[Flux Redux] add prompt & multiple image input

src/diffusers/pipelines/flux/pipeline_flux_prior_redux.py

@@ -142,6 +142,45 @@ def __init__(
             self.tokenizer.model_max_length if hasattr(self, "tokenizer") and self.tokenizer is not None else 77
         )
 
+    def check_inputs(
+        self,
+        image,
+        prompt,
+        prompt_2,
+        prompt_embeds=None,
+        pooled_prompt_embeds=None,
+        prompt_embeds_scale=1.0,
+        pooled_prompt_embeds_scale=1.0,
+    ):
+        if prompt is not None and prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+                " only forward one of the two."
+            )
+        elif prompt_2 is not None and prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+                " only forward one of the two."
+            )
+        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+        elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):
+            raise ValueError(f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}")
+        if prompt is not None and (isinstance(prompt, list) and isinstance(image, list) and len(prompt) != len(image)):
+            raise ValueError(
+                f"number of prompts must be equal to number of images, but {len(prompt)} prompts were provided and {len(image)} images"
+            )
+        if prompt_embeds is not None and pooled_prompt_embeds is None:
+            raise ValueError(
+                "If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`."
+            )
+        if isinstance(prompt_embeds_scale, list) and (
+            isinstance(image, list) and len(prompt_embeds_scale) != len(image)
+        ):
+            raise ValueError(
+                f"number of weights must be equal to number of images, but {len(prompt_embeds_scale)} weights were provided and {len(image)} images"
+            )
+
     def encode_image(self, image, device, num_images_per_prompt):
         dtype = next(self.image_encoder.parameters()).dtype
         image = self.feature_extractor.preprocess(
@@ -334,6 +373,12 @@ def encode_prompt(
     def __call__(
         self,
         image: PipelineImageInput,
+        prompt: Union[str, List[str]] = None,
+        prompt_2: Optional[Union[str, List[str]]] = None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+        prompt_embeds_scale: Optional[Union[float, List[float]]] = 1.0,
+        pooled_prompt_embeds_scale: Optional[Union[float, List[float]]] = 1.0,
         return_dict: bool = True,
     ):
         r"""
@@ -345,6 +390,14 @@ def __call__(
                 numpy array and pytorch tensor, the expected value range is between `[0, 1]` If it's a tensor or a list
                 or tensors, the expected shape should be `(B, C, H, W)` or `(C, H, W)`. If it is a numpy array or a
                 list of arrays, the expected shape should be `(B, H, W, C)` or `(H, W, C)`
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide the image generation.
+            prompt_2 (`str` or `List[str]`, *optional*):
+                The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`.
+            prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
+            pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated pooled text embeddings.
             return_dict (`bool`, *optional*, defaults to `True`):
                 Whether or not to return a [`~pipelines.flux.FluxPriorReduxPipelineOutput`] instead of a plain tuple.
 
@@ -356,13 +409,29 @@ def __call__(
             returning a tuple, the first element is a list with the generated images.
         """
 
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            prompt_2,
+            image,
+            prompt_embeds=prompt_embeds,
+            pooled_prompt_embeds=pooled_prompt_embeds,
+        )
+
         # 2. Define call parameters
         if image is not None and isinstance(image, Image.Image):
             batch_size = 1
         elif image is not None and isinstance(image, list):
             batch_size = len(image)
         else:
             batch_size = image.shape[0]
+        if prompt is not None and isinstance(prompt, str):
+            prompt = batch_size * [prompt]
+        if isinstance(prompt_embeds_scale, float):
+            prompt_embeds_scale = batch_size * [prompt_embeds_scale]
+        if isinstance(pooled_prompt_embeds_scale, float):
+            pooled_prompt_embeds_scale = batch_size * [pooled_prompt_embeds_scale]
+
         device = self._execution_device
 
         # 3. Prepare image embeddings
@@ -378,10 +447,10 @@ def __call__(
                 pooled_prompt_embeds,
                 _,
             ) = self.encode_prompt(
-                prompt=[""] * batch_size,
-                prompt_2=None,
-                prompt_embeds=None,
-                pooled_prompt_embeds=None,
+                prompt=prompt,
+                prompt_2=prompt_2,
+                prompt_embeds=prompt_embeds,
+                pooled_prompt_embeds=pooled_prompt_embeds,
                 device=device,
                 num_images_per_prompt=1,
                 max_sequence_length=512,
@@ -393,9 +462,18 @@ def __call__(
             # pooled_prompt_embeds is 768, clip text encoder hidden size
             pooled_prompt_embeds = torch.zeros((batch_size, 768), device=device, dtype=image_embeds.dtype)
 
-        # Concatenate image and text embeddings
+        # scale & oncatenate image and text embeddings
         prompt_embeds = torch.cat([prompt_embeds, image_embeds], dim=1)
 
+        prompt_embeds *= torch.tensor(prompt_embeds_scale, device=device, dtype=image_embeds.dtype)[:, None, None]
+        pooled_prompt_embeds *= torch.tensor(pooled_prompt_embeds_scale, device=device, dtype=image_embeds.dtype)[
+            :, None
+        ]
+
+        # weighted sum
+        prompt_embeds = torch.sum(prompt_embeds, dim=0, keepdim=True)
+        pooled_prompt_embeds = torch.sum(pooled_prompt_embeds, dim=0, keepdim=True)
+
         # Offload all models
         self.maybe_free_model_hooks()