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[Img2Img2] Re-add K LMS scheduler (#340)

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Patrick von Platen 2022-09-03 12:19:58 +02:00 committed by GitHub
parent e49dd03d2d
commit 9b704f7688
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2 changed files with 102 additions and 13 deletions
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36
src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py
View File

@ -5,12 +5,11 @@ import numpy as np
import torch
import PIL
from tqdm.auto import tqdm
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
from ...models import AutoencoderKL, UNet2DConditionModel
from ...pipeline_utils import DiffusionPipeline
from ...schedulers import DDIMScheduler, PNDMScheduler
from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from .safety_checker import StableDiffusionSafetyChecker
@ -31,7 +30,7 @@ class StableDiffusionImg2ImgPipeline(DiffusionPipeline):
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: Union[DDIMScheduler, PNDMScheduler],
scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPFeatureExtractor,
):
@ -93,12 +92,17 @@ class StableDiffusionImg2ImgPipeline(DiffusionPipeline):
# get the original timestep using init_timestep
init_timestep = int(num_inference_steps * strength) + offset
init_timestep = min(init_timestep, num_inference_steps)
timesteps = self.scheduler.timesteps[-init_timestep]
timesteps = torch.tensor([timesteps] * batch_size, dtype=torch.long, device=self.device)
if isinstance(self.scheduler, LMSDiscreteScheduler):
timesteps = torch.tensor(
[num_inference_steps - init_timestep] * batch_size, dtype=torch.long, device=self.device
)
else:
timesteps = self.scheduler.timesteps[-init_timestep]
timesteps = torch.tensor([timesteps] * batch_size, dtype=torch.long, device=self.device)
# add noise to latents using the timesteps
noise = torch.randn(init_latents.shape, generator=generator, device=self.device)
init_latents = self.scheduler.add_noise(init_latents, noise, timesteps)
init_latents = self.scheduler.add_noise(init_latents, noise, timesteps).to(self.device)
# get prompt text embeddings
text_input = self.tokenizer(
@ -137,11 +141,22 @@ class StableDiffusionImg2ImgPipeline(DiffusionPipeline):
extra_step_kwargs["eta"] = eta
latents = init_latents
t_start = max(num_inference_steps - init_timestep + offset, 0)
for i, t in tqdm(enumerate(self.scheduler.timesteps[t_start:])):
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps[t_start:])):
t_index = t_start + i
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
# if we use LMSDiscreteScheduler, let's make sure latents are mulitplied by sigmas
if isinstance(self.scheduler, LMSDiscreteScheduler):
sigma = self.scheduler.sigmas[t_index]
# the model input needs to be scaled to match the continuous ODE formulation in K-LMS
latent_model_input = latent_model_input / ((sigma**2 + 1) ** 0.5)
latent_model_input = latent_model_input.to(self.unet.dtype)
t = t.to(self.unet.dtype)
# predict the noise residual
noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings)["sample"]
@ -151,11 +166,14 @@ class StableDiffusionImg2ImgPipeline(DiffusionPipeline):
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs)["prev_sample"]
if isinstance(self.scheduler, LMSDiscreteScheduler):
latents = self.scheduler.step(noise_pred, t_index, latents, **extra_step_kwargs)["prev_sample"]
else:
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs)["prev_sample"]
# scale and decode the image latents with vae
latents = 1 / 0.18215 * latents
image = self.vae.decode(latents)
image = self.vae.decode(latents.to(self.vae.dtype))
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).numpy()

79
tests/test_pipelines.py
View File

@ -445,6 +445,49 @@ class PipelineFastTests(unittest.TestCase):
expected_slice = np.array([0.4492, 0.3865, 0.4222, 0.5854, 0.5139, 0.4379, 0.4193, 0.48, 0.4218])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_img2img_k_lms(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
unet = self.dummy_cond_unet
scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear")
vae = self.dummy_vae
bert = self.dummy_text_encoder
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
init_image = self.dummy_image.to(device)
# make sure here that pndm scheduler skips prk
sd_pipe = StableDiffusionImg2ImgPipeline(
unet=unet,
scheduler=scheduler,
vae=vae,
text_encoder=bert,
tokenizer=tokenizer,
safety_checker=self.dummy_safety_checker,
feature_extractor=self.dummy_extractor,
)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
prompt = "A painting of a squirrel eating a burger"
generator = torch.Generator(device=device).manual_seed(0)
output = sd_pipe(
[prompt],
generator=generator,
guidance_scale=6.0,
num_inference_steps=2,
output_type="np",
init_image=init_image,
)
image = output["sample"]
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
expected_slice = np.array([0.4367, 0.4986, 0.4372, 0.6706, 0.5665, 0.444, 0.5864, 0.6019, 0.5203])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_inpaint(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
unet = self.dummy_cond_unet
@ -892,7 +935,7 @@ class PipelineTesterMixin(unittest.TestCase):
def test_stable_diffusion_img2img_pipeline(self):
ds = load_dataset("hf-internal-testing/diffusers-images", split="train")
init_image = ds[1]["image"].resize((768, 512))
init_image = ds[2]["image"].resize((768, 512))
output_image = ds[0]["image"].resize((768, 512))
model_id = "CompVis/stable-diffusion-v1-4"
@ -915,12 +958,40 @@ class PipelineTesterMixin(unittest.TestCase):
@slow
@unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
def test_stable_diffusion_in_paint_pipeline(self):
def test_stable_diffusion_img2img_pipeline_k_lms(self):
ds = load_dataset("hf-internal-testing/diffusers-images", split="train")
init_image = ds[2]["image"].resize((768, 512))
mask_image = ds[3]["image"].resize((768, 512))
output_image = ds[4]["image"].resize((768, 512))
output_image = ds[1]["image"].resize((768, 512))
lms = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear")
model_id = "CompVis/stable-diffusion-v1-4"
pipe = StableDiffusionImg2ImgPipeline.from_pretrained(model_id, scheduler=lms, use_auth_token=True)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
prompt = "A fantasy landscape, trending on artstation"
generator = torch.Generator(device=torch_device).manual_seed(0)
image = pipe(prompt=prompt, init_image=init_image, strength=0.75, guidance_scale=7.5, generator=generator)[
"sample"
][0]
expected_array = np.array(output_image)
sampled_array = np.array(image)
assert sampled_array.shape == (512, 768, 3)
assert np.max(np.abs(sampled_array - expected_array)) < 1e-4
@slow
@unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
def test_stable_diffusion_in_paint_pipeline(self):
ds = load_dataset("hf-internal-testing/diffusers-images", split="train")
init_image = ds[3]["image"].resize((768, 512))
mask_image = ds[4]["image"].resize((768, 512))
output_image = ds[5]["image"].resize((768, 512))
model_id = "CompVis/stable-diffusion-v1-4"
pipe = StableDiffusionInpaintPipeline.from_pretrained(model_id, use_auth_token=True)