Fix typos and minor redundancies (#2029)

fix typos and minor redundancies
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py

@@ -397,7 +397,7 @@ class StableDiffusionImg2ImgPipeline(DiffusionPipeline):
             raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
 
         if strength < 0 or strength > 1:
-            raise ValueError(f"The value of strength should in [1.0, 1.0] but is {strength}")
+            raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
 
         if (callback_steps is None) or (
             callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py

@@ -499,7 +499,7 @@ class StableDiffusionUpscalePipeline(DiffusionPipeline):
                 # perform guidance
                 if do_classifier_free_guidance:
                     noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
 
                 # compute the previous noisy sample x_t -> x_t-1
                 latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample

src/diffusers/schedulers/scheduling_ddim.py

@@ -327,7 +327,7 @@ class DDIMScheduler(SchedulerMixin, ConfigMixin):
                 variance_noise = randn_tensor(
                     model_output.shape, generator=generator, device=device, dtype=model_output.dtype
                 )
-            variance = self._get_variance(timestep, prev_timestep) ** (0.5) * eta * variance_noise
+            variance = std_dev_t * variance_noise
 
             prev_sample = prev_sample + variance