Align PT and Flax API - allow loading checkpoint from PyTorch configs

src/diffusers/pipeline_flax_utils.py

@@ -111,24 +111,27 @@ def register_modules(self, **kwargs):
         from diffusers import pipelines
 
         for name, module in kwargs.items():
-            # retrieve library
-            library = module.__module__.split(".")[0]
+            if module is None:
+                register_dict = {name: (None, None)}
+            else:
+                # retrieve library
+                library = module.__module__.split(".")[0]
 
-            # check if the module is a pipeline module
-            pipeline_dir = module.__module__.split(".")[-2]
-            path = module.__module__.split(".")
-            is_pipeline_module = pipeline_dir in path and hasattr(pipelines, pipeline_dir)
+                # check if the module is a pipeline module
+                pipeline_dir = module.__module__.split(".")[-2]
+                path = module.__module__.split(".")
+                is_pipeline_module = pipeline_dir in path and hasattr(pipelines, pipeline_dir)
 
-            # if library is not in LOADABLE_CLASSES, then it is a custom module.
-            # Or if it's a pipeline module, then the module is inside the pipeline
-            # folder so we set the library to module name.
-            if library not in LOADABLE_CLASSES or is_pipeline_module:
-                library = pipeline_dir
+                # if library is not in LOADABLE_CLASSES, then it is a custom module.
+                # Or if it's a pipeline module, then the module is inside the pipeline
+                # folder so we set the library to module name.
+                if library not in LOADABLE_CLASSES or is_pipeline_module:
+                    library = pipeline_dir
 
-            # retrieve class_name
-            class_name = module.__class__.__name__
+                # retrieve class_name
+                class_name = module.__class__.__name__
 
-            register_dict = {name: (library, class_name)}
+                register_dict = {name: (library, class_name)}
 
             # save model index config
             self.register_to_config(**register_dict)
@@ -320,6 +323,11 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
             pipeline_class = cls
         else:
             diffusers_module = importlib.import_module(cls.__module__.split(".")[0])
+            class_name = (
+                config_dict["_class_name"]
+                if config_dict["_class_name"].startswith("Flax")
+                else "Flax" + config_dict["_class_name"]
+            )
             pipeline_class = getattr(diffusers_module, config_dict["_class_name"])
 
         # some modules can be passed directly to the init
@@ -342,6 +350,7 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
         for name, (library_name, class_name) in init_dict.items():
             is_pipeline_module = hasattr(pipelines, library_name)
             loaded_sub_model = None
+            sub_model_should_be_defined = True
 
             # if the model is in a pipeline module, then we load it from the pipeline
             if name in passed_class_obj:
@@ -362,6 +371,12 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
                             f"{passed_class_obj[name]} is of type: {type(passed_class_obj[name])}, but should be"
                             f" {expected_class_obj}"
                         )
+                elif passed_class_obj[name] is None:
+                    logger.warn(
+                        f"You have passed `None` for {name} to disable its functionality in {pipeline_class}. Note"
+                        f" that this might lead to problems when using {pipeline_class} and is not recommended."
+                    )
+                    sub_model_should_be_defined = False
                 else:
                     logger.warn(
                         f"You have passed a non-standard module {passed_class_obj[name]}. We cannot verify whether it"
@@ -372,25 +387,19 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
                 loaded_sub_model = passed_class_obj[name]
             elif is_pipeline_module:
                 pipeline_module = getattr(pipelines, library_name)
-                if from_pt:
-                    class_obj = import_flax_or_no_model(pipeline_module, class_name)
-                else:
-                    class_obj = getattr(pipeline_module, class_name)
+                class_obj = import_flax_or_no_model(pipeline_module, class_name)
 
                 importable_classes = ALL_IMPORTABLE_CLASSES
                 class_candidates = {c: class_obj for c in importable_classes.keys()}
             else:
                 # else we just import it from the library.
                 library = importlib.import_module(library_name)
-                if from_pt:
-                    class_obj = import_flax_or_no_model(library, class_name)
-                else:
-                    class_obj = getattr(library, class_name)
+                class_obj = import_flax_or_no_model(library, class_name)
 
                 importable_classes = LOADABLE_CLASSES[library_name]
                 class_candidates = {c: getattr(library, c) for c in importable_classes.keys()}
 
