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

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This commit is contained in:
Kashif Rasul
2023-09-12 16:52:25 +02:00
committed by GitHub
parent c806f2fad6
commit 73bf620dec
11 changed files with 13 additions and 13 deletions
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2
examples/community/lpw_stable_diffusion_xl.py
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@@ -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

2
examples/community/stable_diffusion_xl_reference.py
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@@ -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

2
src/diffusers/experimental/rl/value_guided_sampling.py
View File

@@ -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)

2
src/diffusers/pipelines/audio_diffusion/pipeline_audio_diffusion.py
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@@ -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(

2
src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py
View File

@@ -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

2
src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py
View File

@@ -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(

2
src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py
View File

@@ -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(

2
src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_instruct_pix2pix.py
View File

@@ -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

2
src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.py
View File

@@ -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

2
tests/pipelines/consistency_models/test_consistency_models.py
View File

@@ -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)

6
tests/pipelines/unidiffuser/test_unidiffuser.py
View File

@@ -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)