propose an Contrastive Prompt Tuning method (DreamArtist), can super dramatically improve the image quality and diversity #2945

IrisRainbowNeko · 2022-10-17T10:15:18Z

Propose a better method of prompt tuning (train embedding), which can super dramatically improve the image quality compared to the current embedding training method. Excellent performance can be active even with just one image for training (one-shot learning).

Performance Comparison with same extra prompt:

train image:

current method:

my APT method:

or add some details prompt:

no prompt tuning (textual inversion):

Learn Genshin Nahida from single image:

combination Nahida with additional prompt:

**Note. **
The results from this version are currently inconsistent with the version I used before, and there are some discrepancies in performance. ~~The reason for this is currently unknown.~~ Probably due to the scheduler, the training 1000 steps learning rate decreases too fast.

my old version :

new version :

It may be better to use https://github.com/7eu7d7/APT-stable-diffusion-auto-prompt when training embedding

TingTingin · 2022-10-17T10:21:23Z

is this for textual inversion?

IrisRainbowNeko · 2022-10-17T10:25:27Z

is this for textual inversion?

yes, the textual inversion is actually the prompt tuning in NLP.
I have modified the training method of textual inversion to propose a method that can dramatically improve image quality, and the details of the principle are explained in the video https://www.bilibili.com/video/BV1xD4y1C73c/.

IrisRainbowNeko · 2022-10-17T10:30:48Z

Add prompt embedding learning of negative words to dramatically improve the quality of generated images. The concept of high quality can be learned from a single image.

Add reconstruction loss to improve the detail quality and richness of the generated images.

Adding a discriminator trained by artificial annotation (implemented using convnext) allows embedding to learn based on the model.

TingTingin · 2022-10-17T10:35:11Z

thanks for explanation

C43H66N12O12S2 · 2022-10-17T10:37:55Z

Thanks for the PR! Looks interesting. Could you explain in more detail?

Add reconstruction loss to improve the detail quality and richness of the generated images.

Is this specific to each embedding/tuning or a general improvement?

ClashSAN · 2022-10-17T10:41:34Z

Do you have source link for meta files? Is it from this?
https://github.com/facebookresearch/ConvNeXt/blob/main/main.py

IrisRainbowNeko · 2022-10-17T10:45:07Z

Thanks for the PR! Looks interesting. Could you explain in more detail?

Add reconstruction loss to improve the detail quality and richness of the generated images.

Is this specific to each embedding/tuning or a general improvement?

it work for all prompt, once add {name} to prompt and {name}-uc to negative it will improve quality.
some image I genearated use this.
#pixiv https://www.pixiv.net/artworks/102011948
https://www.pixiv.net/artworks/102011852
https://www.pixiv.net/artworks/102011612

IrisRainbowNeko · 2022-10-17T10:47:24Z

Do you have source link for meta files? Is it from this? https://github.com/facebookresearch/ConvNeXt/blob/main/main.py

yes, I copy convnext codes from that, and add an interface

C43H66N12O12S2 · 2022-10-17T11:22:26Z

Great work!

The final word is Automatic's but as far as I see, there's some changes needed for merge:
1 - Please clone the original conv_next to repositories or install it as a module in launch.py
2 - Keep only your changes in modules/ directory, preferably as a single file.
3 - Move requirements to requirements_versions.txt in the repo root. Don't touch torch as that's installed in launch.py

IrisRainbowNeko · 2022-10-17T11:23:40Z

Thanks for the PR! Looks interesting. Could you explain in more detail?

Add reconstruction loss to improve the detail quality and richness of the generated images.

Is this specific to each embedding/tuning or a general improvement?

The reconstruction loss is actually decoding the latent features to get the image and calculate the pixel-level loss between images. This reconstruction loss is a popular approach in some deep learning algorithms.

…xt requirements to requirements.txt

IrisRainbowNeko · 2022-10-17T12:26:03Z

Great work!

