Closed mcqx4 closed 3 months ago
I already reinstalled xformers, but when I start the stable diffusion, i encountered an error with the last line
OSError: /usr/local/lib/python3.10/dist-packages/torchtext/lib/libtorchtext.so: undefined symbol: _ZN2at4_ops5zeros4callEN3c108ArrayRefINS2_6SymIntEEENS2_8optionalINS2_10ScalarTypeEEENS6_INS2_6LayoutEEENS6_INS2_6DeviceEEENS6_IbEE
I already reinstalled xformers, but when I start the stable diffusion, i encountered an error with the last line OSError: /usr/local/lib/python3.10/dist-packages/torchtext/lib/libtorchtext.so: undefined symbol: _ZN2at4_ops5zeros4callEN3c108ArrayRefINS2_6SymIntEEENS2_8optionalINS2_10ScalarTypeEEENS6_INS2_6LayoutEEENS6_INS2_6DeviceEEENS6_IbEE
What version of torchtext
do you have?
I already reinstalled xformers, but when I start the stable diffusion, i encountered an error with the last line OSError: /usr/local/lib/python3.10/dist-packages/torchtext/lib/libtorchtext.so: undefined symbol: _ZN2at4_ops5zeros4callEN3c108ArrayRefINS2_6SymIntEEENS2_8optionalINS2_10ScalarTypeEEENS6_INS2_6LayoutEEENS6_INS2_6DeviceEEENS6_IbEE
What version of
torchtext
do you have?
Im a noob in this with lack of programming skills fyi. How do navigate to find the version of it?
I already reinstalled xformers, but when I start the stable diffusion, i encountered an error with the last line OSError: /usr/local/lib/python3.10/dist-packages/torchtext/lib/libtorchtext.so: undefined symbol: _ZN2at4_ops5zeros4callEN3c108ArrayRefINS2_6SymIntEEENS2_8optionalINS2_10ScalarTypeEEENS6_INS2_6LayoutEEENS6_INS2_6DeviceEEENS6_IbEE
What version of
torchtext
do you have?Im a noob in this with lack of programming skills fyi. How do navigate to find the version of it?
All good. In a new cell:
import torchtext
print(torchtext.__version__)
or
!pip show torchtext
Bare in mind that I don't think updating xformers is going to solve the issue for everyone. The problem seems to be with the colab team updating jax which in turn creates conflicts.
I already reinstalled xformers, but when I start the stable diffusion, i encountered an error with the last line OSError: /usr/local/lib/python3.10/dist-packages/torchtext/lib/libtorchtext.so: undefined symbol: _ZN2at4_ops5zeros4callEN3c108ArrayRefINS2_6SymIntEEENS2_8optionalINS2_10ScalarTypeEEENS6_INS2_6LayoutEEENS6_INS2_6DeviceEEENS6_IbEE
What version of
torchtext
do you have?Im a noob in this with lack of programming skills fyi. How do navigate to find the version of it?
All good. In a new cell:
import torchtext print(torchtext.__version__)
or
!pip show torchtext
Bare in mind that I don't think updating xformers is going to solve the issue for everyone. The problem seems to be with the colab team updating jax which in turn creates conflicts.
So for now, what do I need to do from here. And thanks for helping. I'll bare that in mind that it could be a collab issue.
Temp fix for those still having issues
# Revert (downgrade) JAX back to 0.4.23
!pip uninstall -y jax jaxlib
!pip install jax==0.4.23 jaxlib==0.4.23
# May be redundant — reinstall Xformers
!pip install -U xformers --index-url https://download.pytorch.org/whl/cu121
⬆ New Code-cell Before Start Stable-Diffusion
cell.
Temp fix for those still having issues
# Revert (downgrade) JAX back to 0.4.23 !pip uninstall -y jax jaxlib !pip install jax==0.4.23 jaxlib==0.4.23 # May be redundant — reinstall Xformers !pip install -U xformers --index-url https://download.pytorch.org/whl/cu121
⬆ New Code-cell Before
Start Stable-Diffusion
cell.
Yep, it's working now, thanks for your help. god bless you.
I also have same issue
WARNING[XFORMERS]: xFormers can't load C++/CUDA extensions. xFormers was built for:
PyTorch 2.2.1+cu121 with CUDA 1201 (you have 2.3.0+cu121)
Python 3.10.13 (you have 3.10.12)
Please reinstall xformers (see https://github.com/facebookresearch/xformers#installing-xformers)
Memory-efficient attention, SwiGLU, sparse and more won't be available.
Set XFORMERS_MORE_DETAILS=1 for more details
I try to this code before Start Stable-Diffusion.
# Revert (downgrade) JAX back to 0.4.23
!pip uninstall -y jax jaxlib
!pip install jax==0.4.23 jaxlib==0.4.23
# May be redundant — reinstall Xformers
!pip install -U xformers --index-url https://download.pytorch.org/whl/cu121
However, an error occurs.
*** Error completing request
*** Arguments: ('task(1v7kp0d0fnm46q4)', <gradio.routes.Request object at 0x7d13953a6b00>, 'tree', '', [], 1, 1, 7, 512, 512, False, 0.7, 2, 'Latent', 0, 0, 0, 'Use same checkpoint', 'Use same sampler', 'Use same scheduler', '', '', [], 0, 20, 'DPM++ 2M', 'Automatic', False, '', 0.8, -1, False, -1, 0, 0, 0, ControlNetUnit(is_ui=True, input_mode=<InputMode.SIMPLE: 'simple'>, batch_images='', output_dir='', loopback=False, enabled=False, module='none', model='None', weight=1.0, image=None, resize_mode=<ResizeMode.INNER_FIT: 'Crop and Resize'>, low_vram=False, processor_res=-1, threshold_a=-1.0, threshold_b=-1.0, guidance_start=0.0, guidance_end=1.0, pixel_perfect=False, control_mode=<ControlMode.BALANCED: 'Balanced'>, inpaint_crop_input_image=False, hr_option=<HiResFixOption.BOTH: 'Both'>, save_detected_map=True, advanced_weighting=None, effective_region_mask=None, pulid_mode=<PuLIDMode.FIDELITY: 'Fidelity'>, ipadapter_input=None, mask=None, batch_mask_dir=None, animatediff_batch=False, batch_modifiers=[], batch_image_files=[]), ControlNetUnit(is_ui=True, input_mode=<InputMode.SIMPLE: 'simple'>, batch_images='', output_dir='', loopback=False, enabled=False, module='none', model='None', weight=1.0, image=None, resize_mode=<ResizeMode.INNER_FIT: 'Crop and Resize'>, low_vram=False, processor_res=-1, threshold_a=-1.0, threshold_b=-1.0, guidance_start=0.0, guidance_end=1.0, pixel_perfect=False, control_mode=<ControlMode.BALANCED: 'Balanced'>, inpaint_crop_input_image=False, hr_option=<HiResFixOption.BOTH: 'Both'>, save_detected_map=True, advanced_weighting=None, effective_region_mask=None, pulid_mode=<PuLIDMode.FIDELITY: 'Fidelity'>, ipadapter_input=None, mask=None, batch_mask_dir=None, animatediff_batch=False, batch_modifiers=[], batch_image_files=[]), ControlNetUnit(is_ui=True, input_mode=<InputMode.SIMPLE: 'simple'>, batch_images='', output_dir='', loopback=False, enabled=False, module='none', model='None', weight=1.0, image=None, resize_mode=<ResizeMode.INNER_FIT: 'Crop and Resize'>, low_vram=False, processor_res=-1, threshold_a=-1.0, threshold_b=-1.0, guidance_start=0.0, guidance_end=1.0, pixel_perfect=False, control_mode=<ControlMode.BALANCED: 'Balanced'>, inpaint_crop_input_image=False, hr_option=<HiResFixOption.BOTH: 'Both'>, save_detected_map=True, advanced_weighting=None, effective_region_mask=None, pulid_mode=<PuLIDMode.FIDELITY: 'Fidelity'>, ipadapter_input=None, mask=None, batch_mask_dir=None, animatediff_batch=False, batch_modifiers=[], batch_image_files=[]), False, False, 'positive', 'comma', 0, False, False, 'start', '', 1, '', [], 0, '', [], 0, '', [], True, False, False, False, False, False, False, 0, False, None, None, False, None, None, False, None, None, False, 50) {}
Traceback (most recent call last):
File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/modules/call_queue.py", line 57, in f
res = list(func(*args, **kwargs))
File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/modules/call_queue.py", line 36, in f
res = func(*args, **kwargs)
File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/modules/txt2img.py", line 109, in txt2img
processed = processing.process_images(p)
File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/modules/processing.py", line 845, in process_images
res = process_images_inner(p)
File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/extensions/sd-webui-controlnet/scripts/batch_hijack.py", line 59, in processing_process_images_hijack
return getattr(processing, '__controlnet_original_process_images_inner')(p, *args, **kwargs)
File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/modules/processing.py", line 981, in process_images_inner
samples_ddim = p.sample(conditioning=p.c, unconditional_conditioning=p.uc, seeds=p.seeds, subseeds=p.subseeds, subseed_strength=p.subseed_strength, prompts=p.prompts)
File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/modules/processing.py", line 1328, in sample
samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/modules/sd_samplers_kdiffusion.py", line 218, in sample
samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/modules/sd_samplers_common.py", line 272, in launch_sampling
return func()
File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/modules/sd_samplers_kdiffusion.py", line 218, in <lambda>
samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "/content/gdrive/MyDrive/sd/stablediffusion/src/k-diffusion/k_diffusion/sampling.py", line 594, in sample_dpmpp_2m
denoised = model(x, sigmas[i] * s_in, **extra_args)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1541, in _call_impl
return forward_call(*args, **kwargs)
File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/modules/sd_samplers_cfg_denoiser.py", line 237, in forward
x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict(cond_in, image_cond_in))
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1541, in _call_impl
return forward_call(*args, **kwargs)
File "/content/gdrive/MyDrive/sd/stablediffusion/src/k-diffusion/k_diffusion/external.py", line 112, in forward
eps = self.get_eps(input * c_in, self.sigma_to_t(sigma), **kwargs)
File "/content/gdrive/MyDrive/sd/stablediffusion/src/k-diffusion/k_diffusion/external.py", line 138, in get_eps
return self.inner_model.apply_model(*args, **kwargs)
File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/modules/sd_models_xl.py", line 44, in apply_model
return self.model(x, t, cond)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1541, in _call_impl
return forward_call(*args, **kwargs)
File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/modules/sd_hijack_utils.py", line 18, in <lambda>
setattr(resolved_obj, func_path[-1], lambda *args, **kwargs: self(*args, **kwargs))
File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/modules/sd_hijack_utils.py", line 32, in __call__
return self.__orig_func(*args, **kwargs)
File "/content/gdrive/MyDrive/sd/stablediffusion/generative-models/sgm/modules/diffusionmodules/wrappers.py", line 28, in forward
return self.diffusion_model(
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1541, in _call_impl
return forward_call(*args, **kwargs)
File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/modules/sd_unet.py", line 91, in UNetModel_forward
return original_forward(self, x, timesteps, context, *args, **kwargs)
File "/content/gdrive/MyDrive/sd/stablediffusion/generative-models/sgm/modules/diffusionmodules/openaimodel.py", line 993, in forward
h = module(h, emb, context)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1541, in _call_impl
return forward_call(*args, **kwargs)
File "/content/gdrive/MyDrive/sd/stablediffusion/generative-models/sgm/modules/diffusionmodules/openaimodel.py", line 100, in forward
x = layer(x, context)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1541, in _call_impl
return forward_call(*args, **kwargs)
File "/content/gdrive/MyDrive/sd/stablediffusion/generative-models/sgm/modules/attention.py", line 627, in forward
x = block(x, context=context[i])
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1541, in _call_impl
return forward_call(*args, **kwargs)
File "/content/gdrive/MyDrive/sd/stablediffusion/generative-models/sgm/modules/attention.py", line 459, in forward
return checkpoint(
File "/content/gdrive/MyDrive/sd/stablediffusion/generative-models/sgm/modules/diffusionmodules/util.py", line 165, in checkpoint
return CheckpointFunction.apply(func, len(inputs), *args)
File "/usr/local/lib/python3.10/dist-packages/torch/autograd/function.py", line 598, in apply
return super().apply(*args, **kwargs) # type: ignore[misc]
File "/content/gdrive/MyDrive/sd/stablediffusion/generative-models/sgm/modules/diffusionmodules/util.py", line 182, in forward
output_tensors = ctx.run_function(*ctx.input_tensors)
File "/content/gdrive/MyDrive/sd/stablediffusion/generative-models/sgm/modules/attention.py", line 467, in _forward
self.attn1(
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1541, in _call_impl
return forward_call(*args, **kwargs)
File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/modules/sd_hijack_optimizations.py", line 496, in xformers_attention_forward
out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=get_xformers_flash_attention_op(q, k, v))
File "/usr/local/lib/python3.10/dist-packages/xformers/ops/fmha/__init__.py", line 247, in memory_efficient_attention
