Add SDXL refiner only tests (huggingface#5041)

* add refiner only tests

* make style

Author: DN6

tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_img2img.py

@@ -26,7 +26,12 @@
     StableDiffusionXLImg2ImgPipeline,
     UNet2DConditionModel,
 )
-from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device
+from diffusers.utils.testing_utils import (
+    enable_full_determinism,
+    floats_tensor,
+    require_torch_gpu,
+    torch_device,
+)
 
 from ..pipeline_params import (
     IMAGE_TO_IMAGE_IMAGE_PARAMS,
@@ -159,24 +164,6 @@ def test_stable_diffusion_xl_img2img_euler(self):
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
 
-    def test_stable_diffusion_xl_refiner(self):
-        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
-        components = self.get_dummy_components(skip_first_text_encoder=True)
-
-        sd_pipe = StableDiffusionXLImg2ImgPipeline(**components)
-        sd_pipe = sd_pipe.to(device)
-        sd_pipe.set_progress_bar_config(disable=None)
-
-        inputs = self.get_dummy_inputs(device)
-        image = sd_pipe(**inputs).images
-        image_slice = image[0, -3:, -3:, -1]
-
-        assert image.shape == (1, 32, 32, 3)
-
-        expected_slice = np.array([0.4578, 0.4981, 0.4301, 0.6454, 0.5588, 0.4442, 0.5678, 0.5940, 0.5176])
-
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
-
     def test_attention_slicing_forward_pass(self):
         super().test_attention_slicing_forward_pass(expected_max_diff=3e-3)
 
@@ -195,7 +182,8 @@ def test_stable_diffusion_xl_img2img_negative_prompt_embeds(self):
         sd_pipe.set_progress_bar_config(disable=None)
 
         # forward without prompt embeds
-        inputs = self.get_dummy_inputs(torch_device)
+        generator_device = "cpu"
+        inputs = self.get_dummy_inputs(generator_device)
         negative_prompt = 3 * ["this is a negative prompt"]
         inputs["negative_prompt"] = negative_prompt
         inputs["prompt"] = 3 * [inputs["prompt"]]
@@ -204,7 +192,8 @@ def test_stable_diffusion_xl_img2img_negative_prompt_embeds(self):
         image_slice_1 = output.images[0, -3:, -3:, -1]
 
         # forward with prompt embeds
-        inputs = self.get_dummy_inputs(torch_device)
+        generator_device = "cpu"
+        inputs = self.get_dummy_inputs(generator_device)
         negative_prompt = 3 * ["this is a negative prompt"]
         prompt = 3 * [inputs.pop("prompt")]
 
@@ -248,7 +237,8 @@ def test_stable_diffusion_xl_offloads(self):
         for pipe in pipes:
             pipe.unet.set_default_attn_processor()
 
-            inputs = self.get_dummy_inputs(torch_device)
+            generator_device = "cpu"
+            inputs = self.get_dummy_inputs(generator_device)
             image = pipe(**inputs).images
 
             image_slices.append(image[0, -3:, -3:, -1].flatten())
@@ -261,13 +251,15 @@ def test_stable_diffusion_xl_multi_prompts(self):
         sd_pipe = self.pipeline_class(**components).to(torch_device)
 
         # forward with single prompt
-        inputs = self.get_dummy_inputs(torch_device)
+        generator_device = "cpu"
+        inputs = self.get_dummy_inputs(generator_device)
         inputs["num_inference_steps"] = 5
         output = sd_pipe(**inputs)
         image_slice_1 = output.images[0, -3:, -3:, -1]
 
         # forward with same prompt duplicated
-        inputs = self.get_dummy_inputs(torch_device)
+        generator_device = "cpu"
+        inputs = self.get_dummy_inputs(generator_device)
         inputs["num_inference_steps"] = 5
         inputs["prompt_2"] = inputs["prompt"]
         output = sd_pipe(**inputs)
@@ -277,7 +269,8 @@ def test_stable_diffusion_xl_multi_prompts(self):
         assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4
 
         # forward with different prompt
-        inputs = self.get_dummy_inputs(torch_device)
+        generator_device = "cpu"
+        inputs = self.get_dummy_inputs(generator_device)
         inputs["num_inference_steps"] = 5
         inputs["prompt_2"] = "different prompt"
         output = sd_pipe(**inputs)
@@ -287,14 +280,16 @@ def test_stable_diffusion_xl_multi_prompts(self):
         assert np.abs(image_slice_1.flatten() - image_slice_3.flatten()).max() > 1e-4
 
