Add support for controlnet & sdxl checkpoint conversion (#3905)

## What type of PR is this? (check all applicable)

- [ ] Refactor
- [ X] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission


## Have you discussed this change with the InvokeAI team?
- [X ] Yes
- [ ] No, because:

      
## Have you updated all relevant documentation?
- [ ] Yes
- [ X] No - not yet WIP


## Description

This PR adds support for loading and converting checkpoint-format
ControlNet and SDXL models. The SDXL and SDXL-refiner model conversions
are working; however saving the unet in safetensors format leads to
corrupted model files, so currently is saving in .bin format (after
scanning the input model).

ControlNet conversion seems to be working but needs further testing.

To use this PR, you will need to copy the files
`invokeai/configs/stable-diffusion/sd_xl_base.yaml` and
`invokeai/configs/stable-diffusion/sd_xl_refiner.yaml` into
`INVOKEAI/configs/stable-diffusion`. You will also need to run
`invokeai-configure --yes --skip-sd` in order to install additional core
model files needed by the converter.

invokeai/app/api_app.py

@@ -204,6 +204,9 @@ def invoke_api():
             else:
                 return port
             
+    from invokeai.backend.install.check_root import check_invokeai_root
+    check_invokeai_root(app_config)  # note, may exit with an exception if root not set up
+    
     port = find_port(app_config.port)
     if port != app_config.port:
         logger.warn(f"Port {app_config.port} in use, using port {port}")

invokeai/backend/install/check_root.py

@@ -0,0 +1,31 @@
+"""
+Check that the invokeai_root is correctly configured and exit if not.
+"""
+import sys
+from invokeai.app.services.config import (
+    InvokeAIAppConfig,
+)
+
+def check_invokeai_root(config: InvokeAIAppConfig):
+    try:
+        assert config.model_conf_path.exists()
+        assert config.db_path.exists()
+        assert config.models_path.exists()
+        for model in [
+                'CLIP-ViT-bigG-14-laion2B-39B-b160k',
+                'bert-base-uncased',
+                'clip-vit-large-patch14',
+                'sd-vae-ft-mse',
+                'stable-diffusion-2-clip',
+                'stable-diffusion-safety-checker']:
+            assert (config.models_path / f'core/convert/{model}').exists()
+    except:
+        print()
+        print('== STARTUP ABORTED ==')
+        print('** One or more necessary files is missing from your InvokeAI root directory **')
+        print('** Please rerun the configuration script to fix this problem. **')
+        print('** From the launcher, selection option [7]. **')
+        print('** From the command line, activate the virtual environment and run "invokeai-configure --yes --skip-sd-weights" **')
+        input('Press any key to continue...')
+        sys.exit(0)
+

invokeai/backend/install/invokeai_configure.py

@@ -32,6 +32,7 @@ from omegaconf import OmegaConf
 from tqdm import tqdm
 from transformers import (
     CLIPTextModel,
+    CLIPTextConfig,
     CLIPTokenizer,
     AutoFeatureExtractor,
     BertTokenizerFast,
@@ -55,6 +56,7 @@ from invokeai.frontend.install.widgets import (
 from invokeai.backend.install.legacy_arg_parsing import legacy_parser
 from invokeai.backend.install.model_install_backend import (
     hf_download_from_pretrained,
+    hf_download_with_resume,
     InstallSelections,
     ModelInstall,
 )
@@ -204,6 +206,15 @@ def download_conversion_models():
         pipeline = CLIPTextModel.from_pretrained(repo_id, subfolder="text_encoder", **kwargs)
         pipeline.save_pretrained(target_dir / 'stable-diffusion-2-clip' / 'text_encoder', safe_serialization=True)
 
