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update

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Dhruv Nair
2024-01-17 08:03:40 +00:00
parent e37abaf1ee
commit 1bd8ba3de1
2 changed files with 8 additions and 8 deletions
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4
src/diffusers/loaders/single_file_utils.py
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@@ -1190,8 +1190,8 @@ def create_scheduler(pipeline_class_name, original_config, checkpoint, checkpoin
scheduler_type = "euler"
else:
beta_start = getattr(original_config["model"]["params"], "linear_start", None) or 0.02
beta_end = getattr(original_config["model"]["params"], "linear_end", None) or 0.085
beta_start = original_config["model"]["params"].get("linear_start", 0.02)
beta_end = original_config["model"]["params"].get("linear_end", 0.085)
scheduler_config["beta_start"] = beta_start
scheduler_config["beta_end"] = beta_end
scheduler_config["beta_schedule"] = "scaled_linear"

12
tests/pipelines/stable_diffusion/test_stable_diffusion.py
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@@ -1256,13 +1256,13 @@ class StableDiffusionPipelineCkptTests(unittest.TestCase):
def test_download_ckpt_diff_format_is_same(self):
ckpt_path = "https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.ckpt"
pipe = StableDiffusionPipeline.from_single_file(ckpt_path)
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
pipe.unet.set_attn_processor(AttnProcessor())
pipe.to("cuda")
sf_pipe = StableDiffusionPipeline.from_single_file(ckpt_path)
sf_pipe.scheduler = DDIMScheduler.from_config(sf_pipe.scheduler.config)
sf_pipe.unet.set_attn_processor(AttnProcessor())
sf_pipe.to("cuda")
generator = torch.Generator(device="cpu").manual_seed(0)
image_ckpt = pipe("a turtle", num_inference_steps=2, generator=generator, output_type="np").images[0]
image_single_file = sf_pipe("a turtle", num_inference_steps=2, generator=generator, output_type="np").images[0]
pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
@@ -1272,7 +1272,7 @@ class StableDiffusionPipelineCkptTests(unittest.TestCase):
generator = torch.Generator(device="cpu").manual_seed(0)
image = pipe("a turtle", num_inference_steps=2, generator=generator, output_type="np").images[0]
max_diff = numpy_cosine_similarity_distance(image.flatten(), image_ckpt.flatten())
max_diff = numpy_cosine_similarity_distance(image.flatten(), image_single_file.flatten())
assert max_diff < 1e-3