[InstructPix2Pix] Fix pipeline implementation and add docs (#4844)

* initial evident fixes.

* instructpix2pix fixes.

* add: entry to doc.

* address PR feedback.

* make fix-copies

docs/source/en/api/pipelines/pix2pix.md

@@ -35,4 +35,12 @@ Make sure to check out the Schedulers [guide](/using-diffusers/schedulers) to le
 	- save_lora_weights
 
 ## StableDiffusionPipelineOutput
-[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput
+[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput
\ No newline at end of file
+
+## StableDiffusionXLInstructPix2PixPipeline
+[[autodoc]] StableDiffusionXLInstructPix2PixPipeline
+	- __call__
+	- all
+
+## StableDiffusionXLPipelineOutput
+[[autodoc]] pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput
\ No newline at end of file

tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_instruction_pix2pix.py

@@ -68,7 +68,7 @@ class StableDiffusionXLInstructPix2PixPipelineFastTests(
             addition_embed_type="text_time",
             addition_time_embed_dim=8,
             transformer_layers_per_block=(1, 2),
-            projection_class_embeddings_input_dim=72,  # 5 * 8 + 32
+            projection_class_embeddings_input_dim=80,  # 5 * 8 + 32
             cross_attention_dim=64,
         )
 
@@ -118,12 +118,11 @@ class StableDiffusionXLInstructPix2PixPipelineFastTests(
             "tokenizer": tokenizer,
             "text_encoder_2": text_encoder_2,
             "tokenizer_2": tokenizer_2,
-            "requires_aesthetics_score": True,
         }
         return components
 
     def get_dummy_inputs(self, device, seed=0):
-        image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
+        image = floats_tensor((1, 3, 64, 64), rng=random.Random(seed)).to(device)
         image = image / 2 + 0.5
         if str(device).startswith("mps"):
             generator = torch.manual_seed(seed)
@@ -142,7 +141,6 @@ class StableDiffusionXLInstructPix2PixPipelineFastTests(
 
     def test_components_function(self):
         init_components = self.get_dummy_components()
-        init_components.pop("requires_aesthetics_score")
         pipe = self.pipeline_class(**init_components)
 
         self.assertTrue(hasattr(pipe, "components"))