Model CPU offload fix for BLIPDiffusion (#5174)

cpu offload fix for blip diffusion

src/diffusers/pipelines/blip_diffusion/pipeline_blip_diffusion.py

@@ -98,6 +98,8 @@ class BlipDiffusionPipeline(DiffusionPipeline):
             Position of the context token in the text encoder.
     """
 
+    model_cpu_offload_seq = "qformer->text_encoder->unet->vae"
+
     def __init__(
         self,
         tokenizer: CLIPTokenizer,
@@ -155,7 +157,9 @@ class BlipDiffusionPipeline(DiffusionPipeline):
         latents = latents * self.scheduler.init_noise_sigma
         return latents
 
-    def encode_prompt(self, query_embeds, prompt):
+    def encode_prompt(self, query_embeds, prompt, device=None):
+        device = device or self._execution_device
+
         # embeddings for prompt, with query_embeds as context
         max_len = self.text_encoder.text_model.config.max_position_embeddings
         max_len -= self.qformer.config.num_query_tokens
@@ -166,7 +170,7 @@ class BlipDiffusionPipeline(DiffusionPipeline):
             truncation=True,
             max_length=max_len,
             return_tensors="pt",
-        ).to(self.device)
+        ).to(device)
 
         batch_size = query_embeds.shape[0]
         ctx_begin_pos = [self.config.ctx_begin_pos] * batch_size
@@ -249,11 +253,12 @@ class BlipDiffusionPipeline(DiffusionPipeline):
         Returns:
             [`~pipelines.ImagePipelineOutput`] or `tuple`
         """
+        device = self._execution_device
 
         reference_image = self.image_processor.preprocess(
             reference_image, image_mean=self.config.mean, image_std=self.config.std, return_tensors="pt"
         )["pixel_values"]
-        reference_image = reference_image.to(self.device)
+        reference_image = reference_image.to(device)
 
         if isinstance(prompt, str):
             prompt = [prompt]
@@ -271,7 +276,7 @@ class BlipDiffusionPipeline(DiffusionPipeline):
             prompt_reps=prompt_reps,
         )
         query_embeds = self.get_query_embeddings(reference_image, source_subject_category)
-        text_embeddings = self.encode_prompt(query_embeds, prompt)
+        text_embeddings = self.encode_prompt(query_embeds, prompt, device)
         do_classifier_free_guidance = guidance_scale > 1.0
         if do_classifier_free_guidance:
             max_length = self.text_encoder.text_model.config.max_position_embeddings
@@ -283,7 +288,7 @@ class BlipDiffusionPipeline(DiffusionPipeline):
                 return_tensors="pt",
             )
             uncond_embeddings = self.text_encoder(
-                input_ids=uncond_input.input_ids.to(self.device),
+                input_ids=uncond_input.input_ids.to(device),
                 ctx_embeddings=None,
             )[0]
             # For classifier free guidance, we need to do two forward passes.
@@ -300,7 +305,7 @@ class BlipDiffusionPipeline(DiffusionPipeline):
             generator=generator,
             latents=latents,
             dtype=self.unet.dtype,
-            device=self.device,
+            device=device,
         )
         # set timesteps
         extra_set_kwargs = {}
@@ -330,9 +335,13 @@ class BlipDiffusionPipeline(DiffusionPipeline):
                 t,
                 latents,
             )["prev_sample"]
+
         image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
         image = self.image_processor.postprocess(image, output_type=output_type)
 
+        # Offload all models
+        self.maybe_free_model_hooks()
+
         if not return_dict:
             return (image,)
 

src/diffusers/pipelines/controlnet/pipeline_controlnet_blip_diffusion.py

@@ -107,6 +107,8 @@ class BlipDiffusionControlNetPipeline(DiffusionPipeline):
             Position of the context token in the text encoder.
     """
 
+    model_cpu_offload_seq = "qformer->text_encoder->unet->vae"
+
     def __init__(
         self,
         tokenizer: CLIPTokenizer,
@@ -166,7 +168,9 @@ class BlipDiffusionControlNetPipeline(DiffusionPipeline):
         latents = latents * self.scheduler.init_noise_sigma
         return latents
 
-    def encode_prompt(self, query_embeds, prompt):
+    def encode_prompt(self, query_embeds, prompt, device=None):
+        device = device or self._execution_device
+
         # embeddings for prompt, with query_embeds as context
         max_len = self.text_encoder.text_model.config.max_position_embeddings
         max_len -= self.qformer.config.num_query_tokens
@@ -177,7 +181,7 @@ class BlipDiffusionControlNetPipeline(DiffusionPipeline):
             truncation=True,
             max_length=max_len,
             return_tensors="pt",
-        ).to(self.device)
+        ).to(device)
 
         batch_size = query_embeds.shape[0]
         ctx_begin_pos = [self.config.ctx_begin_pos] * batch_size
@@ -297,11 +301,12 @@ class BlipDiffusionControlNetPipeline(DiffusionPipeline):
         Returns:
             [`~pipelines.ImagePipelineOutput`] or `tuple`
         """
+        device = self._execution_device
 
         reference_image = self.image_processor.preprocess(
             reference_image, image_mean=self.config.mean, image_std=self.config.std, return_tensors="pt"
         )["pixel_values"]
-        reference_image = reference_image.to(self.device)
+        reference_image = reference_image.to(device)
 
         if isinstance(prompt, str):
             prompt = [prompt]
@@ -319,7 +324,7 @@ class BlipDiffusionControlNetPipeline(DiffusionPipeline):
             prompt_reps=prompt_reps,
         )
         query_embeds = self.get_query_embeddings(reference_image, source_subject_category)
-        text_embeddings = self.encode_prompt(query_embeds, prompt)
+        text_embeddings = self.encode_prompt(query_embeds, prompt, device)
         # 3. unconditional embedding
         do_classifier_free_guidance = guidance_scale > 1.0
         if do_classifier_free_guidance:
@@ -332,7 +337,7 @@ class BlipDiffusionControlNetPipeline(DiffusionPipeline):
                 return_tensors="pt",
             )
             uncond_embeddings = self.text_encoder(
-                input_ids=uncond_input.input_ids.to(self.device),
+                input_ids=uncond_input.input_ids.to(device),
                 ctx_embeddings=None,
             )[0]
             # For classifier free guidance, we need to do two forward passes.
@@ -348,7 +353,7 @@ class BlipDiffusionControlNetPipeline(DiffusionPipeline):
             generator=generator,
             latents=latents,
             dtype=self.unet.dtype,
-            device=self.device,
+            device=device,
         )
         # set timesteps
         extra_set_kwargs = {}
@@ -399,6 +404,9 @@ class BlipDiffusionControlNetPipeline(DiffusionPipeline):
         image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
         image = self.image_processor.postprocess(image, output_type=output_type)
 
+        # Offload all models
+        self.maybe_free_model_hooks()
+
         if not return_dict:
             return (image,)