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vladmandic
[Issue]: Cannot set version_counter for inference tensor with face restore on directml
Issue Description
Tried to generate a SDXL image with face restore checked. When doing so, I get the error:
RuntimeError: Cannot set version_counter for inference tensor
I have tried this both on main branch and dev branch
Version Platform Description
No response
Relevant log output
E:\stable_diffusion_sdnext_dev\automatic>webui-user.bat
Using VENV: E:\stable_diffusion_sdnext\automatic\venv
17:07:22-683015 INFO Starting SD.Next
17:07:22-685015 INFO Logger: file="E:\stable_diffusion_sdnext_dev\automatic\sdnext.log" level=INFO size=40293
mode=append
17:07:22-686015 INFO Python: version=3.10.6 platform=Windows
bin="E:\stable_diffusion_sdnext\automatic\venv\Scripts\python.exe"
venv="E:\stable_diffusion_sdnext\automatic\venv"
17:07:22-798753 INFO Version: app=sd.next updated=2024-09-30 hash=684263c1 branch=dev
url=https://github.com/vladmandic/automatic//tree/dev ui=main
17:07:23-206670 INFO Updating main repository
17:07:23-803329 INFO Repository upgraded: 684263c1 Mon Sep 30 16:37:34 2024 -0400
17:07:23-808329 INFO Platform: arch=AMD64 cpu=AMD64 Family 25 Model 97 Stepping 1, AuthenticAMD system=Windows
release=Windows-10-10.0.22621-SP0 python=3.10.6
17:07:23-812330 INFO DirectML: selected
17:07:23-884840 INFO Verifying requirements
17:07:23-887840 INFO Verifying packages
17:07:23-888842 INFO Startup: standard
17:07:23-888842 INFO Verifying submodules
17:07:36-628473 INFO Extensions enabled: ['Lora', 'sd-extension-chainner', 'sd-extension-system-info',
'sd-webui-agent-scheduler', 'sdnext-modernui', 'stable-diffusion-webui-rembg']
17:07:36-629473 INFO Verifying requirements
17:07:36-629473 INFO Updating Wiki
17:07:37-099382 INFO Command line args: ['--upgrade', '--use-directml', '--lowvram'] lowvram=True upgrade=True
use_directml=True
17:07:40-299946 INFO Load packages: {'torch': '2.0.0+cpu', 'diffusers': '0.31.0.dev0', 'gradio': '3.43.2',
'transformers': '4.44.2', 'accelerate': '0.34.2'}
17:07:40-605027 INFO Engine: backend=Backend.DIFFUSERS compute=None device=cpu attention="Scaled-Dot-Product"
mode=no_grad
17:07:41-158093 INFO Device: device=AMD Radeon RX 7900 XTX n=2 directml=0.2.0.dev230426
17:07:41-242521 INFO Available VAEs: path="models\VAE" items=0
17:07:41-244518 INFO Available UNets: path="models\UNET" items=0
17:07:41-244518 INFO Available TEs: path="models\Text-encoder" items=0
17:07:41-245517 INFO Disabled extensions: ['sdnext-modernui']
17:07:41-255523 INFO Available Models: path="models\Stable-diffusion" items=2 time=0.01
17:07:41-344356 INFO Extension: script='extensions-builtin\Lora\scripts\lora_script.py'
[2;36m17:07:41-342354[0m[2;36m [0m[34mINFO [0m Available LoRAs: [33mitems[0m=[1;36m0[0m
[33mfolders[0m=[1;36m2[0m
17:07:41-580715 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
17:07:41-593715 INFO Available Upscalers: items=53 downloaded=0 user=0 time=0.01 types=['None', 'Lanczos',
