fix community pipeline for semantic guidance for flux

Author: Marlon154

examples/community/pipeline_flux_semantic_guidance.py

@@ -230,6 +230,7 @@ def __init__(
         )
         self.default_sample_size = 128
 
+    # Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline._get_t5_prompt_embeds
     def _get_t5_prompt_embeds(
         self,
         prompt: Union[str, List[str]] = None,
@@ -279,6 +280,7 @@ def _get_t5_prompt_embeds(
 
         return prompt_embeds
 
+    # Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline._get_clip_prompt_embeds
     def _get_clip_prompt_embeds(
         self,
         prompt: Union[str, List[str]],
@@ -323,6 +325,7 @@ def _get_clip_prompt_embeds(
 
         return prompt_embeds
 
+    # Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline.encode_prompt
     def encode_prompt(
         self,
         prompt: Union[str, List[str]],
@@ -513,6 +516,7 @@ def encode_text_with_editing(
             enabled_editing_prompts,
         )
 
+    # Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline.encode_image
     def encode_image(self, image, device, num_images_per_prompt):
         dtype = next(self.image_encoder.parameters()).dtype
 
@@ -524,6 +528,7 @@ def encode_image(self, image, device, num_images_per_prompt):
         image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
         return image_embeds
 
+    # Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline.prepare_ip_adapter_image_embeds
     def prepare_ip_adapter_image_embeds(
         self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt
     ):
@@ -555,6 +560,7 @@ def prepare_ip_adapter_image_embeds(
 
         return ip_adapter_image_embeds
 
+    # Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline.check_inputs
     def check_inputs(
         self,
         prompt,
@@ -633,6 +639,7 @@ def check_inputs(
             raise ValueError(f"`max_sequence_length` cannot be greater than 512 but is {max_sequence_length}")
 
     @staticmethod
+    # Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline._prepare_latent_image_ids
     def _prepare_latent_image_ids(batch_size, height, width, device, dtype):
         latent_image_ids = torch.zeros(height, width, 3)
         latent_image_ids[..., 1] = latent_image_ids[..., 1] + torch.arange(height)[:, None]
@@ -647,6 +654,7 @@ def _prepare_latent_image_ids(batch_size, height, width, device, dtype):
         return latent_image_ids.to(device=device, dtype=dtype)
 
     @staticmethod
+    # Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline._pack_latents
     def _pack_latents(latents, batch_size, num_channels_latents, height, width):
         latents = latents.view(batch_size, num_channels_latents, height // 2, 2, width // 2, 2)
         latents = latents.permute(0, 2, 4, 1, 3, 5)
@@ -655,6 +663,7 @@ def _pack_latents(latents, batch_size, num_channels_latents, height, width):
         return latents
 
     @staticmethod
+    # Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline._unpack_latents
     def _unpack_latents(latents, height, width, vae_scale_factor):
         batch_size, num_patches, channels = latents.shape
 
@@ -670,20 +679,23 @@ def _unpack_latents(latents, height, width, vae_scale_factor):
 
         return latents
 
+    # Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline.enable_vae_slicing
     def enable_vae_slicing(self):
         r"""
         Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
         compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
         """
         self.vae.enable_slicing()
 
+    # Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline.disable_vae_slicing
     def disable_vae_slicing(self):
         r"""
         Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
         computing decoding in one step.
         """
         self.vae.disable_slicing()
 
+    # Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline.enable_vae_tiling
     def enable_vae_tiling(self):
         r"""
         Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
@@ -692,13 +704,15 @@ def enable_vae_tiling(self):
         """
         self.vae.enable_tiling()
 
+    # Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline.disable_vae_tiling
     def disable_vae_tiling(self):
         r"""
         Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
         computing decoding in one step.
         """
         self.vae.disable_tiling()
 
+    # Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline.prepare_latents
     def prepare_latents(
         self,
         batch_size,
@@ -1171,7 +1185,7 @@ def __call__(
                         device=device,
                         dtype=noise_guidance.dtype,
                     )
-                    # noise_guidance_edit = torch.zeros_like(noise_guidance)
+
                     warmup_inds = []
                     for c, noise_pred_edit_concept in enumerate(noise_pred_edit_concepts):
                         if isinstance(edit_guidance_scale, list):
@@ -1244,9 +1258,6 @@ def __call__(
                         )
 
                         noise_guidance_edit[c, :, :, :] = noise_guidance_edit_tmp
-                        # noise_guidance_edit[c] = noise_guidance_edit_tmp
-
-                        # noise_guidance_edit = noise_guidance_edit + noise_guidance_edit_tmp
 
                     warmup_inds = torch.tensor(warmup_inds).to(device)
                     if len(noise_pred_edit_concepts) > warmup_inds.shape[0] > 0:
@@ -1258,7 +1269,6 @@ def __call__(
                             concept_weights_tmp < 0, torch.zeros_like(concept_weights_tmp), concept_weights_tmp
                         )
                         concept_weights_tmp = concept_weights_tmp / concept_weights_tmp.sum(dim=0)
-                        # concept_weights_tmp = torch.nan_to_num(concept_weights_tmp)
 
                         noise_guidance_edit_tmp = torch.index_select(noise_guidance_edit.to(device), 0, warmup_inds)
                         noise_guidance_edit_tmp = torch.einsum(