[CI] Nightly Test Updates (#9380)

* update

* update

* update

* update

* update

---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Co-authored-by: YiYi Xu <yixu310@gmail.com>

tests/lora/test_lora_layers_sd.py

@@ -20,7 +20,6 @@ import numpy as np
 import torch
 import torch.nn as nn
 from huggingface_hub import hf_hub_download
-from huggingface_hub.repocard import RepoCard
 from safetensors.torch import load_file
 from transformers import CLIPTextModel, CLIPTokenizer
 
@@ -103,7 +102,7 @@ class StableDiffusionLoRATests(PeftLoraLoaderMixinTests, unittest.TestCase):
     @slow
     @require_torch_gpu
     def test_integration_move_lora_cpu(self):
-        path = "runwayml/stable-diffusion-v1-5"
+        path = "Jiali/stable-diffusion-1.5"
         lora_id = "takuma104/lora-test-text-encoder-lora-target"
 
         pipe = StableDiffusionPipeline.from_pretrained(path, torch_dtype=torch.float16)
@@ -162,7 +161,7 @@ class StableDiffusionLoRATests(PeftLoraLoaderMixinTests, unittest.TestCase):
     def test_integration_move_lora_dora_cpu(self):
         from peft import LoraConfig
 
-        path = "Lykon/dreamshaper-8"
+        path = "Jiali/stable-diffusion-1.5"
         unet_lora_config = LoraConfig(
             init_lora_weights="gaussian",
             target_modules=["to_k", "to_q", "to_v", "to_out.0"],
@@ -222,7 +221,7 @@ class LoraIntegrationTests(unittest.TestCase):
         torch.cuda.empty_cache()
 
     def test_integration_logits_with_scale(self):
-        path = "runwayml/stable-diffusion-v1-5"
+        path = "Jiali/stable-diffusion-1.5"
         lora_id = "takuma104/lora-test-text-encoder-lora-target"
 
         pipe = StableDiffusionPipeline.from_pretrained(path, torch_dtype=torch.float32)
@@ -254,7 +253,7 @@ class LoraIntegrationTests(unittest.TestCase):
         release_memory(pipe)
 
     def test_integration_logits_no_scale(self):
-        path = "runwayml/stable-diffusion-v1-5"
+        path = "Jiali/stable-diffusion-1.5"
         lora_id = "takuma104/lora-test-text-encoder-lora-target"
 
         pipe = StableDiffusionPipeline.from_pretrained(path, torch_dtype=torch.float32)
@@ -284,8 +283,8 @@ class LoraIntegrationTests(unittest.TestCase):
         generator = torch.Generator("cpu").manual_seed(0)
 
         lora_model_id = "hf-internal-testing/lora_dreambooth_dog_example"
-        card = RepoCard.load(lora_model_id)
-        base_model_id = card.data.to_dict()["base_model"]
+
+        base_model_id = "Jiali/stable-diffusion-1.5"
 
         pipe = StableDiffusionPipeline.from_pretrained(base_model_id, safety_checker=None)
         pipe = pipe.to(torch_device)
@@ -308,8 +307,8 @@ class LoraIntegrationTests(unittest.TestCase):
         generator = torch.Generator().manual_seed(0)
 
         lora_model_id = "hf-internal-testing/lora-trained"
-        card = RepoCard.load(lora_model_id)
-        base_model_id = card.data.to_dict()["base_model"]
+
+        base_model_id = "Jiali/stable-diffusion-1.5"
 
         pipe = StableDiffusionPipeline.from_pretrained(base_model_id, safety_checker=None)
         pipe = pipe.to(torch_device)
@@ -420,7 +419,7 @@ class LoraIntegrationTests(unittest.TestCase):
     def test_kohya_sd_v15_with_higher_dimensions(self):
         generator = torch.Generator().manual_seed(0)
 
-        pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", safety_checker=None).to(
+        pipe = StableDiffusionPipeline.from_pretrained("Jiali/stable-diffusion-1.5", safety_checker=None).to(
             torch_device
         )
         lora_model_id = "hf-internal-testing/urushisato-lora"
@@ -444,8 +443,8 @@ class LoraIntegrationTests(unittest.TestCase):
         generator = torch.Generator().manual_seed(0)
 
         lora_model_id = "hf-internal-testing/sd-model-finetuned-lora-t4"
-        card = RepoCard.load(lora_model_id)
-        base_model_id = card.data.to_dict()["base_model"]
+
+        base_model_id = "Jiali/stable-diffusion-1.5"
 
         pipe = StableDiffusionPipeline.from_pretrained(base_model_id, safety_checker=None)
         pipe = pipe.to(torch_device)
@@ -468,7 +467,7 @@ class LoraIntegrationTests(unittest.TestCase):
         prompt = "masterpiece, best quality, mountain"
         num_inference_steps = 2
 
-        pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", safety_checker=None).to(
+        pipe = StableDiffusionPipeline.from_pretrained("Jiali/stable-diffusion-1.5", safety_checker=None).to(
             torch_device
         )
         initial_images = pipe(
@@ -506,7 +505,7 @@ class LoraIntegrationTests(unittest.TestCase):
         prompt = "masterpiece, best quality, mountain"
         num_inference_steps = 2
 
-        pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", safety_checker=None).to(
+        pipe = StableDiffusionPipeline.from_pretrained("Jiali/stable-diffusion-1.5", safety_checker=None).to(
             torch_device
         )
         initial_images = pipe(
@@ -548,9 +547,9 @@ class LoraIntegrationTests(unittest.TestCase):
 
     def test_not_empty_state_dict(self):
         # Makes sure https://github.com/huggingface/diffusers/issues/7054 does not happen again
-        pipe = AutoPipelineForText2Image.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16
-        ).to(torch_device)
+        pipe = AutoPipelineForText2Image.from_pretrained("Jiali/stable-diffusion-1.5", torch_dtype=torch.float16).to(
+            torch_device
+        )
         pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
 
         cached_file = hf_hub_download("hf-internal-testing/lcm-lora-test-sd-v1-5", "test_lora.safetensors")
@@ -562,9 +561,9 @@ class LoraIntegrationTests(unittest.TestCase):
 
     def test_load_unload_load_state_dict(self):
         # Makes sure https://github.com/huggingface/diffusers/issues/7054 does not happen again
-        pipe = AutoPipelineForText2Image.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16
-        ).to(torch_device)
+        pipe = AutoPipelineForText2Image.from_pretrained("Jiali/stable-diffusion-1.5", torch_dtype=torch.float16).to(
+            torch_device
+        )
         pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
 
         cached_file = hf_hub_download("hf-internal-testing/lcm-lora-test-sd-v1-5", "test_lora.safetensors")
@@ -581,7 +580,7 @@ class LoraIntegrationTests(unittest.TestCase):
         release_memory(pipe)
 
     def test_sdv1_5_lcm_lora(self):
-        pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
+        pipe = DiffusionPipeline.from_pretrained("Jiali/stable-diffusion-1.5", torch_dtype=torch.float16)
         pipe.to(torch_device)
         pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
 
@@ -609,7 +608,7 @@ class LoraIntegrationTests(unittest.TestCase):
         release_memory(pipe)
 
     def test_sdv1_5_lcm_lora_img2img(self):
-        pipe = AutoPipelineForImage2Image.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
+        pipe = AutoPipelineForImage2Image.from_pretrained("Jiali/stable-diffusion-1.5", torch_dtype=torch.float16)
         pipe.to(torch_device)
         pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
 
