extraltodeus / depthmap2mask Goto Github PK

hello! the problem is weird


the script does not appear in the list as it is shown in your example. can you help me with this?

Im not sure if this is a bug or not implemented yet. I get all black results in batch img2img

Title says it all, allow the use of your own depth mask instead of generating one.

I installed through web UI, and restarted the UI multiple times. But I'm still getting the following exception:

Error loading script: depthmap_for_depth2img.py
Traceback (most recent call last):
  File "D:\WebUI\stable-diffusion-webui\modules\scripts.py", line 195, in load_scripts
    module = script_loading.load_module(scriptfile.path)
  File "D:\WebUI\stable-diffusion-webui\modules\script_loading.py", line 13, in load_module
    exec(compiled, module.__dict__)
  File "D:\WebUI\stable-diffusion-webui\extensions\depthmap2mask\scripts\depthmap_for_depth2img.py", line 11, in <module>
    from repositories.midas.midas.dpt_depth import DPTDepthModel
  File "D:\WebUI\stable-diffusion-webui\repositories\midas\midas\dpt_depth.py", line 5, in <module>
    from .blocks import (
  File "D:\WebUI\stable-diffusion-webui\repositories\midas\midas\blocks.py", line 21, in <module>
    from .backbones.next_vit import (
  File "D:\WebUI\stable-diffusion-webui\repositories\midas\midas\backbones\next_vit.py", line 8, in <module>
    file = open("./externals/Next_ViT/classification/nextvit.py", "r")
FileNotFoundError: [Errno 2] No such file or directory: './externals/Next_ViT/classification/nextvit.py'

https://github.com/compphoto/BoostingMonocularDepth

I installed extension depthmap2mask via Web UI. Set the settings as shown in the picture. When trying to generate an image, this problem occurred. I set the model(dpt_large) to autoload but it didn't go. How can I solve the problem?

CMD:
Error completing request
Arguments: ('task(f98cgtod4q5r2jz)', 0, '', '', [], <PIL.Image.Image image mode=RGBA size=2560x2560 at 0x295F5E6F040>, None, None, None, None, None, None, 20, 0, 4, 0, 1, False, False, 1, 1, 7, 0, -1.0, -1.0, 0, 0, 0, False, 512, 512, 0, 0, 32, 0, '', '', '', [], 9, '

\n
• CFG Scale should be 2 or lower.
\n

Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8

Will upscale the image by the selected scale factor; use width and height sliders to set tile size

Using the following parms (what I'm doing wrong):

Sampling >140

CFG SCALE = 7

DENOISE = 1

with DDIM

Model = dpt_larg

Thanks!

For 2.0, which cpkt model would be more ideal to use? I thought I should be using 512-depth-ema.ckpt, but I get the error that the midas_models folder doesn't exist.

when we're loading a default model, that we're getting a next error:
"RuntimeError: Error(s) in loading state_dict for DPTDepthModel: Missing key(s) in state_dict: "pretrained.model.layers.3.downsample.reduction.weight", "pretrained.model.layers.3.downsample.norm.weight", "pretrained.model.layers.3.downsample.norm.bias", "pretrained.model.head.fc.weight", "pretrained.model.head.fc.bias". Unexpected key(s) in state_dict: "pretrained.model.layers.0.downsample.reduction.weight", "pretrained.model.layers.0.downsample.norm.weight", "pretrained.model.layers.0.downsample.norm.bias", "pretrained.model.layers.0.blocks.1.attn_mask", "pretrained.model.layers.1.blocks.1.attn_mask", "pretrained.model.head.weight", "pretrained.model.head.bias". size mismatch for pretrained.model.layers.1.downsample.reduction.weight: copying a param with shape torch.Size([512, 1024]) from checkpoint, the shape in current model is torch.Size([256, 512]). size mismatch for pretrained.model.layers.1.downsample.norm.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]). size mismatch for pretrained.model.layers.1.downsample.norm.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]). size mismatch for pretrained.model.layers.2.downsample.reduction.weight: copying a param with shape torch.Size([1024, 2048]) from checkpoint, the shape in current model is torch.Size([512, 1024]). size mismatch for pretrained.model.layers.2.downsample.norm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([512]). size mismatch for pretrained.model.layers.2.downsample.norm.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([512])."

There should be a way to view the generated depth map within the web interface instead of having to save and view them from the img2img folder.

As far as I can tell, this extension currently ignores the manual masking in Auto1111. Taking the intersection of the two masks (multiply in PIL? Not sure what the easiest solution is) would allow users to apply the benefits of this extension to specific areas for touchup, and would allow users to take advantage of the higher local resolution when using "inpaint at full resolution" with a small masked area.

I'm using the latest Stable Diffusion and depthmap2mask. I've tried using all the models, and they download fine but only the midas ones work. All others give an error 'Loading midas model weights'. Every model that starts with 'dpt' gives a KeyError.

Traceback (most recent call last):
File "C:\stable-diffusion\stable-diffusion-webui-master\modules\call_queue.py", line 57, in f
res = list(func(*args, **kwargs))
File "C:\stable-diffusion\stable-diffusion-webui-master\modules\call_queue.py", line 37, in f
res = func(*args, **kwargs)
File "C:\stable-diffusion\stable-diffusion-webui-master\modules\img2img.py", line 179, in img2img
processed = modules.scripts.scripts_img2img.run(p, *args)
File "C:\stable-diffusion\stable-diffusion-webui-master\modules\scripts.py", line 407, in run
processed = script.run(p, *script_args)
File "C:\stable-diffusion\stable-diffusion-webui-master\extensions\depthmap2mask\scripts\depth2image_depthmask.py", line 97, in run
d_m = sdmg.calculate_depth_maps(p.init_images[0],img_x,img_y,model_type,invert_depth)
File "C:\stable-diffusion\stable-diffusion-webui-master\extensions/depthmap2mask/scripts/depthmap_for_depth2img.py", line 294, in calculate_depth_maps
model, transform = load_model(device, model_path, models[model_type_index], (img_x, img_y))
File "C:\stable-diffusion\stable-diffusion-webui-master\extensions/depthmap2mask/scripts/depthmap_for_depth2img.py", line 41, in load_model
model = DPTDepthModel(
File "C:\stable-diffusion\stable-diffusion-webui-master\repositories\midas\midas\dpt_depth.py", line 102, in init
super().init(head, **kwargs)
File "C:\stable-diffusion\stable-diffusion-webui-master\repositories\midas\midas\dpt_depth.py", line 55, in init
hooks=hooks[backbone],
KeyError: 'beitl16_512'

OS: Arch Linux

pip install launch
The conflict is caused by:
    launch 0.1.3 depends on flowdas-meta<1.1 and >=1.0.1
    launch 0.1.2 depends on flowdas-meta<1.1 and >=1.0.1
    launch 0.1.1 depends on flowdas-meta<1.1 and >=1.0.1
    launch 0.1.0 depends on flowdas-meta<1.1 and >=1.0.1

pip install flowdas-meta
ERROR: Could not find a version that satisfies the requirement flowdas-meta (from versions: none)
ERROR: No matching distribution found for flowdas-meta

https://libraries.io/pypi/flowdas-meta
https://pypi.org/search/?q=flowdas-meta

What should the feature do?
When the user selects X/Y plot, there should be a checkbox that enables/disables depthmap2mask on every image generated.

How should that feature be controlled
The user should have control over the same options that are provided by the depthmap2mask script while also including a checkbox to enable/disable depthmap2mask.

What are the parameters we need to apply that control as a user
Enable/Disable through checkbox.

What's up guys. I'm having trouble using Depthmap2mask. I can install as instructed by github. When I go to img2img, enable Depthmap2mask and enable dpt_large, the following error appears in the CMD: File "C:\Users\bypau\Desktop\SD\stable-diffusion-webui\repositories\midas\midas\base_model.py", line 13, in load
if "optimizer" in parameters:
TypeError: argument of type 'NoneType' is not iterable

When I select another model other than dpt_large it works. The problem is only in dpt_large.

I am very grateful to everyone who can help me.

Has anyone got this running on google colab? I managed to install the extension and have tried restarting the UI multiple times but it never shows up on the img2img tab as a script option, it simply doesn't show up there.

