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add HellMeme V2

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songkey-win
2024-12-08 22:13:35 +08:00
parent 24dd59172e
commit c9ca4a689e
16 changed files with 229 additions and 5415 deletions
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hellomeme/models/__init__.py
+1 -1
View File
@@ -9,6 +9,6 @@
"""
from .hm_denoising_motion import HMDenoisingMotion
from .hm_control import HMControlNet, HMControlNet2
from .hm_control import HMControlNet, HMControlNet2, HMV2ControlNet, HMV2ControlNet2
from .hm_blocks import HMReferenceAdapter
from .hm_denoising_3d import HMDenoising3D
hellomeme/models/hm_blocks.py
+20 -16
View File
@@ -50,28 +50,29 @@ class HMReferenceAdapter(ModelMixin, ConfigMixin):
"CrossAttnUpBlock2D",
"CrossAttnUpBlock2D"),
num_attention_heads: Optional[Union[int, Tuple[int]]] = 8,
version='v1'
):
super().__init__()
self.reference_modules_down = nn.ModuleList([])
if isinstance(num_attention_heads, int):
num_attention_heads = (num_attention_heads,) * len(down_block_types)
for i, down_block_type in enumerate(down_block_types):
output_channel = block_out_channels[i]
if version == 'v1':
self.reference_modules_down = nn.ModuleList([])
for i, down_block_type in enumerate(down_block_types):
output_channel = block_out_channels[i]
self.reference_modules_down.append(
SKReferenceAttention(
in_channels=output_channel,
num_attention_heads=num_attention_heads[i],
self.reference_modules_down.append(
SKReferenceAttention(
in_channels=output_channel,
num_attention_heads=num_attention_heads[i],
)
)
)
self.reference_modules_mid = SKReferenceAttention(
in_channels=block_out_channels[-1],
num_attention_heads=num_attention_heads[-1],
)
self.reference_modules_mid = SKReferenceAttention(
in_channels=block_out_channels[-1],
num_attention_heads=num_attention_heads[-1],
)
self.reference_modules_up = nn.ModuleList([])
@@ -326,7 +327,10 @@ class InsertReferenceAdapter(object):
self.reference_modules_up = None
def insert_reference_adapter(self, adapter: HMReferenceAdapter):
self.reference_modules_down = copy.deepcopy(adapter.reference_modules_down)
self.reference_modules_mid = copy.deepcopy(adapter.reference_modules_mid)
self.reference_modules_up = copy.deepcopy(adapter.reference_modules_up)
if hasattr(adapter, "reference_modules_down"):
self.reference_modules_down = copy.deepcopy(adapter.reference_modules_down)
if hasattr(adapter, "reference_modules_mid"):
self.reference_modules_mid = copy.deepcopy(adapter.reference_modules_mid)
if hasattr(adapter, "reference_modules_up"):
self.reference_modules_up = copy.deepcopy(adapter.reference_modules_up)
hellomeme/models/hm_control.py
+127 -2
View File
@@ -24,7 +24,7 @@ class HMControlNet(ModelMixin, ConfigMixin):
@register_to_config
def __init__(
self,
embedding_channels: int,
embedding_channels: int = 1280,
input_channels: int = 3,
scale_factor: int = 8,
cross_attention_dim: int = 320,
@@ -91,7 +91,7 @@ class HMControlNet2(ModelMixin, ConfigMixin):
@register_to_config
def __init__(
self,
embedding_channels: int,
embedding_channels: int = 1280,
input_channels: int = 3,
scale_factor: int = 8,
cross_attention_dim: int = 320,
@@ -143,4 +143,129 @@ class HMControlNet2(ModelMixin, ConfigMixin):
ret_dict[f'down2_{idx}'] = rearrange(embedding,
"(b f) c h w -> b c f h w",
f=video_length)
return ret_dict
class HMV2ControlNet(ModelMixin, ConfigMixin):
def __init__(
self,
embedding_channels: int = 1280,
input_channels: int = 3,
scale_factor: int = 4,
cross_attention_dim: int = 320,
block_out_channels: Tuple[int] = (128, 640, 1280, 1280, 1280),
):
super().__init__()
self.scale_factor = scale_factor