'Nearest', 'ChaiNNer', 'AuraSR', 'ESRGAN', 'LDSR', 'RealESRGAN', 'SCUNet', 'SD', 'SwinIR']
17:07:41-602716 INFO Available Styles: folder="models\styles" items=288 time=0.01
17:07:41-605716 INFO UI theme: type=Standard name="black-teal"
17:07:42-437741 INFO Extension list is empty: refresh required
17:07:43-075162 INFO Local URL: http://127.0.0.1:7860/
17:07:43-185673 INFO [AgentScheduler] Task queue is empty
17:07:43-186671 INFO [AgentScheduler] Registering APIs
17:07:43-261673 INFO Model metadata saved: file="metadata.json" items=2 time=0.01
17:07:43-769671 INFO Torch parameters: backend=directml device=privateuseone:0 config=Auto dtype=torch.float16
vae=torch.float16 unet=torch.float16 context=no_grad nohalf=False nohalfvae=False upscast=False
deterministic=False test-fp16=True test-bf16=False optimization="Scaled-Dot-Product"
17:07:43-770671 INFO Load model: select="animagineXLV31_v31 [e3c47aedb0]"
17:07:43-974239 INFO Autodetect model: detect="Stable Diffusion XL" class=StableDiffusionXLPipeline
file="E:\stable_diffusion_sdnext_dev\automatic\models\Stable-diffusion\animagineXLV31_v31.safet
ensors" size=6617MB
Diffusers 12.96it/s ████████ 100% 7/7 00:00 00:00 Loading pipeline components...
17:07:44-554842 INFO Load network: type=embeddings loaded=0 skipped=0 time=0.00
17:07:44-903878 INFO Load model: time=0.70 load=0.58 move=0.10 native=1024 memory={'ram': {'used': 1.28, 'total':
63.12}, 'gpu': {'used': 0.02, 'total': 0.02}, 'retries': 0, 'oom': 0}
17:07:45-120896 INFO Startup time: 8.02 torch=2.08 gradio=0.59 diffusers=0.15 libraries=1.23 extensions=0.29
ui-networks=0.11 ui-txt2img=0.05 ui-img2img=0.05 ui-control=0.26 ui-models=0.13
ui-settings=0.17 ui-extensions=0.31 launch=0.30 api=0.06 app-started=0.13 checkpoint=1.86
17:08:06-202480 INFO Base: class=StableDiffusionXLPipeline
Progress 3.94s/it █████████████████████████████████ 100% 20/20 01:18 00:00 Base
17:09:28-850245 INFO Load: type=FaceHires model=E:\stable_diffusion_sdnext_dev\automatic\models\yolo\yolov8n-face.pt
17:09:29-323634 ERROR Exception: Cannot set version_counter for inference tensor
17:09:29-325140 ERROR Arguments: args=('task(k5662uv0i6hptn9)', '', 'photo (medium), selfie, 8k, high quality , a man
and a woman sitting by a lake', 'text, watermark, word', [], 20, 14, 29, True, True, False,
False, 1, 1, 6, 6, 0.7, 0, 0.5, 1, 1, -1.0, -1.0, 0, 0, 0, 1248, 832, False, 0.3, 1, 1, 'Add
with forward', 'None', False, 20, 0, 0, 10, 0, '', '', 0, 0, 0, 0, False, 4, 0.95, False, 0.6,
1, '#000000', 0, [], 0, 1, 'None', 'None', 'None', 'None', 0.5, 0.5, 0.5, 0.5, None, None,
None, None, False, False, False, False, 0, 0, 0, 0, 1, 1, 1, 1, None, None, None, None, False,
'', False, 0, '', [], 0, '', [], 0, '', [], False, True, False, True, False, False, 0, 'None',
[], 'FaceID Base', True, True, 1, 1, 1, 0.5, True, 'person', 1, 0.5, True, 'None', 16, 'None',
1, True, 'None', 2, True, 1, 0, True, 'none', 3, 4, 0.25, 0.25, 'THUDM/CogVideoX-2b', 'DDIM',
49, 6, 'balanced', True, 'None', 8, True, 1, 0, None, None, '', 0.5, 5, None, '', 0.5, 5, None,