Can you enable this ? so depth2mask is processing only the part that was masked ? in full resolution inpaint mode it currently process the whole image and thats no good, its fine for regular img2img or non full res inpaint/
The thing is i just want to inpaint a hand on big image, or part of the chest armor, but currently its processing whole image.
You think thats possible to change so the pipeline will check if image was masked or not and then try to process it with depth models to get the proper depth mask ?

Hey, As I constantly Generate Images. I see slow growth in GPU Memory, after generating around 10 images, GPU memory is 100% full. After that, It always Throws OOM Error..

I mainly use two models, one is MiDas_v21 and other is dpt_large. Doesn't matter which model it is.

Thanks for the script!. Depth detection using an NN is very handy for Img2Img editing, and I wouldn't know how to set it up without this handy script.

However, I would request 2 check boxes to allow better control of the masking area:

1. A checkbox to combine the depth mask with your user mask via an AND operation. If checked, the In-Painter will only change the areas of the picture in which you've hand drawn a mask AND the depth mask is on.

This allows you to messily draw an area and have the area be refined by the depth mask. Also, sometimes the depth mask will identify 2 objects as both being in the foreground, so this will let you easily specify only 1 of them which should be changed.

2. A second checkbox to have the depth mask threshold automatically be set at [x%] of the average value of the area you've painted with your user mask. The hard part of using the threshold slider is you don't know what values to put in, and it takes a lot of trial and error to set.

If you check this checkbox, you don't have to guess at the threshold number for objects in the picture anymore. The first time you run it, just draw what you want to change, and the slider bar will be automatically set it for you after the run. Then uncheck the checkbox, or manually adjust the threshhold.

[x] can't be 100% or half the area would not be covered. I wish the [x] could be just hardwired in at say 75%, but I think that'll be too rigid. So I would suggest letting the user set it via a slider, e.g. starting at [x]=75%.

I think these checkboxes are also a solution to the problem identified by @AugmentedRealityCat in #3.

I think these should be both relatively easy to implement. I might try to mod a quick-and-dirty version of this myself, if I can figure out the code.

To reproduce:

In A1111 settings, set "File format for images" to jpg, then run the extension.

The issue:

By default, it is trying to save the image in RBGA format (with an alpha channel.) JPG doesn't have an alpha channel, so there is an error. I don't think this alpha channel is needed, so it may be beneficial to remove it before saving to reduce storage space.

File "I:\SD\sd-webui-aki-v4\modules\img2img.py", line 170, in img2img
processed = modules.scripts.scripts_img2img.run(p, *args)
File "I:\SD\sd-webui-aki-v4\modules\scripts.py", line 407, in run
processed = script.run(p, *script_args)
File "I:\SD\sd-webui-aki-v4\extensions\depthmap2mask\scripts\depth2image_depthmask.py", line 97, in run
d_m = sdmg.calculate_depth_maps(p.init_images[0],img_x,img_y,model_type,invert_depth)
File "I:\SD\sd-webui-aki-v4\extensions/depthmap2mask/scripts/depthmap_for_depth2img.py", line 306, in calculate_depth_maps
prediction = model.forward(sample)
File "I:\SD\sd-webui-aki-v4\repositories\midas\midas\dpt_depth.py", line 166, in forward
return super().forward(x).squeeze(dim=1)
File "I:\SD\sd-webui-aki-v4\repositories\midas\midas\dpt_depth.py", line 114, in forward
layers = self.forward_transformer(self.pretrained, x)
File "I:\SD\sd-webui-aki-v4\repositories\midas\midas\backbones\swin_common.py", line 10, in forward_swin
return forward_default(pretrained, x)
File "I:\SD\sd-webui-aki-v4\repositories\midas\midas\backbones\utils.py", line 64, in forward_default
exec(f"pretrained.model.{function_name}(x)")
File "", line 1, in
File "I:\SD\sd-webui-aki-v4\py310\lib\site-packages\timm\models\swin_transformer_v2.py", line 598, in forward_features
x = self.patch_embed(x)
File "I:\SD\sd-webui-aki-v4\py310\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "I:\SD\sd-webui-aki-v4\py310\lib\site-packages\timm\models\layers\patch_embed.py", line 35, in forward
x = self.proj(x)
File "I:\SD\sd-webui-aki-v4\py310\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "I:\SD\sd-webui-aki-v4\extensions-builtin\Lora\lora.py", line 319, in lora_Conv2d_forward
return torch.nn.Conv2d_forward_before_lora(self, input)
File "I:\SD\sd-webui-aki-v4\py310\lib\site-packages\torch\nn\modules\conv.py", line 463, in forward
return self._conv_forward(input, self.weight, self.bias)
File "I:\SD\sd-webui-aki-v4\py310\lib\site-packages\torch\nn\modules\conv.py", line 459, in _conv_forward
return F.conv2d(input, weight, bias, self.stride,
RuntimeError: Input type (float) and bias type (struct c10::Half) should be the same

