diff --git a/comfy/latent_formats.py b/comfy/latent_formats.py index f975b5e11..894540879 100644 --- a/comfy/latent_formats.py +++ b/comfy/latent_formats.py @@ -606,6 +606,11 @@ class HunyuanImage21(LatentFormat): latent_rgb_factors_bias = [0.0007, -0.0256, -0.0206] +class HunyuanImage21Refiner(LatentFormat): + latent_channels = 64 + latent_dimensions = 3 + scale_factor = 1.03682 + class Hunyuan3Dv2(LatentFormat): latent_channels = 64 latent_dimensions = 1 diff --git a/comfy/ldm/ace/attention.py b/comfy/ldm/ace/attention.py index f20a01669..670eb9783 100644 --- a/comfy/ldm/ace/attention.py +++ b/comfy/ldm/ace/attention.py @@ -133,6 +133,7 @@ class Attention(nn.Module): hidden_states: torch.Tensor, encoder_hidden_states: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, + transformer_options={}, **cross_attention_kwargs, ) -> torch.Tensor: return self.processor( @@ -140,6 +141,7 @@ class Attention(nn.Module): hidden_states, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask, + transformer_options=transformer_options, **cross_attention_kwargs, ) @@ -366,6 +368,7 @@ class CustomerAttnProcessor2_0: encoder_attention_mask: Optional[torch.FloatTensor] = None, rotary_freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]] = None, rotary_freqs_cis_cross: Union[torch.Tensor, Tuple[torch.Tensor]] = None, + transformer_options={}, *args, **kwargs, ) -> torch.Tensor: @@ -433,7 +436,7 @@ class CustomerAttnProcessor2_0: # the output of sdp = (batch, num_heads, seq_len, head_dim) hidden_states = optimized_attention( - query, key, value, heads=query.shape[1], mask=attention_mask, skip_reshape=True, + query, key, value, heads=query.shape[1], mask=attention_mask, skip_reshape=True, transformer_options=transformer_options, ).to(query.dtype) # linear proj @@ -697,6 +700,7 @@ class LinearTransformerBlock(nn.Module): rotary_freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]] = None, rotary_freqs_cis_cross: Union[torch.Tensor, Tuple[torch.Tensor]] = None, temb: torch.FloatTensor = None, + transformer_options={}, ): N = hidden_states.shape[0] @@ -720,6 +724,7 @@ class LinearTransformerBlock(nn.Module): encoder_attention_mask=encoder_attention_mask, rotary_freqs_cis=rotary_freqs_cis, rotary_freqs_cis_cross=rotary_freqs_cis_cross, + transformer_options=transformer_options, ) else: attn_output, _ = self.attn( @@ -729,6 +734,7 @@ class LinearTransformerBlock(nn.Module): encoder_attention_mask=None, rotary_freqs_cis=rotary_freqs_cis, rotary_freqs_cis_cross=None, + transformer_options=transformer_options, ) if self.use_adaln_single: @@ -743,6 +749,7 @@ class LinearTransformerBlock(nn.Module): encoder_attention_mask=encoder_attention_mask, rotary_freqs_cis=rotary_freqs_cis, rotary_freqs_cis_cross=rotary_freqs_cis_cross, + transformer_options=transformer_options, ) hidden_states = attn_output + hidden_states diff --git a/comfy/ldm/ace/model.py b/comfy/ldm/ace/model.py index 41d85eeb5..399329853 100644 --- a/comfy/ldm/ace/model.py +++ b/comfy/ldm/ace/model.py @@ -314,6 +314,7 @@ class ACEStepTransformer2DModel(nn.Module): output_length: int = 0, block_controlnet_hidden_states: Optional[Union[List[torch.Tensor], torch.Tensor]] = None, controlnet_scale: Union[float, torch.Tensor] = 1.0, + transformer_options={}, ): embedded_timestep = self.timestep_embedder(self.time_proj(timestep).to(dtype=hidden_states.dtype)) temb = self.t_block(embedded_timestep) @@ -339,6 +340,7 @@ class ACEStepTransformer2DModel(nn.Module): rotary_freqs_cis=rotary_freqs_cis, rotary_freqs_cis_cross=encoder_rotary_freqs_cis, temb=temb, + transformer_options=transformer_options, ) output = self.final_layer(hidden_states, embedded_timestep, output_length) @@ -393,6 +395,7 @@ class ACEStepTransformer2DModel(nn.Module): output_length = hidden_states.shape[-1] + transformer_options = kwargs.get("transformer_options", {}) output = self.decode( hidden_states=hidden_states, attention_mask=attention_mask, @@ -402,6 +405,7 @@ class ACEStepTransformer2DModel(nn.Module): output_length=output_length, block_controlnet_hidden_states=block_controlnet_hidden_states, controlnet_scale=controlnet_scale, + transformer_options=transformer_options, ) return output diff --git a/comfy/ldm/audio/dit.py b/comfy/ldm/audio/dit.py index d0d69bbdc..ca865189e 100644 --- a/comfy/ldm/audio/dit.py +++ b/comfy/ldm/audio/dit.py @@ -298,7 +298,8 @@ class Attention(nn.Module): mask = None, context_mask = None, rotary_pos_emb = None, - causal = None + causal = None, + transformer_options={}, ): h, kv_h, has_context = self.num_heads, self.kv_heads, context is not None @@ -363,7 +364,7 @@ class Attention(nn.Module): heads_per_kv_head = h // kv_h k, v = map(lambda t: t.repeat_interleave(heads_per_kv_head, dim = 1), (k, v)) - out = optimized_attention(q, k, v, h, skip_reshape=True) + out = optimized_attention(q, k, v, h, skip_reshape=True, transformer_options=transformer_options) out = self.to_out(out) if mask is not None: @@ -488,7 +489,8 @@ class TransformerBlock(nn.Module): global_cond=None, mask = None, context_mask = None, - rotary_pos_emb = None + rotary_pos_emb = None, + transformer_options={} ): if self.global_cond_dim is not None and self.global_cond_dim > 0 and global_cond is not None: @@ -498,12 +500,12 @@ class TransformerBlock(nn.Module): residual = x x = self.pre_norm(x) x = x * (1 + scale_self) + shift_self - x = self.self_attn(x, mask = mask, rotary_pos_emb = rotary_pos_emb) + x = self.self_attn(x, mask = mask, rotary_pos_emb = rotary_pos_emb, transformer_options=transformer_options) x = x * torch.sigmoid(1 - gate_self) x = x + residual if context is not None: - x = x + self.cross_attn(self.cross_attend_norm(x), context = context, context_mask = context_mask) + x = x + self.cross_attn(self.cross_attend_norm(x), context = context, context_mask = context_mask, transformer_options=transformer_options) if self.conformer is not None: x = x + self.conformer(x) @@ -517,10 +519,10 @@ class TransformerBlock(nn.Module): x = x + residual else: - x = x + self.self_attn(self.pre_norm(x), mask = mask, rotary_pos_emb = rotary_pos_emb) + x = x + self.self_attn(self.pre_norm(x), mask = mask, rotary_pos_emb = rotary_pos_emb, transformer_options=transformer_options) if context is not None: - x = x + self.cross_attn(self.cross_attend_norm(x), context = context, context_mask = context_mask) + x = x + self.cross_attn(self.cross_attend_norm(x), context = context, context_mask = context_mask, transformer_options=transformer_options) if self.conformer is not None: x = x + self.conformer(x) @@ -606,7 +608,8 @@ class ContinuousTransformer(nn.Module): return_info = False, **kwargs ): - patches_replace = kwargs.get("transformer_options", {}).get("patches_replace", {}) + transformer_options = kwargs.get("transformer_options", {}) + patches_replace = transformer_options.get("patches_replace", {}) batch, seq, device = *x.shape[:2], x.device context = kwargs["context"] @@ -645,13 +648,13 @@ class ContinuousTransformer(nn.Module): if ("double_block", i) in blocks_replace: def block_wrap(args): out = {} - out["img"] = layer(args["img"], rotary_pos_emb=args["pe"], global_cond=args["vec"], context=args["txt"]) + out["img"] = layer(args["img"], rotary_pos_emb=args["pe"], global_cond=args["vec"], context=args["txt"], transformer_options=args["transformer_options"]) return out - out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": global_cond, "pe": rotary_pos_emb}, {"original_block": block_wrap}) + out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": global_cond, "pe": rotary_pos_emb, "transformer_options": transformer_options}, {"original_block": block_wrap}) x = out["img"] else: - x = layer(x, rotary_pos_emb = rotary_pos_emb, global_cond=global_cond, context=context) + x = layer(x, rotary_pos_emb = rotary_pos_emb, global_cond=global_cond, context=context, transformer_options=transformer_options) # x = checkpoint(layer, x, rotary_pos_emb = rotary_pos_emb, global_cond=global_cond, **kwargs) if return_info: diff --git a/comfy/ldm/aura/mmdit.py b/comfy/ldm/aura/mmdit.py index d7f32b5e8..66d9613b6 100644 --- a/comfy/ldm/aura/mmdit.py +++ b/comfy/ldm/aura/mmdit.py @@ -85,7 +85,7 @@ class SingleAttention(nn.Module): ) #@torch.compile() - def forward(self, c): + def forward(self, c, transformer_options={}): bsz, seqlen1, _ = c.shape @@ -95,7 +95,7 @@ class SingleAttention(nn.Module): v = v.view(bsz, seqlen1, self.n_heads, self.head_dim) q, k = self.q_norm1(q), self.k_norm1(k) - output = optimized_attention(q.permute(0, 2, 1, 3), k.permute(0, 2, 1, 3), v.permute(0, 2, 1, 3), self.n_heads, skip_reshape=True) + output = optimized_attention(q.permute(0, 2, 1, 3), k.permute(0, 2, 1, 3), v.permute(0, 2, 1, 3), self.n_heads, skip_reshape=True, transformer_options=transformer_options) c = self.w1o(output) return c @@ -144,7 +144,7 @@ class DoubleAttention(nn.Module): #@torch.compile() - def forward(self, c, x): + def forward(self, c, x, transformer_options={}): bsz, seqlen1, _ = c.shape bsz, seqlen2, _ = x.shape @@ -168,7 +168,7 @@ class DoubleAttention(nn.Module): torch.cat([cv, xv], dim=1), ) - output = optimized_attention(q.permute(0, 2, 1, 3), k.permute(0, 2, 1, 3), v.permute(0, 2, 1, 3), self.n_heads, skip_reshape=True) + output = optimized_attention(q.permute(0, 2, 1, 3), k.permute(0, 2, 1, 3), v.permute(0, 2, 1, 3), self.n_heads, skip_reshape=True, transformer_options=transformer_options) c, x = output.split([seqlen1, seqlen2], dim=1) c = self.w1o(c) @@ -207,7 +207,7 @@ class MMDiTBlock(nn.Module): self.is_last = is_last #@torch.compile() - def forward(self, c, x, global_cond, **kwargs): + def forward(self, c, x, global_cond, transformer_options={}, **kwargs): cres, xres = c, x @@ -225,7 +225,7 @@ class MMDiTBlock(nn.Module): x = modulate(self.normX1(x), xshift_msa, xscale_msa) # attention - c, x = self.attn(c, x) + c, x = self.attn(c, x, transformer_options=transformer_options) c = self.normC2(cres + cgate_msa.unsqueeze(1) * c) @@ -255,13 +255,13 @@ class DiTBlock(nn.Module): self.mlp = MLP(dim, hidden_dim=dim * 4, dtype=dtype, device=device, operations=operations) #@torch.compile() - def forward(self, cx, global_cond, **kwargs): + def forward(self, cx, global_cond, transformer_options={}, **kwargs): cxres = cx shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.modCX( global_cond ).chunk(6, dim=1) cx = modulate(self.norm1(cx), shift_msa, scale_msa) - cx = self.attn(cx) + cx = self.attn(cx, transformer_options=transformer_options) cx = self.norm2(cxres + gate_msa.unsqueeze(1) * cx) mlpout = self.mlp(modulate(cx, shift_mlp, scale_mlp)) cx = gate_mlp.unsqueeze(1) * mlpout @@ -473,13 +473,14 @@ class MMDiT(nn.Module): out = {} out["txt"], out["img"] = layer(args["txt"], args["img"], - args["vec"]) + args["vec"], + transformer_options=args["transformer_options"]) return out - out = blocks_replace[("double_block", i)]({"img": x, "txt": c, "vec": global_cond}, {"original_block": block_wrap}) + out = blocks_replace[("double_block", i)]({"img": x, "txt": c, "vec": global_cond, "transformer_options": transformer_options}, {"original_block": block_wrap}) c = out["txt"] x = out["img"] else: - c, x = layer(c, x, global_cond, **kwargs) + c, x = layer(c, x, global_cond, transformer_options=transformer_options, **kwargs) if len(self.single_layers) > 0: c_len = c.size(1) @@ -488,13 +489,13 @@ class MMDiT(nn.Module): if ("single_block", i) in blocks_replace: def block_wrap(args): out = {} - out["img"] = layer(args["img"], args["vec"]) + out["img"] = layer(args["img"], args["vec"], transformer_options=args["transformer_options"]) return out - out = blocks_replace[("single_block", i)]({"img": cx, "vec": global_cond}, {"original_block": block_wrap}) + out = blocks_replace[("single_block", i)]({"img": cx, "vec": global_cond, "transformer_options": transformer_options}, {"original_block": block_wrap}) cx = out["img"] else: - cx = layer(cx, global_cond, **kwargs) + cx = layer(cx, global_cond, transformer_options=transformer_options, **kwargs) x = cx[:, c_len:] diff --git a/comfy/ldm/cascade/common.py b/comfy/ldm/cascade/common.py index 3eaa0c821..42ef98c7a 100644 --- a/comfy/ldm/cascade/common.py +++ b/comfy/ldm/cascade/common.py @@ -32,12 +32,12 @@ class OptimizedAttention(nn.Module): self.out_proj = operations.Linear(c, c, bias=True, dtype=dtype, device=device) - def forward(self, q, k, v): + def forward(self, q, k, v, transformer_options={}): q = self.to_q(q) k = self.to_k(k) v = self.to_v(v) - out = optimized_attention(q, k, v, self.heads) + out = optimized_attention(q, k, v, self.heads, transformer_options=transformer_options) return self.out_proj(out) @@ -47,13 +47,13 @@ class Attention2D(nn.Module): self.attn = OptimizedAttention(c, nhead, dtype=dtype, device=device, operations=operations) # self.attn = nn.MultiheadAttention(c, nhead, dropout=dropout, bias=True, batch_first=True, dtype=dtype, device=device) - def forward(self, x, kv, self_attn=False): + def forward(self, x, kv, self_attn=False, transformer_options={}): orig_shape = x.shape x = x.view(x.size(0), x.size(1), -1).permute(0, 2, 1) # Bx4xHxW -> Bx(HxW)x4 if self_attn: kv = torch.cat([x, kv], dim=1) # x = self.attn(x, kv, kv, need_weights=False)[0] - x = self.attn(x, kv, kv) + x = self.attn(x, kv, kv, transformer_options=transformer_options) x = x.permute(0, 2, 1).view(*orig_shape) return x @@ -114,9 +114,9 @@ class AttnBlock(nn.Module): operations.Linear(c_cond, c, dtype=dtype, device=device) ) - def forward(self, x, kv): + def forward(self, x, kv, transformer_options={}): kv = self.kv_mapper(kv) - x = x + self.attention(self.norm(x), kv, self_attn=self.self_attn) + x = x + self.attention(self.norm(x), kv, self_attn=self.self_attn, transformer_options=transformer_options) return x diff --git a/comfy/ldm/cascade/stage_b.py b/comfy/ldm/cascade/stage_b.py index 773830956..428c67fdf 100644 --- a/comfy/ldm/cascade/stage_b.py +++ b/comfy/ldm/cascade/stage_b.py @@ -173,7 +173,7 @@ class StageB(nn.Module): clip = self.clip_norm(clip) return clip - def _down_encode(self, x, r_embed, clip): + def _down_encode(self, x, r_embed, clip, transformer_options={}): level_outputs = [] block_group = zip(self.down_blocks, self.down_downscalers, self.down_repeat_mappers) for down_block, downscaler, repmap in block_group: @@ -187,7 +187,7 @@ class StageB(nn.Module): elif isinstance(block, AttnBlock) or ( hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, AttnBlock)): - x = block(x, clip) + x = block(x, clip, transformer_options=transformer_options) elif isinstance(block, TimestepBlock) or ( hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, TimestepBlock)): @@ -199,7 +199,7 @@ class StageB(nn.Module): level_outputs.insert(0, x) return level_outputs - def _up_decode(self, level_outputs, r_embed, clip): + def _up_decode(self, level_outputs, r_embed, clip, transformer_options={}): x = level_outputs[0] block_group = zip(self.up_blocks, self.up_upscalers, self.up_repeat_mappers) for i, (up_block, upscaler, repmap) in enumerate(block_group): @@ -216,7 +216,7 @@ class StageB(nn.Module): elif isinstance(block, AttnBlock) or ( hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, AttnBlock)): - x = block(x, clip) + x = block(x, clip, transformer_options=transformer_options) elif isinstance(block, TimestepBlock) or ( hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, TimestepBlock)): @@ -228,7 +228,7 @@ class StageB(nn.Module): x = upscaler(x) return x - def forward(self, x, r, effnet, clip, pixels=None, **kwargs): + def forward(self, x, r, effnet, clip, pixels=None, transformer_options={}, **kwargs): if pixels is None: pixels = x.new_zeros(x.size(0), 3, 8, 8) @@ -245,8 +245,8 @@ class StageB(nn.Module): nn.functional.interpolate(effnet, size=x.shape[-2:], mode='bilinear', align_corners=True)) x = x + nn.functional.interpolate(self.pixels_mapper(pixels), size=x.shape[-2:], mode='bilinear', align_corners=True) - level_outputs = self._down_encode(x, r_embed, clip) - x = self._up_decode(level_outputs, r_embed, clip) + level_outputs = self._down_encode(x, r_embed, clip, transformer_options=transformer_options) + x = self._up_decode(level_outputs, r_embed, clip, transformer_options=transformer_options) return self.clf(x) def update_weights_ema(self, src_model, beta=0.999): diff --git a/comfy/ldm/cascade/stage_c.py b/comfy/ldm/cascade/stage_c.py index b952d0349..ebc4434e2 100644 --- a/comfy/ldm/cascade/stage_c.py +++ b/comfy/ldm/cascade/stage_c.py @@ -182,7 +182,7 @@ class StageC(nn.Module): clip = self.clip_norm(clip) return clip - def _down_encode(self, x, r_embed, clip, cnet=None): + def _down_encode(self, x, r_embed, clip, cnet=None, transformer_options={}): level_outputs = [] block_group = zip(self.down_blocks, self.down_downscalers, self.down_repeat_mappers) for down_block, downscaler, repmap in block_group: @@ -201,7 +201,7 @@ class StageC(nn.Module): elif isinstance(block, AttnBlock) or ( hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, AttnBlock)): - x = block(x, clip) + x = block(x, clip, transformer_options=transformer_options) elif isinstance(block, TimestepBlock) or ( hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, TimestepBlock)): @@ -213,7 +213,7 @@ class StageC(nn.Module): level_outputs.insert(0, x) return level_outputs - def _up_decode(self, level_outputs, r_embed, clip, cnet=None): + def _up_decode(self, level_outputs, r_embed, clip, cnet=None, transformer_options={}): x = level_outputs[0] block_group = zip(self.up_blocks, self.up_upscalers, self.up_repeat_mappers) for i, (up_block, upscaler, repmap) in enumerate(block_group): @@ -235,7 +235,7 @@ class StageC(nn.Module): elif isinstance(block, AttnBlock) or ( hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, AttnBlock)): - x = block(x, clip) + x = block(x, clip, transformer_options=transformer_options) elif isinstance(block, TimestepBlock) or ( hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, TimestepBlock)): @@ -247,7 +247,7 @@ class StageC(nn.Module): x = upscaler(x) return x - def forward(self, x, r, clip_text, clip_text_pooled, clip_img, control=None, **kwargs): + def forward(self, x, r, clip_text, clip_text_pooled, clip_img, control=None, transformer_options={}, **kwargs): # Process the conditioning embeddings r_embed = self.gen_r_embedding(r).to(dtype=x.dtype) for c in self.t_conds: @@ -262,8 +262,8 @@ class StageC(nn.Module): # Model Blocks x = self.embedding(x) - level_outputs = self._down_encode(x, r_embed, clip, cnet) - x = self._up_decode(level_outputs, r_embed, clip, cnet) + level_outputs = self._down_encode(x, r_embed, clip, cnet, transformer_options=transformer_options) + x = self._up_decode(level_outputs, r_embed, clip, cnet, transformer_options=transformer_options) return self.clf(x) def update_weights_ema(self, src_model, beta=0.999): diff --git a/comfy/ldm/chroma/layers.py b/comfy/ldm/chroma/layers.py index 2a0dec606..fc7110cce 100644 --- a/comfy/ldm/chroma/layers.py +++ b/comfy/ldm/chroma/layers.py @@ -76,7 +76,7 @@ class DoubleStreamBlock(nn.Module): ) self.flipped_img_txt = flipped_img_txt - def forward(self, img: Tensor, txt: Tensor, pe: Tensor, vec: Tensor, attn_mask=None): + def forward(self, img: Tensor, txt: Tensor, pe: Tensor, vec: Tensor, attn_mask=None, transformer_options={}): (img_mod1, img_mod2), (txt_mod1, txt_mod2) = vec # prepare image for attention @@ -95,7 +95,7 @@ class DoubleStreamBlock(nn.Module): attn = attention(torch.cat((txt_q, img_q), dim=2), torch.cat((txt_k, img_k), dim=2), torch.cat((txt_v, img_v), dim=2), - pe=pe, mask=attn_mask) + pe=pe, mask=attn_mask, transformer_options=transformer_options) txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :] @@ -148,7 +148,7 @@ class SingleStreamBlock(nn.Module): self.mlp_act = nn.GELU(approximate="tanh") - def forward(self, x: Tensor, pe: Tensor, vec: Tensor, attn_mask=None) -> Tensor: + def forward(self, x: Tensor, pe: Tensor, vec: Tensor, attn_mask=None, transformer_options={}) -> Tensor: mod = vec x_mod = torch.addcmul(mod.shift, 1 + mod.scale, self.pre_norm(x)) qkv, mlp = torch.