Update access of configuration attributes (#7343)

Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

docs/source/ko/optimization/fp16.md

@@ -355,7 +355,7 @@ unet_traced = torch.jit.load("unet_traced.pt")
 class TracedUNet(torch.nn.Module):
     def __init__(self):
         super().__init__()
-        self.in_channels = pipe.unet.in_channels
+        self.in_channels = pipe.unet.config.in_channels
         self.device = pipe.unet.device
 
     def forward(self, latent_model_input, t, encoder_hidden_states):

docs/source/ko/using-diffusers/write_own_pipeline.md

@@ -210,7 +210,7 @@ Stable Diffusion 은 text-to-image *latent diffusion* 모델입니다. latent di
 
 ```py
 >>> latents = torch.randn(
-...     (batch_size, unet.in_channels, height // 8, width // 8),
+...     (batch_size, unet.config.in_channels, height // 8, width // 8),
 ...     generator=generator,
 ...     device=torch_device,
 ... )

examples/community/stable_diffusion_ipex.py

@@ -224,7 +224,7 @@ class StableDiffusionIPEXPipeline(
         # 5. Prepare latent variables
         latents = self.prepare_latents(
             batch_size * num_images_per_prompt,
-            self.unet.in_channels,
+            self.unet.config.in_channels,
             height,
             width,
             prompt_embeds.dtype,
@@ -679,7 +679,7 @@ class StableDiffusionIPEXPipeline(
         timesteps = self.scheduler.timesteps
 
         # 5. Prepare latent variables
-        num_channels_latents = self.unet.in_channels
+        num_channels_latents = self.unet.config.in_channels
         latents = self.prepare_latents(
             batch_size * num_images_per_prompt,
             num_channels_latents,

examples/community/stable_diffusion_tensorrt_txt2img.py

@@ -917,7 +917,7 @@ class TensorRTStableDiffusionPipeline(StableDiffusionPipeline):
             text_embeddings = self.__encode_prompt(prompt, negative_prompt)
 
             # Pre-initialize latents
-            num_channels_latents = self.unet.in_channels
+            num_channels_latents = self.unet.config.in_channels
             latents = self.prepare_latents(
                 batch_size,
                 num_channels_latents,

scripts/convert_if.py

@@ -1195,9 +1195,9 @@ def superres_check_against_original(dump_path, unet_checkpoint_path):
         if_II_model = IFStageIII(device="cuda", dir_or_name=orig_path, model_kwargs={"precision": "fp32"}).model
 
     batch_size = 1
-    channels = model.in_channels // 2
-    height = model.sample_size
-    width = model.sample_size
+    channels = model.config.in_channels // 2
+    height = model.config.sample_size
+    width = model.config.sample_size
     height = 1024
     width = 1024
 

src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.py

@@ -613,7 +613,7 @@ class IFImg2ImgPipeline(DiffusionPipeline, LoraLoaderMixin):
 
             for image_ in image:
                 image_ = image_.convert("RGB")
-                image_ = resize(image_, self.unet.sample_size)
+                image_ = resize(image_, self.unet.config.sample_size)
                 image_ = np.array(image_)
                 image_ = image_.astype(np.float32)
                 image_ = image_ / 127.5 - 1

src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.py

@@ -662,7 +662,7 @@ class IFImg2ImgSuperResolutionPipeline(DiffusionPipeline, LoraLoaderMixin):
 
             for image_ in image:
                 image_ = image_.convert("RGB")
-                image_ = resize(image_, self.unet.sample_size)
+                image_ = resize(image_, self.unet.config.sample_size)
                 image_ = np.array(image_)
