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Update model names for CompVis and google
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@@ -969,7 +969,7 @@ class PipelineTesterMixin(unittest.TestCase):
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@slow
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def test_ddim_lsun(self):
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model_id = "google/ddpm-lsun-bedroom-ema"
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model_id = "google/ddpm-ema-bedroom-256"
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unet = UNetUnconditionalModel.from_pretrained(model_id)
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scheduler = DDIMScheduler.from_config(model_id)
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@@ -1028,7 +1028,7 @@ class PipelineTesterMixin(unittest.TestCase):
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@slow
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def test_ldm_text2img(self):
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ldm = LatentDiffusionPipeline.from_pretrained("CompVis/latent-diffusion-text2im-large")
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ldm = LatentDiffusionPipeline.from_pretrained("CompVis/ldm-text2im-large")
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prompt = "A painting of a squirrel eating a burger"
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generator = torch.manual_seed(0)
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@@ -1042,7 +1042,7 @@ class PipelineTesterMixin(unittest.TestCase):
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@slow
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def test_ldm_text2img_fast(self):
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ldm = LatentDiffusionPipeline.from_pretrained("CompVis/latent-diffusion-text2im-large")
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ldm = LatentDiffusionPipeline.from_pretrained("CompVis/ldm-text2im-large")
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prompt = "A painting of a squirrel eating a burger"
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generator = torch.manual_seed(0)
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@@ -1121,7 +1121,7 @@ class PipelineTesterMixin(unittest.TestCase):
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@slow
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def test_ldm_uncond(self):
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ldm = LatentDiffusionUncondPipeline.from_pretrained("CompVis/latent-diffusion-celeba-256")
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ldm = LatentDiffusionUncondPipeline.from_pretrained("CompVis/ldm-celebahq-256")
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generator = torch.manual_seed(0)
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image = ldm(generator=generator, num_inference_steps=5)["sample"]
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