Merge c03a90ecfc into 4064062e7d

* add color type and color to rgb int node

* review fix for allowing output

---------

Co-authored-by: Jedrzej Kosinski <kosinkadink1@gmail.com>

.github/workflows/windows_release_dependencies.yml

@@ -29,7 +29,7 @@ on:
         description: 'python patch version'
         required: true
         type: string
-        default: "9"
+        default: "11"
 #  push:
 #    branches:
 #      - master

comfy/latent_formats.py

@@ -224,6 +224,7 @@ class Flux2(LatentFormat):
 
         self.latent_rgb_factors_bias = [-0.0329, -0.0718, -0.0851]
         self.latent_rgb_factors_reshape = lambda t: t.reshape(t.shape[0], 32, 2, 2, t.shape[-2], t.shape[-1]).permute(0, 1, 4, 2, 5, 3).reshape(t.shape[0], 32, t.shape[-2] * 2, t.shape[-1] * 2)
+        self.taesd_decoder_name = "taef2_decoder"
 
     def process_in(self, latent):
         return latent

comfy/sd.py

@@ -451,7 +451,7 @@ class VAE:
                                                             decoder_config={'target': "comfy.ldm.modules.temporal_ae.VideoDecoder", 'params': decoder_config})
             elif "taesd_decoder.1.weight" in sd:
                 self.latent_channels = sd["taesd_decoder.1.weight"].shape[1]
-                self.first_stage_model = comfy.taesd.taesd.TAESD(latent_channels=self.latent_channels)
+                self.first_stage_model = comfy.taesd.taesd.TAESD(latent_channels=self.latent_channels, use_midblock_gn = True if "taesd_decoder.3.pool.0.weight" in sd else False)
             elif "vquantizer.codebook.weight" in sd: #VQGan: stage a of stable cascade
                 self.first_stage_model = StageA()
                 self.downscale_ratio = 4

comfy/taesd/taesd.py

@@ -17,28 +17,36 @@ class Clamp(nn.Module):
         return torch.tanh(x / 3) * 3
 
 class Block(nn.Module):
-    def __init__(self, n_in, n_out):
+    def __init__(self, n_in, n_out, use_midblock_gn=False):
         super().__init__()
         self.conv = nn.Sequential(conv(n_in, n_out), nn.ReLU(), conv(n_out, n_out), nn.ReLU(), conv(n_out, n_out))
         self.skip = comfy.ops.disable_weight_init.Conv2d(n_in, n_out, 1, bias=False) if n_in != n_out else nn.Identity()
         self.fuse = nn.ReLU()
+        self.pool = None
+        if use_midblock_gn:
+            conv1x1, n_gn = lambda n_in, n_out: comfy.ops.disable_weight_init.Conv2d(n_in, n_out, 1, bias=False), n_in*4
+            self.pool = nn.Sequential(conv1x1(n_in, n_gn), comfy.ops.disable_weight_init.GroupNorm(4, n_gn), nn.ReLU(inplace=True), conv1x1(n_gn, n_in))
     def forward(self, x):
+        if self.pool is not None:
+            x = x + self.pool(x)
         return self.fuse(self.conv(x) + self.skip(x))
 
-def Encoder(latent_channels=4):
+def Encoder(latent_channels=4, use_midblock_gn=False):
+    mb_kw = dict(use_midblock_gn=use_midblock_gn)
     return nn.Sequential(
         conv(3, 64), Block(64, 64),
         conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64),
         conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64),
-        conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64),
+        conv(64, 64, stride=2, bias=False), Block(64, 64, **mb_kw), Block(64, 64, **mb_kw), Block(64, 64, **mb_kw),
         conv(64, latent_channels),
     )
 
