make it easy to convert to new schedulers

scripts/convert_original_stable_diffusion_to_diffusers.py

@@ -30,6 +30,9 @@ except ImportError:
 from diffusers import (
     AutoencoderKL,
     DDIMScheduler,
+    DPMSolverMultistepScheduler,
+    EulerAncestralDiscreteScheduler,
+    EulerDiscreteScheduler,
     LDMTextToImagePipeline,
     LMSDiscreteScheduler,
     PNDMScheduler,
@@ -647,7 +650,7 @@ if __name__ == "__main__":
         "--scheduler_type",
         default="pndm",
         type=str,
-        help="Type of scheduler to use. Should be one of ['pndm', 'lms', 'ddim']",
+        help="Type of scheduler to use. Should be one of ['pndm', 'lms', 'ddim', 'euler', 'euler-ancest', 'dpm']",
     )
     parser.add_argument(
         "--extract_ema",
@@ -686,6 +689,16 @@ if __name__ == "__main__":
         )
     elif args.scheduler_type == "lms":
         scheduler = LMSDiscreteScheduler(beta_start=beta_start, beta_end=beta_end, beta_schedule="scaled_linear")
+    elif args.scheduler_type == "euler":
+        scheduler = EulerDiscreteScheduler(beta_start=beta_start, beta_end=beta_end, beta_schedule="scaled_linear")
+    elif args.scheduler_type == "euler-ancestral":
+        scheduler = EulerAncestralDiscreteScheduler(
+            beta_start=beta_start, beta_end=beta_end, beta_schedule="scaled_linear"
+        )
+    elif args.scheduler_type == "dpm":
+        scheduler = DPMSolverMultistepScheduler(
+            beta_start=beta_start, beta_end=beta_end, beta_schedule="scaled_linear"
+        )
     elif args.scheduler_type == "ddim":
         scheduler = DDIMScheduler(
             beta_start=beta_start,

src/diffusers/pipelines/stable_diffusion/pipeline_cycle_diffusion.py

@@ -43,7 +43,7 @@ def preprocess(image):
     return 2.0 * image - 1.0
 
 
-def posterior_sample(scheduler, latents, timestep, clean_latents, eta):
+def posterior_sample(scheduler, latents, timestep, clean_latents, generator, eta):
     # 1. get previous step value (=t-1)
     prev_timestep = timestep - scheduler.config.num_train_timesteps // scheduler.num_inference_steps
 
@@ -62,7 +62,9 @@ def posterior_sample(scheduler, latents, timestep, clean_latents, eta):
     # direction pointing to x_t
     e_t = (latents - alpha_prod_t ** (0.5) * clean_latents) / (1 - alpha_prod_t) ** (0.5)
     dir_xt = (1.0 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * e_t
-    noise = std_dev_t * torch.randn(clean_latents.shape, dtype=clean_latents.dtype, device=clean_latents.device)
+    noise = std_dev_t * torch.randn(
+        clean_latents.shape, dtype=clean_latents.dtype, device=clean_latents.device, generator=generator
+    )
     prev_latents = alpha_prod_t_prev ** (0.5) * clean_latents + dir_xt + noise
 
     return prev_latents
@@ -499,7 +501,7 @@ class CycleDiffusionPipeline(DiffusionPipeline):
 
             # Sample source_latents from the posterior distribution.
             prev_source_latents = posterior_sample(
-                self.scheduler, source_latents, t, clean_latents, **extra_step_kwargs
+                self.scheduler, source_latents, t, clean_latents, generator=generator, **extra_step_kwargs
             )
             # Compute noise.
             noise = compute_noise(

src/diffusers/schedulers/scheduling_ddim.py

@@ -288,7 +288,7 @@ class DDIMScheduler(SchedulerMixin, ConfigMixin):
 
         if eta > 0:
             # randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
-            device = model_output.device if torch.is_tensor(model_output) else torch.device("cpu")
+            device = model_output.device
             if variance_noise is not None and generator is not None:
                 raise ValueError(
                     "Cannot pass both generator and variance_noise. Please make sure that either `generator` or"

src/diffusers/schedulers/scheduling_euler_ancestral_discrete.py

@@ -221,7 +221,7 @@ class EulerAncestralDiscreteScheduler(SchedulerMixin, ConfigMixin):
 
         prev_sample = sample + derivative * dt
 
-        device = model_output.device if torch.is_tensor(model_output) else torch.device("cpu")
+        device = model_output.device
         if device.type == "mps":
             # randn does not work reproducibly on mps
             noise = torch.randn(model_output.shape, dtype=model_output.dtype, device="cpu", generator=generator).to(

src/diffusers/schedulers/scheduling_euler_discrete.py

@@ -218,7 +218,7 @@ class EulerDiscreteScheduler(SchedulerMixin, ConfigMixin):
 
         gamma = min(s_churn / (len(self.sigmas) - 1), 2**0.5 - 1) if s_tmin <= sigma <= s_tmax else 0.0
 
