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songkey-win
2024-11-13 15:38:02 +08:00
parent 5692a062ab
commit 7e643ae79f
2 changed files with 24 additions and 14 deletions
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hellomeme/pipelines/pipline_hm_video.py
+8 -5
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@@ -18,9 +18,8 @@ from diffusers.utils import deprecate
from diffusers.utils.torch_utils import randn_tensor
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img import retrieve_timesteps, retrieve_latents
from diffusers import StableDiffusionImg2ImgPipeline
from diffusers import StableDiffusionImg2ImgPipeline, MotionAdapter, EulerDiscreteScheduler
from diffusers import MotionAdapter
from ..models import HMDenoising3D, HMDenoisingMotion, HMControlNet
from ..models import HMReferenceAdapter
from ..utils import dicts_to_device, cat_dicts
@@ -234,7 +233,12 @@ class HMVideoPipeline(StableDiffusionImg2ImgPipeline):
if self.do_classifier_free_guidance:
control_latent = cat_dicts([control_latent_neg, control_latent], dim=0)
scheduler = copy.deepcopy(self.scheduler)
scheduler = EulerDiscreteScheduler(
num_train_timesteps=1000,
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
)
tmp_timesteps, _ = retrieve_timesteps(
scheduler, 8, device, None, sigmas
@@ -242,8 +246,7 @@ class HMVideoPipeline(StableDiffusionImg2ImgPipeline):
if idx == 0:
pred_latent = scheduler.add_noise(ref_latents, base_noise, tmp_timesteps[:1])
else:
pred_latent = scheduler.add_noise(pred_latent, base_noise,
tmp_timesteps[:1])
pred_latent = scheduler.add_noise(pred_latent, base_noise, tmp_timesteps[:1])
for i, t in enumerate(tmp_timesteps):
latent_model_input = torch.cat([pred_latent, pred_latent], dim=0) if \
hellomeme/pipelines/pipline_hm_video_simple.py
+16 -9
View File
@@ -19,7 +19,7 @@ from diffusers.utils import deprecate
from diffusers.utils.torch_utils import randn_tensor
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img import retrieve_timesteps, retrieve_latents
from diffusers import StableDiffusionImg2ImgPipeline
from diffusers import StableDiffusionImg2ImgPipeline, EulerDiscreteScheduler
from ..models import HMDenoising3D, HMControlNet
from ..models import HMReferenceAdapter
@@ -217,8 +217,15 @@ class HMVideoSimplePipeline(StableDiffusionImg2ImgPipeline):
if self.do_classifier_free_guidance:
control_latent = cat_dicts([control_latent_neg, control_latent], dim=0)
scheduler = copy.deepcopy(self.scheduler)
scheduler = EulerDiscreteScheduler(
num_train_timesteps=1000,
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
)
tmp_timesteps, _ = retrieve_timesteps(scheduler, 8, device, None, sigmas)
# pred_latent = scheduler.add_noise(ref_latents, base_noise, tmp_timesteps[:1])
if idx == 0:
pred_latent = scheduler.add_noise(ref_latents, base_noise, tmp_timesteps[:1])
else:
@@ -254,15 +261,15 @@ class HMVideoSimplePipeline(StableDiffusionImg2ImgPipeline):
with self.progress_bar(total=raw_video_len) as progress_bar:
for idx in range(raw_video_len):
scheduler = copy.deepcopy(self.scheduler)
scheduler = EulerDiscreteScheduler(
num_train_timesteps=1000,
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
)
tmp_timesteps, _ = retrieve_timesteps(scheduler, num_inference_steps, device, None, sigmas)
if idx == 0:
res_latent = scheduler.add_noise(pre_latents[idx].to(device=device),
base_noise, tmp_timesteps[:1])
else:
res_latent = scheduler.add_noise(pre_latents[idx].to(device=device) * 0.8 + res_latent * 0.2,
base_noise, tmp_timesteps[:1])
res_latent = scheduler.add_noise(pre_latents[idx].to(device=device), base_noise, tmp_timesteps[:1])
for i, t in enumerate(tmp_timesteps):
latent_model_input = torch.cat([res_latent, res_latent], dim=0) if \