 
-def Decoder(latent_channels=4):
+def Decoder(latent_channels=4, use_midblock_gn=False):
+    mb_kw = dict(use_midblock_gn=use_midblock_gn)
     return nn.Sequential(
         Clamp(), conv(latent_channels, 64), nn.ReLU(),
-        Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False),
+        Block(64, 64, **mb_kw), Block(64, 64, **mb_kw), Block(64, 64, **mb_kw), nn.Upsample(scale_factor=2), conv(64, 64, bias=False),
         Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False),
         Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False),
         Block(64, 64), conv(64, 3),
@@ -48,17 +56,30 @@ class TAESD(nn.Module):
     latent_magnitude = 3
     latent_shift = 0.5
 
-    def __init__(self, encoder_path=None, decoder_path=None, latent_channels=4):
+    def __init__(self, encoder_path=None, decoder_path=None, latent_channels=4, use_midblock_gn=False):
         """Initialize pretrained TAESD on the given device from the given checkpoints."""
         super().__init__()
-        self.taesd_encoder = Encoder(latent_channels=latent_channels)
-        self.taesd_decoder = Decoder(latent_channels=latent_channels)
+
+        self.latent_channels = latent_channels
+        self.use_midblock_gn = use_midblock_gn
+        self.taesd_encoder = Encoder(latent_channels=latent_channels, use_midblock_gn=use_midblock_gn)
+        self.taesd_decoder = Decoder(latent_channels=latent_channels, use_midblock_gn=use_midblock_gn)
+
+        if encoder_path is not None:
+            self.taesd_encoder, self.latent_channels = self._load_model(encoder_path, Encoder)
+        if decoder_path is not None:
+            self.taesd_decoder, self.latent_channels = self._load_model(decoder_path, Decoder)
+
         self.vae_scale = torch.nn.Parameter(torch.tensor(1.0))
         self.vae_shift = torch.nn.Parameter(torch.tensor(0.0))
-        if encoder_path is not None:
-            self.taesd_encoder.load_state_dict(comfy.utils.load_torch_file(encoder_path, safe_load=True))
-        if decoder_path is not None:
-            self.taesd_decoder.load_state_dict(comfy.utils.load_torch_file(decoder_path, safe_load=True))
+
+    def _load_model(self, path, model_class):
+        """Load a TAESD encoder or decoder from a file."""
+        sd = comfy.utils.load_torch_file(path, safe_load=True)
+        latent_channels = sd["1.weight"].shape[1]
+        model = model_class(latent_channels=latent_channels, use_midblock_gn="3.pool.0.weight" in sd)
+        model.load_state_dict(sd)
+        return model, latent_channels
 
     @staticmethod
     def scale_latents(x):
@@ -71,9 +92,15 @@ class TAESD(nn.Module):
         return x.sub(TAESD.latent_shift).mul(2 * TAESD.latent_magnitude)
 
     def decode(self, x):
+        if x.shape[1] == self.latent_channels * 4:
+            x = x.reshape(x.shape[0], self.latent_channels, 2, 2, x.shape[-2], x.shape[-1]).permute(0, 1, 4, 2, 5, 3).reshape(x.shape[0], self.latent_channels, x.shape[-2] * 2, x.shape[-1] * 2)
+
         x_sample = self.taesd_decoder((x - self.vae_shift) * self.vae_scale)
         x_sample = x_sample.sub(0.5).mul(2)
         return x_sample
 
     def encode(self, x):
-        return (self.taesd_encoder(x * 0.5 + 0.5) / self.vae_scale) + self.vae_shift
+        x_sample = (self.taesd_encoder(x * 0.5 + 0.5) / self.vae_scale) + self.vae_shift
+        if self.latent_channels == 32 and self.use_midblock_gn: # Only taef2 for Flux2 currently, pack latents: [B, C, H, W] -> [B, C*4, H//2, W//2]
+            x_sample = x_sample.reshape(x_sample.shape[0], self.latent_channels, x_sample.shape[-2] // 2, 2, x_sample.shape[-1] // 2, 2).permute(0, 1, 3, 5, 2, 4).reshape(x_sample.shape[0], self.latent_channels * 4, x_sample.shape[-2] // 2, x_sample.shape[-1] // 2)
+        return x_sample

comfy_api/latest/_io.py

@@ -1146,6 +1146,20 @@ class ImageCompare(ComfyTypeI):
       def as_dict(self):
           return super().as_dict()
 