3, 1, 1, 0.8, 8, 64, True, 0.65, True, False, 1, 1, 1, True, 0.5, 600.0, 1.0, True, None, 1, 0,
0, 0, 0, 0, 0, 0, 1, 1, 1, 0.5, 0.5, 'OpenGVLab/InternVL-14B-224px', False, 0.7, 1.2, 128,
False, False, 'positive', 'comma', 0, False, False, '', 'None', '', 1, '', 'None', 1, True, 10,
'None', True, 0, 'None', 2, True, 1, 0, 0, '', [], 0, '', [], 0, '', [], False, True, False,
False, False, False, 0) kwargs={}
17:09:29-329140 ERROR gradio call: RuntimeError
╭───────────────────────────────────────── Traceback (most recent call last) ──────────────────────────────────────────╮
│ E:\stable_diffusion_sdnext_dev\automatic\modules\call_queue.py:31 in f │
│ │
│ 30 │ │ │ try: │
│ ❱ 31 │ │ │ │ res = func(*args, **kwargs) │
│ 32 │ │ │ │ progress.record_results(id_task, res) │
│ │
│ E:\stable_diffusion_sdnext_dev\automatic\modules\txt2img.py:94 in txt2img │
│ │
│ 93 │ if processed is None: │
│ ❱ 94 │ │ processed = processing.process_images(p) │
│ 95 │ processed = scripts.scripts_txt2img.after(p, processed, *args) │
│ │
│ E:\stable_diffusion_sdnext_dev\automatic\modules\processing.py:201 in process_images │
│ │
│ 200 │ │ │ with context_hypertile_vae(p), context_hypertile_unet(p): │
│ ❱ 201 │ │ │ │ processed = process_images_inner(p) │
│ 202 │
│ │
│ E:\stable_diffusion_sdnext_dev\automatic\modules\processing.py:363 in process_images_inner │
│ │
│ 362 │ │ │ │ │ p.ops.append('face') │
│ ❱ 363 │ │ │ │ │ sample = face_restoration.restore_faces(sample, p) │
│ 364 │ │ │ │ │ if sample is not None: │
│ │
│ E:\stable_diffusion_sdnext_dev\automatic\modules\face_restoration.py:17 in restore_faces │
│ │
│ 16 │ face_restorer = face_restorers[0] │
│ ❱ 17 │ return face_restorer.restore(np_image, p) │
│ 18 │
│ │
│ E:\stable_diffusion_sdnext_dev\automatic\scripts\face_details.py:112 in restore │
│ │
│ 111 │ │ image = Image.fromarray(np_image) │
│ ❱ 112 │ │ faces = self.predict(image) │
│ 113 │ │ if len(faces) == 0: │
│ │
│ E:\stable_diffusion_sdnext_dev\automatic\scripts\face_details.py:57 in predict │
│ │
│ 56 │ │ self.model.to(device) │
│ ❱ 57 │ │ predictions = self.model.predict( │
│ 58 │ │ │ source=[image], │
│ │
│ E:\stable_diffusion_sdnext\automatic\venv\lib\site-packages\ultralytics\engine\model.py:555 in predict │
│ │
│ 554 │ │ │ self.predictor.set_prompts(prompts) │
│ ❱ 555 │ │ return self.predictor.predict_cli(source=source) if is_cli else self.predictor(source=source, stream= │
│ 556 │
│ │
│ ... 8 frames hidden ... │
│ │
│ E:\stable_diffusion_sdnext\automatic\venv\lib\site-packages\ultralytics\nn\tasks.py:147 in _predict_once │
│ │
│ 146 │ │ │ │ self._profile_one_layer(m, x, dt) │
│ ❱ 147 │ │ │ x = m(x) # run │
│ 148 │ │ │ y.append(x if m.i in self.save else None) # save output │
│ │
│ E:\stable_diffusion_sdnext\automatic\venv\lib\site-packages\torch\nn\modules\module.py:1501 in _call_impl │
│ │
│ 1500 │ │ │ │ or _global_forward_hooks or _global_forward_pre_hooks): │
│ ❱ 1501 │ │ │ return forward_call(*args, **kwargs) │
│ 1502 │ │ # Do not call functions when jit is used │
│ │