@@ -650,7 +649,7 @@ class LoraIntegrationTests(unittest.TestCase):
         This test simply checks that loading a LoRA with an empty network alpha works fine
         See: https://github.com/huggingface/diffusers/issues/5606
         """
-        pipeline = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
+        pipeline = StableDiffusionPipeline.from_pretrained("Jiali/stable-diffusion-1.5")
         pipeline.enable_sequential_cpu_offload()
         civitai_path = hf_hub_download("ybelkada/test-ahi-civitai", "ahi_lora_weights.safetensors")
         pipeline.load_lora_weights(civitai_path, adapter_name="ahri")

tests/models/autoencoders/test_models_vae.py

@@ -1051,7 +1051,7 @@ class ConsistencyDecoderVAEIntegrationTests(unittest.TestCase):
 
     def test_sd(self):
         vae = ConsistencyDecoderVAE.from_pretrained("openai/consistency-decoder")  # TODO - update
-        pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", vae=vae, safety_checker=None)
+        pipe = StableDiffusionPipeline.from_pretrained("Jiali/stable-diffusion-1.5", vae=vae, safety_checker=None)
         pipe.to(torch_device)
 
         out = pipe(
@@ -1099,7 +1099,7 @@ class ConsistencyDecoderVAEIntegrationTests(unittest.TestCase):
             "openai/consistency-decoder", torch_dtype=torch.float16
         )  # TODO - update
         pipe = StableDiffusionPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5",
+            "Jiali/stable-diffusion-1.5",
             torch_dtype=torch.float16,
             vae=vae,
             safety_checker=None,
@@ -1124,7 +1124,7 @@ class ConsistencyDecoderVAEIntegrationTests(unittest.TestCase):
     def test_vae_tiling(self):
         vae = ConsistencyDecoderVAE.from_pretrained("openai/consistency-decoder", torch_dtype=torch.float16)
         pipe = StableDiffusionPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", vae=vae, safety_checker=None, torch_dtype=torch.float16
+            "Jiali/stable-diffusion-1.5", vae=vae, safety_checker=None, torch_dtype=torch.float16
         )
         pipe.to(torch_device)
         pipe.set_progress_bar_config(disable=None)

tests/models/unets/test_models_unet_2d_condition.py

@@ -1376,7 +1376,7 @@ class UNet2DConditionModelIntegrationTests(unittest.TestCase):
     @require_torch_accelerator
     @skip_mps
     def test_compvis_sd_v1_5(self, seed, timestep, expected_slice):
-        model = self.get_unet_model(model_id="runwayml/stable-diffusion-v1-5")
+        model = self.get_unet_model(model_id="Jiali/stable-diffusion-1.5")
         latents = self.get_latents(seed)
         encoder_hidden_states = self.get_encoder_hidden_states(seed)
 
@@ -1404,7 +1404,7 @@ class UNet2DConditionModelIntegrationTests(unittest.TestCase):
     )
     @require_torch_accelerator_with_fp16
     def test_compvis_sd_v1_5_fp16(self, seed, timestep, expected_slice):
-        model = self.get_unet_model(model_id="runwayml/stable-diffusion-v1-5", fp16=True)
+        model = self.get_unet_model(model_id="Jiali/stable-diffusion-1.5", fp16=True)
         latents = self.get_latents(seed, fp16=True)
         encoder_hidden_states = self.get_encoder_hidden_states(seed, fp16=True)
 
@@ -1433,7 +1433,7 @@ class UNet2DConditionModelIntegrationTests(unittest.TestCase):
     @require_torch_accelerator
     @skip_mps
     def test_compvis_sd_inpaint(self, seed, timestep, expected_slice):
-        model = self.get_unet_model(model_id="runwayml/stable-diffusion-inpainting")
+        model = self.get_unet_model(model_id="botp/stable-diffusion-v1-5-inpainting")
         latents = self.get_latents(seed, shape=(4, 9, 64, 64))
         encoder_hidden_states = self.get_encoder_hidden_states(seed)
 
@@ -1461,7 +1461,7 @@ class UNet2DConditionModelIntegrationTests(unittest.TestCase):
     )
     @require_torch_accelerator_with_fp16
     def test_compvis_sd_inpaint_fp16(self, seed, timestep, expected_slice):
-        model = self.get_unet_model(model_id="runwayml/stable-diffusion-inpainting", fp16=True)
+        model = self.get_unet_model(model_id="botp/stable-diffusion-v1-5-inpainting", fp16=True)
         latents = self.get_latents(seed, shape=(4, 9, 64, 64), fp16=True)
         encoder_hidden_states = self.get_encoder_hidden_states(seed, fp16=True)
 

tests/pipelines/amused/test_amused.py

@@ -13,7 +13,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-
 import unittest
 
 import numpy as np
@@ -21,7 +20,12 @@ import torch
 from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
 
 from diffusers import AmusedPipeline, AmusedScheduler, UVit2DModel, VQModel
-from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
+from diffusers.utils.testing_utils import (
+    enable_full_determinism,
+    require_torch_gpu,
+    slow,
+    torch_device,
+)
 
 from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
 from ..test_pipelines_common import PipelineTesterMixin
@@ -65,9 +69,7 @@ class AmusedPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
         vqvae = VQModel(
             act_fn="silu",
             block_out_channels=[8],
-            down_block_types=[
-                "DownEncoderBlock2D",
-            ],
+            down_block_types=["DownEncoderBlock2D"],
             in_channels=3,
             latent_channels=8,
             layers_per_block=1,
@@ -75,9 +77,7 @@ class AmusedPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
             num_vq_embeddings=8,
             out_channels=3,
             sample_size=8,
-            up_block_types=[
-                "UpDecoderBlock2D",
-            ],
+            up_block_types=["UpDecoderBlock2D"],
             mid_block_add_attention=False,
             lookup_from_codebook=True,
         )
@@ -96,7 +96,6 @@ class AmusedPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
         )
         text_encoder = CLIPTextModelWithProjection(text_encoder_config)
         tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
-
         components = {
             "transformer": transformer,
             "scheduler": scheduler,
@@ -135,47 +134,37 @@ class AmusedPipelineSlowTests(unittest.TestCase):
     def test_amused_256(self):
         pipe = AmusedPipeline.from_pretrained("amused/amused-256")
         pipe.to(torch_device)
-
         image = pipe("dog", generator=torch.Generator().manual_seed(0), num_inference_steps=2, output_type="np").images
-
         image_slice = image[0, -3:, -3:, -1].flatten()
-
         assert image.shape == (1, 256, 256, 3)
-        expected_slice = np.array([0.4011, 0.3992, 0.3790, 0.3856, 0.3772, 0.3711, 0.3919, 0.3850, 0.3625])
-        assert np.abs(image_slice - expected_slice).max() < 3e-3
+        expected_slice = np.array([0.4011, 0.3992, 0.379, 0.3856, 0.3772, 0.3711, 0.3919, 0.385, 0.3625])
+        assert np.abs(image_slice - expected_slice).max() < 0.003
 
     def test_amused_256_fp16(self):
         pipe = AmusedPipeline.from_pretrained("amused/amused-256", variant="fp16", torch_dtype=torch.float16)
         pipe.to(torch_device)
-
         image = pipe("dog", generator=torch.Generator().manual_seed(0), num_inference_steps=2, output_type="np").images
-
         image_slice = image[0, -3:, -3:, -1].flatten()
-
         assert image.shape == (1, 256, 256, 3)
         expected_slice = np.array([0.0554, 0.05129, 0.0344, 0.0452, 0.0476, 0.0271, 0.0495, 0.0527, 0.0158])
-        assert np.abs(image_slice - expected_slice).max() < 7e-3
+        assert np.abs(image_slice - expected_slice).max() < 0.007
 
