Merge remote-tracking branch 'origin/master' into frontendrefactor

README.md

@@ -135,7 +135,7 @@ You can also set this command line setting to disable the upcasting to fp32 in s
 
 ## Support and dev channel
 
-[Matrix room: #comfyui:matrix.org](https://app.element.io/#/room/%23comfyui%3Amatrix.org) (it's like discord but open source).
+[Matrix space: #comfyui_space:matrix.org](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) (it's like discord but open source).
 
 # QA
 

comfy/ldm/modules/attention.py

@@ -489,6 +489,8 @@ if XFORMERS_IS_AVAILBLE == False or "--disable-xformers" in sys.argv:
         if "--use-pytorch-cross-attention" in sys.argv:
             print("Using pytorch cross attention")
             torch.backends.cuda.enable_math_sdp(False)
+            torch.backends.cuda.enable_flash_sdp(True)
+            torch.backends.cuda.enable_mem_efficient_sdp(True)
             CrossAttention = CrossAttentionPytorch
         else:
             print("Using sub quadratic optimization for cross attention, if you have memory or speed issues try using: --use-split-cross-attention")
@@ -497,6 +499,7 @@ else:
     print("Using xformers cross attention")
     CrossAttention = MemoryEfficientCrossAttention
 
+
 class BasicTransformerBlock(nn.Module):
     def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True,
                  disable_self_attn=False):

comfy/ldm/modules/diffusionmodules/model.py

@@ -7,6 +7,7 @@ from einops import rearrange
 from typing import Optional, Any
 
 from ldm.modules.attention import MemoryEfficientCrossAttention
+import model_management
 
 try:
     import xformers
@@ -199,12 +200,7 @@ class AttnBlock(nn.Module):
 
         r1 = torch.zeros_like(k, device=q.device)
 
-        stats = torch.cuda.memory_stats(q.device)
-        mem_active = stats['active_bytes.all.current']
-        mem_reserved = stats['reserved_bytes.all.current']
-        mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())
-        mem_free_torch = mem_reserved - mem_active
-        mem_free_total = mem_free_cuda + mem_free_torch
+        mem_free_total = model_management.get_free_memory(q.device)
 
         gb = 1024 ** 3
         tensor_size = q.shape[0] * q.shape[1] * k.shape[2] * q.element_size()

comfy/sd.py

@@ -612,8 +612,17 @@ class T2IAdapter:
 
 def load_t2i_adapter(ckpt_path, model=None):
     t2i_data = load_torch_file(ckpt_path)
-    cin = t2i_data['conv_in.weight'].shape[1]
-    model_ad = adapter.Adapter(cin=cin, channels=[320, 640, 1280, 1280][:4], nums_rb=2, ksize=1, sk=True, use_conv=False)
+    keys = t2i_data.keys()
+    if "style_embedding" in keys:
+        pass
+        # TODO
+        # model_ad = adapter.StyleAdapter(width=1024, context_dim=768, num_head=8, n_layes=3, num_token=8)
+    elif "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)
+    else:
+        cin = t2i_data['conv_in.weight'].shape[1]
+        model_ad = adapter.Adapter(cin=cin, channels=[320, 640, 1280, 1280][:4], nums_rb=2, ksize=1, sk=True, use_conv=False)
     model_ad.load_state_dict(t2i_data)
     return T2IAdapter(model_ad, cin // 64)
 

comfy/t2i_adapter/adapter.py

 #taken from https://github.com/TencentARC/T2I-Adapter
-
 import torch
 import torch.nn as nn
-import torch.nn.functional as F
-from ldm.modules.attention import SpatialTransformer, BasicTransformerBlock
+from collections import OrderedDict
+
 
 def conv_nd(dims, *args, **kwargs):
     """
@@ -17,6 +16,7 @@ def conv_nd(dims, *args, **kwargs):
         return nn.Conv3d(*args, **kwargs)
     raise ValueError(f"unsupported dimensions: {dims}")
 
+
 def avg_pool_nd(dims, *args, **kwargs):
     """
     Create a 1D, 2D, or 3D average pooling module.
@@ -29,6 +29,7 @@ def avg_pool_nd(dims, *args, **kwargs):
         return nn.AvgPool3d(*args, **kwargs)
     raise ValueError(f"unsupported dimensions: {dims}")
 
+
 class Downsample(nn.Module):
     """
     A downsampling layer with an optional convolution.
@@ -38,7 +39,7 @@ class Downsample(nn.Module):
                  downsampling occurs in the inner-two dimensions.
     """
 
-    def __init__(self, channels, use_conv, dims=2, out_channels=None,padding=1):
+    def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
         super().__init__()
         self.channels = channels
         self.out_channels = out_channels or channels
@@ -61,8 +62,8 @@ class Downsample(nn.Module):
 class ResnetBlock(nn.Module):
     def __init__(self, in_c, out_c, down, ksize=3, sk=False, use_conv=True):
         super().__init__()
-        ps = ksize//2
-        if in_c != out_c or sk==False:
+        ps = ksize // 2
+        if in_c != out_c or sk == False:
             self.in_conv = nn.Conv2d(in_c, out_c, ksize, 1, ps)
         else:
             # print('n_in')
@@ -70,7 +71,7 @@ class ResnetBlock(nn.Module):
         self.block1 = nn.Conv2d(out_c, out_c, 3, 1, 1)
         self.act = nn.ReLU()
         self.block2 = nn.Conv2d(out_c, out_c, ksize, 1, ps)
-        if sk==False:
+        if sk == False:
             self.skep = nn.Conv2d(in_c, out_c, ksize, 1, ps)
         else:
             self.skep = None
@@ -82,7 +83,7 @@ class ResnetBlock(nn.Module):
     def forward(self, x):
         if self.down == True:
             x = self.down_opt(x)
-        if self.in_conv is not None: # edit
+        if self.in_conv is not None:  # edit
             x = self.in_conv(x)
 
         h = self.block1(x)
@@ -103,12 +104,14 @@ class Adapter(nn.Module):
         self.body = []
         for i in range(len(channels)):
             for j in range(nums_rb):
-                if (i!=0) and (j==0):
-                    self.body.append(ResnetBlock(channels[i-1], channels[i], down=True, ksize=ksize, sk=sk, use_conv=use_conv))
+                if (i != 0) and (j == 0):
+                    self.body.append(
+                        ResnetBlock(channels[i - 1], channels[i], down=True, ksize=ksize, sk=sk, use_conv=use_conv))
                 else:
-                    self.body.append(ResnetBlock(channels[i], channels[i], down=False, ksize=ksize, sk=sk, use_conv=use_conv))
+                    self.body.append(
+                        ResnetBlock(channels[i], channels[i], down=False, ksize=ksize, sk=sk, use_conv=use_conv))
         self.body = nn.ModuleList(self.body)
-        self.conv_in = nn.Conv2d(cin,channels[0], 3, 1, 1)
+        self.conv_in = nn.Conv2d(cin, channels[0], 3, 1, 1)
 
     def forward(self, x):
         # unshuffle
@@ -118,8 +121,139 @@ class Adapter(nn.Module):
         x = self.conv_in(x)
         for i in range(len(self.channels)):
             for j in range(self.nums_rb):
-                idx = i*self.nums_rb +j
+                idx = i * self.nums_rb + j
                 x = self.body[idx](x)
             features.append(x)
 
         return features
+
+
+class LayerNorm(nn.LayerNorm):
+    """Subclass torch's LayerNorm to handle fp16."""
+
+    def forward(self, x: torch.Tensor):
+        orig_type = x.dtype
+        ret = super().forward(x.type(torch.float32))
+        return ret.type(orig_type)
+
+
+class QuickGELU(nn.Module):
+
+    def forward(self, x: torch.Tensor):
+        return x * torch.sigmoid(1.702 * x)
+
+
+class ResidualAttentionBlock(nn.Module):
+
+    def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None):
+        super().__init__()
+
+        self.attn = nn.MultiheadAttention(d_model, n_head)
+        self.ln_1 = LayerNorm(d_model)
+        self.mlp = nn.Sequential(
+            OrderedDict([("c_fc", nn.Linear(d_model, d_model * 4)), ("gelu", QuickGELU()),
+                         ("c_proj", nn.Linear(d_model * 4, d_model))]))
+        self.ln_2 = LayerNorm(d_model)
+        self.attn_mask = attn_mask
+
+    def attention(self, x: torch.Tensor):
+        self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None
+        return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0]
+
+    def forward(self, x: torch.Tensor):
+        x = x + self.attention(self.ln_1(x))
+        x = x + self.mlp(self.ln_2(x))
+        return x
+
+
+class StyleAdapter(nn.Module):
+
+    def __init__(self, width=1024, context_dim=768, num_head=8, n_layes=3, num_token=4):
+        super().__init__()
+
+        scale = width ** -0.5
+        self.transformer_layes = nn.Sequential(*[ResidualAttentionBlock(width, num_head) for _ in range(n_layes)])
+        self.num_token = num_token
+        self.style_embedding = nn.Parameter(torch.randn(1, num_token, width) * scale)
+        self.ln_post = LayerNorm(width)
+        self.ln_pre = LayerNorm(width)
+        self.proj = nn.Parameter(scale * torch.randn(width, context_dim))
+
+    def forward(self, x):
+        # x shape [N, HW+1, C]
+        style_embedding = self.style_embedding + torch.zeros(
+            (x.shape[0], self.num_token, self.style_embedding.shape[-1]), device=x.device)
+        x = torch.cat([x, style_embedding], dim=1)
+        x = self.ln_pre(x)
+        x = x.permute(1, 0, 2)  # NLD -> LND
+        x = self.transformer_layes(x)
+        x = x.permute(1, 0, 2)  # LND -> NLD
+
+        x = self.ln_post(x[:, -self.num_token:, :])
+        x = x @ self.proj
+
+        return x
+
+
+class ResnetBlock_light(nn.Module):
+    def __init__(self, in_c):
+        super().__init__()
+        self.block1 = nn.Conv2d(in_c, in_c, 3, 1, 1)
+        self.act = nn.ReLU()
+        self.block2 = nn.Conv2d(in_c, in_c, 3, 1, 1)
+
+    def forward(self, x):
+        h = self.block1(x)
+        h = self.act(h)
+        h = self.block2(h)
+
+        return h + x
+
+
+class extractor(nn.Module):
+    def __init__(self, in_c, inter_c, out_c, nums_rb, down=False):
+        super().__init__()
+        self.in_conv = nn.Conv2d(in_c, inter_c, 1, 1, 0)
+        self.body = []
+        for _ in range(nums_rb):
+            self.body.append(ResnetBlock_light(inter_c))
+        self.body = nn.Sequential(*self.body)
+        self.out_conv = nn.Conv2d(inter_c, out_c, 1, 1, 0)
+        self.down = down
+        if self.down == True:
+            self.down_opt = Downsample(in_c, use_conv=False)
+
+    def forward(self, x):
+        if self.down == True:
+            x = self.down_opt(x)
+        x = self.in_conv(x)
+        x = self.body(x)
+        x = self.out_conv(x)
+
+        return x
+
+
+class Adapter_light(nn.Module):
+    def __init__(self, channels=[320, 640, 1280, 1280], nums_rb=3, cin=64):
+        super(Adapter_light, self).__init__()
+        self.unshuffle = nn.PixelUnshuffle(8)
+        self.channels = channels
+        self.nums_rb = nums_rb
+        self.body = []
+        for i in range(len(channels)):
+            if i == 0:
+                self.body.append(extractor(in_c=cin, inter_c=channels[i]//4, out_c=channels[i], nums_rb=nums_rb, down=False))
+            else:
+                self.body.append(extractor(in_c=channels[i-1], inter_c=channels[i]//4, out_c=channels[i], nums_rb=nums_rb, down=True))
+        self.body = nn.ModuleList(self.body)
+
+    def forward(self, x):
+        # unshuffle
+        x = self.unshuffle(x)
+        # extract features
+        features = []
+        for i in range(len(self.channels)):
+            x = self.body[i](x)
+            features.append(x)
+
+        return features

notebooks/comfyui_colab.ipynb

@@ -42,7 +42,7 @@
     {
       "cell_type": "markdown",
       "source": [
-        "Download some models/checkpoints/vae (uncomment the wget commands for the ones you want)"
+        "Download some models/checkpoints/vae or custom comfyui nodes (uncomment the commands for the ones you want)"
       ],
       "metadata": {
         "id": "cccccccccc"
@@ -54,43 +54,52 @@
         "# Checkpoints\n",
         "\n",
         "# SD1.5\n",
-        "!wget https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt -P ./models/checkpoints/\n",
+        "!wget -c https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt -P ./models/checkpoints/\n",
         "\n",
         "# SD2\n",
-        "#!wget https://huggingface.co/stabilityai/stable-diffusion-2-1-base/resolve/main/v2-1_512-ema-pruned.safetensors -P ./models/checkpoints/\n",
-        "#!wget https://huggingface.co/stabilityai/stable-diffusion-2-1/resolve/main/v2-1_768-ema-pruned.safetensors -P ./models/checkpoints/\n",
+        "#!wget -c https://huggingface.co/stabilityai/stable-diffusion-2-1-base/resolve/main/v2-1_512-ema-pruned.safetensors -P ./models/checkpoints/\n",
+        "#!wget -c https://huggingface.co/stabilityai/stable-diffusion-2-1/resolve/main/v2-1_768-ema-pruned.safetensors -P ./models/checkpoints/\n",
         "\n",
         "# Some SD1.5 anime style\n",
-        "#!wget https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix2/AbyssOrangeMix2_hard.safetensors -P ./models/checkpoints/\n",
-        "#!wget https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix3/AOM3A1.safetensors -P ./models/checkpoints/\n",
-        "#!wget https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix3/AOM3A3.safetensors -P ./models/checkpoints/\n",
-        "#!wget https://huggingface.co/Linaqruf/anything-v3.0/resolve/main/anything-v3-fp16-pruned.safetensors -P ./models/checkpoints/\n",
+        "#!wget -c https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix2/AbyssOrangeMix2_hard.safetensors -P ./models/checkpoints/\n",
+        "#!wget -c https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix3/AOM3A1.safetensors -P ./models/checkpoints/\n",
+        "#!wget -c https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix3/AOM3A3.safetensors -P ./models/checkpoints/\n",
+        "#!wget -c https://huggingface.co/Linaqruf/anything-v3.0/resolve/main/anything-v3-fp16-pruned.safetensors -P ./models/checkpoints/\n",
         "\n",
         "# Waifu Diffusion 1.5 (anime style SD2.x 768-v)\n",
-        "#!wget https://huggingface.co/waifu-diffusion/wd-1-5-beta2/resolve/main/checkpoints/wd-1-5-beta2-fp16.safetensors -P ./models/checkpoints/\n",
+        "#!wget -c https://huggingface.co/waifu-diffusion/wd-1-5-beta2/resolve/main/checkpoints/wd-1-5-beta2-fp16.safetensors -P ./models/checkpoints/\n",
         "\n",
         "\n",
         "# VAE\n",
-        "!wget https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.safetensors -P ./models/vae/\n",
-        "#!wget https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/VAEs/orangemix.vae.pt -P ./models/vae/\n",
+        "!wget -c https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.safetensors -P ./models/vae/\n",
+        "#!wget -c https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/VAEs/orangemix.vae.pt -P ./models/vae/\n",
         "\n",
         "\n",
         "# Loras\n",
-        "#!wget --content-disposition https://civitai.com/api/download/models/10350 -P ./models/loras/ #theovercomer8sContrastFix SD2.x 768-v\n",
-        "#!wget --content-disposition https://civitai.com/api/download/models/10638 -P ./models/loras/ #theovercomer8sContrastFix SD1.x\n",
+        "#!wget -c --content-disposition https://civitai.com/api/download/models/10350 -P ./models/loras/ #theovercomer8sContrastFix SD2.x 768-v\n",
+        "#!wget -c --content-disposition https://civitai.com/api/download/models/10638 -P ./models/loras/ #theovercomer8sContrastFix SD1.x\n",
         "\n",
         "\n",
         "# T2I-Adapter\n",
-        "#!wget https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_depth_sd14v1.pth -P ./models/t2i_adapter/\n",
-        "#!wget https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_seg_sd14v1.pth -P ./models/t2i_adapter/\n",
-        "#!wget https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_sketch_sd14v1.pth -P ./models/t2i_adapter/\n",
-        "#!wget https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_keypose_sd14v1.pth -P ./models/t2i_adapter/\n",
+        "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_depth_sd14v1.pth -P ./models/t2i_adapter/\n",
+        "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_seg_sd14v1.pth -P ./models/t2i_adapter/\n",
+        "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_sketch_sd14v1.pth -P ./models/t2i_adapter/\n",
+        "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_keypose_sd14v1.pth -P ./models/t2i_adapter/\n",
+        "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_openpose_sd14v1.pth -P ./models/t2i_adapter/\n",
+        "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_color_sd14v1.pth -P ./models/t2i_adapter/\n",
+        "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_canny_sd14v1.pth -P ./models/t2i_adapter/\n",
+        "\n",
         "\n",
         "\n",
         "# ControlNet\n",
-        "#!wget https://huggingface.co/webui/ControlNet-modules-safetensors/resolve/main/control_depth-fp16.safetensors -P ./models/controlnet/\n",
-        "#!wget https://huggingface.co/webui/ControlNet-modules-safetensors/resolve/main/control_scribble-fp16.safetensors -P ./models/controlnet/\n",
-        "#!wget https://huggingface.co/webui/ControlNet-modules-safetensors/resolve/main/control_openpose-fp16.safetensors -P ./models/controlnet/\n"
+        "#!wget -c https://huggingface.co/webui/ControlNet-modules-safetensors/resolve/main/control_depth-fp16.safetensors -P ./models/controlnet/\n",
+        "#!wget -c https://huggingface.co/webui/ControlNet-modules-safetensors/resolve/main/control_scribble-fp16.safetensors -P ./models/controlnet/\n",
+        "#!wget -c https://huggingface.co/webui/ControlNet-modules-safetensors/resolve/main/control_openpose-fp16.safetensors -P ./models/controlnet/\n",
+        "\n",
+        "\n",
+        "# 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"
       ],
       "metadata": {
         "id": "dddddddddd"
@@ -101,7 +110,7 @@
     {
       "cell_type": "markdown",
       "source": [
-        "### Run ComfyUI with localtunnel\n",
+        "### Run ComfyUI with localtunnel (Recommended Way)\n",
         "\n",
         "use the **fp16** model configs for more speed\n",
         "\n"
@@ -146,7 +155,7 @@
     {
       "cell_type": "markdown",
       "source": [
-        "### Run ComfyUI with colab iframe (in case localtunnel doesn't work)\n",
+        "### Run ComfyUI with colab iframe (use only in case the previous way with localtunnel doesn't work)\n",
         "use the **fp16** model configs for more speed\n",
         "\n",
         "You should see the ui appear in an iframe. If you get a 403 error, it's your firefox settings or an extension that's messing things up.\n",

