arenasys / qdiffusion Goto Github PK

Hello
I noticed that you had an update, everything worked well in Colaba half a day ago, and now an error is crashing.
Do you have any suggestions on how this can be fixed?
Thanks for your work

SERVER Traceback (most recent call last):
  File "/content/sd-inference-server/server.py", line 108, in run
    self.wrapper.img2img()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 115, in decorate_context
    return func(*args, **kwargs)
  File "/content/sd-inference-server/wrapper.py", line 752, in img2img
    metadata = self.get_metadata("img2img",  width, height, batch_size, self.prompt, seeds, subseeds)
  File "/content/sd-inference-server/wrapper.py", line 466, in get_metadata
    m["strength"] = format_float(self.strength)
  File "/content/sd-inference-server/wrapper.py", line 92, in format_float
    return f"{x:.4f}".rstrip('0').rstrip('.')
ValueError: Unknown format code 'f' for object of type 'str'

I am using arch and an amd gpu and it seems to be stuck encoding

What all do you need in the wildcards folder do you need in order to make wildcards work, and how do you use them in your prompts? Also, please update the guide to mention wildcards.

After last update, if set Batch size more than 1, program crashes with that log

GUI 2023-06-25 18:10:20.100167
Traceback (most recent call last):
File "F:\qDiffusion-master\source\tabs\basic\basic.py", line 348, in result
metadata = metadata[i] if metadata else None
KeyError: 1

Hey, I have a question and a feature request if what I suspect is right now implemented.
Is Regional Prompter a feature already in this tool? If yes, where can I find it/enable it?

If its not a feature, could it be added? Its a very good extension for automatic111, and with my low end hardware its hard to get it without paying for google collab pro (or restarting each second prompt which takes minutes), which is rather expensive each month.

Describe the bug
Having _ in the model path prevents the GUI from seeing the models.

Screenshots

System:

• Mode: [Local]
• OS: [Windows]
• GPU: [Nvidia]

I can't find the appropriate setting. Has it been implemented?

Also, when you select Control, a gear icon appears on the tile, but it does not work. Is this a stub for the future?

Using subprompts raising an error on upscale. Without upscale - subprompts works normally.

Traceback with generation 540x540 and upscale factor 2.25 with subprompts:

INFERENCE 2023-07-23 06:36:56.336172
Traceback (most recent call last):
  File "F:\NeuralNetworks\Apps\qDiffusion\source\local.py", line 77, in run
    self.wrapper.txt2img()
  File "F:\NeuralNetworks\Apps\qDiffusion\venv\lib\site-packages\torch\utils\_contextlib.py", line 115, in decorate_context
    return func(*args, **kwargs)
  File "F:\NeuralNetworks\Apps\qDiffusion\source\sd-inference-server\wrapper.py", line 720, in txt2img
    latents = inference.img2img(latents, denoiser, sampler, noise, self.hr_steps, True, self.hr_strength, self.on_step)
  File "F:\NeuralNetworks\Apps\qDiffusion\source\sd-inference-server\inference.py", line 34, in img2img
    latents = sampler.step(latents, schedule, i, noise)
  File "F:\NeuralNetworks\Apps\qDiffusion\source\sd-inference-server\samplers_k.py", line 154, in step
    denoised = self.predict(x, sigmas[i])
  File "F:\NeuralNetworks\Apps\qDiffusion\source\sd-inference-server\samplers_k.py", line 57, in predict
    original = self.model.predict_original(latents, timestep, sigma)
  File "F:\NeuralNetworks\Apps\qDiffusion\source\sd-inference-server\guidance.py", line 143, in predict_original
    composed_pred = self.compose_predictions(original_pred)
  File "F:\NeuralNetworks\Apps\qDiffusion\source\sd-inference-server\guidance.py", line 83, in compose_predictions
    pos = pos * masks + (neg * (1 - masks))
RuntimeError: The size of tensor a (151) must match the size of tensor b (67) at non-singleton dimension 3

This time only 1 of my VAES does work, for the rest of them it throws the next message

