Add option for default prompts/negative prompts in eval (#148)

diffusion/evaluation/clean_fid_eval.py

@@ -49,6 +49,9 @@ class CleanFIDEvaluator:
         precision (str): The precision to use for evaluation. Default: ``'amp_fp16'``.
         prompts (List[str], optional): The prompts to use for image visualtization.
             Default: ``["A shiba inu wearing a blue sweater]``.
+        default_prompt (Optional[str]): An optional default prompt to add before each eval prompt. Default: ``None``.
+        default_negative_prompt (Optional[str]): An optional default negative prompt to add before each
+            negative prompt. Default: ``None``.
         additional_generate_kwargs (Dict, optional): Additional keyword arguments to pass to the model.generate method.
 
     """
@@ -70,6 +73,8 @@ def __init__(self,
                  num_samples: Optional[int] = None,
                  precision: str = 'amp_fp16',
                  prompts: Optional[List[str]] = None,
+                 default_prompt: Optional[str] = None,
+                 default_negative_prompt: Optional[str] = None,
                  additional_generate_kwargs: Optional[Dict] = None):
         self.model = model
         self.tokenizer: PreTrainedTokenizerBase = model.tokenizer
@@ -87,6 +92,8 @@ def __init__(self,
         self.num_samples = num_samples if num_samples is not None else float('inf')
         self.precision = precision
         self.prompts = prompts if prompts is not None else ['A shiba inu wearing a blue sweater']
+        self.default_prompt = default_prompt
+        self.default_negative_prompt = default_negative_prompt
         self.additional_generate_kwargs = additional_generate_kwargs if additional_generate_kwargs is not None else {}
         self.sdxl = model.sdxl
 
@@ -141,7 +148,17 @@ def _generate_images(self, guidance_scale: float):
                 break
 
             real_images = batch[self.image_key]
-            captions = batch[self.caption_key]
+            tokenized_captions = batch[self.caption_key]
+            # Get the prompts from the tokens
+            text_captions = self.tokenizer.batch_decode(tokenized_captions, skip_special_tokens=True)
+            # Add default prompts if specified
+            augmented_captions = text_captions
+            augmented_negative_prompt = None
+            if self.default_prompt:
+                augmented_captions = [f'{self.default_prompt} {caption}' for caption in text_captions]
+            if self.default_negative_prompt:
+                augmented_negative_prompt = [f'{self.default_negative_prompt}' for _ in text_captions]
+
             if self.sdxl:
                 crop_params = batch['cond_crops_coords_top_left']
                 input_size_params = batch['cond_original_size']
@@ -153,7 +170,8 @@ def _generate_images(self, guidance_scale: float):
             seed = starting_seed + batch_id
             # Generate images from the captions
             with get_precision_context(self.precision):
-                generated_images = self.model.generate(tokenized_prompts=captions,
+                generated_images = self.model.generate(prompt=augmented_captions,
+                                                       negative_prompt=augmented_negative_prompt,
                                                        height=self.size,
                                                        width=self.size,
                                                        guidance_scale=guidance_scale,
@@ -162,8 +180,6 @@ def _generate_images(self, guidance_scale: float):
                                                        input_size_params=input_size_params,
                                                        progress_bar=False,
                                                        **self.additional_generate_kwargs)  # type: ignore
-            # Get the prompts from the tokens
-            text_captions = self.tokenizer.batch_decode(captions, skip_special_tokens=True)
             self.clip_metric.update((generated_images * 255).to(torch.uint8), text_captions)
             # Save the real images
             # Verify that the real images are in the proper range
@@ -233,8 +249,23 @@ def _compute_metrics(self, guidance_scale: float):
     def _generate_images_from_prompts(self, guidance_scale: float):
         """Generate images from prompts for visualization."""
         if self.prompts:
+            # Augment the prompt
+            augmented_prompts = self.prompts
+            if self.default_prompt:
+                augmented_prompts = [f'{self.default_prompt} {prompt}' for prompt in self.prompts]
+            # Augment the negative prompt
+            augmented_negative_prompts = None
+            if 'negative prompt' in self.additional_generate_kwargs:
+                negative_prompts = self.additional_generate_kwargs['negative prompt']
+                augmented_negative_prompts = [
+                    f'{self.default_negative_prompt} {neg_prompt}' for neg_prompt in negative_prompts
+                ]
+            if self.default_negative_prompt and augmented_negative_prompts is None:
+                augmented_negative_prompts = [f'{self.default_negative_prompt}' for _ in self.prompts]
+
             with get_precision_context(self.precision):
-                generated_images = self.model.generate(prompt=self.prompts,
+                generated_images = self.model.generate(prompt=augmented_prompts,
+                                                       negative_prompt=augmented_negative_prompts,
                                                        height=self.size,
                                                        width=self.size,
                                                        guidance_scale=guidance_scale,