mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2026-03-29 21:13:33 +08:00
Merge 553f71aa9e into fc1fdf3389
This commit is contained in:
Yousef R. Gamaleldin
GitHub
commit
ce0c979e96
@ -755,6 +755,10 @@ class ACEAudio(LatentFormat):
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latent_channels = 8
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latent_dimensions = 2
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class SeedVR2(LatentFormat):
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latent_channels = 16
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latent_dimensions = 16
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class ACEAudio15(LatentFormat):
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latent_channels = 64
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latent_dimensions = 1
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@ -719,7 +719,30 @@ def attention_flash(q, k, v, heads, mask=None, attn_precision=None, skip_reshape
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)
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return out
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def var_attention_pytorch(q, k, v, heads, cu_seqlens_q, cu_seqlens_k, skip_reshape=False, skip_output_reshape=False):
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if not skip_reshape:
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# assumes 2D q, k,v [total_tokens, embed_dim]
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total_tokens, embed_dim = q.shape
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head_dim = embed_dim // heads
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q = q.view(total_tokens, heads, head_dim)
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k = k.view(k.shape[0], heads, head_dim)
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v = v.view(v.shape[0], heads, head_dim)
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q = torch.nested.nested_tensor_from_jagged(q, offsets=cu_seqlens_q.long())
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k = torch.nested.nested_tensor_from_jagged(k, offsets=cu_seqlens_k.long())
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v = torch.nested.nested_tensor_from_jagged(v, offsets=cu_seqlens_k.long())
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q = q.transpose(1, 2)
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k = k.transpose(1, 2)
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v = v.transpose(1, 2)
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out = comfy.ops.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False)
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out = out.transpose(1, 2)
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if not skip_output_reshape:
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return out.values().reshape(-1, heads * (q.shape[-1]))
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return out.values()
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optimized_var_attention = var_attention_pytorch
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optimized_attention = attention_basic
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if model_management.sage_attention_enabled():
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@ -13,6 +13,7 @@ if model_management.xformers_enabled_vae():
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import xformers
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import xformers.ops
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def torch_cat_if_needed(xl, dim):
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xl = [x for x in xl if x is not None and x.shape[dim] > 0]
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if len(xl) > 1:
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@ -22,7 +23,7 @@ def torch_cat_if_needed(xl, dim):
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else:
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return None
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def get_timestep_embedding(timesteps, embedding_dim):
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def get_timestep_embedding(timesteps, embedding_dim, flip_sin_to_cos = False, downscale_freq_shift = 1):
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"""
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This matches the implementation in Denoising Diffusion Probabilistic Models:
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From Fairseq.
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@ -33,11 +34,13 @@ def get_timestep_embedding(timesteps, embedding_dim):
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assert len(timesteps.shape) == 1
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half_dim = embedding_dim // 2
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emb = math.log(10000) / (half_dim - 1)
