mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2026-01-11 14:50:49 +08:00
Merge branch 'master' of github.com:comfyanonymous/ComfyUI
This commit is contained in:
doctorpangloss
commit
a7aff3565b
@ -55,6 +55,9 @@ See what ComfyUI can do with the [example workflows](https://comfyanonymous.gith
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- [Lumina Image 2.0](https://comfyanonymous.github.io/ComfyUI_examples/lumina2/)
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- [HiDream](https://comfyanonymous.github.io/ComfyUI_examples/hidream/)
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- [Cosmos Predict2](https://comfyanonymous.github.io/ComfyUI_examples/cosmos_predict2/)
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- Image Editing Models
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- [Omnigen 2](https://comfyanonymous.github.io/ComfyUI_examples/omnigen/)
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- [Flux Kontext](https://comfyanonymous.github.io/ComfyUI_examples/flux/#flux-kontext-image-editing-model)
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- Video Models
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- [Stable Video Diffusion](https://comfyanonymous.github.io/ComfyUI_examples/video/)
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- [Mochi](https://comfyanonymous.github.io/ComfyUI_examples/mochi/)
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@ -1,3 +1,3 @@
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# This file is automatically generated by the build process when version is
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# updated in pyproject.toml.
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__version__ = "0.3.41"
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__version__ = "0.3.42"
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@ -1,8 +1,7 @@
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# Original code can be found on: https://github.com/black-forest-labs/flux
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from dataclasses import dataclass
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import torch
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from dataclasses import dataclass
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from einops import rearrange, repeat
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from torch import Tensor, nn
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@ -197,20 +196,50 @@ class Flux(nn.Module):
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img = self.final_layer(img, vec) # (N, T, patch_size ** 2 * out_channels)
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return img
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def forward(self, x, timestep, context, y=None, guidance=None, control=None, transformer_options={}, **kwargs):
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def process_img(self, x, index=0, h_offset=0, w_offset=0):
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bs, c, h, w = x.shape
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patch_size = self.patch_size
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x = common_dit.pad_to_patch_size(x, (patch_size, patch_size))
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img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size)
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h_len = ((h + (patch_size // 2)) // patch_size)
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w_len = ((w + (patch_size // 2)) // patch_size)
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h_offset = ((h_offset + (patch_size // 2)) // patch_size)
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w_offset = ((w_offset + (patch_size // 2)) // patch_size)
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img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype)
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img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).unsqueeze(1)
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img_ids[:, :, 2] = img_ids[:, :, 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0)
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img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs)
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img_ids[:, :, 0] = img_ids[:, :, 1] + index
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img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(h_offset, h_len - 1 + h_offset, steps=h_len, device=x.device, dtype=x.dtype).unsqueeze(1)
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img_ids[:, :, 2] = img_ids[:, :, 2] + torch.linspace(w_offset, w_len - 1 + w_offset, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0)
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return img, repeat(img_ids, "h w c -> b (h w) c", b=bs)
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def forward(self, x, timestep, context, y=None, guidance=None, ref_latents=None, control=None, transformer_options={}, **kwargs):
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bs, c, h_orig, w_orig = x.shape
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patch_size = self.patch_size
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h_len = ((h_orig + (patch_size // 2)) // patch_size)
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w_len = ((w_orig + (patch_size // 2)) // patch_size)
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img, img_ids = self.process_img(x)
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img_tokens = img.shape[1]
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if ref_latents is not None:
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h = 0
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w = 0
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for ref in ref_latents:
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h_offset = 0
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w_offset = 0
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if ref.shape[-2] + h > ref.shape[-1] + w:
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w_offset = w
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else:
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h_offset = h
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kontext, kontext_ids = self.process_img(ref, index=1, h_offset=h_offset, w_offset=w_offset)
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img = torch.cat([img, kontext], dim=1)
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img_ids = torch.cat([img_ids, kontext_ids], dim=1)
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h = max(h, ref.shape[-2] + h_offset)
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w = max(w, ref.shape[-1] + w_offset)
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txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype)
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out = self.forward_orig(img, img_ids, context, txt_ids, timestep, y, guidance, control, transformer_options, attn_mask=kwargs.get("attention_mask", None))
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return rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=2, pw=2)[:, :, :h, :w]
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out = out[:, :img_tokens]
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return rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=2, pw=2)[:, :, :h_orig, :w_orig]
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469
comfy/ldm/omnigen/omnigen2.py
Normal file
469
comfy/ldm/omnigen/omnigen2.py
Normal file
@ -0,0 +1,469 @@
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# Original code: https://github.com/VectorSpaceLab/OmniGen2
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from typing import Optional, Tuple
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from einops import rearrange, repeat
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from comfy.ldm.lightricks.model import Timesteps
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from comfy.ldm.flux.layers import EmbedND
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from comfy.ldm.modules.attention import optimized_attention_masked