self.embedding_channels = embedding_channels
self.cross_attention_dim = cross_attention_dim
self.conv_in = nn.Conv2d(input_channels, block_out_channels[0], kernel_size=3, padding=1, bias=False)
self.exp_embedding = Timesteps(cross_attention_dim, True, 0)
self.exp_proj = TimestepEmbedding(cross_attention_dim, cross_attention_dim)
self.face_proj = TimestepEmbedding(1024, cross_attention_dim)
self.blocks_down = nn.ModuleList([])
for i in range(1, len(block_out_channels)):
channel_in = block_out_channels[i - 1]
channel_out = block_out_channels[i]
self.blocks_down.append(
SKCrossAttention(
channel_in=channel_in,
channel_out=channel_out,
cross_attention_dim=cross_attention_dim,
num_positional_embeddings=64,
num_positional_embeddings_hidden=1024
)
)
def forward(self, condition, drive_coeff, face_parts):
bs, _, video_length, h, w = condition.shape
conditioning = rearrange(condition, "b c f h w -> (b f) c h w")
conditioning = F.interpolate(conditioning,
size=(h // self.scale_factor, w // self.scale_factor),
mode='bilinear',
align_corners=False)
embedding = self.conv_in(conditioning)
drive_coeff = rearrange(drive_coeff * 500., "b f c -> (b f c)")
drive_embedding = self.exp_embedding(drive_coeff).to(dtype=embedding.dtype)
drive_embedding = rearrange(drive_embedding, "(b f c) d -> b f c d",
b=bs, f=video_length, d=self.cross_attention_dim)
face_parts = rearrange(face_parts, "b f c d -> (b f) c d")
face_embedding = self.face_proj(face_parts)
face_embedding = rearrange(face_embedding, "(b f) c d -> b f c d", f=video_length)
drive_embedding = torch.cat([face_embedding, drive_embedding], dim=2)
drive_embedding = rearrange(drive_embedding, "b f c d -> (b f) c d")
drive_embedding = self.exp_proj(drive_embedding)
ret_dict = {}
for idx, block in enumerate(self.blocks_down):
embedding = block(embedding, drive_embedding)
ret_dict[f'up_v2_{len(self.blocks_down) - idx - 1}'] = rearrange(embedding,
"(b f) c h w -> b c f h w",
f=video_length)
return ret_dict
class HMV2ControlNet2(ModelMixin, ConfigMixin):
def __init__(
self,
embedding_channels: int = 1280,
input_channels: int = 3,
scale_factor: int = 4,
cross_attention_dim: int = 320,
block_out_channels: Tuple[int] = (128, 640, 1280, 1280, 1280),
):
super().__init__()
self.scale_factor = scale_factor
self.embedding_channels = embedding_channels
self.cross_attention_dim = cross_attention_dim
self.conv_in = nn.Conv2d(input_channels, block_out_channels[0], kernel_size=3, padding=1, bias=False)
self.exp_embedding = Timesteps(cross_attention_dim, True, 0)
self.emo_proj = TimestepEmbedding(cross_attention_dim, cross_attention_dim)
# self.face_proj = TimestepEmbedding(1024, cross_attention_dim)
self.blocks_down = nn.ModuleList([])
for i in range(1, len(block_out_channels)):
channel_in = block_out_channels[i-1]
channel_out = block_out_channels[i]
self.blocks_down.append(
SKCrossAttention(
channel_in=channel_in,
channel_out=channel_out,
cross_attention_dim=cross_attention_dim,
num_positional_embeddings=64,
num_positional_embeddings_hidden=1024
)
)
def forward(self, condition, emo_embedding):
bs, _, video_length, h, w = condition.shape
conditioning = rearrange(condition, "b c f h w -> (b f) c h w")
conditioning = F.interpolate(conditioning,
size=(h // self.scale_factor, w // self.scale_factor),
mode='bilinear',
align_corners=False)
embedding = self.conv_in(conditioning)
drive_coeff = rearrange(emo_embedding * 20., "b f c -> (b f c)")
drive_embedding = self.exp_embedding(drive_coeff).to(dtype=embedding.dtype)
drive_embedding = rearrange(drive_embedding, "(b f c) d -> b f c d",
b=bs, f=video_length, d=self.cross_attention_dim)