                 image_ = image_.astype(np.float32)
                 image_ = image_ / 127.5 - 1

src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py

@@ -654,7 +654,7 @@ class IFInpaintingPipeline(DiffusionPipeline, LoraLoaderMixin):
 
             for image_ in image:
                 image_ = image_.convert("RGB")
-                image_ = resize(image_, self.unet.sample_size)
+                image_ = resize(image_, self.unet.config.sample_size)
                 image_ = np.array(image_)
                 image_ = image_.astype(np.float32)
                 image_ = image_ / 127.5 - 1
@@ -701,7 +701,7 @@ class IFInpaintingPipeline(DiffusionPipeline, LoraLoaderMixin):
 
             for mask_image_ in mask_image:
                 mask_image_ = mask_image_.convert("L")
-                mask_image_ = resize(mask_image_, self.unet.sample_size)
+                mask_image_ = resize(mask_image_, self.unet.config.sample_size)
                 mask_image_ = np.array(mask_image_)
                 mask_image_ = mask_image_[None, None, :]
                 new_mask_image.append(mask_image_)

src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting_superresolution.py

@@ -698,7 +698,7 @@ class IFInpaintingSuperResolutionPipeline(DiffusionPipeline, LoraLoaderMixin):
 
             for image_ in image:
                 image_ = image_.convert("RGB")
-                image_ = resize(image_, self.unet.sample_size)
+                image_ = resize(image_, self.unet.config.sample_size)
                 image_ = np.array(image_)
                 image_ = image_.astype(np.float32)
                 image_ = image_ / 127.5 - 1
@@ -778,7 +778,7 @@ class IFInpaintingSuperResolutionPipeline(DiffusionPipeline, LoraLoaderMixin):
 
             for mask_image_ in mask_image:
                 mask_image_ = mask_image_.convert("L")
-                mask_image_ = resize(mask_image_, self.unet.sample_size)
+                mask_image_ = resize(mask_image_, self.unet.config.sample_size)
                 mask_image_ = np.array(mask_image_)
                 mask_image_ = mask_image_[None, None, :]
                 new_mask_image.append(mask_image_)

src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_upscale.py

@@ -469,7 +469,7 @@ class OnnxStableDiffusionUpscalePipeline(DiffusionPipeline):
 
         latents = self.prepare_latents(
             batch_size * num_images_per_prompt,
-            self.num_latent_channels,
+            self.config.num_latent_channels,
             height,
             width,
             latents_dtype,
@@ -498,12 +498,12 @@ class OnnxStableDiffusionUpscalePipeline(DiffusionPipeline):
 
         # 7. Check that sizes of image and latents match
         num_channels_image = image.shape[1]
-        if self.num_latent_channels + num_channels_image != self.num_unet_input_channels:
+        if self.config.num_latent_channels + num_channels_image != self.config.num_unet_input_channels:
             raise ValueError(
                 "Incorrect configuration settings! The config of `pipeline.unet` expects"
-                f" {self.num_unet_input_channels} but received `num_channels_latents`: {self.num_latent_channels} +"
+                f" {self.config.num_unet_input_channels} but received `num_channels_latents`: {self.config.num_latent_channels} +"
                 f" `num_channels_image`: {num_channels_image} "
-                f" = {self.num_latent_channels + num_channels_image}. Please verify the config of"
+                f" = {self.config.num_latent_channels + num_channels_image}. Please verify the config of"
                 " `pipeline.unet` or your `image` input."