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import comfy.model_management
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import comfy.ldm.common_dit
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def apply_rotary_emb(x, freqs_cis):
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if x.shape[1] == 0:
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return x
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t_ = x.reshape(*x.shape[:-1], -1, 1, 2)
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t_out = freqs_cis[..., 0] * t_[..., 0] + freqs_cis[..., 1] * t_[..., 1]
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return t_out.reshape(*x.shape).to(dtype=x.dtype)
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def swiglu(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
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return F.silu(x) * y
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class TimestepEmbedding(nn.Module):
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def __init__(self, in_channels: int, time_embed_dim: int, dtype=None, device=None, operations=None):
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super().__init__()
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self.linear_1 = operations.Linear(in_channels, time_embed_dim, dtype=dtype, device=device)
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self.act = nn.SiLU()
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self.linear_2 = operations.Linear(time_embed_dim, time_embed_dim, dtype=dtype, device=device)
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def forward(self, sample: torch.Tensor) -> torch.Tensor:
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sample = self.linear_1(sample)
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sample = self.act(sample)
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sample = self.linear_2(sample)
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return sample
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class LuminaRMSNormZero(nn.Module):
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def __init__(self, embedding_dim: int, norm_eps: float = 1e-5, dtype=None, device=None, operations=None):
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super().__init__()
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self.silu = nn.SiLU()
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self.linear = operations.Linear(min(embedding_dim, 1024), 4 * embedding_dim, dtype=dtype, device=device)
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self.norm = operations.RMSNorm(embedding_dim, eps=norm_eps, dtype=dtype, device=device)
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def forward(self, x: torch.Tensor, emb: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
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emb = self.linear(self.silu(emb))
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scale_msa, gate_msa, scale_mlp, gate_mlp = emb.chunk(4, dim=1)
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x = self.norm(x) * (1 + scale_msa[:, None])
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return x, gate_msa, scale_mlp, gate_mlp
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class LuminaLayerNormContinuous(nn.Module):
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def __init__(self, embedding_dim: int, conditioning_embedding_dim: int, elementwise_affine: bool = False, eps: float = 1e-6, out_dim: Optional[int] = None, dtype=None, device=None, operations=None):
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super().__init__()
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self.silu = nn.SiLU()
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self.linear_1 = operations.Linear(conditioning_embedding_dim, embedding_dim, dtype=dtype, device=device)
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self.norm = operations.LayerNorm(embedding_dim, eps, elementwise_affine, dtype=dtype, device=device)
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self.linear_2 = operations.Linear(embedding_dim, out_dim, bias=True, dtype=dtype, device=device) if out_dim is not None else None
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def forward(self, x: torch.Tensor, conditioning_embedding: torch.Tensor) -> torch.Tensor:
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emb = self.linear_1(self.silu(conditioning_embedding).to(x.dtype))
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x = self.norm(x) * (1 + emb)[:, None, :]
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if self.linear_2 is not None:
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x = self.linear_2(x)
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return x
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class LuminaFeedForward(nn.Module):
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def __init__(self, dim: int, inner_dim: int, multiple_of: int = 256, dtype=None, device=None, operations=None):
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super().__init__()
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inner_dim = multiple_of * ((inner_dim + multiple_of - 1) // multiple_of)
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self.linear_1 = operations.Linear(dim, inner_dim, bias=False, dtype=dtype, device=device)
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self.linear_2 = operations.Linear(inner_dim, dim, bias=False, dtype=dtype, device=device)
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self.linear_3 = operations.Linear(dim, inner_dim, bias=False, dtype=dtype, device=device)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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h1, h2 = self.linear_1(x), self.linear_3(x)
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return self.linear_2(swiglu(h1, h2))
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class Lumina2CombinedTimestepCaptionEmbedding(nn.Module):
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def __init__(self, hidden_size: int = 4096, text_feat_dim: int = 2048, frequency_embedding_size: int = 256, norm_eps: float = 1e-5, timestep_scale: float = 1.0, dtype=None, device=None, operations=None):
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super().__init__()
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self.time_proj = Timesteps(num_channels=frequency_embedding_size, flip_sin_to_cos=True, downscale_freq_shift=0.0, scale=timestep_scale)
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self.timestep_embedder = TimestepEmbedding(in_channels=frequency_embedding_size, time_embed_dim=min(hidden_size, 1024), dtype=dtype, device=device, operations=operations)
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self.caption_embedder = nn.Sequential(
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operations.RMSNorm(text_feat_dim, eps=norm_eps, dtype=dtype, device=device),
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operations.Linear(text_feat_dim, hidden_size, bias=True, dtype=dtype, device=device),
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)
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def forward(self, timestep: torch.Tensor, text_hidden_states: torch.Tensor, dtype: torch.dtype) -> Tuple[torch.Tensor, torch.Tensor]:
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timestep_proj = self.time_proj(timestep).to(dtype=dtype)
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time_embed = self.timestep_embedder(timestep_proj)
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caption_embed = self.caption_embedder(text_hidden_states)
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return time_embed, caption_embed
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class Attention(nn.Module):
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def __init__(self, query_dim: int, dim_head: int, heads: int, kv_heads: int, eps: float = 1e-5, bias: bool = False, dtype=None, device=None, operations=None):
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super().__init__()
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self.heads = heads
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self.kv_heads = kv_heads