drive_embedding = rearrange(drive_embedding, "b f c d -> (b f) c d")
drive_embedding = self.emo_proj(drive_embedding)
ret_dict = {}
for idx, block in enumerate(self.blocks_down):
embedding = block(embedding, drive_embedding)
ret_dict[f'up2_v2_{len(self.blocks_down)-idx-1}'] = rearrange(embedding,
"(b f) c h w -> b c f h w",
f=video_length)
return ret_dict
hellomeme/models/hm_denoising_3d.py
+8
View File
@@ -251,6 +251,10 @@ class HMDenoising3D(UNet2DConditionModel, CopyWeights, InsertReferenceAdapter):
if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
res_cache[f"up_{i}"] = sample.clone()
if not control_hidden_states is None and f'up_v2_{i}' in control_hidden_states:
sample += rearrange(control_hidden_states[f'up_v2_{i}'], "b c f h w -> (b f) c h w")
if not control_hidden_states is None and f'up2_v2_{i}' in control_hidden_states:
sample += rearrange(control_hidden_states[f'up2_v2_{i}'], "b c f h w -> (b f) c h w")
if hasattr(self, "reference_modules_up") and not reference_hidden_states is None and f'up_{i}' in reference_hidden_states:
sample = self.reference_modules_up[i-1](sample, reference_hidden_states[f'up_{i}'], num_frames)
@@ -265,6 +269,10 @@ class HMDenoising3D(UNet2DConditionModel, CopyWeights, InsertReferenceAdapter):
encoder_attention_mask=encoder_attention_mask,
)
else:
if not control_hidden_states is None and f'up_v2_{i}' in control_hidden_states:
sample += rearrange(control_hidden_states[f'up_v2_{i}'], "b c f h w -> (b f) c h w")
if not control_hidden_states is None and f'up2_v2_{i}' in control_hidden_states:
sample += rearrange(control_hidden_states[f'up2_v2_{i}'], "b c f h w -> (b f) c h w")
sample = upsample_block(
hidden_states=sample,
temb=emb,
hellomeme/models/hm_denoising_motion.py
+8
View File
@@ -202,6 +202,10 @@ class HMDenoisingMotion(UNetMotionModel, InsertReferenceAdapter):
if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
if not control_hidden_states is None and f'up_v2_{i}' in control_hidden_states:
sample += rearrange(control_hidden_states[f'up_v2_{i}'], "b c f h w -> (b f) c h w")
if not control_hidden_states is None and f'up2_v2_{i}' in control_hidden_states:
sample += rearrange(control_hidden_states[f'up2_v2_{i}'], "b c f h w -> (b f) c h w")
if hasattr(self,
"reference_modules_up") and not reference_hidden_states is None and f'up_{i}' in reference_hidden_states:
sample = self.reference_modules_up[i - 1](sample, reference_hidden_states[f'up_{i}'],
@@ -218,6 +222,10 @@ class HMDenoisingMotion(UNetMotionModel, InsertReferenceAdapter):
cross_attention_kwargs=cross_attention_kwargs,
)
else:
if not control_hidden_states is None and f'up_v2_{i}' in control_hidden_states:
sample += rearrange(control_hidden_states[f'up_v2_{i}'], "b c f h w -> (b f) c h w")
if not control_hidden_states is None and f'up2_v2_{i}' in control_hidden_states:
sample += rearrange(control_hidden_states[f'up2_v2_{i}'], "b c f h w -> (b f) c h w")
sample = upsample_block(
hidden_states=sample,
temb=emb,
hellomeme/pipelines/pipline_hm_image.py
+19 -19
View File
@@ -22,42 +22,44 @@ from diffusers.pipelines.stable_diffusion.pipeline_output import StableDiffusion
from diffusers import StableDiffusionImg2ImgPipeline
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img import retrieve_timesteps, retrieve_latents
from ..models import HMDenoising3D, HMControlNet, HMControlNet2
from ..models import HMDenoising3D, HMControlNet, HMControlNet2, HMV2ControlNet, HMV2ControlNet2
from ..models import HMReferenceAdapter
class HMImagePipeline(StableDiffusionImg2ImgPipeline):
def caryomitosis(self, **kwargs):
if not hasattr(self, "unet_ref"):
self.unet_ref = HMDenoising3D.from_unet2d(self.unet)