     def test_amused_512(self):
         pipe = AmusedPipeline.from_pretrained("amused/amused-512")
         pipe.to(torch_device)
-
         image = pipe("dog", generator=torch.Generator().manual_seed(0), num_inference_steps=2, output_type="np").images
-
         image_slice = image[0, -3:, -3:, -1].flatten()
 
         assert image.shape == (1, 512, 512, 3)
-        expected_slice = np.array([0.9960, 0.9960, 0.9946, 0.9980, 0.9947, 0.9932, 0.9960, 0.9961, 0.9947])
-        assert np.abs(image_slice - expected_slice).max() < 3e-3
+        expected_slice = np.array([0.1199, 0.1171, 0.1229, 0.1188, 0.1210, 0.1147, 0.1260, 0.1346, 0.1152])
+        assert np.abs(image_slice - expected_slice).max() < 0.003
 
     def test_amused_512_fp16(self):
         pipe = AmusedPipeline.from_pretrained("amused/amused-512", variant="fp16", torch_dtype=torch.float16)
         pipe.to(torch_device)
-
         image = pipe("dog", generator=torch.Generator().manual_seed(0), num_inference_steps=2, output_type="np").images
-
         image_slice = image[0, -3:, -3:, -1].flatten()
 
         assert image.shape == (1, 512, 512, 3)
-        expected_slice = np.array([0.9983, 1.0, 1.0, 1.0, 1.0, 0.9989, 0.9994, 0.9976, 0.9977])
-        assert np.abs(image_slice - expected_slice).max() < 3e-3
+        expected_slice = np.array([0.1509, 0.1492, 0.1531, 0.1485, 0.1501, 0.1465, 0.1581, 0.1690, 0.1499])
+        assert np.abs(image_slice - expected_slice).max() < 0.003

tests/pipelines/amused/test_amused_img2img.py

@@ -13,7 +13,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-
 import unittest
 
 import numpy as np
@@ -22,7 +21,12 @@ from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokeni
 
 from diffusers import AmusedImg2ImgPipeline, AmusedScheduler, UVit2DModel, VQModel
 from diffusers.utils import load_image
-from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
+from diffusers.utils.testing_utils import (
+    enable_full_determinism,
+    require_torch_gpu,
+    slow,
+    torch_device,
+)
 
 from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
 from ..test_pipelines_common import PipelineTesterMixin
@@ -35,9 +39,7 @@ class AmusedImg2ImgPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
     pipeline_class = AmusedImg2ImgPipeline
     params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width", "latents"}
     batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
-    required_optional_params = PipelineTesterMixin.required_optional_params - {
-        "latents",
-    }
+    required_optional_params = PipelineTesterMixin.required_optional_params - {"latents"}
 
     def get_dummy_components(self):
         torch.manual_seed(0)
@@ -69,19 +71,15 @@ class AmusedImg2ImgPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
         vqvae = VQModel(
             act_fn="silu",
             block_out_channels=[8],
-            down_block_types=[
-                "DownEncoderBlock2D",
-            ],
+            down_block_types=["DownEncoderBlock2D"],
             in_channels=3,
             latent_channels=8,
             layers_per_block=1,
             norm_num_groups=8,
-            num_vq_embeddings=32,  # reducing this to 16 or 8 -> RuntimeError: "cdist_cuda" not implemented for 'Half'
+            num_vq_embeddings=32,
             out_channels=3,
             sample_size=8,
-            up_block_types=[
-                "UpDecoderBlock2D",
-            ],
+            up_block_types=["UpDecoderBlock2D"],
             mid_block_add_attention=False,
             lookup_from_codebook=True,
         )
@@ -100,7 +98,6 @@ class AmusedImg2ImgPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
         )
         text_encoder = CLIPTextModelWithProjection(text_encoder_config)
         tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
-
         components = {
             "transformer": transformer,
             "scheduler": scheduler,
@@ -139,13 +136,11 @@ class AmusedImg2ImgPipelineSlowTests(unittest.TestCase):
     def test_amused_256(self):
         pipe = AmusedImg2ImgPipeline.from_pretrained("amused/amused-256")
         pipe.to(torch_device)
-
         image = (
             load_image("https://huggingface.co/datasets/diffusers/docs-images/resolve/main/open_muse/mountains.jpg")
             .resize((256, 256))
             .convert("RGB")
         )
-
         image = pipe(
             "winter mountains",
             image,
@@ -153,24 +148,19 @@ class AmusedImg2ImgPipelineSlowTests(unittest.TestCase):
             num_inference_steps=2,
             output_type="np",
         ).images
-
         image_slice = image[0, -3:, -3:, -1].flatten()
-
         assert image.shape == (1, 256, 256, 3)
-        expected_slice = np.array([0.9993, 1.0, 0.9996, 1.0, 0.9995, 0.9925, 0.9990, 0.9954, 1.0])
-
-        assert np.abs(image_slice - expected_slice).max() < 1e-2
+        expected_slice = np.array([0.9993, 1.0, 0.9996, 1.0, 0.9995, 0.9925, 0.999, 0.9954, 1.0])
+        assert np.abs(image_slice - expected_slice).max() < 0.01
 
     def test_amused_256_fp16(self):
         pipe = AmusedImg2ImgPipeline.from_pretrained("amused/amused-256", torch_dtype=torch.float16, variant="fp16")
         pipe.to(torch_device)
-
         image = (
             load_image("https://huggingface.co/datasets/diffusers/docs-images/resolve/main/open_muse/mountains.jpg")
             .resize((256, 256))
             .convert("RGB")
         )
-
         image = pipe(
             "winter mountains",
             image,
@@ -178,24 +168,19 @@ class AmusedImg2ImgPipelineSlowTests(unittest.TestCase):
             num_inference_steps=2,
             output_type="np",
         ).images
-
         image_slice = image[0, -3:, -3:, -1].flatten()
-
         assert image.shape == (1, 256, 256, 3)
-        expected_slice = np.array([0.9980, 0.9980, 0.9940, 0.9944, 0.9960, 0.9908, 1.0, 1.0, 0.9986])
-
-        assert np.abs(image_slice - expected_slice).max() < 1e-2
+        expected_slice = np.array([0.998, 0.998, 0.994, 0.9944, 0.996, 0.9908, 1.0, 1.0, 0.9986])
+        assert np.abs(image_slice - expected_slice).max() < 0.01
 
     def test_amused_512(self):
         pipe = AmusedImg2ImgPipeline.from_pretrained("amused/amused-512")
         pipe.to(torch_device)
-
         image = (
             load_image("https://huggingface.co/datasets/diffusers/docs-images/resolve/main/open_muse/mountains.jpg")
             .resize((512, 512))
             .convert("RGB")
         )
-
         image = pipe(
             "winter mountains",
             image,
@@ -203,23 +188,20 @@ class AmusedImg2ImgPipelineSlowTests(unittest.TestCase):
             num_inference_steps=2,
             output_type="np",
         ).images
-
         image_slice = image[0, -3:, -3:, -1].flatten()
 
         assert image.shape == (1, 512, 512, 3)
-        expected_slice = np.array([0.1344, 0.0985, 0.0, 0.1194, 0.1809, 0.0765, 0.0854, 0.1371, 0.0933])
+        expected_slice = np.array([0.2809, 0.1879, 0.2027, 0.2418, 0.1852, 0.2145, 0.2484, 0.2425, 0.2317])
         assert np.abs(image_slice - expected_slice).max() < 0.1
 
