rename do_classifier_free_guidance->prepare_unconditional_embeds

Author: a-r-r-o-w

src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_modular.py

@@ -233,10 +233,10 @@ def encode_image(self, components, image, device, num_images_per_prompt, output_
 
     # modified from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds
     def prepare_ip_adapter_image_embeds(
-        self, components, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance
+        self, components, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, prepare_unconditional_embeds
     ):
         image_embeds = []
-        if do_classifier_free_guidance:
+        if prepare_unconditional_embeds:
             negative_image_embeds = []
         if ip_adapter_image_embeds is None:
             if not isinstance(ip_adapter_image, list):
@@ -256,19 +256,19 @@ def prepare_ip_adapter_image_embeds(
                 )
 
                 image_embeds.append(single_image_embeds[None, :])
-                if do_classifier_free_guidance:
+                if prepare_unconditional_embeds:
                     negative_image_embeds.append(single_negative_image_embeds[None, :])
         else:
             for single_image_embeds in ip_adapter_image_embeds:
-                if do_classifier_free_guidance:
+                if prepare_unconditional_embeds:
                     single_negative_image_embeds, single_image_embeds = single_image_embeds.chunk(2)
                     negative_image_embeds.append(single_negative_image_embeds)
                 image_embeds.append(single_image_embeds)
 
         ip_adapter_image_embeds = []
         for i, single_image_embeds in enumerate(image_embeds):
             single_image_embeds = torch.cat([single_image_embeds] * num_images_per_prompt, dim=0)
-            if do_classifier_free_guidance:
+            if prepare_unconditional_embeds:
                 single_negative_image_embeds = torch.cat([negative_image_embeds[i]] * num_images_per_prompt, dim=0)
                 single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds], dim=0)
 
@@ -281,7 +281,7 @@ def prepare_ip_adapter_image_embeds(
     def __call__(self, pipeline, state: PipelineState) -> PipelineState:
         data = self.get_block_state(state)
 
-        data.do_classifier_free_guidance = pipeline.guider.num_conditions > 1
+        data.prepare_unconditional_embeds = pipeline.guider.num_conditions > 1
         data.device = pipeline._execution_device
 
         data.ip_adapter_embeds = self.prepare_ip_adapter_image_embeds(
@@ -290,9 +290,9 @@ def __call__(self, pipeline, state: PipelineState) -> PipelineState:
             ip_adapter_image_embeds=None,
             device=data.device,
             num_images_per_prompt=1,
-            do_classifier_free_guidance=data.do_classifier_free_guidance,
+            prepare_unconditional_embeds=data.prepare_unconditional_embeds,
         )
-        if data.do_classifier_free_guidance:
+        if data.prepare_unconditional_embeds:
             data.negative_ip_adapter_embeds = []
             for i, image_embeds in enumerate(data.ip_adapter_embeds):
                 negative_image_embeds, image_embeds = image_embeds.chunk(2)
@@ -355,15 +355,14 @@ def check_inputs(self, pipeline, data):
         elif data.prompt_2 is not None and (not isinstance(data.prompt_2, str) and not isinstance(data.prompt_2, list)):
             raise ValueError(f"`prompt_2` has to be of type `str` or `list` but is {type(data.prompt_2)}")
 
-    # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.encode_prompt with self -> components
     def encode_prompt(
         self,
         components,
         prompt: str,
         prompt_2: Optional[str] = None,
         device: Optional[torch.device] = None,
         num_images_per_prompt: int = 1,
-        do_classifier_free_guidance: bool = True,
+        prepare_unconditional_embeds: bool = True,
         negative_prompt: Optional[str] = None,
         negative_prompt_2: Optional[str] = None,
         prompt_embeds: Optional[torch.Tensor] = None,
@@ -386,8 +385,8 @@ def encode_prompt(
                 torch device
             num_images_per_prompt (`int`):
                 number of images that should be generated per prompt
-            do_classifier_free_guidance (`bool`):
-                whether to use classifier free guidance or not
+            prepare_unconditional_embeds (`bool`):
+                whether to use prepare unconditional embeddings or not
             negative_prompt (`str` or `List[str]`, *optional*):
                 The prompt or prompts not to guide the image generation. If not defined, one has to pass
                 `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
@@ -495,10 +494,10 @@ def encode_prompt(
 
         # get unconditional embeddings for classifier free guidance
         zero_out_negative_prompt = negative_prompt is None and components.config.force_zeros_for_empty_prompt
-        if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:
+        if prepare_unconditional_embeds and negative_prompt_embeds is None and zero_out_negative_prompt:
             negative_prompt_embeds = torch.zeros_like(prompt_embeds)
             negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)
-        elif do_classifier_free_guidance and negative_prompt_embeds is None:
+        elif prepare_unconditional_embeds and negative_prompt_embeds is None:
             negative_prompt = negative_prompt or ""
             negative_prompt_2 = negative_prompt_2 or negative_prompt
 
@@ -559,7 +558,7 @@ def encode_prompt(
         prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
         prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
 
-        if do_classifier_free_guidance:
+        if prepare_unconditional_embeds:
             # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
             seq_len = negative_prompt_embeds.shape[1]
 
@@ -574,7 +573,7 @@ def encode_prompt(
         pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
             bs_embed * num_images_per_prompt, -1
         )
-        if do_classifier_free_guidance:
+        if prepare_unconditional_embeds:
             negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
                 bs_embed * num_images_per_prompt, -1
             )
@@ -598,7 +597,7 @@ def __call__(self, pipeline, state: PipelineState) -> PipelineState:
         data = self.get_block_state(state)
         self.check_inputs(pipeline, data)
 
-        data.do_classifier_free_guidance = pipeline.guider.num_conditions > 1
+        data.prepare_unconditional_embeds = pipeline.guider.num_conditions > 1
         data.device = pipeline._execution_device
 
         # Encode input prompt
@@ -616,7 +615,7 @@ def __call__(self, pipeline, state: PipelineState) -> PipelineState:
             data.prompt_2,
             data.device,
             1,
-            data.do_classifier_free_guidance,
+            data.prepare_unconditional_embeds,
             data.negative_prompt,
             data.negative_prompt_2,
             prompt_embeds=None,

src/diffusers/schedulers/scheduling_euler_discrete.py

@@ -669,7 +669,7 @@ def step(
         dt = self.sigmas[self.step_index + 1] - sigma_hat
 
         prev_sample = sample + derivative * dt
-        
+
         # Cast sample back to model compatible dtype
         prev_sample = prev_sample.to(model_output.dtype)