🎨 fix xl playground device (#8550)

* 🎨 fix xl playground device

* 🎨 run `make fix-copies`

* 🎨 run `make fix-copies`

* edit xl_controlnet_img2img file

* edit playground img2img test slow

* Update tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_img2img.py

---------

Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

src/diffusers/pipelines/controlnet/pipeline_controlnet_sd_xl_img2img.py

@@ -949,8 +949,8 @@ class StableDiffusionXLControlNetImg2ImgPipeline(
 
             init_latents = init_latents.to(dtype)
             if latents_mean is not None and latents_std is not None:
-                latents_mean = latents_mean.to(device=self.device, dtype=dtype)
-                latents_std = latents_std.to(device=self.device, dtype=dtype)
+                latents_mean = latents_mean.to(device=device, dtype=dtype)
+                latents_std = latents_std.to(device=device, dtype=dtype)
                 init_latents = (init_latents - latents_mean) * self.vae.config.scaling_factor / latents_std
             else:
                 init_latents = self.vae.config.scaling_factor * init_latents

src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py

@@ -723,8 +723,8 @@ class StableDiffusionXLImg2ImgPipeline(
 
             init_latents = init_latents.to(dtype)
             if latents_mean is not None and latents_std is not None:
-                latents_mean = latents_mean.to(device=self.device, dtype=dtype)
-                latents_std = latents_std.to(device=self.device, dtype=dtype)
+                latents_mean = latents_mean.to(device=device, dtype=dtype)
+                latents_std = latents_std.to(device=device, dtype=dtype)
                 init_latents = (init_latents - latents_mean) * self.vae.config.scaling_factor / latents_std
             else:
                 init_latents = self.vae.config.scaling_factor * init_latents

tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_img2img.py

@@ -13,6 +13,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import gc
 import random
 import unittest
 
@@ -31,6 +32,7 @@ from transformers import (
 from diffusers import (
     AutoencoderKL,
     AutoencoderTiny,
+    EDMDPMSolverMultistepScheduler,
     EulerDiscreteScheduler,
     LCMScheduler,
     StableDiffusionXLImg2ImgPipeline,
@@ -39,7 +41,9 @@ from diffusers import (
 from diffusers.utils.testing_utils import (
     enable_full_determinism,
     floats_tensor,
+    load_image,
     require_torch_gpu,
+    slow,
     torch_device,
 )
 
@@ -776,3 +780,54 @@ class StableDiffusionXLImg2ImgRefinerOnlyPipelineFastTests(
 
     def test_save_load_optional_components(self):
         self._test_save_load_optional_components()
+
+
+@slow
+class StableDiffusionXLImg2ImgPipelineIntegrationTests(unittest.TestCase):
+    def setUp(self):
+        super().setUp()
+        gc.collect()
+        torch.cuda.empty_cache()
+
+    def tearDown(self):
+        super().tearDown()
+        gc.collect()
+        torch.cuda.empty_cache()
+
+    def test_stable_diffusion_xl_img2img_playground(self):
+        torch.manual_seed(0)
+        model_path = "playgroundai/playground-v2.5-1024px-aesthetic"
+
+        sd_pipe = StableDiffusionXLImg2ImgPipeline.from_pretrained(
+            model_path, torch_dtype=torch.float16, variant="fp16", add_watermarker=False
+        )
+
+        sd_pipe.enable_model_cpu_offload()
+        sd_pipe.scheduler = EDMDPMSolverMultistepScheduler.from_config(
+            sd_pipe.scheduler.config, use_karras_sigmas=True
+        )
+        sd_pipe.set_progress_bar_config(disable=None)
+
+        prompt = "a photo of an astronaut riding a horse on mars"
+
+        url = "https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/aa_xl/000000009.png"
+
+        init_image = load_image(url).convert("RGB")
+
+        image = sd_pipe(
+            prompt,
+            num_inference_steps=30,
+            guidance_scale=8.0,
+            image=init_image,
+            height=1024,
+            width=1024,
+            output_type="np",
+        ).images
+
+        image_slice = image[0, -3:, -3:, -1]
+
+        assert image.shape == (1, 1024, 1024, 3)
+
+        expected_slice = np.array([0.3519, 0.3149, 0.3364, 0.3505, 0.3402, 0.3371, 0.3554, 0.3495, 0.3333])
+
+        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2