Hello,

Thanks for this great work.

I'm wondering if you could provide instructions on how to perform the Upscaling task?

Thanks!

Huge thanks for your code contribution first!

I used your config file "v1-finetune-for-inpainting-laion-iaesthe.yaml" to fine-tune the model for text-conditioned inpainting. The dataset I used is this subset of the Liaon dataset.

It turns out the results finally become the naive inpainting (simply fills the missing region), and were no longer controlled by the text conditioning as the training proceeds (as shown below, the txt prompt is "a cat on the bench", but no cat appears). Maybe i miss some tricks, I wonder did you meet the same issue when you trained the model?

Thank you in advance :)

Got this error:
Traceback (most recent call last):
File "/content/latent-diffusion/scripts/txt2img.py", line 10, in
from ldm.util import instantiate_from_config
File "/usr/local/lib/python3.7/dist-packages/ldm.py", line 20
print self.face_rec_model_path
^
SyntaxError: Missing parentheses in call to 'print'. Did you mean print(self.face_rec_model_path)?

Thank you for this repo. It has more training related stuff, so I can try it on my own.
Can you please point me where 10 % text conditioning dropout is happening?
I'm afraid I will dropout twice if I dropout it on my own.
Thank you again. LDM is really awesome.

keep getting this error even though I tried different version of rich module. How do I go about fixing it?

For some reason, after updating my SD installation today from GitHub, Norton blocked my attempt to connect to the local IP with the following warning:

Web Attack: BeEF Framework Attack

Just happens on Edge and not on Chrome. I've disabled ALL Chrome Add-Ins, but is still happening. Happened to anyone else?

Getting the following error following the instructions

python scripts/txt2img.py --prompt "a virus monster is playing guitar, oil on canvas" --ddim_eta 0.0 --n_samples 4 --n_iter 4 --scale 5.0 --ddim_steps 50
Traceback (most recent call last):
File "scripts/txt2img.py", line 11, in
from pytorch_lightning import seed_everything
File "usr\anaconda3\envs\ldm\lib\site-packages\pytorch_lightning_init_.py", line 20, in
from pytorch_lightning import metrics # noqa: E402
File "usr\anaconda3\envs\ldm\lib\site-packages\pytorch_lightning\metrics_init_.py", line 15, in
from pytorch_lightning.metrics.classification import ( # noqa: F401
File "usr\anaconda3\envs\ldm\lib\site-packages\pytorch_lightning\metrics\classification_init_.py", line 14, in
from pytorch_lightning.metrics.classification.accuracy import Accuracy # noqa: F401
File "usr\anaconda3\envs\ldm\lib\site-packages\pytorch_lightning\metrics\classification\accuracy.py", line 18, in
from pytorch_lightning.metrics.utils import deprecated_metrics, void
File "usr\anaconda3\envs\ldm\lib\site-packages\pytorch_lightning\metrics\utils.py", line 22, in
from torchmetrics.utilities.data import get_num_classes as _get_num_classes
ImportError: cannot import name 'get_num_classes' from 'torchmetrics.utilities.data' (usr\anaconda3\envs\ldm\lib\site-packages\torchmetrics\utilities\data.py)

I'm trying to run the text-to-image model with the example but CUDA keeps running out of memory, despite it barely trying to allocate anything. It's trying to allocate 20MB when there's 7.3GB reserved. Is there any way to fix this? I've searched all over but I couldn't find a clear answer.

Traceback (most recent call last):
File "main.py", line 851, in
trainer.fit(model, data)
File "/opt/conda/envs/ldm/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 553, in fit
self._run(model)
File "/opt/conda/envs/ldm/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 918, in _run
self._dispatch()
File "/opt/conda/envs/ldm/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 986, in _dispatch
self.accelerator.start_training(self)
File "/opt/conda/envs/ldm/lib/python3.8/site-packages/pytorch_lightning/accelerators/accelerator.py", line 92, in start_training
self.training_type_plugin.start_training(trainer)
File "/opt/conda/envs/ldm/lib/python3.8/site-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py", line 161, in start_training
self._results = trainer.run_stage()
File "/opt/conda/envs/ldm/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 996, in run_stage
return self._run_train()
File "/opt/conda/envs/ldm/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1045, in _run_train
self.fit_loop.run()
File "/opt/conda/envs/ldm/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 111, in run
self.advance(*args, **kwargs)
File "/opt/conda/envs/ldm/lib/python3.8/site-packages/pytorch_lightning/loops/fit_loop.py", line 200, in advance
epoch_output = self.epoch_loop.run(train_dataloader)
File "/opt/conda/envs/ldm/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 111, in run
self.advance(*args, **kwargs)
File "/opt/conda/envs/ldm/lib/python3.8/site-packages/pytorch_lightning/loops/epoch/training_epoch_loop.py", line 130, in advance
batch_output = self.batch_loop.run(batch, self.iteration_count, self._dataloader_idx)
File "/opt/conda/envs/ldm/lib/python3.8/site-packages/pytorch_lightning/loops/batch/training_batch_loop.py", line 101, in run
super().run(batch, batch_idx, dataloader_idx)
File "/opt/conda/envs/ldm/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 111, in run
self.advance(*args, **kwargs)
File "/opt/conda/envs/ldm/lib/python3.8/site-packages/pytorch_lightning/loops/batch/training_batch_loop.py", line 148, in advance
result = self._run_optimization(batch_idx, split_batch, opt_idx, optimizer)
File "/opt/conda/envs/ldm/lib/python3.8/site-packages/pytorch_lightning/loops/batch/training_batch_loop.py", line 194, in _run_optimization
closure()
File "/opt/conda/envs/ldm/lib/python3.8/site-packages/pytorch_lightning/loops/batch/training_batch_loop.py", line 236, in _training_step_and_backward_closure
result = self.training_step_and_backward(split_batch, batch_idx, opt_idx, optimizer, hiddens)
File "/opt/conda/envs/ldm/lib/python3.8/site-packages/pytorch_lightning/loops/batch/training_batch_loop.py", line 549, in training_step_and_backward
self.backward(result, optimizer, opt_idx)
File "/opt/conda/envs/ldm/lib/python3.8/site-packages/pytorch_lightning/loops/batch/training_batch_loop.py", line 590, in backward
result.closure_loss = self.trainer.accelerator.backward(result.closure_loss, optimizer, *args, **kwargs)
File "/opt/conda/envs/ldm/lib/python3.8/site-packages/pytorch_lightning/accelerators/accelerator.py", line 276, in backward
self.precision_plugin.backward(self.lightning_module, closure_loss, *args, **kwargs)
File "/opt/conda/envs/ldm/lib/python3.8/site-packages/pytorch_lightning/plugins/precision/precision_plugin.py", line 78, in backward
model.backward(closure_loss, optimizer, *args, **kwargs)
File "/opt/conda/envs/ldm/lib/python3.8/site-packages/pytorch_lightning/core/lightning.py", line 1481, in backward
loss.backward(*args, **kwargs)
File "/opt/conda/envs/ldm/lib/python3.8/site-packages/torch/_tensor.py", line 363, in backward
torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs)
File "/opt/conda/envs/ldm/lib/python3.8/site-packages/torch/autograd/init.py", line 173, in backward
Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
File "/opt/conda/envs/ldm/lib/python3.8/site-packages/torch/autograd/function.py", line 253, in apply
return user_fn(self, *args)
File "/root/data/juicefs_hz_cv_v3/11120102/project/generative-model/pesser-stable-diffusion/ldm/modules/diffusionmodules/util.py", line 138, in backward
output_tensors = ctx.run_function(*shallow_copies)
File "/root/data/juicefs_hz_cv_v3/11120102/project/generative-model/pesser-stable-diffusion/ldm/modules/attention.py", line 215, in _forward
x = self.attn1(self.norm1(x), context=context if self.disable_self_attn else None) + x
File "/opt/conda/envs/ldm/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
return forward_call(*input, **kwargs)
File "/opt/conda/envs/ldm/lib/python3.8/site-packages/torch/nn/modules/normalization.py", line 189, in forward
return F.layer_norm(
File "/opt/conda/envs/ldm/lib/python3.8/site-packages/torch/nn/functional.py", line 2486, in layer_norm
return torch.layer_norm(input, normalized_shape, weight, bias, eps, torch.backends.cudnn.enabled)
RuntimeError: expected scalar type Half but found Float