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emb = math.log(10000) / (half_dim - downscale_freq_shift)
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emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb)
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emb = emb.to(device=timesteps.device)
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emb = timesteps.float()[:, None] * emb[None, :]
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emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
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if flip_sin_to_cos:
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emb = torch.cat([emb[:, half_dim:], emb[:, :half_dim]], dim=-1)
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if embedding_dim % 2 == 1: # zero pad
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emb = torch.nn.functional.pad(emb, (0,1,0,0))
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return emb
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1480
comfy/ldm/seedvr/model.py
Normal file
1480
comfy/ldm/seedvr/model.py
Normal file
File diff suppressed because it is too large
Load Diff
2145
comfy/ldm/seedvr/vae.py
Normal file
2145
comfy/ldm/seedvr/vae.py
Normal file
File diff suppressed because it is too large
Load Diff
@ -48,6 +48,8 @@ import comfy.ldm.chroma.model
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import comfy.ldm.chroma_radiance.model
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import comfy.ldm.ace.model
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import comfy.ldm.omnigen.omnigen2
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import comfy.ldm.seedvr.model
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import comfy.ldm.qwen_image.model
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import comfy.ldm.kandinsky5.model
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import comfy.ldm.anima.model
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@ -828,6 +830,16 @@ class HunyuanDiT(BaseModel):
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out['image_meta_size'] = comfy.conds.CONDRegular(torch.FloatTensor([[height, width, target_height, target_width, 0, 0]]))
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return out
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class SeedVR2(BaseModel):
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def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
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super().__init__(model_config, model_type, device, comfy.ldm.seedvr.model.NaDiT)
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def extra_conds(self, **kwargs):
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out = super().extra_conds(**kwargs)
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condition = kwargs.get("condition", None)
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if condition is not None:
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out["condition"] = comfy.conds.CONDRegular(condition)
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return out
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class PixArt(BaseModel):
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def __init__(self, model_config, model_type=ModelType.EPS, device=None):
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super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.pixart.pixartms.PixArtMS)
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@ -490,6 +490,28 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
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return dit_config
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elif "{}blocks.36.mlp.all.proj_in_gate.weight".format(key_prefix) in state_dict_keys: # seedvr2 7b
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dit_config = {}
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dit_config["image_model"] = "seedvr2"
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dit_config["vid_dim"] = 3072
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dit_config["heads"] = 24
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dit_config["num_layers"] = 36
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dit_config["norm_eps"] = 1e-5
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dit_config["qk_rope"] = True
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dit_config["mlp_type"] = "normal"
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return dit_config
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elif "{}blocks.31.mlp.all.proj_in_gate.weight".format(key_prefix) in state_dict_keys: # seedvr2 3b
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dit_config = {}
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dit_config["image_model"] = "seedvr2"
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dit_config["vid_dim"] = 2560
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dit_config["heads"] = 20