self.unet_ref.cpu()
if not isinstance(self.unet, HMDenoising3D):
unet = HMDenoising3D.from_unet2d(unet=self.unet)
# todo: 不够优雅
del self.unet
self.unet = unet
self.unet.cpu()
self.vae_decode = copy.deepcopy(self.vae)
def insert_hm_modules(self, dtype):
def insert_hm_modules(self, version, dtype):
if isinstance(self.unet, HMDenoising3D):
hm_adapter = HMReferenceAdapter.from_pretrained('songkey/hm_reference')
if version == 'v1':
hm_adapter = HMReferenceAdapter.from_pretrained('songkey/hm_reference')
else:
hm_adapter = HMReferenceAdapter.from_pretrained('songkey/hm2_reference')
self.unet.insert_reference_adapter(hm_adapter)
self.unet_ref.insert_reference_adapter(hm_adapter)
self.unet_ref.to(device='cpu', dtype=dtype).eval()
self.unet.to(device='cpu', dtype=dtype).eval()
if hasattr(self, "mp_control"):
del self.mp_control
self.mp_control = HMControlNet.from_pretrained('songkey/hm_control')
if version == 'v1':
self.mp_control = HMControlNet.from_pretrained('songkey/hm_control')
else:
self.mp_control = HMV2ControlNet.from_pretrained('songkey/hm2_control')
self.mp_control.to(device='cpu', dtype=dtype).eval()
if hasattr(self, "mp_control2"):
del self.mp_control2
self.mp_control2 = HMControlNet2.from_pretrained('songkey/hm_control2')
if version == 'v1':
self.mp_control2 = HMControlNet2.from_pretrained('songkey/hm_control2')
else:
self.mp_control2 = HMV2ControlNet2.from_pretrained('songkey/hm2_control2')
self.mp_control2.to(device='cpu', dtype=dtype).eval()
self.vae.to(device='cpu', dtype=dtype).eval()
self.vae_decode.to(device='cpu', dtype=dtype).eval()
self.text_encoder.to(device='cpu', dtype=dtype).eval()
@torch.no_grad()
@@ -230,15 +232,13 @@ class HMImagePipeline(StableDiffusionImg2ImgPipeline):
)
latent_model_input = torch.cat([torch.zeros_like(ref_latents), ref_latents]) if self.do_classifier_free_guidance else ref_latents
self.unet_ref.to(device=device)
cached_res = self.unet_ref(
self.unet.to(device=device)
cached_res = self.unet(
latent_model_input.unsqueeze(2),
0,
encoder_hidden_states=prompt_embeds,
# control_hidden_states=control_latents,
return_dict=False,
)[1]
self.unet_ref.cpu()
# 7.2 Optionally get Guidance Scale Embedding
timestep_cond = None
@@ -304,14 +304,14 @@ class HMImagePipeline(StableDiffusionImg2ImgPipeline):
self.unet.cpu()
self.vae_decode.to(device=device)
self.vae.to(device=device)
if not output_type == "latent":
image = self.vae_decode.decode(latents / self.vae.config.scaling_factor, return_dict=False, generator=generator)[
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False, generator=generator)[
0
]
else:
image = latents
self.vae_decode.cpu()
self.vae.cpu()
do_denormalize = [True] * image.shape[0]
hellomeme/pipelines/pipline_hm_video.py
+25 -24
View File
@@ -21,54 +21,58 @@ 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, MotionAdapter, EulerDiscreteScheduler
from ..models import HMDenoising3D, HMDenoisingMotion, HMControlNet, HMControlNet2
from ..models import HMDenoising3D, HMDenoisingMotion, HMControlNet, HMControlNet2, HMV2ControlNet, HMV2ControlNet2
from ..models import HMReferenceAdapter
from ..utils import dicts_to_device, cat_dicts, cat_dicts_ref
class HMVideoPipeline(StableDiffusionImg2ImgPipeline):
def caryomitosis(self, patch_frames=12, **kwargs):
def caryomitosis(self, version, **kwargs):
if hasattr(self, "unet_ref"):
del self.unet_ref
self.unet_ref = HMDenoising3D.from_unet2d(self.unet)
self.unet_ref.cpu()
self.unet_pre = HMDenoising3D.from_unet2d(self.unet)
self.unet_pre.cpu()
self.num_frames = 12
adapter = MotionAdapter.from_pretrained("songkey/hm_animatediff_frame12", torch_dtype=torch.float16)