The final word is Automatic's but as far as I see, there's some changes needed for merge: 1 - Please clone the original conv_next to repositories or install it as a module in launch.py 2 - Keep only your changes in modules/ directory, preferably as a single file. 3 - Move requirements to requirements_versions.txt in the repo root. Don't touch torch as that's installed in launch.py

Thanks for your suggestion, I have make the conv_next as repositories in launch.py and put the requirements into requirements.txt

flesnuk · 2022-10-17T13:08:07Z

Having the latest commit, after executing a second time I get:

Commit hash: 04d355a017280054ff88cfa095fc3d0c54998bde
Traceback (most recent call last):
  File "D:\ai\stable-diffusion-webui\launch.py", line 186, in <module>
    prepare_enviroment()
  File "D:\ai\stable-diffusion-webui\launch.py", line 165, in prepare_enviroment
    os.rename(os.path.join(repo_dir('conv_next'), 'models'), os.path.join(repo_dir('conv_next'), 'models_cnx'))
FileNotFoundError: [WinError 2] The system cannot find the file specified: 'repositories\\conv_next\\models' -> 'repositories\\conv_next\\models_cnx'

I only have the models_cnx folder, that's why is failing. Add a check to avoid fail.

I tried running it but I don't have enough VRAM. With 8GB seems impossible to run even using --medvram and convnext_tiny_1k_224_ema.pth for classifier model.
Do you know how much VRAM is needed?

IrisRainbowNeko · 2022-10-17T13:14:38Z

Having the latest commit, after executing a second time I get:
Commit hash: 04d355a017280054ff88cfa095fc3d0c54998bde
Traceback (most recent call last):
  File "D:\ai\stable-diffusion-webui\launch.py", line 186, in <module>
    prepare_enviroment()
  File "D:\ai\stable-diffusion-webui\launch.py", line 165, in prepare_enviroment
    os.rename(os.path.join(repo_dir('conv_next'), 'models'), os.path.join(repo_dir('conv_next'), 'models_cnx'))
FileNotFoundError: [WinError 2] The system cannot find the file specified: 'repositories\\conv_next\\models' -> 'repositories\\conv_next\\models_cnx'
I only have the models_cnx folder, that's why is failing. Add a check to avoid fail.

I tried running it but I don't have enough VRAM. With 8GB seems impossible to run even using --medvram and convnext_tiny_1k_224_ema.pth for classifier model. Do you know how much VRAM is needed?

rename bug is fixed.
convnext is quite a big model, like transformer. I train it use 3090 with 24GB VRAM. You can fine-tuning my per-trained model for fast training https://github.com/7eu7d7/pixiv_AI_crawler/releases/download/v2/checkpoint-best_t5.pth

Evil-Dragon · 2022-10-17T14:05:40Z

I've been testing it for a little while on the latest pull and it does seem pretty promising so far. So far up to about 3000+ steps with 1 image on a RTX 3080 12GB and it does seem to be working well from what I can tell. I'll do some more testing and report my findings.

IrisRainbowNeko · 2022-10-17T14:11:27Z

promising

Actrually, I get above image with just 1000 steps training. This tag with commonly used negative words can generate amazing images. Mix embedding is also work well.

C43H66N12O12S2 · 2022-10-17T14:44:01Z

@flesnuk The speed can probably be increased by fusing ConvNext with https://github.com/NVIDIA/TransformerEngine (if and when it's supported in Windows) and TorchDynamo.

I'll take a look if Automatic merges.

Evil-Dragon · 2022-10-17T15:45:31Z

promising

Actrually, I get above image with just 1000 steps training. This tag with commonly used negative words can generate amazing images. Mix embedding is also work well.

I could have probably been overtraining a bit. Also struggling to train with a 12GB card with even 3 images, keep running out of memory. It's pretty borderline for this type of training, sometimes it will go for a few hundred steps and then OOM.

EDIT: But when it works, it works really well. I've never had much luck with training embeddings but this one seems to actually work for me and capture style/details really well.

CCRcmcpe · 2022-10-17T15:53:36Z

modules/convnext_discriminator.py

+
+import os.path
+import numpy as np
+import cv2


cv2 is not used.

d8ahazard · 2022-10-17T16:08:33Z

requirements.txt

@@ -23,3 +23,7 @@ resize-right
 torchdiffeq
 kornia
 lark
+scikit_learn
+requests
+opencv-python


I'm like, 95% sure opencv is used somewhere else in the project...

I checked, eh, yes. For some reason it's not a requirement.

d8ahazard · 2022-10-17T16:18:28Z

I think it's installed as a dep when the venv is set up, so it's not especially needed, but it still can't hurt to have it. Just don't know if adding it should be done in this commit, or a more general "housekeeping" commit.