     def test_amused_512_fp16(self):
         pipe = AmusedImg2ImgPipeline.from_pretrained("amused/amused-512", variant="fp16", torch_dtype=torch.float16)
         pipe.to(torch_device)
-
         image = (
             load_image("https://huggingface.co/datasets/diffusers/docs-images/resolve/main/open_muse/mountains.jpg")
             .resize((512, 512))
             .convert("RGB")
         )
-
         image = pipe(
             "winter mountains",
             image,
@@ -227,9 +209,8 @@ class AmusedImg2ImgPipelineSlowTests(unittest.TestCase):
             num_inference_steps=2,
             output_type="np",
         ).images
-
         image_slice = image[0, -3:, -3:, -1].flatten()
 
         assert image.shape == (1, 512, 512, 3)
-        expected_slice = np.array([0.1536, 0.1767, 0.0227, 0.1079, 0.2400, 0.1427, 0.1511, 0.1564, 0.1542])
+        expected_slice = np.array([0.2795, 0.1867, 0.2028, 0.2450, 0.1856, 0.2140, 0.2473, 0.2406, 0.2313])
         assert np.abs(image_slice - expected_slice).max() < 0.1

tests/pipelines/amused/test_amused_inpaint.py

@@ -13,7 +13,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-
 import unittest
 
 import numpy as np
@@ -22,7 +21,12 @@ from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokeni
 
 from diffusers import AmusedInpaintPipeline, AmusedScheduler, UVit2DModel, VQModel
 from diffusers.utils import load_image
-from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
+from diffusers.utils.testing_utils import (
+    enable_full_determinism,
+    require_torch_gpu,
+    slow,
+    torch_device,
+)
 
 from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
 from ..test_pipelines_common import PipelineTesterMixin
@@ -35,9 +39,7 @@ class AmusedInpaintPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
     pipeline_class = AmusedInpaintPipeline
     params = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
     batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
-    required_optional_params = PipelineTesterMixin.required_optional_params - {
-        "latents",
-    }
+    required_optional_params = PipelineTesterMixin.required_optional_params - {"latents"}
 
     def get_dummy_components(self):
         torch.manual_seed(0)
@@ -50,7 +52,7 @@ class AmusedInpaintPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
             micro_cond_embed_dim=10,
             encoder_hidden_size=8,
             vocab_size=32,
-            codebook_size=32,  # codebook size needs to be consistent with num_vq_embeddings for inpaint tests
+            codebook_size=32,
             in_channels=8,
             block_out_channels=8,
             num_res_blocks=1,
@@ -69,19 +71,15 @@ class AmusedInpaintPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
         vqvae = VQModel(
             act_fn="silu",
             block_out_channels=[8],
-            down_block_types=[
-                "DownEncoderBlock2D",
-            ],
+            down_block_types=["DownEncoderBlock2D"],
             in_channels=3,
             latent_channels=8,
             layers_per_block=1,
             norm_num_groups=8,
-            num_vq_embeddings=32,  # reducing this to 16 or 8 -> RuntimeError: "cdist_cuda" not implemented for 'Half'
+            num_vq_embeddings=32,
             out_channels=3,
             sample_size=8,
-            up_block_types=[
-                "UpDecoderBlock2D",
-            ],
+            up_block_types=["UpDecoderBlock2D"],
             mid_block_add_attention=False,
             lookup_from_codebook=True,
         )
@@ -100,7 +98,6 @@ class AmusedInpaintPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
         )
         text_encoder = CLIPTextModelWithProjection(text_encoder_config)
         tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
-
         components = {
             "transformer": transformer,
             "scheduler": scheduler,
@@ -143,13 +140,11 @@ class AmusedInpaintPipelineSlowTests(unittest.TestCase):
     def test_amused_256(self):
         pipe = AmusedInpaintPipeline.from_pretrained("amused/amused-256")
         pipe.to(torch_device)
-
         image = (
             load_image("https://huggingface.co/datasets/diffusers/docs-images/resolve/main/open_muse/mountains_1.jpg")
             .resize((256, 256))
             .convert("RGB")
         )
-
         mask_image = (
             load_image(
                 "https://huggingface.co/datasets/diffusers/docs-images/resolve/main/open_muse/mountains_1_mask.png"
@@ -157,7 +152,6 @@ class AmusedInpaintPipelineSlowTests(unittest.TestCase):
             .resize((256, 256))
             .convert("L")
         )
-
         image = pipe(
             "winter mountains",
             image,
@@ -166,9 +160,7 @@ class AmusedInpaintPipelineSlowTests(unittest.TestCase):
             num_inference_steps=2,
             output_type="np",
         ).images
-
         image_slice = image[0, -3:, -3:, -1].flatten()
-
         assert image.shape == (1, 256, 256, 3)
         expected_slice = np.array([0.0699, 0.0716, 0.0608, 0.0715, 0.0797, 0.0638, 0.0802, 0.0924, 0.0634])
         assert np.abs(image_slice - expected_slice).max() < 0.1
@@ -176,13 +168,11 @@ class AmusedInpaintPipelineSlowTests(unittest.TestCase):
     def test_amused_256_fp16(self):
         pipe = AmusedInpaintPipeline.from_pretrained("amused/amused-256", variant="fp16", torch_dtype=torch.float16)
         pipe.to(torch_device)
-
         image = (
             load_image("https://huggingface.co/datasets/diffusers/docs-images/resolve/main/open_muse/mountains_1.jpg")
             .resize((256, 256))
             .convert("RGB")
         )
-
         mask_image = (
             load_image(
                 "https://huggingface.co/datasets/diffusers/docs-images/resolve/main/open_muse/mountains_1_mask.png"
@@ -190,7 +180,6 @@ class AmusedInpaintPipelineSlowTests(unittest.TestCase):
             .resize((256, 256))
             .convert("L")
         )
-
         image = pipe(
             "winter mountains",
             image,
@@ -199,23 +188,19 @@ class AmusedInpaintPipelineSlowTests(unittest.TestCase):
             num_inference_steps=2,
             output_type="np",
         ).images
-
         image_slice = image[0, -3:, -3:, -1].flatten()
-
         assert image.shape == (1, 256, 256, 3)
-        expected_slice = np.array([0.0735, 0.0749, 0.0650, 0.0739, 0.0805, 0.0667, 0.0802, 0.0923, 0.0622])
+        expected_slice = np.array([0.0735, 0.0749, 0.065, 0.0739, 0.0805, 0.0667, 0.0802, 0.0923, 0.0622])
         assert np.abs(image_slice - expected_slice).max() < 0.1
 
     def test_amused_512(self):
         pipe = AmusedInpaintPipeline.from_pretrained("amused/amused-512")
         pipe.to(torch_device)
-
         image = (
             load_image("https://huggingface.co/datasets/diffusers/docs-images/resolve/main/open_muse/mountains_1.jpg")
             .resize((512, 512))
             .convert("RGB")
         )
-
         mask_image = (
             load_image(
                 "https://huggingface.co/datasets/diffusers/docs-images/resolve/main/open_muse/mountains_1_mask.png"
@@ -223,7 +208,6 @@ class AmusedInpaintPipelineSlowTests(unittest.TestCase):
             .resize((512, 512))
             .convert("L")
         )
-
         image = pipe(
             "winter mountains",
             image,
@@ -232,9 +216,7 @@ class AmusedInpaintPipelineSlowTests(unittest.TestCase):
             num_inference_steps=2,
             output_type="np",
         ).images
-
         image_slice = image[0, -3:, -3:, -1].flatten()
-
         assert image.shape == (1, 512, 512, 3)
         expected_slice = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0005, 0.0])
         assert np.abs(image_slice - expected_slice).max() < 0.05
@@ -242,13 +224,11 @@ class AmusedInpaintPipelineSlowTests(unittest.TestCase):
     def test_amused_512_fp16(self):
         pipe = AmusedInpaintPipeline.from_pretrained("amused/amused-512", variant="fp16", torch_dtype=torch.float16)
         pipe.to(torch_device)
-
         image = (
             load_image("https://huggingface.co/datasets/diffusers/docs-images/resolve/main/open_muse/mountains_1.jpg")
             .resize((512, 512))
             .convert("RGB")
         )
-
         mask_image = (
             load_image(
                 "https://huggingface.co/datasets/diffusers/docs-images/resolve/main/open_muse/mountains_1_mask.png"
@@ -256,7 +236,6 @@ class AmusedInpaintPipelineSlowTests(unittest.TestCase):
             .resize((512, 512))
             .convert("L")
         )
-
         image = pipe(
             "winter mountains",
             image,
@@ -265,9 +244,8 @@ class AmusedInpaintPipelineSlowTests(unittest.TestCase):
             num_inference_steps=2,
             output_type="np",
         ).images
-
         image_slice = image[0, -3:, -3:, -1].flatten()
 
