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stochasticai / x-stable-diffusion

Real-time inference for Stable Diffusion - 0.88s latency. Covers AITemplate, nvFuser, TensorRT, FlashAttention. Join our Discord communty: https://discord.com/invite/TgHXuSJEk6
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ValueError: only one element tensors can be converted to Python scalars--Error in line 88 of demo.py: #24

Closed AIManifest closed 1 year ago

AIManifest commented 1 year ago

Hey friends,

Interested in getting the notebook running, would love to solve this! Thanks in advance!

Traceback (most recent call last): File "/content/drive/MyDrive/x-stable-diffusion/TensorRT/demo.py", line 114, in image = model.predict( File "/content/drive/MyDrive/x-stable-diffusion/TensorRT/demo.py", line 88, in predict latents = self.scheduler.step(noise_pred.cuda(), i, latents)["prev_sample"] File "/usr/local/lib/python3.8/dist-packages/diffusers/schedulers/scheduling_lms_discrete.py", line 218, in step step_index = (self.timesteps == timestep).nonzero().item() ValueError: only one element tensors can be converted to Python scalars

akihironitta commented 1 year ago

Same here. @AIManifest Have you found a quick workaround by any chance?

AIManifest commented 1 year ago

Not yet my friend, as far as I know, seems to be a calculation bug, and I'm not sure which value to change when It comes to that.

Toan-Do commented 1 year ago

Hi @akihironitta @AIManifest . Sorry for the late response. We have tested and found the bug in our code and fixed it. Please try it again and let us know if any issue. Note that please use the latest notebook from main branch. Thank you for your rasing it.

akihironitta commented 1 year ago

@Toan-Do Thank you for the update. Which notebook are you running? I'm running this notebook on Colab but getting a different error when running demo.py as in the notebook. I'd like to quickly check if I'm looking at the right notebook before spending more time debugging the error. This is the one, right?: https://colab.research.google.com/github/stochasticai/x-stable-diffusion/blob/main/TensorRT/Notebook.ipynb

the error is here ``` tcmalloc: large alloc 1725005824 bytes == 0x2239c000 @ 0x7f3529ade1e7 0x4d30a0 0x5dede2 0x61033f 0x5aab9b 0x47c416 0x6170f1 0x4f7916 0x4997a2 0x5d8868 0x4997a2 0x5d8868 0x5da092 0x586de6 0x5d8cdf 0x55dc1e 0x5d8868 0x5da092 0x586de6 0x5d8cdf 0x55dc1e 0x55cd91 0x55d743 0x642630 0x6426ae 0x644b78 0x64511c 0x677e5e 0x678029 0x7f35296dbc87 0x5e1baa [01/06/2023-09:06:40] [TRT] [W] TensorRT was linked against cuDNN 8.4.1 but loaded cuDNN 8.3.2 [01/06/2023-09:06:40] [TRT] [W] The getMaxBatchSize() function should not be used with an engine built from a network created with NetworkDefinitionCreationFlag::kEXPLICIT_BATCH flag. This function will always return 1. [01/06/2023-09:06:40] [TRT] [W] The getMaxBatchSize() function should not be used with an engine built from a network created with NetworkDefinitionCreationFlag::kEXPLICIT_BATCH flag. This function will always return 1. [01/06/2023-09:06:40] [TRT] [W] The getMaxBatchSize() function should not be used with an engine built from a network created with NetworkDefinitionCreationFlag::kEXPLICIT_BATCH flag. This function will always return 1. [01/06/2023-09:06:40] [TRT] [W] The getMaxBatchSize() function should not be used with an engine built from a network created with NetworkDefinitionCreationFlag::kEXPLICIT_BATCH flag. This function will always return 1. [01/06/2023-09:06:40] [TRT] [W] TensorRT was linked against cuDNN 8.4.1 but loaded cuDNN 8.3.2 Cannot initialize model with low cpu memory usage because `accelerate` was not found in the environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install `accelerate` for faster and less memory-intense model loading. You can do so with: ``` pip install accelerate ``` . Downloading: 100% 335M/335M [00:03<00:00, 86.7MB/s] Downloading: 100% 522/522 [00:00<00:00, 837kB/s] Downloading: 100% 1.06M/1.06M [00:00<00:00, 61.9MB/s] Downloading: 100% 525k/525k [00:00<00:00, 41.1MB/s] Downloading: 100% 472/472 [00:00<00:00, 549kB/s] Downloading: 100% 806/806 [00:00<00:00, 1.24MB/s] Downloading: 100% 4.52k/4.52k [00:00<00:00, 5.20MB/s] Downloading: 100% 1.71G/1.71G [00:32<00:00, 52.6MB/s] Some weights of the model checkpoint at openai/clip-vit-large-patch14 were not used when initializing CLIPTextModel: ['vision_model.encoder.layers.1.layer_norm2.weight', 'vision_model.encoder.layers.11.mlp.fc1.bias', 'vision_model.encoder.layers.4.self_attn.k_proj.weight', 'vision_model.encoder.layers.17.mlp.fc1.weight', 'vision_model.encoder.layers.2.self_attn.q_proj.bias', 'vision_model.encoder.layers.14.mlp.fc2.weight', 'vision_model.encoder.layers.0.mlp.fc1.weight', 'vision_model.encoder.layers.15.mlp.fc2.weight', 'vision_model.encoder.layers.20.layer_norm1.weight', 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This IS expected if you are initializing CLIPTextModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model). - This IS NOT expected if you are initializing CLIPTextModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). 0% 0/1 [00:00
Toan-Do commented 1 year ago

@akihironitta There is no bug. You can see that it finish generating image in the log 50it [00:09, 5.41it/s] 100% 1/1 [00:12<00:00, 12.32s/it]. Please check if image_generated.png file is generated or not.

akihironitta commented 1 year ago

@Toan-Do You're right. I can see a file generated there. Sorry for the confusion and thank you for your help!