diff --git a/hellomeme/models/__init__.py b/hellomeme/models/__init__.py index dc0b35b..0735937 100644 --- a/hellomeme/models/__init__.py +++ b/hellomeme/models/__init__.py @@ -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 diff --git a/hellomeme/models/hm_blocks.py b/hellomeme/models/hm_blocks.py index f46bb98..b0fc94b 100644 --- a/hellomeme/models/hm_blocks.py +++ b/hellomeme/models/hm_blocks.py @@ -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) diff --git a/hellomeme/models/hm_control.py b/hellomeme/models/hm_control.py index 4809654..5a9c5ef 100644 --- a/hellomeme/models/hm_control.py +++ b/hellomeme/models/hm_control.py @@ -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 \ No newline at end of file diff --git a/hellomeme/models/hm_denoising_3d.py b/hellomeme/models/hm_denoising_3d.py index 0f4743e..d5312d0 100644 --- a/hellomeme/models/hm_denoising_3d.py +++ b/hellomeme/models/hm_denoising_3d.py @@ -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, diff --git a/hellomeme/models/hm_denoising_motion.py b/hellomeme/models/hm_denoising_motion.py index 1297bd2..ada2450 100644 --- a/hellomeme/models/hm_denoising_motion.py +++ b/hellomeme/models/hm_denoising_motion.py @@ -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, diff --git a/hellomeme/pipelines/pipline_hm_image.py b/hellomeme/pipelines/pipline_hm_image.py index 3b9df7b..56275aa 100644 --- a/hellomeme/pipelines/pipline_hm_image.py +++ b/hellomeme/pipelines/pipline_hm_image.py @@ -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] diff --git a/hellomeme/pipelines/pipline_hm_video.py b/hellomeme/pipelines/pipline_hm_video.py index e9aaeff..ce630c1 100644 --- a/hellomeme/pipelines/pipline_hm_video.py +++ b/hellomeme/pipelines/pipline_hm_video.py @@ -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 diff --git a/meme.py b/meme.py index 05f716b..8f63926 100644 --- a/meme.py +++ b/meme.py @@ -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 = 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