         # manually set a negative_prompt
-        inputs = self.get_dummy_inputs(torch_device)
+        generator_device = "cpu"
+        inputs = self.get_dummy_inputs(generator_device)
         inputs["num_inference_steps"] = 5
         inputs["negative_prompt"] = "negative prompt"
         output = sd_pipe(**inputs)
         image_slice_1 = output.images[0, -3:, -3:, -1]
 
         # forward with same negative_prompt duplicated
-        inputs = self.get_dummy_inputs(torch_device)
+        generator_device = "cpu"
+        inputs = self.get_dummy_inputs(generator_device)
         inputs["num_inference_steps"] = 5
         inputs["negative_prompt"] = "negative prompt"
         inputs["negative_prompt_2"] = inputs["negative_prompt"]
@@ -305,7 +300,8 @@ def test_stable_diffusion_xl_multi_prompts(self):
         assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4
 
         # forward with different negative_prompt
-        inputs = self.get_dummy_inputs(torch_device)
+        generator_device = "cpu"
+        inputs = self.get_dummy_inputs(generator_device)
         inputs["num_inference_steps"] = 5
         inputs["negative_prompt"] = "negative prompt"
         inputs["negative_prompt_2"] = "different negative prompt"
@@ -342,3 +338,229 @@ def test_stable_diffusion_xl_img2img_negative_conditions(self):
             np.abs(image_slice_with_no_neg_conditions.flatten() - image_slice_with_neg_conditions.flatten()).max()
             > 1e-4
         )
+
+
+class StableDiffusionXLImg2ImgRefinerOnlyPipelineFastTests(
+    PipelineLatentTesterMixin, PipelineTesterMixin, unittest.TestCase
+):
+    pipeline_class = StableDiffusionXLImg2ImgPipeline
+    params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
+    required_optional_params = PipelineTesterMixin.required_optional_params - {"latents"}
+    batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
+    image_params = IMAGE_TO_IMAGE_IMAGE_PARAMS
+    image_latents_params = IMAGE_TO_IMAGE_IMAGE_PARAMS
+
+    def get_dummy_components(self):
+        torch.manual_seed(0)
+        unet = UNet2DConditionModel(
+            block_out_channels=(32, 64),
+            layers_per_block=2,
+            sample_size=32,
+            in_channels=4,
+            out_channels=4,
+            down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
+            up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
+            # SD2-specific config below
+            attention_head_dim=(2, 4),
+            use_linear_projection=True,
+            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
+            cross_attention_dim=32,
+        )
+        scheduler = EulerDiscreteScheduler(
+            beta_start=0.00085,
+            beta_end=0.012,
+            steps_offset=1,
+            beta_schedule="scaled_linear",
+            timestep_spacing="leading",
+        )
+        torch.manual_seed(0)
+        vae = AutoencoderKL(
+            block_out_channels=[32, 64],
+            in_channels=3,
+            out_channels=3,
+            down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
+            up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
+            latent_channels=4,
+            sample_size=128,
+        )
+        torch.manual_seed(0)
+        text_encoder_config = CLIPTextConfig(
+            bos_token_id=0,
+            eos_token_id=2,
+            hidden_size=32,
+            intermediate_size=37,
+            layer_norm_eps=1e-05,
+            num_attention_heads=4,
+            num_hidden_layers=5,
+            pad_token_id=1,
+            vocab_size=1000,
+            # SD2-specific config below
+            hidden_act="gelu",
+            projection_dim=32,
+        )
+        text_encoder_2 = CLIPTextModelWithProjection(text_encoder_config)
+        tokenizer_2 = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
+
+        components = {
+            "unet": unet,
+            "scheduler": scheduler,
+            "vae": vae,
+            "tokenizer": None,
+            "text_encoder": None,
+            "text_encoder_2": text_encoder_2,
+            "tokenizer_2": tokenizer_2,
+            "requires_aesthetics_score": True,
+        }
+        return components
+
+    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"))
+        self.assertTrue(set(pipe.components.keys()) == set(init_components.keys()))
+
+    def get_dummy_inputs(self, device, seed=0):
+        image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
+        image = image / 2 + 0.5
+        if str(device).startswith("mps"):
+            generator = torch.manual_seed(seed)
+        else:
+            generator = torch.Generator(device=device).manual_seed(seed)
+        inputs = {
+            "prompt": "A painting of a squirrel eating a burger",
+            "image": image,
+            "generator": generator,