I am using this pretrained model for text2img sampling:

and i get a result like this:

when i use this pretrained model:

the result is normal, like this:

is there any suggestion?

for clarity - does this repo actually do stable diffusion?
it doesn't seem like the models for stable diffusion are available.
or am I mistaken?
https://huggingface.co/CompVis/stable-diffusion/blob/main/README.md

UPDATE
seems like they are interchangable. but....
cfreude/stable-diffusion@7fd86d0

Hi,
just asking ... are you planning to make a Dockerfile? Looks like people having problems making the stuff run

I tried running the test command and got this error. I wouldn't be surprised if I screwed something up. I uninstalled and reinstalled torch and tensor to no avail.

H:\stable>python scripts/txt2img.py --prompt "a virus monster is playing guitar, oil on canvas" --ddim_eta 0.0 --n_samples 4 --n_iter 4 --scale 5.0  --ddim_steps 50
Traceback (most recent call last):
  File "H:\stable\scripts\txt2img.py", line 2, in <module>
    import torch
  File "E:\anaconda3\lib\site-packages\torch\__init__.py", line 255, in <module>
    from .random import set_rng_state, get_rng_state, manual_seed, initial_seed, seed
  File "E:\anaconda3\lib\site-packages\torch\random.py", line 9, in <module>
    def set_rng_state(new_state: torch.Tensor) -> None:
AttributeError: partially initialized module 'torch' has no attribute 'Tensor' (most likely due to a circular import)

Trying to set the repo up and get it working but got the below error
Win 11

(ldm) D:\github\stable-diffusion>python scripts/txt2img.py --prompt "a virus monster is playing guitar, oil on canvas" --ddim_eta 0.0 --n_samples 4 --n_iter 4 --scale 5.0 --ddim_steps 50
Traceback (most recent call last):
File "scripts/txt2img.py", line 11, in
from pytorch_lightning import seed_everything
File "C:\Users\nikol\anaconda3\envs\ldm\lib\site-packages\pytorch_lightning_init_.py", line 20, in
from pytorch_lightning import metrics # noqa: E402
File "C:\Users\nikol\anaconda3\envs\ldm\lib\site-packages\pytorch_lightning\metrics_init_.py", line 15, in
from pytorch_lightning.metrics.classification import ( # noqa: F401
File "C:\Users\nikol\anaconda3\envs\ldm\lib\site-packages\pytorch_lightning\metrics\classification_init_.py", line 14, in
from pytorch_lightning.metrics.classification.accuracy import Accuracy # noqa: F401
File "C:\Users\nikol\anaconda3\envs\ldm\lib\site-packages\pytorch_lightning\metrics\classification\accuracy.py", line 18, in
from pytorch_lightning.metrics.utils import deprecated_metrics, void
File "C:\Users\nikol\anaconda3\envs\ldm\lib\site-packages\pytorch_lightning\metrics\utils.py", line 22, in
from torchmetrics.utilities.data import get_num_classes as _get_num_classes
ImportError: cannot import name 'get_num_classes' from 'torchmetrics.utilities.data' (C:\Users\nikol\anaconda3\envs\ldm\lib\site-packages\torchmetrics\utilities\data.py)

Hi everyone.
i recently came across this weird error,
Not sure why i can't load SD through theuser batchweb ui.

