declare DIFFUSERS_REQUEST_TIMEOUT in constants and import when needed

Author: kghamilton89

examples/instruct_pix2pix/train_instruct_pix2pix.py

@@ -49,6 +49,7 @@
 from diffusers.optimization import get_scheduler
 from diffusers.training_utils import EMAModel
 from diffusers.utils import check_min_version, deprecate, is_wandb_available
+from diffusers.utils.constants import DIFFUSERS_REQUEST_TIMEOUT
 from diffusers.utils.import_utils import is_xformers_available
 from diffusers.utils.torch_utils import is_compiled_module
 
@@ -67,10 +68,6 @@
 WANDB_TABLE_COL_NAMES = ["original_image", "edited_image", "edit_prompt"]
 
 
-# Set global timeout
-request_timeout = int(os.environ.get("DIFFUSERS_REQUEST_TIMEOUT", 60))
-
-
 def log_validation(
     pipeline,
     args,
@@ -422,7 +419,7 @@ def convert_to_np(image, resolution):
 
 
 def download_image(url):
-    image = PIL.Image.open(requests.get(url, stream=True, timeout=request_timeout).raw)
+    image = PIL.Image.open(requests.get(url, stream=True, timeout=DIFFUSERS_REQUEST_TIMEOUT).raw)
     image = PIL.ImageOps.exif_transpose(image)
     image = image.convert("RGB")
     return image

examples/research_projects/instructpix2pix_lora/train_instruct_pix2pix_lora.py

@@ -54,6 +54,7 @@
 from diffusers.optimization import get_scheduler
 from diffusers.training_utils import EMAModel, cast_training_params
 from diffusers.utils import check_min_version, convert_state_dict_to_diffusers, deprecate, is_wandb_available
+from diffusers.utils.constants import DIFFUSERS_REQUEST_TIMEOUT
 from diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card
 from diffusers.utils.import_utils import is_xformers_available
 from diffusers.utils.torch_utils import is_compiled_module
@@ -74,10 +75,6 @@
 WANDB_TABLE_COL_NAMES = ["original_image", "edited_image", "edit_prompt"]
 
 
-# Set global timeout
-request_timeout = int(os.environ.get("DIFFUSERS_REQUEST_TIMEOUT", 60))
-
-
 def save_model_card(
     repo_id: str,
     images: list = None,
@@ -479,7 +476,7 @@ def convert_to_np(image, resolution):
 
 
 def download_image(url):
-    image = PIL.Image.open(requests.get(url, stream=True, timeout=request_timeout).raw)
+    image = PIL.Image.open(requests.get(url, stream=True, timeout=DIFFUSERS_REQUEST_TIMEOUT).raw)
     image = PIL.ImageOps.exif_transpose(image)
     image = image.convert("RGB")
     return image

examples/research_projects/promptdiffusion/convert_original_promptdiffusion_to_diffusers.py

@@ -60,6 +60,7 @@
     UnCLIPScheduler,
 )
 from diffusers.utils import is_accelerate_available, logging
+from diffusers.utils.constants import DIFFUSERS_REQUEST_TIMEOUT
 
 
 if is_accelerate_available():
@@ -69,10 +70,6 @@
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
-# Set global timeout
-request_timeout = int(os.environ.get("DIFFUSERS_REQUEST_TIMEOUT", 60))
-
-
 def shave_segments(path, n_shave_prefix_segments=1):
     """
     Removes segments. Positive values shave the first segments, negative shave the last segments.
@@ -1440,7 +1437,7 @@ def download_from_original_stable_diffusion_ckpt(
             config_url = "https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/x4-upscaling.yaml"
 
         if config_url is not None:
-            original_config_file = BytesIO(requests.get(config_url, timeout=request_timeout).content)
+            original_config_file = BytesIO(requests.get(config_url, timeout=DIFFUSERS_REQUEST_TIMEOUT).content)
         else:
             with open(original_config_file, "r") as f:
                 original_config_file = f.read()

scripts/convert_dance_diffusion_to_diffusers.py

@@ -11,6 +11,7 @@
 from torch import nn
 
 from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNet1DModel
+from diffusers.utils.constants import DIFFUSERS_REQUEST_TIMEOUT
 
