adding arguments outside of CLI #3647

WhoseBoatIsThisBoat · 2024-12-22T04:42:05Z

WhoseBoatIsThisBoat
Dec 22, 2024

Hey,

I'm getting an error loading a model and the error message says that the system can recover if I set "ignore_mismatches_sizes"=True
To the question, how do I add custome arguments like that? Is there a file that I can add the argument to or do I need to track down the correct .py-file and add it in there?

So, where do I find the correct message to add this argument?

thanks!

vladmandic · 2024-12-22T05:03:58Z

vladmandic
Dec 22, 2024
Maintainer

you cant, thats not really a user-defined thing.
pls post entire log with error.

1 reply

WhoseBoatIsThisBoat Dec 23, 2024
Author

many thanks for the response!

this is me adding --debug

I load one model successfully, and then fail to load another one:

Using VENV: %ROOT_DIRECTORY%\SD.Next\venv
22:25:40-696030 INFO     Starting SD.Next
22:25:40-698023 INFO     Logger: file="%ROOT_DIRECTORY%\SD.Next\sdnext.log" level=DEBUG size=65 mode=create
22:25:40-699020 INFO     Python: version=3.10.11 platform=Windows bin="%ROOT_DIRECTORY%\SD.Next\venv\Scripts\python.exe"
                         venv="%ROOT_DIRECTORY%\SD.Next\venv"
22:25:40-815581 INFO     Version: app=sd.next updated=2024-11-22 hash=6846f4e5 branch=master
                         url=https://github.com/vladmandic/automatic/tree/master ui=main
22:25:41-066986 INFO     Repository latest available a2f5a39a5919d7edae9b4825a6551c50c854c504 2024-12-09T08:27:14Z
22:25:41-076438 INFO     Platform: arch=AMD64 cpu=Intel64 Family 6 Model 183 Stepping 1, GenuineIntel system=Windows
                         release=Windows-10-10.0.22631-SP0 python=3.10.11 docker=False
22:25:41-077940 DEBUG    Packages: venv=venv site=['venv', 'venv\\lib\\site-packages']
22:25:41-077940 INFO     Args: ['--autolaunch', '--use-cuda', '--debug']
22:25:41-078939 DEBUG    Setting environment tuning
22:25:41-079936 DEBUG    Torch allocator: "garbage_collection_threshold:0.80,max_split_size_mb:512"
22:25:41-082926 DEBUG    Torch overrides: cuda=True rocm=False ipex=False directml=False openvino=False zluda=False
22:25:41-084923 INFO     CUDA: nVidia toolkit detected
22:25:41-154689 INFO     Install: verifying requirements
22:25:41-158672 INFO     Verifying packages
22:25:41-188467 DEBUG    Timestamp repository update time: Fri Nov 22 13:01:15 2024
22:25:41-189463 INFO     Startup: standard
22:25:41-189463 INFO     Verifying submodules
22:25:44-139289 DEBUG    Git submodule: extensions-builtin/sd-extension-chainner / main
22:25:44-199908 DEBUG    Git submodule: extensions-builtin/sd-extension-system-info / main
22:25:44-251241 DEBUG    Git submodule: extensions-builtin/sd-webui-agent-scheduler / main
22:25:44-326516 DEBUG    Git detached head detected: folder="extensions-builtin/sdnext-modernui" reattach=main
22:25:44-327183 DEBUG    Git submodule: extensions-builtin/sdnext-modernui / main
22:25:44-426201 DEBUG    Git submodule: extensions-builtin/stable-diffusion-webui-rembg / master
22:25:44-480316 DEBUG    Git submodule: modules/k-diffusion / master
22:25:44-553110 DEBUG    Git detached head detected: folder="wiki" reattach=master
22:25:44-553777 DEBUG    Git submodule: wiki / master
22:25:44-603425 DEBUG    Register paths
22:25:44-636754 DEBUG    Installed packages: 198
22:25:44-638088 DEBUG    Extensions all: ['Lora', 'sd-extension-chainner', 'sd-extension-system-info',
                         'sd-webui-agent-scheduler', 'sdnext-modernui', 'stable-diffusion-webui-rembg']
22:25:44-733141 DEBUG    Extension installer:
                         %ROOT_DIRECTORY%\SD.Next\extensions-builtin\sd-webui-agent-scheduler\install.py
22:25:47-672595 DEBUG    Extension installer:
                         %ROOT_DIRECTORY%\SD.Next\extensions-builtin\stable-diffusion-webui-rembg\install.py
22:25:52-624425 INFO     Extension installed packages: stable-diffusion-webui-rembg ['rembg==2.0.61']
22:25:52-629759 DEBUG    Extensions all: ['adetailer']
22:25:52-630426 DEBUG    Extension force: name="adetailer" commit=a89c01d
22:25:52-669107 DEBUG    Extension installer: %ROOT_DIRECTORY%\SD.Next\extensions\adetailer\install.py
22:25:52-799888 INFO     Extensions enabled: ['Lora', 'sd-extension-chainner', 'sd-extension-system-info',
                         'sd-webui-agent-scheduler', 'sdnext-modernui', 'stable-diffusion-webui-rembg', 'adetailer']
22:25:52-802028 INFO     Install: verifying requirements
22:25:52-803770 DEBUG    Setup complete without errors: 1734924353
22:25:52-806529 DEBUG    Extension preload: {'extensions-builtin': 0.0, 'extensions': 0.0}
22:25:52-807442 INFO     Command line args: ['--autolaunch', '--use-cuda', '--debug'] autolaunch=True use_cuda=True
                         debug=True
22:25:52-808275 DEBUG    Env flags: []
22:25:52-809160 DEBUG    Starting module: <module 'webui' from '%ROOT_DIRECTORY%\\SD.Next\\webui.py'>
22:25:56-226830 INFO     Torch: torch==2.5.1+cu124 torchvision==0.20.1+cu124
22:25:56-227496 INFO     Packages: diffusers==0.32.0.dev0 transformers==4.46.2 accelerate==1.1.1 gradio==3.43.2
22:25:56-625866 DEBUG    Huggingface cache: folder="%USER_DIRECTORY%\.cache\huggingface\hub"
22:25:56-742870 INFO     Device detect: memory=24.0 optimization=none
22:25:56-744204 DEBUG    Read: file="config.json" json=43 bytes=2150 time=0.000
22:25:56-858277 DEBUG    Setting validation: "prompt_attention"="Fixed attention" default="native" choices=['native',
                         'compel', 'xhinker', 'a1111', 'fixed']
22:25:56-859611 WARNING  Setting validation: unknown=['multiple_tqdm']
22:25:56-860944 INFO     Engine: backend=Backend.DIFFUSERS compute=cuda device=cuda attention="Scaled-Dot-Product"
                         mode=no_grad
22:25:56-863611 DEBUG    Read: file="html\reference.json" json=59 bytes=31585 time=0.000
22:25:56-902945 INFO     Torch parameters: backend=cuda device=cuda config=Auto dtype=torch.bfloat16 vae=torch.bfloat16