         assert image.shape == (1, 512, 512, 3)
-        expected_slice = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0025, 0.0])
-        assert np.abs(image_slice - expected_slice).max() < 3e-3
+        expected_slice = np.array([0.0227, 0.0157, 0.0098, 0.0213, 0.0250, 0.0127, 0.0280, 0.0380, 0.0095])
+        assert np.abs(image_slice - expected_slice).max() < 0.003

tests/pipelines/controlnet/test_controlnet.py

@@ -73,7 +73,7 @@ def _test_stable_diffusion_compile(in_queue, out_queue, timeout):
         controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny")
 
         pipe = StableDiffusionControlNetPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
+            "Jiali/stable-diffusion-1.5", safety_checker=None, controlnet=controlnet
         )
         pipe.to("cuda")
         pipe.set_progress_bar_config(disable=None)
@@ -715,7 +715,7 @@ class ControlNetPipelineSlowTests(unittest.TestCase):
         controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny")
 
         pipe = StableDiffusionControlNetPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
+            "Jiali/stable-diffusion-1.5", safety_checker=None, controlnet=controlnet
         )
         pipe.enable_model_cpu_offload()
         pipe.set_progress_bar_config(disable=None)
@@ -742,7 +742,7 @@ class ControlNetPipelineSlowTests(unittest.TestCase):
         controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-depth")
 
         pipe = StableDiffusionControlNetPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
+            "Jiali/stable-diffusion-1.5", safety_checker=None, controlnet=controlnet
         )
         pipe.enable_model_cpu_offload()
         pipe.set_progress_bar_config(disable=None)
@@ -769,7 +769,7 @@ class ControlNetPipelineSlowTests(unittest.TestCase):
         controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-hed")
 
         pipe = StableDiffusionControlNetPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
+            "Jiali/stable-diffusion-1.5", safety_checker=None, controlnet=controlnet
         )
         pipe.enable_model_cpu_offload()
         pipe.set_progress_bar_config(disable=None)
@@ -796,7 +796,7 @@ class ControlNetPipelineSlowTests(unittest.TestCase):
         controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-mlsd")
 
         pipe = StableDiffusionControlNetPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
+            "Jiali/stable-diffusion-1.5", safety_checker=None, controlnet=controlnet
         )
         pipe.enable_model_cpu_offload()
         pipe.set_progress_bar_config(disable=None)
@@ -823,7 +823,7 @@ class ControlNetPipelineSlowTests(unittest.TestCase):
         controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-normal")
 
         pipe = StableDiffusionControlNetPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
+            "Jiali/stable-diffusion-1.5", safety_checker=None, controlnet=controlnet
         )
         pipe.enable_model_cpu_offload()
         pipe.set_progress_bar_config(disable=None)
@@ -850,7 +850,7 @@ class ControlNetPipelineSlowTests(unittest.TestCase):
         controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-openpose")
 
         pipe = StableDiffusionControlNetPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
+            "Jiali/stable-diffusion-1.5", safety_checker=None, controlnet=controlnet
         )
         pipe.enable_model_cpu_offload()
         pipe.set_progress_bar_config(disable=None)
@@ -877,7 +877,7 @@ class ControlNetPipelineSlowTests(unittest.TestCase):
         controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-scribble")
 
         pipe = StableDiffusionControlNetPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
+            "Jiali/stable-diffusion-1.5", safety_checker=None, controlnet=controlnet
         )
         pipe.enable_model_cpu_offload()
         pipe.set_progress_bar_config(disable=None)
@@ -904,7 +904,7 @@ class ControlNetPipelineSlowTests(unittest.TestCase):
         controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-seg")
 
         pipe = StableDiffusionControlNetPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
+            "Jiali/stable-diffusion-1.5", safety_checker=None, controlnet=controlnet
         )
         pipe.enable_model_cpu_offload()
         pipe.set_progress_bar_config(disable=None)
@@ -935,7 +935,7 @@ class ControlNetPipelineSlowTests(unittest.TestCase):
         controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-seg")
 
         pipe = StableDiffusionControlNetPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
+            "Jiali/stable-diffusion-1.5", safety_checker=None, controlnet=controlnet
         )
         pipe.set_progress_bar_config(disable=None)
         pipe.enable_attention_slicing()
@@ -961,7 +961,7 @@ class ControlNetPipelineSlowTests(unittest.TestCase):
         controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny")
 
         pipe = StableDiffusionControlNetPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
+            "Jiali/stable-diffusion-1.5", safety_checker=None, controlnet=controlnet
         )
         pipe.enable_model_cpu_offload()
         pipe.set_progress_bar_config(disable=None)
@@ -993,7 +993,7 @@ class ControlNetPipelineSlowTests(unittest.TestCase):
         controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny")
 
         pipe = StableDiffusionControlNetPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
+            "Jiali/stable-diffusion-1.5", safety_checker=None, controlnet=controlnet
         )
         pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
         pipe.enable_model_cpu_offload()
@@ -1035,7 +1035,7 @@ class ControlNetPipelineSlowTests(unittest.TestCase):
         controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11e_sd15_shuffle")
 
         pipe = StableDiffusionControlNetPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
+            "Jiali/stable-diffusion-1.5", safety_checker=None, controlnet=controlnet
         )
         pipe.enable_model_cpu_offload()
         pipe.set_progress_bar_config(disable=None)
@@ -1081,7 +1081,7 @@ class StableDiffusionMultiControlNetPipelineSlowTests(unittest.TestCase):
         controlnet_pose = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-openpose")
 
         pipe = StableDiffusionControlNetPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=[controlnet_pose, controlnet_canny]
+            "Jiali/stable-diffusion-1.5", safety_checker=None, controlnet=[controlnet_pose, controlnet_canny]
         )
         pipe.enable_model_cpu_offload()
         pipe.set_progress_bar_config(disable=None)

tests/pipelines/controlnet/test_controlnet_img2img.py

@@ -407,7 +407,7 @@ class ControlNetImg2ImgPipelineSlowTests(unittest.TestCase):
         controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny")
 
         pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
+            "Jiali/stable-diffusion-1.5", safety_checker=None, controlnet=controlnet
         )
         pipe.enable_model_cpu_offload()
         pipe.set_progress_bar_config(disable=None)

tests/pipelines/controlnet/test_controlnet_inpaint.py

@@ -459,7 +459,7 @@ class ControlNetInpaintPipelineSlowTests(unittest.TestCase):
         controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny")
 
         pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
-            "runwayml/stable-diffusion-inpainting", safety_checker=None, controlnet=controlnet
+            "botp/stable-diffusion-v1-5-inpainting", safety_checker=None, controlnet=controlnet
         )
         pipe.enable_model_cpu_offload()
         pipe.set_progress_bar_config(disable=None)
@@ -504,7 +504,7 @@ class ControlNetInpaintPipelineSlowTests(unittest.TestCase):
         controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_inpaint")
 
         pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
+            "Jiali/stable-diffusion-1.5", safety_checker=None, controlnet=controlnet
         )
         pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
         pipe.enable_model_cpu_offload()

tests/pipelines/controlnet/test_flax_controlnet.py

@@ -41,7 +41,7 @@ class FlaxControlNetPipelineIntegrationTests(unittest.TestCase):
             "lllyasviel/sd-controlnet-canny", from_pt=True, dtype=jnp.bfloat16
         )
         pipe, params = FlaxStableDiffusionControlNetPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", controlnet=controlnet, from_pt=True, dtype=jnp.bfloat16
+            "Jiali/stable-diffusion-1.5", controlnet=controlnet, from_pt=True, dtype=jnp.bfloat16
         )
         params["controlnet"] = controlnet_params
 