File "/content/sd-inference-server/server.py", line 112, in run
self.wrapper.txt2img()
File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "/content/sd-inference-server/wrapper.py", line 611, in txt2img
self.load_models(*initial_networks)
File "/content/sd-inference-server/wrapper.py", line 274, in load_models
self.vae = self.storage.get_vae(self.vae_name, self.device)
File "/content/sd-inference-server/storage.py", line 313, in get_vae
return self.get_component(name, "VAE", device)
File "/content/sd-inference-server/storage.py", line 269, in get_component
self.file_cache[file] = self.load_file(file, comp)
File "/content/sd-inference-server/storage.py", line 378, in load_file
state_dict, metadata = convert.convert(file)
File "/content/sd-inference-server/convert.py", line 390, in convert
return convert_checkpoint(model_path)
File "/content/sd-inference-server/convert.py", line 276, in convert_checkpoint
state_dict = torch.load(in_file, map_location="cpu")
File "/usr/local/lib/python3.10/dist-packages/torch/serialization.py", line 809, in load
return _load(opened_zipfile, map_location, pickle_module, **pickle_load_args)
File "/usr/local/lib/python3.10/dist-packages/torch/serialization.py", line 1172, in _load
result = unpickler.load()
File "/usr/local/lib/python3.10/dist-packages/torch/serialization.py", line 1165, in find_class
return super().find_class(mod_name, name)
File "/content/sd-inference-server/venv/lib/python3.10/site-packages/pytorch_lightning/init.py", line 34, in
from pytorch_lightning.callbacks import Callback # noqa: E402
File "/content/sd-inference-server/venv/lib/python3.10/site-packages/pytorch_lightning/callbacks/init.py", line 25, in
from pytorch_lightning.callbacks.progress import ProgressBarBase, RichProgressBar, TQDMProgressBar
File "/content/sd-inference-server/venv/lib/python3.10/site-packages/pytorch_lightning/callbacks/progress/init.py", line 22, in
from pytorch_lightning.callbacks.progress.rich_progress import RichProgressBar # noqa: F401
File "/content/sd-inference-server/venv/lib/python3.10/site-packages/pytorch_lightning/callbacks/progress/rich_progress.py", line 20, in
from torchmetrics.utilities.imports import _compare_version
ImportError: cannot import name '_compare_version' from 'torchmetrics.utilities.imports' (/usr/local/lib/python3.10/dist-packages/torchmetrics/utilities/imports.py)

Whenever I create a mask, and try to edit it by clicking on it twice, the GUI crashes. For a split second, I do see the first image in the img2img window, as if it was ready to be inpainted, but it crashes before I can do anything. The error that it keeps spitting out into crash.log is as follows:

GUI 2023-06-22 11:26:44.956255
Traceback (most recent call last):
File "C:\Users\ryanh\Downloads\qDiffusion-master\source\canvas\renderer.py", line 201, in render
gl.glGetError()
File "C:\Users\ryanh\Downloads\qDiffusion-master\venv\lib\site-packages\OpenGL\platform\baseplatform.py", line 415, in call
return self( *args, **named )
File "C:\Users\ryanh\Downloads\qDiffusion-master\venv\lib\site-packages\OpenGL\error.py", line 230, in glCheckError
raise self._errorClass(
OpenGL.error.GLError: GLError(
err = 1282,
description = b'invalid operation',
baseOperation = glGetError,
cArguments = (),
result = 1282
)

How do I fix this? My computer is a 2011 HP ProBook 6560b, with an Intel Core i5-2420m, and a crappy Intel HD Graphics 3000.

The remote option through colab keeps sending out a non-functional link (wss://api.trycloudflare.com)

Hi,

it was working fine yesterday, after recent update when i open Client it automatically getting closed.

Error Video : https://youtu.be/raRMwou0MeQ

Will you implement this in your GUI sometime in the future?
This is not a request for immediate implementation, this is a question about plans for this ControlNet+Script

After several updates - merging models become impossiple due to very much memory usage. My 32 Gigs of RAM were eaten in first minute. After - it starts using SSD for 2-5 minutes and raised this error. Restart didn't help.

Tried merge 2 models and insert 4 LoRAs in single merge batch.
Generation parameters were standart, nothing changed after lauch.