+
+@comfytype(io_type="COLOR")
+class Color(ComfyTypeIO):
+  Type = str
+
+  class Input(WidgetInput):
+      def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None,
+                   socketless: bool=True, advanced: bool=None, default: str="#ffffff"):
+          super().__init__(id, display_name, optional, tooltip, None, default, socketless, None, None, None, None, advanced)
+          self.default: str
+
+      def as_dict(self):
+          return super().as_dict()
+
 DYNAMIC_INPUT_LOOKUP: dict[str, Callable[[dict[str, Any], dict[str, Any], tuple[str, dict[str, Any]], str, list[str] | None], None]] = {}
 def register_dynamic_input_func(io_type: str, func: Callable[[dict[str, Any], dict[str, Any], tuple[str, dict[str, Any]], str, list[str] | None], None]):
     DYNAMIC_INPUT_LOOKUP[io_type] = func
@@ -1252,23 +1266,6 @@ class NodeInfoV1:
     price_badge: dict | None = None
     search_aliases: list[str]=None
 
-@dataclass
-class NodeInfoV3:
-    input: dict=None
-    output: dict=None
-    hidden: list[str]=None
-    name: str=None
-    display_name: str=None
-    description: str=None
-    python_module: Any = None
-    category: str=None
-    output_node: bool=None
-    deprecated: bool=None
-    experimental: bool=None
-    dev_only: bool=None
-    api_node: bool=None
-    price_badge: dict | None = None
-
 
 @dataclass
 class PriceBadgeDepends:
@@ -1497,40 +1494,6 @@ class Schema:
         )
         return info
 
-
-    def get_v3_info(self, cls) -> NodeInfoV3:
-        input_dict = {}
-        output_dict = {}
-        hidden_list = []
-        # TODO: make sure dynamic types will be handled correctly
-        if self.inputs:
-            for input in self.inputs:
-                add_to_dict_v3(input, input_dict)
-        if self.outputs:
-            for output in self.outputs:
-                add_to_dict_v3(output, output_dict)
-        if self.hidden:
-            for hidden in self.hidden:
-                hidden_list.append(hidden.value)
-
-        info = NodeInfoV3(
-            input=input_dict,
-            output=output_dict,
-            hidden=hidden_list,
-            name=self.node_id,
-            display_name=self.display_name,
-            description=self.description,
-            category=self.category,
-            output_node=self.is_output_node,
-            deprecated=self.is_deprecated,
-            experimental=self.is_experimental,
-            dev_only=self.is_dev_only,
-            api_node=self.is_api_node,
-            python_module=getattr(cls, "RELATIVE_PYTHON_MODULE", "nodes"),
-            price_badge=self.price_badge.as_dict(self.inputs) if self.price_badge is not None else None,
-        )
-        return info
-
 def get_finalized_class_inputs(d: dict[str, Any], live_inputs: dict[str, Any], include_hidden=False) -> tuple[dict[str, Any], V3Data]:
     out_dict = {
         "required": {},
@@ -1585,9 +1548,6 @@ def add_to_dict_v1(i: Input, d: dict):
     as_dict.pop("optional", None)
     d.setdefault(key, {})[i.id] = (i.get_io_type(), as_dict)
 
-def add_to_dict_v3(io: Input | Output, d: dict):
-    d[io.id] = (io.get_io_type(), io.as_dict())
-
 class DynamicPathsDefaultValue:
     EMPTY_DICT = "empty_dict"
 