…

On Mon, Oct 17, 2022 at 11:12 AM RcINS ***@***.***> wrote: ***@***.**** commented on this pull request. ------------------------------ In requirements.txt <#2945 (comment)> : > @@ -23,3 +23,7 @@ resize-right torchdiffeq kornia lark +scikit_learn +requests +opencv-python I checked, eh, yes. For some reason it's not a requirement. — Reply to this email directly, view it on GitHub <#2945 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AAMO4NGFGPMDL6AI3IEWDKDWDV3E7ANCNFSM6AAAAAARG5ELXU> . You are receiving this because you commented.Message ID: ***@***.*** com>

d8ahazard · 2022-10-17T17:08:43Z

@7eu7d7 -

So, I'm running on an 8GB GPU, and with this version of TI using the --medvram flag, if I enable negative prompting, I get an OOM on the first training step.

With the current master branch and --medvram, this does not happen.

I guess, not a dealbreaker, but maybe something to consider for those of us who still need to upgrade their GPU. :D

C43H66N12O12S2 · 2022-10-17T17:11:11Z

@d8ahazard Do you mean that you OOM when using the regular Textual Inversion?

d8ahazard · 2022-10-17T17:12:19Z

With "regular" from Automatic's repo, I do not. With the new changes in the PR, I do. Both using the --medvram flag.

…

On Mon, Oct 17, 2022 at 12:11 PM C43H66N12O12S2 ***@***.***> wrote: @d8ahazard <https://github.com/d8ahazard> Do you mean that you OOM when using the regular Textual Inversion? — Reply to this email directly, view it on GitHub <#2945 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AAMO4NC2C5SNQEKURGMDJE3WDWCDVANCNFSM6AAAAAARG5ELXU> . You are receiving this because you were mentioned.Message ID: ***@***.***>

MarkovInequality · 2022-10-17T18:58:46Z

@d8ahazard I have this problem when training negative prompts as well on 10GB of VRAM. Turning off "train with reconstruction" stops the OOM errors, but I can't get training to converge without it.

Arcsion · 2022-10-24T14:13:56Z

The video seems to be removed or inaccessible. Is there any other explanation of the idea? It sounds promising.

The author said he was going to write a paper about this method, so he take down the video first. I believe we will be able to see his research results better soon.

mekrod · 2022-10-25T00:08:41Z

got an error when trying to run this, APT-stable-diffusion-auto-prompt-master\venv\lib\site-packages\transformers\tokenization_utils_base.py", line 1768, in from_pretrained raise EnvironmentError( OSError: Can't load tokenizer for './models/clip-vit-large-patch14'.

Arcsion · 2022-10-25T00:23:28Z

got an error when trying to run this, APT-stable-diffusion-auto-prompt-master\venv\lib\site-packages\transformers\tokenization_utils_base.py", line 1768, in from_pretrained raise EnvironmentError( OSError: Can't load tokenizer for './models/clip-vit-large-patch14'.

Same issue with this above.

You have to download CLIP from hugginface manually and put that under models/clip-vit-large-patch14/, better clone that repository and put everything in.

TingTingin · 2022-10-25T01:22:32Z

The video seems to be removed or inaccessible. Is there any other explanation of the idea? It sounds promising.

the video was in mandarin

zhupeter010903 · 2022-10-30T18:17:28Z

A weird thing happened. I trained an embedding using this method on 6 images (actually cropped and flipped out of one single image) for 10k steps with the negative prompt. But when I tested, only using the positive prompt gives much better results than using the negative prompt as well.

Lime-Cakes · 2022-11-07T18:30:38Z

The video seems to be removed or inaccessible. Is there any other explanation of the idea? It sounds promising.

the video was in mandarin

If you got a copy of it, could you share it? From a glimpse of the code, it seemed to work by using ConvNeXt as discriminator. Does it use pretrained model as backbone or initialize all weights as random? And how does it modify ConvNeXt to serve as discriminator?

stellaHSR · 2022-11-08T18:24:32Z

@7eu7d7 hi, when I start train apt embedding using [Prompt Tuning (ConvNext)], the error is :

how to solve this?

mekrod · 2022-11-08T20:50:29Z

how to solve this?

@stellaHSR you need to create a new embedding file with the -uc in the end for the negative training, in your case aptllz-uc

Arcsion · 2022-11-08T22:28:59Z

If you got a copy of it, could you share it? From a glimpse of the code, it seemed to work by using ConvNeXt as discriminator. Does it use pretrained model as backbone or initialize all weights as random? And how does it modify ConvNeXt to serve as discriminator?

@Lime-Cakes Yes there is a pretrained model at 7eu7d7/pixiv_AI_crawler, you can download it in the release.

