Use full precision

pipeline_stable_diffusion_xl_opt.py

@@ -831,8 +831,6 @@ class StableDiffusionXLPipeline(DiffusionPipeline, FromSingleFileMixin, LoraLoad
             num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))
             timesteps = timesteps[:num_inference_steps]
 
-        latents = latents.half()
-        prompt_embeds = prompt_embeds.half()
         with self.progress_bar(total=num_inference_steps) as progress_bar:
             for i, t in enumerate(timesteps):
                 latents = self.update_loss(latents, i, t, prompt_embeds, cross_attention_kwargs, add_text_embeds, add_time_ids)