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dit_config["num_layers"] = 32
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dit_config["norm_eps"] = 1.0e-05
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dit_config["qk_rope"] = None
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dit_config["mlp_type"] = "swiglu"
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dit_config["vid_out_norm"] = True
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return dit_config
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if '{}head.modulation'.format(key_prefix) in state_dict_keys: # Wan 2.1
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dit_config = {}
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dit_config["image_model"] = "wan2.1"
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0
comfy/samplers.py
Executable file → Normal file
0
comfy/samplers.py
Executable file → Normal file
16
comfy/sd.py
16
comfy/sd.py
@ -16,6 +16,7 @@ import comfy.ldm.cosmos.vae
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import comfy.ldm.wan.vae
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import comfy.ldm.wan.vae2_2
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import comfy.ldm.hunyuan3d.vae
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import comfy.ldm.seedvr.vae
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import comfy.ldm.ace.vae.music_dcae_pipeline
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import comfy.ldm.hunyuan_video.vae
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import comfy.ldm.mmaudio.vae.autoencoder
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@ -437,7 +438,8 @@ class CLIP:
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class VAE:
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def __init__(self, sd=None, device=None, config=None, dtype=None, metadata=None):
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if 'decoder.up_blocks.0.resnets.0.norm1.weight' in sd.keys(): #diffusers format
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sd = diffusers_convert.convert_vae_state_dict(sd)
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if metadata is None or metadata.get("keep_diffusers_format") != "true":
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sd = diffusers_convert.convert_vae_state_dict(sd)
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if model_management.is_amd():
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VAE_KL_MEM_RATIO = 2.73
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@ -506,6 +508,17 @@ class VAE:
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self.first_stage_model = StageC_coder()
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self.downscale_ratio = 32
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self.latent_channels = 16
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elif "decoder.up_blocks.2.upsamplers.0.upscale_conv.weight" in sd: # seedvr2
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self.first_stage_model = comfy.ldm.seedvr.vae.VideoAutoencoderKLWrapper()
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self.memory_used_decode = lambda shape, dtype: (shape[1] * shape[2] * shape[3] * (4 * 8 * 8)) * model_management.dtype_size(dtype)
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self.memory_used_encode = lambda shape, dtype: (max(shape[1], 5) * shape[2] * shape[3]) * model_management.dtype_size(dtype)
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self.working_dtypes = [torch.bfloat16, torch.float32]
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self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 8, 8)
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self.downscale_index_formula = (4, 8, 8)
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self.upscale_ratio = (lambda a: max(0, a * 4 - 3), 8, 8)
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self.upscale_index_formula = (4, 8, 8)
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self.process_input = lambda image: image
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self.crop_input = False
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elif "decoder.conv_in.weight" in sd:
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if sd['decoder.conv_in.weight'].shape[1] == 64:
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ddconfig = {"block_out_channels": [128, 256, 512, 512, 1024, 1024], "in_channels": 3, "out_channels": 3, "num_res_blocks": 2, "ffactor_spatial": 32, "downsample_match_channel": True, "upsample_match_channel": True}
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@ -626,6 +639,7 @@ class VAE:
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self.downscale_ratio = (lambda a: max(0, math.floor((a + 7) / 8)), 32, 32)
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self.downscale_index_formula = (8, 32, 32)
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self.working_dtypes = [torch.bfloat16, torch.float32]
