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Lora fest + Skip Layer Guidance

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DeepBeepMeep
2025-03-15 01:12:51 +01:00
parent d233dd7ed9 5f07a7092d
commit e554e1a3d6
6 changed files with 136 additions and 45 deletions
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58
wan/image2video.py
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@@ -132,22 +132,25 @@ class WanI2V:
self.sample_neg_prompt = config.sample_neg_prompt
def generate(self,
input_prompt,
img,
max_area=720 * 1280,
frame_num=81,
shift=5.0,
sample_solver='unipc',
sampling_steps=40,
guide_scale=5.0,
n_prompt="",
seed=-1,
offload_model=True,
callback = None,
enable_RIFLEx = False,
VAE_tile_size= 0,
joint_pass = False,
):
input_prompt,
img,
max_area=720 * 1280,
frame_num=81,
shift=5.0,
sample_solver='unipc',
sampling_steps=40,
guide_scale=5.0,
n_prompt="",
seed=-1,
offload_model=True,
callback = None,
enable_RIFLEx = False,
VAE_tile_size= 0,
joint_pass = False,
slg_layers = None,
slg_start = 0.0,
slg_end = 1.0,
):
r"""
Generates video frames from input image and text prompt using diffusion process.
@@ -332,24 +335,41 @@ class WanI2V:
for i, t in enumerate(tqdm(timesteps)):
offload.set_step_no_for_lora(self.model, i)
slg_layers_local = None
if int(slg_start * sampling_steps) <= i < int(slg_end * sampling_steps):
slg_layers_local = slg_layers
latent_model_input = [latent.to(self.device)]
timestep = [t]
timestep = torch.stack(timestep).to(self.device)
if joint_pass:
# if slg_layers is not None:
# raise ValueError('Can not use SLG and joint-pass')
noise_pred_cond, noise_pred_uncond = self.model(
latent_model_input, t=timestep, current_step=i, **arg_both)
latent_model_input, t=timestep, current_step=i, slg_layers=slg_layers_local, **arg_both)
if self._interrupt:
return None
else:
noise_pred_cond = self.model(
latent_model_input, t=timestep, current_step=i, is_uncond = False, **arg_c)[0]
latent_model_input,
t=timestep,
current_step=i,
is_uncond=False,
**arg_c,
)[0]
if self._interrupt:
return None
if offload_model:
torch.cuda.empty_cache()
noise_pred_uncond = self.model(
latent_model_input, t=timestep, current_step=i, is_uncond = True, **arg_null)[0]
latent_model_input,
t=timestep,
current_step=i,
is_uncond=True,
slg_layers=slg_layers_local,
**arg_null,
)[0]
if self._interrupt:
return None
del latent_model_input