Flax: Fix img2img and align with other pipeline (#1824)

* Flax: Add components function

* Flax: Fix img2img and align with other pipeline

* Flax: Fix PRNGKey type

* Refactor strength to start_timestep

* Fix preprocess images

* Fix processed_images dimen

* latents.shape -> latents_shape

* Fix typo

* Remove "static" comment

* Remove unnecessary optional types in _generate

* Apply doc-builder code style.

Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

Author: skirsten

src/diffusers/modeling_flax_utils.py

@@ -189,7 +189,7 @@ 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.PRNGKey) -> Dict:
+    def init_weights(self, rng: jax.random.KeyArray) -> Dict:
         raise NotImplementedError(f"init_weights method has to be implemented for {self}")
 
     @classmethod

src/diffusers/models/unet_2d_condition_flax.py

@@ -112,7 +112,7 @@ class FlaxUNet2DConditionModel(nn.Module, FlaxModelMixin, ConfigMixin):
     flip_sin_to_cos: bool = True
     freq_shift: int = 0
 
-    def init_weights(self, rng: jax.random.PRNGKey) -> FrozenDict:
+    def init_weights(self, rng: jax.random.KeyArray) -> 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

@@ -806,7 +806,7 @@ def setup(self):
             dtype=self.dtype,
         )
 
-    def init_weights(self, rng: jax.random.PRNGKey) -> FrozenDict:
+    def init_weights(self, rng: jax.random.KeyArray) -> 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/pipeline_flax_utils.py

@@ -17,7 +17,7 @@
 import importlib
 import inspect
 import os
-from typing import Dict, List, Optional, Union
+from typing import Any, Dict, List, Optional, Union
 
 import numpy as np
 
@@ -475,6 +475,51 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
         model = pipeline_class(**init_kwargs, dtype=dtype)
         return model, params
 
+    @staticmethod
+    def _get_signature_keys(obj):
+        parameters = inspect.signature(obj.__init__).parameters
+        required_parameters = {k: v for k, v in parameters.items() if v.default == inspect._empty}
+        optional_parameters = set({k for k, v in parameters.items() if v.default != inspect._empty})
+        expected_modules = set(required_parameters.keys()) - set(["self"])
+        return expected_modules, optional_parameters
+
+    @property
+    def components(self) -> Dict[str, Any]:
+        r"""
+
+        The `self.components` property can be useful to run different pipelines with the same weights and
+        configurations to not have to re-allocate memory.
+
+        Examples:
+
+        ```py
+        >>> from diffusers import (
+        ...     FlaxStableDiffusionPipeline,
+        ...     FlaxStableDiffusionImg2ImgPipeline,
+        ... )
+
+        >>> text2img = FlaxStableDiffusionPipeline.from_pretrained(
+        ...     "runwayml/stable-diffusion-v1-5", revision="bf16", dtype=jnp.bfloat16
+        ... )
+        >>> img2img = FlaxStableDiffusionImg2ImgPipeline(**text2img.components)
+        ```
+
+        Returns:
+            A dictionary containing all the modules needed to initialize the pipeline.
+        """
+        expected_modules, optional_parameters = self._get_signature_keys(self)
+        components = {
+            k: getattr(self, k) for k in self.config.keys() if not k.startswith("_") and k not in optional_parameters
+        }
+
+        if set(components.keys()) != expected_modules:
+            raise ValueError(
+                f"{self} has been incorrectly initialized or {self.__class__} is incorrectly implemented. Expected"
+                f" {expected_modules} to be defined, but {components} are defined."
+            )
+
+        return components
+
     @staticmethod
     def numpy_to_pil(images):
         """

src/diffusers/pipeline_utils.py

@@ -764,7 +764,7 @@ def components(self) -> Dict[str, Any]:
         ```
 
         Returns:
-            A dictionaly containing all the modules needed to initialize the pipeline.
+            A dictionary containing all the modules needed to initialize the pipeline.
         """
         expected_modules, optional_parameters = self._get_signature_keys(self)
         components = {

src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion.py

@@ -184,18 +184,14 @@ def _generate(
         self,
         prompt_ids: jnp.array,
         params: Union[Dict, FrozenDict],
-        prng_seed: jax.random.PRNGKey,
-        num_inference_steps: int = 50,
-        height: Optional[int] = None,
-        width: Optional[int] = None,
-        guidance_scale: float = 7.5,
+        prng_seed: jax.random.KeyArray,
+        num_inference_steps: int,
+        height: int,
+        width: int,
+        guidance_scale: float,
         latents: Optional[jnp.array] = None,
-        neg_prompt_ids: jnp.array = None,
+        neg_prompt_ids: Optional[jnp.array] = None,
     ):
-        # 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
-
         if height % 8 != 0 or width % 8 != 0:
             raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
 
@@ -281,15 +277,15 @@ def __call__(
         self,
         prompt_ids: jnp.array,
         params: Union[Dict, FrozenDict],
-        prng_seed: jax.random.PRNGKey,
+        prng_seed: jax.random.KeyArray,
         num_inference_steps: int = 50,
         height: Optional[int] = None,
         width: Optional[int] = None,
         guidance_scale: Union[float, jnp.array] = 7.5,
         latents: jnp.array = None,
+        neg_prompt_ids: jnp.array = None,
         return_dict: bool = True,
         jit: bool = False,
-        neg_prompt_ids: jnp.array = None,
     ):
         r"""
         Function invoked when calling the pipeline for generation.