Hi,

There is a folder where i can upload Lora but i cannot find a folder to Upload LyCORIS (LoCon/LoHA)?. Should i upload this in Lora folder?, will it work?

I want clear my Local Disk (C:) after running locally qDiffusion (installed on other local disk (F:)). I noticed that after closing the program, the memory on the disk becomes 1.7-2.5 GB less. Where qDiffusion saved files?

This is not a suggestion or a bug (unless you think otherwise), it's just curiosity because I haven't been able to figure out why this happens.

When generated using the same model, the same embendings, lora, all parameters (in fact, all), the generated images in your GUI and in Automatic are different. Sometimes - as, for example, in the V-Pred models EasyFluff PreRelease v2 - they are dramatically different.

If you have the opportunity and time, could you explain what this might be related to? This is pure curiosity, since it doesn’t really interfere with using your GUI, which in my eyes is much more convenient, I just want to know the answer to the question that’s stuck in my head.

Will you implement reference controlnet sometime in the future? Mikubill/sd-webui-controlnet#1236

Is it possible to import VAE in qDiffusion on remote mode? If possible, how to do this?

Hi,

Can you make an option which allow to attach more than one Google Drive account.

Google Drive 1 - Models (1-4)
Google Drive 2 - Models ( 4 - 8)
Google Drive 3 - Lora

Etc

Trying to use a different VAE for any model on remote mode (using colab) throws the next message error

File "/content/sd-inference-server/server.py", line 110, in run
self.wrapper.txt2img()
File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "/content/sd-inference-server/wrapper.py", line 563, in txt2img
self.load_models()
File "/content/sd-inference-server/wrapper.py", line 242, in load_models
self.vae = self.storage.get_vae(self.vae_name, self.device)
File "/content/sd-inference-server/storage.py", line 252, in get_vae
return self.get_component(name, "VAE", device)
File "/content/sd-inference-server/storage.py", line 221, in get_component
self.file_cache[file] = self.load_file(file, comp)
File "/content/sd-inference-server/storage.py", line 295, in load_file
state_dict = convert.convert_checkpoint(file)
File "/content/sd-inference-server/convert.py", line 195, in convert_checkpoint
state_dict = torch.load(in_file, map_location="cpu")
File "/usr/local/lib/python3.10/dist-packages/torch/serialization.py", line 809, in load
return _load(opened_zipfile, map_location, pickle_module, **pickle_load_args)
File "/usr/local/lib/python3.10/dist-packages/torch/serialization.py", line 1172, in _load
result = unpickler.load()
File "/usr/local/lib/python3.10/dist-packages/torch/serialization.py", line 1165, in find_class
return super().find_class(mod_name, name)
File "/content/sd-inference-server/venv/lib/python3.10/site-packages/pytorch_lightning/init.py", line 34, in
from pytorch_lightning.callbacks import Callback # noqa: E402
File "/content/sd-inference-server/venv/lib/python3.10/site-packages/pytorch_lightning/callbacks/init.py", line 14, in
from pytorch_lightning.callbacks.callback import Callback
File "/content/sd-inference-server/venv/lib/python3.10/site-packages/pytorch_lightning/callbacks/callback.py", line 25, in
from pytorch_lightning.utilities.types import STEP_OUTPUT
File "/content/sd-inference-server/venv/lib/python3.10/site-packages/pytorch_lightning/utilities/types.py", line 28, in
from torchmetrics import Metric
ModuleNotFoundError: No module named 'torchmetrics'

After this change, all my tiles on the main work area look very bad. On the history tab they look normal. This makes quality harder to asses in the main work area.

Describe the bug
I trying install SwinIR upscaler SwinIR upscaler and get an error. Also, can't use BSRGAN and other, but can use UniScale. What am I doing wrong?