I am runing 1.5

i can only run SD when i double click on the lanchpy and the python webui only but i get a long cmd message

Repeated inpainting leads to saturated pixels. Quick and dirty example:

import subprocess
import os
import numpy as np
from PIL import Image, ImageDraw
import shutil

directory = lambda x: "./Diffusion/Diffusion_{}/".format(x)

for i in range(240):
	if i!=0:
		if os.path.exists(directory(i)):
			shutil.rmtree(directory(i))
for i in range(240):
	im = Image.new('RGB', (512, 512), (0, 0, 0))
	draw = ImageDraw.Draw(im)

	x = np.random.randint(512-128)
	y = np.random.randint(512-128)

	draw.rectangle([(x,y),(x+128,y+128)], fill=(255, 255, 255))
	im.save('{}Diffusion_mask.png'.format(directory(i)))

	os.mkdir(directory(i+1))
	subprocess.run('python scripts/inpaint.py --steps 20 --indir {} --outdir {}'.format(directory(i),directory(i+1)), shell=True)
	
	im = Image.open('{}Diffusion.png'.format(directory(i+1)))
	
	# pixels = 2
	# im = im.crop((pixels, pixels, 512-pixels, 512-pixels))
	# im = im.resize((512,512), resample=Image.BICUBIC, box=None, reducing_gap=None)
	# im.save('{}Diffusion.png'.format(directory(i+1)))
	im.save('./DiffusionOut/{0:06d}.png'.format(i))
	if i!=0:
		shutil.rmtree(directory(i))

Add folders/files:
./Diffusion/Diffusion_0/Diffusion.png
./DiffusionOut/

In scripts/inpainting changing

inpainted = inpainted.cpu().numpy().transpose(0,2,3,1)[0]*255

To

inpainted = np.round(inpainted.cpu().numpy().transpose(0,2,3,1)[0]*255)

Fixes the issue I think

instructions are pretty clear, yet it doesn't work out of the box

1. inpaint_sd.py been placed in root project folder to catch the subfolders and structure
2. inpaint_sd.py, string 109, default="models/ldm/inpainting_big/last.ckpt",
3. inpaint_sd.py, string 122, config="configs/stable-diffusion/inpainting/v1-finetune-for-inpainting-laion-iaesthe.yaml"
4. stable-diffusion\configs\stable-diffusion\inpainting, v1-finetune-for-inpainting-laion-iaesthe.yaml: string 18 changed to ckpt_path: "models/ldm/inpainting_big/last.ckpt

so looks like everything is hooked right now, yet, when I run the script with:
python inpaint_sd.py --indir data/inpainting_examples/ --outdir outputs/inpainting_results

it gives out me these errors:
\stable-diffusion\inpaint_sd.py", line 124, in <module> model = instantiate_from_config(config.model)
\stable-diffusion\ldm\util.py", line 79, in instantiate_from_config return get_obj_from_str(config["target"])(**config.get("params", dict()))
\stable-diffusion\ldm\models\diffusion\ddpm.py", line 1627, in __init__ self.init_from_ckpt(ckpt_path, ignore_keys)
\stable-diffusion\ldm\models\diffusion\ddpm.py", line 1648, in init_from_ckpt new_entry[:, :self.keep_dims, ...] = sd[k]

RuntimeError: The expanded size of the tensor (4) must match the existing size (7) at non-singleton dimension 1. Target sizes: [320, 4, 3, 3]. Tensor sizes: [256, 7, 3, 3]

So, what I can do to fix the issue? why it even happening? 🤔

Everything installed well except I get 2 errors. 1 upon install and another when I try to run it. install error is:
ERROR: File "setup.py" or "setup.cfg" not found. Directory cannot be installed in editable mode: /content
I'm trying to run this on a colab server in standalone mode from the command line.
Thanks for any help!
Traceback (most recent call last):
File "stable-diffusion/scripts/txt2img.py", line 11, in
from pytorch_lightning import seed_everything
File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/init.py", line 20, in
from pytorch_lightning import metrics # noqa: E402
File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/metrics/init.py", line 15, in
from pytorch_lightning.metrics.classification import ( # noqa: F401
File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/metrics/classification/init.py", line 14, in
from pytorch_lightning.metrics.classification.accuracy import Accuracy # noqa: F401
File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/metrics/classification/accuracy.py", line 18, in
from pytorch_lightning.metrics.utils import deprecated_metrics, void
File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/metrics/utils.py", line 29, in
from pytorch_lightning.utilities import rank_zero_deprecation
File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/utilities/init.py", line 18, in
from pytorch_lightning.utilities.apply_func import move_data_to_device # noqa: F401
File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/utilities/apply_func.py", line 31, in
from torchtext.legacy.data import Batch
ModuleNotFoundError: No module named 'torchtext.legacy'

trying to run inpainting with the inpaint_big downloaded model. Changed the checkpoint and config path in inpainting-demo.
but this error appears:
(I think ddpm.py wants 512x512 RGBA image and streamlit gives 2x 512x512 RGB one image and one mask. But I have no clue.)

2022-08-05 11:42:22.357 Uncaught app exception
Traceback (most recent call last):
  File "/usr/local/envs/ldm/lib/python3.8/site-packages/streamlit/scriptrunner/script_runner.py", line 557, in _run_script
    exec(code, module.__dict__)
  File "/content/stable-diffusion/scripts/demo/inpainting.py", line 194, in <module>
    samples = sample(
  File "/content/stable-diffusion/scripts/demo/inpainting.py", line 38, in sample
    z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key, bs=N, return_first_stage_outputs=True)
  File "/usr/local/envs/ldm/lib/python3.8/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
    return func(*args, **kwargs)
  File "/content/stable-diffusion/ldm/models/diffusion/ddpm.py", line 718, in get_input
    x = super().get_input(batch, k)
  File "/content/stable-diffusion/ldm/models/diffusion/ddpm.py", line 383, in get_input
    x = batch[k]
KeyError: 'image'
###
(512, 512, 3)
(512, 512, 3)
###

this is the colab notebook:
https://colab.research.google.com/drive/1iglh0P7CxYtJEf4N5K68RhNr9CJMzYa_?usp=sharing

Hello dear Patrick Esser, I hope you let this thread stay to help newcomers. This is not an issue thread. Thank you.

Expert-Level Tutorials on Stable Diffusion: Master Advanced Techniques and Strategies

Greetings everyone. I am Dr. Furkan Gözükara. I am an Assistant Professor in Software Engineering department of a private university (have PhD in Computer Engineering). My professional programming skill is unfortunately C# not Python :)

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Missing logs/f8-kl-clip-encoder-256x256-run1/configs/2022-06-01T22-11-40-project.yaml

Hey! Needless to say incredible work with Stable Diffusion and latent diffusion in general.

I saw Stable Diffusion is using a old-ish version of PyTorch Lightning (1.4.2), I'm wondering if you'd like help upgrading to Lightning 1.7, happy to provide it. The idea would be to create a test, ensure there's (at least) parity on results and upgrade.

Here's a breakdown of what was released since 1.4.x just in case:

• 1.5 LightningLite, Fault-Tolerant Training, Loop Customization, Lightning Tutorials, LightningCLI v2, RichProgressBar, CheckpointIO Plugin, and Trainer Strategy Flag
• 1.6 Support Intel's Habana Accelerator, New efficient DDP strategy (Bagua), Manual Fault-tolerance, Stability and Reliability
• 1.7 Apple Silicon support, Native FSDP, Collaborative training, and multi-GPU support with Jupyter notebooks

Extra note: Lighting 1.7 supports PyTorch 1.9+

Got the conda env, installs, downloads and everything all working smoothly now, no error messages but upon running this pops up:

Traceback (most recent call last):
File "stable-diffusion/scripts/txt2img.py", line 12, in
from torch import autocast
ImportError: cannot import name 'autocast' from 'torch' (/usr/local/envs/ldm/lib/python3.8/site-packages/torch/init.py)

After following the directions, the txt2img.py script itself doesn't seem to recognize the LDM we created with the .yaml, though it exists in the .\anaconda3\envs. Do I perhaps have the wrong version of python or something? (I'm using 3.10) I don't see anything specified.

txt2img.py", line 15, in <module>
    from ldm.util import instantiate_from_config
ModuleNotFoundError: No module named 'ldm'

Also I found manually running 'pip install ldm' would install the wrong package, then it will ask for ldm.utils if I go this route. ref: CompVis/latent-diffusion#71 but this looks like it was using an online notebook