             )
 

src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.py

@@ -680,7 +680,7 @@ class StableDiffusionGLIGENPipeline(DiffusionPipeline, StableDiffusionMixin):
         timesteps = self.scheduler.timesteps
 
         # 5. Prepare latent variables
-        num_channels_latents = self.unet.in_channels
+        num_channels_latents = self.unet.config.in_channels
         latents = self.prepare_latents(
             batch_size * num_images_per_prompt,
             num_channels_latents,
@@ -713,7 +713,7 @@ class StableDiffusionGLIGENPipeline(DiffusionPipeline, StableDiffusionMixin):
         boxes = torch.zeros(max_objs, 4, device=device, dtype=self.text_encoder.dtype)
         boxes[:n_objs] = torch.tensor(gligen_boxes)
         text_embeddings = torch.zeros(
-            max_objs, self.unet.cross_attention_dim, device=device, dtype=self.text_encoder.dtype
+            max_objs, self.unet.config.cross_attention_dim, device=device, dtype=self.text_encoder.dtype
         )
         text_embeddings[:n_objs] = _text_embeddings
         # Generate a mask for each object that is entity described by phrases

src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py

@@ -847,7 +847,7 @@ class StableDiffusionGLIGENTextImagePipeline(DiffusionPipeline, StableDiffusionM
         timesteps = self.scheduler.timesteps
 
         # 5. Prepare latent variables
-        num_channels_latents = self.unet.in_channels
+        num_channels_latents = self.unet.config.in_channels
         latents = self.prepare_latents(
             batch_size * num_images_per_prompt,
             num_channels_latents,

src/diffusers/schedulers/scheduling_consistency_models.py

@@ -233,7 +233,7 @@ class CMStochasticIterativeScheduler(SchedulerMixin, ConfigMixin):
         sigmas = self._convert_to_karras(ramp)
         timesteps = self.sigma_to_t(sigmas)
 
-        sigmas = np.concatenate([sigmas, [self.sigma_min]]).astype(np.float32)
+        sigmas = np.concatenate([sigmas, [self.config.sigma_min]]).astype(np.float32)
         self.sigmas = torch.from_numpy(sigmas).to(device=device)
 
         if str(device).startswith("mps"):

src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py

@@ -233,7 +233,7 @@ class DPMSolverMultistepInverseScheduler(SchedulerMixin, ConfigMixin):
         """
         # Clipping the minimum of all lambda(t) for numerical stability.
         # This is critical for cosine (squaredcos_cap_v2) noise schedule.
-        clipped_idx = torch.searchsorted(torch.flip(self.lambda_t, [0]), self.lambda_min_clipped).item()
+        clipped_idx = torch.searchsorted(torch.flip(self.lambda_t, [0]), self.config.lambda_min_clipped).item()
         self.noisiest_timestep = self.config.num_train_timesteps - 1 - clipped_idx
 
         # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891

src/diffusers/schedulers/scheduling_dpmsolver_sde.py

@@ -325,7 +325,7 @@ class DPMSolverSDEScheduler(SchedulerMixin, ConfigMixin):
         log_sigmas = np.log(sigmas)
         sigmas = np.interp(timesteps, np.arange(0, len(sigmas)), sigmas)
 
-        if self.use_karras_sigmas:
+        if self.config.use_karras_sigmas:
             sigmas = self._convert_to_karras(in_sigmas=sigmas)
             timesteps = np.array([self._sigma_to_t(sigma, log_sigmas) for sigma in sigmas])
 

src/diffusers/schedulers/scheduling_euler_discrete.py

@@ -343,7 +343,7 @@ class EulerDiscreteScheduler(SchedulerMixin, ConfigMixin):
                 " 'linear' or 'log_linear'"
             )
 
-        if self.use_karras_sigmas:
+        if self.config.use_karras_sigmas:
             sigmas = self._convert_to_karras(in_sigmas=sigmas, num_inference_steps=self.num_inference_steps)
             timesteps = np.array([self._sigma_to_t(sigma, log_sigmas) for sigma in sigmas])
 

src/diffusers/schedulers/scheduling_lms_discrete.py