Traceback
Traceback (most recent call last):
File "F:\qDiffusion-master\source\local.py", line 78, in run
self.wrapper.txt2img()
File "F:\qDiffusion-master\venv\lib\site-packages\torch\utils_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "F:\qDiffusion-master\source\sd-inference-server\wrapper.py", line 635, in txt2img
self.load_models(*initial_networks)
File "F:\qDiffusion-master\source\sd-inference-server\wrapper.py", line 301, in load_models
self.upscale_model = self.storage.get_upscaler(self.hr_upscaler, self.device)
File "F:\qDiffusion-master\source\sd-inference-server\storage.py", line 326, in get_upscaler
return self.get_component(name, "SR", device)
File "F:\qDiffusion-master\source\sd-inference-server\storage.py", line 282, in get_component
model = self.classes[comp].from_model(name, self.file_cache[file][comp], dtype)
File "F:\qDiffusion-master\source\sd-inference-server\upscalers.py", line 75, in from_model
model.load_state_dict(state_dict)
File "F:\qDiffusion-master\venv\lib\site-packages\torch\nn\modules\module.py", line 2041, in load_state_dict
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for SR:
Missing key(s) in state_dict: "body.0.rdb1.conv1.weight", "body.0.rdb1.conv1.bias", "body.0.rdb1.conv2.weight", "body.0.rdb1.conv2.bias", "body.0.rdb1.conv3.weight", "body.0.rdb1.conv3.bias", "body.0.rdb1.conv4.weight", "body.0.rdb1.conv4.bias", "body.0.rdb1.conv5.weight", "body.0.rdb1.conv5.bias", "body.0.rdb2.conv1.weight", "body.0.rdb2.conv1.bias", "body.0.rdb2.conv2.weight", "body.0.rdb2.conv2.bias", "body.0.rdb2.conv3.weight", "body.0.rdb2.conv3.bias", "body.0.rdb2.conv4.weight", "body.0.rdb2.conv4.bias", "body.0.rdb2.conv5.weight", "body.0.rdb2.conv5.bias", "body.0.rdb3.conv1.weight", "body.0.rdb3.conv1.bias", "body.0.rdb3.conv2.weight", "body.0.rdb3.conv2.bias", "body.0.rdb3.conv3.weight", "body.0.rdb3.conv3.bias", "body.0.rdb3.conv4.weight", "body.0.rdb3.conv4.bias", "body.0.rdb3.conv5.weight", "body.0.rdb3.conv5.bias", "conv_body.weight", "conv_body.bias", "conv_up1.weight", "conv_up1.bias", "conv_up2.weight", "conv_up2.bias", "conv_hr.weight", "conv_hr.bias".
Unexpected key(s) in state_dict: "conv_after_body.weight", "conv_after_body.bias", "conv_before_upsample.0.weight", "conv_before_upsample.0.bias", "upsample.0.weight", "upsample.0.bias", "upsample.2.weight", "upsample.2.bias", "patch_embed.norm.weight", "patch_embed.norm.bias", "layers.0.residual_group.blocks.0.norm1.weight", "layers.0.residual_group.blocks.0.norm1.bias", "layers.0.residual_group.blocks.0.attn.relative_position_bias_table", "layers.0.residual_group.blocks.0.attn.relative_position_index", "layers.0.residual_group.blocks.0.attn.qkv.weight", "layers.0.residual_group.blocks.0.attn.qkv.bias", "layers.0.residual_group.blocks.0.attn.proj.weight", "layers.0.residual_group.blocks.0.attn.proj.bias", "layers.0.residual_group.blocks.0.norm2.weight", "layers.0.residual_group.blocks.0.norm2.bias", "layers.0.residual_group.blocks.0.mlp.fc1.weight", "layers.0.residual_group.blocks.0.mlp.fc1.bias", "layers.0.residual_group.blocks.0.mlp.fc2.weight", "layers.0.residual_group.blocks.0.mlp.fc2.bias", "layers.0.residual_group.blocks.1.attn_mask", "layers.0.residual_group.blocks.1.norm1.weight", "layers.0.residual_group.blocks.1.norm1.bias", "layers.0.residual_group.blocks.1.attn.relative_position_bias_table", "layers.0.residual_group.blocks.1.attn.relative_position_index", "layers.0.residual_group.blocks.1.attn.qkv.weight", "layers.0.residual_group.blocks.1.attn.qkv.bias", "layers.0.residual_group.blocks.1.attn.proj.weight", "layers.0.residual_group.blocks.1.attn.proj.bias", "layers.0.residual_group.blocks.1.norm2.weight", "layers.0.residual_group.blocks.1.norm2.bias", "layers.0.residual_group.blocks.1.mlp.fc1.weight", "layers.0.residual_group.blocks.1.mlp.fc1.bias", "layers.0.residual_group.blocks.1.mlp.fc2.weight", "layers.0.residual_group.blocks.1.mlp.fc2.bias", "layers.0.residual_group.blocks.2.norm1.weight", "layers.0.residual_group.blocks.2.norm1.bias", "layers.0.residual_group.blocks.2.attn.relative_position_bias_table", "layers.0.residual_group.blocks.2.attn.relative_position_index", "layers.0.residual_group