if version == 'v1':
adapter = MotionAdapter.from_pretrained("songkey/hm_animatediff_frame12", torch_dtype=torch.float16)
else:
adapter = MotionAdapter.from_pretrained("songkey/hm2_animatediff_frame12", torch_dtype=torch.float16)
unet = HMDenoisingMotion.from_unet2d(unet=self.unet, motion_adapter=adapter, load_weights=True)
# todo: 不够优雅
del self.unet
self.unet = unet
self.vae_decode = copy.deepcopy(self.vae)
def insert_hm_modules(self, dtype):
hm_adapter = HMReferenceAdapter.from_pretrained('songkey/hm_reference')
def insert_hm_modules(self, version, dtype):
if version == 'v1':
hm_adapter = HMReferenceAdapter.from_pretrained('songkey/hm_reference')
else:
hm_adapter = HMReferenceAdapter.from_pretrained('songkey/hm2_reference')
if isinstance(self.unet, HMDenoisingMotion):
self.unet.insert_reference_adapter(hm_adapter)
self.unet.to(device='cpu', dtype=dtype).eval()
if hasattr(self, "unet_ref"):
self.unet_ref.insert_reference_adapter(hm_adapter)
self.unet_ref.to(device='cpu', dtype=dtype).eval()
if hasattr(self, "unet_pre"):
self.unet_pre.insert_reference_adapter(hm_adapter)
self.unet_pre.to(device='cpu', dtype=dtype).eval()
if hasattr(self, "mp_control"):
del self.mp_control
self.mp_control = HMControlNet.from_pretrained('songkey/hm_control')
if version == 'v1':
self.mp_control = HMControlNet.from_pretrained('songkey/hm_control')
else:
self.mp_control = HMV2ControlNet.from_pretrained('songkey/hm2_control')
self.mp_control.to(device='cpu', dtype=dtype).eval()
if hasattr(self, "mp_control2"):
del self.mp_control2
self.mp_control2 = HMControlNet2.from_pretrained('songkey/hm_control2')
if version == 'v1':
self.mp_control2 = HMControlNet2.from_pretrained('songkey/hm_control2')
else:
self.mp_control2 = HMV2ControlNet2.from_pretrained('songkey/hm2_control2')
self.mp_control2.to(device='cpu', dtype=dtype).eval()
self.vae.to(device='cpu', dtype=dtype).eval()
self.vae_decode.to(device='cpu', dtype=dtype).eval()
self.text_encoder.to(device='cpu', dtype=dtype).eval()
@torch.no_grad()
@@ -218,15 +222,14 @@ class HMVideoPipeline(StableDiffusionImg2ImgPipeline):
guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim
).to(device=device, dtype=prompt_embeds.dtype)
self.unet_ref.to(device=device)
cached_res = self.unet_ref(
self.unet_pre.to(device=device)
cached_res = self.unet_pre(
torch.cat([torch.zeros_like(ref_latents), ref_latents], dim=0) if
self.do_classifier_free_guidance else ref_latents,
0,
encoder_hidden_states=prompt_embeds,
return_dict=False,
)[1]
self.unet_ref.cpu()
pre_latents = []
control_latents = []
@@ -234,7 +237,6 @@ class HMVideoPipeline(StableDiffusionImg2ImgPipeline):
base_noise = randn_tensor([batch_size, c, 1, h, w], dtype=prompt_embeds.dtype, generator=generator).to(device=device)
self.mp_control.to(device=device)
self.mp_control2.to(device=device)
self.unet_pre.to(device=device)
with self.progress_bar(total=raw_video_len) as progress_bar:
for idx in range(raw_video_len):
condition = drive_params['condition'][:, :, idx:idx+1].clone().to(device=device)
@@ -313,7 +315,6 @@ class HMVideoPipeline(StableDiffusionImg2ImgPipeline):
self.mp_control.cpu()
self.mp_control2.cpu()
self.unet_pre.cpu()
self.unet_ref.cpu()
chunk_size = self.num_frames
chunk_overlap = min(max(0, chunk_overlap), chunk_size // 2 - 1)
@@ -421,15 +422,15 @@ class HMVideoPipeline(StableDiffusionImg2ImgPipeline):
self.unet.cpu()
latents_res = latents / self.vae.config.scaling_factor
self.vae_decode.to(device=device)
self.vae.to(device=device)
res_frames = []
with self.progress_bar(total=raw_video_len) as progress_bar:
for i in range(raw_video_len):