return _memory_efficient_attention(
File "/usr/local/lib/python3.10/dist-packages/xformers/ops/fmha/__init__.py", line 365, in _memory_efficient_attention
return _memory_efficient_attention_forward(
File "/usr/local/lib/python3.10/dist-packages/xformers/ops/fmha/__init__.py", line 381, in _memory_efficient_attention_forward
op = _dispatch_fw(inp, False)
File "/usr/local/lib/python3.10/dist-packages/xformers/ops/fmha/dispatch.py", line 125, in _dispatch_fw
return _run_priority_list(
File "/usr/local/lib/python3.10/dist-packages/xformers/ops/fmha/dispatch.py", line 65, in _run_priority_list
raise NotImplementedError(msg)
NotImplementedError: No operator found for `memory_efficient_attention_forward` with inputs:
query : shape=(2, 1024, 10, 64) (torch.float16)
key : shape=(2, 1024, 10, 64) (torch.float16)
value : shape=(2, 1024, 10, 64) (torch.float16)
attn_bias : <class 'NoneType'>
p : 0.0
`decoderF` is not supported because:
xFormers wasn't build with CUDA support
attn_bias type is <class 'NoneType'>
operator wasn't built - see `python -m xformers.info` for more info
`flshattF@0.0.0` is not supported because:
xFormers wasn't build with CUDA support
requires device with capability > (8, 0) but your GPU has capability (7, 5) (too old)
operator wasn't built - see `python -m xformers.info` for more info
`cutlassF` is not supported because:
xFormers wasn't build with CUDA support
operator wasn't built - see `python -m xformers.info` for more info
`smallkF` is not supported because:
max(query.shape[-1] != value.shape[-1]) > 32
xFormers wasn't build with CUDA support
dtype=torch.float16 (supported: {torch.float32})
operator wasn't built - see `python -m xformers.info` for more info
unsupported embed per head: 64
---
[chano1025], there are two separeted methods
You have to try to use the first:
!pip uninstall -y jax jaxlib !pip install jax==0.4.23 jaxlib==0.4.23
or the second:
!pip install -U xformers --index-url https://download.pytorch.org/whl/cu121
I'm having the same issue. ill wait it out for a solution.
# Revert (downgrade) JAX back to 0.4.23
!pip uninstall -y jax jaxlib
!pip install jax==0.4.23 jaxlib==0.4.23
# May be redundant — reinstall Xformers
!pip install -U xformers --index-url https://download.pytorch.org/whl/cu121
If this code cell is added right before the "Start Stable-Diffusion" cell is executed, it works correctly. The reason why this code did not work initially was because I added it below the "Connect Google Drive" cell.
I'm having the same issue. ill wait it out for a solution.
https://github.com/TheLastBen/fast-stable-diffusion/issues/2836#issuecomment-2123531245
This should work as a temporary fix until TheLastBen updates the dependencies.
This "fix" doesn't work for me. I run it just before starting up the start stable diffusion code block. It takes forever to load then eventually it uses the entire 12 gigs of system ram for the Google Colab and ends the code block.
the fix works but somehow introduces a new issue: using upscaler takes forever - it somehow adds an additional upscaling cycle with 956 iterations - the first is 86 or something and was quick, then the second cycle needs to run a long time... it was not like this before the fix
Update: and it's getting wilder - after an upscale I tried to do an image2image, the Generate button somehow triggers a new upscaling cycle with 14000 sth. iteration... I had to click on the interupt on the image2image tab, then the upscaling was stopped and the actual generation of img2img started to run... very strange
For those of you who have found this fix to work even partially which version of Google Colabs are you using? Which Runtime? Seems odd that we would all being running in google colabs but it works for some but not for others.
fixed
Getting Error in the last cell during the launch:
WARNING[XFORMERS]: xFormers can't load C++/CUDA extensions. xFormers was built for: PyTorch 2.2.1+cu121 with CUDA 1201 (you have 2.3.0+cu121) Python 3.10.13 (you have 3.10.12) Please reinstall xformers (see https://github.com/facebookresearch/xformers#installing-xformers) Memory-efficient attention, SwiGLU, sparse and more won't be available. Set XFORMERS_MORE_DETAILS=1 for more details
UPD: Reinstalling Xformers worked for me! Put this in the new cell above the "Start Stable Diffusion":
!pip install -U xformers --index-url https://download.pytorch.org/whl/cu121