.blocks.2.attn.qkv.weight", "layers.0.residual_group.blocks.2.attn.qkv.bias", "layers.0.residual_group.blocks.2.attn.proj.weight", "layers.0.residual_group.blocks.2.attn.proj.bias", "layers.0.residual_group.blocks.2.norm2.weight", "layers.0.residual_group.blocks.2.norm2.bias", "layers.0.residual_group.blocks.2.mlp.fc1.weight", "layers.0.residual_group.blocks.2.mlp.fc1.bias", "layers.0.residual_group.blocks.2.mlp.fc2.weight", "layers.0.residual_group.blocks.2.mlp.fc2.bias", "layers.0.residual_group.blocks.3.attn_mask", "layers.0.residual_group.blocks.3.norm1.weight", "layers.0.residual_group.blocks.3.norm1.bias", "layers.0.residual_group.blocks.3.attn.relative_position_bias_table", "layers.0.residual_group.blocks.3.attn.relative_position_index", "layers.0.residual_group.blocks.3.attn.qkv.weight", "layers.0.residual_group.blocks.3.attn.qkv.bias", "layers.0.residual_group.blocks.3.attn.proj.weight", "layers.0.residual_group.blocks.3.attn.proj.bias", "layers.0.residual_group.blocks.3.norm2.weight", "layers.0.residual_group.blocks.3.norm2.bias", "layers.0.residual_group.blocks.3.mlp.fc1.weight", "layers.0.residual_group.blocks.3.mlp.fc1.bias", "layers.0.residual_group.blocks.3.mlp.fc2.weight", "layers.0.residual_group.blocks.3.mlp.fc2.bias", "layers.0.residual_group.blocks.4.norm1.weight", "layers.0.residual_group.blocks.4.norm1.bias", "layers.0.residual_group.blocks.4.attn.relative_position_bias_table", "layers.0.residual_group.blocks.4.attn.relative_position_index", "layers.0.residual_group.blocks.4.attn.qkv.weight", "layers.0.residual_group.blocks.4.attn.qkv.bias", "layers.0.residual_group.blocks.4.attn.proj.weight", "layers.0.residual_group.blocks.4.attn.proj.bias", "layers.0.residual_group.blocks.4.norm2.weight", "layers.0.residual_group.blocks.4.norm2.bias", "layers.0.residual_group.blocks.4.mlp.fc1.weight", "layers.0.residual_group.blocks.4.mlp.fc1.bias", "layers.0.residual_group.blocks.4.mlp.fc2.weight", "layers.0.residual_group.blocks.4.mlp.fc2.bias", "layers.0.residual_group.blocks.5.attn_mask", "layers.0.residual_group.blocks.5.norm1.weight", "layers.0.residual_group.blocks.5.norm1.bias", "layers.0.residual_group.blocks.5.attn.relative_position_bias_table", "layers.0.residual_group.blocks.5.attn.relative_position_index", "layers.0.residual_group.blocks.5.attn.qkv.weight", "layers.0.residual_group.blocks.5.attn.qkv.bias", "layers.0.residual_group.blocks.5.attn.proj.weight", "layers.0.residual_group.blocks.5.attn.proj.bias", "layers.0.residual_group.blocks.5.norm2.weight", "layers.0.residual_group.blocks.5.norm2.bias", "layers.0.residual_group.blocks.5.mlp.fc1.weight", "layers.0.residual_group.blocks.5.mlp.fc1.bias", "layers.0.residual_group.blocks.5.mlp.fc2.weight", "layers.0.residual_group.blocks.5.mlp.fc2.bias", "layers.0.conv.weight", "layers.0.conv.bias", "layers.1.residual_group.blocks.0.norm1.weight", "layers.1.residual_group.blocks.0.norm1.bias", "layers.1.residual_group.blocks.0.attn.relative_position_bias_table", "layers.1.residual_group.blocks.0.attn.relative_position_index", "layers.1.residual_group.blocks.0.attn.qkv.weight", "layers.1.residual_group.blocks.0.attn.qkv.bias", "layers.1.residual_group.blocks.0.attn.proj.weight", "layers.1.residual_group.blocks.0.attn.proj.bias", "layers.1.residual_group.blocks.0.norm2.weight", "layers.1.residual_group.blocks.0.norm2.bias", "layers.1.residual_group.blocks.0.mlp.fc1.weight", "layers.1.residual_group.blocks.0.mlp.fc1.bias", "layers.1.residual_group.blocks.0.mlp.fc2.weight", "layers.1.residual_group.blocks.0.mlp.fc2.bias", "layers.1.residual_group.blocks.1.attn_mask", "layers.1.residual_group.blocks.1.norm1.weight", "layers.1.residual_group.blocks.1.norm1.bias", "layers.1.residual_group.blocks.1.attn.relative_position_bias_table", "layers.1.residual_group.blocks.1.attn.relative_position_index", 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size mismatch for conv_first.weight: copying a param with shape torch.Size([180, 3, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 3, 3, 3]).
size mismatch for conv_first.bias: copying a param with shape torch.Size([180]) from checkpoint, the shape in current model is torch.Size([64]).

