Fix more

src/diffusers/models/unet_2d_condition.py

@@ -820,6 +820,7 @@ class UNet2DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin)
             time_embeds = time_embeds.reshape((text_embeds.shape[0], -1))
 
             add_embeds = torch.concat([text_embeds, time_embeds], dim=-1)
+            add_embeds = add_embeds.to(emb.dtype)
             aug_emb = self.add_embedding(add_embeds)
 
         emb = emb + aug_emb

src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py

@@ -15,7 +15,7 @@
 import inspect
 import warnings
 from typing import Any, Callable, Dict, List, Optional, Union
-from pytorch_lightning import seed_everything
+# from pytorch_lightning import seed_everything
 
 import torch
 from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPTextModelWithProjection
@@ -117,7 +117,6 @@ class StableDiffusionXLPipeline(DiffusionPipeline):
         scheduler: KarrasDiffusionSchedulers,
         # safety_checker: StableDiffusionSafetyChecker,
         # feature_extractor: CLIPImageProcessor,
-        # requires_safety_checker: bool = True,
     ):
         super().__init__()
 
@@ -151,7 +150,6 @@ class StableDiffusionXLPipeline(DiffusionPipeline):
         self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
         self.vae_scale_factor = 8
         self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
-        # self.register_to_config(requires_safety_checker=requires_safety_checker)
 
     def enable_vae_slicing(self):
         r"""
@@ -245,7 +243,6 @@ class StableDiffusionXLPipeline(DiffusionPipeline):
         `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
         hooks.
         """
-        return "cpu"
         if not hasattr(self.unet, "_hf_hook"):
             return self.device
         for module in self.unet.modules():
@@ -635,12 +632,12 @@ class StableDiffusionXLPipeline(DiffusionPipeline):
         height = height or self.unet.config.sample_size * self.vae_scale_factor
         width = width or self.unet.config.sample_size * self.vae_scale_factor
 
-        seed_everything(0)
+        # seed_everything(0)
 
         # 1. Check inputs. Raise error if not correct
-        # self.check_inputs(
-        #     prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds
-        # )
+        self.check_inputs(
+            prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds
+        )
 
         # 2. Define call parameters
         if prompt is not None and isinstance(prompt, str):
@@ -650,8 +647,7 @@ class StableDiffusionXLPipeline(DiffusionPipeline):
         else:
             batch_size = prompt_embeds.shape[0]
 
-        # device = self._execution_device
-        device = "cpu"
+        device = self._execution_device
 
         # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
         # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
@@ -697,7 +693,9 @@ class StableDiffusionXLPipeline(DiffusionPipeline):
         num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
         prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
         add_text_embeds = torch.cat([negative_pooled_prompt_embeds, pooled_prompt_embeds], dim=0)
-        add_time_ids = torch.tensor(2 * [[128, 128, 0, 0, 1024, 1024]], dtype=torch.long)
+
+        # TODO - find better explanations where they come from
+        add_time_ids = torch.tensor(2 * [[128, 128, 0, 0, 1024, 1024]], dtype=torch.long, device=add_text_embeds.device)
 
         with self.progress_bar(total=num_inference_steps) as progress_bar:
             for i, t in enumerate(timesteps):
@@ -736,8 +734,12 @@ class StableDiffusionXLPipeline(DiffusionPipeline):
                         callback(i, t, latents)
 
         if not output_type == "latent":
+            # CHECK there is problem here (PVP)
+            # self.vae = self.vae.to(dtype=torch.float32)
+            # latents = latents.float()
             image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
-            image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
+            #image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
+            has_nsfw_concept = None
         else:
             image = latents
             has_nsfw_concept = None