replace references to deprecated KeyArray & PRNGKeyArray

setup.py

@@ -102,8 +102,8 @@
     "importlib_metadata",
     "invisible-watermark>=0.2.0",
     "isort>=5.5.4",
-    "jax>=0.2.8,!=0.3.2",
-    "jaxlib>=0.1.65",
+    "jax>=0.4.1",
+    "jaxlib>=0.4.1",
     "Jinja2",
     "k-diffusion>=0.0.12",
     "torchsde",

src/diffusers/dependency_versions_table.py

@@ -15,8 +15,8 @@
     "importlib_metadata": "importlib_metadata",
     "invisible-watermark": "invisible-watermark>=0.2.0",
     "isort": "isort>=5.5.4",
-    "jax": "jax>=0.2.8,!=0.3.2",
-    "jaxlib": "jaxlib>=0.1.65",
+    "jax": "jax>=0.4.1",
+    "jaxlib": "jaxlib>=0.4.1",
     "Jinja2": "Jinja2",
     "k-diffusion": "k-diffusion>=0.0.12",
     "torchsde": "torchsde",

src/diffusers/models/controlnet_flax.py

@@ -168,7 +168,7 @@ class FlaxControlNetModel(nn.Module, FlaxModelMixin, ConfigMixin):
     controlnet_conditioning_channel_order: str = "rgb"
     conditioning_embedding_out_channels: Tuple[int] = (16, 32, 96, 256)
 
-    def init_weights(self, rng: jax.random.KeyArray) -> FrozenDict:
+    def init_weights(self, rng: jax.Array) -> FrozenDict:
         # init input tensors
         sample_shape = (1, self.in_channels, self.sample_size, self.sample_size)
         sample = jnp.zeros(sample_shape, dtype=jnp.float32)

src/diffusers/models/modeling_flax_utils.py

@@ -192,7 +192,7 @@ def to_fp16(self, params: Union[Dict, FrozenDict], mask: Any = None):
         ```"""
         return self._cast_floating_to(params, jnp.float16, mask)
 
-    def init_weights(self, rng: jax.random.KeyArray) -> Dict:
+    def init_weights(self, rng: jax.Array) -> Dict:
         raise NotImplementedError(f"init_weights method has to be implemented for {self}")
 
     @classmethod

src/diffusers/models/unet_2d_condition_flax.py

@@ -126,7 +126,7 @@ class FlaxUNet2DConditionModel(nn.Module, FlaxModelMixin, ConfigMixin):
     addition_embed_type_num_heads: int = 64
     projection_class_embeddings_input_dim: Optional[int] = None
 
-    def init_weights(self, rng: jax.random.KeyArray) -> FrozenDict:
+    def init_weights(self, rng: jax.Array) -> FrozenDict:
         # init input tensors
         sample_shape = (1, self.in_channels, self.sample_size, self.sample_size)
         sample = jnp.zeros(sample_shape, dtype=jnp.float32)

src/diffusers/models/vae_flax.py

@@ -817,7 +817,7 @@ def setup(self):
             dtype=self.dtype,
         )
 
-    def init_weights(self, rng: jax.random.KeyArray) -> FrozenDict:
+    def init_weights(self, rng: jax.Array) -> FrozenDict:
         # init input tensors
         sample_shape = (1, self.in_channels, self.sample_size, self.sample_size)
         sample = jnp.zeros(sample_shape, dtype=jnp.float32)

src/diffusers/pipelines/controlnet/pipeline_flax_controlnet.py

@@ -241,7 +241,7 @@ def _generate(
         prompt_ids: jnp.array,
         image: jnp.array,
         params: Union[Dict, FrozenDict],
-        prng_seed: jax.random.KeyArray,
+        prng_seed: jax.Array,
         num_inference_steps: int,
         guidance_scale: float,
         latents: Optional[jnp.array] = None,
@@ -351,7 +351,7 @@ def __call__(
         prompt_ids: jnp.array,
         image: jnp.array,
         params: Union[Dict, FrozenDict],
-        prng_seed: jax.random.KeyArray,
+        prng_seed: jax.Array,
         num_inference_steps: int = 50,
         guidance_scale: Union[float, jnp.array] = 7.5,
         latents: jnp.array = None,
@@ -370,7 +370,7 @@ def __call__(
                 Array representing the ControlNet input condition to provide guidance to the `unet` for generation.
             params (`Dict` or `FrozenDict`):
                 Dictionary containing the model parameters/weights.
-            prng_seed (`jax.random.KeyArray` or `jax.Array`):
+            prng_seed (`jax.Array` or `jax.Array`):
                 Array containing random number generator key.
             num_inference_steps (`int`, *optional*, defaults to 50):
                 The number of denoising steps. More denoising steps usually lead to a higher quality image at the

src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion.py

@@ -215,7 +215,7 @@ def _generate(
         self,
         prompt_ids: jnp.array,
         params: Union[Dict, FrozenDict],
-        prng_seed: jax.random.KeyArray,
+        prng_seed: jax.Array,
         num_inference_steps: int,
         height: int,
         width: int,
@@ -312,7 +312,7 @@ def __call__(
         self,
         prompt_ids: jnp.array,
         params: Union[Dict, FrozenDict],
-        prng_seed: jax.random.KeyArray,
+        prng_seed: jax.Array,
         num_inference_steps: int = 50,
         height: Optional[int] = None,
         width: Optional[int] = None,

