make style && make quality

Author: lawrence-cj

scripts/convert_sana_pag_to_diffusers.py

@@ -7,6 +7,9 @@
 
 import torch
 from accelerate import init_empty_weights
+from termcolor import colored
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
 from diffusers import (
     DCAE,
     DPMSolverMultistepScheduler,
@@ -16,8 +19,7 @@
 )
 from diffusers.models.modeling_utils import load_model_dict_into_meta
 from diffusers.utils.import_utils import is_accelerate_available
-from termcolor import colored
-from transformers import AutoModelForCausalLM, AutoTokenizer
+
 
 CTX = init_empty_weights if is_accelerate_available else nullcontext
 
@@ -203,7 +205,7 @@ def main(args):
         # Scheduler
         if args.scheduler_type == "flow-dpm_solver":
             scheduler = DPMSolverMultistepScheduler(
-                flow_shift=flow_shift, 
+                flow_shift=flow_shift,
                 use_flow_sigmas=True,
                 prediction_type="flow_prediction",
             )

scripts/convert_sana_to_diffusers.py

@@ -7,6 +7,9 @@
 
 import torch
 from accelerate import init_empty_weights
+from termcolor import colored
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
 from diffusers import (
     DCAE,
     DPMSolverMultistepScheduler,
@@ -16,8 +19,7 @@
 )
 from diffusers.models.modeling_utils import load_model_dict_into_meta
 from diffusers.utils.import_utils import is_accelerate_available
-from termcolor import colored
-from transformers import AutoModelForCausalLM, AutoTokenizer
+
 
 CTX = init_empty_weights if is_accelerate_available else nullcontext
 
@@ -203,7 +205,7 @@ def main(args):
         # Scheduler
         if args.scheduler_type == "flow-dpm_solver":
             scheduler = DPMSolverMultistepScheduler(
-                flow_shift=flow_shift, 
+                flow_shift=flow_shift,
                 use_flow_sigmas=True,
                 prediction_type="flow_prediction",
             )

src/diffusers/__init__.py

@@ -803,9 +803,9 @@
             PixArtAlphaPipeline,
             PixArtSigmaPAGPipeline,
             PixArtSigmaPipeline,
-            SanaPipeline,
-            SanaPAGPipeline,
             ReduxImageEncoder,
+            SanaPAGPipeline,
+            SanaPipeline,
             SemanticStableDiffusionPipeline,
             ShapEImg2ImgPipeline,
             ShapEPipeline,

src/diffusers/models/attention.py

@@ -22,7 +22,14 @@
 from .activations import GEGLU, GELU, ApproximateGELU, FP32SiLU, SwiGLU, get_activation
 from .attention_processor import Attention, JointAttnProcessor2_0
 from .embeddings import SinusoidalPositionalEmbedding
-from .normalization import AdaLayerNorm, AdaLayerNormContinuous, AdaLayerNormZero, RMSNorm, SD35AdaLayerNormZeroX, RMSNorm2d
+from .normalization import (
+    AdaLayerNorm,
+    AdaLayerNormContinuous,
+    AdaLayerNormZero,
+    RMSNorm,
+    RMSNorm2d,
+    SD35AdaLayerNormZeroX,
+)
 
 
 logger = logging.get_logger(__name__)
@@ -1267,7 +1274,7 @@ def __init__(
         total_dim = heads * dim
 
         self.dim = dim
-        
+
         qkv = [nn.Conv2d(in_channels=in_channels, out_channels=3 * total_dim, kernel_size=1, bias=use_bias[0])]
         if norm[0] is None:
             pass

src/diffusers/models/autoencoders/__init__.py

@@ -1,4 +1,5 @@
 from .autoencoder_asym_kl import AsymmetricAutoencoderKL
+from .autoencoder_dc import DCAE
 from .autoencoder_kl import AutoencoderKL
 from .autoencoder_kl_allegro import AutoencoderKLAllegro
 from .autoencoder_kl_cogvideox import AutoencoderKLCogVideoX
@@ -7,5 +8,4 @@
 from .autoencoder_oobleck import AutoencoderOobleck
 from .autoencoder_tiny import AutoencoderTiny
 from .consistency_decoder_vae import ConsistencyDecoderVAE
-from .autoencoder_dc import DCAE
 from .vq_model import VQModel

src/diffusers/models/autoencoders/autoencoder_dc.py

@@ -13,25 +13,19 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import Any, Optional, Callable, Union
-from collections import OrderedDict
+from typing import Optional, Union
 
 import torch
 import torch.nn as nn
-import torch.nn.functional as F
 from torch.nn import BatchNorm2d
-from huggingface_hub import PyTorchModelHubMixin
-import ipdb
 
 from ...configuration_utils import ConfigMixin, register_to_config
-from ..modeling_utils import ModelMixin
-
 from ..activations import get_activation
-from ..normalization import RMSNorm2d
-from ..downsampling import ConvPixelUnshuffleDownsample2D, PixelUnshuffleChannelAveragingDownsample2D
-from ..upsampling import ConvPixelShuffleUpsample2D, ChannelDuplicatingPixelUnshuffleUpsample2D, Upsample2D
 from ..attention import DCAELiteMLA
-
+from ..downsampling import ConvPixelUnshuffleDownsample2D, PixelUnshuffleChannelAveragingDownsample2D
+from ..modeling_utils import ModelMixin
+from ..normalization import RMSNorm2d
+from ..upsampling import ChannelDuplicatingPixelUnshuffleUpsample2D, ConvPixelShuffleUpsample2D, Upsample2D
 from .vae import DecoderOutput
 
 
@@ -267,7 +261,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
 
 class Encoder(nn.Module):
     def __init__(
-        self, 
+        self,
         in_channels: int,
         latent_channels: int,
         width_list: list[int] = [128, 256, 512, 512, 1024, 1024],
@@ -291,7 +285,7 @@ def __init__(
             raise ValueError(f"len(depth_list) {len(depth_list)} and len(width_list) {len(width_list)} should be equal to num_stages {num_stages}")
         if not isinstance(block_type, (str, list)) or (isinstance(block_type, list) and len(block_type) != num_stages):
             raise ValueError(f"block_type should be either a str or a list of str with length {num_stages}, but got {block_type}")
-        
+
         # project in
         if depth_list[0] > 0:
             project_in_block = nn.Conv2d(
@@ -422,7 +416,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
 
 class Decoder(nn.Module):
     def __init__(
-        self, 
+        self,
         in_channels: int,
         latent_channels: int,
         in_shortcut: Optional[str] = "duplicating",

src/diffusers/pipelines/pag/pipeline_pag_sana.py

@@ -36,7 +36,6 @@
 from ...utils.torch_utils import randn_tensor
 from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
 from ..pixart_alpha.pipeline_pixart_alpha import (
-    ASPECT_RATIO_256_BIN,
     ASPECT_RATIO_512_BIN,
     ASPECT_RATIO_1024_BIN,
 )