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1) @@ -157,7 +157,7 @@ class SingleStreamBlock(nn.Module): q, k = self.norm(q, k, v) # compute attention - attn = attention(q, k, v, pe=pe, mask=attn_mask) + attn = attention(q, k, v, pe=pe, mask=attn_mask, transformer_options=transformer_options) # compute activation in mlp stream, cat again and run second linear layer output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2)) x.addcmul_(mod.gate, output) diff --git a/comfy/ldm/chroma/model.py b/comfy/ldm/chroma/model.py index 5cff44dc8..4f709f87d 100644 --- a/comfy/ldm/chroma/model.py +++ b/comfy/ldm/chroma/model.py @@ -193,14 +193,16 @@ class Chroma(nn.Module): txt=args["txt"], vec=args["vec"], pe=args["pe"], - attn_mask=args.get("attn_mask")) + attn_mask=args.get("attn_mask"), + transformer_options=args.get("transformer_options")) return out out = blocks_replace[("double_block", i)]({"img": img, "txt": txt, "vec": double_mod, "pe": pe, - "attn_mask": attn_mask}, + "attn_mask": attn_mask, + "transformer_options": transformer_options}, {"original_block": block_wrap}) txt = out["txt"] img = out["img"] @@ -209,7 +211,8 @@ class Chroma(nn.Module): txt=txt, vec=double_mod, pe=pe, - attn_mask=attn_mask) + attn_mask=attn_mask, + transformer_options=transformer_options) if control is not None: # Controlnet control_i = control.get("input") @@ -229,17 +232,19 @@ class Chroma(nn.Module): out["img"] = block(args["img"], vec=args["vec"], pe=args["pe"], - attn_mask=args.get("attn_mask")) + attn_mask=args.get("attn_mask"), + transformer_options=args.get("transformer_options")) return out out = blocks_replace[("single_block", i)]({"img": img, "vec": single_mod, "pe": pe, - "attn_mask": attn_mask}, + "attn_mask": attn_mask, + "transformer_options": transformer_options}, {"original_block": block_wrap}) img = out["img"] else: - img = block(img, vec=single_mod, pe=pe, attn_mask=attn_mask) + img = block(img, vec=single_mod, pe=pe, attn_mask=attn_mask, transformer_options=transformer_options) if control is not None: # Controlnet control_o = control.get("output") diff --git a/comfy/ldm/cosmos/blocks.py b/comfy/ldm/cosmos/blocks.py index 5c4356a3f..afb43d469 100644 --- a/comfy/ldm/cosmos/blocks.py +++ b/comfy/ldm/cosmos/blocks.py @@ -176,6 +176,7 @@ class Attention(nn.Module): context=None, mask=None, rope_emb=None, + transformer_options={}, **kwargs, ): """ @@ -184,7 +185,7 @@ class Attention(nn.Module): context (Optional[Tensor]): The key tensor of shape [B, Mk, K] or use x as context [self attention] if None """ q, k, v = self.cal_qkv(x, context, mask, rope_emb=rope_emb, **kwargs) - out = optimized_attention(q, k, v, self.heads, skip_reshape=True, mask=mask, skip_output_reshape=True) + out = optimized_attention(q, k, v, self.heads, skip_reshape=True, mask=mask, skip_output_reshape=True, transformer_options=transformer_options) del q, k, v out = rearrange(out, " b n s c -> s b (n c)") return self.to_out(out) @@ -546,6 +547,7 @@ class VideoAttn(nn.Module): context: Optional[torch.Tensor] = None, crossattn_mask: Optional[torch.Tensor] = None, rope_emb_L_1_1_D: Optional[torch.Tensor] = None, + transformer_options: Optional[dict] = {}, ) -> torch.Tensor: """ Forward pass for video attention. @@ -571,6 +573,7 @@ class VideoAttn(nn.Module): context_M_B_D, crossattn_mask, rope_emb=rope_emb_L_1_1_D, + transformer_options=transformer_options, ) x_T_H_W_B_D = rearrange(x_THW_B_D, "(t h w) b d -> t h w b d", h=H, w=W) return x_T_H_W_B_D @@ -665,6 +668,7 @@ class DITBuildingBlock(nn.Module): crossattn_mask: Optional[torch.Tensor] = None, rope_emb_L_1_1_D: Optional[torch.Tensor] = None, adaln_lora_B_3D: Optional[torch.Tensor] = None, + transformer_options: Optional[dict] = {}, ) -> torch.Tensor: """ Forward pass for dynamically configured blocks with adaptive normalization. @@ -702,6 +706,7 @@ class DITBuildingBlock(nn.Module): adaln_norm_state(self.norm_state, x, scale_1_1_1_B_D, shift_1_1_1_B_D), context=None, rope_emb_L_1_1_D=rope_emb_L_1_1_D, + transformer_options=transformer_options, ) elif self.block_type in ["cross_attn", "ca"]: x = x + gate_1_1_1_B_D * self.block( @@ -709,6 +714,7 @@ class DITBuildingBlock(nn.Module): context=crossattn_emb, crossattn_mask=crossattn_mask, rope_emb_L_1_1_D=rope_emb_L_1_1_D, + transformer_options=transformer_options, ) else: raise ValueError(f"Unknown block type: {self.block_type}") @@ -784,6 +790,7 @@ class GeneralDITTransformerBlock(nn.Module): crossattn_mask: Optional[torch.Tensor] = None, rope_emb_L_1_1_D: Optional[torch.Tensor] = None, adaln_lora_B_3D: Optional[torch.Tensor] = None, + transformer_options: Optional[dict] = {}, ) -> torch.Tensor: for block in self.blocks: x = block( @@ -793,5 +800,6 @@ class GeneralDITTransformerBlock(nn.Module): crossattn_mask, rope_emb_L_1_1_D=rope_emb_L_1_1_D, adaln_lora_B_3D=adaln_lora_B_3D, + transformer_options=transformer_options, ) return x diff --git a/comfy/ldm/cosmos/model.py b/comfy/ldm/cosmos/model.py index 53698b758..52ef7ef43 100644 --- a/comfy/ldm/cosmos/model.py +++ b/comfy/ldm/cosmos/model.py @@ -520,6 +520,7 @@ class GeneralDIT(nn.Module): x.shape == extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D.shape ), f"{x.shape} != {extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D.shape} {original_shape}" + transformer_options = kwargs.get("transformer_options", {}) for _, block in self.blocks.items(): assert ( self.blocks["block0"].x_format == block.x_format @@ -534,6 +535,7 @@ class GeneralDIT(nn.Module): crossattn_mask, rope_emb_L_1_1_D=rope_emb_L_1_1_D, adaln_lora_B_3D=adaln_lora_B_3D, + transformer_options=transformer_options, ) x_B_T_H_W_D = rearrange(x, "T H W B D -> B T H W D") diff --git a/comfy/ldm/cosmos/predict2.py b/comfy/ldm/cosmos/predict2.py index fcc83ba76..07a4fc79f 100644 --- a/comfy/ldm/cosmos/predict2.py +++ b/comfy/ldm/cosmos/predict2.py @@ -44,7 +44,7 @@ class GPT2FeedForward(nn.Module): return x -def torch_attention_op(q_B_S_H_D: torch.Tensor, k_B_S_H_D: torch.Tensor, v_B_S_H_D: torch.Tensor) -> torch.Tensor: +def torch_attention_op(q_B_S_H_D: torch.Tensor, k_B_S_H_D: torch.Tensor, v_B_S_H_D: torch.Tensor, transformer_options: Optional[dict] = {}) -> torch.Tensor: """Computes multi-head attention using PyTorch's native implementation. This function provides a PyTorch backend alternative to Transformer Engine's attention operation. @@ -71,7 +71,7 @@ def torch_attention_op(q_B_S_H_D: torch.Tensor, k_B_S_H_D: torch.Tensor, v_B_S_H q_B_H_S_D = rearrange(q_B_S_H_D, "b ... h k -> b h ... k").view(in_q_shape[0], in_q_shape[-2], -1, in_q_shape[-1]) k_B_H_S_D = rearrange(k_B_S_H_D, "b ... h v -> b h ... v").view(in_k_shape[0], in_k_shape[-2], -1, in_k_shape[-1]) v_B_H_S_D = rearrange(v_B_S_H_D, "b ... h v -> b h ... v").view(in_k_shape[0], in_k_shape[-2], -1, in_k_shape[-1]) - return optimized_attention(q_B_H_S_D, k_B_H_S_D, v_B_H_S_D, in_q_shape[-2], skip_reshape=True) + return optimized_attention(q_B_H_S_D, k_B_H_S_D, v_B_H_S_D, in_q_shape[-2], skip_reshape=True, transformer_options=transformer_options) class Attention(nn.Module): @@ -180,8 +180,8 @@ class Attention(nn.Module): return q, k, v - def compute_attention(self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor) -> torch.Tensor: - result = self.attn_op(q, k, v) # [B, S, H, D] + def compute_attention(self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, transformer_options: Optional[dict] = {}) -> torch.Tensor: + result = self.attn_op(q, k, v, transformer_options=transformer_options) # [B, S, H, D] return self.output_dropout(self.output_proj(result)) def forward( @@ -189,6 +189,7 @@ class Attention(nn.Module): x: torch.Tensor, context: Optional[torch.Tensor] = None, rope_emb: Optional[torch.Tensor] = None, + transformer_options: Optional[dict] = {}, ) -> torch.Tensor: """ Args: @@ -196,7 +197,7 @@ class Attention(nn.Module): context (Optional[Tensor]): The key tensor of shape [B, Mk, K] or use x as context [self attention] if None """ q, k, v = self.compute_qkv(x, context, rope_emb=rope_emb) - return self.compute_attention(q, k, v) + return self.compute_attention(q, k, v, transformer_options=transformer_options) class Timesteps(nn.Module): @@ -459,6 +460,7 @@ class Block(nn.Module): rope_emb_L_1_1_D: Optional[torch.Tensor] = None, adaln_lora_B_T_3D: Optional[torch.Tensor] = None, extra_per_block_pos_emb: Optional[torch.Tensor] = None, + transformer_options: Optional[dict] = {}, ) -> torch.Tensor: if extra_per_block_pos_emb is not None: x_B_T_H_W_D = x_B_T_H_W_D + extra_per_block_pos_emb @@ -512,6 +514,7 @@ class Block(nn.Module): rearrange(normalized_x_B_T_H_W_D, "b t h w d -> b (t h w) d"), None, rope_emb=rope_emb_L_1_1_D, + transformer_options=transformer_options, ), "b (t h w) d -> b t h w d", t=T, @@ -525,6 +528,7 @@ class Block(nn.Module): layer_norm_cross_attn: Callable, _scale_cross_attn_B_T_1_1_D: torch.Tensor, _shift_cross_attn_B_T_1_1_D: torch.Tensor, + transformer_options: Optional[dict] = {}, ) -> torch.Tensor: _normalized_x_B_T_H_W_D = _fn( _x_B_T_H_W_D, layer_norm_cross_attn, _scale_cross_attn_B_T_1_1_D, _shift_cross_attn_B_T_1_1_D @@ -534,6 +538,7 @@ class Block(nn.Module): rearrange(_normalized_x_B_T_H_W_D, "b t h w d -> b (t h w) d"), crossattn_emb, rope_emb=rope_emb_L_1_1_D, + transformer_options=transformer_options, ), "b (t h w) d -> b t h w d", t=T, @@ -547,6 +552,7 @@ class Block(nn.Module): self.layer_norm_cross_attn, scale_cross_attn_B_T_1_1_D, shift_cross_attn_B_T_1_1_D, + transformer_options=transformer_options, ) x_B_T_H_W_D = result_B_T_H_W_D * gate_cross_attn_B_T_1_1_D + x_B_T_H_W_D @@ -865,6 +871,7 @@ class MiniTrainDIT(nn.Module): "rope_emb_L_1_1_D": rope_emb_L_1_1_D.unsqueeze(1).unsqueeze(0), "adaln_lora_B_T_3D": adaln_lora_B_T_3D, "extra_per_block_pos_emb": extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D, + "transformer_options": kwargs.get("transformer_options", {}), } for block in self.blocks: x_B_T_H_W_D = block( diff --git a/comfy/ldm/flux/layers.py b/comfy/ldm/flux/layers.py index 113eb2096..ef21b416b 100644 --- a/comfy/ldm/flux/layers.py +++ b/comfy/ldm/flux/layers.py @@ -159,7 +159,7 @@ class DoubleStreamBlock(nn.Module): ) self.flipped_img_txt = flipped_img_txt - def forward(self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor, attn_mask=None, modulation_dims_img=None, modulation_dims_txt=None): + def forward(self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor, attn_mask=None, modulation_dims_img=None, modulation_dims_txt=None, transformer_options={}): img_mod1, img_mod2 = self.img_mod(vec) txt_mod1, txt_mod2 = self.txt_mod(vec) @@ -182,7 +182,7 @@ class DoubleStreamBlock(nn.Module): attn = attention(torch.cat((img_q, txt_q), dim=2), torch.cat((img_k, txt_k), dim=2), torch.cat((img_v, txt_v), dim=2), - pe=pe, mask=attn_mask) + pe=pe, mask=attn_mask, transformer_options=transformer_options) img_attn, txt_attn = attn[:, : img.shape[1]], attn[:, img.shape[1]:] else: @@ -190,7 +190,7 @@ class DoubleStreamBlock(nn.Module): attn = attention(torch.cat((txt_q, img_q), dim=2), torch.cat((txt_k, img_k), dim=2), torch.cat((txt_v, img_v), dim=2), - pe=pe, mask=attn_mask) + pe=pe, mask=attn_mask, transformer_options=transformer_options) txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1]:] @@ -244,7 +244,7 @@ class SingleStreamBlock(nn.Module): self.mlp_act = nn.GELU(approximate="tanh") self.modulation = Modulation(hidden_size, double=False, dtype=dtype, device=device, operations=operations) - def forward(self, x: Tensor, vec: Tensor, pe: Tensor, attn_mask=None, modulation_dims=None) -> Tensor: + def forward(self, x: Tensor, vec: Tensor, pe: Tensor, attn_mask=None, modulation_dims=None, transformer_options={}) -> Tensor: mod, _ = self.modulation(vec) qkv, mlp = torch.split(self.linear1(apply_mod(self.pre_norm(x), (1 + mod.scale), mod.shift, modulation_dims)), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1) @@ -252,7 +252,7 @@ class SingleStreamBlock(nn.Module): q, k = self.norm(q, k, v) # compute attention - attn = attention(q, k, v, pe=pe, mask=attn_mask) + attn = attention(q, k, v, pe=pe, mask=attn_mask, transformer_options=transformer_options) # compute activation in mlp stream, cat again and run second linear layer output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2)) x += apply_mod(output, mod.gate, None, modulation_dims) diff --git a/comfy/ldm/flux/math.py b/comfy/ldm/flux/math.py index 3e0978176..4d743cda2 100644 --- a/comfy/ldm/flux/math.py +++ b/comfy/ldm/flux/math.py @@ -6,7 +6,7 @@ from comfy.ldm.modules.attention import optimized_attention import comfy.model_management -def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, mask=None) -> Tensor: +def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, mask=None, transformer_options={}) -> Tensor: q_shape = q.shape k_shape = k.shape @@ -17,7 +17,7 @@ def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, mask=None) -> Tensor: k = (pe[..., 0] * k[..., 0] + pe[..., 1] * k[..., 1]).reshape(*k_shape).type_as(v) heads = q.shape[1] - x = optimized_attention(q, k, v, heads, skip_reshape=True, mask=mask) + x = optimized_attention(q, k, v, heads, skip_reshape=True, mask=mask, transformer_options=transformer_options) return x diff --git a/comfy/ldm/flux/model.py b/comfy/ldm/flux/model.py index 8ea7d4f57..14f90cea5 100644 --- a/comfy/ldm/flux/model.py +++ b/comfy/ldm/flux/model.py @@ -144,14 +144,16 @@ class Flux(nn.Module): txt=args["txt"], vec=args["vec"], pe=args["pe"], - attn_mask=args.get("attn_mask")) + attn_mask=args.get("attn_mask"), + transformer_options=args.get("transformer_options")) return out out = blocks_replace[("double_block", i)]({"img": img, "txt": txt, "vec": vec, "pe": pe, - "attn_mask": attn_mask}, + "attn_mask": attn_mask, + "transformer_options": transformer_options}, {"original_block": block_wrap}) txt = out["txt"] img = out["img"] @@ -160,7 +162,8 @@ class Flux(nn.Module): txt=txt, vec=vec, pe=pe, - attn_mask=attn_mask) + attn_mask=attn_mask, + transformer_options=transformer_options) if control is not None: # Controlnet control_i = control.get("input") @@ -181,17 +184,19 @@ class Flux(nn.Module): out["img"] = block(args["img"], vec=args["vec"], pe=args["pe"], - attn_mask=args.get("attn_mask")) + attn_mask=args.get("attn_mask"), + transformer_options=args.get("transformer_options")) return out out = blocks_replace[("single_block", i)]({"img": img, "vec": vec, "pe": pe, - "attn_mask": attn_mask}, + "attn_mask": attn_mask, + "transformer_options": transformer_options}, {"original_block": block_wrap}) img = out["img"] else: - img = block(img, vec=vec, pe=pe, attn_mask=attn_mask) + img = block(img, vec=vec, pe=pe, attn_mask=attn_mask, transformer_options=transformer_options) if control is not None: # Controlnet control_o = control.get("output") diff --git a/comfy/ldm/genmo/joint_model/asymm_models_joint.py b/comfy/ldm/genmo/joint_model/asymm_models_joint.py index 366a8b713..5c1bb4d42 100644 --- a/comfy/ldm/genmo/joint_model/asymm_models_joint.py +++ b/comfy/ldm/genmo/joint_model/asymm_models_joint.py @@ -109,6 +109,7 @@ class AsymmetricAttention(nn.Module): scale_x: torch.Tensor, # (B, dim_x), modulation for pre-RMSNorm. scale_y: torch.Tensor, # (B, dim_y), modulation for pre-RMSNorm. crop_y, + transformer_options={}, **rope_rotation, ) -> Tuple[torch.Tensor, torch.Tensor]: rope_cos = rope_rotation.get("rope_cos") @@ -143,7 +144,7 @@ class AsymmetricAttention(nn.Module): xy = optimized_attention(q, k, - v, self.num_heads, skip_reshape=True) + v, self.num_heads, skip_reshape=True, transformer_options=transformer_options) x, y = torch.tensor_split(xy, (q_x.shape[1],), dim=1) x = self.proj_x(x) @@ -224,6 +225,7 @@ class AsymmetricJointBlock(nn.Module): x: torch.Tensor, c: torch.Tensor, y: torch.Tensor, + transformer_options={}, **attn_kwargs, ): """Forward pass of a block. @@ -256,6 +258,7 @@ class AsymmetricJointBlock(nn.Module): y, scale_x=scale_msa_x, scale_y=scale_msa_y, + transformer_options=transformer_options, **attn_kwargs, ) @@ -524,10 +527,11 @@ class AsymmDiTJoint(nn.Module): args["txt"], rope_cos=args["rope_cos"], rope_sin=args["rope_sin"], - crop_y=args["num_tokens"] + crop_y=args["num_tokens"], + transformer_options=args["transformer_options"] ) return out - out = blocks_replace[("double_block", i)]({"img": x, "txt": y_feat, "vec": c, "rope_cos": rope_cos, "rope_sin": rope_sin, "num_tokens": num_tokens}, {"original_block": block_wrap}) + out = blocks_replace[("double_block", i)]({"img": x, "txt": y_feat, "vec": c, "rope_cos": rope_cos, "rope_sin": rope_sin, "num_tokens": num_tokens, "transformer_options": transformer_options}, {"original_block": block_wrap}) y_feat = out["txt"] x = out["img"] else: @@ -538,6 +542,7 @@ class AsymmDiTJoint(nn.Module): rope_cos=rope_cos, rope_sin=rope_sin, crop_y=num_tokens, + transformer_options=transformer_options, ) # (B, M, D), (B, L, D) del y_feat # Final layers don't use dense text features. diff --git a/comfy/ldm/hidream/model.py b/comfy/ldm/hidream/model.py index ae49cf945..28d81c79e 100644 --- a/comfy/ldm/hidream/model.py +++ b/comfy/ldm/hidream/model.py @@ -72,8 +72,8 @@ class TimestepEmbed(nn.Module): return t_emb -def attention(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor): - return optimized_attention(query.view(query.shape[0], -1, query.shape[-1] * query.shape[-2]), key.view(key.shape[0], -1, key.shape[-1] * key.shape[-2]), value.view(value.shape[0], -1, value.shape[-1] * value.shape[-2]), query.shape[2]) +def attention(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, transformer_options={}): + return optimized_attention(query.view(query.shape[0], -1, query.shape[-1] * query.shape[-2]), key.view(key.shape[0], -1, key.shape[-1] * key.shape[-2]), value.view(value.shape[0], -1, value.shape[-1] * value.shape[-2]), query.shape[2], transformer_options=transformer_options) class HiDreamAttnProcessor_flashattn: @@ -86,6 +86,7 @@ class HiDreamAttnProcessor_flashattn: image_tokens_masks: Optional[torch.FloatTensor] = None, text_tokens: Optional[torch.FloatTensor] = None, rope: torch.FloatTensor = None, + transformer_options={}, *args, **kwargs, ) -> torch.FloatTensor: @@ -133,7 +134,7 @@ class HiDreamAttnProcessor_flashattn: query = torch.cat([query_1, query_2], dim=-1) key = torch.cat([key_1, key_2], dim=-1) - hidden_states = attention(query, key, value) + hidden_states = attention(query, key, value, transformer_options=transformer_options) if not attn.single: hidden_states_i, hidden_states_t = torch.split(hidden_states, [num_image_tokens, num_text_tokens], dim=1) @@ -199,6 +200,7 @@ class HiDreamAttention(nn.Module): image_tokens_masks: torch.FloatTensor = None, norm_text_tokens: torch.FloatTensor = None, rope: torch.FloatTensor = None, + transformer_options={}, ) -> torch.Tensor: return self.processor( self, @@ -206,6 +208,7 @@ class HiDreamAttention(nn.Module): image_tokens_masks = image_tokens_masks, text_tokens = norm_text_tokens, rope = rope, + transformer_options=transformer_options, ) @@ -406,7 +409,7 @@ class HiDreamImageSingleTransformerBlock(nn.Module): text_tokens: Optional[torch.FloatTensor] = None, adaln_input: Optional[torch.FloatTensor] = None, rope: torch.FloatTensor = None, - + transformer_options={}, ) -> torch.FloatTensor: wtype = image_tokens.dtype shift_msa_i, scale_msa_i, gate_msa_i, shift_mlp_i, scale_mlp_i, gate_mlp_i = \ @@ -419,6 +422,7 @@ class HiDreamImageSingleTransformerBlock(nn.Module): norm_image_tokens, image_tokens_masks, rope = rope, + transformer_options=transformer_options, ) image_tokens = gate_msa_i * attn_output_i + image_tokens @@ -483,6 +487,7 @@ class HiDreamImageTransformerBlock(nn.Module): text_tokens: Optional[torch.FloatTensor] = None, adaln_input: Optional[torch.FloatTensor] = None, rope: torch.FloatTensor = None, + transformer_options={}, ) -> torch.FloatTensor: wtype = image_tokens.dtype shift_msa_i, scale_msa_i, gate_msa_i, shift_mlp_i, scale_mlp_i, gate_mlp_i, \ @@ -500,6 +505,7 @@ class HiDreamImageTransformerBlock(nn.Module): image_tokens_masks, norm_text_tokens, rope = rope, + transformer_options=transformer_options, ) image_tokens = gate_msa_i * attn_output_i + image_tokens @@ -550,6 +556,7 @@ class HiDreamImageBlock(nn.Module): text_tokens: Optional[torch.FloatTensor] = None, adaln_input: torch.FloatTensor = None, rope: torch.FloatTensor = None, + transformer_options={}, ) -> torch.FloatTensor: return self.block( image_tokens, @@ -557,6 +564,7 @@ class HiDreamImageBlock(nn.Module): text_tokens, adaln_input, rope, + transformer_options=transformer_options, ) @@ -786,6 +794,7 @@ class HiDreamImageTransformer2DModel(nn.Module): text_tokens = cur_encoder_hidden_states, adaln_input = adaln_input, rope = rope, + transformer_options=transformer_options, ) initial_encoder_hidden_states = initial_encoder_hidden_states[:, :initial_encoder_hidden_states_seq_len] block_id += 1 @@ -809,6 +818,7 @@ class HiDreamImageTransformer2DModel(nn.Module): text_tokens=None, adaln_input=adaln_input, rope=rope, + transformer_options=transformer_options, ) hidden_states = hidden_states[:, :hidden_states_seq_len] block_id += 1 diff --git a/comfy/ldm/hunyuan3d/model.py b/comfy/ldm/hunyuan3d/model.py index 0fa5e78c1..4991b1645 100644 --- a/comfy/ldm/hunyuan3d/model.py +++ b/comfy/ldm/hunyuan3d/model.py @@ -99,14 +99,16 @@ class Hunyuan3Dv2(nn.Module): txt=args["txt"], vec=args["vec"], pe=args["pe"], - attn_mask=args.get("attn_mask")) + attn_mask=args.get("attn_mask"), + transformer_options=args["transformer_options"]) return out out = blocks_replace[("double_block", i)]({"img": img, "txt": txt, "vec": vec, "pe": pe, - "attn_mask": attn_mask}, + "attn_mask": attn_mask, + "transformer_options": transformer_options}, {"original_block": block_wrap}) txt = out["txt"] img = out["img"] @@ -115,7 +117,8 @@ class Hunyuan3Dv2(nn.Module): txt=txt, vec=vec, pe=pe, - attn_mask=attn_mask) + attn_mask=attn_mask, + transformer_options=transformer_options) img = torch.cat((txt, img), 1) @@ -126,17 +129,19 @@ class Hunyuan3Dv2(nn.Module): out["img"] = block(args["img"], vec=args["vec"], pe=args["pe"], - attn_mask=args.get("attn_mask")) + attn_mask=args.get("attn_mask"), + transformer_options=args["transformer_options"]) return out out = blocks_replace[("single_block", i)]({"img": img, "vec": vec, "pe": pe, - "attn_mask": attn_mask}, + "attn_mask": attn_mask, + "transformer_options": transformer_options}, {"original_block": block_wrap}) img = out["img"] else: - img = block(img, vec=vec, pe=pe, attn_mask=attn_mask) + img = block(img, vec=vec, pe=pe, attn_mask=attn_mask, transformer_options=transformer_options) img = img[:, txt.shape[1]:, ...] img = self.final_layer(img, vec) diff --git a/comfy/ldm/hunyuan_video/model.py b/comfy/ldm/hunyuan_video/model.py index 7732182a4..5132e6c07 100644 --- a/comfy/ldm/hunyuan_video/model.py +++ b/comfy/ldm/hunyuan_video/model.py @@ -80,13 +80,13 @@ class TokenRefinerBlock(nn.Module): operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device), ) - def forward(self, x, c, mask): + def forward(self, x, c, mask, transformer_options={}): mod1, mod2 = self.adaLN_modulation(c).chunk(2, dim=1) norm_x = self.norm1(x) qkv = self.self_attn.qkv(norm_x) q, k, v = qkv.reshape(qkv.shape[0], qkv.shape[1], 3, self.heads, -1).permute(2, 0, 3, 1, 4) - attn = optimized_attention(q, k, v, self.heads, mask=mask, skip_reshape=True) + attn = optimized_attention(q, k, v, self.heads, mask=mask, skip_reshape=True, transformer_options=transformer_options) x = x + self.self_attn.proj(attn) * mod1.unsqueeze(1) x = x + self.mlp(self.norm2(x)) * mod2.unsqueeze(1) @@ -117,14 +117,14 @@ class IndividualTokenRefiner(nn.Module): ] ) - def forward(self, x, c, mask): + def forward(self, x, c, mask, transformer_options={}): m = None if mask is not None: m = mask.view(mask.shape[0], 1, 1, mask.shape[1]).repeat(1, 1, mask.shape[1], 1) m = m + m.transpose(2, 3) for block in self.blocks: - x = block(x, c, m) + x = block(x, c, m, transformer_options=transformer_options) return x @@ -152,6 +152,7 @@ class TokenRefiner(nn.Module): x, timesteps, mask, + transformer_options={}, ): t = self.t_embedder(timestep_embedding(timesteps, 256, time_factor=1.0).to(x.dtype)) # m = mask.float().unsqueeze(-1) @@ -160,7 +161,7 @@ class TokenRefiner(nn.Module): c = t + self.c_embedder(c.to(x.dtype)) x = self.input_embedder(x) - x = self.individual_token_refiner(x, c, mask) + x = self.individual_token_refiner(x, c, mask, transformer_options=transformer_options) return x @@ -278,6 +279,7 @@ class HunyuanVideo(nn.Module): guidance: Tensor = None, guiding_frame_index=None, ref_latent=None, + disable_time_r=False, control=None, transformer_options={}, ) -> Tensor: @@ -288,7 +290,7 @@ class HunyuanVideo(nn.Module): img = self.img_in(img) vec = self.time_in(timestep_embedding(timesteps, 256, time_factor=1.0).to(img.dtype)) - if self.time_r_in is not None: + if (self.time_r_in is not None) and (not disable_time_r): w = torch.where(transformer_options['sigmas'][0] == transformer_options['sample_sigmas'])[0] # This most likely could be improved if len(w) > 0: timesteps_r = transformer_options['sample_sigmas'][w[0] + 1] @@ -327,7 +329,7 @@ class HunyuanVideo(nn.Module): if txt_mask is not None and not torch.is_floating_point(txt_mask): txt_mask = (txt_mask - 1).to(img.dtype) * torch.finfo(img.dtype).max - txt = self.txt_in(txt, timesteps, txt_mask) + txt = self.txt_in(txt, timesteps, txt_mask, transformer_options=transformer_options) if self.byt5_in is not None and txt_byt5 is not None: txt_byt5 = self.byt5_in(txt_byt5) @@ -351,14 +353,14 @@ class HunyuanVideo(nn.Module): if ("double_block", i) in blocks_replace: def block_wrap(args): out = {} - out["img"], out["txt"] = block(img=args["img"], txt=args["txt"], vec=args["vec"], pe=args["pe"], attn_mask=args["attention_mask"], modulation_dims_img=args["modulation_dims_img"], modulation_dims_txt=args["modulation_dims_txt"]) + out["img"], out["txt"] = block(img=args["img"], txt=args["txt"], vec=args["vec"], pe=args["pe"], attn_mask=args["attention_mask"], modulation_dims_img=args["modulation_dims_img"], modulation_dims_txt=args["modulation_dims_txt"], transformer_options=args["transformer_options"]) return out - out = blocks_replace[("double_block", i)]({"img": img, "txt": txt, "vec": vec, "pe": pe, "attention_mask": attn_mask, 'modulation_dims_img': modulation_dims, 'modulation_dims_txt': modulation_dims_txt}, {"original_block": block_wrap}) + out = blocks_replace[("double_block", i)]({"img": img, "txt": txt, "vec": vec, "pe": pe, "attention_mask": attn_mask, 'modulation_dims_img': modulation_dims, 'modulation_dims_txt': modulation_dims_txt, 'transformer_options': transformer_options}, {"original_block": block_wrap}) txt = out["txt"] img = out["img"] else: - img, txt = block(img=img, txt=txt, vec=vec, pe=pe, attn_mask=attn_mask, modulation_dims_img=modulation_dims, modulation_dims_txt=modulation_dims_txt) + img, txt = block(img=img, txt=txt, vec=vec, pe=pe, attn_mask=attn_mask, modulation_dims_img=modulation_dims, modulation_dims_txt=modulation_dims_txt, transformer_options=transformer_options) if control is not None: # Controlnet control_i = control.get("input") @@ -373,13 +375,13 @@ class HunyuanVideo(nn.Module): if ("single_block", i) in blocks_replace: def block_wrap(args): out = {} - out["img"] = block(args["img"], vec=args["vec"], pe=args["pe"], attn_mask=args["attention_mask"], modulation_dims=args["modulation_dims"]) + out["img"] = block(args["img"], vec=args["vec"], pe=args["pe"], attn_mask=args["attention_mask"], modulation_dims=args["modulation_dims"], transformer_options=args["transformer_options"]) return out - out = blocks_replace[("single_block", i)]({"img": img, "vec": vec, "pe": pe, "attention_mask": attn_mask, 'modulation_dims': modulation_dims}, {"original_block": block_wrap}) + out = blocks_replace[("single_block", i)]({"img": img, "vec": vec, "pe": pe, "attention_mask": attn_mask, 'modulation_dims': modulation_dims, 'transformer_options': transformer_options}, {"original_block": block_wrap}) img = out["img"] else: - img = block(img, vec=vec, pe=pe, attn_mask=attn_mask, modulation_dims=modulation_dims) + img = block(img, vec=vec, pe=pe, attn_mask=attn_mask, modulation_dims=modulation_dims, transformer_options=transformer_options) if control is not None: # Controlnet control_o = control.get("output") @@ -428,14 +430,14 @@ class HunyuanVideo(nn.Module): img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0) return repeat(img_ids, "h w c -> b (h w) c", b=bs) - def forward(self, x, timestep, context, y=None, txt_byt5=None, guidance=None, attention_mask=None, guiding_frame_index=None, ref_latent=None, control=None, transformer_options={}, **kwargs): + def forward(self, x, timestep, context, y=None, txt_byt5=None, guidance=None, attention_mask=None, guiding_frame_index=None, ref_latent=None, disable_time_r=False, control=None, transformer_options={}, **kwargs): return comfy.patcher_extension.WrapperExecutor.new_class_executor( self._forward, self, comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options) - ).execute(x, timestep, context, y, txt_byt5, guidance, attention_mask, guiding_frame_index, ref_latent, control, transformer_options, **kwargs) + ).execute(x, timestep, context, y, txt_byt5, guidance, attention_mask, guiding_frame_index, ref_latent, disable_time_r, control, transformer_options, **kwargs) - def _forward(self, x, timestep, context, y=None, txt_byt5=None, guidance=None, attention_mask=None, guiding_frame_index=None, ref_latent=None, control=None, transformer_options={}, **kwargs): + def _forward(self, x, timestep, context, y=None, txt_byt5=None, guidance=None, attention_mask=None, guiding_frame_index=None, ref_latent=None, disable_time_r=False, control=None, transformer_options={}, **kwargs): bs = x.shape[0] if len(self.patch_size) == 3: img_ids = self.img_ids(x) @@ -443,5 +445,5 @@ class HunyuanVideo(nn.Module): else: img_ids = self.img_ids_2d(x) txt_ids = torch.zeros((bs, context.shape[1], 2), device=x.device, dtype=x.dtype) - out = self.forward_orig(x, img_ids, context, txt_ids, attention_mask, timestep, y, txt_byt5, guidance, guiding_frame_index, ref_latent, control=control, transformer_options=transformer_options) + out = self.forward_orig(x, img_ids, context, txt_ids, attention_mask, timestep, y, txt_byt5, guidance, guiding_frame_index, ref_latent, disable_time_r=disable_time_r, control=control, transformer_options=transformer_options) return out diff --git a/comfy/ldm/hunyuan_video/vae_refiner.py b/comfy/ldm/hunyuan_video/vae_refiner.py new file mode 100644 index 000000000..e3fff9bbe --- /dev/null +++ b/comfy/ldm/hunyuan_video/vae_refiner.py @@ -0,0 +1,268 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from comfy.ldm.modules.diffusionmodules.model import ResnetBlock, AttnBlock, VideoConv3d +import comfy.ops +import comfy.ldm.models.autoencoder +ops = comfy.ops.disable_weight_init + +class RMS_norm(nn.Module): + def __init__(self, dim): + super().__init__() + shape = (dim, 1, 1, 1) + self.scale = dim**0.5 + self.gamma = nn.Parameter(torch.empty(shape)) + + def forward(self, x): + return F.normalize(x, dim=1) * self.scale * self.gamma + +class DnSmpl(nn.Module): + def __init__(self, ic, oc, tds=True): + super().__init__() + fct = 2 * 2 * 2 if tds else 1 * 2 * 2 + assert oc % fct == 0 + self.conv = VideoConv3d(ic, oc // fct, kernel_size=3) + + self.tds = tds + self.gs = fct * ic // oc + + def forward(self, x): + r1 = 2 if self.tds else 1 + h = self.conv(x) + + if self.tds: + hf = h[:, :, :1, :, :] + b, c, f, ht, wd = hf.shape + hf = hf.reshape(b, c, f, ht // 2, 2, wd // 2, 2) + hf = hf.permute(0, 4, 6, 1, 2, 3, 5) + hf = hf.reshape(b, 2 * 2 * c, f, ht // 2, wd // 2) + hf = torch.cat([hf, hf], dim=1) + + hn = h[:, :, 1:, :, :] + b, c, frms, ht, wd = hn.shape + nf = frms // r1 + hn = hn.reshape(b, c, nf, r1, ht // 2, 2, wd // 2, 2) + hn = hn.permute(0, 3, 5, 7, 1, 2, 4, 6) + hn = hn.reshape(b, r1 * 2 * 2 * c, nf, ht // 2, wd // 2) + + h = torch.cat([hf, hn], dim=2) + + xf = x[:, :, :1, :, :] + b, ci, f, ht, wd = xf.shape + xf = xf.reshape(b, ci, f, ht // 2, 2, wd // 2, 2) + xf = xf.permute(0, 4, 6, 1, 2, 3, 5) + xf = xf.reshape(b, 2 * 2 * ci, f, ht // 2, wd // 2) + B, C, T, H, W = xf.shape + xf = xf.view(B, h.shape[1], self.gs // 2, T, H, W).mean(dim=2) + + xn = x[:, :, 1:, :, :] + b, ci, frms, ht, wd = xn.shape + nf = frms // r1 + xn = xn.reshape(b, ci, nf, r1, ht // 2, 2, wd // 2, 2) + xn = xn.permute(0, 3, 5, 7, 1, 2, 4, 6) + xn = xn.reshape(b, r1 * 2 * 2 * ci, nf, ht // 2, wd // 2) + B, C, T, H, W = xn.shape + xn = xn.view(B, h.shape[1], self.gs, T, H, W).mean(dim=2) + sc = torch.cat([xf, xn], dim=2) + else: + b, c, frms, ht, wd = h.shape + nf = frms // r1 + h = h.reshape(b, c, nf, r1, ht // 2, 2, wd // 2, 2) + h = h.permute(0, 3, 5, 7, 1, 2, 4, 6) + h = h.reshape(b, r1 * 2 * 2 * c, nf, ht // 2, wd // 2) + + b, ci, frms, ht, wd = x.shape + nf = frms // r1 + sc = x.reshape(b, ci, nf, r1, ht // 2, 2, wd // 2, 2) + sc = sc.permute(0, 3, 5, 7, 1, 2, 4, 6) + sc = sc.reshape(b, r1 * 2 * 2 * ci, nf, ht // 2, wd // 2) + B, C, T, H, W = sc.shape + sc = sc.view(B, h.shape[1], self.gs, T, H, W).mean(dim=2) + + return h + sc + + +class UpSmpl(nn.Module): + def __init__(self, ic, oc, tus=True): + super().__init__() + fct = 2 * 2 * 2 if tus else 1 * 2 * 2 + self.conv = VideoConv3d(ic, oc * fct, kernel_size=3) + + self.tus = tus + self.rp = fct * oc // ic + + def forward(self, x): + r1 = 2 if self.tus else 1 + h = self.conv(x) + + if self.tus: + hf = h[:, :, :1, :, :] + b, c, f, ht, wd = hf.shape + nc = c // (2 * 2) + hf = hf.reshape(b, 2, 2, nc, f, ht, wd) + hf = hf.permute(0, 3, 4, 5, 1, 6, 2) + hf = hf.reshape(b, nc, f, ht * 2, wd * 2) + hf = hf[:, : hf.shape[1] // 2] + + hn = h[:, :, 1:, :, :] + b, c, frms, ht, wd = hn.shape + nc = c // (r1 * 2 * 2) + hn = hn.reshape(b, r1, 2, 2, nc, frms, ht, wd) + hn = hn.permute(0, 4, 5, 1, 6, 2, 7, 3) + hn = hn.reshape(b, nc, frms * r1, ht * 2, wd * 2) + + h = torch.cat([hf, hn], dim=2) + + xf = x[:, :, :1, :, :] + b, ci, f, ht, wd = xf.shape + xf = xf.repeat_interleave(repeats=self.rp // 2, dim=1) + b, c, f, ht, wd = xf.shape + nc = c // (2 * 2) + xf = xf.reshape(b, 2, 2, nc, f, ht, wd) + xf = xf.permute(0, 3, 4, 5, 1, 6, 2) + xf = xf.reshape(b, nc, f, ht * 2, wd * 2) + + xn = x[:, :, 1:, :, :] + xn = xn.repeat_interleave(repeats=self.rp, dim=1) + b, c, frms, ht, wd = xn.shape + nc = c // (r1 * 2 * 2) + xn = xn.reshape(b, r1, 2, 2, nc, frms, ht, wd) + xn = xn.permute(0, 4, 5, 1, 6, 2, 7, 3) + xn = xn.reshape(b, nc, frms * r1, ht * 2, wd * 2) + sc = torch.cat([xf, xn], dim=2) + else: + b, c, frms, ht, wd = h.shape + nc = c // (r1 * 2 * 2) + h = h.reshape(b, r1, 2, 2, nc, frms, ht, wd) + h = h.permute(0, 4, 5, 1, 6, 2, 7, 3) + h = h.reshape(b, nc, frms * r1, ht * 2, wd * 2) + + sc = x.repeat_interleave(repeats=self.rp, dim=1) + b, c, frms, ht, wd = sc.shape + nc = c // (r1 * 2 * 2) + sc = sc.reshape(b, r1, 2, 2, nc, frms, ht, wd) + sc = sc.permute(0, 4, 5, 1, 6, 2, 7, 3) + sc = sc.reshape(b, nc, frms * r1, ht * 2, wd * 2) + + return h + sc + +class Encoder(nn.Module): + def __init__(self, in_channels, z_channels, block_out_channels, num_res_blocks, + ffactor_spatial, ffactor_temporal, downsample_match_channel=True, **_): + super().__init__() + self.z_channels = z_channels + self.block_out_channels = block_out_channels + self.num_res_blocks = num_res_blocks + self.conv_in = VideoConv3d(in_channels, block_out_channels[0], 3, 1, 1) + + self.down = nn.ModuleList() + ch = block_out_channels[0] + depth = (ffactor_spatial >> 1).bit_length() + depth_temporal = ((ffactor_spatial // ffactor_temporal) >> 1).bit_length() + + for i, tgt in enumerate(block_out_channels): + stage = nn.Module() + stage.block = nn.ModuleList([ResnetBlock(in_channels=ch if j == 0 else tgt, + out_channels=tgt, + temb_channels=0, + conv_op=VideoConv3d, norm_op=RMS_norm) + for j in range(num_res_blocks)]) + ch = tgt + if i < depth: + nxt = block_out_channels[i + 1] if i + 1 < len(block_out_channels) and downsample_match_channel else ch + stage.downsample = DnSmpl(ch, nxt, tds=i >= depth_temporal) + ch = nxt + self.down.append(stage) + + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=ch, out_channels=ch, temb_channels=0, conv_op=VideoConv3d, norm_op=RMS_norm) + self.mid.attn_1 = AttnBlock(ch, conv_op=ops.Conv3d, norm_op=RMS_norm) + self.mid.block_2 = ResnetBlock(in_channels=ch, out_channels=ch, temb_channels=0, conv_op=VideoConv3d, norm_op=RMS_norm) + + self.norm_out = RMS_norm(ch) + self.conv_out = VideoConv3d(ch, z_channels << 1, 3, 1, 1) + + self.regul = comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer() + + def forward(self, x): + x = x.unsqueeze(2) + x = self.conv_in(x) + + for stage in self.down: + for blk in stage.block: + x = blk(x) + if hasattr(stage, 'downsample'): + x = stage.downsample(x) + + x = self.mid.block_2(self.mid.attn_1(self.mid.block_1(x))) + + b, c, t, h, w = x.shape + grp = c // (self.z_channels << 1) + skip = x.view(b, c // grp, grp, t, h, w).mean(2) + + out = self.conv_out(F.silu(self.norm_out(x))) + skip + out = self.regul(out)[0] + + out = torch.cat((out[:, :, :1], out), dim=2) + out = out.permute(0, 2, 1, 3, 4) + b, f_times_2, c, h, w = out.shape + out = out.reshape(b, f_times_2 // 2, 2 * c, h, w) + out = out.permute(0, 2, 1, 3, 4).contiguous() + return out + +class Decoder(nn.Module): + def __init__(self, z_channels, out_channels, block_out_channels, num_res_blocks, + ffactor_spatial, ffactor_temporal, upsample_match_channel=True, **_): + super().__init__() + block_out_channels = block_out_channels[::-1] + self.z_channels = z_channels + self.block_out_channels = block_out_channels + self.num_res_blocks = num_res_blocks + + ch = block_out_channels[0] + self.conv_in = VideoConv3d(z_channels, ch, 3) + + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=ch, out_channels=ch, temb_channels=0, conv_op=VideoConv3d, norm_op=RMS_norm) + self.mid.attn_1 = AttnBlock(ch, conv_op=ops.Conv3d, norm_op=RMS_norm) + self.mid.block_2 = ResnetBlock(in_channels=ch, out_channels=ch, temb_channels=0, conv_op=VideoConv3d, norm_op=RMS_norm) + + self.up = nn.ModuleList() + depth = (ffactor_spatial >> 1).bit_length() + depth_temporal = (ffactor_temporal >> 1).bit_length() + + for i, tgt in enumerate(block_out_channels): + stage = nn.Module() + stage.block = nn.ModuleList([ResnetBlock(in_channels=ch if j == 0 else tgt, + out_channels=tgt, + temb_channels=0, + conv_op=VideoConv3d, norm_op=RMS_norm) + for j in range(num_res_blocks + 1)]) + ch = tgt + if i < depth: + nxt = block_out_channels[i + 1] if i + 1 < len(block_out_channels) and upsample_match_channel else ch + stage.upsample = UpSmpl(ch, nxt, tus=i < depth_temporal) + ch = nxt + self.up.append(stage) + + self.norm_out = RMS_norm(ch) + self.conv_out = VideoConv3d(ch, out_channels, 3) + + def forward(self, z): + z = z.permute(0, 2, 1, 3, 4) + b, f, c, h, w = z.shape + z = z.reshape(b, f, 2, c // 2, h, w) + z = z.permute(0, 1, 2, 3, 4, 5).reshape(b, f * 2, c // 2, h, w) + z = z.permute(0, 2, 1, 3, 4) + z = z[:, :, 1:] + + x = self.conv_in(z) + z.repeat_interleave(self.block_out_channels[0] // self.z_channels, 1) + x = self.mid.block_2(self.mid.attn_1(self.mid.block_1(x))) + + for stage in self.up: + for blk in stage.block: + x = blk(x) + if hasattr(stage, 'upsample'): + x = stage.upsample(x) + + return self.conv_out(F.silu(self.norm_out(x))) diff --git a/comfy/ldm/lightricks/model.py b/comfy/ldm/lightricks/model.py index aa2ea62b1..def365ba7 100644 --- a/comfy/ldm/lightricks/model.py +++ b/comfy/ldm/lightricks/model.py @@ -271,7 +271,7 @@ class CrossAttention(nn.Module): self.to_out = nn.Sequential(operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout)) - def forward(self, x, context=None, mask=None, pe=None): + def forward(self, x, context=None, mask=None, pe=None, transformer_options={}): q = self.to_q(x) context = x if context is None else context k = self.to_k(context) @@ -285,9 +285,9 @@ class CrossAttention(nn.Module): k = apply_rotary_emb(k, pe) if mask is None: - out = comfy.ldm.modules.attention.optimized_attention(q, k, v, self.heads, attn_precision=self.attn_precision) + out = comfy.ldm.modules.attention.optimized_attention(q, k, v, self.heads, attn_precision=self.attn_precision, transformer_options=transformer_options) else: - out = comfy.ldm.modules.attention.optimized_attention_masked(q, k, v, self.heads, mask, attn_precision=self.attn_precision) + out = comfy.ldm.modules.attention.optimized_attention_masked(q, k, v, self.heads, mask, attn_precision=self.attn_precision, transformer_options=transformer_options) return self.to_out(out) @@ -303,12 +303,12 @@ class BasicTransformerBlock(nn.Module): self.scale_shift_table = nn.Parameter(torch.empty(6, dim, device=device, dtype=dtype)) - def forward(self, x, context=None, attention_mask=None, timestep=None, pe=None): + def forward(self, x, context=None, attention_mask=None, timestep=None, pe=None, transformer_options={}): shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (self.scale_shift_table[None, None].to(device=x.device, dtype=x.dtype) + timestep.reshape(x.shape[0], timestep.shape[1], self.scale_shift_table.shape[0], -1)).unbind(dim=2) - x += self.attn1(comfy.ldm.common_dit.rms_norm(x) * (1 + scale_msa) + shift_msa, pe=pe) * gate_msa + x += self.attn1(comfy.ldm.common_dit.rms_norm(x) * (1 + scale_msa) + shift_msa, pe=pe, transformer_options=transformer_options) * gate_msa - x += self.attn2(x, context=context, mask=attention_mask) + x += self.attn2(x, context=context, mask=attention_mask, transformer_options=transformer_options) y = comfy.ldm.common_dit.rms_norm(x) * (1 + scale_mlp) + shift_mlp x += self.ff(y) * gate_mlp @@ -479,10 +479,10 @@ class LTXVModel(torch.nn.Module): if ("double_block", i) in blocks_replace: def block_wrap(args): out = {} - out["img"] = block(args["img"], context=args["txt"], attention_mask=args["attention_mask"], timestep=args["vec"], pe=args["pe"]) + out["img"] = block(args["img"], context=args["txt"], attention_mask=args["attention_mask"], timestep=args["vec"], pe=args["pe"], transformer_options=args["transformer_options"]) return out - out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "attention_mask": attention_mask, "vec": timestep, "pe": pe}, {"original_block": block_wrap}) + out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "attention_mask": attention_mask, "vec": timestep, "pe": pe, "transformer_options": transformer_options}, {"original_block": block_wrap}) x = out["img"] else: x = block( @@ -490,7 +490,8 @@ class LTXVModel(torch.nn.Module): context=context, attention_mask=attention_mask, timestep=timestep, - pe=pe + pe=pe, + transformer_options=transformer_options, ) # 3. Output diff --git a/comfy/ldm/lumina/model.py b/comfy/ldm/lumina/model.py index e08ed817d..f87d98ac0 100644 --- a/comfy/ldm/lumina/model.py +++ b/comfy/ldm/lumina/model.py @@ -104,6 +104,7 @@ class JointAttention(nn.Module): x: torch.Tensor, x_mask: torch.Tensor, freqs_cis: torch.Tensor, + transformer_options={}, ) -> torch.Tensor: """ @@ -140,7 +141,7 @@ class JointAttention(nn.Module): if n_rep >= 1: xk = xk.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3) xv = xv.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3) - output = optimized_attention_masked(xq.movedim(1, 2), xk.movedim(1, 2), xv.movedim(1, 2), self.n_local_heads, x_mask, skip_reshape=True) + output = optimized_attention_masked(xq.movedim(1, 2), xk.movedim(1, 2), xv.movedim(1, 2), self.n_local_heads, x_mask, skip_reshape=True, transformer_options=transformer_options) return self.out(output) @@ -268,6 +269,7 @@ class JointTransformerBlock(nn.Module): x_mask: torch.Tensor, freqs_cis: torch.Tensor, adaln_input: Optional[torch.Tensor]=None, + transformer_options={}, ): """ Perform a forward pass through the TransformerBlock. @@ -290,6 +292,7 @@ class JointTransformerBlock(nn.Module): modulate(self.attention_norm1(x), scale_msa), x_mask, freqs_cis, + transformer_options=transformer_options, ) ) x = x + gate_mlp.unsqueeze(1).tanh() * self.ffn_norm2( @@ -304,6 +307,7 @@ class JointTransformerBlock(nn.Module): self.attention_norm1(x), x_mask, freqs_cis, + transformer_options=transformer_options, ) ) x = x + self.ffn_norm2( @@ -494,7 +498,7 @@ class NextDiT(nn.Module): return imgs def patchify_and_embed( - self, x: List[torch.Tensor] | torch.Tensor, cap_feats: torch.Tensor, cap_mask: torch.Tensor, t: torch.Tensor, num_tokens + self, x: List[torch.Tensor] | torch.Tensor, cap_feats: torch.Tensor, cap_mask: torch.Tensor, t: torch.Tensor, num_tokens, transformer_options={} ) -> Tuple[torch.Tensor, torch.Tensor, List[Tuple[int, int]], List[int], torch.Tensor]: bsz = len(x) pH = pW = self.patch_size @@ -554,7 +558,7 @@ class NextDiT(nn.Module): # refine context for layer in self.context_refiner: - cap_feats = layer(cap_feats, cap_mask, cap_freqs_cis) + cap_feats = layer(cap_feats, cap_mask, cap_freqs_cis, transformer_options=transformer_options) # refine image flat_x = [] @@ -573,7 +577,7 @@ class NextDiT(nn.Module): padded_img_embed = self.x_embedder(padded_img_embed) padded_img_mask = padded_img_mask.unsqueeze(1) for layer in self.noise_refiner: - padded_img_embed = layer(padded_img_embed, padded_img_mask, img_freqs_cis, t) + padded_img_embed = layer(padded_img_embed, padded_img_mask, img_freqs_cis, t, transformer_options=transformer_options) if cap_mask is not None: mask = torch.zeros(bsz, max_seq_len, dtype=dtype, device=device) @@ -616,12 +620,13 @@ class NextDiT(nn.Module): cap_feats = self.cap_embedder(cap_feats) # (N, L, D) # todo check if able to batchify w.o. redundant compute + transformer_options = kwargs.get("transformer_options", {}) x_is_tensor = isinstance(x, torch.Tensor) - x, mask, img_size, cap_size, freqs_cis = self.patchify_and_embed(x, cap_feats, cap_mask, t, num_tokens) + x, mask, img_size, cap_size, freqs_cis = self.patchify_and_embed(x, cap_feats, cap_mask, t, num_tokens, transformer_options=transformer_options) freqs_cis = freqs_cis.to(x.device) for layer in self.layers: - x = layer(x, mask, freqs_cis, adaln_input) + x = layer(x, mask, freqs_cis, adaln_input, transformer_options=transformer_options) x = self.final_layer(x, adaln_input) x = self.unpatchify(x, img_size, cap_size, return_tensor=x_is_tensor)[:,:,:h,:w] diff --git a/comfy/ldm/models/autoencoder.py b/comfy/ldm/models/autoencoder.py index 13bd6e16b..611d36a1b 100644 --- a/comfy/ldm/models/autoencoder.py +++ b/comfy/ldm/models/autoencoder.py @@ -26,6 +26,12 @@ class DiagonalGaussianRegularizer(torch.nn.Module): z = posterior.mode() return z, None +class EmptyRegularizer(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward(self, z: torch.Tensor) -> Tuple[torch.Tensor, dict]: + return z, None class AbstractAutoencoder(torch.nn.Module): """ diff --git a/comfy/ldm/modules/attention.py b/comfy/ldm/modules/attention.py index 043df28df..bf2553c37 100644 --- a/comfy/ldm/modules/attention.py +++ b/comfy/ldm/modules/attention.py @@ -5,8 +5,9 @@ import torch import torch.nn.functional as F from torch import nn, einsum from einops import rearrange, repeat -from typing import Optional +from typing import Optional, Any, Callable, Union import logging +import functools from .diffusionmodules.util import AlphaBlender, timestep_embedding from .sub_quadratic_attention import efficient_dot_product_attention @@ -17,23 +18,45 @@ if model_management.xformers_enabled(): import xformers import xformers.ops -if model_management.sage_attention_enabled(): - try: - from sageattention import sageattn - except ModuleNotFoundError as e: +SAGE_ATTENTION_IS_AVAILABLE = False +try: + from sageattention import sageattn + SAGE_ATTENTION_IS_AVAILABLE = True +except ModuleNotFoundError as e: + if model_management.sage_attention_enabled(): if e.name == "sageattention": logging.error(f"\n\nTo use the `--use-sage-attention` feature, the `sageattention` package must be installed first.\ncommand:\n\t{sys.executable} -m pip install sageattention") else: raise e exit(-1) -if model_management.flash_attention_enabled(): - try: - from flash_attn import flash_attn_func - except ModuleNotFoundError: +FLASH_ATTENTION_IS_AVAILABLE = False +try: + from flash_attn import flash_attn_func + FLASH_ATTENTION_IS_AVAILABLE = True +except ModuleNotFoundError: + if model_management.flash_attention_enabled(): logging.error(f"\n\nTo use the `--use-flash-attention` feature, the `flash-attn` package must be installed first.\ncommand:\n\t{sys.executable} -m pip install flash-attn") exit(-1) +REGISTERED_ATTENTION_FUNCTIONS = {} +def register_attention_function(name: str, func: Callable): + # avoid replacing existing functions + if name not in REGISTERED_ATTENTION_FUNCTIONS: + REGISTERED_ATTENTION_FUNCTIONS[name] = func + else: + logging.warning(f"Attention function {name} already registered, skipping registration.") + +def get_attention_function(name: str, default: Any=...) -> Union[Callable, None]: + if name == "optimized": + return optimized_attention + elif name not in REGISTERED_ATTENTION_FUNCTIONS: + if default is ...: + raise KeyError(f"Attention function {name} not found.") + else: + return default + return REGISTERED_ATTENTION_FUNCTIONS[name] + from comfy.cli_args import args import comfy.ops ops = comfy.ops.disable_weight_init @@ -91,7 +114,27 @@ class FeedForward(nn.Module): def Normalize(in_channels, dtype=None, device=None): return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype, device=device) -def attention_basic(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False): + +def wrap_attn(func): + @functools.wraps(func) + def wrapper(*args, **kwargs): + remove_attn_wrapper_key = False + try: + if "_inside_attn_wrapper" not in kwargs: + transformer_options = kwargs.get("transformer_options", None) + remove_attn_wrapper_key = True + kwargs["_inside_attn_wrapper"] = True + if transformer_options is not None: + if "optimized_attention_override" in transformer_options: + return transformer_options["optimized_attention_override"](func, *args, **kwargs) + return func(*args, **kwargs) + finally: + if remove_attn_wrapper_key: + del kwargs["_inside_attn_wrapper"] + return wrapper + +@wrap_attn +def attention_basic(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs): attn_precision = get_attn_precision(attn_precision, q.dtype) if skip_reshape: @@ -159,8 +202,8 @@ def attention_basic(q, k, v, heads, mask=None, attn_precision=None, skip_reshape ) return out - -def attention_sub_quad(query, key, value, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False): +@wrap_attn +def attention_sub_quad(query, key, value, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs): attn_precision = get_attn_precision(attn_precision, query.dtype) if skip_reshape: @@ -230,7 +273,8 @@ def attention_sub_quad(query, key, value, heads, mask=None, attn_precision=None, hidden_states = hidden_states.unflatten(0, (-1, heads)).transpose(1,2).flatten(start_dim=2) return hidden_states -def attention_split(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False): +@wrap_attn +def attention_split(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs): attn_precision = get_attn_precision(attn_precision, q.dtype) if skip_reshape: @@ -359,7 +403,8 @@ try: except: pass -def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False): +@wrap_attn +def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs): b = q.shape[0] dim_head = q.shape[-1] # check to make sure xformers isn't broken @@ -374,7 +419,7 @@ def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_resh disabled_xformers = True if disabled_xformers: - return attention_pytorch(q, k, v, heads, mask, skip_reshape=skip_reshape) + return attention_pytorch(q, k, v, heads, mask, skip_reshape=skip_reshape, **kwargs) if skip_reshape: # b h k d -> b k h d @@ -427,8 +472,8 @@ else: #TODO: other GPUs ? SDP_BATCH_LIMIT = 2**31 - -def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False): +@wrap_attn +def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs): if skip_reshape: b, _, _, dim_head = q.shape else: @@ -470,8 +515,8 @@ def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_resha ).transpose(1, 2).reshape(-1, q.shape[2], heads * dim_head) return out - -def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False): +@wrap_attn +def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs): if skip_reshape: b, _, _, dim_head = q.shape tensor_layout = "HND" @@ -501,7 +546,7 @@ def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape= lambda t: t.transpose(1, 2), (q, k, v), ) - return attention_pytorch(q, k, v, heads, mask=mask, skip_reshape=True, skip_output_reshape=skip_output_reshape) + return attention_pytorch(q, k, v, heads, mask=mask, skip_reshape=True, skip_output_reshape=skip_output_reshape, **kwargs) if tensor_layout == "HND": if not skip_output_reshape: @@ -534,8 +579,8 @@ except AttributeError as error: dropout_p: float = 0.0, causal: bool = False) -> torch.Tensor: assert False, f"Could not define flash_attn_wrapper: {FLASH_ATTN_ERROR}" - -def attention_flash(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False): +@wrap_attn +def attention_flash(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs): if skip_reshape: b, _, _, dim_head = q.shape else: @@ -597,6 +642,19 @@ else: optimized_attention_masked = optimized_attention + +# register core-supported attention functions +if SAGE_ATTENTION_IS_AVAILABLE: + register_attention_function("sage", attention_sage) +if FLASH_ATTENTION_IS_AVAILABLE: + register_attention_function("flash", attention_flash) +if model_management.xformers_enabled(): + register_attention_function("xformers", attention_xformers) +register_attention_function("pytorch", attention_pytorch) +register_attention_function("sub_quad", attention_sub_quad) +register_attention_function("split", attention_split) + + def optimized_attention_for_device(device, mask=False, small_input=False): if small_input: if model_management.pytorch_attention_enabled(): @@ -629,7 +687,7 @@ class CrossAttention(nn.Module): self.to_out = nn.Sequential(operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout)) - def forward(self, x, context=None, value=None, mask=None): + def forward(self, x, context=None, value=None, mask=None, transformer_options={}): q = self.to_q(x) context = default(context, x) k = self.to_k(context) @@ -640,9 +698,9 @@ class CrossAttention(nn.Module): v = self.to_v(context) if mask is None: - out = optimized_attention(q, k, v, self.heads, attn_precision=self.attn_precision) + out = optimized_attention(q, k, v, self.heads, attn_precision=self.attn_precision, transformer_options=transformer_options) else: - out = optimized_attention_masked(q, k, v, self.heads, mask, attn_precision=self.attn_precision) + out = optimized_attention_masked(q, k, v, self.heads, mask, attn_precision=self.attn_precision, transformer_options=transformer_options) return self.to_out(out) @@ -746,7 +804,7 @@ class BasicTransformerBlock(nn.Module): n = attn1_replace_patch[block_attn1](n, context_attn1, value_attn1, extra_options) n = self.attn1.to_out(n) else: - n = self.attn1(n, context=context_attn1, value=value_attn1) + n = self.attn1(n, context=context_attn1, value=value_attn1, transformer_options=transformer_options) if "attn1_output_patch" in transformer_patches: patch = transformer_patches["attn1_output_patch"] @@ -786,7 +844,7 @@ class BasicTransformerBlock(nn.Module): n = attn2_replace_patch[block_attn2](n, context_attn2, value_attn2, extra_options) n = self.attn2.to_out(n) else: - n = self.attn2(n, context=context_attn2, value=value_attn2) + n = self.attn2(n, context=context_attn2, value=value_attn2, transformer_options=transformer_options) if "attn2_output_patch" in transformer_patches: patch = transformer_patches["attn2_output_patch"] @@ -1017,7 +1075,7 @@ class SpatialVideoTransformer(SpatialTransformer): B, S, C = x_mix.shape x_mix = rearrange(x_mix, "(b t) s c -> (b s) t c", t=timesteps) - x_mix = mix_block(x_mix, context=time_context) #TODO: transformer_options + x_mix = mix_block(x_mix, context=time_context, transformer_options=transformer_options) x_mix = rearrange( x_mix, "(b s) t c -> (b t) s c", s=S, b=B // timesteps, c=C, t=timesteps ) diff --git a/comfy/ldm/modules/diffusionmodules/mmdit.py b/comfy/ldm/modules/diffusionmodules/mmdit.py index 4d6beba2d..42f406f1a 100644 --- a/comfy/ldm/modules/diffusionmodules/mmdit.py +++ b/comfy/ldm/modules/diffusionmodules/mmdit.py @@ -606,7 +606,7 @@ def block_mixing(*args, use_checkpoint=True, **kwargs): return _block_mixing(*args, **kwargs) -def _block_mixing(context, x, context_block, x_block, c): +def _block_mixing(context, x, context_block, x_block, c, transformer_options={}): context_qkv, context_intermediates = context_block.pre_attention(context, c) if x_block.x_block_self_attn: @@ -622,6 +622,7 @@ def _block_mixing(context, x, context_block, x_block, c): attn = optimized_attention( qkv[0], qkv[1], qkv[2], heads=x_block.attn.num_heads, + transformer_options=transformer_options, ) context_attn, x_attn = ( attn[:, : context_qkv[0].shape[1]], @@ -637,6 +638,7 @@ def _block_mixing(context, x, context_block, x_block, c): attn2 = optimized_attention( x_qkv2[0], x_qkv2[1], x_qkv2[2], heads=x_block.attn2.num_heads, + transformer_options=transformer_options, ) x = x_block.post_attention_x(x_attn, attn2, *x_intermediates) else: @@ -958,10 +960,10 @@ class MMDiT(nn.Module): if ("double_block", i) in blocks_replace: def block_wrap(args): out = {} - out["txt"], out["img"] = self.joint_blocks[i](args["txt"], args["img"], c=args["vec"]) + out["txt"], out["img"] = self.joint_blocks[i](args["txt"], args["img"], c=args["vec"], transformer_options=args["transformer_options"]) return out - out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": c_mod}, {"original_block": block_wrap}) + out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": c_mod, "transformer_options": transformer_options}, {"original_block": block_wrap}) context = out["txt"] x = out["img"] else: @@ -970,6 +972,7 @@ class MMDiT(nn.Module): x, c=c_mod, use_checkpoint=self.use_checkpoint, + transformer_options=transformer_options, ) if control is not None: control_o = control.get("output") diff --git a/comfy/ldm/modules/diffusionmodules/model.py b/comfy/ldm/modules/diffusionmodules/model.py index 8f598a848..4245eedca 100644 --- a/comfy/ldm/modules/diffusionmodules/model.py +++ b/comfy/ldm/modules/diffusionmodules/model.py @@ -145,7 +145,7 @@ class Downsample(nn.Module): class ResnetBlock(nn.Module): def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False, - dropout=0.0, temb_channels=512, conv_op=ops.Conv2d): + dropout=0.0, temb_channels=512, conv_op=ops.Conv2d, norm_op=Normalize): super().__init__() self.in_channels = in_channels out_channels = in_channels if out_channels is None else out_channels @@ -153,7 +153,7 @@ class ResnetBlock(nn.Module): self.use_conv_shortcut = conv_shortcut self.swish = torch.nn.SiLU(inplace=True) - self.norm1 = Normalize(in_channels) + self.norm1 = norm_op(in_channels) self.conv1 = conv_op(in_channels, out_channels, kernel_size=3, @@ -162,7 +162,7 @@ class ResnetBlock(nn.Module): if temb_channels > 0: self.temb_proj = ops.Linear(temb_channels, out_channels) - self.norm2 = Normalize(out_channels) + self.norm2 = norm_op(out_channels) self.dropout = torch.nn.Dropout(dropout, inplace=True) self.conv2 = conv_op(out_channels, out_channels, @@ -305,11 +305,11 @@ def vae_attention(): return normal_attention class AttnBlock(nn.Module): - def __init__(self, in_channels, conv_op=ops.Conv2d): + def __init__(self, in_channels, conv_op=ops.Conv2d, norm_op=Normalize): super().__init__() self.in_channels = in_channels - self.norm = Normalize(in_channels) + self.norm = norm_op(in_channels) self.q = conv_op(in_channels, in_channels, kernel_size=1, diff --git a/comfy/ldm/omnigen/omnigen2.py b/comfy/ldm/omnigen/omnigen2.py index 4884449f8..82edc92da 100644 --- a/comfy/ldm/omnigen/omnigen2.py +++ b/comfy/ldm/omnigen/omnigen2.py @@ -120,7 +120,7 @@ class Attention(nn.Module): nn.Dropout(0.0) ) - 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: + 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, transformer_options={}) -> torch.Tensor: batch_size, sequence_length, _ = hidden_states.shape query = self.to_q(hidden_states) @@ -146,7 +146,7 @@ class Attention(nn.Module): key = key.repeat_interleave(self.heads // self.kv_heads, dim=1) value = value.repeat_interleave(self.heads // self.kv_heads, dim=1) - hidden_states = optimized_attention_masked(query, key, value, self.heads, attention_mask, skip_reshape=True) + hidden_states = optimized_attention_masked(query, key, value, self.heads, attention_mask, skip_reshape=True, transformer_options=transformer_options) hidden_states = self.to_out[0](hidden_states) return hidden_states @@ -182,16 +182,16 @@ class OmniGen2TransformerBlock(nn.Module): self.norm2 = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device) self.ffn_norm2 = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device) - def forward(self, hidden_states: torch.Tensor, attention_mask: torch.Tensor, image_rotary_emb: torch.Tensor, temb: Optional[torch.Tensor] = None) -> torch.Tensor: + def forward(self, hidden_states: torch.Tensor, attention_mask: torch.Tensor, image_rotary_emb: torch.Tensor, temb: Optional[torch.Tensor] = None, transformer_options={}) -> torch.Tensor: if self.modulation: norm_hidden_states, gate_msa, scale_mlp, gate_mlp = self.norm1(hidden_states, temb) - attn_output = self.attn(norm_hidden_states, norm_hidden_states, attention_mask, image_rotary_emb) + attn_output = self.attn(norm_hidden_states, norm_hidden_states, attention_mask, image_rotary_emb, transformer_options=transformer_options) hidden_states = hidden_states + gate_msa.unsqueeze(1).tanh() * self.norm2(attn_output) mlp_output = self.feed_forward(self.ffn_norm1(hidden_states) * (1 + scale_mlp.unsqueeze(1))) hidden_states = hidden_states + gate_mlp.unsqueeze(1).tanh() * self.ffn_norm2(mlp_output) else: norm_hidden_states = self.norm1(hidden_states) - attn_output = self.attn(norm_hidden_states, norm_hidden_states, attention_mask, image_rotary_emb) + attn_output = self.attn(norm_hidden_states, norm_hidden_states, attention_mask, image_rotary_emb, transformer_options=transformer_options) hidden_states = hidden_states + self.norm2(attn_output) mlp_output = self.feed_forward(self.ffn_norm1(hidden_states)) hidden_states = hidden_states + self.ffn_norm2(mlp_output) @@ -390,7 +390,7 @@ class OmniGen2Transformer2DModel(nn.Module): 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): + 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, transformer_options={}): batch_size = len(hidden_states) hidden_states = self.x_embedder(hidden_states) @@ -405,17 +405,17 @@ class OmniGen2Transformer2DModel(nn.Module): shift += ref_img_len for layer in self.noise_refiner: - hidden_states = layer(hidden_states, padded_img_mask, noise_rotary_emb, temb) + hidden_states = layer(hidden_states, padded_img_mask, noise_rotary_emb, temb, transformer_options=transformer_options) 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) + ref_image_hidden_states = layer(ref_image_hidden_states, padded_ref_img_mask, ref_img_rotary_emb, temb, transformer_options=transformer_options) 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): + def forward(self, x, timesteps, context, num_tokens, ref_latents=None, attention_mask=None, transformer_options={}, **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 @@ -444,7 +444,7 @@ class OmniGen2Transformer2DModel(nn.Module): ) for layer in self.context_refiner: - text_hidden_states = layer(text_hidden_states, text_attention_mask, context_rotary_emb) + text_hidden_states = layer(text_hidden_states, text_attention_mask, context_rotary_emb, transformer_options=transformer_options) img_len = hidden_states.shape[1] combined_img_hidden_states = self.img_patch_embed_and_refine( @@ -453,13 +453,14 @@ class OmniGen2Transformer2DModel(nn.Module): noise_rotary_emb, ref_img_rotary_emb, l_effective_ref_img_len, l_effective_img_len, temb, + transformer_options=transformer_options, ) 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 = layer(hidden_states, attention_mask, rotary_emb, temb, transformer_options=transformer_options) hidden_states = self.norm_out(hidden_states, temb) diff --git a/comfy/ldm/qwen_image/model.py b/comfy/ldm/qwen_image/model.py index 04071f31c..b9f60c2b7 100644 --- a/comfy/ldm/qwen_image/model.py +++ b/comfy/ldm/qwen_image/model.py @@ -132,6 +132,7 @@ class Attention(nn.Module): encoder_hidden_states_mask: torch.FloatTensor = None, attention_mask: Optional[torch.FloatTensor] = None, image_rotary_emb: Optional[torch.Tensor] = None, + transformer_options={}, ) -> Tuple[torch.Tensor, torch.Tensor]: seq_txt = encoder_hidden_states.shape[1] @@ -159,7 +160,7 @@ class Attention(nn.Module): joint_key = joint_key.flatten(start_dim=2) joint_value = joint_value.flatten(start_dim=2) - joint_hidden_states = optimized_attention_masked(joint_query, joint_key, joint_value, self.heads, attention_mask) + joint_hidden_states = optimized_attention_masked(joint_query, joint_key, joint_value, self.heads, attention_mask, transformer_options=transformer_options) txt_attn_output = joint_hidden_states[:, :seq_txt, :] img_attn_output = joint_hidden_states[:, seq_txt:, :] @@ -226,6 +227,7 @@ class QwenImageTransformerBlock(nn.Module): encoder_hidden_states_mask: torch.Tensor, temb: torch.Tensor, image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, + transformer_options={}, ) -> Tuple[torch.Tensor, torch.Tensor]: img_mod_params = self.img_mod(temb) txt_mod_params = self.txt_mod(temb) @@ -242,6 +244,7 @@ class QwenImageTransformerBlock(nn.Module): encoder_hidden_states=txt_modulated, encoder_hidden_states_mask=encoder_hidden_states_mask, image_rotary_emb=image_rotary_emb, + transformer_options=transformer_options, ) hidden_states = hidden_states + img_gate1 * img_attn_output @@ -434,9 +437,9 @@ class QwenImageTransformer2DModel(nn.Module): if ("double_block", i) in blocks_replace: def block_wrap(args): out = {} - out["txt"], out["img"] = block(hidden_states=args["img"], encoder_hidden_states=args["txt"], encoder_hidden_states_mask=encoder_hidden_states_mask, temb=args["vec"], image_rotary_emb=args["pe"]) + out["txt"], out["img"] = block(hidden_states=args["img"], encoder_hidden_states=args["txt"], encoder_hidden_states_mask=encoder_hidden_states_mask, temb=args["vec"], image_rotary_emb=args["pe"], transformer_options=args["transformer_options"]) return out - out = blocks_replace[("double_block", i)]({"img": hidden_states, "txt": encoder_hidden_states, "vec": temb, "pe": image_rotary_emb}, {"original_block": block_wrap}) + out = blocks_replace[("double_block", i)]({"img": hidden_states, "txt": encoder_hidden_states, "vec": temb, "pe": image_rotary_emb, "transformer_options": transformer_options}, {"original_block": block_wrap}) hidden_states = out["img"] encoder_hidden_states = out["txt"] else: @@ -446,11 +449,12 @@ class QwenImageTransformer2DModel(nn.Module): encoder_hidden_states_mask=encoder_hidden_states_mask, temb=temb, image_rotary_emb=image_rotary_emb, + transformer_options=transformer_options, ) if "double_block" in patches: for p in patches["double_block"]: - out = p({"img": hidden_states, "txt": encoder_hidden_states, "x": x, "block_index": i}) + out = p({"img": hidden_states, "txt": encoder_hidden_states, "x": x, "block_index": i, "transformer_options": transformer_options}) hidden_states = out["img"] encoder_hidden_states = out["txt"] diff --git a/comfy/ldm/wan/model.py b/comfy/ldm/wan/model.py index 47857dc2b..63472ada2 100644 --- a/comfy/ldm/wan/model.py +++ b/comfy/ldm/wan/model.py @@ -52,7 +52,7 @@ class WanSelfAttention(nn.Module): self.norm_q = operation_settings.get("operations").RMSNorm(dim, eps=eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if qk_norm else nn.Identity() self.norm_k = operation_settings.get("operations").RMSNorm(dim, eps=eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if qk_norm else nn.Identity() - def forward(self, x, freqs): + def forward(self, x, freqs, transformer_options={}): r""" Args: x(Tensor): Shape [B, L, num_heads, C / num_heads] @@ -75,6 +75,7 @@ class WanSelfAttention(nn.Module): k.view(b, s, n * d), v, heads=self.num_heads, + transformer_options=transformer_options, ) x = self.o(x) @@ -83,7 +84,7 @@ class WanSelfAttention(nn.Module): class WanT2VCrossAttention(WanSelfAttention): - def forward(self, x, context, **kwargs): + def forward(self, x, context, transformer_options={}, **kwargs): r""" Args: x(Tensor): Shape [B, L1, C] @@ -95,7 +96,7 @@ class WanT2VCrossAttention(WanSelfAttention): v = self.v(context) # compute attention - x = optimized_attention(q, k, v, heads=self.num_heads) + x = optimized_attention(q, k, v, heads=self.num_heads, transformer_options=transformer_options) x = self.o(x) return x @@ -116,7 +117,7 @@ class WanI2VCrossAttention(WanSelfAttention): # self.alpha = nn.Parameter(torch.zeros((1, ))) self.norm_k_img = operation_settings.get("operations").RMSNorm(dim, eps=eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if qk_norm else nn.Identity() - def forward(self, x, context, context_img_len): + def forward(self, x, context, context_img_len, transformer_options={}): r""" Args: x(Tensor): Shape [B, L1, C] @@ -131,9 +132,9 @@ class WanI2VCrossAttention(WanSelfAttention): v = self.v(context) k_img = self.norm_k_img(self.k_img(context_img)) v_img = self.v_img(context_img) - img_x = optimized_attention(q, k_img, v_img, heads=self.num_heads) + img_x = optimized_attention(q, k_img, v_img, heads=self.num_heads, transformer_options=transformer_options) # compute attention - x = optimized_attention(q, k, v, heads=self.num_heads) + x = optimized_attention(q, k, v, heads=self.num_heads, transformer_options=transformer_options) # output x = x + img_x @@ -206,6 +207,7 @@ class WanAttentionBlock(nn.Module): freqs, context, context_img_len=257, + transformer_options={}, ): r""" Args: @@ -224,12 +226,12 @@ class WanAttentionBlock(nn.Module): # self-attention y = self.self_attn( torch.addcmul(repeat_e(e[0], x), self.norm1(x), 1 + repeat_e(e[1], x)), - freqs) + freqs, transformer_options=transformer_options) x = torch.addcmul(x, y, repeat_e(e[2], x)) # cross-attention & ffn - x = x + self.cross_attn(self.norm3(x), context, context_img_len=context_img_len) + x = x + self.cross_attn(self.norm3(x), context, context_img_len=context_img_len, transformer_options=transformer_options) y = self.ffn(torch.addcmul(repeat_e(e[3], x), self.norm2(x), 1 + repeat_e(e[4], x))) x = torch.addcmul(x, y, repeat_e(e[5], x)) return x @@ -559,12 +561,12 @@ class WanModel(torch.nn.Module): if ("double_block", i) in blocks_replace: def block_wrap(args): out = {} - out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len) + out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len, transformer_options=args["transformer_options"]) return out - out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs}, {"original_block": block_wrap}) + out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs, "transformer_options": transformer_options}, {"original_block": block_wrap}) x = out["img"] else: - x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len) + x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len, transformer_options=transformer_options) # head x = self.head(x, e) @@ -742,17 +744,17 @@ class VaceWanModel(WanModel): if ("double_block", i) in blocks_replace: def block_wrap(args): out = {} - out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len) + out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len, transformer_options=args["transformer_options"]) return out - out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs}, {"original_block": block_wrap}) + out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs, "transformer_options": transformer_options}, {"original_block": block_wrap}) x = out["img"] else: - x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len) + x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len, transformer_options=transformer_options) ii = self.vace_layers_mapping.get(i, None) if ii is not None: for iii in range(len(c)): - c_skip, c[iii] = self.vace_blocks[ii](c[iii], x=x_orig, e=e0, freqs=freqs, context=context, context_img_len=context_img_len) + c_skip, c[iii] = self.vace_blocks[ii](c[iii], x=x_orig, e=e0, freqs=freqs, context=context, context_img_len=context_img_len, transformer_options=transformer_options) x += c_skip * vace_strength[iii] del c_skip # head @@ -841,12 +843,12 @@ class CameraWanModel(WanModel): if ("double_block", i) in blocks_replace: def block_wrap(args): out = {} - out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len) + out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len, transformer_options=args["transformer_options"]) return out - out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs}, {"original_block": block_wrap}) + out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs, "transformer_options": transformer_options}, {"original_block": block_wrap}) x = out["img"] else: - x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len) + x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len, transformer_options=transformer_options) # head x = self.head(x, e) diff --git a/comfy/model_base.py b/comfy/model_base.py index 993ff65e6..324d89cff 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -1432,3 +1432,31 @@ class HunyuanImage21(BaseModel): out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance])) return out + +class HunyuanImage21Refiner(HunyuanImage21): + def concat_cond(self, **kwargs): + noise = kwargs.get("noise", None) + image = kwargs.get("concat_latent_image", None) + noise_augmentation = kwargs.get("noise_augmentation", 0.0) + device = kwargs["device"] + + if image is None: + shape_image = list(noise.shape) + image = torch.zeros(shape_image, dtype=noise.dtype, layout=noise.layout, device=noise.device) + else: + image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center") + image = self.process_latent_in(image) + image = utils.resize_to_batch_size(image, noise.shape[0]) + if noise_augmentation > 0: + generator = torch.Generator(device="cpu") + generator.manual_seed(kwargs.get("seed", 0) - 10) + noise = torch.randn(image.shape, generator=generator, dtype=image.dtype, device="cpu").to(image.device) + image = noise_augmentation * noise + min(1.0 - noise_augmentation, 0.75) * image + else: + image = 0.75 * image + return image + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + out['disable_time_r'] = comfy.conds.CONDConstant(True) + return out diff --git a/comfy/sd.py b/comfy/sd.py index 9dd9a74d4..02ddc7239 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -285,6 +285,7 @@ class VAE: self.process_output = lambda image: torch.clamp((image + 1.0) / 2.0, min=0.0, max=1.0) self.working_dtypes = [torch.bfloat16, torch.float32] self.disable_offload = False + self.not_video = False self.downscale_index_formula = None self.upscale_index_formula = None @@ -409,6 +410,20 @@ class VAE: self.downscale_ratio = (lambda a: max(0, math.floor((a + 7) / 8)), 32, 32) self.downscale_index_formula = (8, 32, 32) self.working_dtypes = [torch.bfloat16, torch.float32] + elif "decoder.conv_in.conv.weight" in sd and sd['decoder.conv_in.conv.weight'].shape[1] == 32: + 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} + self.latent_channels = ddconfig['z_channels'] = sd["decoder.conv_in.conv.weight"].shape[1] + self.downscale_ratio = 16 + self.upscale_ratio = 16 + self.latent_dim = 3 + self.not_video = True + self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32] + self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "comfy.ldm.models.autoencoder.EmptyRegularizer"}, + encoder_config={'target': "comfy.ldm.hunyuan_video.vae_refiner.Encoder", 'params': ddconfig}, + decoder_config={'target': "comfy.ldm.hunyuan_video.vae_refiner.Decoder", 'params': ddconfig}) + + self.memory_used_encode = lambda shape, dtype: (1400 * shape[-2] * shape[-1]) * model_management.dtype_size(dtype) + self.memory_used_decode = lambda shape, dtype: (1400 * shape[-3] * shape[-2] * shape[-1] * 16 * 16) * model_management.dtype_size(dtype) elif "decoder.conv_in.conv.weight" in sd: ddconfig = {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0} ddconfig["conv3d"] = True @@ -669,7 +684,7 @@ class VAE: self.throw_exception_if_invalid() pixel_samples = self.vae_encode_crop_pixels(pixel_samples) pixel_samples = pixel_samples.movedim(-1, 1) - if self.latent_dim == 3 and pixel_samples.ndim < 5: + if not self.not_video and self.latent_dim == 3 and pixel_samples.ndim < 5: pixel_samples = pixel_samples.movedim(1, 0).unsqueeze(0) try: memory_used = self.memory_used_encode(pixel_samples.shape, self.vae_dtype) diff --git a/comfy/supported_models.py b/comfy/supported_models.py index aa953b462..472ea0ae9 100644 --- a/comfy/supported_models.py +++ b/comfy/supported_models.py @@ -1321,6 +1321,23 @@ class HunyuanImage21(HunyuanVideo): hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen25_7b.transformer.".format(pref)) return supported_models_base.ClipTarget(comfy.text_encoders.hunyuan_image.HunyuanImageTokenizer, comfy.text_encoders.hunyuan_image.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, HunyuanImage21, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, Lumina2, WAN22_T2V, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, WAN22_Camera, WAN22_S2V, Hunyuan3Dv2mini, Hunyuan3Dv2, Hunyuan3Dv2_1, HiDream, Chroma, ACEStep, Omnigen2, QwenImage] +class HunyuanImage21Refiner(HunyuanVideo): + unet_config = { + "image_model": "hunyuan_video", + "patch_size": [1, 1, 1], + "vec_in_dim": None, + } + + sampling_settings = { + "shift": 4.0, + } + + latent_format = latent_formats.HunyuanImage21Refiner + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.HunyuanImage21Refiner(self, device=device) + return out + +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, HunyuanImage21Refiner, HunyuanImage21, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, Lumina2, WAN22_T2V, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, WAN22_Camera, WAN22_S2V, Hunyuan3Dv2mini, Hunyuan3Dv2, Hunyuan3Dv2_1, HiDream, Chroma, ACEStep, Omnigen2, QwenImage] models += [SVD_img2vid] diff --git a/comfy_api/latest/_io.py b/comfy_api/latest/_io.py index f770109d5..4826818df 100644 --- a/comfy_api/latest/_io.py +++ b/comfy_api/latest/_io.py @@ -331,7 +331,7 @@ class String(ComfyTypeIO): }) @comfytype(io_type="COMBO") -class Combo(ComfyTypeI): +class Combo(ComfyTypeIO): Type = str class Input(WidgetInput): """Combo input (dropdown).""" @@ -360,6 +360,14 @@ class Combo(ComfyTypeI): "remote": self.remote.as_dict() if self.remote else None, }) + class Output(Output): + def __init__(self, id: str=None, display_name: str=None, options: list[str]=None, tooltip: str=None, is_output_list=False): + super().__init__(id, display_name, tooltip, is_output_list) + self.options = options if options is not None else [] + + @property + def io_type(self): + return self.options @comfytype(io_type="COMBO") class MultiCombo(ComfyTypeI): diff --git a/comfy_api_nodes/nodes_kling.py b/comfy_api_nodes/nodes_kling.py index 9fa390985..5f55b2cc9 100644 --- a/comfy_api_nodes/nodes_kling.py +++ b/comfy_api_nodes/nodes_kling.py @@ -846,6 +846,8 @@ class KlingStartEndFrameNode(KlingImage2VideoNode): "pro mode / 10s duration / kling-v1-5": ("pro", "10", "kling-v1-5"), "pro mode / 5s duration / kling-v1-6": ("pro", "5", "kling-v1-6"), "pro mode / 10s duration / kling-v1-6": ("pro", "10", "kling-v1-6"), + "pro mode / 5s duration / kling-v2-1": ("pro", "5", "kling-v2-1"), + "pro mode / 10s duration / kling-v2-1": ("pro", "10", "kling-v2-1"), } @classmethod diff --git a/comfy_api_nodes/nodes_minimax.py b/comfy_api_nodes/nodes_minimax.py index bb3c9e710..bf560661c 100644 --- a/comfy_api_nodes/nodes_minimax.py +++ b/comfy_api_nodes/nodes_minimax.py @@ -1,9 +1,10 @@ from inspect import cleandoc -from typing import Union +from typing import Optional import logging import torch -from comfy.comfy_types.node_typing import IO +from typing_extensions import override +from comfy_api.latest import ComfyExtension, io as comfy_io from comfy_api.input_impl.video_types import VideoFromFile from comfy_api_nodes.apis import ( MinimaxVideoGenerationRequest, @@ -11,7 +12,7 @@ from comfy_api_nodes.apis import ( MinimaxFileRetrieveResponse, MinimaxTaskResultResponse, SubjectReferenceItem, - MiniMaxModel + MiniMaxModel, ) from comfy_api_nodes.apis.client import ( ApiEndpoint, @@ -31,372 +32,398 @@ from server import PromptServer I2V_AVERAGE_DURATION = 114 T2V_AVERAGE_DURATION = 234 -class MinimaxTextToVideoNode: + +async def _generate_mm_video( + *, + auth: dict[str, str], + node_id: str, + prompt_text: str, + seed: int, + model: str, + image: Optional[torch.Tensor] = None, # used for ImageToVideo + subject: Optional[torch.Tensor] = None, # used for SubjectToVideo + average_duration: Optional[int] = None, +) -> comfy_io.NodeOutput: + if image is None: + validate_string(prompt_text, field_name="prompt_text") + # upload image, if passed in + image_url = None + if image is not None: + image_url = (await upload_images_to_comfyapi(image, max_images=1, auth_kwargs=auth))[0] + + # TODO: figure out how to deal with subject properly, API returns invalid params when using S2V-01 model + subject_reference = None + if subject is not None: + subject_url = (await upload_images_to_comfyapi(subject, max_images=1, auth_kwargs=auth))[0] + subject_reference = [SubjectReferenceItem(image=subject_url)] + + + video_generate_operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/minimax/video_generation", + method=HttpMethod.POST, + request_model=MinimaxVideoGenerationRequest, + response_model=MinimaxVideoGenerationResponse, + ), + request=MinimaxVideoGenerationRequest( + model=MiniMaxModel(model), + prompt=prompt_text, + callback_url=None, + first_frame_image=image_url, + subject_reference=subject_reference, + prompt_optimizer=None, + ), + auth_kwargs=auth, + ) + response = await video_generate_operation.execute() + + task_id = response.task_id + if not task_id: + raise Exception(f"MiniMax generation failed: {response.base_resp}") + + video_generate_operation = PollingOperation( + poll_endpoint=ApiEndpoint( + path="/proxy/minimax/query/video_generation", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=MinimaxTaskResultResponse, + query_params={"task_id": task_id}, + ), + completed_statuses=["Success"], + failed_statuses=["Fail"], + status_extractor=lambda x: x.status.value, + estimated_duration=average_duration, + node_id=node_id, + auth_kwargs=auth, + ) + task_result = await video_generate_operation.execute() + + file_id = task_result.file_id + if file_id is None: + raise Exception("Request was not successful. Missing file ID.") + file_retrieve_operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/minimax/files/retrieve", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=MinimaxFileRetrieveResponse, + query_params={"file_id": int(file_id)}, + ), + request=EmptyRequest(), + auth_kwargs=auth, + ) + file_result = await file_retrieve_operation.execute() + + file_url = file_result.file.download_url + if file_url is None: + raise Exception( + f"No video was found in the response. Full response: {file_result.model_dump()}" + ) + logging.info("Generated video URL: %s", file_url) + if node_id: + if hasattr(file_result.file, "backup_download_url"): + message = f"Result URL: {file_url}\nBackup URL: {file_result.file.backup_download_url}" + else: + message = f"Result URL: {file_url}" + PromptServer.instance.send_progress_text(message, node_id) + + # Download and return as VideoFromFile + video_io = await download_url_to_bytesio(file_url) + if video_io is None: + error_msg = f"Failed to download video from {file_url}" + logging.error(error_msg) + raise Exception(error_msg) + return comfy_io.NodeOutput(VideoFromFile(video_io)) + + +class MinimaxTextToVideoNode(comfy_io.ComfyNode): """ Generates videos synchronously based on a prompt, and optional parameters using MiniMax's API. """ - AVERAGE_DURATION = T2V_AVERAGE_DURATION + @classmethod + def define_schema(cls) -> comfy_io.Schema: + return comfy_io.Schema( + node_id="MinimaxTextToVideoNode", + display_name="MiniMax Text to Video", + category="api node/video/MiniMax", + description=cleandoc(cls.__doc__ or ""), + inputs=[ + comfy_io.String.Input( + "prompt_text", + multiline=True, + default="", + tooltip="Text prompt to guide the video generation", + ), + comfy_io.Combo.Input( + "model", + options=["T2V-01", "T2V-01-Director"], + default="T2V-01", + tooltip="Model to use for video generation", + ), + comfy_io.Int.Input( + "seed", + default=0, + min=0, + max=0xFFFFFFFFFFFFFFFF, + step=1, + control_after_generate=True, + tooltip="The random seed used for creating the noise.", + optional=True, + ), + ], + outputs=[comfy_io.Video.Output()], + hidden=[ + comfy_io.Hidden.auth_token_comfy_org, + comfy_io.Hidden.api_key_comfy_org, + comfy_io.Hidden.unique_id, + ], + is_api_node=True, + ) @classmethod - def INPUT_TYPES(s): - return { - "required": { - "prompt_text": ( - "STRING", - { - "multiline": True, - "default": "", - "tooltip": "Text prompt to guide the video generation", - }, - ), - "model": ( - [ - "T2V-01", - "T2V-01-Director", - ], - { - "default": "T2V-01", - "tooltip": "Model to use for video generation", - }, - ), + async def execute( + cls, + prompt_text: str, + model: str = "T2V-01", + seed: int = 0, + ) -> comfy_io.NodeOutput: + return await _generate_mm_video( + auth={ + "auth_token": cls.hidden.auth_token_comfy_org, + "comfy_api_key": cls.hidden.api_key_comfy_org, }, - "optional": { - "seed": ( - IO.INT, - { - "default": 0, - "min": 0, - "max": 0xFFFFFFFFFFFFFFFF, - "control_after_generate": True, - "tooltip": "The random seed used for creating the noise.", - }, - ), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - RETURN_TYPES = ("VIDEO",) - DESCRIPTION = "Generates videos from prompts using MiniMax's API" - FUNCTION = "generate_video" - CATEGORY = "api node/video/MiniMax" - API_NODE = True - - async def generate_video( - self, - prompt_text, - seed=0, - model="T2V-01", - image: torch.Tensor=None, # used for ImageToVideo - subject: torch.Tensor=None, # used for SubjectToVideo - unique_id: Union[str, None]=None, - **kwargs, - ): - ''' - Function used between MiniMax nodes - supports T2V, I2V, and S2V, based on provided arguments. - ''' - if image is None: - validate_string(prompt_text, field_name="prompt_text") - # upload image, if passed in - image_url = None - if image is not None: - image_url = (await upload_images_to_comfyapi(image, max_images=1, auth_kwargs=kwargs))[0] - - # TODO: figure out how to deal with subject properly, API returns invalid params when using S2V-01 model - subject_reference = None - if subject is not None: - subject_url = (await upload_images_to_comfyapi(subject, max_images=1, auth_kwargs=kwargs))[0] - subject_reference = [SubjectReferenceItem(image=subject_url)] - - - video_generate_operation = SynchronousOperation( - endpoint=ApiEndpoint( - path="/proxy/minimax/video_generation", - method=HttpMethod.POST, - request_model=MinimaxVideoGenerationRequest, - response_model=MinimaxVideoGenerationResponse, - ), - request=MinimaxVideoGenerationRequest( - model=MiniMaxModel(model), - prompt=prompt_text, - callback_url=None, - first_frame_image=image_url, - subject_reference=subject_reference, - prompt_optimizer=None, - ), - auth_kwargs=kwargs, + node_id=cls.hidden.unique_id, + prompt_text=prompt_text, + seed=seed, + model=model, + image=None, + subject=None, + average_duration=T2V_AVERAGE_DURATION, ) - response = await video_generate_operation.execute() - - task_id = response.task_id - if not task_id: - raise Exception(f"MiniMax generation failed: {response.base_resp}") - - video_generate_operation = PollingOperation( - poll_endpoint=ApiEndpoint( - path="/proxy/minimax/query/video_generation", - method=HttpMethod.GET, - request_model=EmptyRequest, - response_model=MinimaxTaskResultResponse, - query_params={"task_id": task_id}, - ), - completed_statuses=["Success"], - failed_statuses=["Fail"], - status_extractor=lambda x: x.status.value, - estimated_duration=self.AVERAGE_DURATION, - node_id=unique_id, - auth_kwargs=kwargs, - ) - task_result = await video_generate_operation.execute() - - file_id = task_result.file_id - if file_id is None: - raise Exception("Request was not successful. Missing file ID.") - file_retrieve_operation = SynchronousOperation( - endpoint=ApiEndpoint( - path="/proxy/minimax/files/retrieve", - method=HttpMethod.GET, - request_model=EmptyRequest, - response_model=MinimaxFileRetrieveResponse, - query_params={"file_id": int(file_id)}, - ), - request=EmptyRequest(), - auth_kwargs=kwargs, - ) - file_result = await file_retrieve_operation.execute() - - file_url = file_result.file.download_url - if file_url is None: - raise Exception( - f"No video was found in the response. Full response: {file_result.model_dump()}" - ) - logging.info(f"Generated video URL: {file_url}") - if unique_id: - if hasattr(file_result.file, "backup_download_url"): - message = f"Result URL: {file_url}\nBackup URL: {file_result.file.backup_download_url}" - else: - message = f"Result URL: {file_url}" - PromptServer.instance.send_progress_text(message, unique_id) - - video_io = await download_url_to_bytesio(file_url) - if video_io is None: - error_msg = f"Failed to download video from {file_url}" - logging.error(error_msg) - raise Exception(error_msg) - return (VideoFromFile(video_io),) -class MinimaxImageToVideoNode(MinimaxTextToVideoNode): +class MinimaxImageToVideoNode(comfy_io.ComfyNode): """ Generates videos synchronously based on an image and prompt, and optional parameters using MiniMax's API. """ - AVERAGE_DURATION = I2V_AVERAGE_DURATION + @classmethod + def define_schema(cls) -> comfy_io.Schema: + return comfy_io.Schema( + node_id="MinimaxImageToVideoNode", + display_name="MiniMax Image to Video", + category="api node/video/MiniMax", + description=cleandoc(cls.__doc__ or ""), + inputs=[ + comfy_io.Image.Input( + "image", + tooltip="Image to use as first frame of video generation", + ), + comfy_io.String.Input( + "prompt_text", + multiline=True, + default="", + tooltip="Text prompt to guide the video generation", + ), + comfy_io.Combo.Input( + "model", + options=["I2V-01-Director", "I2V-01", "I2V-01-live"], + default="I2V-01", + tooltip="Model to use for video generation", + ), + comfy_io.Int.Input( + "seed", + default=0, + min=0, + max=0xFFFFFFFFFFFFFFFF, + step=1, + control_after_generate=True, + tooltip="The random seed used for creating the noise.", + optional=True, + ), + ], + outputs=[comfy_io.Video.Output()], + hidden=[ + comfy_io.Hidden.auth_token_comfy_org, + comfy_io.Hidden.api_key_comfy_org, + comfy_io.Hidden.unique_id, + ], + is_api_node=True, + ) @classmethod - def INPUT_TYPES(s): - return { - "required": { - "image": ( - IO.IMAGE, - { - "tooltip": "Image to use as first frame of video generation" - }, - ), - "prompt_text": ( - "STRING", - { - "multiline": True, - "default": "", - "tooltip": "Text prompt to guide the video generation", - }, - ), - "model": ( - [ - "I2V-01-Director", - "I2V-01", - "I2V-01-live", - ], - { - "default": "I2V-01", - "tooltip": "Model to use for video generation", - }, - ), + async def execute( + cls, + image: torch.Tensor, + prompt_text: str, + model: str = "I2V-01", + seed: int = 0, + ) -> comfy_io.NodeOutput: + return await _generate_mm_video( + auth={ + "auth_token": cls.hidden.auth_token_comfy_org, + "comfy_api_key": cls.hidden.api_key_comfy_org, }, - "optional": { - "seed": ( - IO.INT, - { - "default": 0, - "min": 0, - "max": 0xFFFFFFFFFFFFFFFF, - "control_after_generate": True, - "tooltip": "The random seed used for creating the noise.", - }, - ), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - RETURN_TYPES = ("VIDEO",) - DESCRIPTION = "Generates videos from an image and prompts using MiniMax's API" - FUNCTION = "generate_video" - CATEGORY = "api node/video/MiniMax" - API_NODE = True + node_id=cls.hidden.unique_id, + prompt_text=prompt_text, + seed=seed, + model=model, + image=image, + subject=None, + average_duration=I2V_AVERAGE_DURATION, + ) -class MinimaxSubjectToVideoNode(MinimaxTextToVideoNode): +class MinimaxSubjectToVideoNode(comfy_io.ComfyNode): """ Generates videos synchronously based on an image and prompt, and optional parameters using MiniMax's API. """ - AVERAGE_DURATION = T2V_AVERAGE_DURATION + @classmethod + def define_schema(cls) -> comfy_io.Schema: + return comfy_io.Schema( + node_id="MinimaxSubjectToVideoNode", + display_name="MiniMax Subject to Video", + category="api node/video/MiniMax", + description=cleandoc(cls.__doc__ or ""), + inputs=[ + comfy_io.Image.Input( + "subject", + tooltip="Image of subject to reference for video generation", + ), + comfy_io.String.Input( + "prompt_text", + multiline=True, + default="", + tooltip="Text prompt to guide the video generation", + ), + comfy_io.Combo.Input( + "model", + options=["S2V-01"], + default="S2V-01", + tooltip="Model to use for video generation", + ), + comfy_io.Int.Input( + "seed", + default=0, + min=0, + max=0xFFFFFFFFFFFFFFFF, + step=1, + control_after_generate=True, + tooltip="The random seed used for creating the noise.", + optional=True, + ), + ], + outputs=[comfy_io.Video.Output()], + hidden=[ + comfy_io.Hidden.auth_token_comfy_org, + comfy_io.Hidden.api_key_comfy_org, + comfy_io.Hidden.unique_id, + ], + is_api_node=True, + ) @classmethod - def INPUT_TYPES(s): - return { - "required": { - "subject": ( - IO.IMAGE, - { - "tooltip": "Image of subject to reference video generation" - }, - ), - "prompt_text": ( - "STRING", - { - "multiline": True, - "default": "", - "tooltip": "Text prompt to guide the video generation", - }, - ), - "model": ( - [ - "S2V-01", - ], - { - "default": "S2V-01", - "tooltip": "Model to use for video generation", - }, - ), + async def execute( + cls, + subject: torch.Tensor, + prompt_text: str, + model: str = "S2V-01", + seed: int = 0, + ) -> comfy_io.NodeOutput: + return await _generate_mm_video( + auth={ + "auth_token": cls.hidden.auth_token_comfy_org, + "comfy_api_key": cls.hidden.api_key_comfy_org, }, - "optional": { - "seed": ( - IO.INT, - { - "default": 0, - "min": 0, - "max": 0xFFFFFFFFFFFFFFFF, - "control_after_generate": True, - "tooltip": "The random seed used for creating the noise.", - }, - ), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - RETURN_TYPES = ("VIDEO",) - DESCRIPTION = "Generates videos from an image and prompts using MiniMax's API" - FUNCTION = "generate_video" - CATEGORY = "api node/video/MiniMax" - API_NODE = True + node_id=cls.hidden.unique_id, + prompt_text=prompt_text, + seed=seed, + model=model, + image=None, + subject=subject, + average_duration=T2V_AVERAGE_DURATION, + ) -class MinimaxHailuoVideoNode: +class MinimaxHailuoVideoNode(comfy_io.ComfyNode): """Generates videos from prompt, with optional start frame using the new MiniMax Hailuo-02 model.""" @classmethod - def INPUT_TYPES(s): - return { - "required": { - "prompt_text": ( - "STRING", - { - "multiline": True, - "default": "", - "tooltip": "Text prompt to guide the video generation.", - }, + def define_schema(cls) -> comfy_io.Schema: + return comfy_io.Schema( + node_id="MinimaxHailuoVideoNode", + display_name="MiniMax Hailuo Video", + category="api node/video/MiniMax", + description=cleandoc(cls.__doc__ or ""), + inputs=[ + comfy_io.String.Input( + "prompt_text", + multiline=True, + default="", + tooltip="Text prompt to guide the video generation.", ), - }, - "optional": { - "seed": ( - IO.INT, - { - "default": 0, - "min": 0, - "max": 0xFFFFFFFFFFFFFFFF, - "control_after_generate": True, - "tooltip": "The random seed used for creating the noise.", - }, + comfy_io.Int.Input( + "seed", + default=0, + min=0, + max=0xFFFFFFFFFFFFFFFF, + step=1, + control_after_generate=True, + tooltip="The random seed used for creating the noise.", + optional=True, ), - "first_frame_image": ( - IO.IMAGE, - { - "tooltip": "Optional image to use as the first frame to generate a video." - }, + comfy_io.Image.Input( + "first_frame_image", + tooltip="Optional image to use as the first frame to generate a video.", + optional=True, ), - "prompt_optimizer": ( - IO.BOOLEAN, - { - "tooltip": "Optimize prompt to improve generation quality when needed.", - "default": True, - }, + comfy_io.Boolean.Input( + "prompt_optimizer", + default=True, + tooltip="Optimize prompt to improve generation quality when needed.", + optional=True, ), - "duration": ( - IO.COMBO, - { - "tooltip": "The length of the output video in seconds.", - "default": 6, - "options": [6, 10], - }, + comfy_io.Combo.Input( + "duration", + options=[6, 10], + default=6, + tooltip="The length of the output video in seconds.", + optional=True, ), - "resolution": ( - IO.COMBO, - { - "tooltip": "The dimensions of the video display. " - "1080p corresponds to 1920 x 1080 pixels, 768p corresponds to 1366 x 768 pixels.", - "default": "768P", - "options": ["768P", "1080P"], - }, + comfy_io.Combo.Input( + "resolution", + options=["768P", "1080P"], + default="768P", + tooltip="The dimensions of the video display. 1080p is 1920x1080, 768p is 1366x768.", + optional=True, ), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, + ], + outputs=[comfy_io.Video.Output()], + hidden=[ + comfy_io.Hidden.auth_token_comfy_org, + comfy_io.Hidden.api_key_comfy_org, + comfy_io.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + prompt_text: str, + seed: int = 0, + first_frame_image: Optional[torch.Tensor] = None, # used for ImageToVideo + prompt_optimizer: bool = True, + duration: int = 6, + resolution: str = "768P", + model: str = "MiniMax-Hailuo-02", + ) -> comfy_io.NodeOutput: + auth = { + "auth_token": cls.hidden.auth_token_comfy_org, + "comfy_api_key": cls.hidden.api_key_comfy_org, } - - RETURN_TYPES = ("VIDEO",) - DESCRIPTION = cleandoc(__doc__ or "") - FUNCTION = "generate_video" - CATEGORY = "api node/video/MiniMax" - API_NODE = True - - async def generate_video( - self, - prompt_text, - seed=0, - first_frame_image: torch.Tensor=None, # used for ImageToVideo - prompt_optimizer=True, - duration=6, - resolution="768P", - model="MiniMax-Hailuo-02", - unique_id: Union[str, None]=None, - **kwargs, - ): if first_frame_image is None: validate_string(prompt_text, field_name="prompt_text") @@ -408,7 +435,7 @@ class MinimaxHailuoVideoNode: # upload image, if passed in image_url = None if first_frame_image is not None: - image_url = (await upload_images_to_comfyapi(first_frame_image, max_images=1, auth_kwargs=kwargs))[0] + image_url = (await upload_images_to_comfyapi(first_frame_image, max_images=1, auth_kwargs=auth))[0] video_generate_operation = SynchronousOperation( endpoint=ApiEndpoint( @@ -426,7 +453,7 @@ class MinimaxHailuoVideoNode: duration=duration, resolution=resolution, ), - auth_kwargs=kwargs, + auth_kwargs=auth, ) response = await video_generate_operation.execute() @@ -447,8 +474,8 @@ class MinimaxHailuoVideoNode: failed_statuses=["Fail"], status_extractor=lambda x: x.status.value, estimated_duration=average_duration, - node_id=unique_id, - auth_kwargs=kwargs, + node_id=cls.hidden.unique_id, + auth_kwargs=auth, ) task_result = await video_generate_operation.execute() @@ -464,7 +491,7 @@ class MinimaxHailuoVideoNode: query_params={"file_id": int(file_id)}, ), request=EmptyRequest(), - auth_kwargs=kwargs, + auth_kwargs=auth, ) file_result = await file_retrieve_operation.execute() @@ -474,34 +501,31 @@ class MinimaxHailuoVideoNode: f"No video was found in the response. Full response: {file_result.model_dump()}" ) logging.info(f"Generated video URL: {file_url}") - if unique_id: + if cls.hidden.unique_id: if hasattr(file_result.file, "backup_download_url"): message = f"Result URL: {file_url}\nBackup URL: {file_result.file.backup_download_url}" else: message = f"Result URL: {file_url}" - PromptServer.instance.send_progress_text(message, unique_id) + PromptServer.instance.send_progress_text(message, cls.hidden.unique_id) video_io = await download_url_to_bytesio(file_url) if video_io is None: error_msg = f"Failed to download video from {file_url}" logging.error(error_msg) raise Exception(error_msg) - return (VideoFromFile(video_io),) + return comfy_io.NodeOutput(VideoFromFile(video_io)) -# A dictionary that contains all nodes you want to export with their names -# NOTE: names should be globally unique -NODE_CLASS_MAPPINGS = { - "MinimaxTextToVideoNode": MinimaxTextToVideoNode, - "MinimaxImageToVideoNode": MinimaxImageToVideoNode, - # "MinimaxSubjectToVideoNode": MinimaxSubjectToVideoNode, - "MinimaxHailuoVideoNode": MinimaxHailuoVideoNode, -} +class MinimaxExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[comfy_io.ComfyNode]]: + return [ + MinimaxTextToVideoNode, + MinimaxImageToVideoNode, + # MinimaxSubjectToVideoNode, + MinimaxHailuoVideoNode, + ] -# A dictionary that contains the friendly/humanly readable titles for the nodes -NODE_DISPLAY_NAME_MAPPINGS = { - "MinimaxTextToVideoNode": "MiniMax Text to Video", - "MinimaxImageToVideoNode": "MiniMax Image to Video", - "MinimaxSubjectToVideoNode": "MiniMax Subject to Video", - "MinimaxHailuoVideoNode": "MiniMax Hailuo Video", -} + +async def comfy_entrypoint() -> MinimaxExtension: + return MinimaxExtension() diff --git a/comfy_api_nodes/nodes_moonvalley.py b/comfy_api_nodes/nodes_moonvalley.py index 806a70e06..08e838fef 100644 --- a/comfy_api_nodes/nodes_moonvalley.py +++ b/comfy_api_nodes/nodes_moonvalley.py @@ -1,6 +1,7 @@ import logging from typing import Any, Callable, Optional, TypeVar import torch +from typing_extensions import override from comfy_api_nodes.util.validation_utils import ( get_image_dimensions, validate_image_dimensions, @@ -26,11 +27,9 @@ from comfy_api_nodes.apinode_utils import ( upload_images_to_comfyapi, upload_video_to_comfyapi, ) -from comfy_api_nodes.mapper_utils import model_field_to_node_input -from comfy_api.input.video_types import VideoInput -from comfy.comfy_types.node_typing import IO -from comfy_api.input_impl import VideoFromFile +from comfy_api.input import VideoInput +from comfy_api.latest import ComfyExtension, InputImpl, io as comfy_io import av import io @@ -362,7 +361,7 @@ def trim_video(video: VideoInput, duration_sec: float) -> VideoInput: # Return as VideoFromFile using the buffer output_buffer.seek(0) - return VideoFromFile(output_buffer) + return InputImpl.VideoFromFile(output_buffer) except Exception as e: # Clean up on error @@ -373,166 +372,150 @@ def trim_video(video: VideoInput, duration_sec: float) -> VideoInput: raise RuntimeError(f"Failed to trim video: {str(e)}") from e -# --- BaseMoonvalleyVideoNode --- -class BaseMoonvalleyVideoNode: - def parseWidthHeightFromRes(self, resolution: str): - # Accepts a string like "16:9 (1920 x 1080)" and returns width, height as a dict - res_map = { - "16:9 (1920 x 1080)": {"width": 1920, "height": 1080}, - "9:16 (1080 x 1920)": {"width": 1080, "height": 1920}, - "1:1 (1152 x 1152)": {"width": 1152, "height": 1152}, - "4:3 (1536 x 1152)": {"width": 1536, "height": 1152}, - "3:4 (1152 x 1536)": {"width": 1152, "height": 1536}, - "21:9 (2560 x 1080)": {"width": 2560, "height": 1080}, - } - if resolution in res_map: - return res_map[resolution] - else: - # Default to 1920x1080 if unknown - return {"width": 1920, "height": 1080} +def parse_width_height_from_res(resolution: str): + # Accepts a string like "16:9 (1920 x 1080)" and returns width, height as a dict + res_map = { + "16:9 (1920 x 1080)": {"width": 1920, "height": 1080}, + "9:16 (1080 x 1920)": {"width": 1080, "height": 1920}, + "1:1 (1152 x 1152)": {"width": 1152, "height": 1152}, + "4:3 (1536 x 1152)": {"width": 1536, "height": 1152}, + "3:4 (1152 x 1536)": {"width": 1152, "height": 1536}, + "21:9 (2560 x 1080)": {"width": 2560, "height": 1080}, + } + return res_map.get(resolution, {"width": 1920, "height": 1080}) - def parseControlParameter(self, value): - control_map = { - "Motion Transfer": "motion_control", - "Canny": "canny_control", - "Pose Transfer": "pose_control", - "Depth": "depth_control", - } - if value in control_map: - return control_map[value] - else: - return control_map["Motion Transfer"] - async def get_response( - self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None - ) -> MoonvalleyPromptResponse: - return await poll_until_finished( - auth_kwargs, - ApiEndpoint( - path=f"{API_PROMPTS_ENDPOINT}/{task_id}", - method=HttpMethod.GET, - request_model=EmptyRequest, - response_model=MoonvalleyPromptResponse, - ), - result_url_extractor=get_video_url_from_response, - node_id=node_id, - ) +def parse_control_parameter(value): + control_map = { + "Motion Transfer": "motion_control", + "Canny": "canny_control", + "Pose Transfer": "pose_control", + "Depth": "depth_control", + } + return control_map.get(value, control_map["Motion Transfer"]) + + +async def get_response( + task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None +) -> MoonvalleyPromptResponse: + return await poll_until_finished( + auth_kwargs, + ApiEndpoint( + path=f"{API_PROMPTS_ENDPOINT}/{task_id}", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=MoonvalleyPromptResponse, + ), + result_url_extractor=get_video_url_from_response, + node_id=node_id, + ) + + +class MoonvalleyImg2VideoNode(comfy_io.ComfyNode): @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "prompt": model_field_to_node_input( - IO.STRING, - MoonvalleyTextToVideoRequest, - "prompt_text", + def define_schema(cls) -> comfy_io.Schema: + return comfy_io.Schema( + node_id="MoonvalleyImg2VideoNode", + display_name="Moonvalley Marey Image to Video", + category="api node/video/Moonvalley Marey", + description="Moonvalley Marey Image to Video Node", + inputs=[ + comfy_io.Image.Input( + "image", + tooltip="The reference image used to generate the video", + ), + comfy_io.String.Input( + "prompt", multiline=True, ), - "negative_prompt": model_field_to_node_input( - IO.STRING, - MoonvalleyTextToVideoInferenceParams, + comfy_io.String.Input( "negative_prompt", multiline=True, - default=" gopro, bright, contrast, static, overexposed, vignette, artifacts, still, noise, texture, scanlines, videogame, 360 camera, VR, transition, flare, saturation, distorted, warped, wide angle, saturated, vibrant, glowing, cross dissolve, cheesy, ugly hands, mutated hands, mutant, disfigured, extra fingers, blown out, horrible, blurry, worst quality, bad, dissolve, melt, fade in, fade out, wobbly, weird, low quality, plastic, stock footage, video camera, boring", + default=" gopro, bright, contrast, static, overexposed, vignette, " + "artifacts, still, noise, texture, scanlines, videogame, 360 camera, VR, transition, " + "flare, saturation, distorted, warped, wide angle, saturated, vibrant, glowing, " + "cross dissolve, cheesy, ugly hands, mutated hands, mutant, disfigured, extra fingers, " + "blown out, horrible, blurry, worst quality, bad, dissolve, melt, fade in, fade out, " + "wobbly, weird, low quality, plastic, stock footage, video camera, boring", + tooltip="Negative prompt text", ), - "resolution": ( - IO.COMBO, - { - "options": [ - "16:9 (1920 x 1080)", - "9:16 (1080 x 1920)", - "1:1 (1152 x 1152)", - "4:3 (1440 x 1080)", - "3:4 (1080 x 1440)", - "21:9 (2560 x 1080)", - ], - "default": "16:9 (1920 x 1080)", - "tooltip": "Resolution of the output video", - }, + comfy_io.Combo.Input( + "resolution", + options=[ + "16:9 (1920 x 1080)", + "9:16 (1080 x 1920)", + "1:1 (1152 x 1152)", + "4:3 (1536 x 1152)", + "3:4 (1152 x 1536)", + "21:9 (2560 x 1080)", + ], + default="16:9 (1920 x 1080)", + tooltip="Resolution of the output video", ), - "prompt_adherence": model_field_to_node_input( - IO.FLOAT, - MoonvalleyTextToVideoInferenceParams, - "guidance_scale", + comfy_io.Float.Input( + "prompt_adherence", default=10.0, - step=1, - min=1, - max=20, + min=1.0, + max=20.0, + step=1.0, + tooltip="Guidance scale for generation control", ), - "seed": model_field_to_node_input( - IO.INT, - MoonvalleyTextToVideoInferenceParams, + comfy_io.Int.Input( "seed", default=9, min=0, max=4294967295, step=1, - display="number", + display_mode=comfy_io.NumberDisplay.number, tooltip="Random seed value", ), - "steps": model_field_to_node_input( - IO.INT, - MoonvalleyTextToVideoInferenceParams, + comfy_io.Int.Input( "steps", default=100, min=1, max=100, + step=1, + tooltip="Number of denoising steps", ), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - "optional": { - "image": model_field_to_node_input( - IO.IMAGE, - MoonvalleyTextToVideoRequest, - "image_url", - tooltip="The reference image used to generate the video", - ), - }, - } - - RETURN_TYPES = ("STRING",) - FUNCTION = "generate" - CATEGORY = "api node/video/Moonvalley Marey" - API_NODE = True - - def generate(self, **kwargs): - return None - - -# --- MoonvalleyImg2VideoNode --- -class MoonvalleyImg2VideoNode(BaseMoonvalleyVideoNode): + ], + outputs=[comfy_io.Video.Output()], + hidden=[ + comfy_io.Hidden.auth_token_comfy_org, + comfy_io.Hidden.api_key_comfy_org, + comfy_io.Hidden.unique_id, + ], + is_api_node=True, + ) @classmethod - def INPUT_TYPES(cls): - return super().INPUT_TYPES() - - RETURN_TYPES = ("VIDEO",) - RETURN_NAMES = ("video",) - DESCRIPTION = "Moonvalley Marey Image to Video Node" - - async def generate( - self, prompt, negative_prompt, unique_id: Optional[str] = None, **kwargs - ): - image = kwargs.get("image", None) - if image is None: - raise MoonvalleyApiError("image is required") - + async def execute( + cls, + image: torch.Tensor, + prompt: str, + negative_prompt: str, + resolution: str, + prompt_adherence: float, + seed: int, + steps: int, + ) -> comfy_io.NodeOutput: validate_input_image(image, True) validate_prompts(prompt, negative_prompt, MOONVALLEY_MAREY_MAX_PROMPT_LENGTH) - width_height = self.parseWidthHeightFromRes(kwargs.get("resolution")) + width_height = parse_width_height_from_res(resolution) + + auth = { + "auth_token": cls.hidden.auth_token_comfy_org, + "comfy_api_key": cls.hidden.api_key_comfy_org, + } inference_params = MoonvalleyTextToVideoInferenceParams( negative_prompt=negative_prompt, - steps=kwargs.get("steps"), - seed=kwargs.get("seed"), - guidance_scale=kwargs.get("prompt_adherence"), + steps=steps, + seed=seed, + guidance_scale=prompt_adherence, num_frames=128, - width=width_height.get("width"), - height=width_height.get("height"), + width=width_height["width"], + height=width_height["height"], use_negative_prompts=True, ) """Upload image to comfy backend to have a URL available for further processing""" @@ -541,7 +524,7 @@ class MoonvalleyImg2VideoNode(BaseMoonvalleyVideoNode): image_url = ( await upload_images_to_comfyapi( - image, max_images=1, auth_kwargs=kwargs, mime_type=mime_type + image, max_images=1, auth_kwargs=auth, mime_type=mime_type ) )[0] @@ -556,127 +539,102 @@ class MoonvalleyImg2VideoNode(BaseMoonvalleyVideoNode): response_model=MoonvalleyPromptResponse, ), request=request, - auth_kwargs=kwargs, + auth_kwargs=auth, ) task_creation_response = await initial_operation.execute() validate_task_creation_response(task_creation_response) task_id = task_creation_response.id - final_response = await self.get_response( - task_id, auth_kwargs=kwargs, node_id=unique_id + final_response = await get_response( + task_id, auth_kwargs=auth, node_id=cls.hidden.unique_id ) video = await download_url_to_video_output(final_response.output_url) - return (video,) + return comfy_io.NodeOutput(video) -# --- MoonvalleyVid2VidNode --- -class MoonvalleyVideo2VideoNode(BaseMoonvalleyVideoNode): - def __init__(self): - super().__init__() +class MoonvalleyVideo2VideoNode(comfy_io.ComfyNode): @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "prompt": model_field_to_node_input( - IO.STRING, - MoonvalleyVideoToVideoRequest, - "prompt_text", + def define_schema(cls) -> comfy_io.Schema: + return comfy_io.Schema( + node_id="MoonvalleyVideo2VideoNode", + display_name="Moonvalley Marey Video to Video", + category="api node/video/Moonvalley Marey", + description="", + inputs=[ + comfy_io.String.Input( + "prompt", multiline=True, + tooltip="Describes the video to generate", ), - "negative_prompt": model_field_to_node_input( - IO.STRING, - MoonvalleyVideoToVideoInferenceParams, + comfy_io.String.Input( "negative_prompt", multiline=True, - default=" gopro, bright, contrast, static, overexposed, vignette, artifacts, still, noise, texture, scanlines, videogame, 360 camera, VR, transition, flare, saturation, distorted, warped, wide angle, saturated, vibrant, glowing, cross dissolve, cheesy, ugly hands, mutated hands, mutant, disfigured, extra fingers, blown out, horrible, blurry, worst quality, bad, dissolve, melt, fade in, fade out, wobbly, weird, low quality, plastic, stock footage, video camera, boring", + default=" gopro, bright, contrast, static, overexposed, vignette, " + "artifacts, still, noise, texture, scanlines, videogame, 360 camera, VR, transition, " + "flare, saturation, distorted, warped, wide angle, saturated, vibrant, glowing, " + "cross dissolve, cheesy, ugly hands, mutated hands, mutant, disfigured, extra fingers, " + "blown out, horrible, blurry, worst quality, bad, dissolve, melt, fade in, fade out, " + "wobbly, weird, low quality, plastic, stock footage, video camera, boring", + tooltip="Negative prompt text", ), - "seed": model_field_to_node_input( - IO.INT, - MoonvalleyVideoToVideoInferenceParams, + comfy_io.Int.Input( "seed", default=9, min=0, max=4294967295, step=1, - display="number", + display_mode=comfy_io.NumberDisplay.number, tooltip="Random seed value", control_after_generate=False, ), - "prompt_adherence": model_field_to_node_input( - IO.FLOAT, - MoonvalleyVideoToVideoInferenceParams, - "guidance_scale", - default=10.0, + comfy_io.Video.Input( + "video", + tooltip="The reference video used to generate the output video. Must be at least 5 seconds long. " + "Videos longer than 5s will be automatically trimmed. Only MP4 format supported.", + ), + comfy_io.Combo.Input( + "control_type", + options=["Motion Transfer", "Pose Transfer"], + default="Motion Transfer", + optional=True, + ), + comfy_io.Int.Input( + "motion_intensity", + default=100, + min=0, + max=100, step=1, - min=1, - max=20, + tooltip="Only used if control_type is 'Motion Transfer'", + optional=True, ), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - "optional": { - "video": ( - IO.VIDEO, - { - "default": "", - "multiline": False, - "tooltip": "The reference video used to generate the output video. Must be at least 5 seconds long. Videos longer than 5s will be automatically trimmed. Only MP4 format supported.", - }, - ), - "control_type": ( - ["Motion Transfer", "Pose Transfer"], - {"default": "Motion Transfer"}, - ), - "motion_intensity": ( - "INT", - { - "default": 100, - "step": 1, - "min": 0, - "max": 100, - "tooltip": "Only used if control_type is 'Motion Transfer'", - }, - ), - "image": model_field_to_node_input( - IO.IMAGE, - MoonvalleyTextToVideoRequest, - "image_url", - tooltip="The reference image used to generate the video", - ), - }, + ], + outputs=[comfy_io.Video.Output()], + hidden=[ + comfy_io.Hidden.auth_token_comfy_org, + comfy_io.Hidden.api_key_comfy_org, + comfy_io.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + prompt: str, + negative_prompt: str, + seed: int, + video: Optional[VideoInput] = None, + control_type: str = "Motion Transfer", + motion_intensity: Optional[int] = 100, + ) -> comfy_io.NodeOutput: + auth = { + "auth_token": cls.hidden.auth_token_comfy_org, + "comfy_api_key": cls.hidden.api_key_comfy_org, } - RETURN_TYPES = ("VIDEO",) - RETURN_NAMES = ("video",) - - async def generate( - self, prompt, negative_prompt, unique_id: Optional[str] = None, **kwargs - ): - video = kwargs.get("video") - image = kwargs.get("image", None) - - if not video: - raise MoonvalleyApiError("video is required") - - video_url = "" - if video: - validated_video = validate_video_to_video_input(video) - video_url = await upload_video_to_comfyapi( - validated_video, auth_kwargs=kwargs - ) - mime_type = "image/png" - - if not image is None: - validate_input_image(image, with_frame_conditioning=True) - image_url = await upload_images_to_comfyapi( - image=image, auth_kwargs=kwargs, max_images=1, mime_type=mime_type - ) - control_type = kwargs.get("control_type") - motion_intensity = kwargs.get("motion_intensity") + validated_video = validate_video_to_video_input(video) + video_url = await upload_video_to_comfyapi(validated_video, auth_kwargs=auth) """Validate prompts and inference input""" validate_prompts(prompt, negative_prompt) @@ -688,11 +646,11 @@ class MoonvalleyVideo2VideoNode(BaseMoonvalleyVideoNode): inference_params = MoonvalleyVideoToVideoInferenceParams( negative_prompt=negative_prompt, - seed=kwargs.get("seed"), + seed=seed, control_params=control_params, ) - control = self.parseControlParameter(control_type) + control = parse_control_parameter(control_type) request = MoonvalleyVideoToVideoRequest( control_type=control, @@ -700,7 +658,6 @@ class MoonvalleyVideo2VideoNode(BaseMoonvalleyVideoNode): prompt_text=prompt, inference_params=inference_params, ) - request.image_url = image_url if not image is None else None initial_operation = SynchronousOperation( endpoint=ApiEndpoint( @@ -710,58 +667,125 @@ class MoonvalleyVideo2VideoNode(BaseMoonvalleyVideoNode): response_model=MoonvalleyPromptResponse, ), request=request, - auth_kwargs=kwargs, + auth_kwargs=auth, ) task_creation_response = await initial_operation.execute() validate_task_creation_response(task_creation_response) task_id = task_creation_response.id - final_response = await self.get_response( - task_id, auth_kwargs=kwargs, node_id=unique_id + final_response = await get_response( + task_id, auth_kwargs=auth, node_id=cls.hidden.unique_id ) video = await download_url_to_video_output(final_response.output_url) - - return (video,) + return comfy_io.NodeOutput(video) -# --- MoonvalleyTxt2VideoNode --- -class MoonvalleyTxt2VideoNode(BaseMoonvalleyVideoNode): - def __init__(self): - super().__init__() - - RETURN_TYPES = ("VIDEO",) - RETURN_NAMES = ("video",) +class MoonvalleyTxt2VideoNode(comfy_io.ComfyNode): @classmethod - def INPUT_TYPES(cls): - input_types = super().INPUT_TYPES() - # Remove image-specific parameters - for param in ["image"]: - if param in input_types["optional"]: - del input_types["optional"][param] - return input_types + def define_schema(cls) -> comfy_io.Schema: + return comfy_io.Schema( + node_id="MoonvalleyTxt2VideoNode", + display_name="Moonvalley Marey Text to Video", + category="api node/video/Moonvalley Marey", + description="", + inputs=[ + comfy_io.String.Input( + "prompt", + multiline=True, + ), + comfy_io.String.Input( + "negative_prompt", + multiline=True, + default=" gopro, bright, contrast, static, overexposed, vignette, " + "artifacts, still, noise, texture, scanlines, videogame, 360 camera, VR, transition, " + "flare, saturation, distorted, warped, wide angle, saturated, vibrant, glowing, " + "cross dissolve, cheesy, ugly hands, mutated hands, mutant, disfigured, extra fingers, " + "blown out, horrible, blurry, worst quality, bad, dissolve, melt, fade in, fade out, " + "wobbly, weird, low quality, plastic, stock footage, video camera, boring", + tooltip="Negative prompt text", + ), + comfy_io.Combo.Input( + "resolution", + options=[ + "16:9 (1920 x 1080)", + "9:16 (1080 x 1920)", + "1:1 (1152 x 1152)", + "4:3 (1536 x 1152)", + "3:4 (1152 x 1536)", + "21:9 (2560 x 1080)", + ], + default="16:9 (1920 x 1080)", + tooltip="Resolution of the output video", + ), + comfy_io.Float.Input( + "prompt_adherence", + default=10.0, + min=1.0, + max=20.0, + step=1.0, + tooltip="Guidance scale for generation control", + ), + comfy_io.Int.Input( + "seed", + default=9, + min=0, + max=4294967295, + step=1, + display_mode=comfy_io.NumberDisplay.number, + tooltip="Random seed value", + ), + comfy_io.Int.Input( + "steps", + default=100, + min=1, + max=100, + step=1, + tooltip="Inference steps", + ), + ], + outputs=[comfy_io.Video.Output()], + hidden=[ + comfy_io.Hidden.auth_token_comfy_org, + comfy_io.Hidden.api_key_comfy_org, + comfy_io.Hidden.unique_id, + ], + is_api_node=True, + ) - async def generate( - self, prompt, negative_prompt, unique_id: Optional[str] = None, **kwargs - ): + @classmethod + async def execute( + cls, + prompt: str, + negative_prompt: str, + resolution: str, + prompt_adherence: float, + seed: int, + steps: int, + ) -> comfy_io.NodeOutput: validate_prompts(prompt, negative_prompt, MOONVALLEY_MAREY_MAX_PROMPT_LENGTH) - width_height = self.parseWidthHeightFromRes(kwargs.get("resolution")) + width_height = parse_width_height_from_res(resolution) + + auth = { + "auth_token": cls.hidden.auth_token_comfy_org, + "comfy_api_key": cls.hidden.api_key_comfy_org, + } inference_params = MoonvalleyTextToVideoInferenceParams( negative_prompt=negative_prompt, - steps=kwargs.get("steps"), - seed=kwargs.get("seed"), - guidance_scale=kwargs.get("prompt_adherence"), + steps=steps, + seed=seed, + guidance_scale=prompt_adherence, num_frames=128, - width=width_height.get("width"), - height=width_height.get("height"), + width=width_height["width"], + height=width_height["height"], ) request = MoonvalleyTextToVideoRequest( prompt_text=prompt, inference_params=inference_params ) - initial_operation = SynchronousOperation( + init_op = SynchronousOperation( endpoint=ApiEndpoint( path=API_TXT2VIDEO_ENDPOINT, method=HttpMethod.POST, @@ -769,29 +793,29 @@ class MoonvalleyTxt2VideoNode(BaseMoonvalleyVideoNode): response_model=MoonvalleyPromptResponse, ), request=request, - auth_kwargs=kwargs, + auth_kwargs=auth, ) - task_creation_response = await initial_operation.execute() + task_creation_response = await init_op.execute() validate_task_creation_response(task_creation_response) task_id = task_creation_response.id - final_response = await self.get_response( - task_id, auth_kwargs=kwargs, node_id=unique_id + final_response = await get_response( + task_id, auth_kwargs=auth, node_id=cls.hidden.unique_id ) video = await download_url_to_video_output(final_response.output_url) - return (video,) + return comfy_io.NodeOutput(video) -NODE_CLASS_MAPPINGS = { - "MoonvalleyImg2VideoNode": MoonvalleyImg2VideoNode, - "MoonvalleyTxt2VideoNode": MoonvalleyTxt2VideoNode, - "MoonvalleyVideo2VideoNode": MoonvalleyVideo2VideoNode, -} +class MoonvalleyExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[comfy_io.ComfyNode]]: + return [ + MoonvalleyImg2VideoNode, + MoonvalleyTxt2VideoNode, + MoonvalleyVideo2VideoNode, + ] -NODE_DISPLAY_NAME_MAPPINGS = { - "MoonvalleyImg2VideoNode": "Moonvalley Marey Image to Video", - "MoonvalleyTxt2VideoNode": "Moonvalley Marey Text to Video", - "MoonvalleyVideo2VideoNode": "Moonvalley Marey Video to Video", -} +async def comfy_entrypoint() -> MoonvalleyExtension: + return MoonvalleyExtension() diff --git a/comfy_extras/nodes_camera_trajectory.py b/comfy_extras/nodes_camera_trajectory.py index 5e0e39f91..eb7ef363c 100644 --- a/comfy_extras/nodes_camera_trajectory.py +++ b/comfy_extras/nodes_camera_trajectory.py @@ -2,12 +2,12 @@ import nodes import torch import numpy as np from einops import rearrange +from typing_extensions import override import comfy.model_management +from comfy_api.latest import ComfyExtension, io -MAX_RESOLUTION = nodes.MAX_RESOLUTION - CAMERA_DICT = { "base_T_norm": 1.5, "base_angle": np.pi/3, @@ -148,32 +148,47 @@ def get_camera_motion(angle, T, speed, n=81): RT = np.stack(RT) return RT -class WanCameraEmbedding: +class WanCameraEmbedding(io.ComfyNode): @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "camera_pose":(["Static","Pan Up","Pan Down","Pan Left","Pan Right","Zoom In","Zoom Out","Anti Clockwise (ACW)", "ClockWise (CW)"],{"default":"Static"}), - "width": ("INT", {"default": 832, "min": 16, "max": MAX_RESOLUTION, "step": 16}), - "height": ("INT", {"default": 480, "min": 16, "max": MAX_RESOLUTION, "step": 16}), - "length": ("INT", {"default": 81, "min": 1, "max": MAX_RESOLUTION, "step": 4}), - }, - "optional":{ - "speed":("FLOAT",{"default":1.0, "min": 0, "max": 10.0, "step": 0.1}), - "fx":("FLOAT",{"default":0.5, "min": 0, "max": 1, "step": 0.000000001}), - "fy":("FLOAT",{"default":0.5, "min": 0, "max": 1, "step": 0.000000001}), - "cx":("FLOAT",{"default":0.5, "min": 0, "max": 1, "step": 0.01}), - "cy":("FLOAT",{"default":0.5, "min": 0, "max": 1, "step": 0.01}), - } + def define_schema(cls): + return io.Schema( + node_id="WanCameraEmbedding", + category="camera", + inputs=[ + io.Combo.Input( + "camera_pose", + options=[ + "Static", + "Pan Up", + "Pan Down", + "Pan Left", + "Pan Right", + "Zoom In", + "Zoom Out", + "Anti Clockwise (ACW)", + "ClockWise (CW)", + ], + default="Static", + ), + io.Int.Input("width", default=832, min=16, max=nodes.MAX_RESOLUTION, step=16), + io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16), + io.Int.Input("length", default=81, min=1, max=nodes.MAX_RESOLUTION, step=4), + io.Float.Input("speed", default=1.0, min=0, max=10.0, step=0.1, optional=True), + io.Float.Input("fx", default=0.5, min=0, max=1, step=0.000000001, optional=True), + io.Float.Input("fy", default=0.5, min=0, max=1, step=0.000000001, optional=True), + io.Float.Input("cx", default=0.5, min=0, max=1, step=0.01, optional=True), + io.Float.Input("cy", default=0.5, min=0, max=1, step=0.01, optional=True), + ], + outputs=[ + io.WanCameraEmbedding.Output(display_name="camera_embedding"), + io.Int.Output(display_name="width"), + io.Int.Output(display_name="height"), + io.Int.Output(display_name="length"), + ], + ) - } - - RETURN_TYPES = ("WAN_CAMERA_EMBEDDING","INT","INT","INT") - RETURN_NAMES = ("camera_embedding","width","height","length") - FUNCTION = "run" - CATEGORY = "camera" - - def run(self, camera_pose, width, height, length, speed=1.0, fx=0.5, fy=0.5, cx=0.5, cy=0.5): + @classmethod + def execute(cls, camera_pose, width, height, length, speed=1.0, fx=0.5, fy=0.5, cx=0.5, cy=0.5) -> io.NodeOutput: """ Use Camera trajectory as extrinsic parameters to calculate Plücker embeddings (Sitzmannet al., 2021) Adapted from https://github.com/aigc-apps/VideoX-Fun/blob/main/comfyui/comfyui_nodes.py @@ -210,9 +225,15 @@ class WanCameraEmbedding: control_camera_video = control_camera_video.contiguous().view(b, f // 4, 4, c, h, w).transpose(2, 3) control_camera_video = control_camera_video.contiguous().view(b, f // 4, c * 4, h, w).transpose(1, 2) - return (control_camera_video, width, height, length) + return io.NodeOutput(control_camera_video, width, height, length) -NODE_CLASS_MAPPINGS = { - "WanCameraEmbedding": WanCameraEmbedding, -} +class CameraTrajectoryExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + WanCameraEmbedding, + ] + +async def comfy_entrypoint() -> CameraTrajectoryExtension: + return CameraTrajectoryExtension() diff --git a/comfy_extras/nodes_canny.py b/comfy_extras/nodes_canny.py index d85e6b856..576f3640a 100644 --- a/comfy_extras/nodes_canny.py +++ b/comfy_extras/nodes_canny.py @@ -1,25 +1,41 @@ from kornia.filters import canny +from typing_extensions import override + import comfy.model_management +from comfy_api.latest import ComfyExtension, io -class Canny: +class Canny(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": {"image": ("IMAGE",), - "low_threshold": ("FLOAT", {"default": 0.4, "min": 0.01, "max": 0.99, "step": 0.01}), - "high_threshold": ("FLOAT", {"default": 0.8, "min": 0.01, "max": 0.99, "step": 0.01}) - }} + def define_schema(cls): + return io.Schema( + node_id="Canny", + category="image/preprocessors", + inputs=[ + io.Image.Input("image"), + io.Float.Input("low_threshold", default=0.4, min=0.01, max=0.99, step=0.01), + io.Float.Input("high_threshold", default=0.8, min=0.01, max=0.99, step=0.01), + ], + outputs=[io.Image.Output()], + ) - RETURN_TYPES = ("IMAGE",) - FUNCTION = "detect_edge" + @classmethod + def detect_edge(cls, image, low_threshold, high_threshold): + # Deprecated: use the V3 schema's `execute` method instead of this. + return cls.execute(image, low_threshold, high_threshold) - CATEGORY = "image/preprocessors" - - def detect_edge(self, image, low_threshold, high_threshold): + @classmethod + def execute(cls, image, low_threshold, high_threshold) -> io.NodeOutput: output = canny(image.to(comfy.model_management.get_torch_device()).movedim(-1, 1), low_threshold, high_threshold) img_out = output[1].to(comfy.model_management.intermediate_device()).repeat(1, 3, 1, 1).movedim(1, -1) - return (img_out,) + return io.NodeOutput(img_out) -NODE_CLASS_MAPPINGS = { - "Canny": Canny, -} + +class CannyExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [Canny] + + +async def comfy_entrypoint() -> CannyExtension: + return CannyExtension() diff --git a/comfy_extras/nodes_cfg.py b/comfy_extras/nodes_cfg.py index 5abdc115a..4ebb4b51e 100644 --- a/comfy_extras/nodes_cfg.py +++ b/comfy_extras/nodes_cfg.py @@ -1,5 +1,10 @@ +from typing_extensions import override + import torch +from comfy_api.latest import ComfyExtension, io + + # https://github.com/WeichenFan/CFG-Zero-star def optimized_scale(positive, negative): positive_flat = positive.reshape(positive.shape[0], -1) @@ -16,17 +21,20 @@ def optimized_scale(positive, negative): return st_star.reshape([positive.shape[0]] + [1] * (positive.ndim - 1)) -class CFGZeroStar: +class CFGZeroStar(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": {"model": ("MODEL",), - }} - RETURN_TYPES = ("MODEL",) - RETURN_NAMES = ("patched_model",) - FUNCTION = "patch" - CATEGORY = "advanced/guidance" + def define_schema(cls) -> io.Schema: + return io.Schema( + node_id="CFGZeroStar", + category="advanced/guidance", + inputs=[ + io.Model.Input("model"), + ], + outputs=[io.Model.Output(display_name="patched_model")], + ) - def patch(self, model): + @classmethod + def execute(cls, model) -> io.NodeOutput: m = model.clone() def cfg_zero_star(args): guidance_scale = args['cond_scale'] @@ -38,21 +46,24 @@ class CFGZeroStar: return out + uncond_p * (alpha - 1.0) + guidance_scale * uncond_p * (1.0 - alpha) m.set_model_sampler_post_cfg_function(cfg_zero_star) - return (m, ) + return io.NodeOutput(m) -class CFGNorm: +class CFGNorm(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": {"model": ("MODEL",), - "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}), - }} - RETURN_TYPES = ("MODEL",) - RETURN_NAMES = ("patched_model",) - FUNCTION = "patch" - CATEGORY = "advanced/guidance" - EXPERIMENTAL = True + def define_schema(cls) -> io.Schema: + return io.Schema( + node_id="CFGNorm", + category="advanced/guidance", + inputs=[ + io.Model.Input("model"), + io.Float.Input("strength", default=1.0, min=0.0, max=100.0, step=0.01), + ], + outputs=[io.Model.Output(display_name="patched_model")], + is_experimental=True, + ) - def patch(self, model, strength): + @classmethod + def execute(cls, model, strength) -> io.NodeOutput: m = model.clone() def cfg_norm(args): cond_p = args['cond_denoised'] @@ -64,9 +75,17 @@ class CFGNorm: return pred_text_ * scale * strength m.set_model_sampler_post_cfg_function(cfg_norm) - return (m, ) + return io.NodeOutput(m) -NODE_CLASS_MAPPINGS = { - "CFGZeroStar": CFGZeroStar, - "CFGNorm": CFGNorm, -} + +class CfgExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + CFGZeroStar, + CFGNorm, + ] + + +async def comfy_entrypoint() -> CfgExtension: + return CfgExtension() diff --git a/comfy_extras/nodes_cond.py b/comfy_extras/nodes_cond.py index 58c16f621..8b06e3de9 100644 --- a/comfy_extras/nodes_cond.py +++ b/comfy_extras/nodes_cond.py @@ -1,15 +1,25 @@ +from typing_extensions import override + +from comfy_api.latest import ComfyExtension, io -class CLIPTextEncodeControlnet: +class CLIPTextEncodeControlnet(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": {"clip": ("CLIP", ), "conditioning": ("CONDITIONING", ), "text": ("STRING", {"multiline": True, "dynamicPrompts": True})}} - RETURN_TYPES = ("CONDITIONING",) - FUNCTION = "encode" + def define_schema(cls) -> io.Schema: + return io.Schema( + node_id="CLIPTextEncodeControlnet", + category="_for_testing/conditioning", + inputs=[ + io.Clip.Input("clip"), + io.Conditioning.Input("conditioning"), + io.String.Input("text", multiline=True, dynamic_prompts=True), + ], + outputs=[io.Conditioning.Output()], + is_experimental=True, + ) - CATEGORY = "_for_testing/conditioning" - - def encode(self, clip, conditioning, text): + @classmethod + def execute(cls, clip, conditioning, text) -> io.NodeOutput: tokens = clip.tokenize(text) cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True) c = [] @@ -18,32 +28,41 @@ class CLIPTextEncodeControlnet: n[1]['cross_attn_controlnet'] = cond n[1]['pooled_output_controlnet'] = pooled c.append(n) - return (c, ) + return io.NodeOutput(c) -class T5TokenizerOptions: +class T5TokenizerOptions(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return { - "required": { - "clip": ("CLIP", ), - "min_padding": ("INT", {"default": 0, "min": 0, "max": 10000, "step": 1}), - "min_length": ("INT", {"default": 0, "min": 0, "max": 10000, "step": 1}), - } - } + def define_schema(cls) -> io.Schema: + return io.Schema( + node_id="T5TokenizerOptions", + category="_for_testing/conditioning", + inputs=[ + io.Clip.Input("clip"), + io.Int.Input("min_padding", default=0, min=0, max=10000, step=1), + io.Int.Input("min_length", default=0, min=0, max=10000, step=1), + ], + outputs=[io.Clip.Output()], + is_experimental=True, + ) - CATEGORY = "_for_testing/conditioning" - RETURN_TYPES = ("CLIP",) - FUNCTION = "set_options" - - def set_options(self, clip, min_padding, min_length): + @classmethod + def execute(cls, clip, min_padding, min_length) -> io.NodeOutput: clip = clip.clone() for t5_type in ["t5xxl", "pile_t5xl", "t5base", "mt5xl", "umt5xxl"]: clip.set_tokenizer_option("{}_min_padding".format(t5_type), min_padding) clip.set_tokenizer_option("{}_min_length".format(t5_type), min_length) - return (clip, ) + return io.NodeOutput(clip) -NODE_CLASS_MAPPINGS = { - "CLIPTextEncodeControlnet": CLIPTextEncodeControlnet, - "T5TokenizerOptions": T5TokenizerOptions, -} + +class CondExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + CLIPTextEncodeControlnet, + T5TokenizerOptions, + ] + + +async def comfy_entrypoint() -> CondExtension: + return CondExtension() diff --git a/comfy_extras/nodes_cosmos.py b/comfy_extras/nodes_cosmos.py index 4f4960551..7dd129d19 100644 --- a/comfy_extras/nodes_cosmos.py +++ b/comfy_extras/nodes_cosmos.py @@ -1,25 +1,32 @@ +from typing_extensions import override import nodes import torch import comfy.model_management import comfy.utils import comfy.latent_formats +from comfy_api.latest import ComfyExtension, io -class EmptyCosmosLatentVideo: + +class EmptyCosmosLatentVideo(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": { "width": ("INT", {"default": 1280, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), - "height": ("INT", {"default": 704, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), - "length": ("INT", {"default": 121, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 8}), - "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})}} - RETURN_TYPES = ("LATENT",) - FUNCTION = "generate" + def define_schema(cls) -> io.Schema: + return io.Schema( + node_id="EmptyCosmosLatentVideo", + category="latent/video", + inputs=[ + io.Int.Input("width", default=1280, min=16, max=nodes.MAX_RESOLUTION, step=16), + io.Int.Input("height", default=704, min=16, max=nodes.MAX_RESOLUTION, step=16), + io.Int.Input("length", default=121, min=1, max=nodes.MAX_RESOLUTION, step=8), + io.Int.Input("batch_size", default=1, min=1, max=4096), + ], + outputs=[io.Latent.Output()], + ) - CATEGORY = "latent/video" - - def generate(self, width, height, length, batch_size=1): + @classmethod + def execute(cls, width, height, length, batch_size=1) -> io.NodeOutput: latent = torch.zeros([batch_size, 16, ((length - 1) // 8) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) - return ({"samples": latent}, ) + return io.NodeOutput({"samples": latent}) def vae_encode_with_padding(vae, image, width, height, length, padding=0): @@ -33,31 +40,31 @@ def vae_encode_with_padding(vae, image, width, height, length, padding=0): return latent_temp[:, :, :latent_len] -class CosmosImageToVideoLatent: +class CosmosImageToVideoLatent(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": {"vae": ("VAE", ), - "width": ("INT", {"default": 1280, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), - "height": ("INT", {"default": 704, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), - "length": ("INT", {"default": 121, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 8}), - "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), - }, - "optional": {"start_image": ("IMAGE", ), - "end_image": ("IMAGE", ), - }} + def define_schema(cls) -> io.Schema: + return io.Schema( + node_id="CosmosImageToVideoLatent", + category="conditioning/inpaint", + inputs=[ + io.Vae.Input("vae"), + io.Int.Input("width", default=1280, min=16, max=nodes.MAX_RESOLUTION, step=16), + io.Int.Input("height", default=704, min=16, max=nodes.MAX_RESOLUTION, step=16), + io.Int.Input("length", default=121, min=1, max=nodes.MAX_RESOLUTION, step=8), + io.Int.Input("batch_size", default=1, min=1, max=4096), + io.Image.Input("start_image", optional=True), + io.Image.Input("end_image", optional=True), + ], + outputs=[io.Latent.Output()], + ) - - RETURN_TYPES = ("LATENT",) - FUNCTION = "encode" - - CATEGORY = "conditioning/inpaint" - - def encode(self, vae, width, height, length, batch_size, start_image=None, end_image=None): + @classmethod + def execute(cls, vae, width, height, length, batch_size, start_image=None, end_image=None) -> io.NodeOutput: latent = torch.zeros([1, 16, ((length - 1) // 8) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) if start_image is None and end_image is None: out_latent = {} out_latent["samples"] = latent - return (out_latent,) + return io.NodeOutput(out_latent) mask = torch.ones([latent.shape[0], 1, ((length - 1) // 8) + 1, latent.shape[-2], latent.shape[-1]], device=comfy.model_management.intermediate_device()) @@ -74,33 +81,33 @@ class CosmosImageToVideoLatent: out_latent = {} out_latent["samples"] = latent.repeat((batch_size, ) + (1,) * (latent.ndim - 1)) out_latent["noise_mask"] = mask.repeat((batch_size, ) + (1,) * (mask.ndim - 1)) - return (out_latent,) + return io.NodeOutput(out_latent) -class CosmosPredict2ImageToVideoLatent: +class CosmosPredict2ImageToVideoLatent(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": {"vae": ("VAE", ), - "width": ("INT", {"default": 848, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), - "height": ("INT", {"default": 480, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), - "length": ("INT", {"default": 93, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}), - "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), - }, - "optional": {"start_image": ("IMAGE", ), - "end_image": ("IMAGE", ), - }} + def define_schema(cls) -> io.Schema: + return io.Schema( + node_id="CosmosPredict2ImageToVideoLatent", + category="conditioning/inpaint", + inputs=[ + io.Vae.Input("vae"), + io.Int.Input("width", default=848, min=16, max=nodes.MAX_RESOLUTION, step=16), + io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16), + io.Int.Input("length", default=93, min=1, max=nodes.MAX_RESOLUTION, step=4), + io.Int.Input("batch_size", default=1, min=1, max=4096), + io.Image.Input("start_image", optional=True), + io.Image.Input("end_image", optional=True), + ], + outputs=[io.Latent.Output()], + ) - - RETURN_TYPES = ("LATENT",) - FUNCTION = "encode" - - CATEGORY = "conditioning/inpaint" - - def encode(self, vae, width, height, length, batch_size, start_image=None, end_image=None): + @classmethod + def execute(cls, vae, width, height, length, batch_size, start_image=None, end_image=None) -> io.NodeOutput: latent = torch.zeros([1, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) if start_image is None and end_image is None: out_latent = {} out_latent["samples"] = latent - return (out_latent,) + return io.NodeOutput(out_latent) mask = torch.ones([latent.shape[0], 1, ((length - 1) // 4) + 1, latent.shape[-2], latent.shape[-1]], device=comfy.model_management.intermediate_device()) @@ -119,10 +126,18 @@ class CosmosPredict2ImageToVideoLatent: latent = latent_format.process_out(latent) * mask + latent * (1.0 - mask) out_latent["samples"] = latent.repeat((batch_size, ) + (1,) * (latent.ndim - 1)) out_latent["noise_mask"] = mask.repeat((batch_size, ) + (1,) * (mask.ndim - 1)) - return (out_latent,) + return io.NodeOutput(out_latent) -NODE_CLASS_MAPPINGS = { - "EmptyCosmosLatentVideo": EmptyCosmosLatentVideo, - "CosmosImageToVideoLatent": CosmosImageToVideoLatent, - "CosmosPredict2ImageToVideoLatent": CosmosPredict2ImageToVideoLatent, -} + +class CosmosExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + EmptyCosmosLatentVideo, + CosmosImageToVideoLatent, + CosmosPredict2ImageToVideoLatent, + ] + + +async def comfy_entrypoint() -> CosmosExtension: + return CosmosExtension() diff --git a/comfy_extras/nodes_hunyuan.py b/comfy_extras/nodes_hunyuan.py index ce031ceb2..db398cdf1 100644 --- a/comfy_extras/nodes_hunyuan.py +++ b/comfy_extras/nodes_hunyuan.py @@ -128,6 +128,28 @@ class EmptyHunyuanImageLatent: latent = torch.zeros([batch_size, 64, height // 32, width // 32], device=comfy.model_management.intermediate_device()) return ({"samples":latent}, ) +class HunyuanRefinerLatent: + @classmethod + def INPUT_TYPES(s): + return {"required": {"positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "latent": ("LATENT", ), + "noise_augmentation": ("FLOAT", {"default": 0.10, "min": 0.0, "max": 1.0, "step": 0.01}), + }} + + RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") + RETURN_NAMES = ("positive", "negative", "latent") + + FUNCTION = "execute" + + def execute(self, positive, negative, latent, noise_augmentation): + latent = latent["samples"] + positive = node_helpers.conditioning_set_values(positive, {"concat_latent_image": latent, "noise_augmentation": noise_augmentation}) + negative = node_helpers.conditioning_set_values(negative, {"concat_latent_image": latent, "noise_augmentation": noise_augmentation}) + out_latent = {} + out_latent["samples"] = torch.zeros([latent.shape[0], 32, latent.shape[-3], latent.shape[-2], latent.shape[-1]], device=comfy.model_management.intermediate_device()) + return (positive, negative, out_latent) + NODE_CLASS_MAPPINGS = { "CLIPTextEncodeHunyuanDiT": CLIPTextEncodeHunyuanDiT, @@ -135,4 +157,5 @@ NODE_CLASS_MAPPINGS = { "EmptyHunyuanLatentVideo": EmptyHunyuanLatentVideo, "HunyuanImageToVideo": HunyuanImageToVideo, "EmptyHunyuanImageLatent": EmptyHunyuanImageLatent, + "HunyuanRefinerLatent": HunyuanRefinerLatent, } diff --git a/requirements.txt b/requirements.txt index f17e2d714..219250f50 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ -comfyui-frontend-package==1.25.11 +comfyui-frontend-package==1.26.11 comfyui-workflow-templates==0.1.81 comfyui-embedded-docs==0.2.6 comfyui_manager==4.0.1b5