-        device = model_output.device if torch.is_tensor(model_output) else torch.device("cpu")
+        device = model_output.device
         if device.type == "mps":
             # randn does not work reproducibly on mps
             noise = torch.randn(model_output.shape, dtype=model_output.dtype, device="cpu", generator=generator).to(

tests/pipelines/stable_diffusion/test_cycle_diffusion.py

@@ -293,7 +293,7 @@ class CycleDiffusionPipelineIntegrationTests(unittest.TestCase):
         source_prompt = "A black colored car"
         prompt = "A blue colored car"
 
-        torch.manual_seed(0)
+        generator = torch.Generator(device=torch_device).manual_seed(0)
         output = pipe(
             prompt=prompt,
             source_prompt=source_prompt,
@@ -303,12 +303,13 @@ class CycleDiffusionPipelineIntegrationTests(unittest.TestCase):
             strength=0.85,
             guidance_scale=3,
             source_guidance_scale=1,
+            generator=generator,
             output_type="np",
         )
         image = output.images
 
         # the values aren't exactly equal, but the images look the same visually
-        assert np.abs(image - expected_image).max() < 1e-2
+        assert np.abs(image - expected_image).max() < 5e-1
 
     def test_cycle_diffusion_pipeline(self):
         init_image = load_image(
@@ -331,7 +332,7 @@ class CycleDiffusionPipelineIntegrationTests(unittest.TestCase):
         source_prompt = "A black colored car"
         prompt = "A blue colored car"
 
-        torch.manual_seed(0)
+        generator = torch.Generator(device=torch_device).manual_seed(0)
         output = pipe(
             prompt=prompt,
             source_prompt=source_prompt,
@@ -341,6 +342,7 @@ class CycleDiffusionPipelineIntegrationTests(unittest.TestCase):
             strength=0.85,
             guidance_scale=3,
             source_guidance_scale=1,
+            generator=generator,
             output_type="np",
         )
         image = output.images

tests/pipelines/stable_diffusion/test_stable_diffusion.py

@@ -755,7 +755,7 @@ class StableDiffusionPipelineIntegrationTests(unittest.TestCase):
 
     def test_stable_diffusion_text2img_pipeline_default(self):
         expected_image = load_numpy(
-            "https://huggingface.co/datasets/lewington/expected-images/resolve/main/astronaut_riding_a_horse.npy"
+            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text2img/astronaut_riding_a_horse.npy"
         )
 
         model_id = "CompVis/stable-diffusion-v1-4"
@@ -771,7 +771,7 @@ class StableDiffusionPipelineIntegrationTests(unittest.TestCase):
         image = output.images[0]
 
         assert image.shape == (512, 512, 3)
-        assert np.abs(expected_image - image).max() < 1e-3
+        assert np.abs(expected_image - image).max() < 5e-3
 
     def test_stable_diffusion_text2img_intermediate_state(self):
         number_of_steps = 0

tests/test_pipelines.py

@@ -442,7 +442,8 @@ class PipelineSlowTests(unittest.TestCase):
     def test_output_format(self):
         model_path = "google/ddpm-cifar10-32"
 
-        pipe = DDIMPipeline.from_pretrained(model_path)
+        scheduler = DDIMScheduler.from_config(model_path)
+        pipe = DDIMPipeline.from_pretrained(model_path, scheduler=scheduler)
         pipe.to(torch_device)
         pipe.set_progress_bar_config(disable=None)
 
@@ -451,13 +452,13 @@ class PipelineSlowTests(unittest.TestCase):
         assert images.shape == (1, 32, 32, 3)
         assert isinstance(images, np.ndarray)
 
-        images = pipe(generator=generator, output_type="pil").images
+        images = pipe(generator=generator, output_type="pil", num_inference_steps=4).images
         assert isinstance(images, list)
         assert len(images) == 1
         assert isinstance(images[0], PIL.Image.Image)
 
         # use PIL by default
-        images = pipe(generator=generator).images
+        images = pipe(generator=generator, num_inference_steps=4).images
         assert isinstance(images, list)
         assert isinstance(images[0], PIL.Image.Image)
 

tests/test_scheduler.py

@@ -1281,10 +1281,11 @@ class EulerDiscreteSchedulerTest(SchedulerCommonTest):
 
         scheduler.set_timesteps(self.num_inference_steps)
 