+            "num_inference_steps": 2,
+            "guidance_scale": 5.0,
+            "output_type": "np",
+            "strength": 0.8,
+        }
+        return inputs
+
+    def test_stable_diffusion_xl_img2img_euler(self):
+        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
+        components = self.get_dummy_components()
+        sd_pipe = StableDiffusionXLImg2ImgPipeline(**components)
+        sd_pipe = sd_pipe.to(device)
+        sd_pipe.set_progress_bar_config(disable=None)
+
+        inputs = self.get_dummy_inputs(device)
+        image = sd_pipe(**inputs).images
+        image_slice = image[0, -3:, -3:, -1]
+
+        assert image.shape == (1, 32, 32, 3)
+
+        expected_slice = np.array([0.4745, 0.4924, 0.4338, 0.6468, 0.5547, 0.4419, 0.5646, 0.5897, 0.5146])
+
+        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
+
+    @require_torch_gpu
+    def test_stable_diffusion_xl_offloads(self):
+        pipes = []
+        components = self.get_dummy_components()
+        sd_pipe = StableDiffusionXLImg2ImgPipeline(**components).to(torch_device)
+        pipes.append(sd_pipe)
+
+        components = self.get_dummy_components()
+        sd_pipe = StableDiffusionXLImg2ImgPipeline(**components)
+        sd_pipe.enable_model_cpu_offload()
+        pipes.append(sd_pipe)
+
+        components = self.get_dummy_components()
+        sd_pipe = StableDiffusionXLImg2ImgPipeline(**components)
+        sd_pipe.enable_sequential_cpu_offload()
+        pipes.append(sd_pipe)
+
+        image_slices = []
+        for pipe in pipes:
+            pipe.unet.set_default_attn_processor()
+
+            generator_device = "cpu"
+            inputs = self.get_dummy_inputs(generator_device)
+            image = pipe(**inputs).images
+
+            image_slices.append(image[0, -3:, -3:, -1].flatten())
+
+        assert np.abs(image_slices[0] - image_slices[1]).max() < 1e-3
+        assert np.abs(image_slices[0] - image_slices[2]).max() < 1e-3
+
+    def test_stable_diffusion_xl_img2img_negative_conditions(self):
+        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
+        components = self.get_dummy_components()
+
+        sd_pipe = self.pipeline_class(**components)
+        sd_pipe = sd_pipe.to(device)
+        sd_pipe.set_progress_bar_config(disable=None)
+
+        inputs = self.get_dummy_inputs(device)
+        image = sd_pipe(**inputs).images
+        image_slice_with_no_neg_conditions = image[0, -3:, -3:, -1]
+
+        image = sd_pipe(
+            **inputs,
+            negative_original_size=(512, 512),
+            negative_crops_coords_top_left=(
+                0,
+                0,
+            ),
+            negative_target_size=(1024, 1024),
+        ).images
+        image_slice_with_neg_conditions = image[0, -3:, -3:, -1]
+
+        assert (
+            np.abs(image_slice_with_no_neg_conditions.flatten() - image_slice_with_neg_conditions.flatten()).max()
+            > 1e-4
+        )
+
+    def test_stable_diffusion_xl_img2img_negative_prompt_embeds(self):
+        components = self.get_dummy_components()
+        sd_pipe = StableDiffusionXLImg2ImgPipeline(**components)
+        sd_pipe = sd_pipe.to(torch_device)
+        sd_pipe = sd_pipe.to(torch_device)
+        sd_pipe.set_progress_bar_config(disable=None)
+
+        # forward without prompt embeds
+        generator_device = "cpu"
+        inputs = self.get_dummy_inputs(generator_device)
+        negative_prompt = 3 * ["this is a negative prompt"]
+        inputs["negative_prompt"] = negative_prompt
+        inputs["prompt"] = 3 * [inputs["prompt"]]
+
+        output = sd_pipe(**inputs)
+        image_slice_1 = output.images[0, -3:, -3:, -1]
+
+        # forward with prompt embeds
+        generator_device = "cpu"
+        inputs = self.get_dummy_inputs(generator_device)
+        negative_prompt = 3 * ["this is a negative prompt"]
+        prompt = 3 * [inputs.pop("prompt")]
+
+        (
+            prompt_embeds,
+            negative_prompt_embeds,
+            pooled_prompt_embeds,
+            negative_pooled_prompt_embeds,
+        ) = sd_pipe.encode_prompt(prompt, negative_prompt=negative_prompt)
+
+        output = sd_pipe(
+            **inputs,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            pooled_prompt_embeds=pooled_prompt_embeds,
+            negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
+        )
+        image_slice_2 = output.images[0, -3:, -3:, -1]
+
+        # make sure that it's equal
+        assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4
+
+    def test_attention_slicing_forward_pass(self):
+        super().test_attention_slicing_forward_pass(expected_max_diff=3e-3)
+
+    def test_inference_batch_single_identical(self):
+        super().test_inference_batch_single_identical(expected_max_diff=3e-3)