script_examples/basic_api_example.py

@@ -37,10 +37,9 @@ prompt_text = """
         }
     },
     "4": {
-        "class_type": "CheckpointLoader",
+        "class_type": "CheckpointLoaderSimple",
         "inputs": {
-            "ckpt_name": "v1-5-pruned-emaonly.ckpt",
-            "config_name": "v1-inference.yaml"
+            "ckpt_name": "v1-5-pruned-emaonly.ckpt"
         }
     },
     "5": {

web/scripts/defaultGraph.js

@@ -86,9 +86,9 @@ export const defaultGraph = {
 		},
 		{
 			id: 4,
-			type: "CheckpointLoader",
+			type: "CheckpointLoaderSimple",
 			pos: [26, 474],
-			size: { 0: 315, 1: 122 },
+			size: { 0: 315, 1: 98 },
 			flags: {},
 			order: 0,
 			mode: 0,
@@ -98,7 +98,7 @@ export const defaultGraph = {
 				{ name: "VAE", type: "VAE", links: [8], slot_index: 2 },
 			],
 			properties: {},
-			widgets_values: ["v1-inference.yaml", "v1-5-pruned-emaonly.ckpt"],
+			widgets_values: ["v1-5-pruned-emaonly.ckpt"],
 		},
 	],
 	links: [