Fix truthy-ness condition in pipelines that use denoising_start (#6912)

* fix denoising start

* fix tests

* remove debug

examples/community/lpw_stable_diffusion_xl.py

@@ -1766,7 +1766,7 @@ class SDXLLongPromptWeightingPipeline(
 
         # 4. Prepare timesteps
         def denoising_value_valid(dnv):
-            return isinstance(self.denoising_end, float) and 0 < dnv < 1
+            return isinstance(dnv, float) and 0 < dnv < 1
 
         timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)
         if image is not None:
@@ -1774,7 +1774,7 @@ class SDXLLongPromptWeightingPipeline(
                 num_inference_steps,
                 strength,
                 device,
-                denoising_start=self.denoising_start if denoising_value_valid else None,
+                denoising_start=self.denoising_start if denoising_value_valid(self.denoising_start) else None,
             )
 
             # check that number of inference steps is not < 1 - as this doesn't make sense

examples/community/pipeline_sdxl_style_aligned.py

@@ -1769,7 +1769,7 @@ class StyleAlignedSDXLPipeline(
 
         # 4. Prepare timesteps
         def denoising_value_valid(dnv):
-            return isinstance(self.denoising_end, float) and 0 < dnv < 1
+            return isinstance(dnv, float) and 0 < dnv < 1
 
         timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)
 
@@ -1778,7 +1778,7 @@ class StyleAlignedSDXLPipeline(
                 num_inference_steps,
                 strength,
                 device,
-                denoising_start=self.denoising_start if denoising_value_valid else None,
+                denoising_start=self.denoising_start if denoising_value_valid(self.denoising_start) else None,
             )
 
             # check that number of inference steps is not < 1 - as this doesn't make sense

examples/community/pipeline_stable_diffusion_xl_controlnet_adapter_inpaint.py

@@ -1563,14 +1563,14 @@ class StableDiffusionXLControlNetAdapterInpaintPipeline(DiffusionPipeline, FromS
 
         # 4. set timesteps
         def denoising_value_valid(dnv):
-            return isinstance(denoising_end, float) and 0 < dnv < 1
+            return isinstance(dnv, float) and 0 < dnv < 1
 
         self.scheduler.set_timesteps(num_inference_steps, device=device)
         timesteps, num_inference_steps = self.get_timesteps(
             num_inference_steps,
             strength,
             device,
-            denoising_start=denoising_start if denoising_value_valid else None,
+            denoising_start=denoising_start if denoising_value_valid(denoising_start) else None,
         )
         # check that number of inference steps is not < 1 - as this doesn't make sense
         if num_inference_steps < 1:

src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py

@@ -1477,11 +1477,14 @@ class StableDiffusionXLControlNetInpaintPipeline(
 
         # 4. set timesteps
         def denoising_value_valid(dnv):
-            return isinstance(denoising_end, float) and 0 < dnv < 1
+            return isinstance(dnv, float) and 0 < dnv < 1
 
         self.scheduler.set_timesteps(num_inference_steps, device=device)
         timesteps, num_inference_steps = self.get_timesteps(
-            num_inference_steps, strength, device, denoising_start=denoising_start if denoising_value_valid else None
+            num_inference_steps,
+            strength,
+            device,
+            denoising_start=denoising_start if denoising_value_valid(denoising_start) else None,
         )
         # check that number of inference steps is not < 1 - as this doesn't make sense
         if num_inference_steps < 1:

src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py

@@ -1315,14 +1315,14 @@ class StableDiffusionXLImg2ImgPipeline(
 
         # 5. Prepare timesteps
         def denoising_value_valid(dnv):
-            return isinstance(self.denoising_end, float) and 0 < dnv < 1
+            return isinstance(dnv, float) and 0 < dnv < 1
 
         timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)
         timesteps, num_inference_steps = self.get_timesteps(
             num_inference_steps,
             strength,
             device,
-            denoising_start=self.denoising_start if denoising_value_valid else None,
+            denoising_start=self.denoising_start if denoising_value_valid(self.denoising_start) else None,
         )
         latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
 

src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py

@@ -1581,14 +1581,14 @@ class StableDiffusionXLInpaintPipeline(
 
         # 4. set timesteps
         def denoising_value_valid(dnv):
-            return isinstance(self.denoising_end, float) and 0 < dnv < 1
+            return isinstance(dnv, float) and 0 < dnv < 1
 
         timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)
         timesteps, num_inference_steps = self.get_timesteps(
             num_inference_steps,
             strength,
             device,
-            denoising_start=self.denoising_start if denoising_value_valid else None,
+            denoising_start=self.denoising_start if denoising_value_valid(self.denoising_start) else None,
         )
         # check that number of inference steps is not < 1 - as this doesn't make sense
         if num_inference_steps < 1: