From 73bf620dec56ee3a24b88bee53763617332223cc Mon Sep 17 00:00:00 2001 From: Kashif Rasul Date: Tue, 12 Sep 2023 16:52:25 +0200 Subject: [PATCH] fix E721 Do not compare types, use `isinstance()` (#4992) --- examples/community/lpw_stable_diffusion_xl.py | 2 +- examples/community/stable_diffusion_xl_reference.py | 2 +- src/diffusers/experimental/rl/value_guided_sampling.py | 2 +- .../pipelines/audio_diffusion/pipeline_audio_diffusion.py | 2 +- .../stable_diffusion_xl/pipeline_stable_diffusion_xl.py | 2 +- .../pipeline_stable_diffusion_xl_img2img.py | 2 +- .../pipeline_stable_diffusion_xl_inpaint.py | 2 +- .../pipeline_stable_diffusion_xl_instruct_pix2pix.py | 2 +- .../t2i_adapter/pipeline_stable_diffusion_xl_adapter.py | 2 +- .../pipelines/consistency_models/test_consistency_models.py | 2 +- tests/pipelines/unidiffuser/test_unidiffuser.py | 6 +++--- 11 files changed, 13 insertions(+), 13 deletions(-) diff --git a/examples/community/lpw_stable_diffusion_xl.py b/examples/community/lpw_stable_diffusion_xl.py index 2ee44b95ab..61a49eb2b3 100644 --- a/examples/community/lpw_stable_diffusion_xl.py +++ b/examples/community/lpw_stable_diffusion_xl.py @@ -1138,7 +1138,7 @@ class SDXLLongPromptWeightingPipeline(DiffusionPipeline, FromSingleFileMixin, Lo num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0) # 7.1 Apply denoising_end - if denoising_end is not None and type(denoising_end) == float and denoising_end > 0 and denoising_end < 1: + if denoising_end is not None and isinstance(denoising_end, float) and denoising_end > 0 and denoising_end < 1: discrete_timestep_cutoff = int( round( self.scheduler.config.num_train_timesteps diff --git a/examples/community/stable_diffusion_xl_reference.py b/examples/community/stable_diffusion_xl_reference.py index 7549135b22..a7654f11bc 100644 --- a/examples/community/stable_diffusion_xl_reference.py +++ b/examples/community/stable_diffusion_xl_reference.py @@ -701,7 +701,7 @@ class StableDiffusionXLReferencePipeline(StableDiffusionXLPipeline): num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0) # 10.1 Apply denoising_end - if denoising_end is not None and type(denoising_end) == float and denoising_end > 0 and denoising_end < 1: + if denoising_end is not None and isinstance(denoising_end, float) and denoising_end > 0 and denoising_end < 1: discrete_timestep_cutoff = int( round( self.scheduler.config.num_train_timesteps diff --git a/src/diffusers/experimental/rl/value_guided_sampling.py b/src/diffusers/experimental/rl/value_guided_sampling.py index 262039be4f..dfb27587d7 100644 --- a/src/diffusers/experimental/rl/value_guided_sampling.py +++ b/src/diffusers/experimental/rl/value_guided_sampling.py @@ -76,7 +76,7 @@ class ValueGuidedRLPipeline(DiffusionPipeline): return x_in * self.stds[key] + self.means[key] def to_torch(self, x_in): - if type(x_in) is dict: + if isinstance(x_in, dict): return {k: self.to_torch(v) for k, v in x_in.items()} elif torch.is_tensor(x_in): return x_in.to(self.unet.device) diff --git a/src/diffusers/pipelines/audio_diffusion/pipeline_audio_diffusion.py b/src/diffusers/pipelines/audio_diffusion/pipeline_audio_diffusion.py index a06217c19b..6c4ae88b22 100644 --- a/src/diffusers/pipelines/audio_diffusion/pipeline_audio_diffusion.py +++ b/src/diffusers/pipelines/audio_diffusion/pipeline_audio_diffusion.py @@ -178,7 +178,7 @@ class AudioDiffusionPipeline(DiffusionPipeline): self.scheduler.set_timesteps(steps) step_generator = step_generator or generator # For backwards compatibility - if type(self.unet.config.sample_size) == int: + if isinstance(self.unet.config.sample_size, int): self.unet.config.sample_size = (self.unet.config.sample_size, self.unet.config.sample_size) if noise is None: noise = randn_tensor( diff --git a/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py b/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py index 10e966b248..84fc9c7c57 100644 --- a/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py +++ b/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py @@ -810,7 +810,7 @@ class StableDiffusionXLPipeline(DiffusionPipeline, FromSingleFileMixin, LoraLoad num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0) # 7.1 Apply denoising_end - if denoising_end is not None and type(denoising_end) == float and denoising_end > 0 and denoising_end < 1: + if denoising_end is not None and isinstance(denoising_end, float) and denoising_end > 0 and denoising_end < 1: discrete_timestep_cutoff = int( round( self.scheduler.config.num_train_timesteps diff --git a/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py b/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py index 8e26a2ad06..4b66193f75 100644 --- a/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py +++ b/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py @@ -885,7 +885,7 @@ class StableDiffusionXLImg2ImgPipeline( # 5. Prepare timesteps def denoising_value_valid(dnv): - return type(denoising_end) == float and 0 < dnv < 1 + return isinstance(denoising_end, float) and 0 < dnv < 1 self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps, num_inference_steps = self.get_timesteps( diff --git a/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py b/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py index 6fdc688d9e..55baada042 100644 --- a/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py +++ b/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py @@ -1120,7 +1120,7 @@ class StableDiffusionXLInpaintPipeline( # 4. set timesteps def denoising_value_valid(dnv): - return type(denoising_end) == float and 0 < dnv < 1 + return isinstance(denoising_end, float) and 0 < dnv < 1 self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps, num_inference_steps = self.get_timesteps( diff --git a/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_instruct_pix2pix.py b/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_instruct_pix2pix.py index 614cc0e647..786231dd5c 100644 --- a/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_instruct_pix2pix.py +++ b/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_instruct_pix2pix.py @@ -837,7 +837,7 @@ class StableDiffusionXLInstructPix2PixPipeline( # 11. Denoising loop num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0) - if denoising_end is not None and type(denoising_end) == float and denoising_end > 0 and denoising_end < 1: + if denoising_end is not None and isinstance(denoising_end, float) and denoising_end > 0 and denoising_end < 1: discrete_timestep_cutoff = int( round( self.scheduler.config.num_train_timesteps diff --git a/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.py b/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.py index 4bf0e33118..d7441db707 100644 --- a/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.py +++ b/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.py @@ -886,7 +886,7 @@ class StableDiffusionXLAdapterPipeline( num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0) # 7.1 Apply denoising_end - if denoising_end is not None and type(denoising_end) == float and denoising_end > 0 and denoising_end < 1: + if denoising_end is not None and isinstance(denoising_end, float) and denoising_end > 0 and denoising_end < 1: discrete_timestep_cutoff = int( round( self.scheduler.config.num_train_timesteps diff --git a/tests/pipelines/consistency_models/test_consistency_models.py b/tests/pipelines/consistency_models/test_consistency_models.py index 6732d5228d..59be333b62 100644 --- a/tests/pipelines/consistency_models/test_consistency_models.py +++ b/tests/pipelines/consistency_models/test_consistency_models.py @@ -193,7 +193,7 @@ class ConsistencyModelPipelineSlowTests(unittest.TestCase): return inputs def get_fixed_latents(self, seed=0, device="cpu", dtype=torch.float32, shape=(1, 3, 64, 64)): - if type(device) == str: + if isinstance(device, str): device = torch.device(device) generator = torch.Generator(device=device).manual_seed(seed) latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) diff --git a/tests/pipelines/unidiffuser/test_unidiffuser.py b/tests/pipelines/unidiffuser/test_unidiffuser.py index 865a7cfa69..eec341db50 100644 --- a/tests/pipelines/unidiffuser/test_unidiffuser.py +++ b/tests/pipelines/unidiffuser/test_unidiffuser.py @@ -109,7 +109,7 @@ class UniDiffuserPipelineFastTests(PipelineTesterMixin, unittest.TestCase): return inputs def get_fixed_latents(self, device, seed=0): - if type(device) == str: + if isinstance(device, str): device = torch.device(device) generator = torch.Generator(device=device).manual_seed(seed) # Hardcode the shapes for now. @@ -545,7 +545,7 @@ class UniDiffuserPipelineSlowTests(unittest.TestCase): return inputs def get_fixed_latents(self, device, seed=0): - if type(device) == str: + if isinstance(device, str): device = torch.device(device) latent_device = torch.device("cpu") generator = torch.Generator(device=latent_device).manual_seed(seed) @@ -648,7 +648,7 @@ class UniDiffuserPipelineNightlyTests(unittest.TestCase): return inputs def get_fixed_latents(self, device, seed=0): - if type(device) == str: + if isinstance(device, str): device = torch.device(device) latent_device = torch.device("cpu") generator = torch.Generator(device=latent_device).manual_seed(seed)