                         unet=torch.bfloat16 context=no_grad nohalf=False nohalfvae=False upscast=False
                         deterministic=False test-fp16=True test-bf16=True optimization="Scaled-Dot-Product"
22:25:56-992076 DEBUG    ONNX: version=1.20.1 provider=CUDAExecutionProvider, available=['TensorrtExecutionProvider',
                         'CUDAExecutionProvider', 'CPUExecutionProvider']
22:25:57-064415 INFO     Device: device=NVIDIA GeForce RTX 4090 n=1 arch=sm_90 capability=(8, 9) cuda=12.4 cudnn=90100
                         driver=560.94
22:25:57-123082 DEBUG    Importing LDM
22:25:57-130085 DEBUG    Entering start sequence
22:25:57-131418 DEBUG    Initializing
22:25:57-148750 INFO     Available VAEs: path="%ROOT_DIRECTORY%\Models\VAE" items=4
22:25:57-150084 INFO     Available UNets: path="%ROOT_DIRECTORY%\Models\UNET" items=4
22:25:57-150751 INFO     Available TEs: path="%ROOT_DIRECTORY%\Models\Text-encoder" items=0
22:25:57-152084 INFO     Disabled extensions: ['sdnext-modernui']
22:25:57-153419 DEBUG    Read: file="cache.json" json=2 bytes=17244 time=0.000
22:25:57-163418 DEBUG    Read: file="metadata.json" json=408 bytes=7312037 time=0.008
22:25:57-166084 DEBUG    Scanning diffusers cache: folder="%ROOT_DIRECTORY%\Models\Diffusers" items=1 time=0.00
22:25:57-167418 INFO     Available Models: path="%ROOT_DIRECTORY%\Models\Stable-diffusion" items=33 time=0.01
22:25:57-204751 INFO     Available Yolo: path="%ROOT_DIRECTORY%\Models\yolo" items=7 downloaded=2
22:25:57-206084 INFO     Load extensions
22:25:57-364154 INFO     Available LoRAs: path="%ROOT_DIRECTORY%\Models\Lora" items=197 folders=2 time=0.01
22:25:57-728198 INFO     Extension: script='extensions-builtin\sd-webui-agent-scheduler\scripts\task_scheduler.py' Using
                         sqlite file: extensions-builtin\sd-webui-agent-scheduler\task_scheduler.sqlite3
22:25:57-843785 INFO     Extension: script='extensions\adetailer\scripts\!adetailer.py' [-] ADetailer initialized.
                         version: 24.5.1, num models: 12
22:25:57-844782 DEBUG    Extensions init time: 0.64 pulid_ext.py=0.10 Lora=0.13 sd-webui-agent-scheduler=0.23
                         adetailer=0.11
22:25:57-853752 DEBUG    Read: file="html/upscalers.json" json=4 bytes=2672 time=0.000
22:25:57-854748 DEBUG    Read: file="extensions-builtin\sd-extension-chainner\models.json" json=24 bytes=2719 time=0.000
22:25:57-855745 DEBUG    chaiNNer models: path="%ROOT_DIRECTORY%\Models\chaiNNer" defined=24 discovered=0 downloaded=0
22:25:57-856741 DEBUG    Upscaler type=ESRGAN folder="%ROOT_DIRECTORY%\Models\ESRGAN"
                         model="1x-ITF-SkinDiffDetail-Lite-v1"
                         path="%ROOT_DIRECTORY%\Models\ESRGAN\1x-ITF-SkinDiffDetail-Lite-v1.pth"
22:25:57-858731 INFO     Available Upscalers: items=54 downloaded=8 user=1 time=0.01 types=['None', 'Lanczos',
                         'Nearest', 'ChaiNNer', 'AuraSR', 'ESRGAN', 'LDSR', 'RealESRGAN', 'SCUNet', 'SD', 'SwinIR']
22:25:57-866705 INFO     Available Styles: folder="%ROOT_DIRECTORY%\Models\styles" items=288 time=0.01
22:25:57-870420 INFO     UI start
22:25:57-871425 DEBUG    UI themes available: type=Standard themes=12
22:25:57-872373 INFO     UI theme: type=Standard name="black-teal"
22:25:57-875363 DEBUG    UI theme: css="%ROOT_DIRECTORY%\SD.Next\javascript\black-teal.css" base="sdnext.css" user="None"
22:25:57-877356 DEBUG    UI initialize: txt2img
22:25:58-013019 DEBUG    Networks: page='model' items=91 subfolders=2 tab=txt2img
                         folders=['%ROOT_DIRECTORY%\\Models\\Stable-diffusion', '%ROOT_DIRECTORY%\\Models\\Diffusers',
                         'models\\Reference'] list=0.11 thumb=0.03 desc=0.02 info=0.06 workers=8 sort=Name [A-Z]
22:25:58-017736 DEBUG    Networks: page='lora' items=197 subfolders=0 tab=txt2img
                         folders=['%ROOT_DIRECTORY%\\Models\\Lora', '%ROOT_DIRECTORY%\\Models\\LyCORIS'] list=0.12
                         thumb=0.01 desc=0.09 info=0.30 workers=8 sort=Name [A-Z]
22:25:58-025102 DEBUG    Networks: page='style' items=288 subfolders=1 tab=txt2img
                         folders=['%ROOT_DIRECTORY%\\Models\\styles', 'html'] list=0.10 thumb=0.00 desc=0.00 info=0.00
                         workers=8 sort=Name [A-Z]
22:25:58-029597 DEBUG    Networks: page='embedding' items=23 subfolders=0 tab=txt2img
                         folders=['%ROOT_DIRECTORY%\\Models\\embeddings'] list=0.09 thumb=0.02 desc=0.01 info=0.02
                         workers=8 sort=Name [A-Z]
22:25:58-033584 DEBUG    Networks: page='vae' items=4 subfolders=0 tab=txt2img folders=['%ROOT_DIRECTORY%\\Models\\VAE']
                         list=0.05 thumb=0.04 desc=0.00 info=0.01 workers=8 sort=Name [A-Z]
22:25:58-035577 DEBUG    Networks: page='history' items=0 subfolders=0 tab=txt2img folders=[] list=0.00 thumb=0.00
                         desc=0.00 info=0.00 workers=8 sort=Name [A-Z]
22:25:58-244816 DEBUG    UI initialize: img2img
22:25:58-399570 DEBUG    UI initialize: control models=%ROOT_DIRECTORY%\Models\control
22:25:58-697840 DEBUG    Read: file="ui-config.json" json=0 bytes=2 time=0.000
22:25:58-802167 DEBUG    UI themes available: type=Standard themes=12
22:25:59-717263 DEBUG    Extension list: processed=393 installed=7 enabled=6 disabled=1 visible=393 hidden=0
22:25:59-822124 DEBUG    Root paths: ['%ROOT_DIRECTORY%\\SD.Next']
22:25:59-887136 INFO     Local URL: http://127.0.0.1:7860/
22:25:59-887819 DEBUG    Gradio functions: registered=2200
22:25:59-889186 DEBUG    API middleware: [<class 'starlette.middleware.base.BaseHTTPMiddleware'>, <class
                         'starlette.middleware.gzip.GZipMiddleware'>]
22:25:59-891919 DEBUG    API initialize
22:26:00-016263 INFO     [AgentScheduler] Task queue is empty
22:26:00-017174 INFO     [AgentScheduler] Registering APIs
22:26:00-259864 DEBUG    Scripts setup: ['IP Adapters:0.023', 'ADetailer:0.068', 'XYZ Grid:0.032', 'Ctrl-X: Controlling
                         Structure and Appearance:0.006', 'Face: Multiple ID Transfers:0.015', 'K-Diffusion
