From 36a4846a00acc54b9bfe4ad2635b559a660f310a Mon Sep 17 00:00:00 2001 From: William Berman Date: Wed, 8 Feb 2023 09:03:39 -0800 Subject: [PATCH 1/2] remove ddpm test_full_inference --- tests/pipelines/ddpm/test_ddpm.py | 26 -------------------------- 1 file changed, 26 deletions(-) diff --git a/tests/pipelines/ddpm/test_ddpm.py b/tests/pipelines/ddpm/test_ddpm.py index a16b3782a421..c3ea0045b4c1 100644 --- a/tests/pipelines/ddpm/test_ddpm.py +++ b/tests/pipelines/ddpm/test_ddpm.py @@ -66,32 +66,6 @@ def test_fast_inference(self): assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2 - def test_full_inference(self): - device = "cpu" - unet = self.dummy_uncond_unet - scheduler = DDPMScheduler() - - ddpm = DDPMPipeline(unet=unet, scheduler=scheduler) - ddpm.to(device) - ddpm.set_progress_bar_config(disable=None) - - generator = torch.Generator(device=device).manual_seed(0) - image = ddpm(generator=generator, output_type="numpy").images - - generator = torch.Generator(device=device).manual_seed(0) - image_from_tuple = ddpm(generator=generator, output_type="numpy", return_dict=False)[0] - - image_slice = image[0, -3:, -3:, -1] - image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1] - - assert image.shape == (1, 32, 32, 3) - expected_slice = np.array( - [1.0, 3.495e-02, 2.939e-01, 9.821e-01, 9.448e-01, 6.261e-03, 7.998e-01, 8.9e-01, 1.122e-02] - ) - - assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 - assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2 - def test_inference_predict_sample(self): unet = self.dummy_uncond_unet scheduler = DDPMScheduler(prediction_type="sample") From 1b8ed965b7c84174b3a58a4502fad236af09d921 Mon Sep 17 00:00:00 2001 From: William Berman Date: Thu, 9 Feb 2023 12:47:03 -0800 Subject: [PATCH 2/2] style --- examples/text_to_image/train_text_to_image_lora.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/text_to_image/train_text_to_image_lora.py b/examples/text_to_image/train_text_to_image_lora.py index a3c5bef73a95..abc535594d8c 100644 --- a/examples/text_to_image/train_text_to_image_lora.py +++ b/examples/text_to_image/train_text_to_image_lora.py @@ -418,9 +418,9 @@ def main(): # freeze parameters of models to save more memory unet.requires_grad_(False) vae.requires_grad_(False) - + text_encoder.requires_grad_(False) - + # For mixed precision training we cast the text_encoder and vae weights to half-precision # as these models are only used for inference, keeping weights in full precision is not required. weight_dtype = torch.float32