Getting the following exception:

Error completing request
Arguments: ('', 'https://github.com/Extraltodeus/depthmap2mask') {}
Traceback (most recent call last):
  File "/home/user/stable-diffusion-webui/modules/ui.py", line 185, in f
    res = list(func(*args, **kwargs))
  File "/home/user/stable-diffusion-webui/modules/ui_extensions.py", line 111, in install_extension_from_url
    check_access()
  File "/home/user/stable-diffusion-webui/modules/ui_extensions.py", line 20, in check_access
    assert not shared.cmd_opts.disable_extension_access, "extension access disabed because of commandline flags"
AssertionError: extension access disabed because of commandline flags

I'm using the command lines arguments: --api --listen --xformers --force-enable-xformers

Any idea what's wrong?

Clicking generate puts an error that says " ModuleNotFoundError: No module named 'repositories.midas' ", not sure what to do

Just found this repo: https://github.com/compphoto/BoostingMonocularDepth
And I was very impressed with the level of detail of the LeRes model.
We can see that by comparing Midas vs LeRes results in complex scene image:

I've been playing with this script and I'm quite liking it so far, but after trying out the other models included I ran into a problem with the dpt_large model. I've been inpainting with the default small model and was having trouble getting the mask to fully cover my subject in a particular picture, so I thought to try the other models and I got better results with the v21 model and even better results with the dpt_large model, so that's good to know. However, after trying the dpt_large model once I immediately became unable to do any further generating without getting a memory error, I couldn't even do txt2img and had to close the console before being able to generate images again. I usually use --xformers but I decided to try --medvram to see if that would help with running the large model but the issue persists. If it helps any I'm running a 2070 Super with 8 gigs of vram on Win11, with up to date drivers for everything. I don't know if it'd be more preferred for me to paste a wall of text or attach a text file, but I'll share the console output both ways just to be sure. Output follows from here, thanks for your work!

Memory Error - Console.txt

Traceback (most recent call last):
File "C:\SDWebUI\modules\call_queue.py", line 45, in f
res = list(func(*args, **kwargs))
File "C:\SDWebUI\modules\call_queue.py", line 28, in f
res = func(*args, **kwargs)
File "C:\SDWebUI\modules\img2img.py", line 137, in img2img
processed = modules.scripts.scripts_img2img.run(p, *args)
File "C:\SDWebUI\modules\scripts.py", line 317, in run
processed = script.run(p, *script_args)
File "C:\SDWebUI\extensions\depthmap2mask\scripts\depth2image_depthmask.py", line 92, in run
proc = process_images(p)
File "C:\SDWebUI\modules\processing.py", line 430, in process_images
res = process_images_inner(p)
File "C:\SDWebUI\modules\processing.py", line 496, in process_images_inner
p.init(p.all_prompts, p.all_seeds, p.all_subseeds)
File "C:\SDWebUI\modules\processing.py", line 841, in init
self.init_latent = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image))
File "C:\SDWebUI\venv\lib\site-packages\torch\autograd\grad_mode.py", line 27, in decorate_context
return func(*args, **kwargs)
File "C:\SDWebUI\repositories\stable-diffusion-stability-ai\ldm\models\diffusion\ddpm.py", line 830, in encode_first_stage
return self.first_stage_model.encode(x)
File "C:\SDWebUI\modules\lowvram.py", line 48, in first_stage_model_encode_wrap
return first_stage_model_encode(x)
File "C:\SDWebUI\repositories\stable-diffusion-stability-ai\ldm\models\autoencoder.py", line 83, in encode
h = self.encoder(x)
File "C:\SDWebUI\venv\lib\site-packages\torch\nn\modules\module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "C:\SDWebUI\repositories\stable-diffusion-stability-ai\ldm\modules\diffusionmodules\model.py", line 526, in forward
h = self.down[i_level].block[i_block](hs[-1], temb)
File "C:\SDWebUI\venv\lib\site-packages\torch\nn\modules\module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "C:\SDWebUI\repositories\stable-diffusion-stability-ai\ldm\modules\diffusionmodules\model.py", line 138, in forward
h = self.norm2(h)
File "C:\SDWebUI\venv\lib\site-packages\torch\nn\modules\module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "C:\SDWebUI\venv\lib\site-packages\torch\nn\modules\normalization.py", line 272, in forward
return F.group_norm(
File "C:\SDWebUI\venv\lib\site-packages\torch\nn\functional.py", line 2516, in group_norm
return torch.group_norm(input, num_groups, weight, bias, eps, torch.backends.cudnn.enabled)
RuntimeError: CUDA out of memory. Tried to allocate 1.12 GiB (GPU 0; 8.00 GiB total capacity; 4.83 GiB already allocated; 0 bytes free; 6.02 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF

`
File "D:\stable-diffusion-webui\extensions\depthmap2mask\scripts\depth2image_depthmask.py", line 76, in run
sdmg = module_from_file("depthmap_for_depth2img",'extensions/depthmap2mask/scripts/depthmap_for_depth2img.py')

File "D:\stable-diffusion-webui\extensions\depthmap2mask\scripts\depth2image_depthmask.py", line 22, in module_from_file
spec.loader.exec_module(module)
File "", line 850, in exec_module
File "", line 228, in _call_with_frames_removed
File "extensions/depthmap2mask/scripts/depthmap_for_depth2img.py", line 11, in
from repositories.midas.midas.dpt_depth import DPTDepthModel
File "D:\stable-diffusion-webui\repositories\midas\midas\dpt_depth.py", line 5, in
from .blocks import (
File "D:\stable-diffusion-webui\repositories\midas\midas\blocks.py", line 21, in
from .backbones.next_vit import (
File "D:\stable-diffusion-webui\repositories\midas\midas\backbones\next_vit.py", line 15, in
file = open("./externals/Next_ViT/classification/nextvit.py", "r")
FileNotFoundError: [Errno 2] No such file or directory: './externals/Next_ViT/classification/nextvit.py'`

Great work.

Any way to implement an ignore user defined masked feature on inpainting?

what it would do.

1-user manually in-paints an area of photo.
2-ignore user defined area checkbox ticked
3-depth img2img works it’s magic and generates a photo
4-all areas inpainted with the depth mask are generated and the user defined area is left unchanged.

could this be as simple as an a-b type equation where a=depthimg2img and b = user defined mask?

I know the script for detection retailer uses a similar process.

last time i used local server in colab its working, now im getting 502 error. i want to use local server for painthua, can anyone help?

If I use midas_v21 or dpt_large on any image, I receive this error:

UnboundLocalError: local variable 'model' referenced before assignment

I have tried installing both manually and using the automatic install method.
midas_v21_small works fine.

Upon launch, I get this error: Error loading script: depthmap_for_depth2img.py

Traceback (most recent call last):
File "A:\Desktop\00 AI Images\stable-diffusion-webui\modules\scripts.py", line 184, in load_scripts
module = script_loading.load_module(scriptfile.path)
File "A:\Desktop\00 AI Images\stable-diffusion-webui\modules\script_loading.py", line 13, in load_module
exec(compiled, module.dict)
File "A:\Desktop\00 AI Images\stable-diffusion-webui\extensions\depthmap2mask\scripts\depthmap_for_depth2img.py", line 11, in
from repositories.midas.midas.dpt_depth import DPTDepthModel
ModuleNotFoundError: No module named 'repositories.midas'

And when I try to run your script, I get this error:

Error completing request
Arguments: (0, 'a mushroom like structure with a colorful sky in the background and plants and rocks in the foreground, and a few mushrooms in the foreground, by Paul Lehr', '', 'None', 'None', <PIL.Image.Image image mode=RGB size=1536x1536 at 0x6FD03640>, None, None, None, None, 0, 150, 0, 4, 0, 0, False, False, 1, 1, 7, 0.75, -1.0, -1.0, 0, 0, 0, False, 512, 512, 0, False, 32, 0, '', '', 10, '

