Fix issue with wan22 14b that woudl load both transformers temporarily resulting in oom on 24GB.

extensions_built_in/diffusion_models/wan22/wan22_14b_model.py

@@ -527,6 +527,44 @@ class Wan2214bModel(Wan21):
 
         return combined_dict
     
+    def generate_single_image(
+        self,
+        pipeline,
+        gen_config: GenerateImageConfig,
+        conditional_embeds: PromptEmbeds,
+        unconditional_embeds: PromptEmbeds,
+        generator: torch.Generator,
+        extra: dict,
+    ):
+        # reactivate progress bar since this is slooooow
+        pipeline.set_progress_bar_config(disable=False)
+        # todo, figure out how to do video
+        output = pipeline(
+            prompt_embeds=conditional_embeds.text_embeds.to(
+                self.device_torch, dtype=self.torch_dtype),
+            negative_prompt_embeds=unconditional_embeds.text_embeds.to(
+                self.device_torch, dtype=self.torch_dtype),
+            height=gen_config.height,
+            width=gen_config.width,
+            num_inference_steps=gen_config.num_inference_steps,
+            guidance_scale=gen_config.guidance_scale,
+            latents=gen_config.latents,
+            num_frames=gen_config.num_frames,
+            generator=generator,
+            return_dict=False,
+            output_type="pil",
+            **extra
+        )[0]
+
+        # shape = [1, frames, channels, height, width]
+        batch_item = output[0]  # list of pil images
+        if gen_config.num_frames > 1:
+            return batch_item  # return the frames.
+        else:
+            # get just the first image
+            img = batch_item[0]
+        return img
+
     def get_model_to_train(self):
         # todo, loras wont load right unless they have the transformer_1 or transformer_2 in the key.
         # called when setting up the LoRA. We only need to get the model for the stages we want to train.