@@ -1748,13 +1708,6 @@ class _ComfyNodeBaseInternal(_ComfyNodeInternal):
         # set hidden
         type_clone.hidden = HiddenHolder.from_v3_data(v3_data)
         return type_clone
-
-    @final
-    @classmethod
-    def GET_NODE_INFO_V3(cls) -> dict[str, Any]:
-        schema = cls.GET_SCHEMA()
-        info = schema.get_v3_info(cls)
-        return asdict(info)
     #############################################
     # V1 Backwards Compatibility code
     #--------------------------------------------
@@ -2099,6 +2052,7 @@ __all__ = [
     "AnyType",
     "MultiType",
     "Tracks",
+    "Color",
     # Dynamic Types
     "MatchType",
     "DynamicCombo",
@@ -2107,12 +2061,10 @@ __all__ = [
     "HiddenHolder",
     "Hidden",
     "NodeInfoV1",
-    "NodeInfoV3",
     "Schema",
     "ComfyNode",
     "NodeOutput",
     "add_to_dict_v1",
-    "add_to_dict_v3",
     "V3Data",
     "ImageCompare",
     "PriceBadgeDepends",

comfy_api_nodes/apis/recraft.py

@@ -1,11 +1,8 @@
 from __future__ import annotations
 
-
-
 from enum import Enum
-from typing import Optional
 
-from pydantic import BaseModel, Field, conint, confloat
+from pydantic import BaseModel, Field
 
 
 class RecraftColor:
@@ -229,24 +226,24 @@ class RecraftColorObject(BaseModel):
 
 
 class RecraftControlsObject(BaseModel):
-    colors: Optional[list[RecraftColorObject]] = Field(None, description='An array of preferable colors')
-    background_color: Optional[RecraftColorObject] = Field(None, description='Use given color as a desired background color')
-    no_text: Optional[bool] = Field(None, description='Do not embed text layouts')
-    artistic_level: Optional[conint(ge=0, le=5)] = Field(None, description='Defines artistic tone of your image. At a simple level, the person looks straight at the camera in a static and clean style. Dynamic and eccentric levels introduce movement and creativity. The value should be in range [0..5].')
+    colors: list[RecraftColorObject] | None = Field(None, description='An array of preferable colors')
+    background_color: RecraftColorObject | None = Field(None, description='Use given color as a desired background color')
+    no_text: bool | None = Field(None, description='Do not embed text layouts')
+    artistic_level: int | None = Field(None, description='Defines artistic tone of your image. At a simple level, the person looks straight at the camera in a static and clean style. Dynamic and eccentric levels introduce movement and creativity. The value should be in range [0..5].')
 
 
 class RecraftImageGenerationRequest(BaseModel):
     prompt: str = Field(..., description='The text prompt describing the image to generate')
-    size: Optional[RecraftImageSize] = Field(None, description='The size of the generated image (e.g., "1024x1024")')
-    n: conint(ge=1, le=6) = Field(..., description='The number of images to generate')
-    negative_prompt: Optional[str] = Field(None, description='A text description of undesired elements on an image')
-    model: Optional[RecraftModel] = Field(RecraftModel.recraftv3, description='The model to use for generation (e.g., "recraftv3")')
-    style: Optional[str] = Field(None, description='The style to apply to the generated image (e.g., "digital_illustration")')
-    substyle: Optional[str] = Field(None, description='The substyle to apply to the generated image, depending on the style input')
-    controls: Optional[RecraftControlsObject] = Field(None, description='A set of custom parameters to tweak generation process')
-    style_id: Optional[str] = Field(None, description='Use a previously uploaded style as a reference; UUID')
-    strength: Optional[confloat(ge=0.0, le=1.0)] = Field(None, description='Defines the difference with the original image, should lie in [0, 1], where 0 means almost identical, and 1 means miserable similarity')
-    random_seed: Optional[int] = Field(None, description="Seed for video generation")
+    size: RecraftImageSize | None = Field(None, description='The size of the generated image (e.g., "1024x1024")')
+    n: int = Field(..., description='The number of images to generate')
+    negative_prompt: str | None = Field(None, description='A text description of undesired elements on an image')
+    model: RecraftModel | None = Field(RecraftModel.recraftv3, description='The model to use for generation (e.g., "recraftv3")')
+    style: str | None = Field(None, description='The style to apply to the generated image (e.g., "digital_illustration")')
+    substyle: str | None = Field(None, description='The substyle to apply to the generated image, depending on the style input')
+    controls: RecraftControlsObject | None = Field(None, description='A set of custom parameters to tweak generation process')
+    style_id: str | None = Field(None, description='Use a previously uploaded style as a reference; UUID')
+    strength: float | None = Field(None, description='Defines the difference with the original image, should lie in [0, 1], where 0 means almost identical, and 1 means miserable similarity')
+    random_seed: int | None = Field(None, description="Seed for video generation")
     # text_layout
 
 
@@ -258,5 +255,13 @@ class RecraftReturnedObject(BaseModel):
 class RecraftImageGenerationResponse(BaseModel):
     created: int = Field(..., description='Unix timestamp when the generation was created')
     credits: int = Field(..., description='Number of credits used for the generation')
-    data: Optional[list[RecraftReturnedObject]] = Field(None, description='Array of generated image information')
-    image: Optional[RecraftReturnedObject] = Field(None, description='Single generated image')
+    data: list[RecraftReturnedObject] | None = Field(None, description='Array of generated image information')
+    image: RecraftReturnedObject | None = Field(None, description='Single generated image')
+
+
+class RecraftCreateStyleRequest(BaseModel):
+    style: str = Field(..., description="realistic_image, digital_illustration, vector_illustration, or icon")
+
+
+class RecraftCreateStyleResponse(BaseModel):
+    id: str = Field(..., description="UUID of the created style")

comfy_api_nodes/nodes_recraft.py

@@ -12,6 +12,8 @@ from comfy_api_nodes.apis.recraft import (
     RecraftColor,
     RecraftColorChain,
     RecraftControls,
+    RecraftCreateStyleRequest,
+    RecraftCreateStyleResponse,
     RecraftImageGenerationRequest,
     RecraftImageGenerationResponse,
     RecraftImageSize,
@@ -323,6 +325,75 @@ class RecraftStyleInfiniteStyleLibrary(IO.ComfyNode):
         return IO.NodeOutput(RecraftStyle(style_id=style_id))
 
 
+class RecraftCreateStyleNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="RecraftCreateStyleNode",
+            display_name="Recraft Create Style",
+            category="api node/image/Recraft",
+            description="Create a custom style from reference images. "
+            "Upload 1-5 images to use as style references. "
+            "Total size of all images is limited to 5 MB.",
+            inputs=[
+                IO.Combo.Input(
+                    "style",
+                    options=["realistic_image", "digital_illustration"],
+                    tooltip="The base style of the generated images.",
+                ),
+                IO.Autogrow.Input(
+                    "images",
+                    template=IO.Autogrow.TemplatePrefix(
+                        IO.Image.Input("image"),
+                        prefix="image",
+                        min=1,
+                        max=5,
+                    ),
+                ),
+            ],
+            outputs=[
+                IO.String.Output(display_name="style_id"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd": 0.04}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        style: str,
+        images: IO.Autogrow.Type,
+    ) -> IO.NodeOutput:
+        files = []
+        total_size = 0
+        max_total_size = 5 * 1024 * 1024  # 5 MB limit
+        for i, img in enumerate(list(images.values())):
+            file_bytes = tensor_to_bytesio(img, total_pixels=2048 * 2048, mime_type="image/webp").read()
+            total_size += len(file_bytes)
+            if total_size > max_total_size:
+                raise Exception("Total size of all images exceeds 5 MB limit.")
+            files.append((f"file{i + 1}", file_bytes))
+
+        response = await sync_op(
+            cls,
+            endpoint=ApiEndpoint(path="/proxy/recraft/styles", method="POST"),
+            response_model=RecraftCreateStyleResponse,
+            files=files,
+            data=RecraftCreateStyleRequest(style=style),
+            content_type="multipart/form-data",
+            max_retries=1,
+        )
+
+        return IO.NodeOutput(response.id)
+
+
 class RecraftTextToImageNode(IO.ComfyNode):
     @classmethod
     def define_schema(cls):
@@ -395,7 +466,7 @@ class RecraftTextToImageNode(IO.ComfyNode):
         negative_prompt: str = None,
         recraft_controls: RecraftControls = None,
     ) -> IO.NodeOutput:
-        validate_string(prompt, strip_whitespace=False, max_length=1000)
+        validate_string(prompt, strip_whitespace=False, min_length=1, max_length=1000)
         default_style = RecraftStyle(RecraftStyleV3.realistic_image)
         if recraft_style is None:
             recraft_style = default_style
@@ -1024,6 +1095,7 @@ class RecraftExtension(ComfyExtension):
             RecraftStyleV3DigitalIllustrationNode,
             RecraftStyleV3LogoRasterNode,
             RecraftStyleInfiniteStyleLibrary,
+            RecraftCreateStyleNode,
             RecraftColorRGBNode,
             RecraftControlsNode,
         ]

