Update all files for DiffusionSat-Single-256

pipeline_diffusionsat.py

@@ -9,7 +9,11 @@ from typing import Any, Callable, Dict, List, Optional, Union
 
 import torch
 from packaging import version
-from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
+from transformers import CLIPTextModel, CLIPTokenizer
+try:
+    from transformers import CLIPImageProcessor
+except ImportError:
+    from transformers import CLIPFeatureExtractor as CLIPImageProcessor
 
 from diffusers.configuration_utils import FrozenDict
 from diffusers.models import AutoencoderKL
@@ -17,10 +21,13 @@ from diffusers.schedulers import KarrasDiffusionSchedulers
 from diffusers.utils import (
     deprecate,
     logging,
-    randn_tensor,
     replace_example_docstring,
     is_accelerate_available,
 )
+try:
+    from diffusers.utils import randn_tensor
+except ImportError:
+    from diffusers.utils.torch_utils import randn_tensor
 from diffusers.pipelines.pipeline_utils import DiffusionPipeline
 from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
 from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
@@ -60,7 +67,7 @@ class DiffusionSatPipeline(DiffusionPipeline):
         unet: Any,
         scheduler: KarrasDiffusionSchedulers,
         safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPFeatureExtractor,
+        feature_extractor: CLIPImageProcessor,
         requires_safety_checker: bool = True,
     ):
         super().__init__()
@@ -198,6 +205,12 @@ class DiffusionSatPipeline(DiffusionPipeline):
         cross_attention_kwargs: Optional[Dict[str, Any]] = None,
         metadata: Optional[List[float]] = None,
     ):
+        """
+        Run inference (text-to-image with optional metadata).
+
+        Examples:
+
+        """
         # 0. Default height and width to unet
         height = height or self.unet.config.sample_size * self.vae_scale_factor
         width = width or self.unet.config.sample_size * self.vae_scale_factor