fix E721 Do not compare types, use `isinstance()` (#4992)

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

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

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)

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(

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

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(

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(

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

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

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)

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)