Instructions to use EulerHuaji/difix_ref with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use EulerHuaji/difix_ref with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("EulerHuaji/difix_ref", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Update vae/autoencoder_kl.py
Browse files- vae/autoencoder_kl.py +2 -1
vae/autoencoder_kl.py
CHANGED
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@@ -19,6 +19,7 @@ from peft import LoraConfig
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from diffusers.configuration_utils import ConfigMixin, register_to_config
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# from diffusers.loaders import FromOriginalVAEMixin
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from diffusers.loaders.single_file_model import FromOriginalModelMixin
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from diffusers.utils.accelerate_utils import apply_forward_hook
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from diffusers.models.attention_processor import (
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@@ -77,7 +78,7 @@ def my_vae_decoder_fwd(self, sample, latent_embeds=None):
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return sample
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class AutoencoderKL(ModelMixin, ConfigMixin, FromOriginalModelMixin):
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r"""
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A VAE model with KL loss for encoding images into latents and decoding latent representations into images.
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from diffusers.configuration_utils import ConfigMixin, register_to_config
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# from diffusers.loaders import FromOriginalVAEMixin
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from diffusers.loaders import PeftAdapterMixin
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from diffusers.loaders.single_file_model import FromOriginalModelMixin
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from diffusers.utils.accelerate_utils import apply_forward_hook
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from diffusers.models.attention_processor import (
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return sample
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class AutoencoderKL(ModelMixin, ConfigMixin, FromOriginalModelMixin, PeftAdapterMixin):
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r"""
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A VAE model with KL loss for encoding images into latents and decoding latent representations into images.
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