@@ -86,7 +86,7 @@ class FlaxControlNetPipelineIntegrationTests(unittest.TestCase):
             "lllyasviel/sd-controlnet-openpose", from_pt=True, dtype=jnp.bfloat16
         )
         pipe, params = FlaxStableDiffusionControlNetPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", controlnet=controlnet, from_pt=True, dtype=jnp.bfloat16
+            "Jiali/stable-diffusion-1.5", controlnet=controlnet, from_pt=True, dtype=jnp.bfloat16
         )
         params["controlnet"] = controlnet_params
 

tests/pipelines/ip_adapters/test_ip_adapter_stable_diffusion.py

@@ -170,7 +170,7 @@ class IPAdapterSDIntegrationTests(IPAdapterNightlyTestsMixin):
     def test_text_to_image(self):
         image_encoder = self.get_image_encoder(repo_id="h94/IP-Adapter", subfolder="models/image_encoder")
         pipeline = StableDiffusionPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", image_encoder=image_encoder, safety_checker=None, torch_dtype=self.dtype
+            "Jiali/stable-diffusion-1.5", image_encoder=image_encoder, safety_checker=None, torch_dtype=self.dtype
         )
         pipeline.to(torch_device)
         pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name="ip-adapter_sd15.bin")
@@ -200,7 +200,7 @@ class IPAdapterSDIntegrationTests(IPAdapterNightlyTestsMixin):
     def test_image_to_image(self):
         image_encoder = self.get_image_encoder(repo_id="h94/IP-Adapter", subfolder="models/image_encoder")
         pipeline = StableDiffusionImg2ImgPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", image_encoder=image_encoder, safety_checker=None, torch_dtype=self.dtype
+            "Jiali/stable-diffusion-1.5", image_encoder=image_encoder, safety_checker=None, torch_dtype=self.dtype
         )
         pipeline.to(torch_device)
         pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name="ip-adapter_sd15.bin")
@@ -232,7 +232,7 @@ class IPAdapterSDIntegrationTests(IPAdapterNightlyTestsMixin):
     def test_inpainting(self):
         image_encoder = self.get_image_encoder(repo_id="h94/IP-Adapter", subfolder="models/image_encoder")
         pipeline = StableDiffusionInpaintPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", image_encoder=image_encoder, safety_checker=None, torch_dtype=self.dtype
+            "Jiali/stable-diffusion-1.5", image_encoder=image_encoder, safety_checker=None, torch_dtype=self.dtype
         )
         pipeline.to(torch_device)
         pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name="ip-adapter_sd15.bin")
@@ -260,7 +260,7 @@ class IPAdapterSDIntegrationTests(IPAdapterNightlyTestsMixin):
     def test_text_to_image_model_cpu_offload(self):
         image_encoder = self.get_image_encoder(repo_id="h94/IP-Adapter", subfolder="models/image_encoder")
         pipeline = StableDiffusionPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", image_encoder=image_encoder, safety_checker=None, torch_dtype=self.dtype
+            "Jiali/stable-diffusion-1.5", image_encoder=image_encoder, safety_checker=None, torch_dtype=self.dtype
         )
         pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name="ip-adapter_sd15.bin")
         pipeline.to(torch_device)
@@ -287,7 +287,7 @@ class IPAdapterSDIntegrationTests(IPAdapterNightlyTestsMixin):
     def test_text_to_image_full_face(self):
         image_encoder = self.get_image_encoder(repo_id="h94/IP-Adapter", subfolder="models/image_encoder")
         pipeline = StableDiffusionPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", image_encoder=image_encoder, safety_checker=None, torch_dtype=self.dtype
+            "Jiali/stable-diffusion-1.5", image_encoder=image_encoder, safety_checker=None, torch_dtype=self.dtype
         )
         pipeline.to(torch_device)
         pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name="ip-adapter-full-face_sd15.bin")
@@ -304,7 +304,7 @@ class IPAdapterSDIntegrationTests(IPAdapterNightlyTestsMixin):
     def test_unload(self):
         image_encoder = self.get_image_encoder(repo_id="h94/IP-Adapter", subfolder="models/image_encoder")
         pipeline = StableDiffusionPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", image_encoder=image_encoder, safety_checker=None, torch_dtype=self.dtype
+            "Jiali/stable-diffusion-1.5", image_encoder=image_encoder, safety_checker=None, torch_dtype=self.dtype
         )
         before_processors = [attn_proc.__class__ for attn_proc in pipeline.unet.attn_processors.values()]
         pipeline.to(torch_device)
@@ -323,7 +323,7 @@ class IPAdapterSDIntegrationTests(IPAdapterNightlyTestsMixin):
     def test_multi(self):
         image_encoder = self.get_image_encoder(repo_id="h94/IP-Adapter", subfolder="models/image_encoder")
         pipeline = StableDiffusionPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", image_encoder=image_encoder, safety_checker=None, torch_dtype=self.dtype
+            "Jiali/stable-diffusion-1.5", image_encoder=image_encoder, safety_checker=None, torch_dtype=self.dtype
         )
         pipeline.to(torch_device)
         pipeline.load_ip_adapter(
@@ -343,7 +343,7 @@ class IPAdapterSDIntegrationTests(IPAdapterNightlyTestsMixin):
 
     def test_text_to_image_face_id(self):
         pipeline = StableDiffusionPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", safety_checker=None, torch_dtype=self.dtype
+            "Jiali/stable-diffusion-1.5", safety_checker=None, torch_dtype=self.dtype
         )
         pipeline.to(torch_device)
         pipeline.load_ip_adapter(

tests/pipelines/ledits_pp/test_ledits_pp_stable_diffusion.py

@@ -224,7 +224,7 @@ class LEditsPPPipelineStableDiffusionSlowTests(unittest.TestCase):
 
     def test_ledits_pp_editing(self):
         pipe = LEditsPPPipelineStableDiffusion.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", safety_checker=None, torch_dtype=torch.float16
+            "Jiali/stable-diffusion-1.5", safety_checker=None, torch_dtype=torch.float16
         )
         pipe = pipe.to(torch_device)
         pipe.set_progress_bar_config(disable=None)

tests/pipelines/pag/test_pag_pixart_sigma.py

@@ -33,7 +33,6 @@ from diffusers.utils import logging
 from diffusers.utils.testing_utils import (
     CaptureLogger,
     enable_full_determinism,
-    print_tensor_test,
     torch_device,
 )
 