ret_tensor = self.vae_decode.decode(latents_res[:, :, i, :, :].to(device=device),
ret_tensor = self.vae.decode(latents_res[:, :, i, :, :].to(device=device),
return_dict=False, generator=generator)[0]
ret_image = self.image_processor.postprocess(ret_tensor, output_type=output_type)
res_frames.append(ret_image)
progress_bar.set_description(f"VAE DECODE")
progress_bar.update()
self.vae_decode.cpu()
self.vae.cpu()
return res_frames, latents_res
meme.py
+13 -11
View File
@@ -72,7 +72,9 @@ def append_pipline_weights(pipeline, checkpoint=None, lora=None, vae=None):
raw_stats = torch.load(checkpoint_path)
if raw_stats:
state_dict = convert_ldm_unet_checkpoint(raw_stats, pipeline.unet_ref.config)
state_dict = convert_ldm_unet_checkpoint(raw_stats,
pipeline.unet_pre.config if hasattr(pipeline, 'unet_pre')
else pipeline.unet.config)
if hasattr(pipeline, 'unet_pre'):
pipeline.unet_pre.load_state_dict(state_dict, strict=False)
pipeline.unet.load_state_dict(state_dict, strict=False)
@@ -92,9 +94,8 @@ def append_pipline_weights(pipeline, checkpoint=None, lora=None, vae=None):
raw_vae_stats = torch.load(real_vae_path)
if raw_vae_stats:
vae_state_dict = convert_ldm_vae_checkpoint(raw_vae_stats, pipeline.vae_decode.config)
if hasattr(pipeline, 'vae_decode'):
pipeline.vae_decode.load_state_dict(vae_state_dict, strict=True)
vae_state_dict = convert_ldm_vae_checkpoint(raw_vae_stats, pipeline.vae.config)
pipeline.vae.load_state_dict(vae_state_dict, strict=True)
### lora
if lora and not lora.startswith('None'):
@@ -113,21 +114,22 @@ class HMImagePipelineLoader:
"checkpoint": (checkpoint_files, ),
"lora": (lora_files, ),
"vae": (vae_files, ),
"version": (['v1', 'v2'], ),
}
}
RETURN_TYPES = ("HMIMAGEPIPELINE", )
RETURN_NAMES = ("hm_image_pipeline", )
FUNCTION = "load_pipeline"
CATEGORY = "hellomeme"
def load_pipeline(self, checkpoint=None, lora=None, vae=None):
def load_pipeline(self, checkpoint=None, lora=None, vae=None, version='v2'):
dtype = torch.float16
pipeline = HMImagePipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5")
pipeline.to(dtype=dtype)
pipeline.caryomitosis()
pipeline.caryomitosis(version=version)
append_pipline_weights(pipeline, checkpoint=checkpoint, lora=lora, vae=vae)
pipeline.insert_hm_modules(dtype=dtype)
pipeline.insert_hm_modules(version=version, dtype=dtype)
return (pipeline, )
@@ -142,7 +144,7 @@ class HMVideoPipelineLoader:
"checkpoint": (checkpoint_files, ),
"lora": (lora_files, ),
"vae": (vae_files, ),
"patch_frames": ([12], ),
"version": (['v1', 'v2'], ),
}
}
@@ -151,15 +153,15 @@ class HMVideoPipelineLoader:
FUNCTION = "load_pipeline"
CATEGORY = "hellomeme"
def load_pipeline(self, checkpoint=None, lora=None, vae=None, patch_frames=12):
def load_pipeline(self, checkpoint=None, lora=None, vae=None, version='v2'):
dtype = torch.float16
pipeline = HMVideoPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5")
pipeline.to(dtype=dtype)
pipeline.caryomitosis(patch_frames=patch_frames)
pipeline.caryomitosis(version=version)
append_pipline_weights(pipeline, checkpoint=checkpoint, lora=lora, vae=vae)
pipeline.insert_hm_modules(dtype=dtype)
pipeline.insert_hm_modules(version=version, dtype=dtype)
return (pipeline,)
workflows/hellomeme_image_sr_workflow.json
+1 -663
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workflows/hellomeme_image_v2_sr_workflow.json
+1 -664
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workflows/hellomeme_image_v2_workflow.json
+1 -569
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@@ -1,569 +1 @@
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workflows/hellomeme_image_workflow.json
+1 -570
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