System:

• Mode: [Local]
• OS: [Windows]
• GPU: [Nvidia]

Describe the bug
Error while Generating.
'UNET' object has no attribute 'determine_prediction_type' (module.py:1614)

Traceback
Traceback (most recent call last):
File "/content/sd-inference-server/server.py", line 209, in run
self.wrapper.txt2img()
File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "/content/sd-inference-server/wrapper.py", line 705, in txt2img
latents = inference.txt2img(denoiser, sampler, noise, self.steps, self.on_step)
File "/content/sd-inference-server/inference.py", line 13, in txt2img
latents = sampler.step(latents, schedule, i, noise)
File "/content/sd-inference-server/samplers_k.py", line 298, in step
denoised = self.predict(x, sigmas[i])
File "/content/sd-inference-server/samplers_k.py", line 57, in predict
original = self.model.predict_original(latents, timestep, sigma)
File "/content/sd-inference-server/guidance.py", line 134, in predict_original
self.unet.determine_prediction_type()
File "/content/sd-inference-server/venv/lib/python3.10/site-packages/diffusers/models/modeling_utils.py", line 186, in getattr
return super().getattr(name)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1614, in getattr
raise AttributeError("'{}' object has no attribute '{}'".format(
AttributeError: 'UNET' object has no attribute 'determine_prediction_type'

Screenshots

System:

• Mode: [Remotel]
• OS: [Windows]

I have problem with "Inpaint":

Error while Generating.
The size of tensor a (53) must match the size of tensor b (54) at non-singleton dimension 3 (controlnet.py:769)

And also when using "Mask":

Error while Decoding.
images do not match (Image.py:1889)

How I can fix it?

I wish there was a big, circular, skeumorphic, red stop button below the generate button. Or if that would clash too much with the UI just a normal stop button below the generate button thank you.

I was hoping for support for this new samplers, the general opinion is they are superior in quality and consistency in image generation.
Thanks!

Highres - we need a more understandable application interface, or at least a tutorial. So, at the moment, you have to apply a mask and regenerate, and not just do Upscale

HR fix - judging by the Readme, it is present, but there is no setting in the interface

For reasons that are not entirely clear, for the same generation parameters, when using the same seed, the results are different. Constantly - when generating several images at once in one batch

I hope I'm not pointing out any obvious or stupid things, and thank you for your hard work - other than the above, everything else works great

p.s. Being able to erase a badly drawn mask with RMB is brilliant

I am unable to understand, How to use ControlNet as there is an no option.

1. Uploaded ControlNet in Correct folder

2. No Option to check ControlNet

3. No Option to choose Installed ControlNet

I want something like with my custom image. Wear sunglasses, change hair colour to red, remove hand tatoo, etc without changing face or unmasked area

ON the collab, after converting the 3 models ( two 2 gb, and sd1.5) mem spikes and the server crashes.

1. Advanced Parameters : I have changed Advanced Parameters to Show but i did not see any changes. what it for?
2. Model folders : It says Model folders. What does it mean Model folders in Local PC or Colab?

Describe the bug
If set third party upscaler ()UniScale, LSDIR and other), get an error on Upscaling process

Traceback
Traceback (most recent call last):
File "F:\qDiffusion-master\source\local.py", line 81, in run
self.wrapper.img2img()
File "F:\qDiffusion-master\venv\lib\site-packages\torch\utils_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "F:\qDiffusion-master\source\sd-inference-server\wrapper.py", line 790, in img2img
return self.tiled_img2img()
File "F:\qDiffusion-master\venv\lib\site-packages\torch\utils_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "F:\qDiffusion-master\source\sd-inference-server\wrapper.py", line 981, in tiled_img2img
upscaled_images = upscalers.upscale(images, UPSCALERS_PIXEL[self.img2img_upscaler], width, height)
KeyError: 'SR\4x-UniScale-Balanced [72000g].pth'

System:

• Mode: [Local]
• OS: [Windows]
• GPU: [Nvidia]

im getting that error when running collab

I am using the v-pred version of the furryrock model and this is all I get

Can you add an Option to Train Models and Lora (With Colab)

When using inpainting with mask and padding (not full image) - GUI freezes, showing "Upscaling" and "Working..." in current state and after this - chashes or keeps working, but without any response to actinos. After closing GUI - python process still hanging with all memory it used.

Img size - 864x1152

Inpaint settings:

• Strength: 0.85
• Upscaler: UltraSHarp-4x
• Padding: 112
• Mask blur: 4
• Mask expand: 0
• Mask fill: Original

GUI 2023-08-03 11:46:29.340816

Traceback (most recent call last):
  File "F:\NeuralNetworks\Apps\qDiffusion\source\parameters.py", line 744, in sync
    closest_match = self.gui.closestModel(value, available) or available[0]
IndexError: list index out of range

1. Sampler Request :

1. DPM Solver++
2. DPM Karras

2. Concurrency Image Generation

During Image generation, It take less than 15% of GPU (Colab) and it generate image one by one. Can you add an option it can generate multiple image simultaneously.

You can add an option where we user can select how many concurrency we want.

3. LoHA :

LoHA is not supported under LoRA in qDiffusion.

4. ControlNet Supporting

Many ControlNet is currently not supported by qDiffusion. Can you make it support wide rang of ControlNet

Describe the bug
I have issue with remote, when I starting this code, but then I getting password, endpoint just generates: wss://api.trycloudflare.com, anyone can fix it?

Traceback
Error while Connecting.
server rejected WebSocket connection: HTTP 400

Screenshots

System:

• Mode: Remote
• OS: Windows
• GPU: T4

At first I thought pasting a seed tend to set in random again sometimes but then when I tried to manually write a 10 digit long seed it deletes the last number when I exit from the input box

At the moment, the Attention setting in the Operation section is reset to Default every time the program is restarted. A suggestion is to save this setting, just like VRAM or Preview mode.

What happened

The current version of the client crashes as soon as I try to generate anything, whereas it was previously working on a version from a few days ago.

Steps to Reproduce

1. Launch the start script
2. Enter a prompt in the UI
3. Click on the Generate button

Actual Results

The client closes and a crash.log file is generated.

Expected result

The generated image appears.

Contents of crash.log

Traceback (most recent call last):
  File "/home/gradient/Documents/qDiffusion/source/tabs/basic/basic.py", line 738, in generate
    request = self.buildRequest()
  File "/home/gradient/Documents/qDiffusion/source/tabs/basic/basic.py", line 726, in buildRequest
    self._requests += [self._parameters.buildRequest(size, batch_images, batch_masks, batch_areas, controls)]
  File "/home/gradient/Documents/qDiffusion/source/parameters.py", line 643, in buildRequest
    del data[k]
KeyError: 'hr_tome_ratio'

This ControlNet, as far as I know, allows you to Upscale images in tiles, which allows you to bypass the memory limitation when Upscaled to high - 2-4k - resolutions. However, in qDiffusion, when used, it immediately tries to Upscale, which causes torch.cuda.OutOfMemoryError.

And, in fact, my question is, is this a feature of ControlNet, implemented without using the analogue of SD Upscale/Ultimate SD Upscale from A1111, or is it still a bug?

Hi, i have asetup with 2 GPUs, how ever even if I select another device the clients wait until the first request is completed. Is it possible to use 2 devices at the same time for 2 different generations on 2 GPUs on the same server?

Do you have plans to add this sampler?
I'm sorry if the question annoys you

May I ask you to write a precise guide for using Subpromts if possible? The fact is that when you try to use them (background promt AND area 1 prompt AND area 2 prompt) during generation, the result is very unpredictable.
Tell me, what am I doing wrong?

Seems like LoRas doesn't work at all. I'm getting default model's results. No one prompt isn't helping to solve this problem. Yesterday everything was fine. Any ideas what's going on and how to fix this?

I've got an error while generating. Description of error:

Error while Sending
Unable to serialize: key 'preview_interval' value: <PyQt5.QtCore.QVariant object at 0x000002B1B203FED0> type: <class 'PyQt5.QtCore.QVariant'>

When I try to generate images, I keep getting this error message:

trace:
Traceback (most recent call last):
File "D:\Other\sd\ui\qDiffusion\source\local.py", line 77, in run
self.wrapper.txt2img()
File "D:\Other\sd\ui\qDiffusion\venv\lib\site-packages\torch\utils_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "D:\Other\sd\ui\qDiffusion\source\sd-inference-server\wrapper.py", line 611, in txt2img
self.load_models(*initial_networks)
File "D:\Other\sd\ui\qDiffusion\source\sd-inference-server\wrapper.py", line 263, in load_models
self.unet = self.storage.get_unet(self.unet_name, self.device, unet_nets)
File "D:\Other\sd\ui\qDiffusion\source\sd-inference-server\storage.py", line 302, in get_unet
unet = self.get_component(name, "UNET", device)
File "D:\Other\sd\ui\qDiffusion\source\sd-inference-server\storage.py", line 269, in get_component
self.file_cache[file] = self.load_file(file, comp)
File "D:\Other\sd\ui\qDiffusion\source\sd-inference-server\storage.py", line 378, in load_file
state_dict, metadata = convert.convert(file)
File "D:\Other\sd\ui\qDiffusion\source\sd-inference-server\convert.py", line 392, in convert
return convert_checkpoint(model_path)
File "D:\Other\sd\ui\qDiffusion\source\sd-inference-server\convert.py", line 278, in convert_checkpoint
state_dict = utils.load_pickle(in_file, map_location="cpu")
File "D:\Other\sd\ui\qDiffusion\source\sd-inference-server\utils.py", line 376, in load_pickle
return torch.load(file, map_location=map_location, pickle_module=SafeUnpickler)
File "D:\Other\sd\ui\qDiffusion\venv\lib\site-packages\torch\serialization.py", line 809, in load
return _load(opened_zipfile, map_location, pickle_module, **pickle_load_args)
File "D:\Other\sd\ui\qDiffusion\venv\lib\site-packages\torch\serialization.py", line 1172, in _load
result = unpickler.load()
_pickle.UnpicklingError: state is not a dictionary

I connect according to guide but i see error:
"Error while Connecting.
server rejected WebSocket connection: HTTP 530"

What should i do to fix this problem?

P.S Didn't use qDiffusion for a month, maybe i need to reinstall programm?

Upd: Yes, reinstall fixed the problem.