add

Author: yiyixuxu

src/diffusers/pipelines/hidream_image/pipeline_hidream_image.py

@@ -315,9 +315,9 @@ def _get_llama3_prompt_embeds(
     def encode_prompt(
         self,
         prompt: Union[str, List[str]],
-        prompt_2: Union[str, List[str]],
-        prompt_3: Union[str, List[str]],
-        prompt_4: Union[str, List[str]],
+        prompt_2: Optional[Union[str, List[str]]] = None,
+        prompt_3: Optional[Union[str, List[str]]] = None,
+        prompt_4: Optional[Union[str, List[str]]] = None,
         device: Optional[torch.device] = None,
         dtype: Optional[torch.dtype] = None,
         num_images_per_prompt: int = 1,
@@ -339,118 +339,104 @@ def encode_prompt(
         else:
             batch_size = prompt_embeds[0].shape[0] if isinstance(prompt_embeds, list) else prompt_embeds.shape[0]
 
-        prompt_embeds, pooled_prompt_embeds = self._encode_prompt(
-            prompt=prompt,
-            prompt_2=prompt_2,
-            prompt_3=prompt_3,
-            prompt_4=prompt_4,
-            device=device,
-            dtype=dtype,
-            num_images_per_prompt=num_images_per_prompt,
-            prompt_embeds=prompt_embeds,
-            pooled_prompt_embeds=pooled_prompt_embeds,
-            max_sequence_length=max_sequence_length,
-        )
-
-        if do_classifier_free_guidance and negative_prompt_embeds is None:
-            negative_prompt = negative_prompt or ""
-            negative_prompt_2 = negative_prompt_2 or negative_prompt
-            negative_prompt_3 = negative_prompt_3 or negative_prompt
-            negative_prompt_4 = negative_prompt_4 or negative_prompt
+        device = device or self._execution_device
 
-            # normalize str to list
-            negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
-            negative_prompt_2 = (
-                batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2
-            )
-            negative_prompt_3 = (
-                batch_size * [negative_prompt_3] if isinstance(negative_prompt_3, str) else negative_prompt_3
-            )
-            negative_prompt_4 = (
-                batch_size * [negative_prompt_4] if isinstance(negative_prompt_4, str) else negative_prompt_4
+        if pooled_prompt_embeds is None:
+            pooled_prompt_embeds_1 = self._get_clip_prompt_embeds(
+                self.tokenizer, self.text_encoder, prompt, max_sequence_length, device, dtype
             )
 
-            if prompt is not None and type(prompt) is not type(negative_prompt):
-                raise TypeError(
-                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
-                    f" {type(prompt)}."
-                )
-            elif batch_size != len(negative_prompt):
-                raise ValueError(
-                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
-                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
-                    " the batch size of `prompt`."
-                )
-
-            negative_prompt_embeds, negative_pooled_prompt_embeds = self._encode_prompt(
-                prompt=negative_prompt,
-                prompt_2=negative_prompt_2,
-                prompt_3=negative_prompt_3,
-                prompt_4=negative_prompt_4,
-                device=device,
-                dtype=dtype,
-                num_images_per_prompt=num_images_per_prompt,
-                prompt_embeds=negative_prompt_embeds,
-                pooled_prompt_embeds=negative_pooled_prompt_embeds,
-                max_sequence_length=max_sequence_length,
+        if do_classifier_free_guidance and negative_pooled_prompt_embeds is None:
+            negative_prompt = negative_prompt or ""
+            negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
+            negative_pooled_prompt_embeds_1 = self._get_clip_prompt_embeds(
+                self.tokenizer, self.text_encoder, negative_prompt, max_sequence_length, device, dtype
             )
-        return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
-
-    def _encode_prompt(
-        self,
-        prompt: Union[str, List[str]],
-        prompt_2: Union[str, List[str]],
-        prompt_3: Union[str, List[str]],
-        prompt_4: Union[str, List[str]],
-        device: Optional[torch.device] = None,
-        dtype: Optional[torch.dtype] = None,
-        num_images_per_prompt: int = 1,
-        prompt_embeds: Optional[List[torch.FloatTensor]] = None,
-        pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
-        max_sequence_length: int = 128,
-    ):
-        device = device or self._execution_device
-        if prompt is not None:
-            batch_size = len(prompt)
-        else:
-            batch_size = prompt_embeds[0].shape[0] if isinstance(prompt_embeds, list) else prompt_embeds.shape[0]
 
         if pooled_prompt_embeds is None:
             prompt_2 = prompt_2 or prompt
             prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2
 
-            pooled_prompt_embeds_1 = self._get_clip_prompt_embeds(
-                self.tokenizer, self.text_encoder, prompt, max_sequence_length, device, dtype
-            )
             pooled_prompt_embeds_2 = self._get_clip_prompt_embeds(
                 self.tokenizer_2, self.text_encoder_2, prompt_2, max_sequence_length, device, dtype
             )
+
+        if do_classifier_free_guidance and negative_pooled_prompt_embeds is None:
+            negative_prompt_2 = negative_prompt_2 or ""
+            negative_prompt_2 = (
+                batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2
+            )
+            negative_pooled_prompt_embeds_2 = self._get_clip_prompt_embeds(
+                self.tokenizer_2, self.text_encoder_2, negative_prompt_2, max_sequence_length, device, dtype
+            )
+
+        if pooled_prompt_embeds is None:
             pooled_prompt_embeds = torch.cat([pooled_prompt_embeds_1, pooled_prompt_embeds_2], dim=-1)
 
-            pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt)
-            pooled_prompt_embeds = pooled_prompt_embeds.view(batch_size * num_images_per_prompt, -1)
+        if do_classifier_free_guidance and negative_pooled_prompt_embeds is None:
+            negative_pooled_prompt_embeds = torch.cat(
+                [negative_pooled_prompt_embeds_1, negative_pooled_prompt_embeds_2], dim=-1
+            )
+
+        pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt)
+        pooled_prompt_embeds = pooled_prompt_embeds.view(batch_size * num_images_per_prompt, -1)
+        negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt)
+        negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.view(batch_size * num_images_per_prompt, -1)
 
         if prompt_embeds is None:
             prompt_3 = prompt_3 or prompt
             prompt_3 = [prompt_3] if isinstance(prompt_3, str) else prompt_3
+            t5_prompt_embeds = self._get_t5_prompt_embeds(prompt_3, max_sequence_length, device, dtype)
+
+        if do_classifier_free_guidance and negative_prompt_embeds is None:
+            negative_prompt_3 = negative_prompt_3 or ""
+            negative_prompt_3 = (
+                batch_size * [negative_prompt_3] if isinstance(negative_prompt_3, str) else negative_prompt_3
+            )
+            negative_t5_prompt_embeds = self._get_t5_prompt_embeds(
+                negative_prompt_3, max_sequence_length, device, dtype
+            )
 
+        if prompt_embeds is None:
             prompt_4 = prompt_4 or prompt
             prompt_4 = [prompt_4] if isinstance(prompt_4, str) else prompt_4
-
-            t5_prompt_embeds = self._get_t5_prompt_embeds(prompt_3, max_sequence_length, device, dtype)
             llama3_prompt_embeds = self._get_llama3_prompt_embeds(prompt_4, max_sequence_length, device, dtype)
 
-            _, seq_len, _ = t5_prompt_embeds.shape
-            t5_prompt_embeds = t5_prompt_embeds.repeat(1, num_images_per_prompt, 1)
-            t5_prompt_embeds = t5_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
-
-            _, _, seq_len, dim = llama3_prompt_embeds.shape
-            llama3_prompt_embeds = llama3_prompt_embeds.repeat(1, 1, num_images_per_prompt, 1)
-            llama3_prompt_embeds = llama3_prompt_embeds.view(-1, batch_size * num_images_per_prompt, seq_len, dim)
+        if do_classifier_free_guidance and negative_prompt_embeds is None:
+            negative_prompt_4 = negative_prompt_4 or ""
+            negative_prompt_4 = (
+                batch_size * [negative_prompt_4] if isinstance(negative_prompt_4, str) else negative_prompt_4
+            )
+            negative_llama3_prompt_embeds = self._get_llama3_prompt_embeds(
+                negative_prompt_4, max_sequence_length, device, dtype
+            )
 
+        if prompt_embeds is None:
             prompt_embeds = [t5_prompt_embeds, llama3_prompt_embeds]
 
-        return prompt_embeds, pooled_prompt_embeds
+        if do_classifier_free_guidance and negative_prompt_embeds is None:
+            negative_prompt_embeds = [negative_t5_prompt_embeds, negative_llama3_prompt_embeds]
+
+        _, seq_len, _ = prompt_embeds[0].shape
+        prompt_embeds[0] = prompt_embeds[0].repeat(1, num_images_per_prompt, 1)
+        prompt_embeds[0] = prompt_embeds[0].view(batch_size * num_images_per_prompt, seq_len, -1)
+
+        _, _, seq_len, dim = prompt_embeds[1].shape
+        prompt_embeds[1] = prompt_embeds[1].repeat(1, 1, num_images_per_prompt, 1)
+        prompt_embeds[1] = prompt_embeds[1].view(-1, batch_size * num_images_per_prompt, seq_len, dim)
+
+        if do_classifier_free_guidance:
+            _, seq_len, _ = negative_prompt_embeds[0].shape
+            negative_prompt_embeds[0] = negative_prompt_embeds[0].repeat(1, num_images_per_prompt, 1)
+            negative_prompt_embeds[0] = negative_prompt_embeds[0].view(batch_size * num_images_per_prompt, seq_len, -1)
+
+            _, _, seq_len, dim = negative_prompt_embeds[1].shape
+            negative_prompt_embeds[1] = negative_prompt_embeds[1].repeat(1, 1, num_images_per_prompt, 1)
+            negative_prompt_embeds[1] = negative_prompt_embeds[1].view(
+                -1, batch_size * num_images_per_prompt, seq_len, dim
+            )
+
+        return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
 
     def enable_vae_slicing(self):
         r"""