+        # sd-xl - tokenizer_2
+        repo_id = "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k"
+        _, model_name = repo_id.split('/')
+        pipeline = CLIPTokenizer.from_pretrained(repo_id, **kwargs)
+        pipeline.save_pretrained(target_dir / model_name, safe_serialization=True)
+        
+        pipeline = CLIPTextConfig.from_pretrained(repo_id, **kwargs)
+        pipeline.save_pretrained(target_dir / model_name, safe_serialization=True)
+        
         # VAE
         logger.info('Downloading stable diffusion VAE')
         vae = AutoencoderKL.from_pretrained('stabilityai/sd-vae-ft-mse', **kwargs)

invokeai/backend/install/model_install_backend.py

@@ -58,7 +58,15 @@ LEGACY_CONFIGS = {
             SchedulerPredictionType.Epsilon: 'v2-inpainting-inference.yaml',
             SchedulerPredictionType.VPrediction: 'v2-inpainting-inference-v.yaml',
         }
-    }
+    },
+    
+    BaseModelType.StableDiffusionXL: {
+        ModelVariantType.Normal: 'sd_xl_base.yaml',
+    },
+    
+    BaseModelType.StableDiffusionXLRefiner: {
+        ModelVariantType.Normal: 'sd_xl_refiner.yaml',
+    },
 }
 
 @dataclass
@@ -329,6 +337,7 @@ class ModelInstall(object):
             description = str(description),
             model_format = info.format,
             )
+        legacy_conf = None
         if info.model_type == ModelType.Main:
             attributes.update(dict(variant = info.variant_type,))
             if info.format=="checkpoint":
@@ -343,11 +352,17 @@ class ModelInstall(object):
                 except KeyError:
                     legacy_conf = Path(self.config.legacy_conf_dir, 'v1-inference.yaml')  # best guess
                     
-                attributes.update(
+        if info.model_type == ModelType.ControlNet and info.format=="checkpoint":
-                    dict(
+            possible_conf = path.with_suffix('.yaml')
-                        config = str(legacy_conf)
+            if possible_conf.exists():
-                    )
+                legacy_conf = str(self.relative_to_root(possible_conf))
+
+        if legacy_conf:
+            attributes.update(
+                dict(
+                    config = str(legacy_conf)
                 )
+            )
         return attributes
 
     def relative_to_root(self, path: Path)->Path:

invokeai/backend/model_management/model_manager.py

@@ -673,6 +673,7 @@ class ModelManager(object):
 
         self.models[model_key] = model_config
         self.commit()
+
         return AddModelResult(
             name = model_name,
             model_type = model_type,
@@ -840,7 +841,7 @@ class ModelManager(object):
         Returns the preamble for the config file.
         """
         return textwrap.dedent(
-            """\
+            """
             # This file describes the alternative machine learning models
             # available to InvokeAI script.
             #

invokeai/backend/model_management/model_probe.py

@@ -253,10 +253,13 @@ class PipelineCheckpointProbe(CheckpointProbeBase):
             return BaseModelType.StableDiffusion1
         if key_name in state_dict and state_dict[key_name].shape[-1] == 1024:
             return BaseModelType.StableDiffusion2
-        # TODO: Verify that this is correct! Need an XL checkpoint file for this.
+        key_name = 'model.diffusion_model.input_blocks.4.1.transformer_blocks.0.attn2.to_k.weight'
         if key_name in state_dict and state_dict[key_name].shape[-1] == 2048:
             return BaseModelType.StableDiffusionXL
-        raise InvalidModelException("Cannot determine base type")
+        elif key_name in state_dict and state_dict[key_name].shape[-1] == 1280:
+            return BaseModelType.StableDiffusionXLRefiner
+        else:
+            raise InvalidModelException("Cannot determine base type")
 
     def get_scheduler_prediction_type(self)->SchedulerPredictionType:
         type = self.get_base_type()

invokeai/backend/model_management/models/controlnet.py

@@ -1,7 +1,8 @@
 import os
 import torch
 from enum import Enum
-from typing import Optional
+from pathlib import Path
+from typing import Optional, Literal
 from .base import (
     ModelBase,
     ModelConfigBase,
@@ -15,6 +16,7 @@ from .base import (
     InvalidModelException,
     ModelNotFoundException,
 )
+from invokeai.app.services.config import InvokeAIAppConfig
 
 class ControlNetModelFormat(str, Enum):
     Checkpoint = "checkpoint"
@@ -24,8 +26,12 @@ class ControlNetModel(ModelBase):
     #model_class: Type
     #model_size: int
 
-    class Config(ModelConfigBase):
+    class DiffusersConfig(ModelConfigBase):
-        model_format: ControlNetModelFormat
+        model_format: Literal[ControlNetModelFormat.Diffusers]
+
+    class CheckpointConfig(ModelConfigBase):
+        model_format: Literal[ControlNetModelFormat.Checkpoint]
+        config: str
 
     def __init__(self, model_path: str, base_model: BaseModelType, model_type: ModelType):
         assert model_type == ModelType.ControlNet
@@ -99,13 +105,51 @@ class ControlNetModel(ModelBase):
 
     @classmethod
     def convert_if_required(
+            cls,
+            model_path: str,
+            output_path: str,
+            config: ModelConfigBase,
+            base_model: BaseModelType,
+    ) -> str:
+        if cls.detect_format(model_path) == ControlNetModelFormat.Checkpoint:
+            return _convert_controlnet_ckpt_and_cache(
+                model_path = model_path,
+                model_config = config.config,
+                output_path = output_path,
+                base_model = base_model,
+                )
+        else:
+            return model_path
+
+@classmethod
+def _convert_controlnet_ckpt_and_cache(
         cls,
         model_path: str,
         output_path: str,
-        config: ModelConfigBase, # empty config or config of parent model
         base_model: BaseModelType,
-    ) -> str:
+        model_config: ControlNetModel.CheckpointConfig,
-        if cls.detect_format(model_path) != ControlNetModelFormat.Diffusers:
+) -> str:
-            raise NotImplementedError("Checkpoint controlnet models currently unsupported")
+    """
-        else:
+    Convert the controlnet from checkpoint format to diffusers format,
-            return model_path
+    cache it to disk, and return Path to converted
+    file. If already on disk then just returns Path.
+    """
+    app_config = InvokeAIAppConfig.get_config()
+    weights = app_config.root_path / model_path
+    output_path = Path(output_path)
+
+    # return cached version if it exists
+    if output_path.exists():
+        return output_path
+
+    # to avoid circular import errors
+    from ..convert_ckpt_to_diffusers import convert_controlnet_to_diffusers
+    convert_controlnet_to_diffusers(
+        weights,
+        output_path,
+        original_config_file = app_config.root_path / model_config,
+        image_size = 512,
+        scan_needed = True,
+        from_safetensors = weights.suffix == ".safetensors"
+    )
+    return output_path

invokeai/backend/model_management/models/sdxl.py

@@ -1,5 +1,6 @@
 import os
 import json
+import invokeai.backend.util.logging as logger
 from enum import Enum
 from pydantic import Field
 from typing import Literal, Optional
@@ -48,7 +49,7 @@ class StableDiffusionXLModel(DiffusersModel):
         if model_format == StableDiffusionXLModelFormat.Checkpoint:
             if ckpt_config_path:
                 ckpt_config = OmegaConf.load(ckpt_config_path)
-                ckpt_config["model"]["params"]["unet_config"]["params"]["in_channels"]
+                in_channels = ckpt_config["model"]["params"]["unet_config"]["params"]["in_channels"]
 
             else:
                 checkpoint = read_checkpoint_meta(path)
@@ -108,7 +109,20 @@ class StableDiffusionXLModel(DiffusersModel):
         config: ModelConfigBase,
         base_model: BaseModelType,
     ) -> str:
+        # The convert script adapted from the diffusers package uses
+        # strings for the base model type. To avoid making too many
+        # source code changes, we simply translate here
+        model_base_to_model_type = {BaseModelType.StableDiffusionXL: 'SDXL',
+                                    BaseModelType.StableDiffusionXLRefiner: 'SDXL-Refiner',
+                                    }
         if isinstance(config, cls.CheckpointConfig):
-            raise NotImplementedError('conversion of SDXL checkpoint models to diffusers format is not yet supported')
+            from invokeai.backend.model_management.models.stable_diffusion import _convert_ckpt_and_cache
+            return _convert_ckpt_and_cache(
+                version=base_model,
+                model_config=config,
+                output_path=output_path,
+                model_type=model_base_to_model_type[base_model],
+                use_safetensors=False, # corrupts sdxl models for some reason
+            )
         else:
             return model_path