                         Samplers:0.084', 'LUT Color grading:0.006', 'PuLID: ID Customization:0.006', 'Video
                         AnimateDiff:0.007', 'Video CogVideoX:0.009', 'Video VGen Image-to-Video:0.007']
22:26:00-261560 DEBUG    Model metadata: file="metadata.json" no changes
22:26:00-262244 DEBUG    Model auto load disabled
22:26:00-264294 DEBUG    Script callback init time: task_scheduler.py:app_started=0.26
22:26:00-265661 DEBUG    Save: file="config.json" json=43 bytes=2089 time=0.002
22:26:00-267027 INFO     Startup time: 7.45 torch=2.67 gradio=0.58 diffusers=0.06 libraries=0.98 extensions=0.64
                         ui-networks=0.27 ui-txt2img=0.19 ui-img2img=0.12 ui-control=0.16 ui-settings=0.33
                         ui-extensions=0.71 ui-defaults=0.06 launch=0.11 api=0.07 app-started=0.30
22:26:00-268394 DEBUG    Settings: unused=['multiple_tqdm']
22:26:00-270444 INFO     Launching browser
22:26:00-303346 DEBUG    Server: alive=True requests=1 memory=1.02/63.75 status='idle' task='' timestamp=None id=''
                         job=0 jobs=0 total=0 step=0 steps=0 queued=0 uptime=4 elapsed=None eta=None progress=0
22:26:02-676508 INFO     MOTD: N/A
22:26:06-961197 DEBUG    UI themes available: type=Standard themes=12
22:26:07-300974 INFO     Browser session: user=None client=127.0.0.1 agent=Mozilla/5.0 (Windows NT 10.0; Win64; x64)
                         AppleWebKit/537.36 (KHTML, like Gecko) Chrome/131.0.0.0 Safari/537.36
22:28:00-272650 DEBUG    Server: alive=True requests=65 memory=1.03/63.75 status='idle' task='' timestamp=None id=''
                         job=0 jobs=0 total=0 step=0 steps=0 queued=0 uptime=124 elapsed=None eta=None progress=0
22:30:00-131059 DEBUG    Server: alive=True requests=67 memory=1.03/63.75 status='idle' task='' timestamp=None id=''
                         job=0 jobs=0 total=0 step=0 steps=0 queued=0 uptime=244 elapsed=None eta=None progress=0
22:32:00-240844 DEBUG    Server: alive=True requests=70 memory=1.03/63.75 status='idle' task='' timestamp=None id=''
                         job=0 jobs=0 total=0 step=0 steps=0 queued=0 uptime=364 elapsed=None eta=None progress=0
22:32:06-474754 DEBUG    Scanning diffusers cache: folder="%ROOT_DIRECTORY%\Models\Diffusers" items=1 time=0.00
22:32:06-477743 INFO     Available Models: path="%ROOT_DIRECTORY%\Models\Stable-diffusion" items=33 time=0.01
22:32:12-966621 INFO     Load model: select="PONY, ChrismixPNY-NSFW v2.0 [03335fed37]"
22:32:12-970608 INFO     Autodetect model: detect="Stable Diffusion XL" class=StableDiffusionXLPipeline
                         file="%ROOT_DIRECTORY%\Models\Stable-diffusion\PONY, ChrismixPNY-NSFW v2.0.safetensors"
                         size=6776MB
22:32:12-989544 DEBUG    Autodetect: modules={'conditioner': {'embedders': {}}, 'first_stage_model': {'decoder': {},
                         'encoder': {}, 'post_quant_conv': {}, 'quant_conv': {}}, 'model': {'diffusion_model': {}}}
                         list=['conditioner.embedders', 'first_stage_model.decoder', 'first_stage_model.encoder',
                         'first_stage_model.post_quant_conv', 'first_stage_model.quant_conv', 'model.diffusion_model']
                         time=0.02
22:32:12-991541 INFO     Autodetect vae: detect="Stable Diffusion XL" class=StableDiffusionXLPipeline
                         file="%ROOT_DIRECTORY%\Models\Stable-diffusion\PONY, ChrismixPNY-NSFW v2.0.safetensors"
                         size=6776MB
22:32:13-000507 DEBUG    Autodetect: modules={'conditioner': {'embedders': {}}, 'first_stage_model': {'decoder': {},
                         'encoder': {}, 'post_quant_conv': {}, 'quant_conv': {}}, 'model': {'diffusion_model': {}}}
                         list=['conditioner.embedders', 'first_stage_model.decoder', 'first_stage_model.encoder',
                         'first_stage_model.post_quant_conv', 'first_stage_model.quant_conv', 'model.diffusion_model']
                         time=0.01
22:32:13-002501 INFO     Load module: type=VAE model="%ROOT_DIRECTORY%\Models\VAE\sdxl_vae.safetensors" source=settings
                         config={'low_cpu_mem_usage': False, 'torch_dtype': torch.bfloat16, 'use_safetensors': True,
                         'config': 'configs/sdxl\\vae'}
Diffusers  5.65s/it ████████ 100% 7/7 00:39 00:00 Loading pipeline components...
22:32:55-474386 DEBUG    Setting model: pipeline=StableDiffusionXLPipeline config={'low_cpu_mem_usage': True,
                         'torch_dtype': torch.bfloat16, 'load_connected_pipeline': True, 'extract_ema': False, 'config':
                         'configs/sdxl', 'use_safetensors': True, 'cache_dir':
                         '%USER_DIRECTORY%\\.cache\\huggingface\\hub'}
22:33:00-445658 INFO     Load network: type=embeddings loaded=10 skipped=13 time=4.97
22:33:00-447406 DEBUG    Setting model: component=VAE name="sdxl_vae.safetensors"
22:33:00-448691 DEBUG    Setting model: component=VAE slicing=True
22:33:00-448691 DEBUG    Setting model: component=VAE tiling=True
22:33:00-449681 DEBUG    Setting model: attention="Scaled-Dot-Product"
22:33:00-469617 DEBUG    Setting model: offload=none
22:33:00-471609 DEBUG    Read: file="%ROOT_DIRECTORY%\SD.Next\configs\sdxl\vae\config.json" json=15 bytes=674 time=0.000
22:33:01-671534 DEBUG    Model move: device=cuda class=StableDiffusionXLPipeline accelerate=False
                         fn=reload_model_weights:load_diffuser time=1.20
22:33:01-861565 DEBUG    GC: utilization={'gpu': 35, 'ram': 11, 'threshold': 80} gc={'collected': 3234, 'saved': 0.0}
                         before={'gpu': 8.38, 'ram': 7.24} after={'gpu': 8.38, 'ram': 7.24, 'retries': 0, 'oom': 0}
                         device=cuda fn=reload_model_weights:load_diffuser time=0.19
22:33:01-868541 INFO     Load model: time=48.70 vae=2.22 load=40.29 embeddings=4.97 move=1.20 native=1024 memory={'ram':
                         {'used': 7.24, 'total': 63.75}, 'gpu': {'used': 8.38, 'total': 23.99}, 'retries': 0, 'oom': 0}
