channels=out_ch,
+                            dims=dims,
+                            use_checkpoint=use_checkpoint,
+                            use_scale_shift_norm=use_scale_shift_norm,
+                            down=True,
+                        )
+                        if resblock_updown
+                        else Downsample(
+                            ch, conv_resample, dims=dims, out_channels=out_ch
+                        )
+                    )
+                )
+                ch = out_ch
+                input_block_chans.append(ch)
+                self.zero_convs.append(self.make_zero_conv(ch))
+                ds *= 2
+                self._feature_size += ch
+
+        if num_head_channels == -1:
+            dim_head = ch // num_heads
+        else:
+            num_heads = ch // num_head_channels
+            dim_head = num_head_channels
+        if legacy:
+            #num_heads = 1
+            dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
+        self.middle_block = TimestepEmbedSequential(
+            ResBlock(
+                ch,
+                time_embed_dim,
+                dropout,
+                dims=dims,
+                use_checkpoint=use_checkpoint,
+                use_scale_shift_norm=use_scale_shift_norm,
+            ),
+            AttentionBlock(
+                ch,
+                use_checkpoint=use_checkpoint,
+                num_heads=num_heads,
+                num_head_channels=dim_head,
+                use_new_attention_order=use_new_attention_order,
+                # always uses a self-attn
+            ) if not use_spatial_transformer else SpatialTransformer(
+                ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
+                disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer,
+                use_checkpoint=use_checkpoint
+            ),
+            ResBlock(
+                ch,
+                time_embed_dim,
+                dropout,
+                dims=dims,
+                use_checkpoint=use_checkpoint,
+                use_scale_shift_norm=use_scale_shift_norm,
+            ),
+        )
+        self.middle_block_out = self.make_zero_conv(ch)
+        self._feature_size += ch
+
+    def make_zero_conv(self, channels):
+        return TimestepEmbedSequential(zero_module(conv_nd(self.dims, channels, channels, 1, padding=0)))
+    
+    def align(self, hint, h, w):
+        c, h1, w1 = hint.shape
+        if h != h1 or w != w1:
+            hint = align(hint.unsqueeze(0), (h, w))
+            return hint.squeeze(0)
+        return hint
+
+    def forward(self, x, hint, timesteps, context, **kwargs):
+        t_emb = timestep_embedding(
+            timesteps, self.model_channels, repeat_only=False)
+        emb = self.time_embed(t_emb)
+
+        guided_hint = self.input_hint_block(hint, emb, context)
+        outs = []
+        
+        h1, w1 = x.shape[-2:]
+        guided_hint = self.align(guided_hint, h1, w1)
+
+        h = x.type(self.dtype)
+        for module, zero_conv in zip(self.input_blocks, self.zero_convs):
+            if guided_hint is not None:
+                h = module(h, emb, context)
+                h += guided_hint
+                guided_hint = None
+            else:
+                h = module(h, emb, context)
+            outs.append(zero_conv(h, emb, context))
+
+        h = self.middle_block(h, emb, context)
+        outs.append(self.middle_block_out(h, emb, context))
+
+        return outs