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self.dim_head = dim_head
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self.scale = dim_head ** -0.5
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self.to_q = operations.Linear(query_dim, heads * dim_head, bias=bias, dtype=dtype, device=device)
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self.to_k = operations.Linear(query_dim, kv_heads * dim_head, bias=bias, dtype=dtype, device=device)
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self.to_v = operations.Linear(query_dim, kv_heads * dim_head, bias=bias, dtype=dtype, device=device)
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self.norm_q = operations.RMSNorm(dim_head, eps=eps, dtype=dtype, device=device)
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self.norm_k = operations.RMSNorm(dim_head, eps=eps, dtype=dtype, device=device)
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self.to_out = nn.Sequential(
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operations.Linear(heads * dim_head, query_dim, bias=bias, dtype=dtype, device=device),
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nn.Dropout(0.0)
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)
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def forward(self, hidden_states: torch.Tensor, encoder_hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, image_rotary_emb: Optional[torch.Tensor] = None) -> torch.Tensor:
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batch_size, sequence_length, _ = hidden_states.shape
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query = self.to_q(hidden_states)
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key = self.to_k(encoder_hidden_states)
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value = self.to_v(encoder_hidden_states)
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query = query.view(batch_size, -1, self.heads, self.dim_head)
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key = key.view(batch_size, -1, self.kv_heads, self.dim_head)
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value = value.view(batch_size, -1, self.kv_heads, self.dim_head)
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query = self.norm_q(query)
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key = self.norm_k(key)
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if image_rotary_emb is not None:
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query = apply_rotary_emb(query, image_rotary_emb)
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key = apply_rotary_emb(key, image_rotary_emb)
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query = query.transpose(1, 2)
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key = key.transpose(1, 2)
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value = value.transpose(1, 2)
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if self.kv_heads < self.heads:
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key = key.repeat_interleave(self.heads // self.kv_heads, dim=1)
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value = value.repeat_interleave(self.heads // self.kv_heads, dim=1)
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hidden_states = optimized_attention_masked(query, key, value, self.heads, attention_mask, skip_reshape=True)
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hidden_states = self.to_out[0](hidden_states)
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return hidden_states
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class OmniGen2TransformerBlock(nn.Module):
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def __init__(self, dim: int, num_attention_heads: int, num_kv_heads: int, multiple_of: int, ffn_dim_multiplier: float, norm_eps: float, modulation: bool = True, dtype=None, device=None, operations=None):
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super().__init__()
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self.modulation = modulation
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self.attn = Attention(
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query_dim=dim,
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dim_head=dim // num_attention_heads,
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heads=num_attention_heads,
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kv_heads=num_kv_heads,
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eps=1e-5,
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bias=False,
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dtype=dtype, device=device, operations=operations,
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)
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self.feed_forward = LuminaFeedForward(
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dim=dim,
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inner_dim=4 * dim,
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multiple_of=multiple_of,
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dtype=dtype, device=device, operations=operations
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)
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if modulation:
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self.norm1 = LuminaRMSNormZero(embedding_dim=dim, norm_eps=norm_eps, dtype=dtype, device=device, operations=operations)
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else:
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self.norm1 = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device)
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self.ffn_norm1 = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device)
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self.norm2 = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device)
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self.ffn_norm2 = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device)
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def forward(self, hidden_states: torch.Tensor, attention_mask: torch.Tensor, image_rotary_emb: torch.Tensor, temb: Optional[torch.Tensor] = None) -> torch.Tensor:
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if self.modulation:
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norm_hidden_states, gate_msa, scale_mlp, gate_mlp = self.norm1(hidden_states, temb)
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attn_output = self.attn(norm_hidden_states, norm_hidden_states, attention_mask, image_rotary_emb)
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hidden_states = hidden_states + gate_msa.unsqueeze(1).tanh() * self.norm2(attn_output)
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mlp_output = self.feed_forward(self.ffn_norm1(hidden_states) * (1 + scale_mlp.unsqueeze(1)))
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hidden_states = hidden_states + gate_mlp.unsqueeze(1).tanh() * self.ffn_norm2(mlp_output)
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else:
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norm_hidden_states = self.norm1(hidden_states)
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attn_output = self.attn(norm_hidden_states, norm_hidden_states, attention_mask, image_rotary_emb)
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hidden_states = hidden_states + self.norm2(attn_output)
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mlp_output = self.feed_forward(self.ffn_norm1(hidden_states))
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hidden_states = hidden_states + self.ffn_norm2(mlp_output)
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return hidden_states
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class OmniGen2RotaryPosEmbed(nn.Module):
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def __init__(self, theta: int, axes_dim: Tuple[int, int, int], axes_lens: Tuple[int, int, int] = (300, 512, 512), patch_size: int = 2):
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super().__init__()
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self.theta = theta
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self.axes_dim = axes_dim
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self.axes_lens = axes_lens
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self.patch_size = patch_size
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self.rope_embedder = EmbedND(dim=sum(axes_dim), theta=self.theta, axes_dim=axes_dim)
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def forward(self, batch_size, encoder_seq_len, l_effective_cap_len, l_effective_ref_img_len, l_effective_img_len, ref_img_sizes, img_sizes, device):
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p = self.patch_size
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seq_lengths = [cap_len + sum(ref_img_len) + img_len for cap_len, ref_img_len, img_len in zip(l_effective_cap_len, l_effective_ref_img_len, l_effective_img_len)]
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max_seq_len = max(seq_lengths)
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max_ref_img_len = max([sum(ref_img_len) for ref_img_len in l_effective_ref_img_len])
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max_img_len = max(l_effective_img_len)
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position_ids = torch.zeros(batch_size, max_seq_len, 3, dtype=torch.int32, device=device)
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for i, (cap_seq_len, seq_len) in enumerate(zip(l_effective_cap_len, seq_lengths)):
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position_ids[i, :cap_seq_len] = repeat(torch.arange(cap_seq_len, dtype=torch.int32, device=device), "l -> l 3")
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pe_shift = cap_seq_len
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pe_shift_len = cap_seq_len
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if ref_img_sizes[i] is not None:
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for ref_img_size, ref_img_len in zip(ref_img_sizes[i], l_effective_ref_img_len[i]):
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H, W = ref_img_size
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ref_H_tokens, ref_W_tokens = H // p, W // p
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row_ids = repeat(torch.arange(ref_H_tokens, dtype=torch.int32, device=device), "h -> h w", w=ref_W_tokens).flatten()
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col_ids = repeat(torch.arange(ref_W_tokens, dtype=torch.int32, device=device), "w -> h w", h=ref_H_tokens).flatten()
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position_ids[i, pe_shift_len:pe_shift_len + ref_img_len, 0] = pe_shift
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position_ids[i, pe_shift_len:pe_shift_len + ref_img_len, 1] = row_ids
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position_ids[i, pe_shift_len:pe_shift_len + ref_img_len, 2] = col_ids
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pe_shift += max(ref_H_tokens, ref_W_tokens)
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pe_shift_len += ref_img_len
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H, W = img_sizes[i]
|
||||
H_tokens, W_tokens = H // p, W // p
|
||||
|
||||
row_ids = repeat(torch.arange(H_tokens, dtype=torch.int32, device=device), "h -> h w", w=W_tokens).flatten()
|
||||
col_ids = repeat(torch.arange(W_tokens, dtype=torch.int32, device=device), "w -> h w", h=H_tokens).flatten()
|
||||
|
||||
position_ids[i, pe_shift_len: seq_len, 0] = pe_shift
|
||||
position_ids[i, pe_shift_len: seq_len, 1] = row_ids
|
||||
position_ids[i, pe_shift_len: seq_len, 2] = col_ids
|
||||
|
||||
freqs_cis = self.rope_embedder(position_ids).movedim(1, 2)
|
||||
|
||||
cap_freqs_cis_shape = list(freqs_cis.shape)
|
||||
cap_freqs_cis_shape[1] = encoder_seq_len
|
||||
cap_freqs_cis = torch.zeros(*cap_freqs_cis_shape, device=device, dtype=freqs_cis.dtype)
|
||||
|
||||
ref_img_freqs_cis_shape = list(freqs_cis.shape)
|
||||
ref_img_freqs_cis_shape[1] = max_ref_img_len
|
||||
ref_img_freqs_cis = torch.zeros(*ref_img_freqs_cis_shape, device=device, dtype=freqs_cis.dtype)
|
||||
|
||||
img_freqs_cis_shape = list(freqs_cis.shape)
|
||||
img_freqs_cis_shape[1] = max_img_len
|
||||
img_freqs_cis = torch.zeros(*img_freqs_cis_shape, device=device, dtype=freqs_cis.dtype)
|
||||
|
||||
for i, (cap_seq_len, ref_img_len, img_len, seq_len) in enumerate(zip(l_effective_cap_len, l_effective_ref_img_len, l_effective_img_len, seq_lengths)):
|
||||
cap_freqs_cis[i, :cap_seq_len] = freqs_cis[i, :cap_seq_len]
|
||||
ref_img_freqs_cis[i, :sum(ref_img_len)] = freqs_cis[i, cap_seq_len:cap_seq_len + sum(ref_img_len)]
|
||||
img_freqs_cis[i, :img_len] = freqs_cis[i, cap_seq_len + sum(ref_img_len):cap_seq_len + sum(ref_img_len) + img_len]
|
||||
|
||||
return cap_freqs_cis, ref_img_freqs_cis, img_freqs_cis, freqs_cis, l_effective_cap_len, seq_lengths
|
||||
|
||||
|
||||
class OmniGen2Transformer2DModel(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
patch_size: int = 2,
|
||||
in_channels: int = 16,
|
||||
out_channels: Optional[int] = None,
|
||||
hidden_size: int = 2304,
|
||||
num_layers: int = 26,
|
||||
num_refiner_layers: int = 2,
|
||||
num_attention_heads: int = 24,
|
||||
num_kv_heads: int = 8,
|
||||
multiple_of: int = 256,
|
||||
ffn_dim_multiplier: Optional[float] = None,
|
||||
norm_eps: float = 1e-5,
|
||||
axes_dim_rope: Tuple[int, int, int] = (32, 32, 32),
|
||||
axes_lens: Tuple[int, int, int] = (300, 512, 512),
|
||||
text_feat_dim: int = 1024,
|
||||
timestep_scale: float = 1.0,
|
||||
image_model=None,
|
||||
device=None,
|
||||
dtype=None,
|
||||
operations=None,
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
self.patch_size = patch_size
|
||||
self.out_channels = out_channels or in_channels
|
||||
self.hidden_size = hidden_size
|
||||
self.dtype = dtype
|
||||
|
||||
self.rope_embedder = OmniGen2RotaryPosEmbed(
|
||||
theta=10000,
|
||||
axes_dim=axes_dim_rope,
|
||||
axes_lens=axes_lens,
|
||||
patch_size=patch_size,
|
||||
)
|
||||
|
||||
self.x_embedder = operations.Linear(patch_size * patch_size * in_channels, hidden_size, dtype=dtype, device=device)
|
||||
self.ref_image_patch_embedder = operations.Linear(patch_size * patch_size * in_channels, hidden_size, dtype=dtype, device=device)
|
||||
|
||||
self.time_caption_embed = Lumina2CombinedTimestepCaptionEmbedding(
|
||||
hidden_size=hidden_size,
|
||||
text_feat_dim=text_feat_dim,
|
||||
norm_eps=norm_eps,
|
||||
timestep_scale=timestep_scale, dtype=dtype, device=device, operations=operations
|
||||
)
|
||||
|
||||
self.noise_refiner = nn.ModuleList([
|
||||
OmniGen2TransformerBlock(
|
||||
hidden_size, num_attention_heads, num_kv_heads,
|
||||
multiple_of, ffn_dim_multiplier, norm_eps, modulation=True, dtype=dtype, device=device, operations=operations
|
||||
) for _ in range(num_refiner_layers)
|
||||
])
|
||||
|
||||
self.ref_image_refiner = nn.ModuleList([
|
||||
OmniGen2TransformerBlock(
|
||||
hidden_size, num_attention_heads, num_kv_heads,
|
||||
multiple_of, ffn_dim_multiplier, norm_eps, modulation=True, dtype=dtype, device=device, operations=operations
|
||||
) for _ in range(num_refiner_layers)
|
||||
])
|
||||
|
||||
self.context_refiner = nn.ModuleList([
|
||||
OmniGen2TransformerBlock(
|
||||
hidden_size, num_attention_heads, num_kv_heads,
|
||||
multiple_of, ffn_dim_multiplier, norm_eps, modulation=False, dtype=dtype, device=device, operations=operations
|
||||
) for _ in range(num_refiner_layers)
|
||||
])
|
||||
|
||||
self.layers = nn.ModuleList([
|
||||
OmniGen2TransformerBlock(
|
||||
hidden_size, num_attention_heads, num_kv_heads,
|
||||
multiple_of, ffn_dim_multiplier, norm_eps, modulation=True, dtype=dtype, device=device, operations=operations
|
||||
) for _ in range(num_layers)
|
||||
])
|
||||
|
||||
self.norm_out = LuminaLayerNormContinuous(
|
||||
embedding_dim=hidden_size,
|
||||
conditioning_embedding_dim=min(hidden_size, 1024),
|
||||
elementwise_affine=False,
|
||||
eps=1e-6,
|
||||
out_dim=patch_size * patch_size * self.out_channels, dtype=dtype, device=device, operations=operations
|
||||
)
|
||||
|
||||
self.image_index_embedding = nn.Parameter(torch.empty(5, hidden_size, device=device, dtype=dtype))
|
||||
|
||||
def flat_and_pad_to_seq(self, hidden_states, ref_image_hidden_states):
|
||||
batch_size = len(hidden_states)
|
||||
p = self.patch_size
|
||||
|
||||
img_sizes = [(img.size(1), img.size(2)) for img in hidden_states]
|
||||
l_effective_img_len = [(H // p) * (W // p) for (H, W) in img_sizes]
|
||||
|
||||
if ref_image_hidden_states is not None:
|
||||
ref_image_hidden_states = list(map(lambda ref: comfy.ldm.common_dit.pad_to_patch_size(ref, (p, p)), ref_image_hidden_states))
|
||||
ref_img_sizes = [[(imgs.size(2), imgs.size(3)) if imgs is not None else None for imgs in ref_image_hidden_states]] * batch_size
|
||||
l_effective_ref_img_len = [[(ref_img_size[0] // p) * (ref_img_size[1] // p) for ref_img_size in _ref_img_sizes] if _ref_img_sizes is not None else [0] for _ref_img_sizes in ref_img_sizes]
|
||||
else:
|
||||
ref_img_sizes = [None for _ in range(batch_size)]
|
||||
l_effective_ref_img_len = [[0] for _ in range(batch_size)]
|
||||
|
||||
flat_ref_img_hidden_states = None
|
||||
if ref_image_hidden_states is not None:
|
||||
imgs = []
|
||||
for ref_img in ref_image_hidden_states:
|
||||
B, C, H, W = ref_img.size()
|
||||
ref_img = rearrange(ref_img, 'b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1=p, p2=p)
|
||||
imgs.append(ref_img)
|
||||
flat_ref_img_hidden_states = torch.cat(imgs, dim=1)
|
||||
|
||||
img = hidden_states
|
||||
B, C, H, W = img.size()
|
||||
flat_hidden_states = rearrange(img, 'b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1=p, p2=p)
|
||||
|
||||
return (
|
||||
flat_hidden_states, flat_ref_img_hidden_states,
|
||||
None, None,
|
||||
l_effective_ref_img_len, l_effective_img_len,
|
||||
ref_img_sizes, img_sizes,
|
||||
)
|
||||
|
||||
def img_patch_embed_and_refine(self, hidden_states, ref_image_hidden_states, padded_img_mask, padded_ref_img_mask, noise_rotary_emb, ref_img_rotary_emb, l_effective_ref_img_len, l_effective_img_len, temb):
|
||||
batch_size = len(hidden_states)
|
||||
|
||||
hidden_states = self.x_embedder(hidden_states)
|
||||
if ref_image_hidden_states is not None:
|
||||
ref_image_hidden_states = self.ref_image_patch_embedder(ref_image_hidden_states)
|
||||
image_index_embedding = comfy.model_management.cast_to(self.image_index_embedding, dtype=hidden_states.dtype, device=hidden_states.device)
|
||||
|
||||
for i in range(batch_size):
|
||||
shift = 0
|
||||
for j, ref_img_len in enumerate(l_effective_ref_img_len[i]):
|
||||
ref_image_hidden_states[i, shift:shift + ref_img_len, :] = ref_image_hidden_states[i, shift:shift + ref_img_len, :] + image_index_embedding[j]
|
||||
shift += ref_img_len
|
||||
|
||||
for layer in self.noise_refiner:
|
||||
hidden_states = layer(hidden_states, padded_img_mask, noise_rotary_emb, temb)
|
||||
|
||||
if ref_image_hidden_states is not None:
|
||||
for layer in self.ref_image_refiner:
|
||||
ref_image_hidden_states = layer(ref_image_hidden_states, padded_ref_img_mask, ref_img_rotary_emb, temb)
|
||||
|
||||
hidden_states = torch.cat([ref_image_hidden_states, hidden_states], dim=1)
|
||||
|
||||
return hidden_states
|
||||
|
||||
def forward(self, x, timesteps, context, num_tokens, ref_latents=None, attention_mask=None, **kwargs):
|
||||
B, C, H, W = x.shape
|
||||
hidden_states = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size))
|
||||
_, _, H_padded, W_padded = hidden_states.shape
|
||||
timestep = 1.0 - timesteps
|
||||
text_hidden_states = context
|
||||
text_attention_mask = attention_mask
|
||||
ref_image_hidden_states = ref_latents
|
||||
device = hidden_states.device
|
||||
|
||||
temb, text_hidden_states = self.time_caption_embed(timestep, text_hidden_states, hidden_states[0].dtype)
|
||||
|
||||
(
|
||||
hidden_states, ref_image_hidden_states,
|
||||
img_mask, ref_img_mask,
|
||||
l_effective_ref_img_len, l_effective_img_len,
|
||||
ref_img_sizes, img_sizes,
|
||||
) = self.flat_and_pad_to_seq(hidden_states, ref_image_hidden_states)
|
||||
|
||||
(
|
||||
context_rotary_emb, ref_img_rotary_emb, noise_rotary_emb,
|
||||
rotary_emb, encoder_seq_lengths, seq_lengths,
|
||||
) = self.rope_embedder(
|
||||
hidden_states.shape[0], text_hidden_states.shape[1], [num_tokens] * text_hidden_states.shape[0],
|
||||
l_effective_ref_img_len, l_effective_img_len,
|
||||
ref_img_sizes, img_sizes, device,
|
||||
)
|
||||
|
||||
for layer in self.context_refiner:
|
||||
text_hidden_states = layer(text_hidden_states, text_attention_mask, context_rotary_emb)
|
||||
|
||||
img_len = hidden_states.shape[1]
|
||||
combined_img_hidden_states = self.img_patch_embed_and_refine(
|
||||
hidden_states, ref_image_hidden_states,
|
||||
img_mask, ref_img_mask,
|
||||
noise_rotary_emb, ref_img_rotary_emb,
|
||||
l_effective_ref_img_len, l_effective_img_len,
|
||||
temb,
|
||||
)
|
||||
|
||||
hidden_states = torch.cat([text_hidden_states, combined_img_hidden_states], dim=1)
|
||||
attention_mask = None
|
||||
|
||||
for layer in self.layers:
|
||||
hidden_states = layer(hidden_states, attention_mask, rotary_emb, temb)
|
||||
|
||||
hidden_states = self.norm_out(hidden_states, temb)
|
||||
|
||||
p = self.patch_size
|
||||
output = rearrange(hidden_states[:, -img_len:], 'b (h w) (p1 p2 c) -> b c (h p1) (w p2)', h=H_padded // p, w=W_padded// p, p1=p, p2=p)[:, :, :H, :W]
|
||||
|
||||
return -output
|
||||
@ -52,6 +52,7 @@ from .ldm.modules.diffusionmodules.upscaling import ImageConcatWithNoiseAugmenta
|
||||
from .ldm.modules.encoders.noise_aug_modules import CLIPEmbeddingNoiseAugmentation
|
||||
from .ldm.pixart.pixartms import PixArtMS
|
||||
from .ldm.wan.model import WanModel, VaceWanModel, CameraWanModel
|
||||
from .ldm.omnigen.omnigen2 import OmniGen2Transformer2DModel
|
||||
from .model_management_types import ModelManageable
|
||||
from .model_sampling import CONST, ModelSamplingDiscreteFlow, ModelSamplingFlux, IMG_TO_IMG
|
||||
from .model_sampling import StableCascadeSampling, COSMOS_RFLOW, ModelSamplingCosmosRFlow, V_PREDICTION, \
|
||||
@ -852,6 +853,7 @@ class PixArt(BaseModel):
|
||||
class Flux(BaseModel):
|
||||
def __init__(self, model_config, model_type=ModelType.FLUX, device=None, unet_model=flux_model.Flux):
|
||||
super().__init__(model_config, model_type, device=device, unet_model=unet_model)
|
||||
self.memory_usage_factor_conds = ("kontext",)
|
||||
|
||||
def concat_cond(self, **kwargs):
|
||||
try:
|
||||
@ -912,6 +914,20 @@ class Flux(BaseModel):
|
||||
guidance = kwargs.get("guidance", 3.5)
|
||||
if guidance is not None:
|
||||
out['guidance'] = conds.CONDRegular(torch.FloatTensor([guidance]))
|
||||
|
||||
ref_latents = kwargs.get("reference_latents", None)
|
||||
if ref_latents is not None:
|
||||
latents = []
|
||||
for lat in ref_latents:
|
||||
latents.append(self.process_latent_in(lat))
|
||||
out['ref_latents'] = conds.CONDList(latents)
|
||||
return out
|
||||
|
||||
def extra_conds_shapes(self, **kwargs):
|
||||
out = {}
|
||||
ref_latents = kwargs.get("reference_latents", None)
|
||||
if ref_latents is not None:
|
||||
out['ref_latents'] = list([1, 16, sum(map(lambda a: math.prod(a.size()), ref_latents)) // 16])
|
||||
return out
|
||||
|
||||
|
||||
@ -1279,3 +1295,33 @@ class ACEStep(BaseModel):
|
||||
out['speaker_embeds'] = conds.CONDRegular(torch.zeros(noise.shape[0], 512, device=noise.device, dtype=noise.dtype))
|
||||
out['lyrics_strength'] = conds.CONDConstant(kwargs.get("lyrics_strength", 1.0))
|
||||
return out
|
||||
|
||||
class Omnigen2(BaseModel):
|
||||
def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
|
||||
super().__init__(model_config, model_type, device=device, unet_model=OmniGen2Transformer2DModel)
|
||||
self.memory_usage_factor_conds = ("ref_latents",)
|
||||
|
||||
def extra_conds(self, **kwargs):
|
||||
out = super().extra_conds(**kwargs)
|
||||
attention_mask = kwargs.get("attention_mask", None)
|
||||
if attention_mask is not None:
|
||||
if torch.numel(attention_mask) != attention_mask.sum():
|
||||
out['attention_mask'] = conds.CONDRegular(attention_mask)
|
||||
out['num_tokens'] = conds.CONDConstant(max(1, torch.sum(attention_mask).item()))
|
||||
cross_attn = kwargs.get("cross_attn", None)
|
||||
if cross_attn is not None:
|
||||
out['c_crossattn'] = conds.CONDRegular(cross_attn)
|
||||
ref_latents = kwargs.get("reference_latents", None)
|
||||
if ref_latents is not None:
|
||||
latents = []
|
||||
for lat in ref_latents:
|
||||
latents.append(self.process_latent_in(lat))
|
||||
out['ref_latents'] = conds.CONDList(latents)
|
||||
return out
|
||||
|
||||
def extra_conds_shapes(self, **kwargs):
|
||||
out = {}
|
||||
ref_latents = kwargs.get("reference_latents", None)
|
||||
if ref_latents is not None:
|
||||
out['ref_latents'] = list([1, 16, sum(map(lambda a: math.prod(a.size()), ref_latents)) // 16])
|
||||
return out
|
||||
|
||||
@ -463,6 +463,26 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
|
||||
|
||||
return dit_config
|
||||
|
||||
if '{}time_caption_embed.timestep_embedder.linear_1.bias'.format(key_prefix) in state_dict_keys: # Omnigen2
|
||||
dit_config = {}
|
||||
dit_config["image_model"] = "omnigen2"
|
||||
dit_config["axes_dim_rope"] = [40, 40, 40]
|
||||
dit_config["axes_lens"] = [1024, 1664, 1664]
|
||||
dit_config["ffn_dim_multiplier"] = None
|
||||
dit_config["hidden_size"] = 2520
|
||||
dit_config["in_channels"] = 16
|
||||
dit_config["multiple_of"] = 256
|
||||
dit_config["norm_eps"] = 1e-05
|
||||
dit_config["num_attention_heads"] = 21
|
||||
dit_config["num_kv_heads"] = 7
|
||||
dit_config["num_layers"] = 32
|
||||
dit_config["num_refiner_layers"] = 2
|
||||
dit_config["out_channels"] = None
|
||||
dit_config["patch_size"] = 2
|
||||
dit_config["text_feat_dim"] = 2048
|
||||
dit_config["timestep_scale"] = 1000.0
|
||||
return dit_config
|
||||
|
||||
if '{}input_blocks.0.0.weight'.format(key_prefix) not in state_dict_keys:
|
||||
return None
|
||||
|
||||
|
||||
@ -340,7 +340,6 @@ try:
|
||||
except:
|
||||
pass
|
||||
|
||||
|
||||
SUPPORT_FP8_OPS = args.supports_fp8_compute
|
||||
try:
|
||||
if is_amd():
|
||||
@ -1075,6 +1074,8 @@ if args.async_offload:
|
||||
logging.info("Using async weight offloading with {} streams".format(NUM_STREAMS))
|
||||
|
||||
stream_counters = {}
|
||||
|
||||
|
||||
def get_offload_stream(device):
|
||||
stream_counter = stream_counters.get(device, 0)
|
||||
if NUM_STREAMS <= 1:
|
||||
@ -1099,12 +1100,14 @@ def get_offload_stream(device):
|
||||
return s
|
||||
return None
|
||||
|
||||
|
||||
def sync_stream(device, stream):
|
||||
if stream is None:
|
||||
return
|
||||
if is_device_cuda(device):
|
||||
torch.cuda.current_stream().wait_stream(stream)
|
||||
|
||||
|
||||
def cast_to(weight, dtype=None, device=None, non_blocking=False, copy=False, stream=None):
|
||||
if device is None or weight.device == device:
|
||||
if not copy:
|
||||
@ -1466,6 +1469,14 @@ def supports_fp8_compute(device=None):
|
||||
return True
|
||||
|
||||
|
||||
def extended_fp16_support():
|
||||
# TODO: check why some models work with fp16 on newer torch versions but not on older
|
||||
if torch_version_numeric < (2, 7):
|
||||
return False
|
||||
|
||||
return True
|
||||
|
||||
|
||||
def soft_empty_cache(force=False):
|
||||
with model_management_lock:
|
||||
_soft_empty_cache(force=force)
|
||||
|
||||
@ -952,7 +952,7 @@ class CLIPLoader:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": { "clip_name": (get_filename_list_with_downloadable("text_encoders", KNOWN_CLIP_MODELS),),
|
||||
"type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi", "ltxv", "pixart", "cosmos", "lumina2", "wan", "hidream", "chroma", "ace"], ),
|
||||
"type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi", "ltxv", "pixart", "cosmos", "lumina2", "wan", "hidream", "chroma", "ace", "omnigen2"], ),
|
||||
},
|
||||
"optional": {
|
||||
"device": (["default", "cpu"], {"advanced": True}),
|
||||
@ -962,7 +962,7 @@ class CLIPLoader:
|
||||
|
||||
CATEGORY = "advanced/loaders"
|
||||
|
||||
DESCRIPTION = "[Recipes]\n\nstable_diffusion: clip-l\nstable_cascade: clip-g\nsd3: t5 xxl/ clip-g / clip-l\nstable_audio: t5 base\nmochi: t5 xxl\ncosmos: old t5 xxl\nlumina2: gemma 2 2B\nwan: umt5 xxl\n hidream: llama-3.1 (Recommend) or t5"
|
||||
DESCRIPTION = "[Recipes]\n\nstable_diffusion: clip-l\nstable_cascade: clip-g\nsd3: t5 xxl/ clip-g / clip-l\nstable_audio: t5 base\nmochi: t5 xxl\ncosmos: old t5 xxl\nlumina2: gemma 2 2B\nwan: umt5 xxl\n hidream: llama-3.1 (Recommend) or t5\nomnigen2: qwen vl 2.5 3B"
|
||||
|
||||
def load_clip(self, clip_name, type="stable_diffusion", device="default"):
|
||||
clip_type = getattr(sd.CLIPType, type.upper(), sd.CLIPType.STABLE_DIFFUSION)
|
||||
|
||||
12
comfy/sd.py
12
comfy/sd.py
@ -54,6 +54,7 @@ from .text_encoders import sd2_clip
|
||||
from .text_encoders import sd3_clip
|
||||
from .text_encoders import wan
|
||||
from .text_encoders import ace
|
||||
from .text_encoders import omnigen2
|
||||
from .utils import ProgressBar
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
@ -773,6 +774,7 @@ class CLIPType(Enum):
|
||||
HIDREAM = 14
|
||||
CHROMA = 15
|
||||
ACE = 16
|
||||
OMNIGEN2 = 17
|
||||
|
||||
|
||||
@dataclasses.dataclass
|
||||
@ -802,6 +804,7 @@ class TEModel(Enum):
|
||||
LLAMA3_8 = 7
|
||||
T5_XXL_OLD = 8
|
||||
GEMMA_2_2B = 9
|
||||
QWEN25_3B = 10
|
||||
|
||||
|
||||
def detect_te_model(sd):
|
||||
@ -823,6 +826,8 @@ def detect_te_model(sd):
|
||||
return TEModel.T5_BASE
|
||||
if 'model.layers.0.post_feedforward_layernorm.weight' in sd:
|
||||
return TEModel.GEMMA_2_2B
|
||||
if 'model.layers.0.self_attn.k_proj.bias' in sd:
|
||||
return TEModel.QWEN25_3B
|
||||
if "model.layers.0.post_attention_layernorm.weight" in sd:
|
||||
return TEModel.LLAMA3_8
|
||||
return None
|
||||
@ -926,6 +931,9 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
|
||||
clip_target.clip = hidream.hidream_clip(**llama_detect(clip_data),
|
||||
clip_l=False, clip_g=False, t5=False, llama=True, dtype_t5=None, t5xxl_scaled_fp8=None)
|
||||
clip_target.tokenizer = hidream.HiDreamTokenizer
|
||||
elif te_model == TEModel.QWEN25_3B:
|
||||
clip_target.clip = omnigen2.te(**llama_detect(clip_data))
|
||||
clip_target.tokenizer = omnigen2.Omnigen2Tokenizer
|
||||
else:
|
||||
# clip_l
|
||||
if clip_type == CLIPType.SD3:
|
||||
@ -1207,7 +1215,7 @@ def load_diffusion_model_state_dict(sd, model_options: dict = None, ckpt_path: O
|
||||
model.load_model_weights(new_sd, "")
|
||||
left_over = sd.keys()
|
||||
if len(left_over) > 0:
|
||||
logger.info("left over keys in unet: {}".format(left_over))
|
||||
logger.info("left over keys in diffusion model: {}".format(left_over))
|
||||
return model_patcher.ModelPatcher(model, load_device=load_device, offload_device=offload_device, ckpt_name=os.path.basename(ckpt_path))
|
||||
|
||||
|
||||
@ -1217,7 +1225,7 @@ def load_diffusion_model(unet_path, model_options: dict = None):
|
||||
sd = utils.load_torch_file(unet_path)
|
||||
model = load_diffusion_model_state_dict(sd, model_options=model_options, ckpt_path=unet_path)
|
||||
if model is None:
|
||||
logger.error("ERROR UNSUPPORTED UNET {}".format(unet_path))
|
||||
logger.error("ERROR UNSUPPORTED DIFFUSION MODEL {}".format(unet_path))
|
||||
raise RuntimeError("ERROR: Could not detect model type of: {}".format(unet_path))
|
||||
return model
|
||||
|
||||
|
||||
@ -576,7 +576,8 @@ class SDTokenizer:
|
||||
if end_token is not None:
|
||||
self.end_token = end_token
|
||||
else:
|
||||
self.end_token = empty[0]
|
||||
if has_end_token:
|
||||
self.end_token = empty[0]
|
||||
|
||||
if pad_token is not None:
|
||||
self.pad_token = pad_token
|
||||
|
||||
@ -21,6 +21,7 @@ from .text_encoders import sa_t5
|
||||
from .text_encoders import sd2_clip
|
||||
from .text_encoders import sd3_clip
|
||||
from .text_encoders import wan
|
||||
from .text_encoders import omnigen2
|
||||
|
||||
|
||||
class SD15(supported_models_base.BASE):
|
||||
@ -1276,6 +1277,41 @@ class ACEStep(supported_models_base.BASE):
|
||||
return supported_models_base.ClipTarget(ace.AceT5Tokenizer, ace.AceT5Model)
|
||||
|
||||
|
||||
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, FluxSchnell, GenmoMochi, LTXV, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, Lumina2, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, Hunyuan3Dv2mini, Hunyuan3Dv2, HiDream, Chroma, ACEStep]
|
||||
class Omnigen2(supported_models_base.BASE):
|
||||
unet_config = {
|
||||
"image_model": "omnigen2",
|
||||
}
|
||||
|
||||
sampling_settings = {
|
||||
"multiplier": 1.0,
|
||||
"shift": 2.6,
|
||||
}
|
||||
|
||||
memory_usage_factor = 1.65 # TODO
|
||||
|
||||
unet_extra_config = {}
|
||||
latent_format = latent_formats.Flux
|
||||
|
||||
supported_inference_dtypes = [torch.bfloat16, torch.float32]
|
||||
|
||||
vae_key_prefix = ["vae."]
|
||||
text_encoder_key_prefix = ["text_encoders."]
|
||||
|
||||
def __init__(self, unet_config):
|
||||
super().__init__(unet_config)
|
||||
if model_management.extended_fp16_support():
|
||||
self.supported_inference_dtypes = [torch.float16] + self.supported_inference_dtypes
|
||||
|
||||
def get_model(self, state_dict, prefix="", device=None):
|
||||
out = model_base.Omnigen2(self, device=device)
|
||||
return out
|
||||
|
||||
def clip_target(self, state_dict={}):
|
||||
pref = self.text_encoder_key_prefix[0]
|
||||
hunyuan_detect = hunyuan_video.llama_detect(state_dict, "{}qwen25_3b.transformer.".format(pref))
|
||||
return supported_models_base.ClipTarget(omnigen2.LuminaTokenizer, omnigen2.te(**hunyuan_detect))
|
||||
|
||||
|
||||
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, FluxSchnell, GenmoMochi, LTXV, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, Lumina2, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, Hunyuan3Dv2mini, Hunyuan3Dv2, HiDream, Chroma, ACEStep, Omnigen2]
|
||||
|
||||
models += [SVD_img2vid]
|
||||
|
||||
@ -23,6 +23,24 @@ class Llama2Config:
|
||||
head_dim = 128
|
||||
rms_norm_add = False
|
||||
mlp_activation = "silu"
|
||||
qkv_bias = False
|
||||
|
||||
@dataclass
|
||||
class Qwen25_3BConfig:
|
||||
vocab_size: int = 151936
|
||||
hidden_size: int = 2048
|
||||
intermediate_size: int = 11008
|
||||
num_hidden_layers: int = 36
|
||||
num_attention_heads: int = 16
|
||||
num_key_value_heads: int = 2
|
||||
max_position_embeddings: int = 128000
|
||||
rms_norm_eps: float = 1e-6
|
||||
rope_theta: float = 1000000.0
|
||||
transformer_type: str = "llama"
|
||||
head_dim = 128
|
||||
rms_norm_add = False
|
||||
mlp_activation = "silu"
|
||||
qkv_bias = True
|
||||
|
||||
|
||||
@dataclass
|
||||
@ -40,6 +58,7 @@ class Gemma2_2B_Config:
|
||||
head_dim = 256
|
||||
rms_norm_add = True
|
||||
mlp_activation = "gelu_pytorch_tanh"
|
||||
qkv_bias = False
|
||||
|
||||
|
||||
class RMSNorm(nn.Module):
|
||||
@ -98,9 +117,9 @@ class Attention(nn.Module):
|
||||
self.inner_size = self.num_heads * self.head_dim
|
||||
|
||||
ops = ops or nn
|
||||
self.q_proj = ops.Linear(config.hidden_size, self.inner_size, bias=False, device=device, dtype=dtype)
|
||||
self.k_proj = ops.Linear(config.hidden_size, self.num_kv_heads * self.head_dim, bias=False, device=device, dtype=dtype)
|
||||
self.v_proj = ops.Linear(config.hidden_size, self.num_kv_heads * self.head_dim, bias=False, device=device, dtype=dtype)
|
||||
self.q_proj = ops.Linear(config.hidden_size, self.inner_size, bias=config.qkv_bias, device=device, dtype=dtype)
|
||||
self.k_proj = ops.Linear(config.hidden_size, self.num_kv_heads * self.head_dim, bias=config.qkv_bias, device=device, dtype=dtype)
|
||||
self.v_proj = ops.Linear(config.hidden_size, self.num_kv_heads * self.head_dim, bias=config.qkv_bias, device=device, dtype=dtype)
|
||||
self.o_proj = ops.Linear(self.inner_size, config.hidden_size, bias=False, device=device, dtype=dtype)
|
||||
|
||||
def forward(
|
||||
@ -327,6 +346,14 @@ class Llama2(BaseLlama, torch.nn.Module):
|
||||
self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)
|
||||
self.dtype = dtype
|
||||
|
||||
class Qwen25_3B(BaseLlama, torch.nn.Module):
|
||||
def __init__(self, config_dict, dtype, device, operations):
|
||||
super().__init__()
|
||||
config = Qwen25_3BConfig(**config_dict)
|
||||
self.num_layers = config.num_hidden_layers
|
||||
|
||||
self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)
|
||||
self.dtype = dtype
|
||||
|
||||
class Gemma2_2B(BaseLlama, torch.nn.Module):
|
||||
def __init__(self, config_dict, dtype, device, operations):
|
||||
|
||||
44
comfy/text_encoders/omnigen2.py
Normal file
44
comfy/text_encoders/omnigen2.py
Normal file
@ -0,0 +1,44 @@
|
||||
from transformers import Qwen2Tokenizer
|
||||
from comfy import sd1_clip
|
||||
import comfy.text_encoders.llama
|
||||
import os
|
||||
|
||||
|
||||
class Qwen25_3BTokenizer(sd1_clip.SDTokenizer):
|
||||
def __init__(self, embedding_directory=None, tokenizer_data={}):
|
||||
tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "qwen25_tokenizer")
|
||||
super().__init__(tokenizer_path, pad_with_end=False, embedding_size=2048, embedding_key='qwen25_3b', tokenizer_class=Qwen2Tokenizer, has_start_token=False, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, pad_token=151643, tokenizer_data=tokenizer_data)
|
||||
|
||||
|
||||
class Omnigen2Tokenizer(sd1_clip.SD1Tokenizer):
|
||||
def __init__(self, embedding_directory=None, tokenizer_data={}):
|
||||
super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name="qwen25_3b", tokenizer=Qwen25_3BTokenizer)
|
||||
self.llama_template = '<|im_start|>system\nYou are a helpful assistant that generates high-quality images based on user instructions.<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n'
|
||||
|
||||
def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None,**kwargs):
|
||||
if llama_template is None:
|
||||
llama_text = self.llama_template.format(text)
|
||||
else:
|
||||
llama_text = llama_template.format(text)
|
||||
return super().tokenize_with_weights(llama_text, return_word_ids=return_word_ids, **kwargs)
|
||||
|
||||
class Qwen25_3BModel(sd1_clip.SDClipModel):
|
||||
def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, attention_mask=True, model_options={}):
|
||||
super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"pad": 151643}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Qwen25_3B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
|
||||
|
||||
|
||||
class Omnigen2Model(sd1_clip.SD1ClipModel):
|
||||
def __init__(self, device="cpu", dtype=None, model_options={}):
|
||||
super().__init__(device=device, dtype=dtype, name="qwen25_3b", clip_model=Qwen25_3BModel, model_options=model_options)
|
||||
|
||||
|
||||
def te(dtype_llama=None, llama_scaled_fp8=None):
|
||||
class Omnigen2TEModel_(Omnigen2Model):
|
||||
def __init__(self, device="cpu", dtype=None, model_options={}):
|
||||
if llama_scaled_fp8 is not None and "scaled_fp8" not in model_options:
|
||||
model_options = model_options.copy()
|
||||
model_options["scaled_fp8"] = llama_scaled_fp8
|
||||
if dtype_llama is not None:
|
||||
dtype = dtype_llama
|
||||
super().__init__(device=device, dtype=dtype, model_options=model_options)
|
||||
return Omnigen2TEModel_
|
||||
151388
comfy/text_encoders/qwen25_tokenizer/merges.txt
Normal file
151388
comfy/text_encoders/qwen25_tokenizer/merges.txt
Normal file
File diff suppressed because it is too large
Load Diff
241
comfy/text_encoders/qwen25_tokenizer/tokenizer_config.json
Normal file
241
comfy/text_encoders/qwen25_tokenizer/tokenizer_config.json
Normal file
@ -0,0 +1,241 @@
|
||||
{
|
||||
"add_bos_token": false,
|
||||
"add_prefix_space": false,
|
||||
"added_tokens_decoder": {
|
||||
"151643": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151644": {
|
||||
"content": "<|im_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151645": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151646": {
|
||||
"content": "<|object_ref_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151647": {
|
||||
"content": "<|object_ref_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151648": {
|
||||
"content": "<|box_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151649": {
|
||||
"content": "<|box_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151650": {
|
||||
"content": "<|quad_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151651": {
|
||||
"content": "<|quad_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151652": {
|
||||
"content": "<|vision_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151653": {
|
||||
"content": "<|vision_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151654": {
|
||||
"content": "<|vision_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151655": {
|
||||
"content": "<|image_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151656": {
|
||||
"content": "<|video_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151657": {
|
||||
"content": "<tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151658": {
|
||||
"content": "</tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151659": {
|
||||
"content": "<|fim_prefix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151660": {
|
||||
"content": "<|fim_middle|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151661": {
|
||||
"content": "<|fim_suffix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151662": {
|
||||
"content": "<|fim_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151665": {
|
||||
"content": "<|img|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151666": {
|
||||
"content": "<|endofimg|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151667": {
|
||||
"content": "<|meta|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151668": {
|
||||
"content": "<|endofmeta|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"processor_class": "Qwen2_5_VLProcessor",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
1
comfy/text_encoders/qwen25_tokenizer/vocab.json
Normal file
1
comfy/text_encoders/qwen25_tokenizer/vocab.json
Normal file
File diff suppressed because one or more lines are too long
@ -1,15 +1,17 @@
|
||||
from comfy import node_helpers
|
||||
import comfy.utils
|
||||
|
||||
|
||||
class CLIPTextEncodeFlux:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": {
|
||||
"clip": ("CLIP", ),
|
||||
"clip": ("CLIP",),
|
||||
"clip_l": ("STRING", {"multiline": True, "dynamicPrompts": True}),
|
||||
"t5xxl": ("STRING", {"multiline": True, "dynamicPrompts": True}),
|
||||
"guidance": ("FLOAT", {"default": 3.5, "min": 0.0, "max": 100.0, "step": 0.1}),
|
||||
}}
|
||||
}}
|
||||
|
||||
RETURN_TYPES = ("CONDITIONING",)
|
||||
FUNCTION = "encode"
|
||||
|
||||
@ -19,15 +21,16 @@ class CLIPTextEncodeFlux:
|
||||
tokens = clip.tokenize(clip_l)
|
||||
tokens["t5xxl"] = clip.tokenize(t5xxl)["t5xxl"]
|
||||
|
||||
return (clip.encode_from_tokens_scheduled(tokens, add_dict={"guidance": guidance}), )
|
||||
return (clip.encode_from_tokens_scheduled(tokens, add_dict={"guidance": guidance}),)
|
||||
|
||||
|
||||
class FluxGuidance:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": {
|
||||
"conditioning": ("CONDITIONING", ),
|
||||
"conditioning": ("CONDITIONING",),
|
||||
"guidance": ("FLOAT", {"default": 3.5, "min": -100.0, "max": 100.0, "step": 0.1}),
|
||||
}}
|
||||
}}
|
||||
|
||||
RETURN_TYPES = ("CONDITIONING",)
|
||||
FUNCTION = "append"
|
||||
@ -36,15 +39,15 @@ class FluxGuidance:
|
||||
|
||||
def append(self, conditioning, guidance):
|
||||
c = node_helpers.conditioning_set_values(conditioning, {"guidance": guidance})
|
||||
return (c, )
|
||||
return (c,)
|
||||
|
||||
|
||||
class FluxDisableGuidance:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": {
|
||||
"conditioning": ("CONDITIONING", ),
|
||||
}}
|
||||
"conditioning": ("CONDITIONING",),
|
||||
}}
|
||||
|
||||
RETURN_TYPES = ("CONDITIONING",)
|
||||
FUNCTION = "append"
|
||||
@ -54,11 +57,55 @@ class FluxDisableGuidance:
|
||||
|
||||
def append(self, conditioning):
|
||||
c = node_helpers.conditioning_set_values(conditioning, {"guidance": None})
|
||||
return (c, )
|
||||
return (c,)
|
||||
|
||||
|
||||
PREFERED_KONTEXT_RESOLUTIONS = [
|
||||
(672, 1568),
|
||||
(688, 1504),
|
||||
(720, 1456),
|
||||
(752, 1392),
|
||||
(800, 1328),
|
||||
(832, 1248),
|
||||
(880, 1184),
|
||||
(944, 1104),
|
||||
(1024, 1024),
|
||||
(1104, 944),
|
||||
(1184, 880),
|
||||
(1248, 832),
|
||||
(1328, 800),
|
||||
(1392, 752),
|
||||
(1456, 720),
|
||||
(1504, 688),
|
||||
(1568, 672),
|
||||
]
|
||||
|
||||
|
||||
class FluxKontextImageScale:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": {"image": ("IMAGE",),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("IMAGE",)
|
||||
FUNCTION = "scale"
|
||||
|
||||
CATEGORY = "advanced/conditioning/flux"
|
||||
DESCRIPTION = "This node resizes the image to one that is more optimal for flux kontext."
|
||||
|
||||
def scale(self, image):
|
||||
width = image.shape[2]
|
||||
height = image.shape[1]
|
||||
aspect_ratio = width / height
|
||||
_, width, height = min((abs(aspect_ratio - w / h), w, h) for w, h in PREFERED_KONTEXT_RESOLUTIONS)
|
||||
image = comfy.utils.common_upscale(image.movedim(-1, 1), width, height, "lanczos", "center").movedim(1, -1)
|
||||
return (image,)
|
||||
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"CLIPTextEncodeFlux": CLIPTextEncodeFlux,
|
||||
"FluxGuidance": FluxGuidance,
|
||||
"FluxDisableGuidance": FluxDisableGuidance,
|
||||
"FluxKontextImageScale": FluxKontextImageScale,
|
||||
}
|
||||
|
||||
26
comfy_extras/nodes_edit_model.py
Normal file
26
comfy_extras/nodes_edit_model.py
Normal file
@ -0,0 +1,26 @@
|
||||
import node_helpers
|
||||
|
||||
|
||||
class ReferenceLatent:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": {"conditioning": ("CONDITIONING", ),
|
||||
},
|
||||
"optional": {"latent": ("LATENT", ),}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("CONDITIONING",)
|
||||
FUNCTION = "append"
|
||||
|
||||
CATEGORY = "advanced/conditioning/edit_models"
|
||||
DESCRIPTION = "This node sets the guiding latent for an edit model. If the model supports it you can chain multiple to set multiple reference images."
|
||||
|
||||
def append(self, conditioning, latent=None):
|
||||
if latent is not None:
|
||||
conditioning = node_helpers.conditioning_set_values(conditioning, {"reference_latents": [latent["samples"]]}, append=True)
|
||||
return (conditioning, )
|
||||
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"ReferenceLatent": ReferenceLatent,
|
||||
}
|
||||
@ -1,6 +1,6 @@
|
||||
[project]
|
||||
name = "comfyui"
|
||||
version = "0.3.41"
|
||||
version = "0.3.42"
|
||||
description = "An installable version of ComfyUI"
|
||||
readme = "README.md"
|
||||
authors = [
|
||||
|
||||
Loading…
Reference in New Issue
Block a user