\n
• CFG Scale should be 2 or lower.
\n

Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8

Will upscale the image to twice the dimensions; use width and height sliders to set tile size

Deforum v0.5-webui-beta

This script is deprecated. Please use the full Deforum extension instead.
\nUpdate instructions:

github.com/deforum-art/deforum-for-automatic1111-webui/blob/automatic1111-webui/README.md

discord.gg/deforum

I would like to be able to use my own depthmap from a depth camera. As you can see the depth maps have much more detail for th AI to work with and would create some amazing results.
Could we have the input image be our own 512 depth map? Or folder of depth maps.
Please and thank you.

1. What should the feature do?

With this new feature it should be possible to determine at which depth the image should be masked, and at which depth it should be visible.

2. How should that feature be controlled

We will call the portion of the image we want to keep visible a slice.
The user should have control over where to cut the scene to obtain the slice he wants to keep.

3. What are the parameters we need to apply that control as a user

The first parameter would be the near cut distance. This would determine how far from the camera the image slice begins to be visible. Everything closer to the camera than the near cut distance would be cut out.
The second parameter would be the far cut distance. This would determine at which distance the visible slice of our image should be cut again, and everything further away simply becomes invisible.

4. How should those parameters be presented to the user?

Since we already have a depthmap to deal with, it is logical to use it as a reference.
It uses a 8 bit channel (values of 0 to 255) to determine distance.
A distance of 0 should be the closest to the camera.
A distance of 255 should be infinity - basically everything further away than the furthest distance measured.
This means that for both near cut and far cut the user must provide a value between 0 and 255.
Logically, the far cut value should always be higher than the near cut.

5. How should those parameters be used by the system to obtain the desired results?

We will transform the near cut and far cut parameters into control parameters for a color adjustments procedure
First we will invert the depthmap colors - This is just to make it easier to understand with black=0=close and white=255=far.
Then we will make all the pixels that are darker than the near cut distance value completely black. This cuts the near part.
Then we will make all the pixels that are brighter than the far cut distance value completely black as well. This cuts the far part.
Then we will make all the pixels that are brighter than the near cut value but lower than the far cut completely white. This makes the slice we have just cut out completely opaque.

6. How can we make this even better

Feathered distance : this would soften the cut. It would act like a blurred mask, but blurred in z-space, all by adjusting the masks colors. The parameter would be controlled as a 0-255 distance, but would be limited to a certain maximal value that make sense.
Keep semi transparent : this checkbox would allow the user to keep the sliced portion in greyscale instead of forcing it to be opaque
Semi-transparency normalization : This checkbox sub-parameter would only be used if keep semi-transparent is selected. This would normalize the greyscale gradient that has remained visible after the slicing procedure by making the darkest grey almost black, and the lightest grey almost white, and spreading all other greyscale values in between evenly.

Let me know if you'd like examples based on pictures - I'm doing this process manually right now so I am very familiar with it.

File "/root/StableDiffusion/stable-diffusion-webui/repositories/midas/midas/dpt_depth.py", line 55, in init
hooks=hooks[backbone],
KeyError: 'swin2b24_384'

I manually downloaded midas models and placed them in models/midas, when i ran generate the error occured

OS:
linux

Hi i hope you are fine i have a problem with the extension

FileNotFoundError: [Errno 2] No such file or directory: 'extensions/depthmap2mask/scripts/depthmap_for_depth2img.py'

Connected
Error completing request
Arguments: (0, '', '', 'None', 'None', <PIL.Image.Image image mode=RGB size=512x512 at 0x7FD06CA987D0>, None, None, None, 0, 20, 0, 4, 1, False, False, 1, 1, 7, 0.75, -1.0, -1.0, 0, 0, 0, False, 512, 512, 0, False, 32, 0, '', '', 9, '

\n
• CFG Scale should be 2 or lower.
\n

Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8

Will upscale the image to twice the dimensions; use width and height sliders to set tile size

File "", line 724, in exec_module
File "", line 859, in get_code
File "", line 916, in get_data
FileNotFoundError: [Errno 2] No such file or directory: 'extensions/depthmap2mask/scripts/depthmap_for_depth2img.py'

I am not 100% sure about this, but it felt like increasing the contrast cut had a 'negative' effect when the inverted mask open was used.

Sorry for my english,
This si great plugin, just have question

is here way, to add prompts to this plugin?

Like a masked image, with prompts?

like include or exlude some of parts of image from masking?

Thanx you

Im getting this error after installing this extension in Stable Diffusion, any1 know how to fix this?

Error loading script: depthmap_for_depth2img.py
Traceback (most recent call last):
File "C:\A1111 Web UI Autoinstaller\stable-diffusion-webui\modules\scripts.py", line 256, in load_scripts
script_module = script_loading.load_module(scriptfile.path)
File "C:\A1111 Web UI Autoinstaller\stable-diffusion-webui\modules\script_loading.py", line 11, in load_module
module_spec.loader.exec_module(module)
File "", line 883, in exec_module
File "", line 241, in _call_with_frames_removed
File "C:\A1111 Web UI Autoinstaller\stable-diffusion-webui\extensions\depthmap2mask\scripts\depthmap_for_depth2img.py", line 13, in
from repositories.midas.midas.dpt_depth import DPTDepthModel
File "C:\A1111 Web UI Autoinstaller\stable-diffusion-webui\repositories\midas\midas\dpt_depth.py", line 5, in
from .blocks import (
File "C:\A1111 Web UI Autoinstaller\stable-diffusion-webui\repositories\midas\midas\blocks.py", line 4, in
from .backbones.beit import (
File "C:\A1111 Web UI Autoinstaller\stable-diffusion-webui\repositories\midas\midas\backbones\beit.py", line 9, in
from timm.models.beit import gen_relative_position_index
ModuleNotFoundError: No module named 'timm.models.beit'

Currently custom scripts can be run through the automatic1111 api but not extensions.
I would like to be able to use this through unity but my knowledge of phyton is too limited to convert this.
Can someone help me or point me to what exactly has to be changed to run this as a script?

Good day.
When executing the script I get an error

model
dpt_beit_large_512

AttributeError: 'Block' object has no attribute 'drop_path'

The script Depth aware img2img mask does not work, gives the following error TypeError: argument of - type 'NoneType' is not iterable. Why can such an error occur?

When using the depthmap mask option its way to slow, loads model, generated depthmap and unloads again, thats to slow. It should not be unloaded the model or just generate first the dedpthmaps first, but in that case add an option to use an already existing depth map. Thing is, right now a batch is like 3-4 times slower compared to not use a depthmap.

Hi, great script !! :)
Would it be possible for it to work as a tillable on AUTO1111?

I used the script a few days ago on colab pro with no problem. Today I can't get it to work.

Maybe it has something to do with this Midas update yesterday?

They added this file which references the now missing module as a dependency.

Thats at least as far as I got. Might be completely off.

Here is the full error message:

Error loading script: depthmap_for_depth2img.py
Traceback (most recent call last):
  File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/modules/scripts.py", line 195, in load_scripts
    module = script_loading.load_module(scriptfile.path)
  File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/modules/script_loading.py", line 13, in load_module
    exec(compiled, module.__dict__)
  File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/extensions/depthmap2mask/scripts/depthmap_for_depth2img.py", line 11, in <module>
    from repositories.midas.midas.dpt_depth import DPTDepthModel
  File "/content/gdrive/MyDrive/sd/stablediffusion/repositories/midas/midas/dpt_depth.py", line 5, in <module>
    from .blocks import (
  File "/content/gdrive/MyDrive/sd/stablediffusion/repositories/midas/midas/blocks.py", line 4, in <module>
    from .backbones.beit import (
  File "/content/gdrive/MyDrive/sd/stablediffusion/repositories/midas/midas/backbones/beit.py", line 9, in <module>
    from timm.models.beit import gen_relative_position_index
ModuleNotFoundError: No module named 'timm.models.beit'

Thanks for the awesome script btw! :D

Been having this problem when trying to batch img2img

File "\stable-diffusion-webui\extensions/depthmap2mask/scripts/depthmap_for_depth2img.py", line 161, in calculate_depth_maps
img_output2[:,:,0] = img_output / 256.0
IndexError: too many indices for array: array is 2-dimensional, but 3 were indexed

Do you consider to having script that can integrate to Automatic1111 as API?