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elif "decoder.conv_in.conv.weight" in sd and sd['decoder.conv_in.conv.weight'].shape[1] == 32:
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ddconfig = {"block_out_channels": [128, 256, 512, 1024, 1024], "in_channels": 3, "out_channels": 3, "num_res_blocks": 2, "ffactor_spatial": 16, "ffactor_temporal": 4, "downsample_match_channel": True, "upsample_match_channel": True}
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ddconfig['z_channels'] = sd["decoder.conv_in.conv.weight"].shape[1]
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@ -1415,6 +1415,25 @@ class Chroma(supported_models_base.BASE):
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t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref))
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return supported_models_base.ClipTarget(comfy.text_encoders.pixart_t5.PixArtTokenizer, comfy.text_encoders.pixart_t5.pixart_te(**t5_detect))
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class SeedVR2(supported_models_base.BASE):
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unet_config = {
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"image_model": "seedvr2"
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}
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latent_format = comfy.latent_formats.SeedVR2
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vae_key_prefix = ["vae."]
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text_encoder_key_prefix = ["text_encoders."]
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supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32]
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sampling_settings = {
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"shift": 1.0,
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}
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def get_model(self, state_dict, prefix = "", device=None):
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out = model_base.SeedVR2(self, device=device)
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return out
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def clip_target(self, state_dict={}):
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return None
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class ChromaRadiance(Chroma):
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unet_config = {
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"image_model": "chroma_radiance",
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@ -1734,6 +1753,6 @@ class LongCatImage(supported_models_base.BASE):
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hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen25_7b.transformer.".format(pref))
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return supported_models_base.ClipTarget(comfy.text_encoders.longcat_image.LongCatImageTokenizer, comfy.text_encoders.longcat_image.te(**hunyuan_detect))
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models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, LongCatImage, FluxSchnell, GenmoMochi, LTXV, LTXAV, HunyuanVideo15_SR_Distilled, HunyuanVideo15, HunyuanImage21Refiner, HunyuanImage21, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, ZImagePixelSpace, ZImage, Lumina2, WAN22_T2V, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, WAN22_Camera, WAN22_S2V, WAN21_HuMo, WAN22_Animate, WAN21_FlowRVS, WAN21_SCAIL, Hunyuan3Dv2mini, Hunyuan3Dv2, Hunyuan3Dv2_1, HiDream, Chroma, ChromaRadiance, ACEStep, ACEStep15, Omnigen2, QwenImage, Flux2, Kandinsky5Image, Kandinsky5, Anima]
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models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, LongCatImage, FluxSchnell, GenmoMochi, LTXV, LTXAV, HunyuanVideo15_SR_Distilled, HunyuanVideo15, HunyuanImage21Refiner, HunyuanImage21, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, ZImagePixelSpace, ZImage, Lumina2, WAN22_T2V, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, WAN22_Camera, WAN22_S2V, WAN21_HuMo, WAN22_Animate, WAN21_FlowRVS, WAN21_SCAIL, Hunyuan3Dv2mini, Hunyuan3Dv2, Hunyuan3Dv2_1, HiDream, Chroma, ChromaRadiance, ACEStep, ACEStep15, Omnigen2, QwenImage, Flux2, Kandinsky5Image, Kandinsky5, Anima, SeedVR2]
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models += [SVD_img2vid]
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470
comfy_extras/nodes_seedvr.py
Normal file
470
comfy_extras/nodes_seedvr.py
Normal file
@ -0,0 +1,470 @@
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from typing_extensions import override
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from comfy_api.latest import ComfyExtension, io
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import torch
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import math
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from einops import rearrange
