Revert cond + uncond batching

Author: Ednaordinary

src/diffusers/pipelines/chroma/pipeline_chroma.py

@@ -694,9 +694,6 @@ def __call__(
                 max_sequence_length=max_sequence_length,
                 lora_scale=lora_scale,
             )
-
-        if self.do_classifier_free_guidance:
-            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
 
         # 4. Prepare latent variables
         num_channels_latents = self.transformer.config.in_channels // 4
@@ -773,13 +770,11 @@ def __call__(
                 if image_embeds is not None:
                     self._joint_attention_kwargs["ip_adapter_image_embeds"] = image_embeds
 
-                # expand the latents if we are doing classifier free guidance
-                latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
                 # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
-                timestep = t.expand(latent_model_input.shape[0]).to(latents.dtype)
+                timestep = t.expand(latents.shape[0]).to(latents.dtype)
 
                 noise_pred = self.transformer(
-                    hidden_states=latent_model_input,
+                    hidden_states=latents,
                     timestep=timestep / 1000,
                     encoder_hidden_states=prompt_embeds,
                     txt_ids=text_ids,
@@ -791,8 +786,16 @@ def __call__(
                 if self.do_classifier_free_guidance:
                     if negative_image_embeds is not None:
                         self._joint_attention_kwargs["ip_adapter_image_embeds"] = negative_image_embeds
-                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                    noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
+                    neg_noise_pred = self.transformer(
+                        hidden_states=latents,
+                        timestep=timestep / 1000,
+                        encoder_hidden_states=negative_prompt_embeds,
+                        txt_ids=negative_text_ids,
+                        img_ids=latent_image_ids,
+                        joint_attention_kwargs=self.joint_attention_kwargs,
+                        return_dict=False,
+                    )[0]
+                    noise_pred = neg_noise_pred + guidance_scale * (noise_pred - neg_noise_pred)
 
                 # compute the previous noisy sample x_t -> x_t-1
                 latents_dtype = latents.dtype