Custom Lora the long generation does not work either

I’m reaching out to report an issue with image generation when using a custom 164 MB Lora that I created with Replicate, in conjunction with the Flux model. The generation time is significantly longer, ranging from 20 to 40 minutes per image. Moreover, it appears that the Lora is not being applied correctly, as the results are markedly different from what I would expect.

For comparison, when using a Lora downloaded from Civitai, the generation time is much shorter—around 1.5 minutes—and the results are as expected. I also tested the custom Lora on [Hugging Face](https://huggingface.co/), where it performed as anticipated.

This discrepancy is impacting my workflow, and I would appreciate any guidance or solutions you can provide to address this issue.

Please let me know if you need any additional information or if there are steps I should take to resolve this.

version: [f2.0.1v1.10.1-previous-401-g08f74875](https://github.com/lllyasviel/stable-diffusion-webui-forge/commit/08f7487590d2b48ae53098a4247f4d067549b0c9)  •  python: 3.10.6  •  torch: 2.4.0+cu124  •  xformers: N/A  •  gradio: 4.40.0  •  checkpoint: [c161224931](https://google.com/search?q=c161224931ab6280cf2ac6cf9060cdbffe78a6ac9b4ff55b89bc23264adc16db)

Python 3.10.6 (tags/v3.10.6:9c7b4bd, Aug 1 2022, 21:53:49) [MSC v.1932 64 bit (AMD64)]
Version: f2.0.1v1.10.1-previous-401-g08f74875
Commit hash: 08f7487590d2b48ae53098a4247f4d067549b0c9
Launching Web UI with arguments: --share
Total VRAM 12288 MB, total RAM 32612 MB
pytorch version: 2.4.0+cu124
Set vram state to: NORMAL_VRAM
Device: cuda:0 NVIDIA GeForce RTX 3060 : native
Hint: your device supports --cuda-malloc for potential speed improvements.
VAE dtype preferences: [torch.bfloat16, torch.float32] -> torch.bfloat16
CUDA Using Stream: False
G:\webui_forge_cu124_torch24\system\python\lib\site-packages\transformers\utils\hub.py:127: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.
warnings.warn(
Using pytorch cross attention
Using pytorch attention for VAE
ControlNet preprocessor location: G:\webui_forge_cu124_torch24\webui\models\ControlNetPreprocessor
2024-08-22 18:03:38,547 - ControlNet - INFO - ControlNet UI callback registered.
Model selected: {'checkpoint_info': {'filename': 'G:\\webui_forge_cu124_torch24\\webui\\models\\Stable-diffusion\\flux1-dev-bnb-nf4.safetensors', 'hash': '0184473b'}, 'additional_modules': [], 'unet_storage_dtype': None}
Using online LoRAs in FP16: False
Running on local URL: http://127.0.0.1:7860

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: 26.2s (prepare environment: 5.3s, import torch: 10.4s, initialize shared: 0.3s, other imports: 0.8s, load scripts: 2.5s, create ui: 3.5s, gradio launch: 3.3s).
Environment vars changed: {'stream': False, 'inference_memory': 1024.0, 'pin_shared_memory': False}
[GPU Setting] You will use 91.67% GPU memory (11263.00 MB) to load weights, and use 8.33% GPU memory (1024.00 MB) to do matrix computation.
Loading Model: {'checkpoint_info': {'filename': 'G:\\webui_forge_cu124_torch24\\webui\\models\\Stable-diffusion\\flux1-dev-bnb-nf4.safetensors', 'hash': '0184473b'}, 'additional_modules': [], 'unet_storage_dtype': None}
[Unload] Trying to free all memory for cuda:0 with 0 models keep loaded ...
StateDict Keys: {'transformer': 2350, 'vae': 244, 'text_encoder': 198, 'text_encoder_2': 220, 'ignore': 0}
Using Detected T5 Data Type: torch.float8_e4m3fn
Using Detected UNet Type: nf4
Using pre-quant state dict!
Working with z of shape (1, 16, 32, 32) = 16384 dimensions.
K-Model Created: {'storage_dtype': 'nf4', 'computation_dtype': torch.bfloat16}
Model loaded in 2.3s (unload existing model: 0.2s, forge model load: 2.1s).
[LORA] Loaded G:\webui_forge_cu124_torch24\webui\models\Lora\tok.safetensors for KModel-UNet with 494 keys at weight 0.94 (skipped 0 keys)
Skipping unconditional conditioning when CFG = 1. Negative Prompts are ignored.
To load target model JointTextEncoder
Begin to load 1 model
[Unload] Trying to free 7725.00 MB for cuda:0 with 0 models keep loaded ...
[Memory Management] Current Free GPU Memory: 11235.00 MB
[Memory Management] Required Model Memory: 5154.62 MB
[Memory Management] Required Inference Memory: 1024.00 MB
[Memory Management] Estimated Remaining GPU Memory: 5056.38 MB
Moving model(s) has taken 6.92 seconds
Distilled CFG Scale: 3.5
To load target model KModel
Begin to load 1 model
[Unload] Trying to free 9411.13 MB for cuda:0 with 0 models keep loaded ...
[Unload] Current free memory is 5956.43 MB ...
[Unload] Unload model JointTextEncoder
[Memory Management] Current Free GPU Memory: 11191.04 MB
[Memory Management] Required Model Memory: 6246.84 MB
[Memory Management] Required Inference Memory: 1024.00 MB
[Memory Management] Estimated Remaining GPU Memory: 3920.19 MB
Patching LoRAs for KModel: 100%|█████████████████████████████████████████████████████| 304/304 [00:42<00:00, 7.20it/s]
LoRA patching has taken 42.20 seconds
Moving model(s) has taken 46.29 seconds
100%|██████████████████████████████████████████████████████████████████████████████████| 20/20 [07:58<00:00, 23.90s/it]
To load target model IntegratedAutoencoderKL███████████████████████████████████████████| 20/20 [07:38<00:00, 24.98s/it]
Begin to load 1 model
[Unload] Trying to free 4495.77 MB for cuda:0 with 0 models keep loaded ...
[Unload] Current free memory is 19691.11 MB ...
[Memory Management] Current Free GPU Memory: 10137.11 MB
[Memory Management] Required Model Memory: 159.87 MB
[Memory Management] Required Inference Memory: 1024.00 MB
[Memory Management] Estimated Remaining GPU Memory: 8953.23 MB
Moving model(s) has taken 0.92 seconds
Total progress: 100%|██████████████████████████████████████████████████████████████████| 20/20 [07:41<00:00, 23.09s/it]
Total progress: 100%|██████████████████████████████████████████████████████████████████| 20/20 [07:41<00:00, 24.98s/it]

The report provides no source file or test entry point; start by reproducing the custom 164 MB LoRA with Flux using the listed Python, PyTorch, Gradio, and RTX 3060 environment. Compare its timing and output with the Civitai LoRA, using the supplied generation log to identify where the delay or incorrect application occurs; done means the custom LoRA is applied as expected without the 20–40 minute generation time.

Written by the indexing model from the issue text.