-            if loaded_sub_model is None:
+            if loaded_sub_model is None and sub_model_should_be_defined:
                 load_method_name = None
                 for class_name, class_candidate in class_candidates.items():
                     if issubclass(class_obj, class_candidate):

src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion.py

@@ -14,10 +14,14 @@
 from ...models import FlaxAutoencoderKL, FlaxUNet2DConditionModel
 from ...pipeline_flax_utils import FlaxDiffusionPipeline
 from ...schedulers import FlaxDDIMScheduler, FlaxLMSDiscreteScheduler, FlaxPNDMScheduler
+from ...utils import logging
 from . import FlaxStableDiffusionPipelineOutput
 from .safety_checker_flax import FlaxStableDiffusionSafetyChecker
 
 
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
 class FlaxStableDiffusionPipeline(FlaxDiffusionPipeline):
     r"""
     Pipeline for text-to-image generation using Stable Diffusion.
@@ -60,6 +64,16 @@ def __init__(
         super().__init__()
         self.dtype = dtype
 
+        if safety_checker is None:
+            logger.warn(
+                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
+                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
+                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
+                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
+                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
+                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
+            )
+
         self.register_modules(
             vae=vae,
             text_encoder=text_encoder,
@@ -265,10 +279,23 @@ def __call__(
                 prompt_ids, params, prng_seed, num_inference_steps, height, width, guidance_scale, latents, debug
             )
 
-        safety_params = params["safety_checker"]
-        images = (images * 255).round().astype("uint8")
-        images = np.asarray(images).reshape(-1, height, width, 3)
-        images, has_nsfw_concept = self._run_safety_checker(images, safety_params, jit)
+        if self.safety_checker is not None:
+            safety_params = params["safety_checker"]
+            images_uint8_casted = (images * 255).round().astype("uint8")
+            num_devices, batch_size = images.shape[:2]
+
+            images_uint8_casted = np.asarray(images_uint8_casted).reshape(num_devices * batch_size, height, width, 3)
+            images_uint8_casted, has_nsfw_concept = self._run_safety_checker(images_uint8_casted, safety_params, jit)
+            images = np.asarray(images)
+
+            # block images
+            if any(has_nsfw_concept):
+                for i, is_nsfw in enumerate(has_nsfw_concept):
+                    images[i] = np.asarray(images_uint8_casted[i])
+
+            images = images.reshape(num_devices, batch_size, height, width, 3)
+        else:
+            has_nsfw_concept = False
 
         if not return_dict:
             return (images, has_nsfw_concept)

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py

@@ -73,7 +73,7 @@ def __init__(
 
         if safety_checker is None:
             logger.warn(
-                f"You have disabed the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
+                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
                 " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
                 " results in services or applications open to the public. Both the diffusers team and Hugging Face"
                 " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py

@@ -85,7 +85,7 @@ def __init__(
 
         if safety_checker is None:
             logger.warn(
-                f"You have disabed the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
+                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
                 " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
                 " results in services or applications open to the public. Both the diffusers team and Hugging Face"
                 " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py

@@ -100,7 +100,7 @@ def __init__(
 
         if safety_checker is None:
             logger.warn(
-                f"You have disabed the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
+                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
                 " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
                 " results in services or applications open to the public. Both the diffusers team and Hugging Face"
                 " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"

tests/test_pipelines_flax.py

@@ -23,6 +23,7 @@
 
 if is_flax_available():
     import jax
+    import jax.numpy as jnp
     from diffusers import FlaxStableDiffusionPipeline
     from flax.jax_utils import replicate
     from flax.training.common_utils import shard
@@ -34,7 +35,7 @@
 class FlaxPipelineTests(unittest.TestCase):
     def test_dummy_all_tpus(self):
         pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(
-            "hf-internal-testing/tiny-stable-diffusion-pipe"
+            "hf-internal-testing/tiny-stable-diffusion-pipe", safety_checker=None
         )
 
         prompt = (
@@ -57,6 +58,103 @@ def test_dummy_all_tpus(self):
         prompt_ids = shard(prompt_ids)
 
