[Bug]: LoRA doesn't have impact on the output

Checklist

[X] The issue exists after disabling all extensions
[X] The issue exists on a clean installation of webui
[ ] The issue is caused by an extension, but I believe it is caused by a bug in the webui
[X] The issue exists in the current version of the webui
[ ] The issue has not been reported before recently
[ ] The issue has been reported before but has not been fixed yet

What happened?

I have been using train_dreambooth_lora_sdxl.py and convert_diffusers_sdxl_lora_to_webui.py to train LoRA for specific character

I am able to use my LoRA using diffusers script and in ComfyUI, but it doesnt work in A1111. It loads without any errors, but has no desired effect on output

Steps to reproduce the problem

Create VM with this docker image: pytorch/pytorch:2.0.0-cuda11.7-cudnn8-devel

Install dependencies:


apt update
apt install vim git tmux ffmpeg libsm6 libxext6 wget python3 python3-venv libgl1 libglib2.0-0 google-perftools -y

git clone https://github.com/huggingface/diffusers.git cd diffusers pip install -e . cd examples/dreambooth pip install -r requirements.txt accelerate config default pip install bitsandbytes xformers==0.0.19

3. Download baseline SDXL model:

wget https://civitai.com/api/download/models/333449 -O DreamShaperXL.safetensors

4. Convert `.safetensors` to suitable format using python:

import diffusers pipe = diffusers.StableDiffusionXLPipeline.from_single_file("DreamShaperXL.safetensors") pipe.save_pretrained("DreamShaperXL")

5. Train LoRA (6 images with the same woman on white background):

export MODEL_NAME="DreamShaperXL" export INSTANCE_DIR="data/claire" export MAX_TRAIN_STEPS=5000 export CHECKPOINTING_STEPS=500

export OUTPUT_DIR="outputs/$(basename ${MODELNAME})$(basename ${INSTANCE_DIR})" export CUDA_LAUNCH_BLOCKING=1 export TORCH_USE_CUDA_DSA=1

printf "\n\nTraining Claire model with $MODEL_NAME on $INSTANCE_DIR, saving to $OUTPUT_DIR\n\n"

accelerate launch diffusers/examples/dreambooth/train_dreambooth_lora_sdxl.py \ --instance_prompt="photo of wff woman, isolated on white background" \ --pretrained_model_name_or_path=$MODEL_NAME \ --instance_data_dir=$INSTANCE_DIR \ --output_dir=$OUTPUT_DIR \ --resolution=1024 \ --train_batch_size=2 \ --gradient_accumulation_steps=4 \ --learning_rate=1e-4 \ --lr_scheduler="constant" \ --lr_warmup_steps=0 \ --max_train_steps=$MAX_TRAIN_STEPS \ --seed="0" \ --train_text_encoder \ --enable_xformers_memory_efficient_attention \ --gradient_checkpointing \ --use_8bit_adam \ --checkpointing_steps=$CHECKPOINTING_STEPS

6. Convert to Kohya format:

python /diffusers/scripts/convert_diffusers_sdxl_lora_to_webui.py outputs/DreamShaperXL_claire/pytorch_lora_weights.safetensors test.safetensors

7. Move to A1111:

mv test.safetensors stable-diffusion-webui/models/Lora/


### What should have happened?

A1111 Config:

photo of wff woman, rides gondola in Venice, Negative prompt: text, watermark, low quality, medium quality, blurry, censored, wrinkles, deformed, mutated text, watermark, low quality, medium quality, blurry, censored, wrinkles, deformed, mutated, BadDream, UnrealisticDream Steps: 7, Sampler: DPM++ SDE Karras, CFG scale: 2, Seed: 420, Size: 1024x1024, Model hash: 676f0d60c8, Model: DreamShaperXL, Version: v1.7.0

Image without any LoRA generated in A1111:
![image](https://github.com/AUTOMATIC1111/stable-diffusion-webui/assets/44069677/f4d0de5d-7b16-49fd-b648-3d76e53b79af)
Image with LoRA generated in A1111:
![image](https://github.com/AUTOMATIC1111/stable-diffusion-webui/assets/44069677/ed72252d-a269-4c6f-af20-6c455031ea74)
Image generated with custom code:

import torch from diffusers import DiffusionPipeline

pretrained_model = "DreamShaperXL" lora_weights = "./outputs/DreamShaperXL_claire/checkpoint-4000/"

prompt = "photo of wff woman, rides gondola in Venice," negative_prompt = "text, watermark, low quality, medium quality, blurry, censored, wrinkles, deformed, mutated text, watermark, low quality, medium quality, blurry, censored, wrinkles, deformed, mutated"

pipe = DiffusionPipeline.from_pretrained(pretrained_model, torch_dtype=torch.float32) pipe = pipe.to("cuda") pipe.load_lora_weights(lora_weights)

image = pipe( prompt=prompt, negative_prompt=negative_prompt, num_inference_steps=50, seed=420, ).images[0]

image.save("lora_inference.png")

![image](https://github.com/AUTOMATIC1111/stable-diffusion-webui/assets/44069677/e27a9836-8bab-4543-ae2d-374c4debc123)

Note:
Obiviously locally generated file does not have desired quality, but as you can see it changes the image in the way I want

### What browsers do you use to access the UI ?

Google Chrome

### Sysinfo

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### Console logs

```Shell
################################################################
Install script for stable-diffusion + Web UI
Tested on Debian 11 (Bullseye), Fedora 34+ and openSUSE Leap 15.4 or newer.
################################################################

################################################################
Running on root user
################################################################

################################################################
Repo already cloned, using it as install directory
################################################################

################################################################
Create and activate python venv
################################################################

################################################################
Launching launch.py...
################################################################
Using TCMalloc: libtcmalloc_minimal.so.4
Python 3.10.9 (main, Mar  8 2023, 10:47:38) [GCC 11.2.0]
Version: v1.7.0
Commit hash: cf2772fab0af5573da775e7437e6acdca424f26e
Launching Web UI with arguments: --xformers --api --share
Style database not found: /project/stable-diffusion-webui/styles.csv
Loading weights [aeb7e9e689] from /project/stable-diffusion-webui/models/Stable-diffusion/juggernautXL_v8Rundiffusion.safetensors
Running on local URL:  http://127.0.0.1:7860
Creating model from config: /project/stable-diffusion-webui/repositories/generative-models/configs/inference/sd_xl_base.yaml
Applying attention optimization: xformers... done.
Model loaded in 6.1s (load weights from disk: 1.9s, create model: 0.7s, apply weights to model: 3.1s, apply half(): 0.1s, calculate empty prompt: 0.1s).
Running on public URL: https://90a558500b31edaa81.gradio.live

This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)
Startup time: 29.8s (prepare environment: 4.1s, import torch: 6.2s, import gradio: 2.0s, setup paths: 3.5s, initialize shared: 0.4s, other imports: 1.1s, setup codeformer: 0.3s, setup gfpgan: 0.4s, load scripts: 0.8s, create ui: 0.9s, gradio launch: 9.9s, add APIs: 0.1s).
Reusing loaded model juggernautXL_v8Rundiffusion.safetensors [aeb7e9e689] to load DreamShaperXL.safetensors [676f0d60c8]
Loading weights [676f0d60c8] from /project/stable-diffusion-webui/models/Stable-diffusion/DreamShaperXL.safetensors
Applying attention optimization: xformers... done.
Weights loaded in 27.0s (send model to cpu: 2.8s, load weights from disk: 0.9s, apply weights to model: 12.7s, move model to device: 10.6s).
Calculating sha256 for /project/stable-diffusion-webui/models/Lora/test.safetensors: d90adf86741514ae25e56c370cca19f51cf547cf5087ce8ce921d33e1df507f0
100%|____________________________________________________________________________________________________________________________________________________________________| 7/7 [00:04<00:00,  1.43it/s]
==========================================================================================_______________________________________________________________________________| 7/7 [00:03<00:00,  1.92it/s]
A tensor with all NaNs was produced in VAE.
Web UI will now convert VAE into 32-bit float and retry.
To disable this behavior, disable the 'Automatically revert VAE to 32-bit floats' setting.
To always start with 32-bit VAE, use --no-half-vae commandline flag.
==========================================================================================
Total progress: 100%|____________________________________________________________________________________________________________________________________________________| 7/7 [00:04<00:00,  1.55it/s]
100%|____________________________________________________________________________________________________________________________________________________________________| 7/7 [00:03<00:00,  1.93it/s]
Total progress: 100%|____________________________________________________________________________________________________________________________________________________| 7/7 [00:04<00:00,  1.65it/s]
100%|____________________________________________________________________________________________________________________________________________________________________| 7/7 [00:03<00:00,  1.92it/s]
Total progress: 100%|____________________________________________________________________________________________________________________________________________________| 7/7 [00:04<00:00,  1.65it/s]
Total progress: 100%|____________________________________________________________________________________________________________________________________________________| 7/7 [00:04<00:00,  1.90it/s]

Additional information

I used exactly the same pipline two weeks ago and everything worked fine, I saw this issue from a week ago, maybe the same has to be applied in A1111? https://github.com/huggingface/diffusers/issues/6777

AUTOMATIC1111 / stable-diffusion-webui