invokeai/backend/model_management/models/stable_diffusion.py

@@ -15,9 +15,12 @@ from .base import (
     classproperty,
     InvalidModelException,
 )
+from .sdxl import StableDiffusionXLModel
+import invokeai.backend.util.logging as logger
 from invokeai.app.services.config import InvokeAIAppConfig
 from omegaconf import OmegaConf
 
+
 class StableDiffusion1ModelFormat(str, Enum):
     Checkpoint = "checkpoint"
     Diffusers = "diffusers"
@@ -235,42 +238,17 @@ class StableDiffusion2Model(DiffusersModel):
         else:
             return model_path
 
-def _select_ckpt_config(version: BaseModelType, variant: ModelVariantType):
-    ckpt_configs = {
-        BaseModelType.StableDiffusion1: {
-            ModelVariantType.Normal: "v1-inference.yaml",
-            ModelVariantType.Inpaint: "v1-inpainting-inference.yaml",
-        },
-        BaseModelType.StableDiffusion2: {
-            ModelVariantType.Normal: "v2-inference-v.yaml", # best guess, as we can't differentiate with base(512)
-            ModelVariantType.Inpaint: "v2-inpainting-inference.yaml",
-            ModelVariantType.Depth: "v2-midas-inference.yaml",
-        },
-        # note that these .yaml files don't yet exist!
-        BaseModelType.StableDiffusionXL: {
-            ModelVariantType.Normal: "xl-inference-v.yaml",
-            ModelVariantType.Inpaint: "xl-inpainting-inference.yaml",
-            ModelVariantType.Depth: "xl-midas-inference.yaml",
-        }
-    }
-
-    app_config = InvokeAIAppConfig.get_config()
-    try:
-        config_path = app_config.legacy_conf_path / ckpt_configs[version][variant]
-        if config_path.is_relative_to(app_config.root_path):
-            config_path = config_path.relative_to(app_config.root_path)
-        return str(config_path)
-            
-    except:
-        return None
-
-
 # TODO: rework
-# Note that convert_ckpt_to_diffuses does not currently support conversion of SDXL models
+# pass precision - currently defaulting to fp16
 def _convert_ckpt_and_cache(
-    version: BaseModelType,
+        version: BaseModelType,
-    model_config: Union[StableDiffusion1Model.CheckpointConfig, StableDiffusion2Model.CheckpointConfig],
+        model_config: Union[StableDiffusion1Model.CheckpointConfig,
-    output_path: str,
+                            StableDiffusion2Model.CheckpointConfig,
+                            StableDiffusionXLModel.CheckpointConfig,
+                            ],
+        output_path: str,
+        use_save_model: bool=False,
+        **kwargs,
 ) -> str:
     """
     Convert the checkpoint model indicated in mconfig into a
@@ -289,6 +267,9 @@ def _convert_ckpt_and_cache(
 
     # to avoid circular import errors
     from ..convert_ckpt_to_diffusers import convert_ckpt_to_diffusers
+    from ...util.devices import choose_torch_device, torch_dtype
+    
+    logger.info(f'Converting {weights} to diffusers format')
     with SilenceWarnings():        
         convert_ckpt_to_diffusers(
             weights,
@@ -298,5 +279,43 @@ def _convert_ckpt_and_cache(
             original_config_file=config_file,
             extract_ema=True,
             scan_needed=True,
+            from_safetensors = weights.suffix == ".safetensors",
+            precision = torch_dtype(choose_torch_device()),
+            **kwargs,
         )
     return output_path
+
+def _select_ckpt_config(version: BaseModelType, variant: ModelVariantType):
+    ckpt_configs = {
+        BaseModelType.StableDiffusion1: {
+            ModelVariantType.Normal: "v1-inference.yaml",
+            ModelVariantType.Inpaint: "v1-inpainting-inference.yaml",
+        },
+        BaseModelType.StableDiffusion2: {
+            ModelVariantType.Normal: "v2-inference-v.yaml", # best guess, as we can't differentiate with base(512)
+            ModelVariantType.Inpaint: "v2-inpainting-inference.yaml",
+            ModelVariantType.Depth: "v2-midas-inference.yaml",
+        },
+        BaseModelType.StableDiffusionXL: {
+            ModelVariantType.Normal: "sd_xl_base.yaml",
+            ModelVariantType.Inpaint: None,
+            ModelVariantType.Depth: None,
+        },
+        BaseModelType.StableDiffusionXLRefiner: {
+            ModelVariantType.Normal: "sd_xl_refiner.yaml",
+            ModelVariantType.Inpaint: None,
+            ModelVariantType.Depth: None,
+        },
+    }
+
+    app_config = InvokeAIAppConfig.get_config()
+    try:
+        config_path = app_config.legacy_conf_path / ckpt_configs[version][variant]
+        if config_path.is_relative_to(app_config.root_path):
+            config_path = config_path.relative_to(app_config.root_path)
+        return str(config_path)
+            
+    except:
+        return None
+
+

invokeai/backend/util/logging.py

@@ -1,7 +1,7 @@
 # Copyright (c) 2023 Lincoln D. Stein and The InvokeAI Development Team
 
 """
-invokeai.util.logging
+invokeai.backend.util.logging
 
 Logging class for InvokeAI that produces console messages
 

invokeai/configs/stable-diffusion/sd_xl_base.yaml

@@ -0,0 +1,98 @@
+model:
+  target: sgm.models.diffusion.DiffusionEngine
+  params:
+    scale_factor: 0.13025
+    disable_first_stage_autocast: True
+
+    denoiser_config:
+      target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser
+      params:
+        num_idx: 1000
+
+        weighting_config:
+          target: sgm.modules.diffusionmodules.denoiser_weighting.EpsWeighting
+        scaling_config:
+          target: sgm.modules.diffusionmodules.denoiser_scaling.EpsScaling
+        discretization_config:
+          target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
+
+    network_config:
+      target: sgm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        adm_in_channels: 2816
+        num_classes: sequential
+        use_checkpoint: True
+        in_channels: 4
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [4, 2]
+        num_res_blocks: 2
+        channel_mult: [1, 2, 4]
+        num_head_channels: 64
+        use_spatial_transformer: True
+        use_linear_in_transformer: True
+        transformer_depth: [1, 2, 10]  # note: the first is unused (due to attn_res starting at 2) 32, 16, 8 --> 64, 32, 16
+        context_dim: 2048
+        spatial_transformer_attn_type: softmax-xformers
+        legacy: False
+
+    conditioner_config:
+      target: sgm.modules.GeneralConditioner
+      params:
+        emb_models:
+          # crossattn cond
+          - is_trainable: False
+            input_key: txt
+            target: sgm.modules.encoders.modules.FrozenCLIPEmbedder
+            params:
+              layer: hidden
+              layer_idx: 11
+          # crossattn and vector cond
+          - is_trainable: False
+            input_key: txt
+            target: sgm.modules.encoders.modules.FrozenOpenCLIPEmbedder2
+            params:
+              arch: ViT-bigG-14
+              version: laion2b_s39b_b160k
+              freeze: True
+              layer: penultimate
+              always_return_pooled: True
+              legacy: False
+          # vector cond
+          - is_trainable: False
+            input_key: original_size_as_tuple
+            target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+            params:
+              outdim: 256  # multiplied by two
+          # vector cond
+          - is_trainable: False
+            input_key: crop_coords_top_left
+            target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+            params:
+              outdim: 256  # multiplied by two
+          # vector cond
+          - is_trainable: False
+            input_key: target_size_as_tuple
+            target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+            params:
+              outdim: 256  # multiplied by two
+
+    first_stage_config:
+      target: sgm.models.autoencoder.AutoencoderKLInferenceWrapper
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          attn_type: vanilla-xformers
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult: [1, 2, 4, 4]
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity

invokeai/configs/stable-diffusion/sd_xl_refiner.yaml

@@ -0,0 +1,91 @@
+model:
+  target: sgm.models.diffusion.DiffusionEngine
+  params:
+    scale_factor: 0.13025
+    disable_first_stage_autocast: True
+
+    denoiser_config:
+      target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser
+      params:
+        num_idx: 1000
+
+        weighting_config:
+          target: sgm.modules.diffusionmodules.denoiser_weighting.EpsWeighting
+        scaling_config:
+          target: sgm.modules.diffusionmodules.denoiser_scaling.EpsScaling
+        discretization_config:
+          target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
+
+    network_config:
+      target: sgm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        adm_in_channels: 2560
+        num_classes: sequential
+        use_checkpoint: True
+        in_channels: 4
+        out_channels: 4
+        model_channels: 384
+        attention_resolutions: [4, 2]
+        num_res_blocks: 2
+        channel_mult: [1, 2, 4, 4]
+        num_head_channels: 64
+        use_spatial_transformer: True
+        use_linear_in_transformer: True
+        transformer_depth: 4
+        context_dim: [1280, 1280, 1280, 1280]  # 1280
+        spatial_transformer_attn_type: softmax-xformers
+        legacy: False
+
+    conditioner_config:
+      target: sgm.modules.GeneralConditioner
+      params:
+        emb_models:
+          # crossattn and vector cond
+          - is_trainable: False
+            input_key: txt
+            target: sgm.modules.encoders.modules.FrozenOpenCLIPEmbedder2
+            params:
+              arch: ViT-bigG-14
+              version: laion2b_s39b_b160k
+              legacy: False
+              freeze: True
+              layer: penultimate
+              always_return_pooled: True
+          # vector cond
+          - is_trainable: False
+            input_key: original_size_as_tuple
+            target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+            params:
+              outdim: 256  # multiplied by two
+          # vector cond
+          - is_trainable: False
+            input_key: crop_coords_top_left
+            target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+            params:
+              outdim: 256  # multiplied by two
+          # vector cond
+          - is_trainable: False
+            input_key: aesthetic_score
+            target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+            params:
+              outdim: 256  # multiplied by one
+
+    first_stage_config:
+      target: sgm.models.autoencoder.AutoencoderKLInferenceWrapper
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          attn_type: vanilla-xformers
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult: [1, 2, 4, 4]
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity

invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/ModelManagerPanel/CheckpointModelEdit.tsx

@@ -1,6 +1,5 @@
 import { Badge, Divider, Flex, Text } from '@chakra-ui/react';
 import { useForm } from '@mantine/form';
-import { makeToast } from 'features/system/util/makeToast';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import IAIButton from 'common/components/IAIButton';
 import IAIMantineTextInput from 'common/components/IAIMantineInput';
@@ -8,6 +7,7 @@ import IAISimpleCheckbox from 'common/components/IAISimpleCheckbox';
 import { MODEL_TYPE_MAP } from 'features/parameters/types/constants';
 import { selectIsBusy } from 'features/system/store/systemSelectors';
 import { addToast } from 'features/system/store/systemSlice';
+import { makeToast } from 'features/system/util/makeToast';
 import { useCallback, useEffect, useState } from 'react';
 import { useTranslation } from 'react-i18next';
 import {
@@ -115,7 +115,7 @@ export default function CheckpointModelEdit(props: CheckpointModelEditProps) {
             {MODEL_TYPE_MAP[model.base_model]} Model
           </Text>
         </Flex>
-        {!['sdxl', 'sdxl-refiner'].includes(model.base_model) ? (
+        {![''].includes(model.base_model) ? (
           <ModelConvert model={model} />
         ) : (
           <Badge