         images = p_sample(prompt_ids, params, prng_seed, num_inference_steps).images
+
+        assert images.shape == (8, 1, 64, 64, 3)
+        assert np.abs((np.abs(images[0, 0, :2, :2, -2:], dtype=np.float32).sum() - 4.151474)) < 1e-3
+        assert np.abs((np.abs(images, dtype=np.float32).sum() - 49947.875)) < 1e-2
+
         images_pil = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))
 
         assert len(images_pil) == 8
+
+    def test_stable_diffusion_v1_4(self):
+        pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(
+            "CompVis/stable-diffusion-v1-4", revision="flax", safety_checker=None
+        )
+
+        prompt = (
+            "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
+            " field, close up, split lighting, cinematic"
+        )
+
+        prng_seed = jax.random.PRNGKey(0)
+        num_inference_steps = 50
+
+        num_samples = jax.device_count()
+        prompt = num_samples * [prompt]
+        prompt_ids = pipeline.prepare_inputs(prompt)
+
+        p_sample = pmap(pipeline.__call__, static_broadcasted_argnums=(3,))
+
+        # shard inputs and rng
+        params = replicate(params)
+        prng_seed = jax.random.split(prng_seed, 8)
+        prompt_ids = shard(prompt_ids)
+
+        images = p_sample(prompt_ids, params, prng_seed, num_inference_steps).images
+
+        images_pil = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))
+        for i, image in enumerate(images_pil):
+            image.save(f"/home/patrick/images/flax-test-{i}_fp32.png")
+
+        assert images.shape == (8, 1, 512, 512, 3)
+        assert np.abs((np.abs(images[0, 0, :2, :2, -2:], dtype=np.float32).sum() - 0.05652401)) < 1e-3
+        assert np.abs((np.abs(images, dtype=np.float32).sum() - 2383808.2)) < 1e-2
+
+    def test_stable_diffusion_v1_4_bfloat_16(self):
+        pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(
+            "CompVis/stable-diffusion-v1-4", revision="bf16", dtype=jnp.bfloat16, safety_checker=None
+        )
+
+        prompt = (
+            "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
+            " field, close up, split lighting, cinematic"
+        )
+
+        prng_seed = jax.random.PRNGKey(0)
+        num_inference_steps = 50
+
+        num_samples = jax.device_count()
+        prompt = num_samples * [prompt]
+        prompt_ids = pipeline.prepare_inputs(prompt)
+
+        p_sample = pmap(pipeline.__call__, static_broadcasted_argnums=(3,))
+
+        # shard inputs and rng
+        params = replicate(params)
+        prng_seed = jax.random.split(prng_seed, 8)
+        prompt_ids = shard(prompt_ids)
+
+        images = p_sample(prompt_ids, params, prng_seed, num_inference_steps).images
+
+        assert images.shape == (8, 1, 512, 512, 3)
+        assert np.abs((np.abs(images[0, 0, :2, :2, -2:], dtype=np.float32).sum() - 0.06652832)) < 1e-3
+        assert np.abs((np.abs(images, dtype=np.float32).sum() - 2384849.8)) < 1e-2
+
+    def test_stable_diffusion_v1_4_bfloat_16_with_safety(self):
+        pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(
+            "CompVis/stable-diffusion-v1-4", revision="bf16", dtype=jnp.bfloat16
+        )
+
+        prompt = (
+            "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
+            " field, close up, split lighting, cinematic"
+        )
+
+        prng_seed = jax.random.PRNGKey(0)
+        num_inference_steps = 50
+
+        num_samples = jax.device_count()
+        prompt = num_samples * [prompt]
+        prompt_ids = pipeline.prepare_inputs(prompt)
+
+        # shard inputs and rng
+        params = replicate(params)
+        prng_seed = jax.random.split(prng_seed, 8)
+        prompt_ids = shard(prompt_ids)
+
+        images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images
+
+        assert images.shape == (8, 1, 512, 512, 3)
+        assert np.abs((np.abs(images[0, 0, :2, :2, -2:], dtype=np.float32).sum() - 0.06652832)) < 1e-3
+        assert np.abs((np.abs(images, dtype=np.float32).sum() - 2384849.8)) < 1e-2