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import gc
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import comfy.model_management
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from comfy.utils import ProgressBar
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import torch.nn.functional as F
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from torchvision.transforms import functional as TVF
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from torchvision.transforms import Lambda, Normalize
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from torchvision.transforms.functional import InterpolationMode
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@torch.inference_mode()
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def tiled_vae(x, vae_model, tile_size=(512, 512), tile_overlap=(64, 64), temporal_size=16, encode=True):
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gc.collect()
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torch.cuda.empty_cache()
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x = x.to(next(vae_model.parameters()).dtype)
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if x.ndim != 5:
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x = x.unsqueeze(2)
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b, c, d, h, w = x.shape
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sf_s = getattr(vae_model, "spatial_downsample_factor", 8)
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sf_t = getattr(vae_model, "temporal_downsample_factor", 4)
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if encode:
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ti_h, ti_w = tile_size
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ov_h, ov_w = tile_overlap
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target_d = (d + sf_t - 1) // sf_t
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target_h = (h + sf_s - 1) // sf_s
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target_w = (w + sf_s - 1) // sf_s
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else:
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ti_h = max(1, tile_size[0] // sf_s)
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ti_w = max(1, tile_size[1] // sf_s)
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ov_h = max(0, tile_overlap[0] // sf_s)
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ov_w = max(0, tile_overlap[1] // sf_s)
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target_d = d * sf_t
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target_h = h * sf_s
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target_w = w * sf_s
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stride_h = max(1, ti_h - ov_h)
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stride_w = max(1, ti_w - ov_w)
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storage_device = vae_model.device
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result = None
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count = None
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def run_temporal_chunks(spatial_tile):
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chunk_results = []
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t_dim_size = spatial_tile.shape[2]
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if encode:
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input_chunk = temporal_size
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else:
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input_chunk = max(1, temporal_size // sf_t)
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for i in range(0, t_dim_size, input_chunk):
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t_chunk = spatial_tile[:, :, i : i + input_chunk, :, :]
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current_valid_len = t_chunk.shape[2]
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pad_amount = 0
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if current_valid_len < input_chunk:
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pad_amount = input_chunk - current_valid_len
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last_frame = t_chunk[:, :, -1:, :, :]
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padding = last_frame.repeat(1, 1, pad_amount, 1, 1)
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t_chunk = torch.cat([t_chunk, padding], dim=2)
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t_chunk = t_chunk.contiguous()
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if encode:
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out = vae_model.encode(t_chunk)[0]
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else:
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out = vae_model.decode_(t_chunk)
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if isinstance(out, (tuple, list)):
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out = out[0]
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if out.ndim == 4:
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out = out.unsqueeze(2)
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if pad_amount > 0:
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if encode:
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expected_valid_out = (current_valid_len + sf_t - 1) // sf_t
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out = out[:, :, :expected_valid_out, :, :]
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else:
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expected_valid_out = current_valid_len * sf_t
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out = out[:, :, :expected_valid_out, :, :]
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chunk_results.append(out.to(storage_device))
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return torch.cat(chunk_results, dim=2)
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ramp_cache = {}
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def get_ramp(steps):
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if steps not in ramp_cache:
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t = torch.linspace(0, 1, steps=steps, device=storage_device, dtype=torch.float32)
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ramp_cache[steps] = 0.5 - 0.5 * torch.cos(t * torch.pi)
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return ramp_cache[steps]
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total_tiles = len(range(0, h, stride_h)) * len(range(0, w, stride_w))
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bar = ProgressBar(total_tiles)
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for y_idx in range(0, h, stride_h):
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y_end = min(y_idx + ti_h, h)
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for x_idx in range(0, w, stride_w):
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x_end = min(x_idx + ti_w, w)
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tile_x = x[:, :, :, y_idx:y_end, x_idx:x_end]
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# Run VAE
|
||||
tile_out = run_temporal_chunks(tile_x)
|
||||
|
||||
if result is None:
|
||||
b_out, c_out = tile_out.shape[0], tile_out.shape[1]
|
||||
result = torch.zeros((b_out, c_out, target_d, target_h, target_w), device=storage_device, dtype=torch.float32)
|
||||
count = torch.zeros((1, 1, 1, target_h, target_w), device=storage_device, dtype=torch.float32)
|
||||
|
||||
if encode:
|
||||
ys, ye = y_idx // sf_s, (y_idx // sf_s) + tile_out.shape[3]
|
||||
xs, xe = x_idx // sf_s, (x_idx // sf_s) + tile_out.shape[4]
|
||||
cur_ov_h = max(0, min(ov_h // sf_s, tile_out.shape[3] // 2))
|
||||
cur_ov_w = max(0, min(ov_w // sf_s, tile_out.shape[4] // 2))
|
||||
else:
|
||||
ys, ye = y_idx * sf_s, (y_idx * sf_s) + tile_out.shape[3]
|
||||
xs, xe = x_idx * sf_s, (x_idx * sf_s) + tile_out.shape[4]
|
||||
cur_ov_h = max(0, min(ov_h, tile_out.shape[3] // 2))
|
||||
cur_ov_w = max(0, min(ov_w, tile_out.shape[4] // 2))
|
||||
|
||||
w_h = torch.ones((tile_out.shape[3],), device=storage_device)
|
||||
w_w = torch.ones((tile_out.shape[4],), device=storage_device)
|
||||
|
||||
if cur_ov_h > 0:
|
||||
r = get_ramp(cur_ov_h)
|
||||
if y_idx > 0:
|
||||
w_h[:cur_ov_h] = r
|
||||
if y_end < h:
|
||||
w_h[-cur_ov_h:] = 1.0 - r
|
||||
|
||||
if cur_ov_w > 0:
|
||||
r = get_ramp(cur_ov_w)
|
||||
if x_idx > 0:
|
||||
w_w[:cur_ov_w] = r
|
||||
if x_end < w:
|
||||
w_w[-cur_ov_w:] = 1.0 - r
|
||||
|
||||
final_weight = w_h.view(1,1,1,-1,1) * w_w.view(1,1,1,1,-1)
|
||||
|
||||
valid_d = min(tile_out.shape[2], result.shape[2])
|
||||
tile_out = tile_out[:, :, :valid_d, :, :]
|
||||
|
||||
tile_out.mul_(final_weight)
|
||||
|
||||
result[:, :, :valid_d, ys:ye, xs:xe] += tile_out
|
||||
count[:, :, :, ys:ye, xs:xe] += final_weight
|
||||
|
||||
del tile_out, final_weight, tile_x, w_h, w_w
|
||||
bar.update(1)
|
||||
|
||||
result.div_(count.clamp(min=1e-6))
|
||||
|
||||
if result.device != x.device:
|
||||
result = result.to(x.device).to(x.dtype)
|
||||
|
||||
if x.shape[2] == 1 and sf_t == 1:
|
||||
result = result.squeeze(2)
|
||||
|
||||
return result
|
||||
|
||||
def pad_video_temporal(videos: torch.Tensor, count: int = 0, temporal_dim: int = 1, prepend: bool = False):
|
||||
t = videos.size(temporal_dim)
|
||||
|
||||
if count == 0 and not prepend:
|
||||
if t % 4 == 1:
|
||||
return videos
|
||||
count = ((t - 1) // 4 + 1) * 4 + 1 - t
|
||||
|
||||
if count <= 0:
|
||||
return videos
|
||||
|
||||
def select(start, end):
|
||||
return videos[start:end] if temporal_dim == 0 else videos[:, start:end]
|
||||
|
||||
if count >= t:
|
||||
repeat_count = count - t + 1
|
||||
last = select(-1, None)
|
||||
|
||||
if temporal_dim == 0:
|
||||
repeated = last.repeat(repeat_count, 1, 1, 1)
|
||||
reversed_frames = select(1, None).flip(temporal_dim) if t > 1 else last[:0]