src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion_img2img.py

@@ -235,7 +235,7 @@ def _generate(
         prompt_ids: jnp.array,
         image: jnp.array,
         params: Union[Dict, FrozenDict],
-        prng_seed: jax.random.KeyArray,
+        prng_seed: jax.Array,
         start_timestep: int,
         num_inference_steps: int,
         height: int,
@@ -340,7 +340,7 @@ def __call__(
         prompt_ids: jnp.array,
         image: jnp.array,
         params: Union[Dict, FrozenDict],
-        prng_seed: jax.random.KeyArray,
+        prng_seed: jax.Array,
         strength: float = 0.8,
         num_inference_steps: int = 50,
         height: Optional[int] = None,
@@ -361,7 +361,7 @@ def __call__(
                 Array representing an image batch to be used as the starting point.
             params (`Dict` or `FrozenDict`):
                 Dictionary containing the model parameters/weights.
-            prng_seed (`jax.random.KeyArray` or `jax.Array`):
+            prng_seed (`jax.Array` or `jax.Array`):
                 Array containing random number generator key.
             strength (`float`, *optional*, defaults to 0.8):
                 Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a

src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion_inpaint.py

@@ -270,7 +270,7 @@ def _generate(
         mask: jnp.array,
         masked_image: jnp.array,
         params: Union[Dict, FrozenDict],
-        prng_seed: jax.random.KeyArray,
+        prng_seed: jax.Array,
         num_inference_steps: int,
         height: int,
         width: int,
@@ -398,7 +398,7 @@ def __call__(
         mask: jnp.array,
         masked_image: jnp.array,
         params: Union[Dict, FrozenDict],
-        prng_seed: jax.random.KeyArray,
+        prng_seed: jax.Array,
         num_inference_steps: int = 50,
         height: Optional[int] = None,
         width: Optional[int] = None,

src/diffusers/pipelines/stable_diffusion/safety_checker_flax.py

@@ -87,7 +87,7 @@ def __init__(
         module = self.module_class(config=config, dtype=dtype, **kwargs)
         super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init)
 
-    def init_weights(self, rng: jax.random.KeyArray, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict:
+    def init_weights(self, rng: jax.Array, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict:
         # init input tensor
         clip_input = jax.random.normal(rng, input_shape)
 

src/diffusers/pipelines/stable_diffusion_xl/pipeline_flax_stable_diffusion_xl.py

@@ -89,7 +89,7 @@ def __call__(
         self,
         prompt_ids: jax.Array,
         params: Union[Dict, FrozenDict],
-        prng_seed: jax.random.KeyArray,
+        prng_seed: jax.Array,
         num_inference_steps: int = 50,
         guidance_scale: Union[float, jax.Array] = 7.5,
         height: Optional[int] = None,
@@ -170,7 +170,7 @@ def _generate(
         self,
         prompt_ids: jnp.array,
         params: Union[Dict, FrozenDict],
-        prng_seed: jax.random.KeyArray,
+        prng_seed: jax.Array,
         num_inference_steps: int,
         height: int,
         width: int,

src/diffusers/schedulers/scheduling_ddpm_flax.py

@@ -198,7 +198,7 @@ def step(
         model_output: jnp.ndarray,
         timestep: int,
         sample: jnp.ndarray,
-        key: Optional[jax.random.KeyArray] = None,
+        key: Optional[jax.Array] = None,
         return_dict: bool = True,
     ) -> Union[FlaxDDPMSchedulerOutput, Tuple]:
         """
@@ -211,7 +211,7 @@ def step(
             timestep (`int`): current discrete timestep in the diffusion chain.
             sample (`jnp.ndarray`):
                 current instance of sample being created by diffusion process.
-            key (`jax.random.KeyArray`): a PRNG key.
+            key (`jax.Array`): a PRNG key.
             return_dict (`bool`): option for returning tuple rather than FlaxDDPMSchedulerOutput class
 
         Returns:

src/diffusers/schedulers/scheduling_karras_ve_flax.py

@@ -17,6 +17,7 @@
 from typing import Optional, Tuple, Union
 
 import flax
+import jax
 import jax.numpy as jnp
 from jax import random
 
@@ -139,7 +140,7 @@ def add_noise_to_input(
         state: KarrasVeSchedulerState,
         sample: jnp.ndarray,
         sigma: float,
-        key: random.KeyArray,
+        key: jax.Array,
     ) -> Tuple[jnp.ndarray, float]:
         """
         Explicit Langevin-like "churn" step of adding noise to the sample according to a factor gamma_i ≥ 0 to reach a

src/diffusers/schedulers/scheduling_sde_ve_flax.py

@@ -18,6 +18,7 @@
 from typing import Optional, Tuple, Union
 
 import flax
+import jax
 import jax.numpy as jnp
 from jax import random
 
@@ -169,7 +170,7 @@ def step_pred(
         model_output: jnp.ndarray,
         timestep: int,
         sample: jnp.ndarray,
-        key: random.KeyArray,
+        key: jax.Array,
         return_dict: bool = True,
     ) -> Union[FlaxSdeVeOutput, Tuple]:
         """
@@ -228,7 +229,7 @@ def step_correct(
         state: ScoreSdeVeSchedulerState,
         model_output: jnp.ndarray,
         sample: jnp.ndarray,
-        key: random.KeyArray,
+        key: jax.Array,
         return_dict: bool = True,
     ) -> Union[FlaxSdeVeOutput, Tuple]:
         """