22:33:01-870535 DEBUG    Setting changed: sd_model_checkpoint=PONY, ChrismixPNY-NSFW v2.0 [03335fed37] progress=True
22:33:01-871532 DEBUG    Save: file="config.json" json=43 bytes=2105 time=0.001
22:33:01-874521 DEBUG    Settings: unused=['multiple_tqdm']
22:33:12-793715 INFO     Load model: select="PONY, Chrismix NSFW v3.0"
22:33:15-636755 DEBUG    Model move: device=cpu class=StableDiffusionXLPipeline accelerate=False
                         fn=<lambda>:reload_model_weights time=2.84
22:33:16-052962 DEBUG    GC: utilization={'gpu': 35, 'ram': 12, 'threshold': 80} gc={'collected': 621, 'saved': 6.78}
                         before={'gpu': 8.38, 'ram': 7.62} after={'gpu': 1.6, 'ram': 7.62, 'retries': 0, 'oom': 0}
                         device=cuda fn=reload_model_weights:unload_model_weights time=0.36
22:33:16-054956 DEBUG    Unload weights model: {'ram': {'used': 7.62, 'total': 63.75}, 'gpu': {'used': 1.6, 'total':
                         23.99}, 'retries': 0, 'oom': 0}
22:33:16-069907 INFO     Autodetect model: detect="Stable Diffusion" class=StableDiffusionPipeline
                         file="%ROOT_DIRECTORY%\Models\Stable-diffusion\PONY, Chrismix NSFW v3.0.safetensors" size=13233MB
22:33:16-085532 DEBUG    Autodetect: modules={'conditioner': {'embedders': {}}, 'first_stage_model': {'decoder': {},
                         'encoder': {}, 'post_quant_conv': {}, 'quant_conv': {}}, 'model': {'diffusion_model': {}}}
                         list=['conditioner.embedders', 'first_stage_model.decoder', 'first_stage_model.encoder',
                         'first_stage_model.post_quant_conv', 'first_stage_model.quant_conv', 'model.diffusion_model']
                         time=0.01
22:33:16-087525 INFO     Autodetect vae: detect="Stable Diffusion" class=StableDiffusionPipeline
                         file="%ROOT_DIRECTORY%\Models\Stable-diffusion\PONY, Chrismix NSFW v3.0.safetensors" size=13233MB
22:33:16-106954 DEBUG    Autodetect: modules={'conditioner': {'embedders': {}}, 'first_stage_model': {'decoder': {},
                         'encoder': {}, 'post_quant_conv': {}, 'quant_conv': {}}, 'model': {'diffusion_model': {}}}
                         list=['conditioner.embedders', 'first_stage_model.decoder', 'first_stage_model.encoder',
                         'first_stage_model.post_quant_conv', 'first_stage_model.quant_conv', 'model.diffusion_model']
                         time=0.02
22:33:16-112931 INFO     Load module: type=VAE model="%ROOT_DIRECTORY%\Models\VAE\sdxl_vae.safetensors" source=settings
                         config={'low_cpu_mem_usage': False, 'torch_dtype': torch.bfloat16, 'use_safetensors': True,
                         'config': 'configs/sd15\\vae'}
Diffusers  1.14it/s █████▎    67% 4/6 00:03 00:01 Loading pipeline components...
22:33:20-347678 ERROR    Load model: file="%ROOT_DIRECTORY%\Models\Stable-diffusion\PONY, Chrismix NSFW v3.0.safetensors"
                         pipeline=Autodetect/NoneType config={'low_cpu_mem_usage': True, 'torch_dtype': torch.bfloat16,
                         'load_connected_pipeline': True, 'safety_checker': None, 'requires_safety_checker': False,
                         'vae': AutoencoderKL(
                           (encoder): Encoder(
                             (conv_in): Conv2d(3, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                             (down_blocks): ModuleList(
                               (0): DownEncoderBlock2D(
                                 (resnets): ModuleList(
                                   (0-1): 2 x ResnetBlock2D(
                                     (norm1): GroupNorm(32, 128, eps=1e-06, affine=True)
                                     (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                                     (norm2): GroupNorm(32, 128, eps=1e-06, affine=True)
                                     (dropout): Dropout(p=0.0, inplace=False)
                                     (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                                     (nonlinearity): SiLU()
                                   )
                                 )
                                 (downsamplers): ModuleList(
                                   (0): Downsample2D(
                                     (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2))
                                   )
                                 )
                               )
                               (1): DownEncoderBlock2D(
                                 (resnets): ModuleList(
                                   (0): ResnetBlock2D(
                                     (norm1): GroupNorm(32, 128, eps=1e-06, affine=True)
                                     (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                                     (norm2): GroupNorm(32, 256, eps=1e-06, affine=True)
                                     (dropout): Dropout(p=0.0, inplace=False)
                                     (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                                     (nonlinearity): SiLU()
                                     (conv_shortcut): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1))
                                   )
                                   (1): ResnetBlock2D(
                                     (norm1): GroupNorm(32, 256, eps=1e-06, affine=True)
                                     (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                                     (norm2): GroupNorm(32, 256, eps=1e-06, affine=True)
                                     (dropout): Dropout(p=0.0, inplace=False)
                                     (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                                     (nonlinearity): SiLU()
                                   )
                                 )