|
||||
else:
|
||||
repeated = last.expand(-1, repeat_count, -1, -1).contiguous()
|
||||
reversed_frames = select(1, None).flip(temporal_dim) if t > 1 else last[:, :0]
|
||||
|
||||
return torch.cat([repeated, reversed_frames, videos] if prepend else
|
||||
[videos, reversed_frames, repeated], dim=temporal_dim)
|
||||
|
||||
if prepend:
|
||||
reversed_frames = select(1, count+1).flip(temporal_dim)
|
||||
else:
|
||||
reversed_frames = select(-count-1, -1).flip(temporal_dim)
|
||||
|
||||
return torch.cat([reversed_frames, videos] if prepend else
|
||||
[videos, reversed_frames], dim=temporal_dim)
|
||||
|
||||
def clear_vae_memory(vae_model):
|
||||
for module in vae_model.modules():
|
||||
if hasattr(module, "memory"):
|
||||
module.memory = None
|
||||
gc.collect()
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
def expand_dims(tensor, ndim):
|
||||
shape = tensor.shape + (1,) * (ndim - tensor.ndim)
|
||||
return tensor.reshape(shape)
|
||||
|
||||
def get_conditions(latent, latent_blur):
|
||||
t, h, w, c = latent.shape
|
||||
cond = torch.ones([t, h, w, c + 1], device=latent.device, dtype=latent.dtype)
|
||||
cond[:, ..., :-1] = latent_blur[:]
|
||||
cond[:, ..., -1:] = 1.0
|
||||
return cond
|
||||
|
||||
def timestep_transform(timesteps, latents_shapes):
|
||||
vt = 4
|
||||
vs = 8
|
||||
frames = (latents_shapes[:, 0] - 1) * vt + 1
|
||||
heights = latents_shapes[:, 1] * vs
|
||||
widths = latents_shapes[:, 2] * vs
|
||||
|
||||
# Compute shift factor.
|
||||
def get_lin_function(x1, y1, x2, y2):
|
||||
m = (y2 - y1) / (x2 - x1)
|
||||
b = y1 - m * x1
|
||||
return lambda x: m * x + b
|
||||
|
||||
img_shift_fn = get_lin_function(x1=256 * 256, y1=1.0, x2=1024 * 1024, y2=3.2)
|
||||
vid_shift_fn = get_lin_function(x1=256 * 256 * 37, y1=1.0, x2=1280 * 720 * 145, y2=5.0)
|
||||
shift = torch.where(
|
||||
frames > 1,
|
||||
vid_shift_fn(heights * widths * frames),
|
||||
img_shift_fn(heights * widths),
|
||||
).to(timesteps.device)
|
||||
|
||||
# Shift timesteps.
|
||||
T = 1000.0
|
||||
timesteps = timesteps / T
|
||||
timesteps = shift * timesteps / (1 + (shift - 1) * timesteps)
|
||||
timesteps = timesteps * T
|
||||
return timesteps
|
||||
|
||||
def inter(x_0, x_T, t):
|
||||
t = expand_dims(t, x_0.ndim)
|
||||
T = 1000.0
|
||||
B = lambda t: t / T
|
||||
A = lambda t: 1 - (t / T)
|
||||
return A(t) * x_0 + B(t) * x_T
|
||||
def area_resize(image, max_area):
|
||||
|
||||
height, width = image.shape[-2:]
|
||||
scale = math.sqrt(max_area / (height * width))
|
||||
|
||||
resized_height, resized_width = round(height * scale), round(width * scale)
|
||||
|
||||
return TVF.resize(
|
||||
image,
|
||||
size=(resized_height, resized_width),
|
||||
interpolation=InterpolationMode.BICUBIC,
|
||||
)
|
||||
|
||||
def div_pad(image, factor):
|
||||
|
||||
height_factor, width_factor = factor
|
||||
height, width = image.shape[-2:]
|
||||
|
||||
pad_height = (height_factor - (height % height_factor)) % height_factor
|
||||
pad_width = (width_factor - (width % width_factor)) % width_factor
|
||||
|
||||
if pad_height == 0 and pad_width == 0:
|
||||
return image
|
||||
|
||||
if isinstance(image, torch.Tensor):
|
||||
padding = (0, pad_width, 0, pad_height)
|
||||
image = torch.nn.functional.pad(image, padding, mode='constant', value=0.0)
|
||||
|
||||
return image
|
||||
|
||||
def cut_videos(videos):
|
||||
t = videos.size(1)
|
||||
if t == 1:
|
||||
return videos
|
||||
if t <= 4 :
|
||||
padding = [videos[:, -1].unsqueeze(1)] * (4 - t + 1)
|
||||
padding = torch.cat(padding, dim=1)
|
||||
videos = torch.cat([videos, padding], dim=1)
|
||||
return videos
|
||||
if (t - 1) % (4) == 0:
|
||||
return videos
|
||||
else:
|
||||
padding = [videos[:, -1].unsqueeze(1)] * (
|
||||
4 - ((t - 1) % (4))
|
||||
)
|
||||
padding = torch.cat(padding, dim=1)
|
||||
videos = torch.cat([videos, padding], dim=1)
|
||||
assert (videos.size(1) - 1) % (4) == 0
|
||||
return videos
|
||||
|
||||
def side_resize(image, size):
|
||||
antialias = not (isinstance(image, torch.Tensor) and image.device.type == 'mps')
|
||||
resized = TVF.resize(image, size, InterpolationMode.BICUBIC, antialias=antialias)
|
||||
return resized
|
||||
|
||||
class SeedVR2InputProcessing(io.ComfyNode):
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id = "SeedVR2InputProcessing",
|
||||
category="image/video",
|
||||
inputs = [
|
||||
io.Image.Input("images"),
|
||||
io.Vae.Input("vae"),
|
||||
io.Int.Input("resolution", default = 1280, min = 120), # just non-zero value
|
||||
io.Int.Input("spatial_tile_size", default = 512, min = 1),
|
||||
io.Int.Input("spatial_overlap", default = 64, min = 1),
|
||||
io.Int.Input("temporal_tile_size", default=5, min=1, max=16384, step=4),
|
||||
io.Boolean.Input("enable_tiling", default=False),
|
||||
],
|
||||
outputs = [
|
||||
io.Latent.Output("vae_conditioning")
|
||||
]
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, images, vae, resolution, spatial_tile_size, spatial_overlap, temporal_tile_size, enable_tiling):