In 7eu7d7's modified version of WEBUI which adds APT, APT-stable-diffusion-auto-prompt, you can find a README which says:

Add model-based prompt tuning, based on the convnext model trained in my previous pixiv_AI_crawler, and use another AI to evaluate the quality of the generated images as a discriminator to aid in model training. It would allow prompt to learn the concept of high quality, or learn your fetish.

stellaHSR · 2022-11-09T12:32:13Z

how to solve this?

@stellaHSR you need to create a new embedding file with the -uc in the end for the negative training, in your case aptllz-uc

Thx, it works!
But it seems need a specified model ? I use the animefull-latest model, the loss is quite large (0.98) and the output image looks like noise figure

IrisRainbowNeko · 2022-11-09T13:03:48Z

The new version of the method has some changes, and recently will consider making it as an extension.

TWIISTED-STUDIOS · 2022-11-10T04:37:41Z

The new version of the method has some changes, and recently will consider making it as an extension.

this is would love a version that is an extension that way its easier to manage for the end user.

f8upd8 · 2022-11-11T13:41:01Z

The new version of the method has some changes, and recently will consider making it as an extension.

Please do

Raz0rStorm · 2022-11-11T20:04:47Z

I tried to train at 384*384 but got TypeError: only integer tensors of a single element can be converted to an index

aliencaocao · 2022-11-12T03:37:42Z

Please update us here when the plugin for this is available. thank you

IrisRainbowNeko · 2022-11-12T12:35:40Z

The new version of the method is renamed to DreamArtist.
Extension Version: https://github.com/7eu7d7/DreamArtist-sd-webui-extension
Regular version: https://github.com/7eu7d7/DreamArtist-stable-diffusion

The paper will be published on arxiv soon.

IrisRainbowNeko · 2022-11-12T12:36:37Z

Please update us here when the plugin for this is available. thank you

Extension version is available.

aliencaocao · 2022-11-12T12:49:47Z

Someone should add it to the list: https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Extensions
cc @ClashSAN

zhupeter010903 · 2022-11-12T22:30:32Z

When I try to train with the option 'Read parameters (prompt, etc...) from txt2img tab when making previews' turned on, I got the following error message:

Traceback (most recent call last):
  File "/content/gdrive/.shortcut-targets-by-id/1VNhtnoJC8AkKUr4-kCbT3hxMSzfiOGCd/sd/stable-diffusion-webui/modules/ui.py", line 185, in f
    res = list(func(*args, **kwargs))
  File "/content/gdrive/.shortcut-targets-by-id/1VNhtnoJC8AkKUr4-kCbT3hxMSzfiOGCd/sd/stable-diffusion-webui/webui.py", line 54, in f
    res = func(*args, **kwargs)
  File "/content/gdrive/.shortcut-targets-by-id/1VNhtnoJC8AkKUr4-kCbT3hxMSzfiOGCd/sd/stable-diffusion-webui/extensions/DreamArtist/scripts/dream_artist/ui.py", line 30, in train_embedding
    embedding, filename = dream_artist.cptuning.train_embedding(*args)
  File "/content/gdrive/.shortcut-targets-by-id/1VNhtnoJC8AkKUr4-kCbT3hxMSzfiOGCd/sd/stable-diffusion-webui/extensions/DreamArtist/scripts/dream_artist/cptuning.py", line 486, in train_embedding
    processed = processing.process_images(p)
  File "/content/gdrive/.shortcut-targets-by-id/1VNhtnoJC8AkKUr4-kCbT3hxMSzfiOGCd/sd/stable-diffusion-webui/modules/processing.py", line 423, in process_images
    res = process_images_inner(p)
  File "/content/gdrive/.shortcut-targets-by-id/1VNhtnoJC8AkKUr4-kCbT3hxMSzfiOGCd/sd/stable-diffusion-webui/modules/processing.py", line 442, in process_images_inner
    file.write(processed.infotext(p, 0))
  File "/content/gdrive/.shortcut-targets-by-id/1VNhtnoJC8AkKUr4-kCbT3hxMSzfiOGCd/sd/stable-diffusion-webui/modules/processing.py", line 281, in infotext
    return create_infotext(p, self.all_prompts, self.all_seeds, self.all_subseeds, comments=[], position_in_batch=index % self.batch_size, iteration=index // self.batch_size)
  File "/content/gdrive/.shortcut-targets-by-id/1VNhtnoJC8AkKUr4-kCbT3hxMSzfiOGCd/sd/stable-diffusion-webui/modules/processing.py", line 411, in create_infotext
    negative_prompt_text = "\nNegative prompt: " + p.negative_prompt if p.negative_prompt else ""
TypeError: can only concatenate str (not "tuple") to str

This is happening with both the extension version and the regular version. Also, when I tried to use reconstruction, I got

Traceback (most recent call last):
  File "/content/stable-diffusion-webui/modules/ui.py", line 185, in f
    res = list(func(*args, **kwargs))
  File "/content/stable-diffusion-webui/webui.py", line 54, in f
    res = func(*args, **kwargs)
  File "/content/stable-diffusion-webui/modules/dream_artist/ui.py", line 36, in train_embedding
    embedding, filename = modules.dream_artist.cptuning.train_embedding(*args)
  File "/content/stable-diffusion-webui/modules/dream_artist/cptuning.py", line 413, in train_embedding
    x_samples_ddim = shared.sd_model.decode_first_stage.__wrapped__(shared.sd_model, output[2])  # forward with grad
  File "/content/stable-diffusion-webui/repositories/stable-diffusion/ldm/models/diffusion/ddpm.py", line 763, in decode_first_stage
    return self.first_stage_model.decode(z)
  File "/content/stable-diffusion-webui/repositories/stable-diffusion/ldm/models/autoencoder.py", line 331, in decode
    z = self.post_quant_conv(z)
  File "/usr/local/envs/automatic/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
    return forward_call(*input, **kwargs)
  File "/usr/local/envs/automatic/lib/python3.10/site-packages/torch/nn/modules/conv.py", line 457, in forward
    return self._conv_forward(input, self.weight, self.bias)
  File "/usr/local/envs/automatic/lib/python3.10/site-packages/torch/nn/modules/conv.py", line 453, in _conv_forward
    return F.conv2d(input, weight, bias, self.stride,
RuntimeError: Input type (torch.cuda.HalfTensor) and weight type (torch.HalfTensor) should be the same

devNegative-asm · 2022-11-15T21:24:23Z

Has anyone gotten the extension to actually work yet? everyone I asked on the SD discord said the results looked nothing like the training image.

Add elem ids for interrogate tabs

propose a more advanced Prompt Tuning method (APT)

f173782

IrisRainbowNeko requested a review from AUTOMATIC1111 as a code owner October 17, 2022 10:15

IrisRainbowNeko changed the title ~~propose a more advanced Prompt Tuning method (APT)~~ propose a more advanced Prompt Tuning method (APT), can super dramatically improve the image quality Oct 17, 2022

IrisRainbowNeko added 2 commits October 17, 2022 20:16

clone the original conv_next to repositories in launch.py, add convne…

b6d1741

…xt requirements to requirements.txt

fix

04d355a

fix

20ecb5a

CCRcmcpe reviewed Oct 17, 2022

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modules/convnext_discriminator.py

import os.path

import numpy as np

import cv2

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cv2 is not used.

CCRcmcpe reviewed Oct 17, 2022

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sotokisehiro mentioned this pull request Oct 27, 2022

做成webui插件/Make this an extension to stable-diffusion-webui IrisRainbowNeko/DreamArtist-stable-diffusion#4

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fix reconstruction loss

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IrisRainbowNeko closed this Nov 9, 2022

IrisRainbowNeko reopened this Nov 9, 2022

IrisRainbowNeko closed this Nov 9, 2022

IrisRainbowNeko changed the title ~~propose an advanced Prompt Tuning method (APT), can super dramatically improve the image quality and diversity~~ propose an Contrastive Prompt Tuning method (DreamArtist), can super dramatically improve the image quality and diversity Nov 13, 2022

WangShuXian6 mentioned this pull request Jul 30, 2023

stable diffusion webui WangShuXian6/blog#155

Open

DrakeRichards pushed a commit to DrakeRichards/stable-diffusion-webui that referenced this pull request Mar 22, 2024

Merge pull request AUTOMATIC1111#2945 from BinaryQuantumSoul/dev

4830d9f

Add elem ids for interrogate tabs

Atry pushed a commit to Atry/stable-diffusion-webui that referenced this pull request Jul 11, 2024

Lift ipadapter api input type constraint (AUTOMATIC1111#2945)

bb3c2a9

propose an Contrastive Prompt Tuning method (DreamArtist), can super dramatically improve the image quality and diversity #2945

propose an Contrastive Prompt Tuning method (DreamArtist), can super dramatically improve the image quality and diversity #2945

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