comfy_extras/nodes_color.py

@@ -0,0 +1,42 @@
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io
+
+
+class ColorToRGBInt(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="ColorToRGBInt",
+            display_name="Color to RGB Int",
+            category="utils",
+            description="Convert a color to a RGB integer value.",
+            inputs=[
+                io.Color.Input("color"),
+            ],
+            outputs=[
+                io.Int.Output(display_name="rgb_int"),
+            ],
+        )
+
+    @classmethod
+    def execute(
+        cls,
+        color: str,
+    ) -> io.NodeOutput:
+        # expect format #RRGGBB
+        if len(color) != 7 or color[0] != "#":
+            raise ValueError("Color must be in format #RRGGBB")
+        r = int(color[1:3], 16)
+        g = int(color[3:5], 16)
+        b = int(color[5:7], 16)
+        return io.NodeOutput(r * 256 * 256 + g * 256 + b)
+
+
+class ColorExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [ColorToRGBInt]
+
+
+async def comfy_entrypoint() -> ColorExtension:
+    return ColorExtension()

nodes.py

@@ -724,7 +724,7 @@ class LoraLoaderModelOnly(LoraLoader):
 
 class VAELoader:
     video_taes = ["taehv", "lighttaew2_2", "lighttaew2_1", "lighttaehy1_5", "taeltx_2"]
-    image_taes = ["taesd", "taesdxl", "taesd3", "taef1"]
+    image_taes = ["taesd", "taesdxl", "taesd3", "taef1", "taef2"]
     @staticmethod
     def vae_list(s):
         vaes = folder_paths.get_filename_list("vae")
@@ -737,6 +737,8 @@ class VAELoader:
         sd3_taesd_dec = False
         f1_taesd_enc = False
         f1_taesd_dec = False
+        f2_taesd_enc = False
+        f2_taesd_dec = False
 
         for v in approx_vaes:
             if v.startswith("taesd_decoder."):
@@ -755,6 +757,10 @@ class VAELoader:
                 f1_taesd_dec = True
             elif v.startswith("taef1_decoder."):
                 f1_taesd_enc = True
+            elif v.startswith("taef2_encoder."):
+                f2_taesd_dec = True
+            elif v.startswith("taef2_decoder."):
+                f2_taesd_enc = True
             else:
                 for tae in s.video_taes:
                     if v.startswith(tae):
@@ -768,6 +774,8 @@ class VAELoader:
             vaes.append("taesd3")
         if f1_taesd_dec and f1_taesd_enc:
             vaes.append("taef1")
+        if f2_taesd_dec and f2_taesd_enc:
+            vaes.append("taef2")
         vaes.append("pixel_space")
         return vaes
 
@@ -799,6 +807,9 @@ class VAELoader:
         elif name == "taef1":
             sd["vae_scale"] = torch.tensor(0.3611)
             sd["vae_shift"] = torch.tensor(0.1159)
+        elif name == "taef2":
+            sd["vae_scale"] = torch.tensor(1.0)
+            sd["vae_shift"] = torch.tensor(0.0)
         return sd
 
     @classmethod
@@ -2432,7 +2443,8 @@ async def init_builtin_extra_nodes():
         "nodes_wanmove.py",
         "nodes_image_compare.py",
         "nodes_zimage.py",
-        "nodes_lora_debug.py"
+        "nodes_lora_debug.py",
+        "nodes_color.py"
     ]
 
     import_failed = []