[Feature] Use default args for pretrained weights name

[okotaku]

Motivation

Use default args when building model components.

Modification

Before

from diffusers import AutoencoderKL, DDPMScheduler, UNet2DConditionModel
from transformers import CLIPTextModel, CLIPTokenizer

from diffengine.models.editors import StableDiffusion

base_model = "runwayml/stable-diffusion-v1-5"
model = dict(type=StableDiffusion,
             model=base_model,
             tokenizer=dict(type=CLIPTokenizer.from_pretrained,
                            pretrained_model_name_or_path=base_model,
                            subfolder="tokenizer"),
             scheduler=dict(type=DDPMScheduler.from_pretrained,
                            pretrained_model_name_or_path=base_model,
                            subfolder="scheduler"),
             text_encoder=dict(type=CLIPTextModel.from_pretrained,
                               pretrained_model_name_or_path=base_model,
                               subfolder="text_encoder"),
             vae=dict(
                type=AutoencoderKL.from_pretrained,
                pretrained_model_name_or_path=base_model,
                subfolder="vae"),
             unet=dict(type=UNet2DConditionModel.from_pretrained,
                             pretrained_model_name_or_path=base_model,
                             subfolder="unet"))

After

from diffusers import AutoencoderKL, DDPMScheduler, UNet2DConditionModel
from transformers import CLIPTextModel, CLIPTokenizer

from diffengine.models.editors import StableDiffusion

base_model = "runwayml/stable-diffusion-v1-5"
model = dict(type=StableDiffusion,
             model=base_model,
             tokenizer=dict(type=CLIPTokenizer.from_pretrained,
                            subfolder="tokenizer"),
             scheduler=dict(type=DDPMScheduler.from_pretrained,
                            subfolder="scheduler"),
             text_encoder=dict(type=CLIPTextModel.from_pretrained,
                               subfolder="text_encoder"),
             vae=dict(
                type=AutoencoderKL.from_pretrained,
                subfolder="vae"),
             unet=dict(type=UNet2DConditionModel.from_pretrained,
                             subfolder="unet"))

Checklist

Before PR:

• Pre-commit or other linting tools are used to fix the potential lint issues.
• Bug fixes are fully covered by unit tests, the case that causes the bug should be added in the unit tests.
• The modification is covered by complete unit tests. If not, please add more unit test to ensure the correctness.
• The documentation has been modified accordingly, like docstring or example tutorials.

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📚 Documentation preview 📚: https://DiffEngine--122.org.readthedocs.build/en/122/