@@ -173,7 +172,6 @@ class PixArtSigmaPAGPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
         inputs = self.get_dummy_inputs(device)
         image = pipe_pag(**inputs).images
         image_slice = image[0, -3:, -3:, -1]
-        print_tensor_test(image_slice)
 
         assert image.shape == (
             1,

tests/pipelines/pag/test_pag_sd.py

@@ -283,7 +283,7 @@ class StableDiffusionPAGPipelineFastTests(
 @require_torch_gpu
 class StableDiffusionPAGPipelineIntegrationTests(unittest.TestCase):
     pipeline_class = StableDiffusionPAGPipeline
-    repo_id = "runwayml/stable-diffusion-v1-5"
+    repo_id = "Jiali/stable-diffusion-1.5"
 
     def setUp(self):
         super().setUp()

tests/pipelines/semantic_stable_diffusion/test_semantic_diffusion.py

@@ -287,7 +287,7 @@ class SemanticDiffusionPipelineIntegrationTests(unittest.TestCase):
 
     def test_positive_guidance(self):
         torch_device = "cuda"
-        pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
+        pipe = StableDiffusionPipeline.from_pretrained("Jiali/stable-diffusion-1.5")
         pipe = pipe.to(torch_device)
         pipe.set_progress_bar_config(disable=None)
 
@@ -370,7 +370,7 @@ class SemanticDiffusionPipelineIntegrationTests(unittest.TestCase):
 
     def test_negative_guidance(self):
         torch_device = "cuda"
-        pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
+        pipe = StableDiffusionPipeline.from_pretrained("Jiali/stable-diffusion-1.5")
         pipe = pipe.to(torch_device)
         pipe.set_progress_bar_config(disable=None)
 
@@ -453,7 +453,7 @@ class SemanticDiffusionPipelineIntegrationTests(unittest.TestCase):
 
     def test_multi_cond_guidance(self):
         torch_device = "cuda"
-        pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
+        pipe = StableDiffusionPipeline.from_pretrained("Jiali/stable-diffusion-1.5")
         pipe = pipe.to(torch_device)
         pipe.set_progress_bar_config(disable=None)
 
@@ -536,7 +536,7 @@ class SemanticDiffusionPipelineIntegrationTests(unittest.TestCase):
 
     def test_guidance_fp16(self):
         torch_device = "cuda"
-        pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
+        pipe = StableDiffusionPipeline.from_pretrained("Jiali/stable-diffusion-1.5", torch_dtype=torch.float16)
         pipe = pipe.to(torch_device)
         pipe.set_progress_bar_config(disable=None)
 

tests/pipelines/stable_diffusion/test_onnx_stable_diffusion.py

@@ -250,10 +250,10 @@ class OnnxStableDiffusionPipelineIntegrationTests(unittest.TestCase):
 
     def test_inference_ddim(self):
         ddim_scheduler = DDIMScheduler.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", subfolder="scheduler", revision="onnx"
+            "Jiali/stable-diffusion-1.5", subfolder="scheduler", revision="onnx"
         )
         sd_pipe = OnnxStableDiffusionPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5",
+            "Jiali/stable-diffusion-1.5",
             revision="onnx",
             scheduler=ddim_scheduler,
             safety_checker=None,
@@ -276,10 +276,10 @@ class OnnxStableDiffusionPipelineIntegrationTests(unittest.TestCase):
 
     def test_inference_k_lms(self):
         lms_scheduler = LMSDiscreteScheduler.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", subfolder="scheduler", revision="onnx"
+            "Jiali/stable-diffusion-1.5", subfolder="scheduler", revision="onnx"
         )
         sd_pipe = OnnxStableDiffusionPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5",
+            "Jiali/stable-diffusion-1.5",
             revision="onnx",
             scheduler=lms_scheduler,
             safety_checker=None,
@@ -327,7 +327,7 @@ class OnnxStableDiffusionPipelineIntegrationTests(unittest.TestCase):
         test_callback_fn.has_been_called = False
 
         pipe = OnnxStableDiffusionPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5",
+            "Jiali/stable-diffusion-1.5",
             revision="onnx",
             safety_checker=None,
             feature_extractor=None,
@@ -352,7 +352,7 @@ class OnnxStableDiffusionPipelineIntegrationTests(unittest.TestCase):
 
     def test_stable_diffusion_no_safety_checker(self):
         pipe = OnnxStableDiffusionPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5",
+            "Jiali/stable-diffusion-1.5",
             revision="onnx",
             safety_checker=None,
             feature_extractor=None,

tests/pipelines/stable_diffusion/test_onnx_stable_diffusion_img2img.py

@@ -210,10 +210,10 @@ class OnnxStableDiffusionImg2ImgPipelineIntegrationTests(unittest.TestCase):
         )
         init_image = init_image.resize((768, 512))
         lms_scheduler = LMSDiscreteScheduler.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", subfolder="scheduler", revision="onnx"
+            "Jiali/stable-diffusion-1.5", subfolder="scheduler", revision="onnx"
         )
         pipe = OnnxStableDiffusionImg2ImgPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5",
+            "Jiali/stable-diffusion-1.5",
             revision="onnx",
             scheduler=lms_scheduler,
             safety_checker=None,

tests/pipelines/stable_diffusion/test_onnx_stable_diffusion_inpaint.py

@@ -68,7 +68,7 @@ class OnnxStableDiffusionInpaintPipelineIntegrationTests(unittest.TestCase):
             "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"
         )
         pipe = OnnxStableDiffusionInpaintPipeline.from_pretrained(
-            "runwayml/stable-diffusion-inpainting",
+            "botp/stable-diffusion-v1-5-inpainting",
             revision="onnx",
             safety_checker=None,
             feature_extractor=None,
@@ -107,10 +107,10 @@ class OnnxStableDiffusionInpaintPipelineIntegrationTests(unittest.TestCase):
             "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"
         )
         lms_scheduler = LMSDiscreteScheduler.from_pretrained(
-            "runwayml/stable-diffusion-inpainting", subfolder="scheduler", revision="onnx"
+            "botp/stable-diffusion-v1-5-inpainting", subfolder="scheduler", revision="onnx"
         )
         pipe = OnnxStableDiffusionInpaintPipeline.from_pretrained(
-            "runwayml/stable-diffusion-inpainting",
+            "botp/stable-diffusion-v1-5-inpainting",
             revision="onnx",
             scheduler=lms_scheduler,
             safety_checker=None,

tests/pipelines/stable_diffusion/test_stable_diffusion.py

@@ -1332,7 +1332,7 @@ class StableDiffusionPipelineCkptTests(unittest.TestCase):
 
     def test_download_from_hub(self):
         ckpt_paths = [
-            "https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.safetensors",
+            "https://huggingface.co/Jiali/stable-diffusion-1.5/blob/main/v1-5-pruned-emaonly.safetensors",
             "https://huggingface.co/WarriorMama777/OrangeMixs/blob/main/Models/AbyssOrangeMix/AbyssOrangeMix.safetensors",
         ]
 
@@ -1346,8 +1346,8 @@ class StableDiffusionPipelineCkptTests(unittest.TestCase):
         assert image_out.shape == (512, 512, 3)
 
     def test_download_local(self):
-        ckpt_filename = hf_hub_download("runwayml/stable-diffusion-v1-5", filename="v1-5-pruned-emaonly.safetensors")
-        config_filename = hf_hub_download("runwayml/stable-diffusion-v1-5", filename="v1-inference.yaml")
+        ckpt_filename = hf_hub_download("Jiali/stable-diffusion-1.5", filename="v1-5-pruned-emaonly.safetensors")
+        config_filename = hf_hub_download("Jiali/stable-diffusion-1.5", filename="v1-inference.yaml")
 
         pipe = StableDiffusionPipeline.from_single_file(
             ckpt_filename, config_files={"v1": config_filename}, torch_dtype=torch.float16
@@ -1402,7 +1402,7 @@ class StableDiffusionPipelineNightlyTests(unittest.TestCase):
         assert max_diff < 1e-3
 
     def test_stable_diffusion_1_5_pndm(self):
-        sd_pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5").to(torch_device)
+        sd_pipe = StableDiffusionPipeline.from_pretrained("Jiali/stable-diffusion-1.5").to(torch_device)
         sd_pipe.set_progress_bar_config(disable=None)
 
         inputs = self.get_inputs(torch_device)
@@ -1483,9 +1483,9 @@ class StableDiffusionPipelineDeviceMapTests(unittest.TestCase):
         return inputs
 
     def get_pipeline_output_without_device_map(self):
-        sd_pipe = StableDiffusionPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16
-        ).to(torch_device)
+        sd_pipe = StableDiffusionPipeline.from_pretrained("Jiali/stable-diffusion-1.5", torch_dtype=torch.float16).to(
+            torch_device
+        )
         sd_pipe.set_progress_bar_config(disable=True)
         inputs = self.get_inputs()
         no_device_map_image = sd_pipe(**inputs).images
@@ -1498,7 +1498,7 @@ class StableDiffusionPipelineDeviceMapTests(unittest.TestCase):
         no_device_map_image = self.get_pipeline_output_without_device_map()
 
         sd_pipe_with_device_map = StableDiffusionPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", device_map="balanced", torch_dtype=torch.float16
+            "Jiali/stable-diffusion-1.5", device_map="balanced", torch_dtype=torch.float16
         )
         sd_pipe_with_device_map.set_progress_bar_config(disable=True)
         inputs = self.get_inputs()
@@ -1509,7 +1509,7 @@ class StableDiffusionPipelineDeviceMapTests(unittest.TestCase):
 
     def test_components_put_in_right_devices(self):
         sd_pipe_with_device_map = StableDiffusionPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", device_map="balanced", torch_dtype=torch.float16
+            "Jiali/stable-diffusion-1.5", device_map="balanced", torch_dtype=torch.float16
         )
 
         assert len(set(sd_pipe_with_device_map.hf_device_map.values())) >= 2
@@ -1518,7 +1518,7 @@ class StableDiffusionPipelineDeviceMapTests(unittest.TestCase):
         no_device_map_image = self.get_pipeline_output_without_device_map()
 
         sd_pipe_with_device_map = StableDiffusionPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5",
+            "Jiali/stable-diffusion-1.5",
             device_map="balanced",
             max_memory={0: "1GB", 1: "1GB"},
             torch_dtype=torch.float16,
@@ -1532,7 +1532,7 @@ class StableDiffusionPipelineDeviceMapTests(unittest.TestCase):
 
     def test_reset_device_map(self):
         sd_pipe_with_device_map = StableDiffusionPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", device_map="balanced", torch_dtype=torch.float16
+            "Jiali/stable-diffusion-1.5", device_map="balanced", torch_dtype=torch.float16
         )
         sd_pipe_with_device_map.reset_device_map()
 
@@ -1544,7 +1544,7 @@ class StableDiffusionPipelineDeviceMapTests(unittest.TestCase):
 
     def test_reset_device_map_to(self):
         sd_pipe_with_device_map = StableDiffusionPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", device_map="balanced", torch_dtype=torch.float16
+            "Jiali/stable-diffusion-1.5", device_map="balanced", torch_dtype=torch.float16
         )
         sd_pipe_with_device_map.reset_device_map()
 
@@ -1556,7 +1556,7 @@ class StableDiffusionPipelineDeviceMapTests(unittest.TestCase):
 
     def test_reset_device_map_enable_model_cpu_offload(self):
         sd_pipe_with_device_map = StableDiffusionPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", device_map="balanced", torch_dtype=torch.float16
+            "Jiali/stable-diffusion-1.5", device_map="balanced", torch_dtype=torch.float16
         )
         sd_pipe_with_device_map.reset_device_map()
 
@@ -1568,7 +1568,7 @@ class StableDiffusionPipelineDeviceMapTests(unittest.TestCase):
 
     def test_reset_device_map_enable_sequential_cpu_offload(self):
         sd_pipe_with_device_map = StableDiffusionPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", device_map="balanced", torch_dtype=torch.float16
+            "Jiali/stable-diffusion-1.5", device_map="balanced", torch_dtype=torch.float16
         )
         sd_pipe_with_device_map.reset_device_map()
 

tests/pipelines/stable_diffusion/test_stable_diffusion_img2img.py

@@ -566,7 +566,7 @@ class StableDiffusionImg2ImgPipelineSlowTests(unittest.TestCase):
             assert module.device == torch.device("cpu")
 
     def test_img2img_2nd_order(self):
-        sd_pipe = StableDiffusionImg2ImgPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
+        sd_pipe = StableDiffusionImg2ImgPipeline.from_pretrained("Jiali/stable-diffusion-1.5")
         sd_pipe.scheduler = HeunDiscreteScheduler.from_config(sd_pipe.scheduler.config)
         sd_pipe.to(torch_device)
         sd_pipe.set_progress_bar_config(disable=None)
@@ -630,7 +630,7 @@ class StableDiffusionImg2ImgPipelineSlowTests(unittest.TestCase):
         assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-3
 
     def test_img2img_safety_checker_works(self):
-        sd_pipe = StableDiffusionImg2ImgPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
+        sd_pipe = StableDiffusionImg2ImgPipeline.from_pretrained("Jiali/stable-diffusion-1.5")
         sd_pipe.to(torch_device)
         sd_pipe.set_progress_bar_config(disable=None)
 
@@ -686,7 +686,7 @@ class StableDiffusionImg2ImgPipelineNightlyTests(unittest.TestCase):
         return inputs
 
     def test_img2img_pndm(self):
-        sd_pipe = StableDiffusionImg2ImgPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
+        sd_pipe = StableDiffusionImg2ImgPipeline.from_pretrained("Jiali/stable-diffusion-1.5")
         sd_pipe.to(torch_device)
         sd_pipe.set_progress_bar_config(disable=None)
 
@@ -701,7 +701,7 @@ class StableDiffusionImg2ImgPipelineNightlyTests(unittest.TestCase):
         assert max_diff < 1e-3
 
     def test_img2img_ddim(self):
-        sd_pipe = StableDiffusionImg2ImgPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
+        sd_pipe = StableDiffusionImg2ImgPipeline.from_pretrained("Jiali/stable-diffusion-1.5")
         sd_pipe.scheduler = DDIMScheduler.from_config(sd_pipe.scheduler.config)
         sd_pipe.to(torch_device)
         sd_pipe.set_progress_bar_config(disable=None)
@@ -717,7 +717,7 @@ class StableDiffusionImg2ImgPipelineNightlyTests(unittest.TestCase):
         assert max_diff < 1e-3
 
     def test_img2img_lms(self):
-        sd_pipe = StableDiffusionImg2ImgPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
+        sd_pipe = StableDiffusionImg2ImgPipeline.from_pretrained("Jiali/stable-diffusion-1.5")
         sd_pipe.scheduler = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)
         sd_pipe.to(torch_device)
         sd_pipe.set_progress_bar_config(disable=None)
@@ -733,7 +733,7 @@ class StableDiffusionImg2ImgPipelineNightlyTests(unittest.TestCase):
         assert max_diff < 1e-3
 
     def test_img2img_dpm(self):
-        sd_pipe = StableDiffusionImg2ImgPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
+        sd_pipe = StableDiffusionImg2ImgPipeline.from_pretrained("Jiali/stable-diffusion-1.5")
         sd_pipe.scheduler = DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config)
         sd_pipe.to(torch_device)
         sd_pipe.set_progress_bar_config(disable=None)