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Make LoRACompatibleConv padding_mode work. #6031

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merged 9 commits into from
Feb 27, 2024
Merged

Make LoRACompatibleConv padding_mode work. #6031

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2994616
Make LoRACompatibleConv padding_mode work.
jinghuan-Chen Dec 3, 2023
d5ea454
Format code style.
jinghuan-Chen Dec 4, 2023
c2de7c7
add fast test
jinghuan-Chen Dec 5, 2023
234a405
Update src/diffusers/models/lora.py
jinghuan-Chen Jan 7, 2024
a441798
code refactor
jinghuan-Chen Jan 10, 2024
5dcc27d
apply patrickvonplaten suggestion to simplify the code.
jinghuan-Chen Feb 11, 2024
949b01e
rm test_lora_layers_old_backend.py and add test case in test_lora_lay…
jinghuan-Chen Feb 24, 2024
f7ef216
update test case.
jinghuan-Chen Feb 25, 2024
87ec26a
Merge branch 'main' into make-LoRACompatibleConv-paddingmode-work
yiyixuxu Feb 26, 2024
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19 changes: 13 additions & 6 deletions src/diffusers/models/lora.py
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Original file line number Diff line number Diff line change
Expand Up @@ -355,13 +355,20 @@ def forward(self, hidden_states: torch.Tensor, scale: float = 1.0) -> torch.Tens
if self.lora_layer is None:
# make sure to the functional Conv2D function as otherwise torch.compile's graph will break
# see: https://github.com/huggingface/diffusers/pull/4315
return F.conv2d(
hidden_states, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups
)
if self.padding_mode != 'zeros':
return F.conv2d(F.pad(hidden_states, self._reversed_padding_repeated_twice, mode=self.padding_mode),
self.weight, self.bias, self.stride,
(0, 0), self.dilation, self.groups)
return F.conv2d(hidden_states, self.weight, self.bias, self.stride,
self.padding, self.dilation, self.groups)
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Can we compute original_outputs one time and the reuse it? That way, I think the code will remain cleaner.

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@jinghuan-Chen jinghuan-Chen Dec 4, 2023 •
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Thanks for your guidance.
There is currently no better, cleaner code solution. If you have, please give me some guidance.

testing script

import torch
from diffusers import DiffusionPipeline


# Modify the padding mode of Conv2d
def set_pad_mode(network, mode="circular"):
    for _, module in network.named_children():
        if len(module._modules) > 0:
            set_pad_mode(module, mode)
        else:
            if isinstance(module, torch.nn.Conv2d):
                module.padding_mode = mode


base = DiffusionPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0",
    torch_dtype=torch.float16,
    variant="fp16",
    use_safetensors=True,
)
base.to("cuda")
n_steps = 30
prompt = "interior design, Equirectangular, Panoramic, Panorama and 360"

set_pad_mode(base.vae, "circular")
set_pad_mode(base.unet, "circular")


image = base(
    prompt=prompt,
    height=1024,
    width=2048,
    num_inference_steps=n_steps,
    output_type="pil",
).images[0]

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I don't this comment was addressed:

#6031 (comment)

Also, by testing, I meant adding a test to our testing suite. In this case, you add one here and here.

Does that make sense?

else:
original_outputs = F.conv2d(
hidden_states, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups
)
if self.padding_mode != 'zeros':
original_outputs = F.conv2d(F.pad(hidden_states, self._reversed_padding_repeated_twice, mode=self.padding_mode),
self.weight, self.bias, self.stride,
(0, 0), self.dilation, self.groups)
else:
original_outputs = F.conv2d(hidden_states, self.weight, self.bias, self.stride,
self.padding, self.dilation, self.groups)
return original_outputs + (scale * self.lora_layer(hidden_states))


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