│ E:\stable_diffusion_sdnext\automatic\venv\lib\site-packages\ultralytics\nn\modules\conv.py:54 in forward_fuse │
│ │
│ 53 │ │ """Perform transposed convolution of 2D data.""" │
│ ❱ 54 │ │ return self.act(self.conv(x)) │
│ 55 │
│ │
│ E:\stable_diffusion_sdnext\automatic\venv\lib\site-packages\torch\nn\modules\module.py:1501 in _call_impl │
│ │
│ 1500 │ │ │ │ or _global_forward_hooks or _global_forward_pre_hooks): │
│ ❱ 1501 │ │ │ return forward_call(*args, **kwargs) │
│ 1502 │ │ # Do not call functions when jit is used │
│ │
│ E:\stable_diffusion_sdnext\automatic\venv\lib\site-packages\torch\nn\modules\conv.py:463 in forward │
│ │
│ 462 │ def forward(self, input: Tensor) -> Tensor: │
│ ❱ 463 │ │ return self._conv_forward(input, self.weight, self.bias) │
│ 464 │
│ │
│ E:\stable_diffusion_sdnext\automatic\venv\lib\site-packages\torch\nn\modules\conv.py:459 in _conv_forward │
│ │
│ 458 │ │ │ │ │ │ │ _pair(0), self.dilation, self.groups) │
│ ❱ 459 │ │ return F.conv2d(input, weight, bias, self.stride, │
│ 460 │ │ │ │ │ │ self.padding, self.dilation, self.groups) │
│ │
│ E:\stable_diffusion_sdnext_dev\automatic\modules\dml\amp\autocast_mode.py:43 in <lambda> │
│ │
│ 42 │ │ op = getattr(resolved_obj, func_path[-1]) │
│ ❱ 43 │ │ setattr(resolved_obj, func_path[-1], lambda *args, **kwargs: forward(op, args, kwargs)) │
│ 44 │
│ │
│ E:\stable_diffusion_sdnext_dev\automatic\modules\dml\amp\autocast_mode.py:15 in forward │
│ │
│ 14 │ if not torch.dml.is_autocast_enabled: │
│ ❱ 15 │ │ return op(*args, **kwargs) │
│ 16 │ args = list(map(cast, args)) │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
RuntimeError: Cannot set version_counter for inference tensor
Backend
Diffusers
UI
Standard
Branch
Dev
Model
StableDiffusion XL
Acknowledgements
- [X] I have read the above and searched for existing issues
- [X] I confirm that this is classified correctly and its not an extension issue
this is happening in ultralytics package which is used to execute yolo face detection model per https://github.com/ultralytics/ultralytics/issues/13620 and https://github.com/ultralytics/ultralytics/issues/907 directml is not supported with ultralytics. i'll see what can be done from sdnext side, but i'm affraid its pretty limited.
Thank you for looking into this. Older builds of SDNext had a different face fix button that did work under directml. Is it possible to bring this back for directml users?
I've always used directml, and in older builds I thought there was a button that fixed faces similar to the current face restore. And this button didn't have any issues on directml.
there was never any button. there was and is a checkbox to enable/disable and what it does did not change recently.
It's highly recommended you switch to ZLUDA over DirectML; better speeds, better memory management. https://github.com/vladmandic/automatic/wiki/ZLUDA
It's highly recommended you switch to ZLUDA over DirectML; better speeds, better memory management. https://github.com/vladmandic/automatic/wiki/ZLUDA
I've done that, you're right! Thanks, it's so much better.