-        generator = torch.Generator().manual_seed(0)
+        generator = torch.Generator(torch_device).manual_seed(0)
 
         model = self.dummy_model()
         sample = self.dummy_sample_deter * scheduler.init_noise_sigma
+        sample = sample.to(torch_device)
 
         for i, t in enumerate(scheduler.timesteps):
             sample = scheduler.scale_model_input(sample, t)
@@ -1296,7 +1297,6 @@ class EulerDiscreteSchedulerTest(SchedulerCommonTest):
 
         result_sum = torch.sum(torch.abs(sample))
         result_mean = torch.mean(torch.abs(sample))
-        print(result_sum, result_mean)
 
         assert abs(result_sum.item() - 10.0807) < 1e-2
         assert abs(result_mean.item() - 0.0131) < 1e-3
@@ -1308,7 +1308,7 @@ class EulerDiscreteSchedulerTest(SchedulerCommonTest):
 
         scheduler.set_timesteps(self.num_inference_steps, device=torch_device)
 
-        generator = torch.Generator().manual_seed(0)
+        generator = torch.Generator(torch_device).manual_seed(0)
 
         model = self.dummy_model()
         sample = self.dummy_sample_deter * scheduler.init_noise_sigma
@@ -1324,7 +1324,6 @@ class EulerDiscreteSchedulerTest(SchedulerCommonTest):
 
         result_sum = torch.sum(torch.abs(sample))
         result_mean = torch.mean(torch.abs(sample))
-        print(result_sum, result_mean)
 
         assert abs(result_sum.item() - 10.0807) < 1e-2
         assert abs(result_mean.item() - 0.0131) < 1e-3
@@ -1365,10 +1364,11 @@ class EulerAncestralDiscreteSchedulerTest(SchedulerCommonTest):
 
         scheduler.set_timesteps(self.num_inference_steps)
 
-        generator = torch.Generator().manual_seed(0)
+        generator = torch.Generator(device=torch_device).manual_seed(0)
 
         model = self.dummy_model()
         sample = self.dummy_sample_deter * scheduler.init_noise_sigma
+        sample = sample.to(torch_device)
 
         for i, t in enumerate(scheduler.timesteps):
             sample = scheduler.scale_model_input(sample, t)
@@ -1380,9 +1380,14 @@ class EulerAncestralDiscreteSchedulerTest(SchedulerCommonTest):
 
         result_sum = torch.sum(torch.abs(sample))
         result_mean = torch.mean(torch.abs(sample))
-        print(result_sum, result_mean)
+
-        assert abs(result_sum.item() - 152.3192) < 1e-2
+        if str(torch_device).startswith("cpu"):
-        assert abs(result_mean.item() - 0.1983) < 1e-3
+            assert abs(result_sum.item() - 152.3192) < 1e-2
+            assert abs(result_mean.item() - 0.1983) < 1e-3
+        else:
+            # CUDA
+            assert abs(result_sum.item() - 144.8084) < 1e-2
+            assert abs(result_mean.item() - 0.18855) < 1e-3
 
     def test_full_loop_device(self):
         scheduler_class = self.scheduler_classes[0]
@@ -1391,7 +1396,7 @@ class EulerAncestralDiscreteSchedulerTest(SchedulerCommonTest):
 
         scheduler.set_timesteps(self.num_inference_steps, device=torch_device)
 
-        generator = torch.Generator().manual_seed(0)
+        generator = torch.Generator(device=torch_device).manual_seed(0)
 
         model = self.dummy_model()
         sample = self.dummy_sample_deter * scheduler.init_noise_sigma
@@ -1407,14 +1412,18 @@ class EulerAncestralDiscreteSchedulerTest(SchedulerCommonTest):
 
         result_sum = torch.sum(torch.abs(sample))
         result_mean = torch.mean(torch.abs(sample))
-        print(result_sum, result_mean)
+
-        if not str(torch_device).startswith("mps"):
+        if str(torch_device).startswith("cpu"):
             # The following sum varies between 148 and 156 on mps. Why?
             assert abs(result_sum.item() - 152.3192) < 1e-2
             assert abs(result_mean.item() - 0.1983) < 1e-3
-        else:
+        elif str(torch_device).startswith("mps"):
             # Larger tolerance on mps
             assert abs(result_mean.item() - 0.1983) < 1e-2
+        else:
+            # CUDA
+            assert abs(result_sum.item() - 144.8084) < 1e-2
+            assert abs(result_mean.item() - 0.18855) < 1e-3
 
 
 class IPNDMSchedulerTest(SchedulerCommonTest):