misc fixes (#2282)

Co-authored-by: Patrick von Platen <[email protected]>

Author: williamberman

scripts/convert_original_stable_diffusion_to_diffusers.py

@@ -48,7 +48,10 @@
         "--pipeline_type",
         default=None,
         type=str,
-        help="The pipeline type. If `None` pipeline will be automatically inferred.",
+        help=(
+            "The pipeline type. One of 'FrozenOpenCLIPEmbedder', 'FrozenCLIPEmbedder', 'PaintByExample'"
+            ". If `None` pipeline will be automatically inferred."
+        ),
     )
     parser.add_argument(
         "--image_size",
@@ -65,7 +68,7 @@
         type=str,
         help=(
             "The prediction type that the model was trained on. Use 'epsilon' for Stable Diffusion v1.X and Stable"
-            " Siffusion v2 Base. Use 'v-prediction' for Stable Diffusion v2."
+            " Diffusion v2 Base. Use 'v_prediction' for Stable Diffusion v2."
         ),
     )
     parser.add_argument(
@@ -79,8 +82,7 @@
     )
     parser.add_argument(
         "--upcast_attention",
-        default=False,
-        type=bool,
+        action="store_true",
         help=(
             "Whether the attention computation should always be upcasted. This is necessary when running stable"
             " diffusion 2.1."
@@ -111,5 +113,6 @@
         num_in_channels=args.num_in_channels,
         upcast_attention=args.upcast_attention,
         from_safetensors=args.from_safetensors,
+        device=args.device,
     )
     pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)

src/diffusers/pipelines/audio_diffusion/mel.py

@@ -13,17 +13,12 @@
 # limitations under the License.
 
 
-import warnings
+import numpy as np  # noqa: E402
 
 from ...configuration_utils import ConfigMixin, register_to_config
 from ...schedulers.scheduling_utils import SchedulerMixin
 
 
-warnings.filterwarnings("ignore")
-
-import numpy as np  # noqa: E402
-
-
 try:
     import librosa  # noqa: E402
 

src/diffusers/pipelines/stable_diffusion/convert_from_ckpt.py

@@ -39,10 +39,13 @@
 from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder, PaintByExamplePipeline
 from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
 
-from ...utils import is_omegaconf_available, is_safetensors_available
+from ...utils import is_omegaconf_available, is_safetensors_available, logging
 from ...utils.import_utils import BACKENDS_MAPPING
 
 
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
 def shave_segments(path, n_shave_prefix_segments=1):
     """
     Removes segments. Positive values shave the first segments, negative shave the last segments.
@@ -801,11 +804,11 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
     corresponding to the original architecture. If `None`, will be
             automatically inferred by looking for a key that only exists in SD2.0 models.
     :param image_size: The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable
-    Siffusion v2
+    Diffusion v2
             Base. Use 768 for Stable Diffusion v2.
     :param prediction_type: The prediction type that the model was trained on. Use `'epsilon'` for Stable Diffusion
     v1.X and Stable
-            Siffusion v2 Base. Use `'v-prediction'` for Stable Diffusion v2.
+            Diffusion v2 Base. Use `'v_prediction'` for Stable Diffusion v2.
     :param num_in_channels: The number of input channels. If `None` number of input channels will be automatically
     inferred. :param scheduler_type: Type of scheduler to use. Should be one of `["pndm", "lms", "heun", "euler",
     "euler-ancestral", "dpm", "ddim"]`. :param model_type: The pipeline type. `None` to automatically infer, or one of
@@ -820,6 +823,8 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
     `checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch. :return: A
     StableDiffusionPipeline object representing the passed-in `.ckpt`/`.safetensors` file.
     """
+    if prediction_type == "v-prediction":
+        prediction_type = "v_prediction"
 
     if not is_omegaconf_available():
         raise ValueError(BACKENDS_MAPPING["omegaconf"][1])
@@ -957,6 +962,7 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
     # Convert the text model.
     if model_type is None:
         model_type = original_config.model.params.cond_stage_config.target.split(".")[-1]
+        logger.debug(f"no `model_type` given, `model_type` inferred as: {model_type}")
 
     if model_type == "FrozenOpenCLIPEmbedder":
         text_model = convert_open_clip_checkpoint(checkpoint)

src/diffusers/schedulers/scheduling_ddim.py

@@ -305,7 +305,6 @@ def step(
         prev_sample = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction
 
         if eta > 0:
-            # randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
             device = model_output.device
             if variance_noise is not None and generator is not None:
                 raise ValueError(

src/diffusers/schedulers/scheduling_unclip.py

@@ -106,8 +106,11 @@ def __init__(
         clip_sample: bool = True,
         clip_sample_range: Optional[float] = 1.0,
         prediction_type: str = "epsilon",
+        beta_schedule: str = "squaredcos_cap_v2",
     ):
-        # beta scheduler is "squaredcos_cap_v2"
+        if beta_schedule != "squaredcos_cap_v2":
+            raise ValueError("UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'")
+
         self.betas = betas_for_alpha_bar(num_train_timesteps)
 
         self.alphas = 1.0 - self.betas

src/diffusers/utils/testing_utils.py

@@ -17,23 +17,36 @@
 from packaging import version
 
 from .import_utils import is_flax_available, is_onnx_available, is_torch_available
+from .logging import get_logger
 
 
 global_rng = random.Random()
 
+logger = get_logger(__name__)
 
 if is_torch_available():
     import torch
 
-    torch_device = "cuda" if torch.cuda.is_available() else "cpu"
-    is_torch_higher_equal_than_1_12 = version.parse(version.parse(torch.__version__).base_version) >= version.parse(
-        "1.12"
-    )
+    if "DIFFUSERS_TEST_DEVICE" in os.environ:
+        torch_device = os.environ["DIFFUSERS_TEST_DEVICE"]
 
-    if is_torch_higher_equal_than_1_12:
-        # Some builds of torch 1.12 don't have the mps backend registered. See #892 for more details
-        mps_backend_registered = hasattr(torch.backends, "mps")
-        torch_device = "mps" if (mps_backend_registered and torch.backends.mps.is_available()) else torch_device
+        available_backends = ["cuda", "cpu", "mps"]
+        if torch_device not in available_backends:
+            raise ValueError(
+                f"unknown torch backend for diffusers tests: {torch_device}. Available backends are:"
+                f" {available_backends}"
+            )
+        logger.info(f"torch_device overrode to {torch_device}")
+    else:
+        torch_device = "cuda" if torch.cuda.is_available() else "cpu"
+        is_torch_higher_equal_than_1_12 = version.parse(
+            version.parse(torch.__version__).base_version
+        ) >= version.parse("1.12")
+
+        if is_torch_higher_equal_than_1_12:
+            # Some builds of torch 1.12 don't have the mps backend registered. See #892 for more details
+            mps_backend_registered = hasattr(torch.backends, "mps")
+            torch_device = "mps" if (mps_backend_registered and torch.backends.mps.is_available()) else torch_device
 
 
 def torch_all_close(a, b, *args, **kwargs):

tests/pipelines/unclip/test_unclip.py

@@ -30,6 +30,7 @@
 
 class UnCLIPPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
     pipeline_class = UnCLIPPipeline
+    test_xformers_attention = False
 
     required_optional_params = [
         "generator",

tests/test_pipelines_common.py

@@ -259,6 +259,7 @@ def _test_inference_batch_single_identical(
                 # Taking the median of the largest <n> differences
                 # is resilient to outliers
                 diff = np.abs(output_batch[0][0] - output[0][0])
+                diff = diff.flatten()
                 diff.sort()
                 max_diff = np.median(diff[-5:])
             else: