Error when using blockwise scales with sd_xl_offset_example-lora_1.0.safetensors

[asomoza]

Describe the bug

When loading the sd_xl_offset_example-lora_1.0.safetensors from the official repository and setting the scales by blocks, it throws this error:

RuntimeError: No LoRA weight found for module down_blocks.0.resnets.0.conv1

Loading the same lora with just a float e.g.: pipe.set_adapters(["offset"], [1.0]) doesn't raise the error.

Reproduction

import torch

from diffusers import StableDiffusionXLPipeline


pipe = StableDiffusionXLPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16"
).to("cuda")

pipe.load_lora_weights(
    "stabilityai/stable-diffusion-xl-base-1.0",
    weight_name="sd_xl_offset_example-lora_1.0.safetensors",
    adapter_name="offset",
)
offset_scales = {
    "unet": {
        "down": {"block_1": [1.0, 1.0], "block_2": [1.0, 1.0]},
        "mid": 0,
        "up": {"block_0": [1.0, 1.0, 1.0], "block_1": [1.0, 1.0, 1.0]},
    },
    "text_encoder": 1.0,
    "text_encoder_2": 1.0,
}
pipe.set_adapters(["offset"], [offset_scales])

prompt = "high quality photo of a panther in the jungle, night, dark, low light, dim"
generator = torch.Generator(device="cpu").manual_seed(899041154)

image = pipe(
    prompt=prompt,
    negative_prompt="",
    guidance_scale=6.5,
    num_inference_steps=25,
    generator=generator,
).images[0]
image.save("result.png")

Logs

No response

System Info

• diffusers version: 0.28.0.dev0
• Platform: Linux-6.8.7-arch1-1-x86_64-with-glibc2.39
• Python version: 3.11.8
• PyTorch version (GPU?): 2.2.2+cu121 (True)
• Huggingface_hub version: 0.20.3
• Transformers version: 4.40.1
• Accelerate version: 0.30.0
• xFormers version: not installed

Who can help?

@sayakpaul @UmerHA

[asomoza]

I can open a PR to fix it but first I would like to know if there is a reason behind raising the error here:

diffusers/src/diffusers/utils/peft_utils.py

Line 249 in 7fa3e5b

raise RuntimeError(f"No LoRA weight found for module {module_name}.")

instead of just returning a default value.

[UmerHA]

@asomoza At this point the lora weight dict is expanded and should contain an entry for every peft-tunable module. So that error should never happen.

My naive guess is that convolutions are now also peft-tunable (ie a subclass of BaseTunerLayer) but the dict expansion doesn't reflect that.

Imo returning a default value is fine.

[asomoza]

thanks for the clarification, I really appreciate it.