 
 MODELS_MAP = {
@@ -46,9 +47,6 @@
     },
 }
 
-# Set global timeout
-request_timeout = int(os.environ.get("DIFFUSERS_REQUEST_TIMEOUT", 60))
-
 
 def alpha_sigma_to_t(alpha, sigma):
     """Returns a timestep, given the scaling factors for the clean image and for
@@ -77,7 +75,7 @@ def __init__(self, global_args):
 
 def download(model_name):
     url = MODELS_MAP[model_name]["url"]
-    r = requests.get(url, stream=True, timeout=request_timeout)
+    r = requests.get(url, stream=True, timeout=DIFFUSERS_REQUEST_TIMEOUT)
 
     local_filename = f"./{model_name}.ckpt"
     with open(local_filename, "wb") as fp:

scripts/convert_vae_pt_to_diffusers.py

@@ -1,6 +1,5 @@
 import argparse
 import io
-import os
 
 import requests
 import torch
@@ -14,10 +13,7 @@
     renew_vae_attention_paths,
     renew_vae_resnet_paths,
 )
-
-
-# Set global timeout
-request_timeout = int(os.environ.get("DIFFUSERS_REQUEST_TIMEOUT", 60))
+from diffusers.utils.constants import DIFFUSERS_REQUEST_TIMEOUT
 
 
 def custom_convert_ldm_vae_checkpoint(checkpoint, config):
@@ -127,7 +123,7 @@ def vae_pt_to_vae_diffuser(
 ):
     # Only support V1
     r = requests.get(
-        " https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml", timeout=request_timeout
+        " https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml", timeout=DIFFUSERS_REQUEST_TIMEOUT
     )
     io_obj = io.BytesIO(r.content)
 

src/diffusers/loaders/single_file_utils.py

@@ -44,6 +44,7 @@
     is_transformers_available,
     logging,
 )
+from ..utils.constants import DIFFUSERS_REQUEST_TIMEOUT
 from ..utils.hub_utils import _get_model_file
 
 
@@ -57,9 +58,6 @@
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
-# Set global timeout
-request_timeout = int(os.environ.get("DIFFUSERS_REQUEST_TIMEOUT", 60))
-
 CHECKPOINT_KEY_NAMES = {
     "v2": "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight",
     "xl_base": "conditioner.embedders.1.model.transformer.resblocks.9.mlp.c_proj.bias",
@@ -446,7 +444,7 @@ def fetch_original_config(original_config_file, local_files_only=False):
                 "Please provide a valid local file path."
             )
 
-        original_config_file = BytesIO(requests.get(original_config_file, timeout=request_timeout).content)
+        original_config_file = BytesIO(requests.get(original_config_file, timeout=DIFFUSERS_REQUEST_TIMEOUT).content)
 
     else:
         raise ValueError("Invalid `original_config_file` provided. Please set it to a valid file path or URL.")

src/diffusers/pipelines/stable_diffusion/convert_from_ckpt.py

@@ -53,6 +53,7 @@
     UnCLIPScheduler,
 )
 from ...utils import is_accelerate_available, logging
+from ...utils.constants import DIFFUSERS_REQUEST_TIMEOUT
 from ..latent_diffusion.pipeline_latent_diffusion import LDMBertConfig, LDMBertModel
 from ..paint_by_example import PaintByExampleImageEncoder
 from ..pipeline_utils import DiffusionPipeline
@@ -67,10 +68,6 @@
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
-# Set global timeout
-request_timeout = int(os.environ.get("DIFFUSERS_REQUEST_TIMEOUT", 60))
-
-
 def shave_segments(path, n_shave_prefix_segments=1):
     """
     Removes segments. Positive values shave the first segments, negative shave the last segments.
@@ -1329,7 +1326,7 @@ def download_from_original_stable_diffusion_ckpt(
             config_url = "https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/x4-upscaling.yaml"
 
         if config_url is not None:
-            original_config_file = BytesIO(requests.get(config_url, timeout=request_timeout).content)
+            original_config_file = BytesIO(requests.get(config_url, timeout=DIFFUSERS_REQUEST_TIMEOUT).content)
         else:
             with open(original_config_file, "r") as f:
                 original_config_file = f.read()