                                 (downsamplers): ModuleList(
                                   (0): Downsample2D(
                                     (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2))
                                   )
                                 )
                               )
                               (2): DownEncoderBlock2D(
                                 (resnets): ModuleList(
                                   (0): ResnetBlock2D(
                                     (norm1): GroupNorm(32, 256, eps=1e-06, affine=True)
                                     (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                                     (norm2): GroupNorm(32, 512, eps=1e-06, affine=True)
                                     (dropout): Dropout(p=0.0, inplace=False)
                                     (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                                     (nonlinearity): SiLU()
                                     (conv_shortcut): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1))
                                   )
                                   (1): ResnetBlock2D(
                                     (norm1): GroupNorm(32, 512, eps=1e-06, affine=True)
                                     (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                                     (norm2): GroupNorm(32, 512, eps=1e-06, affine=True)
                                     (dropout): Dropout(p=0.0, inplace=False)
                                     (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                                     (nonlinearity): SiLU()
                                   )
                                 )
                                 (downsamplers): ModuleList(
                                   (0): Downsample2D(
                                     (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2))
                                   )
                                 )
                               )
                               (3): DownEncoderBlock2D(
                                 (resnets): ModuleList(
                                   (0-1): 2 x ResnetBlock2D(
                                     (norm1): GroupNorm(32, 512, eps=1e-06, affine=True)
                                     (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                                     (norm2): GroupNorm(32, 512, eps=1e-06, affine=True)
                                     (dropout): Dropout(p=0.0, inplace=False)
                                     (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                                     (nonlinearity): SiLU()
                                   )
                                 )
                               )
                             )
                             (mid_block): UNetMidBlock2D(
                               (attentions): ModuleList(
                                 (0): Attention(
                                   (group_norm): GroupNorm(32, 512, eps=1e-06, affine=True)
                                   (to_q): Linear(in_features=512, out_features=512, bias=True)
                                   (to_k): Linear(in_features=512, out_features=512, bias=True)
                                   (to_v): Linear(in_features=512, out_features=512, bias=True)
                                   (to_out): ModuleList(
                                     (0): Linear(in_features=512, out_features=512, bias=True)
                                     (1): Dropout(p=0.0, inplace=False)
                                   )
                                 )
                               )
                               (resnets): ModuleList(
                                 (0-1): 2 x ResnetBlock2D(
                                   (norm1): GroupNorm(32, 512, eps=1e-06, affine=True)
                                   (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                                   (norm2): GroupNorm(32, 512, eps=1e-06, affine=True)
                                   (dropout): Dropout(p=0.0, inplace=False)
                                   (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                                   (nonlinearity): SiLU()
                                 )
                               )
                             )
                             (conv_norm_out): GroupNorm(32, 512, eps=1e-06, affine=True)
                             (conv_act): SiLU()
                             (conv_out): Conv2d(512, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                           )
                           (decoder): Decoder(
                             (conv_in): Conv2d(4, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                             (up_blocks): ModuleList(
                               (0-1): 2 x UpDecoderBlock2D(
                                 (resnets): ModuleList(
                                   (0-2): 3 x ResnetBlock2D(
                                     (norm1): GroupNorm(32, 512, eps=1e-06, affine=True)
                                     (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                                     (norm2): GroupNorm(32, 512, eps=1e-06, affine=True)
                                     (dropout): Dropout(p=0.0, inplace=False)
                                     (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                                     (nonlinearity): SiLU()
                                   )
                                 )
                                 (upsamplers): ModuleList(
                                   (0): Upsample2D(
                                     (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                                   )
                                 )
                               )
                               (2): UpDecoderBlock2D(
                                 (resnets): ModuleList(
                                   (0): ResnetBlock2D(
                                     (norm1): GroupNorm(32, 512, eps=1e-06, affine=True)
                                     (conv1): Conv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                                     (norm2): GroupNorm(32, 256, eps=1e-06, affine=True)
                                     (dropout): Dropout(p=0.0, inplace=False)
                                     (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                                     (nonlinearity): SiLU()
                                     (conv_shortcut): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
                                   )
                                   (1-2): 2 x ResnetBlock2D(
                                     (norm1): GroupNorm(32, 256, eps=1e-06, affine=True)
                                     (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                                     (norm2): GroupNorm(32, 256, eps=1e-06, affine=True)
                                     (dropout): Dropout(p=0.0, inplace=False)
                                     (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                                     (nonlinearity): SiLU()
                                   )
                                 )
                                 (upsamplers): ModuleList(
                                   (0): Upsample2D(
                                     (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                                   )
                                 )
                               )
                               (3): UpDecoderBlock2D(
                                 (resnets): ModuleList(
                                   (0): ResnetBlock2D(
                                     (norm1): GroupNorm(32, 256, eps=1e-06, affine=True)
                                     (conv1): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                                     (norm2): GroupNorm(32, 128, eps=1e-06, affine=True)
                                     (dropout): Dropout(p=0.0, inplace=False)
                                     (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                                     (nonlinearity): SiLU()
                                     (conv_shortcut): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
                                   )
                                   (1-2): 2 x ResnetBlock2D(
                                     (norm1): GroupNorm(32, 128, eps=1e-06, affine=True)
                                     (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                                     (norm2): GroupNorm(32, 128, eps=1e-06, affine=True)
                                     (dropout): Dropout(p=0.0, inplace=False)
                                     (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                                     (nonlinearity): SiLU()
                                   )
                                 )
                               )
                             )
                             (mid_block): UNetMidBlock2D(
                               (attentions): ModuleList(
                                 (0): Attention(
                                   (group_norm): GroupNorm(32, 512, eps=1e-06, affine=True)
                                   (to_q): Linear(in_features=512, out_features=512, bias=True)
                                   (to_k): Linear(in_features=512, out_features=512, bias=True)
                                   (to_v): Linear(in_features=512, out_features=512, bias=True)
                                   (to_out): ModuleList(
                                     (0): Linear(in_features=512, out_features=512, bias=True)
                                     (1): Dropout(p=0.0, inplace=False)
                                   )
                                 )
                               )
                               (resnets): ModuleList(
                                 (0-1): 2 x ResnetBlock2D(
                                   (norm1): GroupNorm(32, 512, eps=1e-06, affine=True)
                                   (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                                   (norm2): GroupNorm(32, 512, eps=1e-06, affine=True)
                                   (dropout): Dropout(p=0.0, inplace=False)
                                   (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                                   (nonlinearity): SiLU()
                                 )
                               )
                             )
                             (conv_norm_out): GroupNorm(32, 128, eps=1e-06, affine=True)
                             (conv_act): SiLU()
                             (conv_out): Conv2d(128, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                           )
                           (quant_conv): Conv2d(8, 8, kernel_size=(1, 1), stride=(1, 1))
                           (post_quant_conv): Conv2d(4, 4, kernel_size=(1, 1), stride=(1, 1))
                         ), 'local_files_only': False, 'extract_ema': False, 'config': 'configs/sd15',
                         'use_safetensors': True, 'cache_dir': '%USER_DIRECTORY%\\.cache\\huggingface\\hub'} Cannot load
                         because down_blocks.1.attentions.0.proj_in.weight expected shape tensor(..., device='meta',
                         size=(640, 640, 1, 1)), but got torch.Size([640, 640]). If you want to instead overwrite
                         randomly initialized weights, please make sure to pass both `low_cpu_mem_usage=False` and
                         `ignore_mismatched_sizes=True`. For more information, see also:
                         https://github.com/huggingface/diffusers/issues/1619#issuecomment-1345604389 as an example.
22:33:20-395497 ERROR    Load: ValueError
╭───────────────────────────────────────── Traceback (most recent call last) ──────────────────────────────────────────╮
│ %ROOT_DIRECTORY%\SD.Next\modules\sd_models.py:745 in load_diffuser_file                                                │
│                                                                                                                      │
│    744 │   │   │   │   sd_hijack_accelerate.restore_accelerate()                                                     │
│ ❱  745 │   │   │   sd_model = pipeline.from_single_file(checkpoint_info.path, **diffusers_load_config)               │
│    746 │   │   │   # sd_model = patch_diffuser_config(sd_model, checkpoint_info.path)                                │
│                                                                                                                      │
│ %ROOT_DIRECTORY%\SD.Next\venv\lib\site-packages\huggingface_hub\utils\_validators.py:114 in _inner_fn                  │
│                                                                                                                      │
│   113 │   │                                                                                                          │
│ ❱ 114 │   │   return fn(*args, **kwargs)                                                                             │
│   115                                                                                                                │
│                                                                                                                      │
│ %ROOT_DIRECTORY%\SD.Next\venv\lib\site-packages\diffusers\loaders\single_file.py:495 in from_single_file               │
│                                                                                                                      │
│   494 │   │   │   │   try:                                                                                           │
│ ❱ 495 │   │   │   │   │   loaded_sub_model = load_single_file_sub_model(                                             │
│   496 │   │   │   │   │   │   library_name=library_name,                                                             │
│                                                                                                                      │
│ %ROOT_DIRECTORY%\SD.Next\venv\lib\site-packages\diffusers\loaders\single_file.py:102 in load_single_file_sub_model     │
│                                                                                                                      │
│   101 │   │                                                                                                          │
│ ❱ 102 │   │   loaded_sub_model = load_method(                                                                        │
│   103 │   │   │   pretrained_model_link_or_path_or_dict=checkpoint,                                                  │
│                                                                                                                      │
│ %ROOT_DIRECTORY%\SD.Next\venv\lib\site-packages\huggingface_hub\utils\_validators.py:114 in _inner_fn                  │
│                                                                                                                      │
│   113 │   │                                                                                                          │
│ ❱ 114 │   │   return fn(*args, **kwargs)                                                                             │
│   115                                                                                                                │
│                                                                                                                      │
│ %ROOT_DIRECTORY%\SD.Next\venv\lib\site-packages\diffusers\loaders\single_file_model.py:299 in from_single_file         │
│                                                                                                                      │
│   298 │   │   if is_accelerate_available():                                                                          │
│ ❱ 299 │   │   │   unexpected_keys = load_model_dict_into_meta(model, diffusers_format_checkpoint, dtype=torch_dtype) │
│   300                                                                                                                │
│                                                                                                                      │
│ %ROOT_DIRECTORY%\SD.Next\venv\lib\site-packages\diffusers\models\model_loading_utils.py:223 in load_model_dict_into_me │
│                                                                                                                      │
│   222 │   │   │   │   model_name_or_path_str = f"{model_name_or_path} " if model_name_or_path is not None else ""    │
│ ❱ 223 │   │   │   │   raise ValueError(                                                                              │
│   224 │   │   │   │   │   f"Cannot load {model_name_or_path_str} because {param_name} expected shape {empty_state_di │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
ValueError: Cannot load  because down_blocks.1.attentions.0.proj_in.weight expected shape tensor(..., device='meta', size=(640, 640, 1, 1)), but got torch.Size([640, 640]). If you want to instead overwrite randomly initialized weights, please make sure to pass both `low_cpu_mem_usage=False` and `ignore_mismatched_sizes=True`. For more information, see also: https://github.com/huggingface/diffusers/issues/1619#issuecomment-1345604389 as an example.
22:33:20-642708 ERROR    Load model: name="PONY, Chrismix NSFW v3.0" not loaded
22:33:20-645700 DEBUG    Setting changed: sd_model_checkpoint=PONY, Chrismix NSFW v3.0 progress=True
22:33:20-648689 DEBUG    Save: file="config.json" json=43 bytes=2089 time=0.004
22:33:20-649686 DEBUG    Settings: unused=['multiple_tqdm']

vladmandic · 2024-12-23T04:34:30Z

vladmandic
Dec 23, 2024
Maintainer

loading first model:

22:32:12-966621 INFO     Load model: select="PONY, ChrismixPNY-NSFW v2.0 [03335fed37]"
22:32:12-970608 INFO     Autodetect model: detect="Stable Diffusion XL" class=StableDiffusionXLPipeline

loading second model:

22:33:12-793715 INFO     Load model: select="PONY, Chrismix NSFW v3.0"
22:33:16-069907 INFO     Autodetect model: detect="Stable Diffusion" class=StableDiffusionPipeline
                         file="%ROOT_DIRECTORY%\Models\Stable-diffusion\PONY, Chrismix NSFW v3.0.safetensors" size=13233MB
22:33:20-347678 ERROR    Load model: file="%ROOT_DIRECTORY%\Models\Stable-diffusion\PONY, Chrismix NSFW v3.0.safetensors"
                         pipeline=Autodetect/NoneType config={'low_cpu_mem_usage': True, 'torch_dtype': torch.bfloat16,

that second model has a weird size and its not autodetected as XL model, so loading failed.
you can force pipiline type in settings to SDXL instead of autodetect.
i dont know why autodetect fails, but any SDXL model that is 13gb in size is not worth paying attention to in my book, that's just bloatware.

3 replies

WhoseBoatIsThisBoat Dec 23, 2024
Author

Thanks for the reply!

How do I force SD.Next to load it as an SDXL model?

vladmandic Dec 23, 2024
Maintainer

in settings - select pipine type instead of auto detect.

WhoseBoatIsThisBoat Dec 23, 2024
Author

that worked

cheers!

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

adding arguments outside of CLI #3647

{{title}}

{{editor}}'s edit

{{editor}}'s edit

Replies: 2 comments 4 replies

{{title}}

{{title}}

{{editor}}'s edit

{{editor}}'s edit

{{title}}

{{title}}

{{title}}

{{title}}

Select a reply

adding arguments outside of CLI #3647

WhoseBoatIsThisBoat Dec 22, 2024

Replies: 2 comments · 4 replies

vladmandic Dec 22, 2024 Maintainer

WhoseBoatIsThisBoat Dec 23, 2024 Author

vladmandic Dec 23, 2024 Maintainer

WhoseBoatIsThisBoat Dec 23, 2024 Author

vladmandic Dec 23, 2024 Maintainer

WhoseBoatIsThisBoat Dec 23, 2024 Author

WhoseBoatIsThisBoat
Dec 22, 2024

Replies: 2 comments 4 replies

vladmandic
Dec 22, 2024
Maintainer

WhoseBoatIsThisBoat Dec 23, 2024
Author

vladmandic
Dec 23, 2024
Maintainer

WhoseBoatIsThisBoat Dec 23, 2024
Author

vladmandic Dec 23, 2024
Maintainer

WhoseBoatIsThisBoat Dec 23, 2024
Author