|
||||
|
||||
comfy.model_management.load_models_gpu([vae.patcher])
|
||||
vae_model = vae.first_stage_model
|
||||
scale = 0.9152
|
||||
shift = 0
|
||||
if images.dim() != 5: # add the t dim
|
||||
images = images.unsqueeze(0)
|
||||
images = images.permute(0, 1, 4, 2, 3)
|
||||
|
||||
b, t, c, h, w = images.shape
|
||||
images = images.reshape(b * t, c, h, w)
|
||||
|
||||
clip = Lambda(lambda x: torch.clamp(x, 0.0, 1.0))
|
||||
normalize = Normalize(0.5, 0.5)
|
||||
images = side_resize(images, resolution)
|
||||
|
||||
images = clip(images)
|
||||
o_h, o_w = images.shape[-2:]
|
||||
images = div_pad(images, (16, 16))
|
||||
images = normalize(images)
|
||||
_, _, new_h, new_w = images.shape
|
||||
|
||||
images = images.reshape(b, t, c, new_h, new_w)
|
||||
images = cut_videos(images)
|
||||
|
||||
images = rearrange(images, "b t c h w -> b c t h w")
|
||||
|
||||
# in case users a non-compatiable number for tiling
|
||||
def make_divisible(val, divisor):
|
||||
return max(divisor, round(val / divisor) * divisor)
|
||||
|
||||
spatial_tile_size = make_divisible(spatial_tile_size, 32)
|
||||
spatial_overlap = make_divisible(spatial_overlap, 32)
|
||||
|
||||
if spatial_overlap >= spatial_tile_size:
|
||||
spatial_overlap = max(0, spatial_tile_size - 8)
|
||||
|
||||
args = {"tile_size": (spatial_tile_size, spatial_tile_size), "tile_overlap": (spatial_overlap, spatial_overlap),
|
||||
"temporal_size":temporal_tile_size}
|
||||
if enable_tiling:
|
||||
latent = tiled_vae(images, vae_model, encode=True, **args)
|
||||
else:
|
||||
latent = vae_model.encode(images, orig_dims = [o_h, o_w])[0]
|
||||
|
||||
clear_vae_memory(vae_model)
|
||||
#images = images.to(offload_device)
|
||||
#vae_model = vae_model.to(offload_device)
|
||||
|
||||
vae_model.img_dims = [o_h, o_w]
|
||||
args["enable_tiling"] = enable_tiling
|
||||
vae_model.tiled_args = args
|
||||
vae_model.original_image_video = images
|
||||
|
||||
latent = latent.unsqueeze(2) if latent.ndim == 4 else latent
|
||||
latent = rearrange(latent, "b c ... -> b ... c")
|
||||
|
||||
latent = (latent - shift) * scale
|
||||
|
||||
return io.NodeOutput({"samples": latent})
|
||||
|
||||
class SeedVR2Conditioning(io.ComfyNode):
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="SeedVR2Conditioning",
|
||||
category="image/video",
|
||||
inputs=[
|
||||
io.Latent.Input("vae_conditioning"),
|
||||
io.Model.Input("model"),
|
||||
io.Float.Input("latent_noise_scale", default=0.0, step=0.001)
|
||||
],
|
||||
outputs=[io.Conditioning.Output(display_name = "positive"),
|
||||
io.Conditioning.Output(display_name = "negative"),
|
||||
io.Latent.Output(display_name = "latent")],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, vae_conditioning, model, latent_noise_scale) -> io.NodeOutput:
|
||||
|
||||
vae_conditioning = vae_conditioning["samples"]
|
||||
device = vae_conditioning.device
|
||||
model = model.model.diffusion_model
|
||||
pos_cond = model.positive_conditioning
|
||||
neg_cond = model.negative_conditioning
|
||||
|
||||
for module in model.modules():
|
||||
if hasattr(module, 'rope') and hasattr(module.rope, 'freqs'):
|
||||
module.rope.freqs.data = module.rope.freqs.data.to(torch.float32)
|
||||
|
||||
noises = torch.randn_like(vae_conditioning, dtype=vae_conditioning.dtype).to(device)
|
||||
aug_noises = torch.randn_like(vae_conditioning, dtype=vae_conditioning.dtype).to(device)
|
||||
aug_noises = noises * 0.1 + aug_noises * 0.05
|
||||
cond_noise_scale = latent_noise_scale
|
||||
t = (
|
||||
torch.tensor([1000.0])
|
||||
* cond_noise_scale
|
||||
).to(device)
|
||||
shape = torch.tensor(vae_conditioning.shape[1:]).to(device)[None] # avoid batch dim
|
||||
t = timestep_transform(t, shape)
|
||||
cond = inter(vae_conditioning, aug_noises, t)
|
||||
condition = torch.stack([get_conditions(noise, c) for noise, c in zip(noises, cond)])
|
||||
condition = condition.movedim(-1, 1)
|
||||
noises = noises.movedim(-1, 1)
|
||||
|
||||
pos_shape = pos_cond.shape[0]
|
||||
neg_shape = neg_cond.shape[0]
|
||||
diff = abs(pos_shape - neg_shape)
|
||||
if pos_shape > neg_shape:
|
||||
neg_cond = F.pad(neg_cond, (0, 0, 0, diff))
|
||||
else:
|
||||
pos_cond = F.pad(pos_cond, (0, 0, 0, diff))
|
||||
|
||||
noises = rearrange(noises, "b c t h w -> b (c t) h w")
|
||||
condition = rearrange(condition, "b c t h w -> b (c t) h w")
|
||||
|
||||
negative = [[neg_cond.unsqueeze(0), {"condition": condition}]]
|
||||
positive = [[pos_cond.unsqueeze(0), {"condition": condition}]]
|
||||
|
||||
return io.NodeOutput(positive, negative, {"samples": noises})
|
||||
|
||||
class SeedVRExtension(ComfyExtension):
|
||||
@override
|
||||
async def get_node_list(self) -> list[type[io.ComfyNode]]:
|
||||
return [
|
||||
SeedVR2Conditioning,
|
||||
SeedVR2InputProcessing
|
||||
]
|
||||
|
||||
async def comfy_entrypoint() -> SeedVRExtension:
|
||||
return SeedVRExtension()
|
||||
Loading…
Reference in New Issue
Block a user