@@ -288,7 +288,7 @@ class LMSDiscreteScheduler(SchedulerMixin, ConfigMixin):
         log_sigmas = np.log(sigmas)
         sigmas = np.interp(timesteps, np.arange(0, len(sigmas)), sigmas)
 
-        if self.use_karras_sigmas:
+        if self.config.use_karras_sigmas:
             sigmas = self._convert_to_karras(in_sigmas=sigmas)
             timesteps = np.array([self._sigma_to_t(sigma, log_sigmas) for sigma in sigmas])
 

tests/models/unets/test_models_unet_2d_condition.py

@@ -782,7 +782,7 @@ class UNet2DConditionModelTests(ModelTesterMixin, UNetTesterMixin, unittest.Test
         # update inputs_dict for ip-adapter
         batch_size = inputs_dict["encoder_hidden_states"].shape[0]
         # for ip-adapter image_embeds has shape [batch_size, num_image, embed_dim]
-        image_embeds = floats_tensor((batch_size, 1, model.cross_attention_dim)).to(torch_device)
+        image_embeds = floats_tensor((batch_size, 1, model.config.cross_attention_dim)).to(torch_device)
         inputs_dict["added_cond_kwargs"] = {"image_embeds": [image_embeds]}
 
         # make ip_adapter_1 and ip_adapter_2
@@ -854,7 +854,7 @@ class UNet2DConditionModelTests(ModelTesterMixin, UNetTesterMixin, unittest.Test
         # update inputs_dict for ip-adapter
         batch_size = inputs_dict["encoder_hidden_states"].shape[0]
         # for ip-adapter-plus image_embeds has shape [batch_size, num_image, sequence_length, embed_dim]
-        image_embeds = floats_tensor((batch_size, 1, 1, model.cross_attention_dim)).to(torch_device)
+        image_embeds = floats_tensor((batch_size, 1, 1, model.config.cross_attention_dim)).to(torch_device)
         inputs_dict["added_cond_kwargs"] = {"image_embeds": [image_embeds]}
 
         # make ip_adapter_1 and ip_adapter_2

tests/others/test_config.py

@@ -272,17 +272,17 @@ class ConfigTester(unittest.TestCase):
             # now loading it with SampleObject2 should put f into `_use_default_values`
             config = SampleObject2.from_config(tmpdirname)
 
-            assert "f" in config._use_default_values
-            assert config.f == [1, 3]
+            assert "f" in config.config._use_default_values
+            assert config.config.f == [1, 3]
 
         # now loading the config, should **NOT** use [1, 3] for `f`, but the default [1, 4] value
-        # **BECAUSE** it is part of `config._use_default_values`
+        # **BECAUSE** it is part of `config.config._use_default_values`
         new_config = SampleObject4.from_config(config.config)
-        assert new_config.f == [5, 4]
+        assert new_config.config.f == [5, 4]
 
         config.config._use_default_values.pop()
         new_config_2 = SampleObject4.from_config(config.config)
-        assert new_config_2.f == [1, 3]
+        assert new_config_2.config.f == [1, 3]
 
         # Nevertheless "e" should still be correctly loaded to [1, 3] from SampleObject2 instead of defaulting to [1, 5]
-        assert new_config_2.e == [1, 3]
+        assert new_config_2.config.e == [1, 3]

tests/pipelines/dance_diffusion/test_dance_diffusion.py

@@ -137,7 +137,7 @@ class PipelineIntegrationTests(unittest.TestCase):
 
         audio_slice = audio[0, -3:, -3:]
 
-        assert audio.shape == (1, 2, pipe.unet.sample_size)
+        assert audio.shape == (1, 2, pipe.unet.config.sample_size)
         expected_slice = np.array([-0.0192, -0.0231, -0.0318, -0.0059, 0.0002, -0.0020])
 
         assert np.abs(audio_slice.flatten() - expected_slice).max() < 1e-2
@@ -155,7 +155,7 @@ class PipelineIntegrationTests(unittest.TestCase):
 
         audio_slice = audio[0, -3:, -3:]
 
-        assert audio.shape == (1, 2, pipe.unet.sample_size)
+        assert audio.shape == (1, 2, pipe.unet.config.sample_size)
         expected_slice = np.array([-0.0367, -0.0488, -0.0771, -0.0525, -0.0444, -0.0341])
 
         assert np.abs(audio_slice.flatten() - expected_slice).max() < 1e-2