src/diffusers/utils/constants.py

@@ -40,6 +40,7 @@
 DIFFUSERS_DYNAMIC_MODULE_NAME = "diffusers_modules"
 HF_MODULES_CACHE = os.getenv("HF_MODULES_CACHE", os.path.join(HF_HOME, "modules"))
 DEPRECATED_REVISION_ARGS = ["fp16", "non-ema"]
+DIFFUSERS_REQUEST_TIMEOUT = 60
 
 # Below should be `True` if the current version of `peft` and `transformers` are compatible with
 # PEFT backend. Will automatically fall back to PEFT backend if the correct versions of the libraries are

src/diffusers/utils/loading_utils.py

@@ -7,13 +7,10 @@
 import PIL.ImageOps
 import requests
 
+from .constants import DIFFUSERS_REQUEST_TIMEOUT
 from .import_utils import BACKENDS_MAPPING, is_imageio_available
 
 
-# Set global timeout
-request_timeout = int(os.environ.get("DIFFUSERS_REQUEST_TIMEOUT", 60))
-
-
 def load_image(
     image: Union[str, PIL.Image.Image], convert_method: Optional[Callable[[PIL.Image.Image], PIL.Image.Image]] = None
 ) -> PIL.Image.Image:
@@ -33,7 +30,7 @@ def load_image(
     """
     if isinstance(image, str):
         if image.startswith("http://") or image.startswith("https://"):
-            image = PIL.Image.open(requests.get(image, stream=True, timeout=request_timeout).raw)
+            image = PIL.Image.open(requests.get(image, stream=True, timeout=DIFFUSERS_REQUEST_TIMEOUT).raw)
         elif os.path.isfile(image):
             image = PIL.Image.open(image)
         else:

src/diffusers/utils/testing_utils.py

@@ -26,6 +26,7 @@
 from numpy.linalg import norm
 from packaging import version
 
+from .constants import DIFFUSERS_REQUEST_TIMEOUT
 from .import_utils import (
     BACKENDS_MAPPING,
     is_accelerate_available,
@@ -47,9 +48,6 @@
 from .logging import get_logger
 
 
-# Set global timeout
-request_timeout = int(os.environ.get("DIFFUSERS_REQUEST_TIMEOUT", 60))
-
 global_rng = random.Random()
 
 logger = get_logger(__name__)
@@ -597,7 +595,7 @@ def load_numpy(arry: Union[str, np.ndarray], local_path: Optional[str] = None) -
             # local_path can be passed to correct images of tests
             return Path(local_path, arry.split("/")[-5], arry.split("/")[-2], arry.split("/")[-1]).as_posix()
         elif arry.startswith("http://") or arry.startswith("https://"):
-            response = requests.get(arry, timeout=request_timeout)
+            response = requests.get(arry, timeout=DIFFUSERS_REQUEST_TIMEOUT)
             response.raise_for_status()
             arry = np.load(BytesIO(response.content))
         elif os.path.isfile(arry):
@@ -618,7 +616,7 @@ def load_numpy(arry: Union[str, np.ndarray], local_path: Optional[str] = None) -
 
 
 def load_pt(url: str, map_location: str):
-    response = requests.get(url, timeout=request_timeout)
+    response = requests.get(url, timeout=DIFFUSERS_REQUEST_TIMEOUT)
     response.raise_for_status()
     arry = torch.load(BytesIO(response.content), map_location=map_location)
     return arry
@@ -637,7 +635,7 @@ def load_image(image: Union[str, PIL.Image.Image]) -> PIL.Image.Image:
     """
     if isinstance(image, str):
         if image.startswith("http://") or image.startswith("https://"):
-            image = PIL.Image.open(requests.get(image, stream=True, timeout=request_timeout).raw)
+            image = PIL.Image.open(requests.get(image, stream=True, timeout=DIFFUSERS_REQUEST_TIMEOUT).raw)
         elif os.path.isfile(image):
             image = PIL.Image.open(image)
         else: