diff --git a/README.md b/README.md index 28beec427..91fb510e1 100644 --- a/README.md +++ b/README.md @@ -67,6 +67,8 @@ See what ComfyUI can do with the [example workflows](https://comfyanonymous.gith - [HiDream](https://comfyanonymous.github.io/ComfyUI_examples/hidream/) - [Qwen Image](https://comfyanonymous.github.io/ComfyUI_examples/qwen_image/) - [Hunyuan Image 2.1](https://comfyanonymous.github.io/ComfyUI_examples/hunyuan_image/) + - [Flux 2](https://comfyanonymous.github.io/ComfyUI_examples/flux2/) + - [Z Image](https://comfyanonymous.github.io/ComfyUI_examples/z_image/) - Image Editing Models - [Omnigen 2](https://comfyanonymous.github.io/ComfyUI_examples/omnigen/) - [Flux Kontext](https://comfyanonymous.github.io/ComfyUI_examples/flux/#flux-kontext-image-editing-model) diff --git a/comfy/latent_formats.py b/comfy/latent_formats.py index 204fc048d..8e110f45d 100644 --- a/comfy/latent_formats.py +++ b/comfy/latent_formats.py @@ -6,6 +6,7 @@ class LatentFormat: latent_dimensions = 2 latent_rgb_factors = None latent_rgb_factors_bias = None + latent_rgb_factors_reshape = None taesd_decoder_name = None def process_in(self, latent): @@ -178,6 +179,54 @@ class Flux(SD3): def process_out(self, latent): return (latent / self.scale_factor) + self.shift_factor +class Flux2(LatentFormat): + latent_channels = 128 + + def __init__(self): + self.latent_rgb_factors =[ + [0.0058, 0.0113, 0.0073], + [0.0495, 0.0443, 0.0836], + [-0.0099, 0.0096, 0.0644], + [0.2144, 0.3009, 0.3652], + [0.0166, -0.0039, -0.0054], + [0.0157, 0.0103, -0.0160], + [-0.0398, 0.0902, -0.0235], + [-0.0052, 0.0095, 0.0109], + [-0.3527, -0.2712, -0.1666], + [-0.0301, -0.0356, -0.0180], + [-0.0107, 0.0078, 0.0013], + [0.0746, 0.0090, -0.0941], + [0.0156, 0.0169, 0.0070], + [-0.0034, -0.0040, -0.0114], + [0.0032, 0.0181, 0.0080], + [-0.0939, -0.0008, 0.0186], + [0.0018, 0.0043, 0.0104], + [0.0284, 0.0056, -0.0127], + [-0.0024, -0.0022, -0.0030], + [0.1207, -0.0026, 0.0065], + [0.0128, 0.0101, 0.0142], + [0.0137, -0.0072, -0.0007], + [0.0095, 0.0092, -0.0059], + [0.0000, -0.0077, -0.0049], + [-0.0465, -0.0204, -0.0312], + [0.0095, 0.0012, -0.0066], + [0.0290, -0.0034, 0.0025], + [0.0220, 0.0169, -0.0048], + [-0.0332, -0.0457, -0.0468], + [-0.0085, 0.0389, 0.0609], + [-0.0076, 0.0003, -0.0043], + [-0.0111, -0.0460, -0.0614], + ] + + self.latent_rgb_factors_bias = [-0.0329, -0.0718, -0.0851] + self.latent_rgb_factors_reshape = lambda t: t.reshape(t.shape[0], 32, 2, 2, t.shape[-2], t.shape[-1]).permute(0, 1, 4, 2, 5, 3).reshape(t.shape[0], 32, t.shape[-2] * 2, t.shape[-1] * 2) + + def process_in(self, latent): + return latent + + def process_out(self, latent): + return latent + class Mochi(LatentFormat): latent_channels = 12 latent_dimensions = 3 diff --git a/comfy/ldm/chroma/model.py b/comfy/ldm/chroma/model.py index 67bf70eb1..a72f8cc47 100644 --- a/comfy/ldm/chroma/model.py +++ b/comfy/ldm/chroma/model.py @@ -179,7 +179,10 @@ class Chroma(nn.Module): pe = self.pe_embedder(ids) blocks_replace = patches_replace.get("dit", {}) + transformer_options["total_blocks"] = len(self.double_blocks) + transformer_options["block_type"] = "double" for i, block in enumerate(self.double_blocks): + transformer_options["block_index"] = i if i not in self.skip_mmdit: double_mod = ( self.get_modulations(mod_vectors, "double_img", idx=i), @@ -222,7 +225,10 @@ class Chroma(nn.Module): img = torch.cat((txt, img), 1) + transformer_options["total_blocks"] = len(self.single_blocks) + transformer_options["block_type"] = "single" for i, block in enumerate(self.single_blocks): + transformer_options["block_index"] = i if i not in self.skip_dit: single_mod = self.get_modulations(mod_vectors, "single", idx=i) if ("single_block", i) in blocks_replace: diff --git a/comfy/ldm/flux/layers.py b/comfy/ldm/flux/layers.py index 23150a712..2472ab79c 100644 --- a/comfy/ldm/flux/layers.py +++ b/comfy/ldm/flux/layers.py @@ -48,11 +48,11 @@ def timestep_embedding(t: Tensor, dim, max_period=10000, time_factor: float = 10 return embedding class MLPEmbedder(nn.Module): - def __init__(self, in_dim: int, hidden_dim: int, dtype=None, device=None, operations=None): + def __init__(self, in_dim: int, hidden_dim: int, bias=True, dtype=None, device=None, operations=None): super().__init__() - self.in_layer = operations.Linear(in_dim, hidden_dim, bias=True, dtype=dtype, device=device) + self.in_layer = operations.Linear(in_dim, hidden_dim, bias=bias, dtype=dtype, device=device) self.silu = nn.SiLU() - self.out_layer = operations.Linear(hidden_dim, hidden_dim, bias=True, dtype=dtype, device=device) + self.out_layer = operations.Linear(hidden_dim, hidden_dim, bias=bias, dtype=dtype, device=device) def forward(self, x: Tensor) -> Tensor: return self.out_layer(self.silu(self.in_layer(x))) @@ -80,14 +80,14 @@ class QKNorm(torch.nn.Module): class SelfAttention(nn.Module): - def __init__(self, dim: int, num_heads: int = 8, qkv_bias: bool = False, dtype=None, device=None, operations=None): + def __init__(self, dim: int, num_heads: int = 8, qkv_bias: bool = False, proj_bias: bool = True, dtype=None, device=None, operations=None): super().__init__() self.num_heads = num_heads head_dim = dim // num_heads self.qkv = operations.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device) self.norm = QKNorm(head_dim, dtype=dtype, device=device, operations=operations) - self.proj = operations.Linear(dim, dim, dtype=dtype, device=device) + self.proj = operations.Linear(dim, dim, bias=proj_bias, dtype=dtype, device=device) @dataclass @@ -98,11 +98,11 @@ class ModulationOut: class Modulation(nn.Module): - def __init__(self, dim: int, double: bool, dtype=None, device=None, operations=None): + def __init__(self, dim: int, double: bool, bias=True, dtype=None, device=None, operations=None): super().__init__() self.is_double = double self.multiplier = 6 if double else 3 - self.lin = operations.Linear(dim, self.multiplier * dim, bias=True, dtype=dtype, device=device) + self.lin = operations.Linear(dim, self.multiplier * dim, bias=bias, dtype=dtype, device=device) def forward(self, vec: Tensor) -> tuple: if vec.ndim == 2: @@ -129,8 +129,18 @@ def apply_mod(tensor, m_mult, m_add=None, modulation_dims=None): return tensor +class SiLUActivation(nn.Module): + def __init__(self): + super().__init__() + self.gate_fn = nn.SiLU() + + def forward(self, x: Tensor) -> Tensor: + x1, x2 = x.chunk(2, dim=-1) + return self.gate_fn(x1) * x2 + + class DoubleStreamBlock(nn.Module): - def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False, flipped_img_txt=False, modulation=True, dtype=None, device=None, operations=None): + def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False, flipped_img_txt=False, modulation=True, mlp_silu_act=False, proj_bias=True, dtype=None, device=None, operations=None): super().__init__() mlp_hidden_dim = int(hidden_size * mlp_ratio) @@ -142,27 +152,44 @@ class DoubleStreamBlock(nn.Module): self.img_mod = Modulation(hidden_size, double=True, dtype=dtype, device=device, operations=operations) self.img_norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) - self.img_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, dtype=dtype, device=device, operations=operations) + self.img_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, proj_bias=proj_bias, dtype=dtype, device=device, operations=operations) self.img_norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) - self.img_mlp = nn.Sequential( - operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device), - nn.GELU(approximate="tanh"), - operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device), - ) + + if mlp_silu_act: + self.img_mlp = nn.Sequential( + operations.Linear(hidden_size, mlp_hidden_dim * 2, bias=False, dtype=dtype, device=device), + SiLUActivation(), + operations.Linear(mlp_hidden_dim, hidden_size, bias=False, dtype=dtype, device=device), + ) + else: + self.img_mlp = nn.Sequential( + operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device), + nn.GELU(approximate="tanh"), + operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device), + ) if self.modulation: self.txt_mod = Modulation(hidden_size, double=True, dtype=dtype, device=device, operations=operations) self.txt_norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) - self.txt_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, dtype=dtype, device=device, operations=operations) + self.txt_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, proj_bias=proj_bias, dtype=dtype, device=device, operations=operations) self.txt_norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) - self.txt_mlp = nn.Sequential( - operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device), - nn.GELU(approximate="tanh"), - operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device), - ) + + if mlp_silu_act: + self.txt_mlp = nn.Sequential( + operations.Linear(hidden_size, mlp_hidden_dim * 2, bias=False, dtype=dtype, device=device), + SiLUActivation(), + operations.Linear(mlp_hidden_dim, hidden_size, bias=False, dtype=dtype, device=device), + ) + else: + self.txt_mlp = nn.Sequential( + operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device), + nn.GELU(approximate="tanh"), + operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device), + ) + 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, transformer_options={}): @@ -246,6 +273,8 @@ class SingleStreamBlock(nn.Module): mlp_ratio: float = 4.0, qk_scale: float = None, modulation=True, + mlp_silu_act=False, + bias=True, dtype=None, device=None, operations=None @@ -257,17 +286,24 @@ class SingleStreamBlock(nn.Module): self.scale = qk_scale or head_dim**-0.5 self.mlp_hidden_dim = int(hidden_size * mlp_ratio) + + self.mlp_hidden_dim_first = self.mlp_hidden_dim + if mlp_silu_act: + self.mlp_hidden_dim_first = int(hidden_size * mlp_ratio * 2) + self.mlp_act = SiLUActivation() + else: + self.mlp_act = nn.GELU(approximate="tanh") + # qkv and mlp_in - self.linear1 = operations.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim, dtype=dtype, device=device) + self.linear1 = operations.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim_first, bias=bias, dtype=dtype, device=device) # proj and mlp_out - self.linear2 = operations.Linear(hidden_size + self.mlp_hidden_dim, hidden_size, dtype=dtype, device=device) + self.linear2 = operations.Linear(hidden_size + self.mlp_hidden_dim, hidden_size, bias=bias, dtype=dtype, device=device) self.norm = QKNorm(head_dim, dtype=dtype, device=device, operations=operations) self.hidden_size = hidden_size self.pre_norm = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) - self.mlp_act = nn.GELU(approximate="tanh") if modulation: self.modulation = Modulation(hidden_size, double=False, dtype=dtype, device=device, operations=operations) else: @@ -279,7 +315,7 @@ class SingleStreamBlock(nn.Module): else: mod = 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) + 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_first], dim=-1) q, k, v = qkv.view(qkv.shape[0], qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) del qkv @@ -298,11 +334,11 @@ class SingleStreamBlock(nn.Module): class LastLayer(nn.Module): - def __init__(self, hidden_size: int, patch_size: int, out_channels: int, dtype=None, device=None, operations=None): + def __init__(self, hidden_size: int, patch_size: int, out_channels: int, bias=True, dtype=None, device=None, operations=None): super().__init__() self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) - self.linear = operations.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device) - self.adaLN_modulation = nn.Sequential(nn.SiLU(), operations.Linear(hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device)) + self.linear = operations.Linear(hidden_size, patch_size * patch_size * out_channels, bias=bias, dtype=dtype, device=device) + self.adaLN_modulation = nn.Sequential(nn.SiLU(), operations.Linear(hidden_size, 2 * hidden_size, bias=bias, dtype=dtype, device=device)) def forward(self, x: Tensor, vec: Tensor, modulation_dims=None) -> Tensor: if vec.ndim == 2: diff --git a/comfy/ldm/flux/model.py b/comfy/ldm/flux/model.py index b9d36f202..d5674dea6 100644 --- a/comfy/ldm/flux/model.py +++ b/comfy/ldm/flux/model.py @@ -15,6 +15,7 @@ from .layers import ( MLPEmbedder, SingleStreamBlock, timestep_embedding, + Modulation ) @dataclass @@ -33,6 +34,11 @@ class FluxParams: patch_size: int qkv_bias: bool guidance_embed: bool + global_modulation: bool = False + mlp_silu_act: bool = False + ops_bias: bool = True + default_ref_method: str = "offset" + ref_index_scale: float = 1.0 class Flux(nn.Module): @@ -58,13 +64,17 @@ class Flux(nn.Module): self.hidden_size = params.hidden_size self.num_heads = params.num_heads self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim) - self.img_in = operations.Linear(self.in_channels, self.hidden_size, bias=True, dtype=dtype, device=device) - self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, dtype=dtype, device=device, operations=operations) - self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size, dtype=dtype, device=device, operations=operations) + self.img_in = operations.Linear(self.in_channels, self.hidden_size, bias=params.ops_bias, dtype=dtype, device=device) + self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, bias=params.ops_bias, dtype=dtype, device=device, operations=operations) + if params.vec_in_dim is not None: + self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size, dtype=dtype, device=device, operations=operations) + else: + self.vector_in = None + self.guidance_in = ( - MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, dtype=dtype, device=device, operations=operations) if params.guidance_embed else nn.Identity() + MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, bias=params.ops_bias, dtype=dtype, device=device, operations=operations) if params.guidance_embed else nn.Identity() ) - self.txt_in = operations.Linear(params.context_in_dim, self.hidden_size, dtype=dtype, device=device) + self.txt_in = operations.Linear(params.context_in_dim, self.hidden_size, bias=params.ops_bias, dtype=dtype, device=device) self.double_blocks = nn.ModuleList( [ @@ -73,6 +83,9 @@ class Flux(nn.Module): self.num_heads, mlp_ratio=params.mlp_ratio, qkv_bias=params.qkv_bias, + modulation=params.global_modulation is False, + mlp_silu_act=params.mlp_silu_act, + proj_bias=params.ops_bias, dtype=dtype, device=device, operations=operations ) for _ in range(params.depth) @@ -81,13 +94,30 @@ class Flux(nn.Module): self.single_blocks = nn.ModuleList( [ - SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio, dtype=dtype, device=device, operations=operations) + SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio, modulation=params.global_modulation is False, mlp_silu_act=params.mlp_silu_act, bias=params.ops_bias, dtype=dtype, device=device, operations=operations) for _ in range(params.depth_single_blocks) ] ) if final_layer: - self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels, dtype=dtype, device=device, operations=operations) + self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels, bias=params.ops_bias, dtype=dtype, device=device, operations=operations) + + if params.global_modulation: + self.double_stream_modulation_img = Modulation( + self.hidden_size, + double=True, + bias=False, + dtype=dtype, device=device, operations=operations + ) + self.double_stream_modulation_txt = Modulation( + self.hidden_size, + double=True, + bias=False, + dtype=dtype, device=device, operations=operations + ) + self.single_stream_modulation = Modulation( + self.hidden_size, double=False, bias=False, dtype=dtype, device=device, operations=operations + ) def forward_orig( self, @@ -103,9 +133,6 @@ class Flux(nn.Module): attn_mask: Tensor = None, ) -> Tensor: - if y is None: - y = torch.zeros((img.shape[0], self.params.vec_in_dim), device=img.device, dtype=img.dtype) - patches = transformer_options.get("patches", {}) patches_replace = transformer_options.get("patches_replace", {}) if img.ndim != 3 or txt.ndim != 3: @@ -118,9 +145,17 @@ class Flux(nn.Module): if guidance is not None: vec = vec + self.guidance_in(timestep_embedding(guidance, 256).to(img.dtype)) - vec = vec + self.vector_in(y[:, :self.params.vec_in_dim]) + if self.vector_in is not None: + if y is None: + y = torch.zeros((img.shape[0], self.params.vec_in_dim), device=img.device, dtype=img.dtype) + vec = vec + self.vector_in(y[:, :self.params.vec_in_dim]) + txt = self.txt_in(txt) + vec_orig = vec + if self.params.global_modulation: + vec = (self.double_stream_modulation_img(vec_orig), self.double_stream_modulation_txt(vec_orig)) + if "post_input" in patches: for p in patches["post_input"]: out = p({"img": img, "txt": txt, "img_ids": img_ids, "txt_ids": txt_ids}) @@ -136,7 +171,10 @@ class Flux(nn.Module): pe = None blocks_replace = patches_replace.get("dit", {}) + transformer_options["total_blocks"] = len(self.double_blocks) + transformer_options["block_type"] = "double" for i, block in enumerate(self.double_blocks): + transformer_options["block_index"] = i if ("double_block", i) in blocks_replace: def block_wrap(args): out = {} @@ -177,7 +215,13 @@ class Flux(nn.Module): img = torch.cat((txt, img), 1) + if self.params.global_modulation: + vec, _ = self.single_stream_modulation(vec_orig) + + transformer_options["total_blocks"] = len(self.single_blocks) + transformer_options["block_type"] = "single" for i, block in enumerate(self.single_blocks): + transformer_options["block_index"] = i if ("single_block", i) in blocks_replace: def block_wrap(args): out = {} @@ -207,7 +251,7 @@ class Flux(nn.Module): img = img[:, txt.shape[1] :, ...] - img = self.final_layer(img, vec) # (N, T, patch_size ** 2 * out_channels) + img = self.final_layer(img, vec_orig) # (N, T, patch_size ** 2 * out_channels) return img def process_img(self, x, index=0, h_offset=0, w_offset=0, transformer_options={}): @@ -234,10 +278,10 @@ class Flux(nn.Module): h_offset += rope_options.get("shift_y", 0.0) w_offset += rope_options.get("shift_x", 0.0) - img_ids = torch.zeros((steps_h, steps_w, 3), device=x.device, dtype=x.dtype) + img_ids = torch.zeros((steps_h, steps_w, len(self.params.axes_dim)), device=x.device, dtype=torch.float32) img_ids[:, :, 0] = img_ids[:, :, 1] + index - img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(h_offset, h_len - 1 + h_offset, steps=steps_h, device=x.device, dtype=x.dtype).unsqueeze(1) - img_ids[:, :, 2] = img_ids[:, :, 2] + torch.linspace(w_offset, w_len - 1 + w_offset, steps=steps_w, device=x.device, dtype=x.dtype).unsqueeze(0) + img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(h_offset, h_len - 1 + h_offset, steps=steps_h, device=x.device, dtype=torch.float32).unsqueeze(1) + img_ids[:, :, 2] = img_ids[:, :, 2] + torch.linspace(w_offset, w_len - 1 + w_offset, steps=steps_w, device=x.device, dtype=torch.float32).unsqueeze(0) return img, repeat(img_ids, "h w c -> b (h w) c", b=bs) def forward(self, x, timestep, context, y=None, guidance=None, ref_latents=None, control=None, transformer_options={}, **kwargs): @@ -259,10 +303,10 @@ class Flux(nn.Module): h = 0 w = 0 index = 0 - ref_latents_method = kwargs.get("ref_latents_method", "offset") + ref_latents_method = kwargs.get("ref_latents_method", self.params.default_ref_method) for ref in ref_latents: if ref_latents_method == "index": - index += 1 + index += self.params.ref_index_scale h_offset = 0 w_offset = 0 elif ref_latents_method == "uxo": @@ -286,7 +330,11 @@ class Flux(nn.Module): img = torch.cat([img, kontext], dim=1) img_ids = torch.cat([img_ids, kontext_ids], dim=1) - txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype) + txt_ids = torch.zeros((bs, context.shape[1], len(self.params.axes_dim)), device=x.device, dtype=torch.float32) + + if len(self.params.axes_dim) == 4: # Flux 2 + txt_ids[:, :, 3] = torch.linspace(0, context.shape[1] - 1, steps=context.shape[1], device=x.device, dtype=torch.float32) + out = self.forward_orig(img, img_ids, context, txt_ids, timestep, y, guidance, control, transformer_options, attn_mask=kwargs.get("attention_mask", None)) out = out[:, :img_tokens] - return rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=2, pw=2)[:,:,:h_orig,:w_orig] + return rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=self.patch_size, pw=self.patch_size)[:,:,:h_orig,:w_orig] diff --git a/comfy/ldm/hunyuan_video/model.py b/comfy/ldm/hunyuan_video/model.py index f75c6e0e1..2749c53f5 100644 --- a/comfy/ldm/hunyuan_video/model.py +++ b/comfy/ldm/hunyuan_video/model.py @@ -389,7 +389,10 @@ class HunyuanVideo(nn.Module): attn_mask = None blocks_replace = patches_replace.get("dit", {}) + transformer_options["total_blocks"] = len(self.double_blocks) + transformer_options["block_type"] = "double" for i, block in enumerate(self.double_blocks): + transformer_options["block_index"] = i if ("double_block", i) in blocks_replace: def block_wrap(args): out = {} @@ -411,7 +414,10 @@ class HunyuanVideo(nn.Module): img = torch.cat((img, txt), 1) + transformer_options["total_blocks"] = len(self.single_blocks) + transformer_options["block_type"] = "single" for i, block in enumerate(self.single_blocks): + transformer_options["block_index"] = i if ("single_block", i) in blocks_replace: def block_wrap(args): out = {} diff --git a/comfy/ldm/lumina/model.py b/comfy/ldm/lumina/model.py index b4494a51d..565400b54 100644 --- a/comfy/ldm/lumina/model.py +++ b/comfy/ldm/lumina/model.py @@ -11,6 +11,7 @@ import comfy.ldm.common_dit from comfy.ldm.modules.diffusionmodules.mmdit import TimestepEmbedder from comfy.ldm.modules.attention import optimized_attention_masked from comfy.ldm.flux.layers import EmbedND +from comfy.ldm.flux.math import apply_rope import comfy.patcher_extension @@ -31,6 +32,7 @@ class JointAttention(nn.Module): n_heads: int, n_kv_heads: Optional[int], qk_norm: bool, + out_bias: bool = False, operation_settings={}, ): """ @@ -59,7 +61,7 @@ class JointAttention(nn.Module): self.out = operation_settings.get("operations").Linear( n_heads * self.head_dim, dim, - bias=False, + bias=out_bias, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"), ) @@ -70,35 +72,6 @@ class JointAttention(nn.Module): else: self.q_norm = self.k_norm = nn.Identity() - @staticmethod - def apply_rotary_emb( - x_in: torch.Tensor, - freqs_cis: torch.Tensor, - ) -> torch.Tensor: - """ - Apply rotary embeddings to input tensors using the given frequency - tensor. - - This function applies rotary embeddings to the given query 'xq' and - key 'xk' tensors using the provided frequency tensor 'freqs_cis'. The - input tensors are reshaped as complex numbers, and the frequency tensor - is reshaped for broadcasting compatibility. The resulting tensors - contain rotary embeddings and are returned as real tensors. - - Args: - x_in (torch.Tensor): Query or Key tensor to apply rotary embeddings. - freqs_cis (torch.Tensor): Precomputed frequency tensor for complex - exponentials. - - Returns: - Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor - and key tensor with rotary embeddings. - """ - - t_ = x_in.reshape(*x_in.shape[:-1], -1, 1, 2) - t_out = freqs_cis[..., 0] * t_[..., 0] + freqs_cis[..., 1] * t_[..., 1] - return t_out.reshape(*x_in.shape) - def forward( self, x: torch.Tensor, @@ -134,8 +107,7 @@ class JointAttention(nn.Module): xq = self.q_norm(xq) xk = self.k_norm(xk) - xq = JointAttention.apply_rotary_emb(xq, freqs_cis=freqs_cis) - xk = JointAttention.apply_rotary_emb(xk, freqs_cis=freqs_cis) + xq, xk = apply_rope(xq, xk, freqs_cis) n_rep = self.n_local_heads // self.n_local_kv_heads if n_rep >= 1: @@ -215,6 +187,8 @@ class JointTransformerBlock(nn.Module): norm_eps: float, qk_norm: bool, modulation=True, + z_image_modulation=False, + attn_out_bias=False, operation_settings={}, ) -> None: """ @@ -235,10 +209,10 @@ class JointTransformerBlock(nn.Module): super().__init__() self.dim = dim self.head_dim = dim // n_heads - self.attention = JointAttention(dim, n_heads, n_kv_heads, qk_norm, operation_settings=operation_settings) + self.attention = JointAttention(dim, n_heads, n_kv_heads, qk_norm, out_bias=attn_out_bias, operation_settings=operation_settings) self.feed_forward = FeedForward( dim=dim, - hidden_dim=4 * dim, + hidden_dim=dim, multiple_of=multiple_of, ffn_dim_multiplier=ffn_dim_multiplier, operation_settings=operation_settings, @@ -252,16 +226,27 @@ class JointTransformerBlock(nn.Module): self.modulation = modulation if modulation: - self.adaLN_modulation = nn.Sequential( - nn.SiLU(), - operation_settings.get("operations").Linear( - min(dim, 1024), - 4 * dim, - bias=True, - device=operation_settings.get("device"), - dtype=operation_settings.get("dtype"), - ), - ) + if z_image_modulation: + self.adaLN_modulation = nn.Sequential( + operation_settings.get("operations").Linear( + min(dim, 256), + 4 * dim, + bias=True, + device=operation_settings.get("device"), + dtype=operation_settings.get("dtype"), + ), + ) + else: + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + operation_settings.get("operations").Linear( + min(dim, 1024), + 4 * dim, + bias=True, + device=operation_settings.get("device"), + dtype=operation_settings.get("dtype"), + ), + ) def forward( self, @@ -323,7 +308,7 @@ class FinalLayer(nn.Module): The final layer of NextDiT. """ - def __init__(self, hidden_size, patch_size, out_channels, operation_settings={}): + def __init__(self, hidden_size, patch_size, out_channels, z_image_modulation=False, operation_settings={}): super().__init__() self.norm_final = operation_settings.get("operations").LayerNorm( hidden_size, @@ -340,10 +325,15 @@ class FinalLayer(nn.Module): dtype=operation_settings.get("dtype"), ) + if z_image_modulation: + min_mod = 256 + else: + min_mod = 1024 + self.adaLN_modulation = nn.Sequential( nn.SiLU(), operation_settings.get("operations").Linear( - min(hidden_size, 1024), + min(hidden_size, min_mod), hidden_size, bias=True, device=operation_settings.get("device"), @@ -373,12 +363,16 @@ class NextDiT(nn.Module): n_heads: int = 32, n_kv_heads: Optional[int] = None, multiple_of: int = 256, - ffn_dim_multiplier: Optional[float] = None, + ffn_dim_multiplier: float = 4.0, norm_eps: float = 1e-5, qk_norm: bool = False, cap_feat_dim: int = 5120, axes_dims: List[int] = (16, 56, 56), axes_lens: List[int] = (1, 512, 512), + rope_theta=10000.0, + z_image_modulation=False, + time_scale=1.0, + pad_tokens_multiple=None, image_model=None, device=None, dtype=None, @@ -390,6 +384,8 @@ class NextDiT(nn.Module): self.in_channels = in_channels self.out_channels = in_channels self.patch_size = patch_size + self.time_scale = time_scale + self.pad_tokens_multiple = pad_tokens_multiple self.x_embedder = operation_settings.get("operations").Linear( in_features=patch_size * patch_size * in_channels, @@ -411,6 +407,7 @@ class NextDiT(nn.Module): norm_eps, qk_norm, modulation=True, + z_image_modulation=z_image_modulation, operation_settings=operation_settings, ) for layer_id in range(n_refiner_layers) @@ -434,7 +431,7 @@ class NextDiT(nn.Module): ] ) - self.t_embedder = TimestepEmbedder(min(dim, 1024), **operation_settings) + self.t_embedder = TimestepEmbedder(min(dim, 1024), output_size=256 if z_image_modulation else None, **operation_settings) self.cap_embedder = nn.Sequential( operation_settings.get("operations").RMSNorm(cap_feat_dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")), operation_settings.get("operations").Linear( @@ -457,18 +454,24 @@ class NextDiT(nn.Module): ffn_dim_multiplier, norm_eps, qk_norm, + z_image_modulation=z_image_modulation, + attn_out_bias=False, operation_settings=operation_settings, ) for layer_id in range(n_layers) ] ) self.norm_final = operation_settings.get("operations").RMSNorm(dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) - self.final_layer = FinalLayer(dim, patch_size, self.out_channels, operation_settings=operation_settings) + self.final_layer = FinalLayer(dim, patch_size, self.out_channels, z_image_modulation=z_image_modulation, operation_settings=operation_settings) + + if self.pad_tokens_multiple is not None: + self.x_pad_token = nn.Parameter(torch.empty((1, dim), device=device, dtype=dtype)) + self.cap_pad_token = nn.Parameter(torch.empty((1, dim), device=device, dtype=dtype)) assert (dim // n_heads) == sum(axes_dims) self.axes_dims = axes_dims self.axes_lens = axes_lens - self.rope_embedder = EmbedND(dim=dim // n_heads, theta=10000.0, axes_dim=axes_dims) + self.rope_embedder = EmbedND(dim=dim // n_heads, theta=rope_theta, axes_dim=axes_dims) self.dim = dim self.n_heads = n_heads @@ -503,108 +506,42 @@ class NextDiT(nn.Module): bsz = len(x) pH = pW = self.patch_size device = x[0].device - dtype = x[0].dtype - if cap_mask is not None: - l_effective_cap_len = cap_mask.sum(dim=1).tolist() - else: - l_effective_cap_len = [num_tokens] * bsz + if self.pad_tokens_multiple is not None: + pad_extra = (-cap_feats.shape[1]) % self.pad_tokens_multiple + cap_feats = torch.cat((cap_feats, self.cap_pad_token.to(device=cap_feats.device, dtype=cap_feats.dtype, copy=True).unsqueeze(0).repeat(cap_feats.shape[0], pad_extra, 1)), dim=1) - if cap_mask is not None and not torch.is_floating_point(cap_mask): - cap_mask = (cap_mask - 1).to(dtype) * torch.finfo(dtype).max + cap_pos_ids = torch.zeros(bsz, cap_feats.shape[1], 3, dtype=torch.float32, device=device) + cap_pos_ids[:, :, 0] = torch.arange(cap_feats.shape[1], dtype=torch.float32, device=device) + 1.0 - img_sizes = [(img.size(1), img.size(2)) for img in x] - l_effective_img_len = [(H // pH) * (W // pW) for (H, W) in img_sizes] + B, C, H, W = x.shape + x = self.x_embedder(x.view(B, C, H // pH, pH, W // pW, pW).permute(0, 2, 4, 3, 5, 1).flatten(3).flatten(1, 2)) - max_seq_len = max( - (cap_len+img_len for cap_len, img_len in zip(l_effective_cap_len, l_effective_img_len)) - ) - max_cap_len = max(l_effective_cap_len) - max_img_len = max(l_effective_img_len) + H_tokens, W_tokens = H // pH, W // pW + x_pos_ids = torch.zeros((bsz, x.shape[1], 3), dtype=torch.float32, device=device) + x_pos_ids[:, :, 0] = cap_feats.shape[1] + 1 + x_pos_ids[:, :, 1] = torch.arange(H_tokens, dtype=torch.float32, device=device).view(-1, 1).repeat(1, W_tokens).flatten() + x_pos_ids[:, :, 2] = torch.arange(W_tokens, dtype=torch.float32, device=device).view(1, -1).repeat(H_tokens, 1).flatten() - position_ids = torch.zeros(bsz, max_seq_len, 3, dtype=torch.float32, device=device) + if self.pad_tokens_multiple is not None: + pad_extra = (-x.shape[1]) % self.pad_tokens_multiple + x = torch.cat((x, self.x_pad_token.to(device=x.device, dtype=x.dtype, copy=True).unsqueeze(0).repeat(x.shape[0], pad_extra, 1)), dim=1) + x_pos_ids = torch.nn.functional.pad(x_pos_ids, (0, 0, 0, pad_extra)) - for i in range(bsz): - cap_len = l_effective_cap_len[i] - img_len = l_effective_img_len[i] - H, W = img_sizes[i] - H_tokens, W_tokens = H // pH, W // pW - assert H_tokens * W_tokens == img_len - - rope_options = transformer_options.get("rope_options", None) - h_scale = 1.0 - w_scale = 1.0 - h_start = 0 - w_start = 0 - if rope_options is not None: - h_scale = rope_options.get("scale_y", 1.0) - w_scale = rope_options.get("scale_x", 1.0) - - h_start = rope_options.get("shift_y", 0.0) - w_start = rope_options.get("shift_x", 0.0) - - position_ids[i, :cap_len, 0] = torch.arange(cap_len, dtype=torch.float32, device=device) - position_ids[i, cap_len:cap_len+img_len, 0] = cap_len - row_ids = (torch.arange(H_tokens, dtype=torch.float32, device=device) * h_scale + h_start).view(-1, 1).repeat(1, W_tokens).flatten() - col_ids = (torch.arange(W_tokens, dtype=torch.float32, device=device) * w_scale + w_start).view(1, -1).repeat(H_tokens, 1).flatten() - position_ids[i, cap_len:cap_len+img_len, 1] = row_ids - position_ids[i, cap_len:cap_len+img_len, 2] = col_ids - - freqs_cis = self.rope_embedder(position_ids).movedim(1, 2).to(dtype) - - # build freqs_cis for cap and image individually - cap_freqs_cis_shape = list(freqs_cis.shape) - # cap_freqs_cis_shape[1] = max_cap_len - cap_freqs_cis_shape[1] = cap_feats.shape[1] - cap_freqs_cis = torch.zeros(*cap_freqs_cis_shape, device=device, dtype=freqs_cis.dtype) - - img_freqs_cis_shape = list(freqs_cis.shape) - img_freqs_cis_shape[1] = max_img_len - img_freqs_cis = torch.zeros(*img_freqs_cis_shape, device=device, dtype=freqs_cis.dtype) - - for i in range(bsz): - cap_len = l_effective_cap_len[i] - img_len = l_effective_img_len[i] - cap_freqs_cis[i, :cap_len] = freqs_cis[i, :cap_len] - img_freqs_cis[i, :img_len] = freqs_cis[i, cap_len:cap_len+img_len] + freqs_cis = self.rope_embedder(torch.cat((cap_pos_ids, x_pos_ids), dim=1)).movedim(1, 2) # refine context for layer in self.context_refiner: - cap_feats = layer(cap_feats, cap_mask, cap_freqs_cis, transformer_options=transformer_options) + cap_feats = layer(cap_feats, cap_mask, freqs_cis[:, :cap_pos_ids.shape[1]], transformer_options=transformer_options) - # refine image - flat_x = [] - for i in range(bsz): - img = x[i] - C, H, W = img.size() - img = img.view(C, H // pH, pH, W // pW, pW).permute(1, 3, 2, 4, 0).flatten(2).flatten(0, 1) - flat_x.append(img) - x = flat_x - padded_img_embed = torch.zeros(bsz, max_img_len, x[0].shape[-1], device=device, dtype=x[0].dtype) - padded_img_mask = torch.zeros(bsz, max_img_len, dtype=dtype, device=device) - for i in range(bsz): - padded_img_embed[i, :l_effective_img_len[i]] = x[i] - padded_img_mask[i, l_effective_img_len[i]:] = -torch.finfo(dtype).max - - padded_img_embed = self.x_embedder(padded_img_embed) - padded_img_mask = padded_img_mask.unsqueeze(1) + padded_img_mask = None for layer in self.noise_refiner: - 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) - mask[:, :max_cap_len] = cap_mask[:, :max_cap_len] - else: - mask = None - - padded_full_embed = torch.zeros(bsz, max_seq_len, self.dim, device=device, dtype=x[0].dtype) - for i in range(bsz): - cap_len = l_effective_cap_len[i] - img_len = l_effective_img_len[i] - - padded_full_embed[i, :cap_len] = cap_feats[i, :cap_len] - padded_full_embed[i, cap_len:cap_len+img_len] = padded_img_embed[i, :img_len] + x = layer(x, padded_img_mask, freqs_cis[:, cap_pos_ids.shape[1]:], t, transformer_options=transformer_options) + padded_full_embed = torch.cat((cap_feats, x), dim=1) + mask = None + img_sizes = [(H, W)] * bsz + l_effective_cap_len = [cap_feats.shape[1]] * bsz return padded_full_embed, mask, img_sizes, l_effective_cap_len, freqs_cis def forward(self, x, timesteps, context, num_tokens, attention_mask=None, **kwargs): @@ -627,7 +564,7 @@ class NextDiT(nn.Module): y: (N,) tensor of text tokens/features """ - t = self.t_embedder(t, dtype=x.dtype) # (N, D) + t = self.t_embedder(t * self.time_scale, dtype=x.dtype) # (N, D) adaln_input = t cap_feats = self.cap_embedder(cap_feats) # (N, L, D) # todo check if able to batchify w.o. redundant compute diff --git a/comfy/ldm/models/autoencoder.py b/comfy/ldm/models/autoencoder.py index 611d36a1b..4f50810dc 100644 --- a/comfy/ldm/models/autoencoder.py +++ b/comfy/ldm/models/autoencoder.py @@ -9,6 +9,8 @@ from comfy.ldm.modules.distributions.distributions import DiagonalGaussianDistri from comfy.ldm.util import get_obj_from_str, instantiate_from_config from comfy.ldm.modules.ema import LitEma import comfy.ops +from einops import rearrange +import comfy.model_management class DiagonalGaussianRegularizer(torch.nn.Module): def __init__(self, sample: bool = False): @@ -179,6 +181,21 @@ class AutoencodingEngineLegacy(AutoencodingEngine): self.post_quant_conv = conv_op(embed_dim, ddconfig["z_channels"], 1) self.embed_dim = embed_dim + if ddconfig.get("batch_norm_latent", False): + self.bn_eps = 1e-4 + self.bn_momentum = 0.1 + self.ps = [2, 2] + self.bn = torch.nn.BatchNorm2d(math.prod(self.ps) * ddconfig["z_channels"], + eps=self.bn_eps, + momentum=self.bn_momentum, + affine=False, + track_running_stats=True, + ) + self.bn.eval() + else: + self.bn = None + + def get_autoencoder_params(self) -> list: params = super().get_autoencoder_params() return params @@ -201,11 +218,36 @@ class AutoencodingEngineLegacy(AutoencodingEngine): z = torch.cat(z, 0) z, reg_log = self.regularization(z) + + if self.bn is not None: + z = rearrange(z, + "... c (i pi) (j pj) -> ... (c pi pj) i j", + pi=self.ps[0], + pj=self.ps[1], + ) + + z = torch.nn.functional.batch_norm(z, + comfy.model_management.cast_to(self.bn.running_mean, dtype=z.dtype, device=z.device), + comfy.model_management.cast_to(self.bn.running_var, dtype=z.dtype, device=z.device), + momentum=self.bn_momentum, + eps=self.bn_eps) + if return_reg_log: return z, reg_log return z def decode(self, z: torch.Tensor, **decoder_kwargs) -> torch.Tensor: + if self.bn is not None: + s = torch.sqrt(comfy.model_management.cast_to(self.bn.running_var.view(1, -1, 1, 1), dtype=z.dtype, device=z.device) + self.bn_eps) + m = comfy.model_management.cast_to(self.bn.running_mean.view(1, -1, 1, 1), dtype=z.dtype, device=z.device) + z = z * s + m + z = rearrange( + z, + "... (c pi pj) i j -> ... c (i pi) (j pj)", + pi=self.ps[0], + pj=self.ps[1], + ) + if self.max_batch_size is None: dec = self.post_quant_conv(z) dec = self.decoder(dec, **decoder_kwargs) diff --git a/comfy/ldm/modules/diffusionmodules/mmdit.py b/comfy/ldm/modules/diffusionmodules/mmdit.py index 42f406f1a..0dc8fe789 100644 --- a/comfy/ldm/modules/diffusionmodules/mmdit.py +++ b/comfy/ldm/modules/diffusionmodules/mmdit.py @@ -211,12 +211,14 @@ class TimestepEmbedder(nn.Module): Embeds scalar timesteps into vector representations. """ - def __init__(self, hidden_size, frequency_embedding_size=256, dtype=None, device=None, operations=None): + def __init__(self, hidden_size, frequency_embedding_size=256, output_size=None, dtype=None, device=None, operations=None): super().__init__() + if output_size is None: + output_size = hidden_size self.mlp = nn.Sequential( operations.Linear(frequency_embedding_size, hidden_size, bias=True, dtype=dtype, device=device), nn.SiLU(), - operations.Linear(hidden_size, hidden_size, bias=True, dtype=dtype, device=device), + operations.Linear(hidden_size, output_size, bias=True, dtype=dtype, device=device), ) self.frequency_embedding_size = frequency_embedding_size diff --git a/comfy/ldm/qwen_image/model.py b/comfy/ldm/qwen_image/model.py index 427ea19c1..8c75670cd 100644 --- a/comfy/ldm/qwen_image/model.py +++ b/comfy/ldm/qwen_image/model.py @@ -439,7 +439,10 @@ class QwenImageTransformer2DModel(nn.Module): patches = transformer_options.get("patches", {}) blocks_replace = patches_replace.get("dit", {}) + transformer_options["total_blocks"] = len(self.transformer_blocks) + transformer_options["block_type"] = "double" for i, block in enumerate(self.transformer_blocks): + transformer_options["block_index"] = i if ("double_block", i) in blocks_replace: def block_wrap(args): out = {} diff --git a/comfy/model_base.py b/comfy/model_base.py index e14b552c5..9b76c285e 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -898,12 +898,13 @@ class Flux(BaseModel): attention_mask = kwargs.get("attention_mask", None) if attention_mask is not None: shape = kwargs["noise"].shape - mask_ref_size = kwargs["attention_mask_img_shape"] - # the model will pad to the patch size, and then divide - # essentially dividing and rounding up - (h_tok, w_tok) = (math.ceil(shape[2] / self.diffusion_model.patch_size), math.ceil(shape[3] / self.diffusion_model.patch_size)) - attention_mask = utils.upscale_dit_mask(attention_mask, mask_ref_size, (h_tok, w_tok)) - out['attention_mask'] = comfy.conds.CONDRegular(attention_mask) + mask_ref_size = kwargs.get("attention_mask_img_shape", None) + if mask_ref_size is not None: + # the model will pad to the patch size, and then divide + # essentially dividing and rounding up + (h_tok, w_tok) = (math.ceil(shape[2] / self.diffusion_model.patch_size), math.ceil(shape[3] / self.diffusion_model.patch_size)) + attention_mask = utils.upscale_dit_mask(attention_mask, mask_ref_size, (h_tok, w_tok)) + out['attention_mask'] = comfy.conds.CONDRegular(attention_mask) guidance = kwargs.get("guidance", 3.5) if guidance is not None: @@ -925,9 +926,19 @@ class Flux(BaseModel): out = {} ref_latents = kwargs.get("reference_latents", None) if ref_latents is not None: - out['ref_latents'] = list([1, 16, sum(map(lambda a: math.prod(a.size()), ref_latents)) // 16]) + out['ref_latents'] = list([1, 16, sum(map(lambda a: math.prod(a.size()[2:]), ref_latents))]) return out +class Flux2(Flux): + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + target_text_len = 512 + if cross_attn.shape[1] < target_text_len: + cross_attn = torch.nn.functional.pad(cross_attn, (0, 0, target_text_len - cross_attn.shape[1], 0)) + out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) + return out class GenmoMochi(BaseModel): def __init__(self, model_config, model_type=ModelType.FLOW, device=None): @@ -1103,9 +1114,13 @@ class Lumina2(BaseModel): if torch.numel(attention_mask) != attention_mask.sum(): out['attention_mask'] = comfy.conds.CONDRegular(attention_mask) out['num_tokens'] = comfy.conds.CONDConstant(max(1, torch.sum(attention_mask).item())) + cross_attn = kwargs.get("cross_attn", None) if cross_attn is not None: out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) + if 'num_tokens' not in out: + out['num_tokens'] = comfy.conds.CONDConstant(cross_attn.shape[1]) + return out class WAN21(BaseModel): diff --git a/comfy/model_detection.py b/comfy/model_detection.py index 0131ca25a..7afe4a798 100644 --- a/comfy/model_detection.py +++ b/comfy/model_detection.py @@ -200,26 +200,54 @@ def detect_unet_config(state_dict, key_prefix, metadata=None): if '{}double_blocks.0.img_attn.norm.key_norm.scale'.format(key_prefix) in state_dict_keys and ('{}img_in.weight'.format(key_prefix) in state_dict_keys or f"{key_prefix}distilled_guidance_layer.norms.0.scale" in state_dict_keys): #Flux, Chroma or Chroma Radiance (has no img_in.weight) dit_config = {} - dit_config["image_model"] = "flux" + if '{}double_stream_modulation_img.lin.weight'.format(key_prefix) in state_dict_keys: + dit_config["image_model"] = "flux2" + dit_config["axes_dim"] = [32, 32, 32, 32] + dit_config["num_heads"] = 48 + dit_config["mlp_ratio"] = 3.0 + dit_config["theta"] = 2000 + dit_config["out_channels"] = 128 + dit_config["global_modulation"] = True + dit_config["vec_in_dim"] = None + dit_config["mlp_silu_act"] = True + dit_config["qkv_bias"] = False + dit_config["ops_bias"] = False + dit_config["default_ref_method"] = "index" + dit_config["ref_index_scale"] = 10.0 + patch_size = 1 + else: + dit_config["image_model"] = "flux" + dit_config["axes_dim"] = [16, 56, 56] + dit_config["num_heads"] = 24 + dit_config["mlp_ratio"] = 4.0 + dit_config["theta"] = 10000 + dit_config["out_channels"] = 16 + dit_config["qkv_bias"] = True + patch_size = 2 + dit_config["in_channels"] = 16 - patch_size = 2 + dit_config["hidden_size"] = 3072 + dit_config["context_in_dim"] = 4096 + dit_config["patch_size"] = patch_size in_key = "{}img_in.weight".format(key_prefix) if in_key in state_dict_keys: - dit_config["in_channels"] = state_dict[in_key].shape[1] // (patch_size * patch_size) - dit_config["out_channels"] = 16 + w = state_dict[in_key] + dit_config["in_channels"] = w.shape[1] // (patch_size * patch_size) + dit_config["hidden_size"] = w.shape[0] + + txt_in_key = "{}txt_in.weight".format(key_prefix) + if txt_in_key in state_dict_keys: + w = state_dict[txt_in_key] + dit_config["context_in_dim"] = w.shape[1] + dit_config["hidden_size"] = w.shape[0] + vec_in_key = '{}vector_in.in_layer.weight'.format(key_prefix) if vec_in_key in state_dict_keys: dit_config["vec_in_dim"] = state_dict[vec_in_key].shape[1] - dit_config["context_in_dim"] = 4096 - dit_config["hidden_size"] = 3072 - dit_config["mlp_ratio"] = 4.0 - dit_config["num_heads"] = 24 + dit_config["depth"] = count_blocks(state_dict_keys, '{}double_blocks.'.format(key_prefix) + '{}.') dit_config["depth_single_blocks"] = count_blocks(state_dict_keys, '{}single_blocks.'.format(key_prefix) + '{}.') - dit_config["axes_dim"] = [16, 56, 56] - dit_config["theta"] = 10000 - dit_config["qkv_bias"] = True if '{}distilled_guidance_layer.0.norms.0.scale'.format(key_prefix) in state_dict_keys or '{}distilled_guidance_layer.norms.0.scale'.format(key_prefix) in state_dict_keys: #Chroma dit_config["image_model"] = "chroma" dit_config["in_channels"] = 64 @@ -388,14 +416,31 @@ def detect_unet_config(state_dict, key_prefix, metadata=None): dit_config["image_model"] = "lumina2" dit_config["patch_size"] = 2 dit_config["in_channels"] = 16 - dit_config["dim"] = 2304 - dit_config["cap_feat_dim"] = state_dict['{}cap_embedder.1.weight'.format(key_prefix)].shape[1] + w = state_dict['{}cap_embedder.1.weight'.format(key_prefix)] + dit_config["dim"] = w.shape[0] + dit_config["cap_feat_dim"] = w.shape[1] dit_config["n_layers"] = count_blocks(state_dict_keys, '{}layers.'.format(key_prefix) + '{}.') - dit_config["n_heads"] = 24 - dit_config["n_kv_heads"] = 8 dit_config["qk_norm"] = True - dit_config["axes_dims"] = [32, 32, 32] - dit_config["axes_lens"] = [300, 512, 512] + + if dit_config["dim"] == 2304: # Original Lumina 2 + dit_config["n_heads"] = 24 + dit_config["n_kv_heads"] = 8 + dit_config["axes_dims"] = [32, 32, 32] + dit_config["axes_lens"] = [300, 512, 512] + dit_config["rope_theta"] = 10000.0 + dit_config["ffn_dim_multiplier"] = 4.0 + elif dit_config["dim"] == 3840: # Z image + dit_config["n_heads"] = 30 + dit_config["n_kv_heads"] = 30 + dit_config["axes_dims"] = [32, 48, 48] + dit_config["axes_lens"] = [1536, 512, 512] + dit_config["rope_theta"] = 256.0 + dit_config["ffn_dim_multiplier"] = (8.0 / 3.0) + dit_config["z_image_modulation"] = True + dit_config["time_scale"] = 1000.0 + if '{}cap_pad_token'.format(key_prefix) in state_dict_keys: + dit_config["pad_tokens_multiple"] = 32 + return dit_config if '{}head.modulation'.format(key_prefix) in state_dict_keys: # Wan 2.1 diff --git a/comfy/model_management.py b/comfy/model_management.py index a21df54b3..9c403d580 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -689,7 +689,7 @@ def load_models_gpu(models, memory_required=0, force_patch_weights=False, minimu loaded_memory = loaded_model.model_loaded_memory() current_free_mem = get_free_memory(torch_dev) + loaded_memory - lowvram_model_memory = max(128 * 1024 * 1024, (current_free_mem - minimum_memory_required), min(current_free_mem * MIN_WEIGHT_MEMORY_RATIO, current_free_mem - minimum_inference_memory())) + lowvram_model_memory = max(0, (current_free_mem - minimum_memory_required), min(current_free_mem * MIN_WEIGHT_MEMORY_RATIO, current_free_mem - minimum_inference_memory())) lowvram_model_memory = lowvram_model_memory - loaded_memory if lowvram_model_memory == 0: @@ -1012,7 +1012,7 @@ def force_channels_last(): STREAMS = {} -NUM_STREAMS = 1 +NUM_STREAMS = 0 if args.async_offload: NUM_STREAMS = 2 logging.info("Using async weight offloading with {} streams".format(NUM_STREAMS)) @@ -1030,7 +1030,7 @@ def current_stream(device): stream_counters = {} def get_offload_stream(device): stream_counter = stream_counters.get(device, 0) - if NUM_STREAMS <= 1: + if NUM_STREAMS == 0: return None if device in STREAMS: @@ -1098,13 +1098,14 @@ if not args.disable_pinned_memory: MAX_PINNED_MEMORY = get_total_memory(torch.device("cpu")) * 0.95 logging.info("Enabled pinned memory {}".format(MAX_PINNED_MEMORY // (1024 * 1024))) +PINNING_ALLOWED_TYPES = set(["Parameter", "QuantizedTensor"]) def pin_memory(tensor): global TOTAL_PINNED_MEMORY if MAX_PINNED_MEMORY <= 0: return False - if type(tensor) is not torch.nn.parameter.Parameter: + if type(tensor).__name__ not in PINNING_ALLOWED_TYPES: return False if not is_device_cpu(tensor.device): @@ -1124,6 +1125,9 @@ def pin_memory(tensor): return False ptr = tensor.data_ptr() + if ptr == 0: + return False + if torch.cuda.cudart().cudaHostRegister(ptr, size, 1) == 0: PINNED_MEMORY[ptr] = size TOTAL_PINNED_MEMORY += size diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py index cf1b0d441..3eac77275 100644 --- a/comfy/model_patcher.py +++ b/comfy/model_patcher.py @@ -132,7 +132,7 @@ class LowVramPatch: def __call__(self, weight): intermediate_dtype = weight.dtype if self.convert_func is not None: - weight = self.convert_func(weight.to(dtype=torch.float32, copy=True), inplace=True) + weight = self.convert_func(weight, inplace=False) if intermediate_dtype not in [torch.float32, torch.float16, torch.bfloat16]: #intermediate_dtype has to be one that is supported in math ops intermediate_dtype = torch.float32 @@ -148,6 +148,15 @@ class LowVramPatch: else: return out +#The above patch logic may cast up the weight to fp32, and do math. Go with fp32 x 3 +LOWVRAM_PATCH_ESTIMATE_MATH_FACTOR = 3 + +def low_vram_patch_estimate_vram(model, key): + weight, set_func, convert_func = get_key_weight(model, key) + if weight is None: + return 0 + return weight.numel() * torch.float32.itemsize * LOWVRAM_PATCH_ESTIMATE_MATH_FACTOR + def get_key_weight(model, key): set_func = None convert_func = None @@ -231,7 +240,6 @@ class ModelPatcher: self.object_patches_backup = {} self.weight_wrapper_patches = {} self.model_options = {"transformer_options":{}} - self.model_size() self.load_device = load_device self.offload_device = offload_device self.weight_inplace_update = weight_inplace_update @@ -270,6 +278,9 @@ class ModelPatcher: if not hasattr(self.model, 'current_weight_patches_uuid'): self.model.current_weight_patches_uuid = None + if not hasattr(self.model, 'model_offload_buffer_memory'): + self.model.model_offload_buffer_memory = 0 + def model_size(self): if self.size > 0: return self.size @@ -286,7 +297,7 @@ class ModelPatcher: return self.model.lowvram_patch_counter def clone(self): - n = self.__class__(self.model, self.load_device, self.offload_device, self.size, weight_inplace_update=self.weight_inplace_update) + n = self.__class__(self.model, self.load_device, self.offload_device, self.model_size(), weight_inplace_update=self.weight_inplace_update) n.patches = {} for k in self.patches: n.patches[k] = self.patches[k][:] @@ -663,7 +674,16 @@ class ModelPatcher: skip = True # skip random weights in non leaf modules break if not skip and (hasattr(m, "comfy_cast_weights") or len(params) > 0): - loading.append((comfy.model_management.module_size(m), n, m, params)) + module_mem = comfy.model_management.module_size(m) + module_offload_mem = module_mem + if hasattr(m, "comfy_cast_weights"): + weight_key = "{}.weight".format(n) + bias_key = "{}.bias".format(n) + if weight_key in self.patches: + module_offload_mem += low_vram_patch_estimate_vram(self.model, weight_key) + if bias_key in self.patches: + module_offload_mem += low_vram_patch_estimate_vram(self.model, bias_key) + loading.append((module_offload_mem, module_mem, n, m, params)) return loading def load(self, device_to=None, lowvram_model_memory=0, force_patch_weights=False, full_load=False): @@ -677,20 +697,22 @@ class ModelPatcher: load_completely = [] offloaded = [] + offload_buffer = 0 loading.sort(reverse=True) for x in loading: - n = x[1] - m = x[2] - params = x[3] - module_mem = x[0] + module_offload_mem, module_mem, n, m, params = x lowvram_weight = False + potential_offload = max(offload_buffer, module_offload_mem * (comfy.model_management.NUM_STREAMS + 1)) + lowvram_fits = mem_counter + module_mem + potential_offload < lowvram_model_memory + weight_key = "{}.weight".format(n) bias_key = "{}.bias".format(n) if not full_load and hasattr(m, "comfy_cast_weights"): - if mem_counter + module_mem >= lowvram_model_memory: + if not lowvram_fits: + offload_buffer = potential_offload lowvram_weight = True lowvram_counter += 1 lowvram_mem_counter += module_mem @@ -724,9 +746,11 @@ class ModelPatcher: if hasattr(m, "comfy_cast_weights"): wipe_lowvram_weight(m) - if full_load or mem_counter + module_mem < lowvram_model_memory: + if full_load or lowvram_fits: mem_counter += module_mem load_completely.append((module_mem, n, m, params)) + else: + offload_buffer = potential_offload if cast_weight and hasattr(m, "comfy_cast_weights"): m.prev_comfy_cast_weights = m.comfy_cast_weights @@ -767,7 +791,7 @@ class ModelPatcher: self.pin_weight_to_device("{}.{}".format(n, param)) if lowvram_counter > 0: - logging.info("loaded partially; {:.2f} MB usable, {:.2f} MB loaded, {:.2f} MB offloaded, lowvram patches: {}".format(lowvram_model_memory / (1024 * 1024), mem_counter / (1024 * 1024), lowvram_mem_counter / (1024 * 1024), patch_counter)) + logging.info("loaded partially; {:.2f} MB usable, {:.2f} MB loaded, {:.2f} MB offloaded, {:.2f} MB buffer reserved, lowvram patches: {}".format(lowvram_model_memory / (1024 * 1024), mem_counter / (1024 * 1024), lowvram_mem_counter / (1024 * 1024), offload_buffer / (1024 * 1024), patch_counter)) self.model.model_lowvram = True else: logging.info("loaded completely; {:.2f} MB usable, {:.2f} MB loaded, full load: {}".format(lowvram_model_memory / (1024 * 1024), mem_counter / (1024 * 1024), full_load)) @@ -779,6 +803,7 @@ class ModelPatcher: self.model.lowvram_patch_counter += patch_counter self.model.device = device_to self.model.model_loaded_weight_memory = mem_counter + self.model.model_offload_buffer_memory = offload_buffer self.model.current_weight_patches_uuid = self.patches_uuid for callback in self.get_all_callbacks(CallbacksMP.ON_LOAD): @@ -832,6 +857,7 @@ class ModelPatcher: self.model.to(device_to) self.model.device = device_to self.model.model_loaded_weight_memory = 0 + self.model.model_offload_buffer_memory = 0 for m in self.model.modules(): if hasattr(m, "comfy_patched_weights"): @@ -850,13 +876,14 @@ class ModelPatcher: patch_counter = 0 unload_list = self._load_list() unload_list.sort() + offload_buffer = self.model.model_offload_buffer_memory + for unload in unload_list: - if memory_to_free < memory_freed: + if memory_to_free + offload_buffer - self.model.model_offload_buffer_memory < memory_freed: break - module_mem = unload[0] - n = unload[1] - m = unload[2] - params = unload[3] + module_offload_mem, module_mem, n, m, params = unload + + potential_offload = (comfy.model_management.NUM_STREAMS + 1) * module_offload_mem lowvram_possible = hasattr(m, "comfy_cast_weights") if hasattr(m, "comfy_patched_weights") and m.comfy_patched_weights == True: @@ -907,15 +934,18 @@ class ModelPatcher: m.comfy_cast_weights = True m.comfy_patched_weights = False memory_freed += module_mem + offload_buffer = max(offload_buffer, potential_offload) logging.debug("freed {}".format(n)) for param in params: self.pin_weight_to_device("{}.{}".format(n, param)) + self.model.model_lowvram = True self.model.lowvram_patch_counter += patch_counter self.model.model_loaded_weight_memory -= memory_freed - logging.info("loaded partially: {:.2f} MB loaded, lowvram patches: {}".format(self.model.model_loaded_weight_memory / (1024 * 1024), self.model.lowvram_patch_counter)) + self.model.model_offload_buffer_memory = offload_buffer + logging.info("Unloaded partially: {:.2f} MB freed, {:.2f} MB remains loaded, {:.2f} MB buffer reserved, lowvram patches: {}".format(memory_freed / (1024 * 1024), self.model.model_loaded_weight_memory / (1024 * 1024), offload_buffer / (1024 * 1024), self.model.lowvram_patch_counter)) return memory_freed def partially_load(self, device_to, extra_memory=0, force_patch_weights=False): diff --git a/comfy/ops.py b/comfy/ops.py index 640622fd1..a0ff4e8f1 100644 --- a/comfy/ops.py +++ b/comfy/ops.py @@ -117,6 +117,8 @@ def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None, of if weight_has_function or weight.dtype != dtype: with wf_context: weight = weight.to(dtype=dtype) + if isinstance(weight, QuantizedTensor): + weight = weight.dequantize() for f in s.weight_function: weight = f(weight) @@ -502,7 +504,7 @@ def scaled_fp8_ops(fp8_matrix_mult=False, scale_input=False, override_dtype=None weight *= self.scale_weight.to(device=weight.device, dtype=weight.dtype) return weight else: - return weight * self.scale_weight.to(device=weight.device, dtype=weight.dtype) + return weight.to(dtype=torch.float32) * self.scale_weight.to(device=weight.device, dtype=torch.float32) def set_weight(self, weight, inplace_update=False, seed=None, return_weight=False, **kwargs): weight = comfy.float.stochastic_rounding(weight / self.scale_weight.to(device=weight.device, dtype=weight.dtype), self.weight.dtype, seed=seed) @@ -540,115 +542,136 @@ if CUBLAS_IS_AVAILABLE: # ============================================================================== from .quant_ops import QuantizedTensor, QUANT_ALGOS -class MixedPrecisionOps(disable_weight_init): - _layer_quant_config = {} - _compute_dtype = torch.bfloat16 - class Linear(torch.nn.Module, CastWeightBiasOp): - def __init__( - self, - in_features: int, - out_features: int, - bias: bool = True, - device=None, - dtype=None, - ) -> None: - super().__init__() +def mixed_precision_ops(layer_quant_config={}, compute_dtype=torch.bfloat16, full_precision_mm=False): + class MixedPrecisionOps(manual_cast): + _layer_quant_config = layer_quant_config + _compute_dtype = compute_dtype + _full_precision_mm = full_precision_mm - self.factory_kwargs = {"device": device, "dtype": MixedPrecisionOps._compute_dtype} - # self.factory_kwargs = {"device": device, "dtype": dtype} + class Linear(torch.nn.Module, CastWeightBiasOp): + def __init__( + self, + in_features: int, + out_features: int, + bias: bool = True, + device=None, + dtype=None, + ) -> None: + super().__init__() - self.in_features = in_features - self.out_features = out_features - if bias: - self.bias = torch.nn.Parameter(torch.empty(out_features, **self.factory_kwargs)) - else: - self.register_parameter("bias", None) + self.factory_kwargs = {"device": device, "dtype": MixedPrecisionOps._compute_dtype} + # self.factory_kwargs = {"device": device, "dtype": dtype} - self.tensor_class = None + self.in_features = in_features + self.out_features = out_features + if bias: + self.bias = torch.nn.Parameter(torch.empty(out_features, **self.factory_kwargs)) + else: + self.register_parameter("bias", None) - def reset_parameters(self): - return None + self.tensor_class = None + self._full_precision_mm = MixedPrecisionOps._full_precision_mm - def _load_from_state_dict(self, state_dict, prefix, local_metadata, - strict, missing_keys, unexpected_keys, error_msgs): + def reset_parameters(self): + return None - device = self.factory_kwargs["device"] - layer_name = prefix.rstrip('.') - weight_key = f"{prefix}weight" - weight = state_dict.pop(weight_key, None) - if weight is None: - raise ValueError(f"Missing weight for layer {layer_name}") + def _load_from_state_dict(self, state_dict, prefix, local_metadata, + strict, missing_keys, unexpected_keys, error_msgs): - manually_loaded_keys = [weight_key] + device = self.factory_kwargs["device"] + layer_name = prefix.rstrip('.') + weight_key = f"{prefix}weight" + weight = state_dict.pop(weight_key, None) + if weight is None: + raise ValueError(f"Missing weight for layer {layer_name}") - if layer_name not in MixedPrecisionOps._layer_quant_config: - self.weight = torch.nn.Parameter(weight.to(device=device, dtype=MixedPrecisionOps._compute_dtype), requires_grad=False) - else: - quant_format = MixedPrecisionOps._layer_quant_config[layer_name].get("format", None) - if quant_format is None: - raise ValueError(f"Unknown quantization format for layer {layer_name}") + manually_loaded_keys = [weight_key] - qconfig = QUANT_ALGOS[quant_format] - self.layout_type = qconfig["comfy_tensor_layout"] + if layer_name not in MixedPrecisionOps._layer_quant_config: + self.weight = torch.nn.Parameter(weight.to(device=device, dtype=MixedPrecisionOps._compute_dtype), requires_grad=False) + else: + quant_format = MixedPrecisionOps._layer_quant_config[layer_name].get("format", None) + if quant_format is None: + raise ValueError(f"Unknown quantization format for layer {layer_name}") - weight_scale_key = f"{prefix}weight_scale" - layout_params = { - 'scale': state_dict.pop(weight_scale_key, None), - 'orig_dtype': MixedPrecisionOps._compute_dtype, - 'block_size': qconfig.get("group_size", None), - } - if layout_params['scale'] is not None: - manually_loaded_keys.append(weight_scale_key) + qconfig = QUANT_ALGOS[quant_format] + self.layout_type = qconfig["comfy_tensor_layout"] - self.weight = torch.nn.Parameter( - QuantizedTensor(weight.to(device=device), self.layout_type, layout_params), - requires_grad=False - ) + weight_scale_key = f"{prefix}weight_scale" + layout_params = { + 'scale': state_dict.pop(weight_scale_key, None), + 'orig_dtype': MixedPrecisionOps._compute_dtype, + 'block_size': qconfig.get("group_size", None), + } + if layout_params['scale'] is not None: + manually_loaded_keys.append(weight_scale_key) - for param_name in qconfig["parameters"]: - param_key = f"{prefix}{param_name}" - _v = state_dict.pop(param_key, None) - if _v is None: - continue - setattr(self, param_name, torch.nn.Parameter(_v.to(device=device), requires_grad=False)) - manually_loaded_keys.append(param_key) + self.weight = torch.nn.Parameter( + QuantizedTensor(weight.to(device=device), self.layout_type, layout_params), + requires_grad=False + ) - super()._load_from_state_dict(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs) + for param_name in qconfig["parameters"]: + param_key = f"{prefix}{param_name}" + _v = state_dict.pop(param_key, None) + if _v is None: + continue + setattr(self, param_name, torch.nn.Parameter(_v.to(device=device), requires_grad=False)) + manually_loaded_keys.append(param_key) - for key in manually_loaded_keys: - if key in missing_keys: - missing_keys.remove(key) + super()._load_from_state_dict(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs) - def _forward(self, input, weight, bias): - return torch.nn.functional.linear(input, weight, bias) + for key in manually_loaded_keys: + if key in missing_keys: + missing_keys.remove(key) - def forward_comfy_cast_weights(self, input): - weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True) - x = self._forward(input, weight, bias) - uncast_bias_weight(self, weight, bias, offload_stream) - return x + def _forward(self, input, weight, bias): + return torch.nn.functional.linear(input, weight, bias) - def forward(self, input, *args, **kwargs): - run_every_op() + def forward_comfy_cast_weights(self, input): + weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True) + x = self._forward(input, weight, bias) + uncast_bias_weight(self, weight, bias, offload_stream) + return x - if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0: - return self.forward_comfy_cast_weights(input, *args, **kwargs) - if (getattr(self, 'layout_type', None) is not None and - getattr(self, 'input_scale', None) is not None and - not isinstance(input, QuantizedTensor)): - input = QuantizedTensor.from_float(input, self.layout_type, scale=self.input_scale, dtype=self.weight.dtype) - return self._forward(input, self.weight, self.bias) + def forward(self, input, *args, **kwargs): + run_every_op() + if self._full_precision_mm or self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0: + return self.forward_comfy_cast_weights(input, *args, **kwargs) + if (getattr(self, 'layout_type', None) is not None and + getattr(self, 'input_scale', None) is not None and + not isinstance(input, QuantizedTensor)): + input = QuantizedTensor.from_float(input, self.layout_type, scale=self.input_scale, dtype=self.weight.dtype) + return self._forward(input, self.weight, self.bias) + + def convert_weight(self, weight, inplace=False, **kwargs): + if isinstance(weight, QuantizedTensor): + return weight.dequantize() + else: + return weight + + def set_weight(self, weight, inplace_update=False, seed=None, return_weight=False, **kwargs): + if getattr(self, 'layout_type', None) is not None: + weight = QuantizedTensor.from_float(weight, self.layout_type, scale=None, dtype=self.weight.dtype, stochastic_rounding=seed, inplace_ops=True) + else: + weight = weight.to(self.weight.dtype) + if return_weight: + return weight + + assert inplace_update is False # TODO: eventually remove the inplace_update stuff + self.weight = torch.nn.Parameter(weight, requires_grad=False) + + return MixedPrecisionOps def pick_operations(weight_dtype, compute_dtype, load_device=None, disable_fast_fp8=False, fp8_optimizations=False, scaled_fp8=None, model_config=None): - if model_config and hasattr(model_config, 'layer_quant_config') and model_config.layer_quant_config: - MixedPrecisionOps._layer_quant_config = model_config.layer_quant_config - MixedPrecisionOps._compute_dtype = compute_dtype - logging.info(f"Using mixed precision operations: {len(model_config.layer_quant_config)} quantized layers") - return MixedPrecisionOps + fp8_compute = comfy.model_management.supports_fp8_compute(load_device) # TODO: if we support more ops this needs to be more granular + + if model_config and hasattr(model_config, 'layer_quant_config') and model_config.layer_quant_config: + logging.info(f"Using mixed precision operations: {len(model_config.layer_quant_config)} quantized layers") + return mixed_precision_ops(model_config.layer_quant_config, compute_dtype, full_precision_mm=not fp8_compute) - fp8_compute = comfy.model_management.supports_fp8_compute(load_device) if scaled_fp8 is not None: return scaled_fp8_ops(fp8_matrix_mult=fp8_compute and fp8_optimizations, scale_input=fp8_optimizations, override_dtype=scaled_fp8) diff --git a/comfy/quant_ops.py b/comfy/quant_ops.py index 1d058bece..d2f3e7397 100644 --- a/comfy/quant_ops.py +++ b/comfy/quant_ops.py @@ -1,6 +1,7 @@ import torch import logging from typing import Tuple, Dict +import comfy.float _LAYOUT_REGISTRY = {} _GENERIC_UTILS = {} @@ -228,6 +229,14 @@ class QuantizedTensor(torch.Tensor): new_kwargs = dequant_arg(kwargs) return func(*new_args, **new_kwargs) + def data_ptr(self): + return self._qdata.data_ptr() + + def is_pinned(self): + return self._qdata.is_pinned() + + def is_contiguous(self): + return self._qdata.is_contiguous() # ============================================================================== # Generic Utilities (Layout-Agnostic Operations) @@ -338,6 +347,18 @@ def generic_copy_(func, args, kwargs): return func(*args, **kwargs) +@register_generic_util(torch.ops.aten.to.dtype) +def generic_to_dtype(func, args, kwargs): + """Handle .to(dtype) calls - dtype conversion only.""" + src = args[0] + if isinstance(src, QuantizedTensor): + # For dtype-only conversion, just change the orig_dtype, no real cast is needed + target_dtype = args[1] if len(args) > 1 else kwargs.get('dtype') + src._layout_params["orig_dtype"] = target_dtype + return src + return func(*args, **kwargs) + + @register_generic_util(torch.ops.aten._has_compatible_shallow_copy_type.default) def generic_has_compatible_shallow_copy_type(func, args, kwargs): return True @@ -373,7 +394,7 @@ class TensorCoreFP8Layout(QuantizedLayout): - orig_dtype: Original dtype before quantization (for casting back) """ @classmethod - def quantize(cls, tensor, scale=None, dtype=torch.float8_e4m3fn): + def quantize(cls, tensor, scale=None, dtype=torch.float8_e4m3fn, stochastic_rounding=0, inplace_ops=False): orig_dtype = tensor.dtype if scale is None: @@ -383,17 +404,23 @@ class TensorCoreFP8Layout(QuantizedLayout): scale = torch.tensor(scale) scale = scale.to(device=tensor.device, dtype=torch.float32) - tensor_scaled = tensor * (1.0 / scale).to(tensor.dtype) - # TODO: uncomment this if it's actually needed because the clamp has a small performance penality' - # lp_amax = torch.finfo(dtype).max - # torch.clamp(tensor_scaled, min=-lp_amax, max=lp_amax, out=tensor_scaled) - qdata = tensor_scaled.to(dtype, memory_format=torch.contiguous_format) + if inplace_ops: + tensor *= (1.0 / scale).to(tensor.dtype) + else: + tensor = tensor * (1.0 / scale).to(tensor.dtype) + + if stochastic_rounding > 0: + tensor = comfy.float.stochastic_rounding(tensor, dtype=dtype, seed=stochastic_rounding) + else: + lp_amax = torch.finfo(dtype).max + torch.clamp(tensor, min=-lp_amax, max=lp_amax, out=tensor) + tensor = tensor.to(dtype, memory_format=torch.contiguous_format) layout_params = { 'scale': scale, 'orig_dtype': orig_dtype } - return qdata, layout_params + return tensor, layout_params @staticmethod def dequantize(qdata, scale, orig_dtype, **kwargs): diff --git a/comfy/sd.py b/comfy/sd.py index dc0905ada..350fae92b 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -52,6 +52,7 @@ import comfy.text_encoders.ace import comfy.text_encoders.omnigen2 import comfy.text_encoders.qwen_image import comfy.text_encoders.hunyuan_image +import comfy.text_encoders.z_image import comfy.model_patcher import comfy.lora @@ -356,7 +357,7 @@ class VAE: self.memory_used_encode = lambda shape, dtype: (700 * shape[2] * shape[3]) * model_management.dtype_size(dtype) self.memory_used_decode = lambda shape, dtype: (700 * shape[2] * shape[3] * 32 * 32) * model_management.dtype_size(dtype) - elif sd['decoder.conv_in.weight'].shape[1] == 32: + elif sd['decoder.conv_in.weight'].shape[1] == 32 and sd['decoder.conv_in.weight'].ndim == 5: 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, "refiner_vae": False} self.latent_channels = ddconfig['z_channels'] = sd["decoder.conv_in.weight"].shape[1] self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32] @@ -382,6 +383,17 @@ class VAE: self.upscale_ratio = 4 self.latent_channels = ddconfig['z_channels'] = sd["decoder.conv_in.weight"].shape[1] + if 'decoder.post_quant_conv.weight' in sd: + sd = comfy.utils.state_dict_prefix_replace(sd, {"decoder.post_quant_conv.": "post_quant_conv.", "encoder.quant_conv.": "quant_conv."}) + + if 'bn.running_mean' in sd: + ddconfig["batch_norm_latent"] = True + self.downscale_ratio *= 2 + self.upscale_ratio *= 2 + self.latent_channels *= 4 + old_memory_used_decode = self.memory_used_decode + self.memory_used_decode = lambda shape, dtype: old_memory_used_decode(shape, dtype) * 4.0 + if 'post_quant_conv.weight' in sd: self.first_stage_model = AutoencoderKL(ddconfig=ddconfig, embed_dim=sd['post_quant_conv.weight'].shape[1]) else: @@ -917,7 +929,12 @@ class CLIPType(Enum): def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION, model_options={}): clip_data = [] for p in ckpt_paths: - clip_data.append(comfy.utils.load_torch_file(p, safe_load=True)) + sd, metadata = comfy.utils.load_torch_file(p, safe_load=True, return_metadata=True) + if metadata is not None: + quant_metadata = metadata.get("_quantization_metadata", None) + if quant_metadata is not None: + sd["_quantization_metadata"] = quant_metadata + clip_data.append(sd) return load_text_encoder_state_dicts(clip_data, embedding_directory=embedding_directory, clip_type=clip_type, model_options=model_options) @@ -935,6 +952,10 @@ class TEModel(Enum): QWEN25_7B = 11 BYT5_SMALL_GLYPH = 12 GEMMA_3_4B = 13 + MISTRAL3_24B = 14 + MISTRAL3_24B_PRUNED_FLUX2 = 15 + QWEN3_4B = 16 + def detect_te_model(sd): if "text_model.encoder.layers.30.mlp.fc1.weight" in sd: @@ -967,6 +988,15 @@ def detect_te_model(sd): if weight.shape[0] == 512: return TEModel.QWEN25_7B if "model.layers.0.post_attention_layernorm.weight" in sd: + if 'model.layers.0.self_attn.q_norm.weight' in sd: + return TEModel.QWEN3_4B + weight = sd['model.layers.0.post_attention_layernorm.weight'] + if weight.shape[0] == 5120: + if "model.layers.39.post_attention_layernorm.weight" in sd: + return TEModel.MISTRAL3_24B + else: + return TEModel.MISTRAL3_24B_PRUNED_FLUX2 + return TEModel.LLAMA3_8 return None @@ -1081,6 +1111,13 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip else: clip_target.clip = comfy.text_encoders.qwen_image.te(**llama_detect(clip_data)) clip_target.tokenizer = comfy.text_encoders.qwen_image.QwenImageTokenizer + elif te_model == TEModel.MISTRAL3_24B or te_model == TEModel.MISTRAL3_24B_PRUNED_FLUX2: + clip_target.clip = comfy.text_encoders.flux.flux2_te(**llama_detect(clip_data), pruned=te_model == TEModel.MISTRAL3_24B_PRUNED_FLUX2) + clip_target.tokenizer = comfy.text_encoders.flux.Flux2Tokenizer + tokenizer_data["tekken_model"] = clip_data[0].get("tekken_model", None) + elif te_model == TEModel.QWEN3_4B: + clip_target.clip = comfy.text_encoders.z_image.te(**llama_detect(clip_data)) + clip_target.tokenizer = comfy.text_encoders.z_image.ZImageTokenizer else: # clip_l if clip_type == CLIPType.SD3: @@ -1142,6 +1179,8 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip parameters = 0 for c in clip_data: + if "_quantization_metadata" in c: + c.pop("_quantization_metadata") parameters += comfy.utils.calculate_parameters(c) tokenizer_data, model_options = comfy.text_encoders.long_clipl.model_options_long_clip(c, tokenizer_data, model_options) diff --git a/comfy/sd1_clip.py b/comfy/sd1_clip.py index 3066de2d7..0fc9ab3db 100644 --- a/comfy/sd1_clip.py +++ b/comfy/sd1_clip.py @@ -90,7 +90,6 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder): special_tokens={"start": 49406, "end": 49407, "pad": 49407}, layer_norm_hidden_state=True, enable_attention_masks=False, zero_out_masked=False, return_projected_pooled=True, return_attention_masks=False, model_options={}): # clip-vit-base-patch32 super().__init__() - assert layer in self.LAYERS if textmodel_json_config is None: textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_clip_config.json") @@ -109,13 +108,23 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder): operations = model_options.get("custom_operations", None) scaled_fp8 = None + quantization_metadata = model_options.get("quantization_metadata", None) if operations is None: - scaled_fp8 = model_options.get("scaled_fp8", None) - if scaled_fp8 is not None: - operations = comfy.ops.scaled_fp8_ops(fp8_matrix_mult=False, override_dtype=scaled_fp8) + layer_quant_config = None + if quantization_metadata is not None: + layer_quant_config = json.loads(quantization_metadata).get("layers", None) + + if layer_quant_config is not None: + operations = comfy.ops.mixed_precision_ops(layer_quant_config, dtype, full_precision_mm=True) + logging.info(f"Using MixedPrecisionOps for text encoder: {len(layer_quant_config)} quantized layers") else: - operations = comfy.ops.manual_cast + # Fallback to scaled_fp8_ops for backward compatibility + scaled_fp8 = model_options.get("scaled_fp8", None) + if scaled_fp8 is not None: + operations = comfy.ops.scaled_fp8_ops(fp8_matrix_mult=False, override_dtype=scaled_fp8) + else: + operations = comfy.ops.manual_cast self.operations = operations self.transformer = model_class(config, dtype, device, self.operations) @@ -154,7 +163,7 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder): def set_clip_options(self, options): layer_idx = options.get("layer", self.layer_idx) self.return_projected_pooled = options.get("projected_pooled", self.return_projected_pooled) - if self.layer == "all": + if isinstance(self.layer, list) or self.layer == "all": pass elif layer_idx is None or abs(layer_idx) > self.num_layers: self.layer = "last" @@ -256,7 +265,9 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder): if self.enable_attention_masks: attention_mask_model = attention_mask - if self.layer == "all": + if isinstance(self.layer, list): + intermediate_output = self.layer + elif self.layer == "all": intermediate_output = "all" else: intermediate_output = self.layer_idx diff --git a/comfy/supported_models.py b/comfy/supported_models.py index 2e64b85e8..af8120400 100644 --- a/comfy/supported_models.py +++ b/comfy/supported_models.py @@ -21,6 +21,7 @@ import comfy.text_encoders.ace import comfy.text_encoders.omnigen2 import comfy.text_encoders.qwen_image import comfy.text_encoders.hunyuan_image +import comfy.text_encoders.z_image from . import supported_models_base from . import latent_formats @@ -741,6 +742,37 @@ class FluxSchnell(Flux): out = model_base.Flux(self, model_type=model_base.ModelType.FLOW, device=device) return out +class Flux2(Flux): + unet_config = { + "image_model": "flux2", + } + + sampling_settings = { + "shift": 2.02, + } + + unet_extra_config = {} + latent_format = latent_formats.Flux2 + + supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32] + + vae_key_prefix = ["vae."] + text_encoder_key_prefix = ["text_encoders."] + + def __init__(self, unet_config): + super().__init__(unet_config) + self.memory_usage_factor = self.memory_usage_factor * (2.0 * 2.0) * 2.36 + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.Flux2(self, device=device) + return out + + def clip_target(self, state_dict={}): + return None # TODO + pref = self.text_encoder_key_prefix[0] + t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref)) + return supported_models_base.ClipTarget(comfy.text_encoders.flux.FluxTokenizer, comfy.text_encoders.flux.flux_clip(**t5_detect)) + class GenmoMochi(supported_models_base.BASE): unet_config = { "image_model": "mochi_preview", @@ -963,7 +995,7 @@ class Lumina2(supported_models_base.BASE): "shift": 6.0, } - memory_usage_factor = 1.2 + memory_usage_factor = 1.4 unet_extra_config = {} latent_format = latent_formats.Flux @@ -982,6 +1014,24 @@ class Lumina2(supported_models_base.BASE): hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}gemma2_2b.transformer.".format(pref)) return supported_models_base.ClipTarget(comfy.text_encoders.lumina2.LuminaTokenizer, comfy.text_encoders.lumina2.te(**hunyuan_detect)) +class ZImage(Lumina2): + unet_config = { + "image_model": "lumina2", + "dim": 3840, + } + + sampling_settings = { + "multiplier": 1.0, + "shift": 3.0, + } + + memory_usage_factor = 1.7 + + def clip_target(self, state_dict={}): + pref = self.text_encoder_key_prefix[0] + hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen3_4b.transformer.".format(pref)) + return supported_models_base.ClipTarget(comfy.text_encoders.z_image.ZImageTokenizer, comfy.text_encoders.z_image.te(**hunyuan_detect)) + class WAN21_T2V(supported_models_base.BASE): unet_config = { "image_model": "wan2.1", @@ -1422,6 +1472,7 @@ class HunyuanVideo15_SR_Distilled(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_video.HunyuanVideo15Tokenizer, 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, HunyuanVideo15_SR_Distilled, HunyuanVideo15, 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, WAN21_HuMo, WAN22_Animate, Hunyuan3Dv2mini, Hunyuan3Dv2, Hunyuan3Dv2_1, HiDream, Chroma, ChromaRadiance, ACEStep, Omnigen2, QwenImage] +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, HunyuanVideo15_SR_Distilled, HunyuanVideo15, HunyuanImage21Refiner, HunyuanImage21, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, ZImage, Lumina2, WAN22_T2V, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, WAN22_Camera, WAN22_S2V, WAN21_HuMo, WAN22_Animate, Hunyuan3Dv2mini, Hunyuan3Dv2, Hunyuan3Dv2_1, HiDream, Chroma, ChromaRadiance, ACEStep, Omnigen2, QwenImage, Flux2] + models += [SVD_img2vid] diff --git a/comfy/text_encoders/flux.py b/comfy/text_encoders/flux.py index d61ef6668..99f4812bb 100644 --- a/comfy/text_encoders/flux.py +++ b/comfy/text_encoders/flux.py @@ -1,10 +1,13 @@ from comfy import sd1_clip import comfy.text_encoders.t5 import comfy.text_encoders.sd3_clip +import comfy.text_encoders.llama import comfy.model_management -from transformers import T5TokenizerFast +from transformers import T5TokenizerFast, LlamaTokenizerFast import torch import os +import json +import base64 class T5XXLTokenizer(sd1_clip.SDTokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): @@ -68,3 +71,106 @@ def flux_clip(dtype_t5=None, t5xxl_scaled_fp8=None): model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8 super().__init__(dtype_t5=dtype_t5, device=device, dtype=dtype, model_options=model_options) return FluxClipModel_ + +def load_mistral_tokenizer(data): + if torch.is_tensor(data): + data = data.numpy().tobytes() + + try: + from transformers.integrations.mistral import MistralConverter + except ModuleNotFoundError: + from transformers.models.pixtral.convert_pixtral_weights_to_hf import MistralConverter + + mistral_vocab = json.loads(data) + + special_tokens = {} + vocab = {} + + max_vocab = mistral_vocab["config"]["default_vocab_size"] + max_vocab -= len(mistral_vocab["special_tokens"]) + + for w in mistral_vocab["vocab"]: + r = w["rank"] + if r >= max_vocab: + continue + + vocab[base64.b64decode(w["token_bytes"])] = r + + for w in mistral_vocab["special_tokens"]: + if "token_bytes" in w: + special_tokens[base64.b64decode(w["token_bytes"])] = w["rank"] + else: + special_tokens[w["token_str"]] = w["rank"] + + all_special = [] + for v in special_tokens: + all_special.append(v) + + special_tokens.update(vocab) + vocab = special_tokens + return {"tokenizer_object": MistralConverter(vocab=vocab, additional_special_tokens=all_special).converted(), "legacy": False} + +class MistralTokenizerClass: + @staticmethod + def from_pretrained(path, **kwargs): + return LlamaTokenizerFast(**kwargs) + +class Mistral3Tokenizer(sd1_clip.SDTokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + self.tekken_data = tokenizer_data.get("tekken_model", None) + super().__init__("", pad_with_end=False, embedding_size=5120, embedding_key='mistral3_24b', tokenizer_class=MistralTokenizerClass, has_end_token=False, pad_to_max_length=False, pad_token=11, max_length=99999999, min_length=1, pad_left=True, tokenizer_args=load_mistral_tokenizer(self.tekken_data), tokenizer_data=tokenizer_data) + + def state_dict(self): + return {"tekken_model": self.tekken_data} + +class Flux2Tokenizer(sd1_clip.SD1Tokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name="mistral3_24b", tokenizer=Mistral3Tokenizer) + self.llama_template = '[SYSTEM_PROMPT]You are an AI that reasons about image descriptions. You give structured responses focusing on object relationships, object\nattribution and actions without speculation.[/SYSTEM_PROMPT][INST]{}[/INST]' + + def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, **kwargs): + if llama_template is None: + llama_text = self.llama_template.format(text) + else: + llama_text = llama_template.format(text) + + tokens = super().tokenize_with_weights(llama_text, return_word_ids=return_word_ids, disable_weights=True, **kwargs) + return tokens + +class Mistral3_24BModel(sd1_clip.SDClipModel): + def __init__(self, device="cpu", layer=[10, 20, 30], layer_idx=None, dtype=None, attention_mask=True, model_options={}): + textmodel_json_config = {} + num_layers = model_options.get("num_layers", None) + if num_layers is not None: + textmodel_json_config["num_hidden_layers"] = num_layers + if num_layers < 40: + textmodel_json_config["final_norm"] = False + super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"start": 1, "pad": 0}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Mistral3Small24B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options) + +class Flux2TEModel(sd1_clip.SD1ClipModel): + def __init__(self, device="cpu", dtype=None, model_options={}, name="mistral3_24b", clip_model=Mistral3_24BModel): + super().__init__(device=device, dtype=dtype, name=name, clip_model=clip_model, model_options=model_options) + + def encode_token_weights(self, token_weight_pairs): + out, pooled, extra = super().encode_token_weights(token_weight_pairs) + + out = torch.stack((out[:, 0], out[:, 1], out[:, 2]), dim=1) + out = out.movedim(1, 2) + out = out.reshape(out.shape[0], out.shape[1], -1) + return out, pooled, extra + +def flux2_te(dtype_llama=None, llama_scaled_fp8=None, llama_quantization_metadata=None, pruned=False): + class Flux2TEModel_(Flux2TEModel): + def __init__(self, device="cpu", dtype=None, model_options={}): + if llama_scaled_fp8 is not None and "scaled_fp8" not in model_options: + model_options = model_options.copy() + model_options["scaled_fp8"] = llama_scaled_fp8 + if dtype_llama is not None: + dtype = dtype_llama + if llama_quantization_metadata is not None: + model_options["quantization_metadata"] = llama_quantization_metadata + if pruned: + model_options = model_options.copy() + model_options["num_layers"] = 30 + super().__init__(device=device, dtype=dtype, model_options=model_options) + return Flux2TEModel_ diff --git a/comfy/text_encoders/hunyuan_video.py b/comfy/text_encoders/hunyuan_video.py index 557094f49..0110517bb 100644 --- a/comfy/text_encoders/hunyuan_video.py +++ b/comfy/text_encoders/hunyuan_video.py @@ -18,6 +18,9 @@ def llama_detect(state_dict, prefix=""): if scaled_fp8_key in state_dict: out["llama_scaled_fp8"] = state_dict[scaled_fp8_key].dtype + if "_quantization_metadata" in state_dict: + out["llama_quantization_metadata"] = state_dict["_quantization_metadata"] + return out diff --git a/comfy/text_encoders/llama.py b/comfy/text_encoders/llama.py index feb44bbb0..cd4b5f76c 100644 --- a/comfy/text_encoders/llama.py +++ b/comfy/text_encoders/llama.py @@ -34,6 +34,28 @@ class Llama2Config: rope_scale = None final_norm: bool = True +@dataclass +class Mistral3Small24BConfig: + vocab_size: int = 131072 + hidden_size: int = 5120 + intermediate_size: int = 32768 + num_hidden_layers: int = 40 + num_attention_heads: int = 32 + num_key_value_heads: int = 8 + max_position_embeddings: int = 8192 + rms_norm_eps: float = 1e-5 + rope_theta: float = 1000000000.0 + transformer_type: str = "llama" + head_dim = 128 + rms_norm_add = False + mlp_activation = "silu" + qkv_bias = False + rope_dims = None + q_norm = None + k_norm = None + rope_scale = None + final_norm: bool = True + @dataclass class Qwen25_3BConfig: vocab_size: int = 151936 @@ -56,6 +78,28 @@ class Qwen25_3BConfig: rope_scale = None final_norm: bool = True +@dataclass +class Qwen3_4BConfig: + vocab_size: int = 151936 + hidden_size: int = 2560 + intermediate_size: int = 9728 + num_hidden_layers: int = 36 + num_attention_heads: int = 32 + num_key_value_heads: int = 8 + max_position_embeddings: int = 40960 + rms_norm_eps: float = 1e-6 + rope_theta: float = 1000000.0 + transformer_type: str = "llama" + head_dim = 128 + rms_norm_add = False + mlp_activation = "silu" + qkv_bias = False + rope_dims = None + q_norm = "gemma3" + k_norm = "gemma3" + rope_scale = None + final_norm: bool = True + @dataclass class Qwen25_7BVLI_Config: vocab_size: int = 152064 @@ -412,8 +456,12 @@ class Llama2_(nn.Module): intermediate = None all_intermediate = None + only_layers = None if intermediate_output is not None: - if intermediate_output == "all": + if isinstance(intermediate_output, list): + all_intermediate = [] + only_layers = set(intermediate_output) + elif intermediate_output == "all": all_intermediate = [] intermediate_output = None elif intermediate_output < 0: @@ -421,7 +469,8 @@ class Llama2_(nn.Module): for i, layer in enumerate(self.layers): if all_intermediate is not None: - all_intermediate.append(x.unsqueeze(1).clone()) + if only_layers is None or (i in only_layers): + all_intermediate.append(x.unsqueeze(1).clone()) x = layer( x=x, attention_mask=mask, @@ -435,7 +484,8 @@ class Llama2_(nn.Module): x = self.norm(x) if all_intermediate is not None: - all_intermediate.append(x.unsqueeze(1).clone()) + if only_layers is None or ((i + 1) in only_layers): + all_intermediate.append(x.unsqueeze(1).clone()) if all_intermediate is not None: intermediate = torch.cat(all_intermediate, dim=1) @@ -465,6 +515,15 @@ class Llama2(BaseLlama, torch.nn.Module): self.model = Llama2_(config, device=device, dtype=dtype, ops=operations) self.dtype = dtype +class Mistral3Small24B(BaseLlama, torch.nn.Module): + def __init__(self, config_dict, dtype, device, operations): + super().__init__() + config = Mistral3Small24BConfig(**config_dict) + self.num_layers = config.num_hidden_layers + + self.model = Llama2_(config, device=device, dtype=dtype, ops=operations) + self.dtype = dtype + class Qwen25_3B(BaseLlama, torch.nn.Module): def __init__(self, config_dict, dtype, device, operations): super().__init__() @@ -474,6 +533,15 @@ class Qwen25_3B(BaseLlama, torch.nn.Module): self.model = Llama2_(config, device=device, dtype=dtype, ops=operations) self.dtype = dtype +class Qwen3_4B(BaseLlama, torch.nn.Module): + def __init__(self, config_dict, dtype, device, operations): + super().__init__() + config = Qwen3_4BConfig(**config_dict) + self.num_layers = config.num_hidden_layers + + self.model = Llama2_(config, device=device, dtype=dtype, ops=operations) + self.dtype = dtype + class Qwen25_7BVLI(BaseLlama, torch.nn.Module): def __init__(self, config_dict, dtype, device, operations): super().__init__() diff --git a/comfy/text_encoders/z_image.py b/comfy/text_encoders/z_image.py new file mode 100644 index 000000000..bb9273b20 --- /dev/null +++ b/comfy/text_encoders/z_image.py @@ -0,0 +1,48 @@ +from transformers import Qwen2Tokenizer +import comfy.text_encoders.llama +from comfy import sd1_clip +import os + +class Qwen3Tokenizer(sd1_clip.SDTokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "qwen25_tokenizer") + super().__init__(tokenizer_path, pad_with_end=False, embedding_size=2560, embedding_key='qwen3_4b', tokenizer_class=Qwen2Tokenizer, has_start_token=False, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, pad_token=151643, tokenizer_data=tokenizer_data) + + +class ZImageTokenizer(sd1_clip.SD1Tokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name="qwen3_4b", tokenizer=Qwen3Tokenizer) + self.llama_template = "<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n" + + def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, **kwargs): + if llama_template is None: + llama_text = self.llama_template.format(text) + else: + llama_text = llama_template.format(text) + + tokens = super().tokenize_with_weights(llama_text, return_word_ids=return_word_ids, disable_weights=True, **kwargs) + return tokens + + +class Qwen3_4BModel(sd1_clip.SDClipModel): + def __init__(self, device="cpu", layer="hidden", layer_idx=-2, dtype=None, attention_mask=True, model_options={}): + super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"pad": 151643}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Qwen3_4B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options) + + +class ZImageTEModel(sd1_clip.SD1ClipModel): + def __init__(self, device="cpu", dtype=None, model_options={}): + super().__init__(device=device, dtype=dtype, name="qwen3_4b", clip_model=Qwen3_4BModel, model_options=model_options) + + +def te(dtype_llama=None, llama_scaled_fp8=None, llama_quantization_metadata=None): + class ZImageTEModel_(ZImageTEModel): + def __init__(self, device="cpu", dtype=None, model_options={}): + if llama_scaled_fp8 is not None and "scaled_fp8" not in model_options: + model_options = model_options.copy() + model_options["scaled_fp8"] = llama_scaled_fp8 + if dtype_llama is not None: + dtype = dtype_llama + if llama_quantization_metadata is not None: + model_options["quantization_metadata"] = llama_quantization_metadata + super().__init__(device=device, dtype=dtype, model_options=model_options) + return ZImageTEModel_ diff --git a/comfy/weight_adapter/lora.py b/comfy/weight_adapter/lora.py index 4db004e50..3cc60bb1b 100644 --- a/comfy/weight_adapter/lora.py +++ b/comfy/weight_adapter/lora.py @@ -194,6 +194,7 @@ class LoRAAdapter(WeightAdapterBase): lora_diff = torch.mm( mat1.flatten(start_dim=1), mat2.flatten(start_dim=1) ).reshape(weight.shape) + del mat1, mat2 if dora_scale is not None: weight = weight_decompose( dora_scale, diff --git a/comfy_api/internal/async_to_sync.py b/comfy_api/internal/async_to_sync.py index f5f805a62..257ade82e 100644 --- a/comfy_api/internal/async_to_sync.py +++ b/comfy_api/internal/async_to_sync.py @@ -8,7 +8,7 @@ import os import textwrap import threading from enum import Enum -from typing import Optional, Type, get_origin, get_args +from typing import Optional, Type, get_origin, get_args, get_type_hints class TypeTracker: @@ -220,11 +220,18 @@ class AsyncToSyncConverter: self._async_instance = async_class(*args, **kwargs) # Handle annotated class attributes (like execution: Execution) - # Get all annotations from the class hierarchy - all_annotations = {} - for base_class in reversed(inspect.getmro(async_class)): - if hasattr(base_class, "__annotations__"): - all_annotations.update(base_class.__annotations__) + # Get all annotations from the class hierarchy and resolve string annotations + try: + # get_type_hints resolves string annotations to actual type objects + # This handles classes using 'from __future__ import annotations' + all_annotations = get_type_hints(async_class) + except Exception: + # Fallback to raw annotations if get_type_hints fails + # (e.g., for undefined forward references) + all_annotations = {} + for base_class in reversed(inspect.getmro(async_class)): + if hasattr(base_class, "__annotations__"): + all_annotations.update(base_class.__annotations__) # For each annotated attribute, check if it needs to be created or wrapped for attr_name, attr_type in all_annotations.items(): @@ -625,15 +632,19 @@ class AsyncToSyncConverter: """Extract class attributes that are classes themselves.""" class_attributes = [] + # Get resolved type hints to handle string annotations + try: + type_hints = get_type_hints(async_class) + except Exception: + type_hints = {} + # Look for class attributes that are classes for name, attr in sorted(inspect.getmembers(async_class)): if isinstance(attr, type) and not name.startswith("_"): class_attributes.append((name, attr)) - elif ( - hasattr(async_class, "__annotations__") - and name in async_class.__annotations__ - ): - annotation = async_class.__annotations__[name] + elif name in type_hints: + # Use resolved type hint instead of raw annotation + annotation = type_hints[name] if isinstance(annotation, type): class_attributes.append((name, annotation)) @@ -908,11 +919,15 @@ class AsyncToSyncConverter: attribute_mappings = {} # First check annotations for typed attributes (including from parent classes) - # Collect all annotations from the class hierarchy - all_annotations = {} - for base_class in reversed(inspect.getmro(async_class)): - if hasattr(base_class, "__annotations__"): - all_annotations.update(base_class.__annotations__) + # Resolve string annotations to actual types + try: + all_annotations = get_type_hints(async_class) + except Exception: + # Fallback to raw annotations + all_annotations = {} + for base_class in reversed(inspect.getmro(async_class)): + if hasattr(base_class, "__annotations__"): + all_annotations.update(base_class.__annotations__) for attr_name, attr_type in sorted(all_annotations.items()): for class_name, class_type in class_attributes: diff --git a/comfy_api/latest/_input/video_types.py b/comfy_api/latest/_input/video_types.py index a335df4d0..87c81d73a 100644 --- a/comfy_api/latest/_input/video_types.py +++ b/comfy_api/latest/_input/video_types.py @@ -1,5 +1,6 @@ from __future__ import annotations from abc import ABC, abstractmethod +from fractions import Fraction from typing import Optional, Union, IO import io import av @@ -72,6 +73,33 @@ class VideoInput(ABC): frame_count = components.images.shape[0] return float(frame_count / components.frame_rate) + def get_frame_count(self) -> int: + """ + Returns the number of frames in the video. + + Default implementation uses :meth:`get_components`, which may require + loading all frames into memory. File-based implementations should + override this method and use container/stream metadata instead. + + Returns: + Total number of frames as an integer. + """ + return int(self.get_components().images.shape[0]) + + def get_frame_rate(self) -> Fraction: + """ + Returns the frame rate of the video. + + Default implementation materializes the video into memory via + `get_components()`. Subclasses that can inspect the underlying + container (e.g. `VideoFromFile`) should override this with a more + efficient implementation. + + Returns: + Frame rate as a Fraction. + """ + return self.get_components().frame_rate + def get_container_format(self) -> str: """ Returns the container format of the video (e.g., 'mp4', 'mov', 'avi'). diff --git a/comfy_api/latest/_input_impl/video_types.py b/comfy_api/latest/_input_impl/video_types.py index f646504c8..bde37f90a 100644 --- a/comfy_api/latest/_input_impl/video_types.py +++ b/comfy_api/latest/_input_impl/video_types.py @@ -121,6 +121,71 @@ class VideoFromFile(VideoInput): raise ValueError(f"Could not determine duration for file '{self.__file}'") + def get_frame_count(self) -> int: + """ + Returns the number of frames in the video without materializing them as + torch tensors. + """ + if isinstance(self.__file, io.BytesIO): + self.__file.seek(0) + + with av.open(self.__file, mode="r") as container: + video_stream = self._get_first_video_stream(container) + # 1. Prefer the frames field if available + if video_stream.frames and video_stream.frames > 0: + return int(video_stream.frames) + + # 2. Try to estimate from duration and average_rate using only metadata + if container.duration is not None and video_stream.average_rate: + duration_seconds = float(container.duration / av.time_base) + estimated_frames = int(round(duration_seconds * float(video_stream.average_rate))) + if estimated_frames > 0: + return estimated_frames + + if ( + getattr(video_stream, "duration", None) is not None + and getattr(video_stream, "time_base", None) is not None + and video_stream.average_rate + ): + duration_seconds = float(video_stream.duration * video_stream.time_base) + estimated_frames = int(round(duration_seconds * float(video_stream.average_rate))) + if estimated_frames > 0: + return estimated_frames + + # 3. Last resort: decode frames and count them (streaming) + frame_count = 0 + container.seek(0) + for packet in container.demux(video_stream): + for _ in packet.decode(): + frame_count += 1 + + if frame_count == 0: + raise ValueError(f"Could not determine frame count for file '{self.__file}'") + return frame_count + + def get_frame_rate(self) -> Fraction: + """ + Returns the average frame rate of the video using container metadata + without decoding all frames. + """ + if isinstance(self.__file, io.BytesIO): + self.__file.seek(0) + + with av.open(self.__file, mode="r") as container: + video_stream = self._get_first_video_stream(container) + # Preferred: use PyAV's average_rate (usually already a Fraction-like) + if video_stream.average_rate: + return Fraction(video_stream.average_rate) + + # Fallback: estimate from frames + duration if available + if video_stream.frames and container.duration: + duration_seconds = float(container.duration / av.time_base) + if duration_seconds > 0: + return Fraction(video_stream.frames / duration_seconds).limit_denominator() + + # Last resort: match get_components_internal default + return Fraction(1) + def get_container_format(self) -> str: """ Returns the container format of the video (e.g., 'mp4', 'mov', 'avi'). @@ -238,6 +303,13 @@ class VideoFromFile(VideoInput): packet.stream = stream_map[packet.stream] output_container.mux(packet) + def _get_first_video_stream(self, container: InputContainer): + video_stream = next((s for s in container.streams if s.type == "video"), None) + if video_stream is None: + raise ValueError(f"No video stream found in file '{self.__file}'") + return video_stream + + class VideoFromComponents(VideoInput): """ Class representing video input from tensors. diff --git a/comfy_api_nodes/apis/bfl_api.py b/comfy_api_nodes/apis/bfl_api.py index 0fc8c0607..d8d3557b3 100644 --- a/comfy_api_nodes/apis/bfl_api.py +++ b/comfy_api_nodes/apis/bfl_api.py @@ -70,6 +70,29 @@ class BFLFluxProGenerateRequest(BaseModel): # ) +class Flux2ProGenerateRequest(BaseModel): + prompt: str = Field(...) + width: int = Field(1024, description="Must be a multiple of 32.") + height: int = Field(768, description="Must be a multiple of 32.") + seed: int | None = Field(None) + prompt_upsampling: bool | None = Field(None) + input_image: str | None = Field(None, description="Base64 encoded image for image-to-image generation") + input_image_2: str | None = Field(None, description="Base64 encoded image for image-to-image generation") + input_image_3: str | None = Field(None, description="Base64 encoded image for image-to-image generation") + input_image_4: str | None = Field(None, description="Base64 encoded image for image-to-image generation") + input_image_5: str | None = Field(None, description="Base64 encoded image for image-to-image generation") + input_image_6: str | None = Field(None, description="Base64 encoded image for image-to-image generation") + input_image_7: str | None = Field(None, description="Base64 encoded image for image-to-image generation") + input_image_8: str | None = Field(None, description="Base64 encoded image for image-to-image generation") + input_image_9: str | None = Field(None, description="Base64 encoded image for image-to-image generation") + safety_tolerance: int | None = Field( + 5, description="Tolerance level for input and output moderation. Value 0 being most strict.", ge=0, le=5 + ) + output_format: str | None = Field( + "png", description="Output format for the generated image. Can be 'jpeg' or 'png'." + ) + + class BFLFluxKontextProGenerateRequest(BaseModel): prompt: str = Field(..., description='The text prompt for what you wannt to edit.') input_image: Optional[str] = Field(None, description='Image to edit in base64 format') @@ -109,8 +132,9 @@ class BFLFluxProUltraGenerateRequest(BaseModel): class BFLFluxProGenerateResponse(BaseModel): - id: str = Field(..., description='The unique identifier for the generation task.') - polling_url: str = Field(..., description='URL to poll for the generation result.') + id: str = Field(..., description="The unique identifier for the generation task.") + polling_url: str = Field(..., description="URL to poll for the generation result.") + cost: float | None = Field(None, description="Price in cents") class BFLStatus(str, Enum): diff --git a/comfy_api_nodes/apis/gemini_api.py b/comfy_api_nodes/apis/gemini_api.py index 710f173f1..a380ecc86 100644 --- a/comfy_api_nodes/apis/gemini_api.py +++ b/comfy_api_nodes/apis/gemini_api.py @@ -58,8 +58,14 @@ class GeminiInlineData(BaseModel): mimeType: GeminiMimeType | None = Field(None) +class GeminiFileData(BaseModel): + fileUri: str | None = Field(None) + mimeType: GeminiMimeType | None = Field(None) + + class GeminiPart(BaseModel): inlineData: GeminiInlineData | None = Field(None) + fileData: GeminiFileData | None = Field(None) text: str | None = Field(None) @@ -113,9 +119,9 @@ class GeminiGenerationConfig(BaseModel): maxOutputTokens: int | None = Field(None, ge=16, le=8192) seed: int | None = Field(None) stopSequences: list[str] | None = Field(None) - temperature: float | None = Field(1, ge=0.0, le=2.0) - topK: int | None = Field(40, ge=1) - topP: float | None = Field(0.95, ge=0.0, le=1.0) + temperature: float | None = Field(None, ge=0.0, le=2.0) + topK: int | None = Field(None, ge=1) + topP: float | None = Field(None, ge=0.0, le=1.0) class GeminiImageConfig(BaseModel): diff --git a/comfy_api_nodes/apis/veo_api.py b/comfy_api_nodes/apis/veo_api.py index a55137afb..8328d1aa4 100644 --- a/comfy_api_nodes/apis/veo_api.py +++ b/comfy_api_nodes/apis/veo_api.py @@ -1,34 +1,21 @@ -from typing import Optional, Union -from enum import Enum +from typing import Optional from pydantic import BaseModel, Field -class Image2(BaseModel): - bytesBase64Encoded: str - gcsUri: Optional[str] = None - mimeType: Optional[str] = None +class VeoRequestInstanceImage(BaseModel): + bytesBase64Encoded: str | None = Field(None) + gcsUri: str | None = Field(None) + mimeType: str | None = Field(None) -class Image3(BaseModel): - bytesBase64Encoded: Optional[str] = None - gcsUri: str - mimeType: Optional[str] = None - - -class Instance1(BaseModel): - image: Optional[Union[Image2, Image3]] = Field( - None, description='Optional image to guide video generation' - ) +class VeoRequestInstance(BaseModel): + image: VeoRequestInstanceImage | None = Field(None) + lastFrame: VeoRequestInstanceImage | None = Field(None) prompt: str = Field(..., description='Text description of the video') -class PersonGeneration1(str, Enum): - ALLOW = 'ALLOW' - BLOCK = 'BLOCK' - - -class Parameters1(BaseModel): +class VeoRequestParameters(BaseModel): aspectRatio: Optional[str] = Field(None, examples=['16:9']) durationSeconds: Optional[int] = None enhancePrompt: Optional[bool] = None @@ -37,17 +24,18 @@ class Parameters1(BaseModel): description='Generate audio for the video. Only supported by veo 3 models.', ) negativePrompt: Optional[str] = None - personGeneration: Optional[PersonGeneration1] = None + personGeneration: str | None = Field(None, description="ALLOW or BLOCK") sampleCount: Optional[int] = None seed: Optional[int] = None storageUri: Optional[str] = Field( None, description='Optional Cloud Storage URI to upload the video' ) + resolution: str | None = Field(None) class VeoGenVidRequest(BaseModel): - instances: Optional[list[Instance1]] = None - parameters: Optional[Parameters1] = None + instances: list[VeoRequestInstance] | None = Field(None) + parameters: VeoRequestParameters | None = Field(None) class VeoGenVidResponse(BaseModel): diff --git a/comfy_api_nodes/nodes_bfl.py b/comfy_api_nodes/nodes_bfl.py index 1740fb377..8826dea0c 100644 --- a/comfy_api_nodes/nodes_bfl.py +++ b/comfy_api_nodes/nodes_bfl.py @@ -1,7 +1,7 @@ from inspect import cleandoc -from typing import Optional import torch +from pydantic import BaseModel from typing_extensions import override from comfy_api.latest import IO, ComfyExtension @@ -9,15 +9,16 @@ from comfy_api_nodes.apis.bfl_api import ( BFLFluxExpandImageRequest, BFLFluxFillImageRequest, BFLFluxKontextProGenerateRequest, - BFLFluxProGenerateRequest, BFLFluxProGenerateResponse, BFLFluxProUltraGenerateRequest, BFLFluxStatusResponse, BFLStatus, + Flux2ProGenerateRequest, ) from comfy_api_nodes.util import ( ApiEndpoint, download_url_to_image_tensor, + get_number_of_images, poll_op, resize_mask_to_image, sync_op, @@ -116,7 +117,7 @@ class FluxProUltraImageNode(IO.ComfyNode): prompt_upsampling: bool = False, raw: bool = False, seed: int = 0, - image_prompt: Optional[torch.Tensor] = None, + image_prompt: torch.Tensor | None = None, image_prompt_strength: float = 0.1, ) -> IO.NodeOutput: if image_prompt is None: @@ -230,7 +231,7 @@ class FluxKontextProImageNode(IO.ComfyNode): aspect_ratio: str, guidance: float, steps: int, - input_image: Optional[torch.Tensor] = None, + input_image: torch.Tensor | None = None, seed=0, prompt_upsampling=False, ) -> IO.NodeOutput: @@ -280,124 +281,6 @@ class FluxKontextMaxImageNode(FluxKontextProImageNode): DISPLAY_NAME = "Flux.1 Kontext [max] Image" -class FluxProImageNode(IO.ComfyNode): - """ - Generates images synchronously based on prompt and resolution. - """ - - @classmethod - def define_schema(cls) -> IO.Schema: - return IO.Schema( - node_id="FluxProImageNode", - display_name="Flux 1.1 [pro] Image", - category="api node/image/BFL", - description=cleandoc(cls.__doc__ or ""), - inputs=[ - IO.String.Input( - "prompt", - multiline=True, - default="", - tooltip="Prompt for the image generation", - ), - IO.Boolean.Input( - "prompt_upsampling", - default=False, - tooltip="Whether to perform upsampling on the prompt. " - "If active, automatically modifies the prompt for more creative generation, " - "but results are nondeterministic (same seed will not produce exactly the same result).", - ), - IO.Int.Input( - "width", - default=1024, - min=256, - max=1440, - step=32, - ), - IO.Int.Input( - "height", - default=768, - min=256, - max=1440, - step=32, - ), - IO.Int.Input( - "seed", - default=0, - min=0, - max=0xFFFFFFFFFFFFFFFF, - control_after_generate=True, - tooltip="The random seed used for creating the noise.", - ), - IO.Image.Input( - "image_prompt", - optional=True, - ), - # "image_prompt_strength": ( - # IO.FLOAT, - # { - # "default": 0.1, - # "min": 0.0, - # "max": 1.0, - # "step": 0.01, - # "tooltip": "Blend between the prompt and the image prompt.", - # }, - # ), - ], - outputs=[IO.Image.Output()], - hidden=[ - IO.Hidden.auth_token_comfy_org, - IO.Hidden.api_key_comfy_org, - IO.Hidden.unique_id, - ], - is_api_node=True, - ) - - @classmethod - async def execute( - cls, - prompt: str, - prompt_upsampling, - width: int, - height: int, - seed=0, - image_prompt=None, - # image_prompt_strength=0.1, - ) -> IO.NodeOutput: - image_prompt = image_prompt if image_prompt is None else tensor_to_base64_string(image_prompt) - initial_response = await sync_op( - cls, - ApiEndpoint( - path="/proxy/bfl/flux-pro-1.1/generate", - method="POST", - ), - response_model=BFLFluxProGenerateResponse, - data=BFLFluxProGenerateRequest( - prompt=prompt, - prompt_upsampling=prompt_upsampling, - width=width, - height=height, - seed=seed, - image_prompt=image_prompt, - ), - ) - response = await poll_op( - cls, - ApiEndpoint(initial_response.polling_url), - response_model=BFLFluxStatusResponse, - status_extractor=lambda r: r.status, - progress_extractor=lambda r: r.progress, - completed_statuses=[BFLStatus.ready], - failed_statuses=[ - BFLStatus.request_moderated, - BFLStatus.content_moderated, - BFLStatus.error, - BFLStatus.task_not_found, - ], - queued_statuses=[], - ) - return IO.NodeOutput(await download_url_to_image_tensor(response.result["sample"])) - - class FluxProExpandNode(IO.ComfyNode): """ Outpaints image based on prompt. @@ -640,16 +523,125 @@ class FluxProFillNode(IO.ComfyNode): return IO.NodeOutput(await download_url_to_image_tensor(response.result["sample"])) +class Flux2ProImageNode(IO.ComfyNode): + + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="Flux2ProImageNode", + display_name="Flux.2 [pro] Image", + category="api node/image/BFL", + description="Generates images synchronously based on prompt and resolution.", + inputs=[ + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="Prompt for the image generation or edit", + ), + IO.Int.Input( + "width", + default=1024, + min=256, + max=2048, + step=32, + ), + IO.Int.Input( + "height", + default=768, + min=256, + max=2048, + step=32, + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=0xFFFFFFFFFFFFFFFF, + control_after_generate=True, + tooltip="The random seed used for creating the noise.", + ), + IO.Boolean.Input( + "prompt_upsampling", + default=False, + tooltip="Whether to perform upsampling on the prompt. " + "If active, automatically modifies the prompt for more creative generation, " + "but results are nondeterministic (same seed will not produce exactly the same result).", + ), + IO.Image.Input("images", optional=True, tooltip="Up to 4 images to be used as references."), + ], + outputs=[IO.Image.Output()], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + prompt: str, + width: int, + height: int, + seed: int, + prompt_upsampling: bool, + images: torch.Tensor | None = None, + ) -> IO.NodeOutput: + reference_images = {} + if images is not None: + if get_number_of_images(images) > 9: + raise ValueError("The current maximum number of supported images is 9.") + for image_index in range(images.shape[0]): + key_name = f"input_image_{image_index + 1}" if image_index else "input_image" + reference_images[key_name] = tensor_to_base64_string(images[image_index], total_pixels=2048 * 2048) + initial_response = await sync_op( + cls, + ApiEndpoint(path="/proxy/bfl/flux-2-pro/generate", method="POST"), + response_model=BFLFluxProGenerateResponse, + data=Flux2ProGenerateRequest( + prompt=prompt, + width=width, + height=height, + seed=seed, + prompt_upsampling=prompt_upsampling, + **reference_images, + ), + ) + + def price_extractor(_r: BaseModel) -> float | None: + return None if initial_response.cost is None else initial_response.cost / 100 + + response = await poll_op( + cls, + ApiEndpoint(initial_response.polling_url), + response_model=BFLFluxStatusResponse, + status_extractor=lambda r: r.status, + progress_extractor=lambda r: r.progress, + price_extractor=price_extractor, + completed_statuses=[BFLStatus.ready], + failed_statuses=[ + BFLStatus.request_moderated, + BFLStatus.content_moderated, + BFLStatus.error, + BFLStatus.task_not_found, + ], + queued_statuses=[], + ) + return IO.NodeOutput(await download_url_to_image_tensor(response.result["sample"])) + + class BFLExtension(ComfyExtension): @override async def get_node_list(self) -> list[type[IO.ComfyNode]]: return [ FluxProUltraImageNode, - # FluxProImageNode, FluxKontextProImageNode, FluxKontextMaxImageNode, FluxProExpandNode, FluxProFillNode, + Flux2ProImageNode, ] diff --git a/comfy_api_nodes/nodes_gemini.py b/comfy_api_nodes/nodes_gemini.py index be752c885..08f7b0f64 100644 --- a/comfy_api_nodes/nodes_gemini.py +++ b/comfy_api_nodes/nodes_gemini.py @@ -4,10 +4,7 @@ See: https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/infer """ import base64 -import json import os -import time -import uuid from enum import Enum from io import BytesIO from typing import Literal @@ -20,6 +17,7 @@ from comfy_api.latest import IO, ComfyExtension, Input from comfy_api.util import VideoCodec, VideoContainer from comfy_api_nodes.apis.gemini_api import ( GeminiContent, + GeminiFileData, GeminiGenerateContentRequest, GeminiGenerateContentResponse, GeminiImageConfig, @@ -38,10 +36,10 @@ from comfy_api_nodes.util import ( get_number_of_images, sync_op, tensor_to_base64_string, + upload_images_to_comfyapi, validate_string, video_to_base64_string, ) -from server import PromptServer GEMINI_BASE_ENDPOINT = "/proxy/vertexai/gemini" GEMINI_MAX_INPUT_FILE_SIZE = 20 * 1024 * 1024 # 20 MB @@ -68,24 +66,43 @@ class GeminiImageModel(str, Enum): gemini_2_5_flash_image = "gemini-2.5-flash-image" -def create_image_parts(image_input: torch.Tensor) -> list[GeminiPart]: - """ - Convert image tensor input to Gemini API compatible parts. - - Args: - image_input: Batch of image tensors from ComfyUI. - - Returns: - List of GeminiPart objects containing the encoded images. - """ +async def create_image_parts( + cls: type[IO.ComfyNode], + images: torch.Tensor, + image_limit: int = 0, +) -> list[GeminiPart]: image_parts: list[GeminiPart] = [] - for image_index in range(image_input.shape[0]): - image_as_b64 = tensor_to_base64_string(image_input[image_index].unsqueeze(0)) + if image_limit < 0: + raise ValueError("image_limit must be greater than or equal to 0 when creating Gemini image parts.") + total_images = get_number_of_images(images) + if total_images <= 0: + raise ValueError("No images provided to create_image_parts; at least one image is required.") + + # If image_limit == 0 --> use all images; otherwise clamp to image_limit. + effective_max = total_images if image_limit == 0 else min(total_images, image_limit) + + # Number of images we'll send as URLs (fileData) + num_url_images = min(effective_max, 10) # Vertex API max number of image links + reference_images_urls = await upload_images_to_comfyapi( + cls, + images, + max_images=num_url_images, + ) + for reference_image_url in reference_images_urls: + image_parts.append( + GeminiPart( + fileData=GeminiFileData( + mimeType=GeminiMimeType.image_png, + fileUri=reference_image_url, + ) + ) + ) + for idx in range(num_url_images, effective_max): image_parts.append( GeminiPart( inlineData=GeminiInlineData( mimeType=GeminiMimeType.image_png, - data=image_as_b64, + data=tensor_to_base64_string(images[idx]), ) ) ) @@ -104,14 +121,14 @@ def get_parts_by_type(response: GeminiGenerateContentResponse, part_type: Litera List of response parts matching the requested type. """ if response.candidates is None: - if response.promptFeedback.blockReason: + if response.promptFeedback and response.promptFeedback.blockReason: feedback = response.promptFeedback raise ValueError( f"Gemini API blocked the request. Reason: {feedback.blockReason} ({feedback.blockReasonMessage})" ) - raise NotImplementedError( - "Gemini returned no response candidates. " - "Please report to ComfyUI repository with the example of workflow to reproduce this." + raise ValueError( + "Gemini API returned no response candidates. If you are using the `IMAGE` modality, " + "try changing it to `IMAGE+TEXT` to view the model's reasoning and understand why image generation failed." ) parts = [] for part in response.candidates[0].content.parts: @@ -182,11 +199,12 @@ def calculate_tokens_price(response: GeminiGenerateContentResponse) -> float | N else: return None final_price = response.usageMetadata.promptTokenCount * input_tokens_price - for i in response.usageMetadata.candidatesTokensDetails: - if i.modality == Modality.IMAGE: - final_price += output_image_tokens_price * i.tokenCount # for Nano Banana models - else: - final_price += output_text_tokens_price * i.tokenCount + if response.usageMetadata.candidatesTokensDetails: + for i in response.usageMetadata.candidatesTokensDetails: + if i.modality == Modality.IMAGE: + final_price += output_image_tokens_price * i.tokenCount # for Nano Banana models + else: + final_price += output_text_tokens_price * i.tokenCount if response.usageMetadata.thoughtsTokenCount: final_price += output_text_tokens_price * response.usageMetadata.thoughtsTokenCount return final_price / 1_000_000.0 @@ -337,8 +355,7 @@ class GeminiNode(IO.ComfyNode): # Add other modal parts if images is not None: - image_parts = create_image_parts(images) - parts.extend(image_parts) + parts.extend(await create_image_parts(cls, images)) if audio is not None: parts.extend(cls.create_audio_parts(audio)) if video is not None: @@ -363,29 +380,6 @@ class GeminiNode(IO.ComfyNode): ) output_text = get_text_from_response(response) - if output_text: - # Not a true chat history like the OpenAI Chat node. It is emulated so the frontend can show a copy button. - render_spec = { - "node_id": cls.hidden.unique_id, - "component": "ChatHistoryWidget", - "props": { - "history": json.dumps( - [ - { - "prompt": prompt, - "response": output_text, - "response_id": str(uuid.uuid4()), - "timestamp": time.time(), - } - ] - ), - }, - } - PromptServer.instance.send_sync( - "display_component", - render_spec, - ) - return IO.NodeOutput(output_text or "Empty response from Gemini model...") @@ -561,8 +555,7 @@ class GeminiImage(IO.ComfyNode): image_config = GeminiImageConfig(aspectRatio=aspect_ratio) if images is not None: - image_parts = create_image_parts(images) - parts.extend(image_parts) + parts.extend(await create_image_parts(cls, images)) if files is not None: parts.extend(files) @@ -581,30 +574,7 @@ class GeminiImage(IO.ComfyNode): response_model=GeminiGenerateContentResponse, price_extractor=calculate_tokens_price, ) - - output_text = get_text_from_response(response) - if output_text: - render_spec = { - "node_id": cls.hidden.unique_id, - "component": "ChatHistoryWidget", - "props": { - "history": json.dumps( - [ - { - "prompt": prompt, - "response": output_text, - "response_id": str(uuid.uuid4()), - "timestamp": time.time(), - } - ] - ), - }, - } - PromptServer.instance.send_sync( - "display_component", - render_spec, - ) - return IO.NodeOutput(get_image_from_response(response), output_text) + return IO.NodeOutput(get_image_from_response(response), get_text_from_response(response)) class GeminiImage2(IO.ComfyNode): @@ -645,7 +615,7 @@ class GeminiImage2(IO.ComfyNode): options=["auto", "1:1", "2:3", "3:2", "3:4", "4:3", "4:5", "5:4", "9:16", "16:9", "21:9"], default="auto", tooltip="If set to 'auto', matches your input image's aspect ratio; " - "if no image is provided, generates a 1:1 square.", + "if no image is provided, a 16:9 square is usually generated.", ), IO.Combo.Input( "resolution", @@ -701,7 +671,7 @@ class GeminiImage2(IO.ComfyNode): if images is not None: if get_number_of_images(images) > 14: raise ValueError("The current maximum number of supported images is 14.") - parts.extend(create_image_parts(images)) + parts.extend(await create_image_parts(cls, images)) if files is not None: parts.extend(files) @@ -724,30 +694,7 @@ class GeminiImage2(IO.ComfyNode): response_model=GeminiGenerateContentResponse, price_extractor=calculate_tokens_price, ) - - output_text = get_text_from_response(response) - if output_text: - render_spec = { - "node_id": cls.hidden.unique_id, - "component": "ChatHistoryWidget", - "props": { - "history": json.dumps( - [ - { - "prompt": prompt, - "response": output_text, - "response_id": str(uuid.uuid4()), - "timestamp": time.time(), - } - ] - ), - }, - } - PromptServer.instance.send_sync( - "display_component", - render_spec, - ) - return IO.NodeOutput(get_image_from_response(response), output_text) + return IO.NodeOutput(get_image_from_response(response), get_text_from_response(response)) class GeminiExtension(ComfyExtension): diff --git a/comfy_api_nodes/nodes_openai.py b/comfy_api_nodes/nodes_openai.py index acf35d276..c8da5464b 100644 --- a/comfy_api_nodes/nodes_openai.py +++ b/comfy_api_nodes/nodes_openai.py @@ -1,15 +1,10 @@ from io import BytesIO -from typing import Optional, Union -import json import os -import time -import uuid from enum import Enum from inspect import cleandoc import numpy as np import torch from PIL import Image -from server import PromptServer import folder_paths import base64 from comfy_api.latest import IO, ComfyExtension @@ -587,11 +582,11 @@ class OpenAIChatNode(IO.ComfyNode): def create_input_message_contents( cls, prompt: str, - image: Optional[torch.Tensor] = None, - files: Optional[list[InputFileContent]] = None, + image: torch.Tensor | None = None, + files: list[InputFileContent] | None = None, ) -> InputMessageContentList: """Create a list of input message contents from prompt and optional image.""" - content_list: list[Union[InputContent, InputTextContent, InputImageContent, InputFileContent]] = [ + content_list: list[InputContent | InputTextContent | InputImageContent | InputFileContent] = [ InputTextContent(text=prompt, type="input_text"), ] if image is not None: @@ -617,9 +612,9 @@ class OpenAIChatNode(IO.ComfyNode): prompt: str, persist_context: bool = False, model: SupportedOpenAIModel = SupportedOpenAIModel.gpt_5.value, - images: Optional[torch.Tensor] = None, - files: Optional[list[InputFileContent]] = None, - advanced_options: Optional[CreateModelResponseProperties] = None, + images: torch.Tensor | None = None, + files: list[InputFileContent] | None = None, + advanced_options: CreateModelResponseProperties | None = None, ) -> IO.NodeOutput: validate_string(prompt, strip_whitespace=False) @@ -660,30 +655,7 @@ class OpenAIChatNode(IO.ComfyNode): status_extractor=lambda response: response.status, completed_statuses=["incomplete", "completed"] ) - output_text = cls.get_text_from_message_content(cls.get_message_content_from_response(result_response)) - - # Update history - render_spec = { - "node_id": cls.hidden.unique_id, - "component": "ChatHistoryWidget", - "props": { - "history": json.dumps( - [ - { - "prompt": prompt, - "response": output_text, - "response_id": str(uuid.uuid4()), - "timestamp": time.time(), - } - ] - ), - }, - } - PromptServer.instance.send_sync( - "display_component", - render_spec, - ) - return IO.NodeOutput(output_text) + return IO.NodeOutput(cls.get_text_from_message_content(cls.get_message_content_from_response(result_response))) class OpenAIInputFiles(IO.ComfyNode): @@ -790,8 +762,8 @@ class OpenAIChatConfig(IO.ComfyNode): def execute( cls, truncation: bool, - instructions: Optional[str] = None, - max_output_tokens: Optional[int] = None, + instructions: str | None = None, + max_output_tokens: int | None = None, ) -> IO.NodeOutput: """ Configure advanced options for the OpenAI Chat Node. diff --git a/comfy_api_nodes/nodes_topaz.py b/comfy_api_nodes/nodes_topaz.py index 79c7bf43d..f522756e5 100644 --- a/comfy_api_nodes/nodes_topaz.py +++ b/comfy_api_nodes/nodes_topaz.py @@ -5,8 +5,7 @@ import aiohttp import torch from typing_extensions import override -from comfy_api.input.video_types import VideoInput -from comfy_api.latest import IO, ComfyExtension +from comfy_api.latest import IO, ComfyExtension, Input from comfy_api_nodes.apis import topaz_api from comfy_api_nodes.util import ( ApiEndpoint, @@ -282,7 +281,7 @@ class TopazVideoEnhance(IO.ComfyNode): @classmethod async def execute( cls, - video: VideoInput, + video: Input.Video, upscaler_enabled: bool, upscaler_model: str, upscaler_resolution: str, @@ -297,12 +296,10 @@ class TopazVideoEnhance(IO.ComfyNode): ) -> IO.NodeOutput: if upscaler_enabled is False and interpolation_enabled is False: raise ValueError("There is nothing to do: both upscaling and interpolation are disabled.") - src_width, src_height = video.get_dimensions() - video_components = video.get_components() - src_frame_rate = int(video_components.frame_rate) - duration_sec = video.get_duration() - estimated_frames = int(duration_sec * src_frame_rate) validate_container_format_is_mp4(video) + src_width, src_height = video.get_dimensions() + src_frame_rate = int(video.get_frame_rate()) + duration_sec = video.get_duration() src_video_stream = video.get_stream_source() target_width = src_width target_height = src_height @@ -338,7 +335,7 @@ class TopazVideoEnhance(IO.ComfyNode): container="mp4", size=get_fs_object_size(src_video_stream), duration=int(duration_sec), - frameCount=estimated_frames, + frameCount=video.get_frame_count(), frameRate=src_frame_rate, resolution=topaz_api.Resolution(width=src_width, height=src_height), ), diff --git a/comfy_api_nodes/nodes_veo2.py b/comfy_api_nodes/nodes_veo2.py index d37e9e9b4..a54dc13ab 100644 --- a/comfy_api_nodes/nodes_veo2.py +++ b/comfy_api_nodes/nodes_veo2.py @@ -1,6 +1,7 @@ import base64 from io import BytesIO +import torch from typing_extensions import override from comfy_api.input_impl.video_types import VideoFromFile @@ -10,6 +11,9 @@ from comfy_api_nodes.apis.veo_api import ( VeoGenVidPollResponse, VeoGenVidRequest, VeoGenVidResponse, + VeoRequestInstance, + VeoRequestInstanceImage, + VeoRequestParameters, ) from comfy_api_nodes.util import ( ApiEndpoint, @@ -346,12 +350,163 @@ class Veo3VideoGenerationNode(VeoVideoGenerationNode): ) +class Veo3FirstLastFrameNode(IO.ComfyNode): + + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="Veo3FirstLastFrameNode", + display_name="Google Veo 3 First-Last-Frame to Video", + category="api node/video/Veo", + description="Generate video using prompt and first and last frames.", + inputs=[ + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="Text description of the video", + ), + IO.String.Input( + "negative_prompt", + multiline=True, + default="", + tooltip="Negative text prompt to guide what to avoid in the video", + ), + IO.Combo.Input("resolution", options=["720p", "1080p"]), + IO.Combo.Input( + "aspect_ratio", + options=["16:9", "9:16"], + default="16:9", + tooltip="Aspect ratio of the output video", + ), + IO.Int.Input( + "duration", + default=8, + min=4, + max=8, + step=2, + display_mode=IO.NumberDisplay.slider, + tooltip="Duration of the output video in seconds", + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=0xFFFFFFFF, + step=1, + display_mode=IO.NumberDisplay.number, + control_after_generate=True, + tooltip="Seed for video generation", + ), + IO.Image.Input("first_frame", tooltip="Start frame"), + IO.Image.Input("last_frame", tooltip="End frame"), + IO.Combo.Input( + "model", + options=["veo-3.1-generate", "veo-3.1-fast-generate"], + default="veo-3.1-fast-generate", + ), + IO.Boolean.Input( + "generate_audio", + default=True, + tooltip="Generate audio for the video.", + ), + ], + outputs=[ + IO.Video.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + prompt: str, + negative_prompt: str, + resolution: str, + aspect_ratio: str, + duration: int, + seed: int, + first_frame: torch.Tensor, + last_frame: torch.Tensor, + model: str, + generate_audio: bool, + ): + model = MODELS_MAP[model] + initial_response = await sync_op( + cls, + ApiEndpoint(path=f"/proxy/veo/{model}/generate", method="POST"), + response_model=VeoGenVidResponse, + data=VeoGenVidRequest( + instances=[ + VeoRequestInstance( + prompt=prompt, + image=VeoRequestInstanceImage( + bytesBase64Encoded=tensor_to_base64_string(first_frame), mimeType="image/png" + ), + lastFrame=VeoRequestInstanceImage( + bytesBase64Encoded=tensor_to_base64_string(last_frame), mimeType="image/png" + ), + ), + ], + parameters=VeoRequestParameters( + aspectRatio=aspect_ratio, + personGeneration="ALLOW", + durationSeconds=duration, + enhancePrompt=True, # cannot be False for Veo3 + seed=seed, + generateAudio=generate_audio, + negativePrompt=negative_prompt, + resolution=resolution, + ), + ), + ) + poll_response = await poll_op( + cls, + ApiEndpoint(path=f"/proxy/veo/{model}/poll", method="POST"), + response_model=VeoGenVidPollResponse, + status_extractor=lambda r: "completed" if r.done else "pending", + data=VeoGenVidPollRequest( + operationName=initial_response.name, + ), + poll_interval=5.0, + estimated_duration=AVERAGE_DURATION_VIDEO_GEN, + ) + + if poll_response.error: + raise Exception(f"Veo API error: {poll_response.error.message} (code: {poll_response.error.code})") + + response = poll_response.response + filtered_count = response.raiMediaFilteredCount + if filtered_count: + reasons = response.raiMediaFilteredReasons or [] + reason_part = f": {reasons[0]}" if reasons else "" + raise Exception( + f"Content blocked by Google's Responsible AI filters{reason_part} " + f"({filtered_count} video{'s' if filtered_count != 1 else ''} filtered)." + ) + + if response.videos: + video = response.videos[0] + if video.bytesBase64Encoded: + return IO.NodeOutput(VideoFromFile(BytesIO(base64.b64decode(video.bytesBase64Encoded)))) + if video.gcsUri: + return IO.NodeOutput(await download_url_to_video_output(video.gcsUri)) + raise Exception("Video returned but no data or URL was provided") + raise Exception("Video generation completed but no video was returned") + + class VeoExtension(ComfyExtension): @override async def get_node_list(self) -> list[type[IO.ComfyNode]]: return [ VeoVideoGenerationNode, Veo3VideoGenerationNode, + Veo3FirstLastFrameNode, ] diff --git a/comfy_api_nodes/util/__init__.py b/comfy_api_nodes/util/__init__.py index 21013b591..80292fb3c 100644 --- a/comfy_api_nodes/util/__init__.py +++ b/comfy_api_nodes/util/__init__.py @@ -36,6 +36,7 @@ from .upload_helpers import ( upload_video_to_comfyapi, ) from .validation_utils import ( + get_image_dimensions, get_number_of_images, validate_aspect_ratio_string, validate_audio_duration, @@ -82,6 +83,7 @@ __all__ = [ "trim_video", "video_to_base64_string", # Validation utilities + "get_image_dimensions", "get_number_of_images", "validate_aspect_ratio_string", "validate_audio_duration", diff --git a/comfy_api_nodes/util/upload_helpers.py b/comfy_api_nodes/util/upload_helpers.py index 632450d9b..b9019841f 100644 --- a/comfy_api_nodes/util/upload_helpers.py +++ b/comfy_api_nodes/util/upload_helpers.py @@ -4,7 +4,7 @@ import logging import time import uuid from io import BytesIO -from typing import Optional, Union +from typing import Optional from urllib.parse import urlparse import aiohttp @@ -48,8 +48,9 @@ async def upload_images_to_comfyapi( image: torch.Tensor, *, max_images: int = 8, - mime_type: Optional[str] = None, - wait_label: Optional[str] = "Uploading", + mime_type: str | None = None, + wait_label: str | None = "Uploading", + show_batch_index: bool = True, ) -> list[str]: """ Uploads images to ComfyUI API and returns download URLs. @@ -59,11 +60,18 @@ async def upload_images_to_comfyapi( download_urls: list[str] = [] is_batch = len(image.shape) > 3 batch_len = image.shape[0] if is_batch else 1 + num_to_upload = min(batch_len, max_images) + batch_start_ts = time.monotonic() - for idx in range(min(batch_len, max_images)): + for idx in range(num_to_upload): tensor = image[idx] if is_batch else image img_io = tensor_to_bytesio(tensor, mime_type=mime_type) - url = await upload_file_to_comfyapi(cls, img_io, img_io.name, mime_type, wait_label) + + effective_label = wait_label + if wait_label and show_batch_index and num_to_upload > 1: + effective_label = f"{wait_label} ({idx + 1}/{num_to_upload})" + + url = await upload_file_to_comfyapi(cls, img_io, img_io.name, mime_type, effective_label, batch_start_ts) download_urls.append(url) return download_urls @@ -126,8 +134,9 @@ async def upload_file_to_comfyapi( cls: type[IO.ComfyNode], file_bytes_io: BytesIO, filename: str, - upload_mime_type: Optional[str], - wait_label: Optional[str] = "Uploading", + upload_mime_type: str | None, + wait_label: str | None = "Uploading", + progress_origin_ts: float | None = None, ) -> str: """Uploads a single file to ComfyUI API and returns its download URL.""" if upload_mime_type is None: @@ -148,6 +157,7 @@ async def upload_file_to_comfyapi( file_bytes_io, content_type=upload_mime_type, wait_label=wait_label, + progress_origin_ts=progress_origin_ts, ) return create_resp.download_url @@ -155,27 +165,18 @@ async def upload_file_to_comfyapi( async def upload_file( cls: type[IO.ComfyNode], upload_url: str, - file: Union[BytesIO, str], + file: BytesIO | str, *, - content_type: Optional[str] = None, + content_type: str | None = None, max_retries: int = 3, retry_delay: float = 1.0, retry_backoff: float = 2.0, - wait_label: Optional[str] = None, + wait_label: str | None = None, + progress_origin_ts: float | None = None, ) -> None: """ Upload a file to a signed URL (e.g., S3 pre-signed PUT) with retries, Comfy progress display, and interruption. - Args: - cls: Node class (provides auth context + UI progress hooks). - upload_url: Pre-signed PUT URL. - file: BytesIO or path string. - content_type: Explicit MIME type. If None, we *suppress* Content-Type. - max_retries: Maximum retry attempts. - retry_delay: Initial delay in seconds. - retry_backoff: Exponential backoff factor. - wait_label: Progress label shown in Comfy UI. - Raises: ProcessingInterrupted, LocalNetworkError, ApiServerError, Exception """ @@ -198,7 +199,7 @@ async def upload_file( attempt = 0 delay = retry_delay - start_ts = time.monotonic() + start_ts = progress_origin_ts if progress_origin_ts is not None else time.monotonic() op_uuid = uuid.uuid4().hex[:8] while True: attempt += 1 diff --git a/comfy_extras/nodes_custom_sampler.py b/comfy_extras/nodes_custom_sampler.py index d011f433b..fbb080886 100644 --- a/comfy_extras/nodes_custom_sampler.py +++ b/comfy_extras/nodes_custom_sampler.py @@ -3,272 +3,312 @@ import comfy.samplers import comfy.sample from comfy.k_diffusion import sampling as k_diffusion_sampling from comfy.k_diffusion import sa_solver -from comfy.comfy_types import IO, ComfyNodeABC, InputTypeDict import latent_preview import torch import comfy.utils import node_helpers +from typing_extensions import override +from comfy_api.latest import ComfyExtension, io -class BasicScheduler: +class BasicScheduler(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": - {"model": ("MODEL",), - "scheduler": (comfy.samplers.SCHEDULER_NAMES, ), - "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), - "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), - } - } - RETURN_TYPES = ("SIGMAS",) - CATEGORY = "sampling/custom_sampling/schedulers" + def define_schema(cls): + return io.Schema( + node_id="BasicScheduler", + category="sampling/custom_sampling/schedulers", + inputs=[ + io.Model.Input("model"), + io.Combo.Input("scheduler", options=comfy.samplers.SCHEDULER_NAMES), + io.Int.Input("steps", default=20, min=1, max=10000), + io.Float.Input("denoise", default=1.0, min=0.0, max=1.0, step=0.01), + ], + outputs=[io.Sigmas.Output()] + ) - FUNCTION = "get_sigmas" - - def get_sigmas(self, model, scheduler, steps, denoise): + @classmethod + def execute(cls, model, scheduler, steps, denoise) -> io.NodeOutput: total_steps = steps if denoise < 1.0: if denoise <= 0.0: - return (torch.FloatTensor([]),) + return io.NodeOutput(torch.FloatTensor([])) total_steps = int(steps/denoise) sigmas = comfy.samplers.calculate_sigmas(model.get_model_object("model_sampling"), scheduler, total_steps).cpu() sigmas = sigmas[-(steps + 1):] - return (sigmas, ) + return io.NodeOutput(sigmas) + + get_sigmas = execute -class KarrasScheduler: +class KarrasScheduler(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": - {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}), - "sigma_max": ("FLOAT", {"default": 14.614642, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), - "sigma_min": ("FLOAT", {"default": 0.0291675, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), - "rho": ("FLOAT", {"default": 7.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), - } - } - RETURN_TYPES = ("SIGMAS",) - CATEGORY = "sampling/custom_sampling/schedulers" + def define_schema(cls): + return io.Schema( + node_id="KarrasScheduler", + category="sampling/custom_sampling/schedulers", + inputs=[ + io.Int.Input("steps", default=20, min=1, max=10000), + io.Float.Input("sigma_max", default=14.614642, min=0.0, max=5000.0, step=0.01, round=False), + io.Float.Input("sigma_min", default=0.0291675, min=0.0, max=5000.0, step=0.01, round=False), + io.Float.Input("rho", default=7.0, min=0.0, max=100.0, step=0.01, round=False), + ], + outputs=[io.Sigmas.Output()] + ) - FUNCTION = "get_sigmas" - - def get_sigmas(self, steps, sigma_max, sigma_min, rho): + @classmethod + def execute(cls, steps, sigma_max, sigma_min, rho) -> io.NodeOutput: sigmas = k_diffusion_sampling.get_sigmas_karras(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, rho=rho) - return (sigmas, ) + return io.NodeOutput(sigmas) -class ExponentialScheduler: + get_sigmas = execute + +class ExponentialScheduler(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": - {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}), - "sigma_max": ("FLOAT", {"default": 14.614642, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), - "sigma_min": ("FLOAT", {"default": 0.0291675, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), - } - } - RETURN_TYPES = ("SIGMAS",) - CATEGORY = "sampling/custom_sampling/schedulers" + def define_schema(cls): + return io.Schema( + node_id="ExponentialScheduler", + category="sampling/custom_sampling/schedulers", + inputs=[ + io.Int.Input("steps", default=20, min=1, max=10000), + io.Float.Input("sigma_max", default=14.614642, min=0.0, max=5000.0, step=0.01, round=False), + io.Float.Input("sigma_min", default=0.0291675, min=0.0, max=5000.0, step=0.01, round=False), + ], + outputs=[io.Sigmas.Output()] + ) - FUNCTION = "get_sigmas" - - def get_sigmas(self, steps, sigma_max, sigma_min): + @classmethod + def execute(cls, steps, sigma_max, sigma_min) -> io.NodeOutput: sigmas = k_diffusion_sampling.get_sigmas_exponential(n=steps, sigma_min=sigma_min, sigma_max=sigma_max) - return (sigmas, ) + return io.NodeOutput(sigmas) -class PolyexponentialScheduler: + get_sigmas = execute + +class PolyexponentialScheduler(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": - {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}), - "sigma_max": ("FLOAT", {"default": 14.614642, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), - "sigma_min": ("FLOAT", {"default": 0.0291675, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), - "rho": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), - } - } - RETURN_TYPES = ("SIGMAS",) - CATEGORY = "sampling/custom_sampling/schedulers" + def define_schema(cls): + return io.Schema( + node_id="PolyexponentialScheduler", + category="sampling/custom_sampling/schedulers", + inputs=[ + io.Int.Input("steps", default=20, min=1, max=10000), + io.Float.Input("sigma_max", default=14.614642, min=0.0, max=5000.0, step=0.01, round=False), + io.Float.Input("sigma_min", default=0.0291675, min=0.0, max=5000.0, step=0.01, round=False), + io.Float.Input("rho", default=1.0, min=0.0, max=100.0, step=0.01, round=False), + ], + outputs=[io.Sigmas.Output()] + ) - FUNCTION = "get_sigmas" - - def get_sigmas(self, steps, sigma_max, sigma_min, rho): + @classmethod + def execute(cls, steps, sigma_max, sigma_min, rho) -> io.NodeOutput: sigmas = k_diffusion_sampling.get_sigmas_polyexponential(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, rho=rho) - return (sigmas, ) + return io.NodeOutput(sigmas) -class LaplaceScheduler: + get_sigmas = execute + +class LaplaceScheduler(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": - {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}), - "sigma_max": ("FLOAT", {"default": 14.614642, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), - "sigma_min": ("FLOAT", {"default": 0.0291675, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), - "mu": ("FLOAT", {"default": 0.0, "min": -10.0, "max": 10.0, "step":0.1, "round": False}), - "beta": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 10.0, "step":0.1, "round": False}), - } - } - RETURN_TYPES = ("SIGMAS",) - CATEGORY = "sampling/custom_sampling/schedulers" + def define_schema(cls): + return io.Schema( + node_id="LaplaceScheduler", + category="sampling/custom_sampling/schedulers", + inputs=[ + io.Int.Input("steps", default=20, min=1, max=10000), + io.Float.Input("sigma_max", default=14.614642, min=0.0, max=5000.0, step=0.01, round=False), + io.Float.Input("sigma_min", default=0.0291675, min=0.0, max=5000.0, step=0.01, round=False), + io.Float.Input("mu", default=0.0, min=-10.0, max=10.0, step=0.1, round=False), + io.Float.Input("beta", default=0.5, min=0.0, max=10.0, step=0.1, round=False), + ], + outputs=[io.Sigmas.Output()] + ) - FUNCTION = "get_sigmas" - - def get_sigmas(self, steps, sigma_max, sigma_min, mu, beta): + @classmethod + def execute(cls, steps, sigma_max, sigma_min, mu, beta) -> io.NodeOutput: sigmas = k_diffusion_sampling.get_sigmas_laplace(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, mu=mu, beta=beta) - return (sigmas, ) + return io.NodeOutput(sigmas) + + get_sigmas = execute -class SDTurboScheduler: +class SDTurboScheduler(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": - {"model": ("MODEL",), - "steps": ("INT", {"default": 1, "min": 1, "max": 10}), - "denoise": ("FLOAT", {"default": 1.0, "min": 0, "max": 1.0, "step": 0.01}), - } - } - RETURN_TYPES = ("SIGMAS",) - CATEGORY = "sampling/custom_sampling/schedulers" + def define_schema(cls): + return io.Schema( + node_id="SDTurboScheduler", + category="sampling/custom_sampling/schedulers", + inputs=[ + io.Model.Input("model"), + io.Int.Input("steps", default=1, min=1, max=10), + io.Float.Input("denoise", default=1.0, min=0, max=1.0, step=0.01), + ], + outputs=[io.Sigmas.Output()] + ) - FUNCTION = "get_sigmas" - - def get_sigmas(self, model, steps, denoise): + @classmethod + def execute(cls, model, steps, denoise) -> io.NodeOutput: start_step = 10 - int(10 * denoise) timesteps = torch.flip(torch.arange(1, 11) * 100 - 1, (0,))[start_step:start_step + steps] sigmas = model.get_model_object("model_sampling").sigma(timesteps) sigmas = torch.cat([sigmas, sigmas.new_zeros([1])]) - return (sigmas, ) + return io.NodeOutput(sigmas) -class BetaSamplingScheduler: + get_sigmas = execute + +class BetaSamplingScheduler(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": - {"model": ("MODEL",), - "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), - "alpha": ("FLOAT", {"default": 0.6, "min": 0.0, "max": 50.0, "step":0.01, "round": False}), - "beta": ("FLOAT", {"default": 0.6, "min": 0.0, "max": 50.0, "step":0.01, "round": False}), - } - } - RETURN_TYPES = ("SIGMAS",) - CATEGORY = "sampling/custom_sampling/schedulers" + def define_schema(cls): + return io.Schema( + node_id="BetaSamplingScheduler", + category="sampling/custom_sampling/schedulers", + inputs=[ + io.Model.Input("model"), + io.Int.Input("steps", default=20, min=1, max=10000), + io.Float.Input("alpha", default=0.6, min=0.0, max=50.0, step=0.01, round=False), + io.Float.Input("beta", default=0.6, min=0.0, max=50.0, step=0.01, round=False), + ], + outputs=[io.Sigmas.Output()] + ) - FUNCTION = "get_sigmas" - - def get_sigmas(self, model, steps, alpha, beta): + @classmethod + def execute(cls, model, steps, alpha, beta) -> io.NodeOutput: sigmas = comfy.samplers.beta_scheduler(model.get_model_object("model_sampling"), steps, alpha=alpha, beta=beta) - return (sigmas, ) + return io.NodeOutput(sigmas) -class VPScheduler: + get_sigmas = execute + +class VPScheduler(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": - {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}), - "beta_d": ("FLOAT", {"default": 19.9, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), #TODO: fix default values - "beta_min": ("FLOAT", {"default": 0.1, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), - "eps_s": ("FLOAT", {"default": 0.001, "min": 0.0, "max": 1.0, "step":0.0001, "round": False}), - } - } - RETURN_TYPES = ("SIGMAS",) - CATEGORY = "sampling/custom_sampling/schedulers" + def define_schema(cls): + return io.Schema( + node_id="VPScheduler", + category="sampling/custom_sampling/schedulers", + inputs=[ + io.Int.Input("steps", default=20, min=1, max=10000), + io.Float.Input("beta_d", default=19.9, min=0.0, max=5000.0, step=0.01, round=False), #TODO: fix default values + io.Float.Input("beta_min", default=0.1, min=0.0, max=5000.0, step=0.01, round=False), + io.Float.Input("eps_s", default=0.001, min=0.0, max=1.0, step=0.0001, round=False), + ], + outputs=[io.Sigmas.Output()] + ) - FUNCTION = "get_sigmas" - - def get_sigmas(self, steps, beta_d, beta_min, eps_s): + @classmethod + def execute(cls, steps, beta_d, beta_min, eps_s) -> io.NodeOutput: sigmas = k_diffusion_sampling.get_sigmas_vp(n=steps, beta_d=beta_d, beta_min=beta_min, eps_s=eps_s) - return (sigmas, ) + return io.NodeOutput(sigmas) -class SplitSigmas: + get_sigmas = execute + +class SplitSigmas(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": - {"sigmas": ("SIGMAS", ), - "step": ("INT", {"default": 0, "min": 0, "max": 10000}), - } - } - RETURN_TYPES = ("SIGMAS","SIGMAS") - RETURN_NAMES = ("high_sigmas", "low_sigmas") - CATEGORY = "sampling/custom_sampling/sigmas" + def define_schema(cls): + return io.Schema( + node_id="SplitSigmas", + category="sampling/custom_sampling/sigmas", + inputs=[ + io.Sigmas.Input("sigmas"), + io.Int.Input("step", default=0, min=0, max=10000), + ], + outputs=[ + io.Sigmas.Output(display_name="high_sigmas"), + io.Sigmas.Output(display_name="low_sigmas"), + ] + ) - FUNCTION = "get_sigmas" - - def get_sigmas(self, sigmas, step): + @classmethod + def execute(cls, sigmas, step) -> io.NodeOutput: sigmas1 = sigmas[:step + 1] sigmas2 = sigmas[step:] - return (sigmas1, sigmas2) + return io.NodeOutput(sigmas1, sigmas2) -class SplitSigmasDenoise: + get_sigmas = execute + +class SplitSigmasDenoise(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": - {"sigmas": ("SIGMAS", ), - "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), - } - } - RETURN_TYPES = ("SIGMAS","SIGMAS") - RETURN_NAMES = ("high_sigmas", "low_sigmas") - CATEGORY = "sampling/custom_sampling/sigmas" + def define_schema(cls): + return io.Schema( + node_id="SplitSigmasDenoise", + category="sampling/custom_sampling/sigmas", + inputs=[ + io.Sigmas.Input("sigmas"), + io.Float.Input("denoise", default=1.0, min=0.0, max=1.0, step=0.01), + ], + outputs=[ + io.Sigmas.Output(display_name="high_sigmas"), + io.Sigmas.Output(display_name="low_sigmas"), + ] + ) - FUNCTION = "get_sigmas" - - def get_sigmas(self, sigmas, denoise): + @classmethod + def execute(cls, sigmas, denoise) -> io.NodeOutput: steps = max(sigmas.shape[-1] - 1, 0) total_steps = round(steps * denoise) sigmas1 = sigmas[:-(total_steps)] sigmas2 = sigmas[-(total_steps + 1):] - return (sigmas1, sigmas2) + return io.NodeOutput(sigmas1, sigmas2) -class FlipSigmas: + get_sigmas = execute + +class FlipSigmas(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": - {"sigmas": ("SIGMAS", ), - } - } - RETURN_TYPES = ("SIGMAS",) - CATEGORY = "sampling/custom_sampling/sigmas" + def define_schema(cls): + return io.Schema( + node_id="FlipSigmas", + category="sampling/custom_sampling/sigmas", + inputs=[io.Sigmas.Input("sigmas")], + outputs=[io.Sigmas.Output()] + ) - FUNCTION = "get_sigmas" - - def get_sigmas(self, sigmas): + @classmethod + def execute(cls, sigmas) -> io.NodeOutput: if len(sigmas) == 0: - return (sigmas,) + return io.NodeOutput(sigmas) sigmas = sigmas.flip(0) if sigmas[0] == 0: sigmas[0] = 0.0001 - return (sigmas,) + return io.NodeOutput(sigmas) -class SetFirstSigma: + get_sigmas = execute + +class SetFirstSigma(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": - {"sigmas": ("SIGMAS", ), - "sigma": ("FLOAT", {"default": 136.0, "min": 0.0, "max": 20000.0, "step": 0.001, "round": False}), - } - } - RETURN_TYPES = ("SIGMAS",) - CATEGORY = "sampling/custom_sampling/sigmas" + def define_schema(cls): + return io.Schema( + node_id="SetFirstSigma", + category="sampling/custom_sampling/sigmas", + inputs=[ + io.Sigmas.Input("sigmas"), + io.Float.Input("sigma", default=136.0, min=0.0, max=20000.0, step=0.001, round=False), + ], + outputs=[io.Sigmas.Output()] + ) - FUNCTION = "set_first_sigma" - - def set_first_sigma(self, sigmas, sigma): + @classmethod + def execute(cls, sigmas, sigma) -> io.NodeOutput: sigmas = sigmas.clone() sigmas[0] = sigma - return (sigmas, ) + return io.NodeOutput(sigmas) -class ExtendIntermediateSigmas: + set_first_sigma = execute + +class ExtendIntermediateSigmas(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": - {"sigmas": ("SIGMAS", ), - "steps": ("INT", {"default": 2, "min": 1, "max": 100}), - "start_at_sigma": ("FLOAT", {"default": -1.0, "min": -1.0, "max": 20000.0, "step": 0.01, "round": False}), - "end_at_sigma": ("FLOAT", {"default": 12.0, "min": 0.0, "max": 20000.0, "step": 0.01, "round": False}), - "spacing": (['linear', 'cosine', 'sine'],), - } - } - RETURN_TYPES = ("SIGMAS",) - CATEGORY = "sampling/custom_sampling/sigmas" + def define_schema(cls): + return io.Schema( + node_id="ExtendIntermediateSigmas", + category="sampling/custom_sampling/sigmas", + inputs=[ + io.Sigmas.Input("sigmas"), + io.Int.Input("steps", default=2, min=1, max=100), + io.Float.Input("start_at_sigma", default=-1.0, min=-1.0, max=20000.0, step=0.01, round=False), + io.Float.Input("end_at_sigma", default=12.0, min=0.0, max=20000.0, step=0.01, round=False), + io.Combo.Input("spacing", options=['linear', 'cosine', 'sine']), + ], + outputs=[io.Sigmas.Output()] + ) - FUNCTION = "extend" - - def extend(self, sigmas: torch.Tensor, steps: int, start_at_sigma: float, end_at_sigma: float, spacing: str): + @classmethod + def execute(cls, sigmas: torch.Tensor, steps: int, start_at_sigma: float, end_at_sigma: float, spacing: str) -> io.NodeOutput: if start_at_sigma < 0: start_at_sigma = float("inf") @@ -299,27 +339,27 @@ class ExtendIntermediateSigmas: extended_sigmas = torch.FloatTensor(extended_sigmas) - return (extended_sigmas,) + return io.NodeOutput(extended_sigmas) + + extend = execute -class SamplingPercentToSigma: +class SamplingPercentToSigma(io.ComfyNode): @classmethod - def INPUT_TYPES(cls) -> InputTypeDict: - return { - "required": { - "model": (IO.MODEL, {}), - "sampling_percent": (IO.FLOAT, {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.0001}), - "return_actual_sigma": (IO.BOOLEAN, {"default": False, "tooltip": "Return the actual sigma value instead of the value used for interval checks.\nThis only affects results at 0.0 and 1.0."}), - } - } + def define_schema(cls): + return io.Schema( + node_id="SamplingPercentToSigma", + category="sampling/custom_sampling/sigmas", + inputs=[ + io.Model.Input("model"), + io.Float.Input("sampling_percent", default=0.0, min=0.0, max=1.0, step=0.0001), + io.Boolean.Input("return_actual_sigma", default=False, tooltip="Return the actual sigma value instead of the value used for interval checks.\nThis only affects results at 0.0 and 1.0."), + ], + outputs=[io.Float.Output(display_name="sigma_value")] + ) - RETURN_TYPES = (IO.FLOAT,) - RETURN_NAMES = ("sigma_value",) - CATEGORY = "sampling/custom_sampling/sigmas" - - FUNCTION = "get_sigma" - - def get_sigma(self, model, sampling_percent, return_actual_sigma): + @classmethod + def execute(cls, model, sampling_percent, return_actual_sigma) -> io.NodeOutput: model_sampling = model.get_model_object("model_sampling") sigma_val = model_sampling.percent_to_sigma(sampling_percent) if return_actual_sigma: @@ -327,212 +367,234 @@ class SamplingPercentToSigma: sigma_val = model_sampling.sigma_max.item() elif sampling_percent == 1.0: sigma_val = model_sampling.sigma_min.item() - return (sigma_val,) + return io.NodeOutput(sigma_val) + + get_sigma = execute -class KSamplerSelect: +class KSamplerSelect(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": - {"sampler_name": (comfy.samplers.SAMPLER_NAMES, ), - } - } - RETURN_TYPES = ("SAMPLER",) - CATEGORY = "sampling/custom_sampling/samplers" + def define_schema(cls): + return io.Schema( + node_id="KSamplerSelect", + category="sampling/custom_sampling/samplers", + inputs=[io.Combo.Input("sampler_name", options=comfy.samplers.SAMPLER_NAMES)], + outputs=[io.Sampler.Output()] + ) - FUNCTION = "get_sampler" - - def get_sampler(self, sampler_name): + @classmethod + def execute(cls, sampler_name) -> io.NodeOutput: sampler = comfy.samplers.sampler_object(sampler_name) - return (sampler, ) + return io.NodeOutput(sampler) -class SamplerDPMPP_3M_SDE: + get_sampler = execute + +class SamplerDPMPP_3M_SDE(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": - {"eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), - "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), - "noise_device": (['gpu', 'cpu'], ), - } - } - RETURN_TYPES = ("SAMPLER",) - CATEGORY = "sampling/custom_sampling/samplers" + def define_schema(cls): + return io.Schema( + node_id="SamplerDPMPP_3M_SDE", + category="sampling/custom_sampling/samplers", + inputs=[ + io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False), + io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False), + io.Combo.Input("noise_device", options=['gpu', 'cpu']), + ], + outputs=[io.Sampler.Output()] + ) - FUNCTION = "get_sampler" - - def get_sampler(self, eta, s_noise, noise_device): + @classmethod + def execute(cls, eta, s_noise, noise_device) -> io.NodeOutput: if noise_device == 'cpu': sampler_name = "dpmpp_3m_sde" else: sampler_name = "dpmpp_3m_sde_gpu" sampler = comfy.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise}) - return (sampler, ) + return io.NodeOutput(sampler) -class SamplerDPMPP_2M_SDE: + get_sampler = execute + +class SamplerDPMPP_2M_SDE(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": - {"solver_type": (['midpoint', 'heun'], ), - "eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), - "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), - "noise_device": (['gpu', 'cpu'], ), - } - } - RETURN_TYPES = ("SAMPLER",) - CATEGORY = "sampling/custom_sampling/samplers" + def define_schema(cls): + return io.Schema( + node_id="SamplerDPMPP_2M_SDE", + category="sampling/custom_sampling/samplers", + inputs=[ + io.Combo.Input("solver_type", options=['midpoint', 'heun']), + io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False), + io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False), + io.Combo.Input("noise_device", options=['gpu', 'cpu']), + ], + outputs=[io.Sampler.Output()] + ) - FUNCTION = "get_sampler" - - def get_sampler(self, solver_type, eta, s_noise, noise_device): + @classmethod + def execute(cls, solver_type, eta, s_noise, noise_device) -> io.NodeOutput: if noise_device == 'cpu': sampler_name = "dpmpp_2m_sde" else: sampler_name = "dpmpp_2m_sde_gpu" sampler = comfy.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise, "solver_type": solver_type}) - return (sampler, ) + return io.NodeOutput(sampler) + + get_sampler = execute -class SamplerDPMPP_SDE: +class SamplerDPMPP_SDE(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": - {"eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), - "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), - "r": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), - "noise_device": (['gpu', 'cpu'], ), - } - } - RETURN_TYPES = ("SAMPLER",) - CATEGORY = "sampling/custom_sampling/samplers" + def define_schema(cls): + return io.Schema( + node_id="SamplerDPMPP_SDE", + category="sampling/custom_sampling/samplers", + inputs=[ + io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False), + io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False), + io.Float.Input("r", default=0.5, min=0.0, max=100.0, step=0.01, round=False), + io.Combo.Input("noise_device", options=['gpu', 'cpu']), + ], + outputs=[io.Sampler.Output()] + ) - FUNCTION = "get_sampler" - - def get_sampler(self, eta, s_noise, r, noise_device): + @classmethod + def execute(cls, eta, s_noise, r, noise_device) -> io.NodeOutput: if noise_device == 'cpu': sampler_name = "dpmpp_sde" else: sampler_name = "dpmpp_sde_gpu" sampler = comfy.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise, "r": r}) - return (sampler, ) + return io.NodeOutput(sampler) -class SamplerDPMPP_2S_Ancestral: + get_sampler = execute + +class SamplerDPMPP_2S_Ancestral(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": - {"eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), - "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), - } - } - RETURN_TYPES = ("SAMPLER",) - CATEGORY = "sampling/custom_sampling/samplers" + def define_schema(cls): + return io.Schema( + node_id="SamplerDPMPP_2S_Ancestral", + category="sampling/custom_sampling/samplers", + inputs=[ + io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False), + io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False), + ], + outputs=[io.Sampler.Output()] + ) - FUNCTION = "get_sampler" - - def get_sampler(self, eta, s_noise): + @classmethod + def execute(cls, eta, s_noise) -> io.NodeOutput: sampler = comfy.samplers.ksampler("dpmpp_2s_ancestral", {"eta": eta, "s_noise": s_noise}) - return (sampler, ) + return io.NodeOutput(sampler) -class SamplerEulerAncestral: + get_sampler = execute + +class SamplerEulerAncestral(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": - {"eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), - "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), - } - } - RETURN_TYPES = ("SAMPLER",) - CATEGORY = "sampling/custom_sampling/samplers" + def define_schema(cls): + return io.Schema( + node_id="SamplerEulerAncestral", + category="sampling/custom_sampling/samplers", + inputs=[ + io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False), + io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False), + ], + outputs=[io.Sampler.Output()] + ) - FUNCTION = "get_sampler" - - def get_sampler(self, eta, s_noise): + @classmethod + def execute(cls, eta, s_noise) -> io.NodeOutput: sampler = comfy.samplers.ksampler("euler_ancestral", {"eta": eta, "s_noise": s_noise}) - return (sampler, ) + return io.NodeOutput(sampler) -class SamplerEulerAncestralCFGPP: + get_sampler = execute + +class SamplerEulerAncestralCFGPP(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return { - "required": { - "eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step":0.01, "round": False}), - "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step":0.01, "round": False}), - }} - RETURN_TYPES = ("SAMPLER",) - CATEGORY = "sampling/custom_sampling/samplers" + def define_schema(cls): + return io.Schema( + node_id="SamplerEulerAncestralCFGPP", + display_name="SamplerEulerAncestralCFG++", + category="sampling/custom_sampling/samplers", + inputs=[ + io.Float.Input("eta", default=1.0, min=0.0, max=1.0, step=0.01, round=False), + io.Float.Input("s_noise", default=1.0, min=0.0, max=10.0, step=0.01, round=False), + ], + outputs=[io.Sampler.Output()] + ) - FUNCTION = "get_sampler" - - def get_sampler(self, eta, s_noise): + @classmethod + def execute(cls, eta, s_noise) -> io.NodeOutput: sampler = comfy.samplers.ksampler( "euler_ancestral_cfg_pp", {"eta": eta, "s_noise": s_noise}) - return (sampler, ) + return io.NodeOutput(sampler) -class SamplerLMS: + get_sampler = execute + +class SamplerLMS(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": - {"order": ("INT", {"default": 4, "min": 1, "max": 100}), - } - } - RETURN_TYPES = ("SAMPLER",) - CATEGORY = "sampling/custom_sampling/samplers" + def define_schema(cls): + return io.Schema( + node_id="SamplerLMS", + category="sampling/custom_sampling/samplers", + inputs=[io.Int.Input("order", default=4, min=1, max=100)], + outputs=[io.Sampler.Output()] + ) - FUNCTION = "get_sampler" - - def get_sampler(self, order): + @classmethod + def execute(cls, order) -> io.NodeOutput: sampler = comfy.samplers.ksampler("lms", {"order": order}) - return (sampler, ) + return io.NodeOutput(sampler) -class SamplerDPMAdaptative: + get_sampler = execute + +class SamplerDPMAdaptative(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": - {"order": ("INT", {"default": 3, "min": 2, "max": 3}), - "rtol": ("FLOAT", {"default": 0.05, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), - "atol": ("FLOAT", {"default": 0.0078, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), - "h_init": ("FLOAT", {"default": 0.05, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), - "pcoeff": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), - "icoeff": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), - "dcoeff": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), - "accept_safety": ("FLOAT", {"default": 0.81, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), - "eta": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), - "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), - } - } - RETURN_TYPES = ("SAMPLER",) - CATEGORY = "sampling/custom_sampling/samplers" + def define_schema(cls): + return io.Schema( + node_id="SamplerDPMAdaptative", + category="sampling/custom_sampling/samplers", + inputs=[ + io.Int.Input("order", default=3, min=2, max=3), + io.Float.Input("rtol", default=0.05, min=0.0, max=100.0, step=0.01, round=False), + io.Float.Input("atol", default=0.0078, min=0.0, max=100.0, step=0.01, round=False), + io.Float.Input("h_init", default=0.05, min=0.0, max=100.0, step=0.01, round=False), + io.Float.Input("pcoeff", default=0.0, min=0.0, max=100.0, step=0.01, round=False), + io.Float.Input("icoeff", default=1.0, min=0.0, max=100.0, step=0.01, round=False), + io.Float.Input("dcoeff", default=0.0, min=0.0, max=100.0, step=0.01, round=False), + io.Float.Input("accept_safety", default=0.81, min=0.0, max=100.0, step=0.01, round=False), + io.Float.Input("eta", default=0.0, min=0.0, max=100.0, step=0.01, round=False), + io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False), + ], + outputs=[io.Sampler.Output()] + ) - FUNCTION = "get_sampler" - - def get_sampler(self, order, rtol, atol, h_init, pcoeff, icoeff, dcoeff, accept_safety, eta, s_noise): + @classmethod + def execute(cls, order, rtol, atol, h_init, pcoeff, icoeff, dcoeff, accept_safety, eta, s_noise) -> io.NodeOutput: sampler = comfy.samplers.ksampler("dpm_adaptive", {"order": order, "rtol": rtol, "atol": atol, "h_init": h_init, "pcoeff": pcoeff, "icoeff": icoeff, "dcoeff": dcoeff, "accept_safety": accept_safety, "eta": eta, "s_noise":s_noise }) - return (sampler, ) + return io.NodeOutput(sampler) + + get_sampler = execute -class SamplerER_SDE(ComfyNodeABC): +class SamplerER_SDE(io.ComfyNode): @classmethod - def INPUT_TYPES(cls) -> InputTypeDict: - return { - "required": { - "solver_type": (IO.COMBO, {"options": ["ER-SDE", "Reverse-time SDE", "ODE"]}), - "max_stage": (IO.INT, {"default": 3, "min": 1, "max": 3}), - "eta": ( - IO.FLOAT, - {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01, "round": False, "tooltip": "Stochastic strength of reverse-time SDE.\nWhen eta=0, it reduces to deterministic ODE. This setting doesn't apply to ER-SDE solver type."}, - ), - "s_noise": (IO.FLOAT, {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01, "round": False}), - } - } + def define_schema(cls): + return io.Schema( + node_id="SamplerER_SDE", + category="sampling/custom_sampling/samplers", + inputs=[ + io.Combo.Input("solver_type", options=["ER-SDE", "Reverse-time SDE", "ODE"]), + io.Int.Input("max_stage", default=3, min=1, max=3), + io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False, tooltip="Stochastic strength of reverse-time SDE.\nWhen eta=0, it reduces to deterministic ODE. This setting doesn't apply to ER-SDE solver type."), + io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False), + ], + outputs=[io.Sampler.Output()] + ) - RETURN_TYPES = (IO.SAMPLER,) - CATEGORY = "sampling/custom_sampling/samplers" - - FUNCTION = "get_sampler" - - def get_sampler(self, solver_type, max_stage, eta, s_noise): + @classmethod + def execute(cls, solver_type, max_stage, eta, s_noise) -> io.NodeOutput: if solver_type == "ODE" or (solver_type == "Reverse-time SDE" and eta == 0): eta = 0 s_noise = 0 @@ -548,32 +610,33 @@ class SamplerER_SDE(ComfyNodeABC): sampler_name = "er_sde" sampler = comfy.samplers.ksampler(sampler_name, {"s_noise": s_noise, "noise_scaler": noise_scaler, "max_stage": max_stage}) - return (sampler,) + return io.NodeOutput(sampler) + + get_sampler = execute -class SamplerSASolver(ComfyNodeABC): +class SamplerSASolver(io.ComfyNode): @classmethod - def INPUT_TYPES(cls) -> InputTypeDict: - return { - "required": { - "model": (IO.MODEL, {}), - "eta": (IO.FLOAT, {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01, "round": False},), - "sde_start_percent": (IO.FLOAT, {"default": 0.2, "min": 0.0, "max": 1.0, "step": 0.001},), - "sde_end_percent": (IO.FLOAT, {"default": 0.8, "min": 0.0, "max": 1.0, "step": 0.001},), - "s_noise": (IO.FLOAT, {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01, "round": False},), - "predictor_order": (IO.INT, {"default": 3, "min": 1, "max": 6}), - "corrector_order": (IO.INT, {"default": 4, "min": 0, "max": 6}), - "use_pece": (IO.BOOLEAN, {}), - "simple_order_2": (IO.BOOLEAN, {}), - } - } + def define_schema(cls): + return io.Schema( + node_id="SamplerSASolver", + category="sampling/custom_sampling/samplers", + inputs=[ + io.Model.Input("model"), + io.Float.Input("eta", default=1.0, min=0.0, max=10.0, step=0.01, round=False), + io.Float.Input("sde_start_percent", default=0.2, min=0.0, max=1.0, step=0.001), + io.Float.Input("sde_end_percent", default=0.8, min=0.0, max=1.0, step=0.001), + io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False), + io.Int.Input("predictor_order", default=3, min=1, max=6), + io.Int.Input("corrector_order", default=4, min=0, max=6), + io.Boolean.Input("use_pece"), + io.Boolean.Input("simple_order_2"), + ], + outputs=[io.Sampler.Output()] + ) - RETURN_TYPES = (IO.SAMPLER,) - CATEGORY = "sampling/custom_sampling/samplers" - - FUNCTION = "get_sampler" - - def get_sampler(self, model, eta, sde_start_percent, sde_end_percent, s_noise, predictor_order, corrector_order, use_pece, simple_order_2): + @classmethod + def execute(cls, model, eta, sde_start_percent, sde_end_percent, s_noise, predictor_order, corrector_order, use_pece, simple_order_2) -> io.NodeOutput: model_sampling = model.get_model_object("model_sampling") start_sigma = model_sampling.percent_to_sigma(sde_start_percent) end_sigma = model_sampling.percent_to_sigma(sde_end_percent) @@ -591,7 +654,9 @@ class SamplerSASolver(ComfyNodeABC): "simple_order_2": simple_order_2, }, ) - return (sampler,) + return io.NodeOutput(sampler) + + get_sampler = execute class Noise_EmptyNoise: @@ -612,30 +677,31 @@ class Noise_RandomNoise: batch_inds = input_latent["batch_index"] if "batch_index" in input_latent else None return comfy.sample.prepare_noise(latent_image, self.seed, batch_inds) -class SamplerCustom: +class SamplerCustom(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": - {"model": ("MODEL",), - "add_noise": ("BOOLEAN", {"default": True}), - "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "control_after_generate": True}), - "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}), - "positive": ("CONDITIONING", ), - "negative": ("CONDITIONING", ), - "sampler": ("SAMPLER", ), - "sigmas": ("SIGMAS", ), - "latent_image": ("LATENT", ), - } - } + def define_schema(cls): + return io.Schema( + node_id="SamplerCustom", + category="sampling/custom_sampling", + inputs=[ + io.Model.Input("model"), + io.Boolean.Input("add_noise", default=True), + io.Int.Input("noise_seed", default=0, min=0, max=0xffffffffffffffff, control_after_generate=True), + io.Float.Input("cfg", default=8.0, min=0.0, max=100.0, step=0.1, round=0.01), + io.Conditioning.Input("positive"), + io.Conditioning.Input("negative"), + io.Sampler.Input("sampler"), + io.Sigmas.Input("sigmas"), + io.Latent.Input("latent_image"), + ], + outputs=[ + io.Latent.Output(display_name="output"), + io.Latent.Output(display_name="denoised_output"), + ] + ) - RETURN_TYPES = ("LATENT","LATENT") - RETURN_NAMES = ("output", "denoised_output") - - FUNCTION = "sample" - - CATEGORY = "sampling/custom_sampling" - - def sample(self, model, add_noise, noise_seed, cfg, positive, negative, sampler, sigmas, latent_image): + @classmethod + def execute(cls, model, add_noise, noise_seed, cfg, positive, negative, sampler, sigmas, latent_image) -> io.NodeOutput: latent = latent_image latent_image = latent["samples"] latent = latent.copy() @@ -664,52 +730,58 @@ class SamplerCustom: out_denoised["samples"] = model.model.process_latent_out(x0_output["x0"].cpu()) else: out_denoised = out - return (out, out_denoised) + return io.NodeOutput(out, out_denoised) + + sample = execute class Guider_Basic(comfy.samplers.CFGGuider): def set_conds(self, positive): self.inner_set_conds({"positive": positive}) -class BasicGuider: +class BasicGuider(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": - {"model": ("MODEL",), - "conditioning": ("CONDITIONING", ), - } - } + def define_schema(cls): + return io.Schema( + node_id="BasicGuider", + category="sampling/custom_sampling/guiders", + inputs=[ + io.Model.Input("model"), + io.Conditioning.Input("conditioning"), + ], + outputs=[io.Guider.Output()] + ) - RETURN_TYPES = ("GUIDER",) - - FUNCTION = "get_guider" - CATEGORY = "sampling/custom_sampling/guiders" - - def get_guider(self, model, conditioning): + @classmethod + def execute(cls, model, conditioning) -> io.NodeOutput: guider = Guider_Basic(model) guider.set_conds(conditioning) - return (guider,) + return io.NodeOutput(guider) -class CFGGuider: + get_guider = execute + +class CFGGuider(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": - {"model": ("MODEL",), - "positive": ("CONDITIONING", ), - "negative": ("CONDITIONING", ), - "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}), - } - } + def define_schema(cls): + return io.Schema( + node_id="CFGGuider", + category="sampling/custom_sampling/guiders", + inputs=[ + io.Model.Input("model"), + io.Conditioning.Input("positive"), + io.Conditioning.Input("negative"), + io.Float.Input("cfg", default=8.0, min=0.0, max=100.0, step=0.1, round=0.01), + ], + outputs=[io.Guider.Output()] + ) - RETURN_TYPES = ("GUIDER",) - - FUNCTION = "get_guider" - CATEGORY = "sampling/custom_sampling/guiders" - - def get_guider(self, model, positive, negative, cfg): + @classmethod + def execute(cls, model, positive, negative, cfg) -> io.NodeOutput: guider = comfy.samplers.CFGGuider(model) guider.set_conds(positive, negative) guider.set_cfg(cfg) - return (guider,) + return io.NodeOutput(guider) + + get_guider = execute class Guider_DualCFG(comfy.samplers.CFGGuider): def set_cfg(self, cfg1, cfg2, nested=False): @@ -740,84 +812,88 @@ class Guider_DualCFG(comfy.samplers.CFGGuider): out = comfy.samplers.calc_cond_batch(self.inner_model, [negative_cond, middle_cond, positive_cond], x, timestep, model_options) return comfy.samplers.cfg_function(self.inner_model, out[1], out[0], self.cfg2, x, timestep, model_options=model_options, cond=middle_cond, uncond=negative_cond) + (out[2] - out[1]) * self.cfg1 -class DualCFGGuider: +class DualCFGGuider(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": - {"model": ("MODEL",), - "cond1": ("CONDITIONING", ), - "cond2": ("CONDITIONING", ), - "negative": ("CONDITIONING", ), - "cfg_conds": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}), - "cfg_cond2_negative": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}), - "style": (["regular", "nested"],), - } - } + def define_schema(cls): + return io.Schema( + node_id="DualCFGGuider", + category="sampling/custom_sampling/guiders", + inputs=[ + io.Model.Input("model"), + io.Conditioning.Input("cond1"), + io.Conditioning.Input("cond2"), + io.Conditioning.Input("negative"), + io.Float.Input("cfg_conds", default=8.0, min=0.0, max=100.0, step=0.1, round=0.01), + io.Float.Input("cfg_cond2_negative", default=8.0, min=0.0, max=100.0, step=0.1, round=0.01), + io.Combo.Input("style", options=["regular", "nested"]), + ], + outputs=[io.Guider.Output()] + ) - RETURN_TYPES = ("GUIDER",) - - FUNCTION = "get_guider" - CATEGORY = "sampling/custom_sampling/guiders" - - def get_guider(self, model, cond1, cond2, negative, cfg_conds, cfg_cond2_negative, style): + @classmethod + def execute(cls, model, cond1, cond2, negative, cfg_conds, cfg_cond2_negative, style) -> io.NodeOutput: guider = Guider_DualCFG(model) guider.set_conds(cond1, cond2, negative) guider.set_cfg(cfg_conds, cfg_cond2_negative, nested=(style == "nested")) - return (guider,) + return io.NodeOutput(guider) -class DisableNoise: + get_guider = execute + +class DisableNoise(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required":{ - } - } + def define_schema(cls): + return io.Schema( + node_id="DisableNoise", + category="sampling/custom_sampling/noise", + inputs=[], + outputs=[io.Noise.Output()] + ) - RETURN_TYPES = ("NOISE",) - FUNCTION = "get_noise" - CATEGORY = "sampling/custom_sampling/noise" - - def get_noise(self): - return (Noise_EmptyNoise(),) - - -class RandomNoise(DisableNoise): @classmethod - def INPUT_TYPES(s): - return { - "required": { - "noise_seed": ("INT", { - "default": 0, - "min": 0, - "max": 0xffffffffffffffff, - "control_after_generate": True, - }), - } - } + def execute(cls) -> io.NodeOutput: + return io.NodeOutput(Noise_EmptyNoise()) - def get_noise(self, noise_seed): - return (Noise_RandomNoise(noise_seed),) + get_noise = execute -class SamplerCustomAdvanced: +class RandomNoise(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": - {"noise": ("NOISE", ), - "guider": ("GUIDER", ), - "sampler": ("SAMPLER", ), - "sigmas": ("SIGMAS", ), - "latent_image": ("LATENT", ), - } - } + def define_schema(cls): + return io.Schema( + node_id="RandomNoise", + category="sampling/custom_sampling/noise", + inputs=[io.Int.Input("noise_seed", default=0, min=0, max=0xffffffffffffffff, control_after_generate=True)], + outputs=[io.Noise.Output()] + ) - RETURN_TYPES = ("LATENT","LATENT") - RETURN_NAMES = ("output", "denoised_output") + @classmethod + def execute(cls, noise_seed) -> io.NodeOutput: + return io.NodeOutput(Noise_RandomNoise(noise_seed)) - FUNCTION = "sample" + get_noise = execute - CATEGORY = "sampling/custom_sampling" - def sample(self, noise, guider, sampler, sigmas, latent_image): +class SamplerCustomAdvanced(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="SamplerCustomAdvanced", + category="sampling/custom_sampling", + inputs=[ + io.Noise.Input("noise"), + io.Guider.Input("guider"), + io.Sampler.Input("sampler"), + io.Sigmas.Input("sigmas"), + io.Latent.Input("latent_image"), + ], + outputs=[ + io.Latent.Output(display_name="output"), + io.Latent.Output(display_name="denoised_output"), + ] + ) + + @classmethod + def execute(cls, noise, guider, sampler, sigmas, latent_image) -> io.NodeOutput: latent = latent_image latent_image = latent["samples"] latent = latent.copy() @@ -842,28 +918,32 @@ class SamplerCustomAdvanced: out_denoised["samples"] = guider.model_patcher.model.process_latent_out(x0_output["x0"].cpu()) else: out_denoised = out - return (out, out_denoised) + return io.NodeOutput(out, out_denoised) -class AddNoise: + sample = execute + +class AddNoise(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": - {"model": ("MODEL",), - "noise": ("NOISE", ), - "sigmas": ("SIGMAS", ), - "latent_image": ("LATENT", ), - } - } + def define_schema(cls): + return io.Schema( + node_id="AddNoise", + category="_for_testing/custom_sampling/noise", + is_experimental=True, + inputs=[ + io.Model.Input("model"), + io.Noise.Input("noise"), + io.Sigmas.Input("sigmas"), + io.Latent.Input("latent_image"), + ], + outputs=[ + io.Latent.Output(), + ] + ) - RETURN_TYPES = ("LATENT",) - - FUNCTION = "add_noise" - - CATEGORY = "_for_testing/custom_sampling/noise" - - def add_noise(self, model, noise, sigmas, latent_image): + @classmethod + def execute(cls, model, noise, sigmas, latent_image) -> io.NodeOutput: if len(sigmas) == 0: - return latent_image + return io.NodeOutput(latent_image) latent = latent_image latent_image = latent["samples"] @@ -887,46 +967,50 @@ class AddNoise: out = latent.copy() out["samples"] = noisy - return (out,) + return io.NodeOutput(out) + + add_noise = execute -NODE_CLASS_MAPPINGS = { - "SamplerCustom": SamplerCustom, - "BasicScheduler": BasicScheduler, - "KarrasScheduler": KarrasScheduler, - "ExponentialScheduler": ExponentialScheduler, - "PolyexponentialScheduler": PolyexponentialScheduler, - "LaplaceScheduler": LaplaceScheduler, - "VPScheduler": VPScheduler, - "BetaSamplingScheduler": BetaSamplingScheduler, - "SDTurboScheduler": SDTurboScheduler, - "KSamplerSelect": KSamplerSelect, - "SamplerEulerAncestral": SamplerEulerAncestral, - "SamplerEulerAncestralCFGPP": SamplerEulerAncestralCFGPP, - "SamplerLMS": SamplerLMS, - "SamplerDPMPP_3M_SDE": SamplerDPMPP_3M_SDE, - "SamplerDPMPP_2M_SDE": SamplerDPMPP_2M_SDE, - "SamplerDPMPP_SDE": SamplerDPMPP_SDE, - "SamplerDPMPP_2S_Ancestral": SamplerDPMPP_2S_Ancestral, - "SamplerDPMAdaptative": SamplerDPMAdaptative, - "SamplerER_SDE": SamplerER_SDE, - "SamplerSASolver": SamplerSASolver, - "SplitSigmas": SplitSigmas, - "SplitSigmasDenoise": SplitSigmasDenoise, - "FlipSigmas": FlipSigmas, - "SetFirstSigma": SetFirstSigma, - "ExtendIntermediateSigmas": ExtendIntermediateSigmas, - "SamplingPercentToSigma": SamplingPercentToSigma, +class CustomSamplersExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + SamplerCustom, + BasicScheduler, + KarrasScheduler, + ExponentialScheduler, + PolyexponentialScheduler, + LaplaceScheduler, + VPScheduler, + BetaSamplingScheduler, + SDTurboScheduler, + KSamplerSelect, + SamplerEulerAncestral, + SamplerEulerAncestralCFGPP, + SamplerLMS, + SamplerDPMPP_3M_SDE, + SamplerDPMPP_2M_SDE, + SamplerDPMPP_SDE, + SamplerDPMPP_2S_Ancestral, + SamplerDPMAdaptative, + SamplerER_SDE, + SamplerSASolver, + SplitSigmas, + SplitSigmasDenoise, + FlipSigmas, + SetFirstSigma, + ExtendIntermediateSigmas, + SamplingPercentToSigma, + CFGGuider, + DualCFGGuider, + BasicGuider, + RandomNoise, + DisableNoise, + AddNoise, + SamplerCustomAdvanced, + ] - "CFGGuider": CFGGuider, - "DualCFGGuider": DualCFGGuider, - "BasicGuider": BasicGuider, - "RandomNoise": RandomNoise, - "DisableNoise": DisableNoise, - "AddNoise": AddNoise, - "SamplerCustomAdvanced": SamplerCustomAdvanced, -} -NODE_DISPLAY_NAME_MAPPINGS = { - "SamplerEulerAncestralCFGPP": "SamplerEulerAncestralCFG++", -} +async def comfy_entrypoint() -> CustomSamplersExtension: + return CustomSamplersExtension() diff --git a/comfy_extras/nodes_dataset.py b/comfy_extras/nodes_dataset.py new file mode 100644 index 000000000..4789d7d53 --- /dev/null +++ b/comfy_extras/nodes_dataset.py @@ -0,0 +1,1432 @@ +import logging +import os +import json + +import numpy as np +import torch +from PIL import Image +from typing_extensions import override + +import folder_paths +import node_helpers +from comfy_api.latest import ComfyExtension, io + + +def load_and_process_images(image_files, input_dir): + """Utility function to load and process a list of images. + + Args: + image_files: List of image filenames + input_dir: Base directory containing the images + resize_method: How to handle images of different sizes ("None", "Stretch", "Crop", "Pad") + + Returns: + torch.Tensor: Batch of processed images + """ + if not image_files: + raise ValueError("No valid images found in input") + + output_images = [] + + for file in image_files: + image_path = os.path.join(input_dir, file) + img = node_helpers.pillow(Image.open, image_path) + + if img.mode == "I": + img = img.point(lambda i: i * (1 / 255)) + img = img.convert("RGB") + img_array = np.array(img).astype(np.float32) / 255.0 + img_tensor = torch.from_numpy(img_array)[None,] + output_images.append(img_tensor) + + return output_images + + +class LoadImageDataSetFromFolderNode(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="LoadImageDataSetFromFolder", + display_name="Load Image Dataset from Folder", + category="dataset", + is_experimental=True, + inputs=[ + io.Combo.Input( + "folder", + options=folder_paths.get_input_subfolders(), + tooltip="The folder to load images from.", + ) + ], + outputs=[ + io.Image.Output( + display_name="images", + is_output_list=True, + tooltip="List of loaded images", + ) + ], + ) + + @classmethod + def execute(cls, folder): + sub_input_dir = os.path.join(folder_paths.get_input_directory(), folder) + valid_extensions = [".png", ".jpg", ".jpeg", ".webp"] + image_files = [ + f + for f in os.listdir(sub_input_dir) + if any(f.lower().endswith(ext) for ext in valid_extensions) + ] + output_tensor = load_and_process_images(image_files, sub_input_dir) + return io.NodeOutput(output_tensor) + + +class LoadImageTextDataSetFromFolderNode(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="LoadImageTextDataSetFromFolder", + display_name="Load Image and Text Dataset from Folder", + category="dataset", + is_experimental=True, + inputs=[ + io.Combo.Input( + "folder", + options=folder_paths.get_input_subfolders(), + tooltip="The folder to load images from.", + ) + ], + outputs=[ + io.Image.Output( + display_name="images", + is_output_list=True, + tooltip="List of loaded images", + ), + io.String.Output( + display_name="texts", + is_output_list=True, + tooltip="List of text captions", + ), + ], + ) + + @classmethod + def execute(cls, folder): + logging.info(f"Loading images from folder: {folder}") + + sub_input_dir = os.path.join(folder_paths.get_input_directory(), folder) + valid_extensions = [".png", ".jpg", ".jpeg", ".webp"] + + image_files = [] + for item in os.listdir(sub_input_dir): + path = os.path.join(sub_input_dir, item) + if any(item.lower().endswith(ext) for ext in valid_extensions): + image_files.append(path) + elif os.path.isdir(path): + # Support kohya-ss/sd-scripts folder structure + repeat = 1 + if item.split("_")[0].isdigit(): + repeat = int(item.split("_")[0]) + image_files.extend( + [ + os.path.join(path, f) + for f in os.listdir(path) + if any(f.lower().endswith(ext) for ext in valid_extensions) + ] + * repeat + ) + + caption_file_path = [ + f.replace(os.path.splitext(f)[1], ".txt") for f in image_files + ] + captions = [] + for caption_file in caption_file_path: + caption_path = os.path.join(sub_input_dir, caption_file) + if os.path.exists(caption_path): + with open(caption_path, "r", encoding="utf-8") as f: + caption = f.read().strip() + captions.append(caption) + else: + captions.append("") + + output_tensor = load_and_process_images(image_files, sub_input_dir) + + logging.info(f"Loaded {len(output_tensor)} images from {sub_input_dir}.") + return io.NodeOutput(output_tensor, captions) + + +def save_images_to_folder(image_list, output_dir, prefix="image"): + """Utility function to save a list of image tensors to disk. + + Args: + image_list: List of image tensors (each [1, H, W, C] or [H, W, C] or [C, H, W]) + output_dir: Directory to save images to + prefix: Filename prefix + + Returns: + List of saved filenames + """ + os.makedirs(output_dir, exist_ok=True) + saved_files = [] + + for idx, img_tensor in enumerate(image_list): + # Handle different tensor shapes + if isinstance(img_tensor, torch.Tensor): + # Remove batch dimension if present [1, H, W, C] -> [H, W, C] + if img_tensor.dim() == 4 and img_tensor.shape[0] == 1: + img_tensor = img_tensor.squeeze(0) + + # If tensor is [C, H, W], permute to [H, W, C] + if img_tensor.dim() == 3 and img_tensor.shape[0] in [1, 3, 4]: + if ( + img_tensor.shape[0] <= 4 + and img_tensor.shape[1] > 4 + and img_tensor.shape[2] > 4 + ): + img_tensor = img_tensor.permute(1, 2, 0) + + # Convert to numpy and scale to 0-255 + img_array = img_tensor.cpu().numpy() + img_array = np.clip(img_array * 255.0, 0, 255).astype(np.uint8) + + # Convert to PIL Image + img = Image.fromarray(img_array) + else: + raise ValueError(f"Expected torch.Tensor, got {type(img_tensor)}") + + # Save image + filename = f"{prefix}_{idx:05d}.png" + filepath = os.path.join(output_dir, filename) + img.save(filepath) + saved_files.append(filename) + + return saved_files + + +class SaveImageDataSetToFolderNode(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="SaveImageDataSetToFolder", + display_name="Save Image Dataset to Folder", + category="dataset", + is_experimental=True, + is_output_node=True, + is_input_list=True, # Receive images as list + inputs=[ + io.Image.Input("images", tooltip="List of images to save."), + io.String.Input( + "folder_name", + default="dataset", + tooltip="Name of the folder to save images to (inside output directory).", + ), + io.String.Input( + "filename_prefix", + default="image", + tooltip="Prefix for saved image filenames.", + ), + ], + outputs=[], + ) + + @classmethod + def execute(cls, images, folder_name, filename_prefix): + # Extract scalar values + folder_name = folder_name[0] + filename_prefix = filename_prefix[0] + + output_dir = os.path.join(folder_paths.get_output_directory(), folder_name) + saved_files = save_images_to_folder(images, output_dir, filename_prefix) + + logging.info(f"Saved {len(saved_files)} images to {output_dir}.") + return io.NodeOutput() + + +class SaveImageTextDataSetToFolderNode(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="SaveImageTextDataSetToFolder", + display_name="Save Image and Text Dataset to Folder", + category="dataset", + is_experimental=True, + is_output_node=True, + is_input_list=True, # Receive both images and texts as lists + inputs=[ + io.Image.Input("images", tooltip="List of images to save."), + io.String.Input("texts", tooltip="List of text captions to save."), + io.String.Input( + "folder_name", + default="dataset", + tooltip="Name of the folder to save images to (inside output directory).", + ), + io.String.Input( + "filename_prefix", + default="image", + tooltip="Prefix for saved image filenames.", + ), + ], + outputs=[], + ) + + @classmethod + def execute(cls, images, texts, folder_name, filename_prefix): + # Extract scalar values + folder_name = folder_name[0] + filename_prefix = filename_prefix[0] + + output_dir = os.path.join(folder_paths.get_output_directory(), folder_name) + saved_files = save_images_to_folder(images, output_dir, filename_prefix) + + # Save captions + for idx, (filename, caption) in enumerate(zip(saved_files, texts)): + caption_filename = filename.replace(".png", ".txt") + caption_path = os.path.join(output_dir, caption_filename) + with open(caption_path, "w", encoding="utf-8") as f: + f.write(caption) + + logging.info(f"Saved {len(saved_files)} images and captions to {output_dir}.") + return io.NodeOutput() + + +# ========== Helper Functions for Transform Nodes ========== + + +def tensor_to_pil(img_tensor): + """Convert tensor to PIL Image.""" + if img_tensor.dim() == 4 and img_tensor.shape[0] == 1: + img_tensor = img_tensor.squeeze(0) + img_array = (img_tensor.cpu().numpy() * 255).clip(0, 255).astype(np.uint8) + return Image.fromarray(img_array) + + +def pil_to_tensor(img): + """Convert PIL Image to tensor.""" + img_array = np.array(img).astype(np.float32) / 255.0 + return torch.from_numpy(img_array)[None,] + + +# ========== Base Classes for Transform Nodes ========== + + +class ImageProcessingNode(io.ComfyNode): + """Base class for image processing nodes that operate on images. + + Child classes should set: + node_id: Unique node identifier (required) + display_name: Display name (optional, defaults to node_id) + description: Node description (optional) + extra_inputs: List of additional io.Input objects beyond "images" (optional) + is_group_process: None (auto-detect), True (group), or False (individual) (optional) + is_output_list: True (list output) or False (single output) (optional, default True) + + Child classes must implement ONE of: + _process(cls, image, **kwargs) -> tensor (for single-item processing) + _group_process(cls, images, **kwargs) -> list[tensor] (for group processing) + """ + + node_id = None + display_name = None + description = None + extra_inputs = [] + is_group_process = None # None = auto-detect, True/False = explicit + is_output_list = None # None = auto-detect based on processing mode + + @classmethod + def _detect_processing_mode(cls): + """Detect whether this node uses group or individual processing. + + Returns: + bool: True if group processing, False if individual processing + """ + # Explicit setting takes precedence + if cls.is_group_process is not None: + return cls.is_group_process + + # Check which method is overridden by looking at the defining class in MRO + base_class = ImageProcessingNode + + # Find which class in MRO defines _process + process_definer = None + for klass in cls.__mro__: + if "_process" in klass.__dict__: + process_definer = klass + break + + # Find which class in MRO defines _group_process + group_definer = None + for klass in cls.__mro__: + if "_group_process" in klass.__dict__: + group_definer = klass + break + + # Check what was overridden (not defined in base class) + has_process = process_definer is not None and process_definer is not base_class + has_group = group_definer is not None and group_definer is not base_class + + if has_process and has_group: + raise ValueError( + f"{cls.__name__}: Cannot override both _process and _group_process. " + "Override only one, or set is_group_process explicitly." + ) + if not has_process and not has_group: + raise ValueError( + f"{cls.__name__}: Must override either _process or _group_process" + ) + + return has_group + + @classmethod + def define_schema(cls): + if cls.node_id is None: + raise NotImplementedError(f"{cls.__name__} must set node_id class variable") + + is_group = cls._detect_processing_mode() + + # Auto-detect is_output_list if not explicitly set + # Single processing: False (backend collects results into list) + # Group processing: True by default (can be False for single-output nodes) + output_is_list = ( + cls.is_output_list if cls.is_output_list is not None else is_group + ) + + inputs = [ + io.Image.Input( + "images", + tooltip=( + "List of images to process." if is_group else "Image to process." + ), + ) + ] + inputs.extend(cls.extra_inputs) + + return io.Schema( + node_id=cls.node_id, + display_name=cls.display_name or cls.node_id, + category="dataset/image", + is_experimental=True, + is_input_list=is_group, # True for group, False for individual + inputs=inputs, + outputs=[ + io.Image.Output( + display_name="images", + is_output_list=output_is_list, + tooltip="Processed images", + ) + ], + ) + + @classmethod + def execute(cls, images, **kwargs): + """Execute the node. Routes to _process or _group_process based on mode.""" + is_group = cls._detect_processing_mode() + + # Extract scalar values from lists for parameters + params = {} + for k, v in kwargs.items(): + if isinstance(v, list) and len(v) == 1: + params[k] = v[0] + else: + params[k] = v + + if is_group: + # Group processing: images is list, call _group_process + result = cls._group_process(images, **params) + else: + # Individual processing: images is single item, call _process + result = cls._process(images, **params) + + return io.NodeOutput(result) + + @classmethod + def _process(cls, image, **kwargs): + """Override this method for single-item processing. + + Args: + image: tensor - Single image tensor + **kwargs: Additional parameters (already extracted from lists) + + Returns: + tensor - Processed image + """ + raise NotImplementedError(f"{cls.__name__} must implement _process method") + + @classmethod + def _group_process(cls, images, **kwargs): + """Override this method for group processing. + + Args: + images: list[tensor] - List of image tensors + **kwargs: Additional parameters (already extracted from lists) + + Returns: + list[tensor] - Processed images + """ + raise NotImplementedError( + f"{cls.__name__} must implement _group_process method" + ) + + +class TextProcessingNode(io.ComfyNode): + """Base class for text processing nodes that operate on texts. + + Child classes should set: + node_id: Unique node identifier (required) + display_name: Display name (optional, defaults to node_id) + description: Node description (optional) + extra_inputs: List of additional io.Input objects beyond "texts" (optional) + is_group_process: None (auto-detect), True (group), or False (individual) (optional) + is_output_list: True (list output) or False (single output) (optional, default True) + + Child classes must implement ONE of: + _process(cls, text, **kwargs) -> str (for single-item processing) + _group_process(cls, texts, **kwargs) -> list[str] (for group processing) + """ + + node_id = None + display_name = None + description = None + extra_inputs = [] + is_group_process = None # None = auto-detect, True/False = explicit + is_output_list = None # None = auto-detect based on processing mode + + @classmethod + def _detect_processing_mode(cls): + """Detect whether this node uses group or individual processing. + + Returns: + bool: True if group processing, False if individual processing + """ + # Explicit setting takes precedence + if cls.is_group_process is not None: + return cls.is_group_process + + # Check which method is overridden by looking at the defining class in MRO + base_class = TextProcessingNode + + # Find which class in MRO defines _process + process_definer = None + for klass in cls.__mro__: + if "_process" in klass.__dict__: + process_definer = klass + break + + # Find which class in MRO defines _group_process + group_definer = None + for klass in cls.__mro__: + if "_group_process" in klass.__dict__: + group_definer = klass + break + + # Check what was overridden (not defined in base class) + has_process = process_definer is not None and process_definer is not base_class + has_group = group_definer is not None and group_definer is not base_class + + if has_process and has_group: + raise ValueError( + f"{cls.__name__}: Cannot override both _process and _group_process. " + "Override only one, or set is_group_process explicitly." + ) + if not has_process and not has_group: + raise ValueError( + f"{cls.__name__}: Must override either _process or _group_process" + ) + + return has_group + + @classmethod + def define_schema(cls): + if cls.node_id is None: + raise NotImplementedError(f"{cls.__name__} must set node_id class variable") + + is_group = cls._detect_processing_mode() + + inputs = [ + io.String.Input( + "texts", + tooltip="List of texts to process." if is_group else "Text to process.", + ) + ] + inputs.extend(cls.extra_inputs) + + return io.Schema( + node_id=cls.node_id, + display_name=cls.display_name or cls.node_id, + category="dataset/text", + is_experimental=True, + is_input_list=is_group, # True for group, False for individual + inputs=inputs, + outputs=[ + io.String.Output( + display_name="texts", + is_output_list=cls.is_output_list, + tooltip="Processed texts", + ) + ], + ) + + @classmethod + def execute(cls, texts, **kwargs): + """Execute the node. Routes to _process or _group_process based on mode.""" + is_group = cls._detect_processing_mode() + + # Extract scalar values from lists for parameters + params = {} + for k, v in kwargs.items(): + if isinstance(v, list) and len(v) == 1: + params[k] = v[0] + else: + params[k] = v + + if is_group: + # Group processing: texts is list, call _group_process + result = cls._group_process(texts, **params) + else: + # Individual processing: texts is single item, call _process + result = cls._process(texts, **params) + + # Wrap result based on is_output_list + if cls.is_output_list: + # Result should already be a list (or will be for individual) + return io.NodeOutput(result if is_group else [result]) + else: + # Single output - wrap in list for NodeOutput + return io.NodeOutput([result]) + + @classmethod + def _process(cls, text, **kwargs): + """Override this method for single-item processing. + + Args: + text: str - Single text string + **kwargs: Additional parameters (already extracted from lists) + + Returns: + str - Processed text + """ + raise NotImplementedError(f"{cls.__name__} must implement _process method") + + @classmethod + def _group_process(cls, texts, **kwargs): + """Override this method for group processing. + + Args: + texts: list[str] - List of text strings + **kwargs: Additional parameters (already extracted from lists) + + Returns: + list[str] - Processed texts + """ + raise NotImplementedError( + f"{cls.__name__} must implement _group_process method" + ) + + +# ========== Image Transform Nodes ========== + + +class ResizeImagesByShorterEdgeNode(ImageProcessingNode): + node_id = "ResizeImagesByShorterEdge" + display_name = "Resize Images by Shorter Edge" + description = "Resize images so that the shorter edge matches the specified length while preserving aspect ratio." + extra_inputs = [ + io.Int.Input( + "shorter_edge", + default=512, + min=1, + max=8192, + tooltip="Target length for the shorter edge.", + ), + ] + + @classmethod + def _process(cls, image, shorter_edge): + img = tensor_to_pil(image) + w, h = img.size + if w < h: + new_w = shorter_edge + new_h = int(h * (shorter_edge / w)) + else: + new_h = shorter_edge + new_w = int(w * (shorter_edge / h)) + img = img.resize((new_w, new_h), Image.Resampling.LANCZOS) + return pil_to_tensor(img) + + +class ResizeImagesByLongerEdgeNode(ImageProcessingNode): + node_id = "ResizeImagesByLongerEdge" + display_name = "Resize Images by Longer Edge" + description = "Resize images so that the longer edge matches the specified length while preserving aspect ratio." + extra_inputs = [ + io.Int.Input( + "longer_edge", + default=1024, + min=1, + max=8192, + tooltip="Target length for the longer edge.", + ), + ] + + @classmethod + def _process(cls, image, longer_edge): + img = tensor_to_pil(image) + w, h = img.size + if w > h: + new_w = longer_edge + new_h = int(h * (longer_edge / w)) + else: + new_h = longer_edge + new_w = int(w * (longer_edge / h)) + img = img.resize((new_w, new_h), Image.Resampling.LANCZOS) + return pil_to_tensor(img) + + +class CenterCropImagesNode(ImageProcessingNode): + node_id = "CenterCropImages" + display_name = "Center Crop Images" + description = "Center crop all images to the specified dimensions." + extra_inputs = [ + io.Int.Input("width", default=512, min=1, max=8192, tooltip="Crop width."), + io.Int.Input("height", default=512, min=1, max=8192, tooltip="Crop height."), + ] + + @classmethod + def _process(cls, image, width, height): + img = tensor_to_pil(image) + left = max(0, (img.width - width) // 2) + top = max(0, (img.height - height) // 2) + right = min(img.width, left + width) + bottom = min(img.height, top + height) + img = img.crop((left, top, right, bottom)) + return pil_to_tensor(img) + + +class RandomCropImagesNode(ImageProcessingNode): + node_id = "RandomCropImages" + display_name = "Random Crop Images" + description = ( + "Randomly crop all images to the specified dimensions (for data augmentation)." + ) + extra_inputs = [ + io.Int.Input("width", default=512, min=1, max=8192, tooltip="Crop width."), + io.Int.Input("height", default=512, min=1, max=8192, tooltip="Crop height."), + io.Int.Input( + "seed", default=0, min=0, max=0xFFFFFFFFFFFFFFFF, tooltip="Random seed." + ), + ] + + @classmethod + def _process(cls, image, width, height, seed): + np.random.seed(seed % (2**32 - 1)) + img = tensor_to_pil(image) + max_left = max(0, img.width - width) + max_top = max(0, img.height - height) + left = np.random.randint(0, max_left + 1) if max_left > 0 else 0 + top = np.random.randint(0, max_top + 1) if max_top > 0 else 0 + right = min(img.width, left + width) + bottom = min(img.height, top + height) + img = img.crop((left, top, right, bottom)) + return pil_to_tensor(img) + + +class NormalizeImagesNode(ImageProcessingNode): + node_id = "NormalizeImages" + display_name = "Normalize Images" + description = "Normalize images using mean and standard deviation." + extra_inputs = [ + io.Float.Input( + "mean", + default=0.5, + min=0.0, + max=1.0, + tooltip="Mean value for normalization.", + ), + io.Float.Input( + "std", + default=0.5, + min=0.001, + max=1.0, + tooltip="Standard deviation for normalization.", + ), + ] + + @classmethod + def _process(cls, image, mean, std): + return (image - mean) / std + + +class AdjustBrightnessNode(ImageProcessingNode): + node_id = "AdjustBrightness" + display_name = "Adjust Brightness" + description = "Adjust brightness of all images." + extra_inputs = [ + io.Float.Input( + "factor", + default=1.0, + min=0.0, + max=2.0, + tooltip="Brightness factor. 1.0 = no change, <1.0 = darker, >1.0 = brighter.", + ), + ] + + @classmethod + def _process(cls, image, factor): + return (image * factor).clamp(0.0, 1.0) + + +class AdjustContrastNode(ImageProcessingNode): + node_id = "AdjustContrast" + display_name = "Adjust Contrast" + description = "Adjust contrast of all images." + extra_inputs = [ + io.Float.Input( + "factor", + default=1.0, + min=0.0, + max=2.0, + tooltip="Contrast factor. 1.0 = no change, <1.0 = less contrast, >1.0 = more contrast.", + ), + ] + + @classmethod + def _process(cls, image, factor): + return ((image - 0.5) * factor + 0.5).clamp(0.0, 1.0) + + +class ShuffleDatasetNode(ImageProcessingNode): + node_id = "ShuffleDataset" + display_name = "Shuffle Image Dataset" + description = "Randomly shuffle the order of images in the dataset." + is_group_process = True # Requires full list to shuffle + extra_inputs = [ + io.Int.Input( + "seed", default=0, min=0, max=0xFFFFFFFFFFFFFFFF, tooltip="Random seed." + ), + ] + + @classmethod + def _group_process(cls, images, seed): + np.random.seed(seed % (2**32 - 1)) + indices = np.random.permutation(len(images)) + return [images[i] for i in indices] + + +class ShuffleImageTextDatasetNode(io.ComfyNode): + """Special node that shuffles both images and texts together.""" + + @classmethod + def define_schema(cls): + return io.Schema( + node_id="ShuffleImageTextDataset", + display_name="Shuffle Image-Text Dataset", + category="dataset/image", + is_experimental=True, + is_input_list=True, + inputs=[ + io.Image.Input("images", tooltip="List of images to shuffle."), + io.String.Input("texts", tooltip="List of texts to shuffle."), + io.Int.Input( + "seed", + default=0, + min=0, + max=0xFFFFFFFFFFFFFFFF, + tooltip="Random seed.", + ), + ], + outputs=[ + io.Image.Output( + display_name="images", + is_output_list=True, + tooltip="Shuffled images", + ), + io.String.Output( + display_name="texts", is_output_list=True, tooltip="Shuffled texts" + ), + ], + ) + + @classmethod + def execute(cls, images, texts, seed): + seed = seed[0] # Extract scalar + np.random.seed(seed % (2**32 - 1)) + indices = np.random.permutation(len(images)) + shuffled_images = [images[i] for i in indices] + shuffled_texts = [texts[i] for i in indices] + return io.NodeOutput(shuffled_images, shuffled_texts) + + +# ========== Text Transform Nodes ========== + + +class TextToLowercaseNode(TextProcessingNode): + node_id = "TextToLowercase" + display_name = "Text to Lowercase" + description = "Convert all texts to lowercase." + + @classmethod + def _process(cls, text): + return text.lower() + + +class TextToUppercaseNode(TextProcessingNode): + node_id = "TextToUppercase" + display_name = "Text to Uppercase" + description = "Convert all texts to uppercase." + + @classmethod + def _process(cls, text): + return text.upper() + + +class TruncateTextNode(TextProcessingNode): + node_id = "TruncateText" + display_name = "Truncate Text" + description = "Truncate all texts to a maximum length." + extra_inputs = [ + io.Int.Input( + "max_length", default=77, min=1, max=10000, tooltip="Maximum text length." + ), + ] + + @classmethod + def _process(cls, text, max_length): + return text[:max_length] + + +class AddTextPrefixNode(TextProcessingNode): + node_id = "AddTextPrefix" + display_name = "Add Text Prefix" + description = "Add a prefix to all texts." + extra_inputs = [ + io.String.Input("prefix", default="", tooltip="Prefix to add."), + ] + + @classmethod + def _process(cls, text, prefix): + return prefix + text + + +class AddTextSuffixNode(TextProcessingNode): + node_id = "AddTextSuffix" + display_name = "Add Text Suffix" + description = "Add a suffix to all texts." + extra_inputs = [ + io.String.Input("suffix", default="", tooltip="Suffix to add."), + ] + + @classmethod + def _process(cls, text, suffix): + return text + suffix + + +class ReplaceTextNode(TextProcessingNode): + node_id = "ReplaceText" + display_name = "Replace Text" + description = "Replace text in all texts." + extra_inputs = [ + io.String.Input("find", default="", tooltip="Text to find."), + io.String.Input("replace", default="", tooltip="Text to replace with."), + ] + + @classmethod + def _process(cls, text, find, replace): + return text.replace(find, replace) + + +class StripWhitespaceNode(TextProcessingNode): + node_id = "StripWhitespace" + display_name = "Strip Whitespace" + description = "Strip leading and trailing whitespace from all texts." + + @classmethod + def _process(cls, text): + return text.strip() + + +# ========== Group Processing Example Nodes ========== + + +class ImageDeduplicationNode(ImageProcessingNode): + """Remove duplicate or very similar images from the dataset using perceptual hashing.""" + + node_id = "ImageDeduplication" + display_name = "Image Deduplication" + description = "Remove duplicate or very similar images from the dataset." + is_group_process = True # Requires full list to compare images + extra_inputs = [ + io.Float.Input( + "similarity_threshold", + default=0.95, + min=0.0, + max=1.0, + tooltip="Similarity threshold (0-1). Higher means more similar. Images above this threshold are considered duplicates.", + ), + ] + + @classmethod + def _group_process(cls, images, similarity_threshold): + """Remove duplicate images using perceptual hashing.""" + if len(images) == 0: + return [] + + # Compute simple perceptual hash for each image + def compute_hash(img_tensor): + """Compute a simple perceptual hash by resizing to 8x8 and comparing to average.""" + img = tensor_to_pil(img_tensor) + # Resize to 8x8 + img_small = img.resize((8, 8), Image.Resampling.LANCZOS).convert("L") + # Get pixels + pixels = list(img_small.getdata()) + # Compute average + avg = sum(pixels) / len(pixels) + # Create hash (1 if above average, 0 otherwise) + hash_bits = "".join("1" if p > avg else "0" for p in pixels) + return hash_bits + + def hamming_distance(hash1, hash2): + """Compute Hamming distance between two hash strings.""" + return sum(c1 != c2 for c1, c2 in zip(hash1, hash2)) + + # Compute hashes for all images + hashes = [compute_hash(img) for img in images] + + # Find duplicates + keep_indices = [] + for i in range(len(images)): + is_duplicate = False + for j in keep_indices: + # Compare hashes + distance = hamming_distance(hashes[i], hashes[j]) + similarity = 1.0 - (distance / 64.0) # 64 bits total + if similarity >= similarity_threshold: + is_duplicate = True + logging.info( + f"Image {i} is similar to image {j} (similarity: {similarity:.3f}), skipping" + ) + break + + if not is_duplicate: + keep_indices.append(i) + + # Return only unique images + unique_images = [images[i] for i in keep_indices] + logging.info( + f"Deduplication: kept {len(unique_images)} out of {len(images)} images" + ) + return unique_images + + +class ImageGridNode(ImageProcessingNode): + """Combine multiple images into a single grid/collage.""" + + node_id = "ImageGrid" + display_name = "Image Grid" + description = "Arrange multiple images into a grid layout." + is_group_process = True # Requires full list to create grid + is_output_list = False # Outputs single grid image + extra_inputs = [ + io.Int.Input( + "columns", + default=4, + min=1, + max=20, + tooltip="Number of columns in the grid.", + ), + io.Int.Input( + "cell_width", + default=256, + min=32, + max=2048, + tooltip="Width of each cell in the grid.", + ), + io.Int.Input( + "cell_height", + default=256, + min=32, + max=2048, + tooltip="Height of each cell in the grid.", + ), + io.Int.Input( + "padding", default=4, min=0, max=50, tooltip="Padding between images." + ), + ] + + @classmethod + def _group_process(cls, images, columns, cell_width, cell_height, padding): + """Arrange images into a grid.""" + if len(images) == 0: + raise ValueError("Cannot create grid from empty image list") + + # Calculate grid dimensions + num_images = len(images) + rows = (num_images + columns - 1) // columns # Ceiling division + + # Calculate total grid size + grid_width = columns * cell_width + (columns - 1) * padding + grid_height = rows * cell_height + (rows - 1) * padding + + # Create blank grid + grid = Image.new("RGB", (grid_width, grid_height), (0, 0, 0)) + + # Place images + for idx, img_tensor in enumerate(images): + row = idx // columns + col = idx % columns + + # Convert to PIL and resize to cell size + img = tensor_to_pil(img_tensor) + img = img.resize((cell_width, cell_height), Image.Resampling.LANCZOS) + + # Calculate position + x = col * (cell_width + padding) + y = row * (cell_height + padding) + + # Paste into grid + grid.paste(img, (x, y)) + + logging.info( + f"Created {columns}x{rows} grid with {num_images} images ({grid_width}x{grid_height})" + ) + return pil_to_tensor(grid) + + +class MergeImageListsNode(ImageProcessingNode): + """Merge multiple image lists into a single list.""" + + node_id = "MergeImageLists" + display_name = "Merge Image Lists" + description = "Concatenate multiple image lists into one." + is_group_process = True # Receives images as list + + @classmethod + def _group_process(cls, images): + """Simply return the images list (already merged by input handling).""" + # When multiple list inputs are connected, they're concatenated + # For now, this is a simple pass-through + logging.info(f"Merged image list contains {len(images)} images") + return images + + +class MergeTextListsNode(TextProcessingNode): + """Merge multiple text lists into a single list.""" + + node_id = "MergeTextLists" + display_name = "Merge Text Lists" + description = "Concatenate multiple text lists into one." + is_group_process = True # Receives texts as list + + @classmethod + def _group_process(cls, texts): + """Simply return the texts list (already merged by input handling).""" + # When multiple list inputs are connected, they're concatenated + # For now, this is a simple pass-through + logging.info(f"Merged text list contains {len(texts)} texts") + return texts + + +# ========== Training Dataset Nodes ========== + + +class MakeTrainingDataset(io.ComfyNode): + """Encode images with VAE and texts with CLIP to create a training dataset.""" + + @classmethod + def define_schema(cls): + return io.Schema( + node_id="MakeTrainingDataset", + display_name="Make Training Dataset", + category="dataset", + is_experimental=True, + is_input_list=True, # images and texts as lists + inputs=[ + io.Image.Input("images", tooltip="List of images to encode."), + io.Vae.Input( + "vae", tooltip="VAE model for encoding images to latents." + ), + io.Clip.Input( + "clip", tooltip="CLIP model for encoding text to conditioning." + ), + io.String.Input( + "texts", + optional=True, + tooltip="List of text captions. Can be length n (matching images), 1 (repeated for all), or omitted (uses empty string).", + ), + ], + outputs=[ + io.Latent.Output( + display_name="latents", + is_output_list=True, + tooltip="List of latent dicts", + ), + io.Conditioning.Output( + display_name="conditioning", + is_output_list=True, + tooltip="List of conditioning lists", + ), + ], + ) + + @classmethod + def execute(cls, images, vae, clip, texts=None): + # Extract scalars (vae and clip are single values wrapped in lists) + vae = vae[0] + clip = clip[0] + + # Handle text list + num_images = len(images) + + if texts is None or len(texts) == 0: + # Treat as [""] for unconditional training + texts = [""] + + if len(texts) == 1 and num_images > 1: + # Repeat single text for all images + texts = texts * num_images + elif len(texts) != num_images: + raise ValueError( + f"Number of texts ({len(texts)}) does not match number of images ({num_images}). " + f"Text list should have length {num_images}, 1, or 0." + ) + + # Encode images with VAE + logging.info(f"Encoding {num_images} images with VAE...") + latents_list = [] # list[{"samples": tensor}] + for img_tensor in images: + # img_tensor is [1, H, W, 3] + latent_tensor = vae.encode(img_tensor[:, :, :, :3]) + latents_list.append({"samples": latent_tensor}) + + # Encode texts with CLIP + logging.info(f"Encoding {len(texts)} texts with CLIP...") + conditioning_list = [] # list[list[cond]] + for text in texts: + if text == "": + cond = clip.encode_from_tokens_scheduled(clip.tokenize("")) + else: + tokens = clip.tokenize(text) + cond = clip.encode_from_tokens_scheduled(tokens) + conditioning_list.append(cond) + + logging.info( + f"Created dataset with {len(latents_list)} latents and {len(conditioning_list)} conditioning." + ) + return io.NodeOutput(latents_list, conditioning_list) + + +class SaveTrainingDataset(io.ComfyNode): + """Save encoded training dataset (latents + conditioning) to disk.""" + + @classmethod + def define_schema(cls): + return io.Schema( + node_id="SaveTrainingDataset", + display_name="Save Training Dataset", + category="dataset", + is_experimental=True, + is_output_node=True, + is_input_list=True, # Receive lists + inputs=[ + io.Latent.Input( + "latents", + tooltip="List of latent dicts from MakeTrainingDataset.", + ), + io.Conditioning.Input( + "conditioning", + tooltip="List of conditioning lists from MakeTrainingDataset.", + ), + io.String.Input( + "folder_name", + default="training_dataset", + tooltip="Name of folder to save dataset (inside output directory).", + ), + io.Int.Input( + "shard_size", + default=1000, + min=1, + max=100000, + tooltip="Number of samples per shard file.", + ), + ], + outputs=[], + ) + + @classmethod + def execute(cls, latents, conditioning, folder_name, shard_size): + # Extract scalars + folder_name = folder_name[0] + shard_size = shard_size[0] + + # latents: list[{"samples": tensor}] + # conditioning: list[list[cond]] + + # Validate lengths match + if len(latents) != len(conditioning): + raise ValueError( + f"Number of latents ({len(latents)}) does not match number of conditions ({len(conditioning)}). " + f"Something went wrong in dataset preparation." + ) + + # Create output directory + output_dir = os.path.join(folder_paths.get_output_directory(), folder_name) + os.makedirs(output_dir, exist_ok=True) + + # Prepare data pairs + num_samples = len(latents) + num_shards = (num_samples + shard_size - 1) // shard_size # Ceiling division + + logging.info( + f"Saving {num_samples} samples to {num_shards} shards in {output_dir}..." + ) + + # Save data in shards + for shard_idx in range(num_shards): + start_idx = shard_idx * shard_size + end_idx = min(start_idx + shard_size, num_samples) + + # Get shard data (list of latent dicts and conditioning lists) + shard_data = { + "latents": latents[start_idx:end_idx], + "conditioning": conditioning[start_idx:end_idx], + } + + # Save shard + shard_filename = f"shard_{shard_idx:04d}.pkl" + shard_path = os.path.join(output_dir, shard_filename) + + with open(shard_path, "wb") as f: + torch.save(shard_data, f) + + logging.info( + f"Saved shard {shard_idx + 1}/{num_shards}: {shard_filename} ({end_idx - start_idx} samples)" + ) + + # Save metadata + metadata = { + "num_samples": num_samples, + "num_shards": num_shards, + "shard_size": shard_size, + } + metadata_path = os.path.join(output_dir, "metadata.json") + with open(metadata_path, "w") as f: + json.dump(metadata, f, indent=2) + + logging.info(f"Successfully saved {num_samples} samples to {output_dir}.") + return io.NodeOutput() + + +class LoadTrainingDataset(io.ComfyNode): + """Load encoded training dataset from disk.""" + + @classmethod + def define_schema(cls): + return io.Schema( + node_id="LoadTrainingDataset", + display_name="Load Training Dataset", + category="dataset", + is_experimental=True, + inputs=[ + io.String.Input( + "folder_name", + default="training_dataset", + tooltip="Name of folder containing the saved dataset (inside output directory).", + ), + ], + outputs=[ + io.Latent.Output( + display_name="latents", + is_output_list=True, + tooltip="List of latent dicts", + ), + io.Conditioning.Output( + display_name="conditioning", + is_output_list=True, + tooltip="List of conditioning lists", + ), + ], + ) + + @classmethod + def execute(cls, folder_name): + # Get dataset directory + dataset_dir = os.path.join(folder_paths.get_output_directory(), folder_name) + + if not os.path.exists(dataset_dir): + raise ValueError(f"Dataset directory not found: {dataset_dir}") + + # Find all shard files + shard_files = sorted( + [ + f + for f in os.listdir(dataset_dir) + if f.startswith("shard_") and f.endswith(".pkl") + ] + ) + + if not shard_files: + raise ValueError(f"No shard files found in {dataset_dir}") + + logging.info(f"Loading {len(shard_files)} shards from {dataset_dir}...") + + # Load all shards + all_latents = [] # list[{"samples": tensor}] + all_conditioning = [] # list[list[cond]] + + for shard_file in shard_files: + shard_path = os.path.join(dataset_dir, shard_file) + + with open(shard_path, "rb") as f: + shard_data = torch.load(f, weights_only=True) + + all_latents.extend(shard_data["latents"]) + all_conditioning.extend(shard_data["conditioning"]) + + logging.info(f"Loaded {shard_file}: {len(shard_data['latents'])} samples") + + logging.info( + f"Successfully loaded {len(all_latents)} samples from {dataset_dir}." + ) + return io.NodeOutput(all_latents, all_conditioning) + + +# ========== Extension Setup ========== + + +class DatasetExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + # Data loading/saving nodes + LoadImageDataSetFromFolderNode, + LoadImageTextDataSetFromFolderNode, + SaveImageDataSetToFolderNode, + SaveImageTextDataSetToFolderNode, + # Image transform nodes + ResizeImagesByShorterEdgeNode, + ResizeImagesByLongerEdgeNode, + CenterCropImagesNode, + RandomCropImagesNode, + NormalizeImagesNode, + AdjustBrightnessNode, + AdjustContrastNode, + ShuffleDatasetNode, + ShuffleImageTextDatasetNode, + # Text transform nodes + TextToLowercaseNode, + TextToUppercaseNode, + TruncateTextNode, + AddTextPrefixNode, + AddTextSuffixNode, + ReplaceTextNode, + StripWhitespaceNode, + # Group processing examples + ImageDeduplicationNode, + ImageGridNode, + MergeImageListsNode, + MergeTextListsNode, + # Training dataset nodes + MakeTrainingDataset, + SaveTrainingDataset, + LoadTrainingDataset, + ] + + +async def comfy_entrypoint() -> DatasetExtension: + return DatasetExtension() diff --git a/comfy_extras/nodes_flux.py b/comfy_extras/nodes_flux.py index ce1b2e89f..d9c4bba81 100644 --- a/comfy_extras/nodes_flux.py +++ b/comfy_extras/nodes_flux.py @@ -2,7 +2,10 @@ import node_helpers import comfy.utils from typing_extensions import override from comfy_api.latest import ComfyExtension, io - +import comfy.model_management +import torch +import math +import nodes class CLIPTextEncodeFlux(io.ComfyNode): @classmethod @@ -30,6 +33,27 @@ class CLIPTextEncodeFlux(io.ComfyNode): encode = execute # TODO: remove +class EmptyFlux2LatentImage(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="EmptyFlux2LatentImage", + display_name="Empty Flux 2 Latent", + category="latent", + inputs=[ + io.Int.Input("width", default=1024, min=16, max=nodes.MAX_RESOLUTION, step=16), + io.Int.Input("height", default=1024, min=16, max=nodes.MAX_RESOLUTION, step=16), + io.Int.Input("batch_size", default=1, min=1, max=4096), + ], + outputs=[ + io.Latent.Output(), + ], + ) + + @classmethod + def execute(cls, width, height, batch_size=1) -> io.NodeOutput: + latent = torch.zeros([batch_size, 128, height // 16, width // 16], device=comfy.model_management.intermediate_device()) + return io.NodeOutput({"samples": latent}) class FluxGuidance(io.ComfyNode): @classmethod @@ -154,6 +178,58 @@ class FluxKontextMultiReferenceLatentMethod(io.ComfyNode): append = execute # TODO: remove +def generalized_time_snr_shift(t, mu: float, sigma: float): + return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma) + + +def compute_empirical_mu(image_seq_len: int, num_steps: int) -> float: + a1, b1 = 8.73809524e-05, 1.89833333 + a2, b2 = 0.00016927, 0.45666666 + + if image_seq_len > 4300: + mu = a2 * image_seq_len + b2 + return float(mu) + + m_200 = a2 * image_seq_len + b2 + m_10 = a1 * image_seq_len + b1 + + a = (m_200 - m_10) / 190.0 + b = m_200 - 200.0 * a + mu = a * num_steps + b + + return float(mu) + + +def get_schedule(num_steps: int, image_seq_len: int) -> list[float]: + mu = compute_empirical_mu(image_seq_len, num_steps) + timesteps = torch.linspace(1, 0, num_steps + 1) + timesteps = generalized_time_snr_shift(timesteps, mu, 1.0) + return timesteps + + +class Flux2Scheduler(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="Flux2Scheduler", + category="sampling/custom_sampling/schedulers", + inputs=[ + io.Int.Input("steps", default=20, min=1, max=4096), + io.Int.Input("width", default=1024, min=16, max=nodes.MAX_RESOLUTION, step=1), + io.Int.Input("height", default=1024, min=16, max=nodes.MAX_RESOLUTION, step=1), + ], + outputs=[ + io.Sigmas.Output(), + ], + ) + + @classmethod + def execute(cls, steps, width, height) -> io.NodeOutput: + seq_len = (width * height / (16 * 16)) + sigmas = get_schedule(steps, round(seq_len)) + return io.NodeOutput(sigmas) + + class FluxExtension(ComfyExtension): @override async def get_node_list(self) -> list[type[io.ComfyNode]]: @@ -163,6 +239,8 @@ class FluxExtension(ComfyExtension): FluxDisableGuidance, FluxKontextImageScale, FluxKontextMultiReferenceLatentMethod, + EmptyFlux2LatentImage, + Flux2Scheduler, ] diff --git a/comfy_extras/nodes_load_3d.py b/comfy_extras/nodes_load_3d.py index 899608149..54c66ef68 100644 --- a/comfy_extras/nodes_load_3d.py +++ b/comfy_extras/nodes_load_3d.py @@ -7,6 +7,10 @@ from comfy_api.input_impl import VideoFromFile from pathlib import Path +from PIL import Image +import numpy as np + +import uuid def normalize_path(path): return path.replace('\\', '/') @@ -34,58 +38,6 @@ class Load3D(): "height": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}), }} - RETURN_TYPES = ("IMAGE", "MASK", "STRING", "IMAGE", "IMAGE", "LOAD3D_CAMERA", IO.VIDEO) - RETURN_NAMES = ("image", "mask", "mesh_path", "normal", "lineart", "camera_info", "recording_video") - - FUNCTION = "process" - EXPERIMENTAL = True - - CATEGORY = "3d" - - def process(self, model_file, image, **kwargs): - image_path = folder_paths.get_annotated_filepath(image['image']) - mask_path = folder_paths.get_annotated_filepath(image['mask']) - normal_path = folder_paths.get_annotated_filepath(image['normal']) - lineart_path = folder_paths.get_annotated_filepath(image['lineart']) - - load_image_node = nodes.LoadImage() - output_image, ignore_mask = load_image_node.load_image(image=image_path) - ignore_image, output_mask = load_image_node.load_image(image=mask_path) - normal_image, ignore_mask2 = load_image_node.load_image(image=normal_path) - lineart_image, ignore_mask3 = load_image_node.load_image(image=lineart_path) - - video = None - - if image['recording'] != "": - recording_video_path = folder_paths.get_annotated_filepath(image['recording']) - - video = VideoFromFile(recording_video_path) - - return output_image, output_mask, model_file, normal_image, lineart_image, image['camera_info'], video - -class Load3DAnimation(): - @classmethod - def INPUT_TYPES(s): - input_dir = os.path.join(folder_paths.get_input_directory(), "3d") - - os.makedirs(input_dir, exist_ok=True) - - input_path = Path(input_dir) - base_path = Path(folder_paths.get_input_directory()) - - files = [ - normalize_path(str(file_path.relative_to(base_path))) - for file_path in input_path.rglob("*") - if file_path.suffix.lower() in {'.gltf', '.glb', '.fbx'} - ] - - return {"required": { - "model_file": (sorted(files), {"file_upload": True}), - "image": ("LOAD_3D_ANIMATION", {}), - "width": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}), - "height": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}), - }} - RETURN_TYPES = ("IMAGE", "MASK", "STRING", "IMAGE", "LOAD3D_CAMERA", IO.VIDEO) RETURN_NAMES = ("image", "mask", "mesh_path", "normal", "camera_info", "recording_video") @@ -120,7 +72,8 @@ class Preview3D(): "model_file": ("STRING", {"default": "", "multiline": False}), }, "optional": { - "camera_info": ("LOAD3D_CAMERA", {}) + "camera_info": ("LOAD3D_CAMERA", {}), + "bg_image": ("IMAGE", {}) }} OUTPUT_NODE = True @@ -133,50 +86,33 @@ class Preview3D(): def process(self, model_file, **kwargs): camera_info = kwargs.get("camera_info", None) + bg_image = kwargs.get("bg_image", None) + + bg_image_path = None + if bg_image is not None: + + img_array = (bg_image[0].cpu().numpy() * 255).astype(np.uint8) + img = Image.fromarray(img_array) + + temp_dir = folder_paths.get_temp_directory() + filename = f"bg_{uuid.uuid4().hex}.png" + bg_image_path = os.path.join(temp_dir, filename) + img.save(bg_image_path, compress_level=1) + + bg_image_path = f"temp/{filename}" return { "ui": { - "result": [model_file, camera_info] - } - } - -class Preview3DAnimation(): - @classmethod - def INPUT_TYPES(s): - return {"required": { - "model_file": ("STRING", {"default": "", "multiline": False}), - }, - "optional": { - "camera_info": ("LOAD3D_CAMERA", {}) - }} - - OUTPUT_NODE = True - RETURN_TYPES = () - - CATEGORY = "3d" - - FUNCTION = "process" - EXPERIMENTAL = True - - def process(self, model_file, **kwargs): - camera_info = kwargs.get("camera_info", None) - - return { - "ui": { - "result": [model_file, camera_info] + "result": [model_file, camera_info, bg_image_path] } } NODE_CLASS_MAPPINGS = { "Load3D": Load3D, - "Load3DAnimation": Load3DAnimation, "Preview3D": Preview3D, - "Preview3DAnimation": Preview3DAnimation } NODE_DISPLAY_NAME_MAPPINGS = { - "Load3D": "Load 3D", - "Load3DAnimation": "Load 3D - Animation", - "Preview3D": "Preview 3D", - "Preview3DAnimation": "Preview 3D - Animation" + "Load3D": "Load 3D & Animation", + "Preview3D": "Preview 3D & Animation", } diff --git a/comfy_extras/nodes_train.py b/comfy_extras/nodes_train.py index 9e6ec6780..cb24ab709 100644 --- a/comfy_extras/nodes_train.py +++ b/comfy_extras/nodes_train.py @@ -1,15 +1,13 @@ -import datetime -import json import logging import os import numpy as np import safetensors import torch -from PIL import Image, ImageDraw, ImageFont -from PIL.PngImagePlugin import PngInfo import torch.utils.checkpoint -import tqdm +from tqdm.auto import trange +from PIL import Image, ImageDraw, ImageFont +from typing_extensions import override import comfy.samplers import comfy.sd @@ -18,9 +16,9 @@ import comfy.model_management import comfy_extras.nodes_custom_sampler import folder_paths import node_helpers -from comfy.cli_args import args -from comfy.comfy_types.node_typing import IO from comfy.weight_adapter import adapters, adapter_maps +from comfy_api.latest import ComfyExtension, io, ui +from comfy.utils import ProgressBar def make_batch_extra_option_dict(d, indicies, full_size=None): @@ -56,7 +54,18 @@ def process_cond_list(d, prefix=""): class TrainSampler(comfy.samplers.Sampler): - def __init__(self, loss_fn, optimizer, loss_callback=None, batch_size=1, grad_acc=1, total_steps=1, seed=0, training_dtype=torch.bfloat16): + def __init__( + self, + loss_fn, + optimizer, + loss_callback=None, + batch_size=1, + grad_acc=1, + total_steps=1, + seed=0, + training_dtype=torch.bfloat16, + real_dataset=None, + ): self.loss_fn = loss_fn self.optimizer = optimizer self.loss_callback = loss_callback @@ -65,54 +74,138 @@ class TrainSampler(comfy.samplers.Sampler): self.grad_acc = grad_acc self.seed = seed self.training_dtype = training_dtype + self.real_dataset: list[torch.Tensor] | None = real_dataset - def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False): + def fwd_bwd( + self, + model_wrap, + batch_sigmas, + batch_noise, + batch_latent, + cond, + indicies, + extra_args, + dataset_size, + bwd=True, + ): + xt = model_wrap.inner_model.model_sampling.noise_scaling( + batch_sigmas, batch_noise, batch_latent, False + ) + x0 = model_wrap.inner_model.model_sampling.noise_scaling( + torch.zeros_like(batch_sigmas), + torch.zeros_like(batch_noise), + batch_latent, + False, + ) + + model_wrap.conds["positive"] = [cond[i] for i in indicies] + batch_extra_args = make_batch_extra_option_dict( + extra_args, indicies, full_size=dataset_size + ) + + with torch.autocast(xt.device.type, dtype=self.training_dtype): + x0_pred = model_wrap( + xt.requires_grad_(True), + batch_sigmas.requires_grad_(True), + **batch_extra_args, + ) + loss = self.loss_fn(x0_pred, x0) + if bwd: + bwd_loss = loss / self.grad_acc + bwd_loss.backward() + return loss + + def sample( + self, + model_wrap, + sigmas, + extra_args, + callback, + noise, + latent_image=None, + denoise_mask=None, + disable_pbar=False, + ): model_wrap.conds = process_cond_list(model_wrap.conds) cond = model_wrap.conds["positive"] dataset_size = sigmas.size(0) torch.cuda.empty_cache() - for i in (pbar:=tqdm.trange(self.total_steps, desc="Training LoRA", smoothing=0.01, disable=not comfy.utils.PROGRESS_BAR_ENABLED)): - noisegen = comfy_extras.nodes_custom_sampler.Noise_RandomNoise(self.seed + i * 1000) - indicies = torch.randperm(dataset_size)[:self.batch_size].tolist() - - batch_latent = torch.stack([latent_image[i] for i in indicies]) - batch_noise = noisegen.generate_noise({"samples": batch_latent}).to(batch_latent.device) - batch_sigmas = [ - model_wrap.inner_model.model_sampling.percent_to_sigma( - torch.rand((1,)).item() - ) for _ in range(min(self.batch_size, dataset_size)) - ] - batch_sigmas = torch.tensor(batch_sigmas).to(batch_latent.device) - - xt = model_wrap.inner_model.model_sampling.noise_scaling( - batch_sigmas, - batch_noise, - batch_latent, - False + ui_pbar = ProgressBar(self.total_steps) + for i in ( + pbar := trange( + self.total_steps, + desc="Training LoRA", + smoothing=0.01, + disable=not comfy.utils.PROGRESS_BAR_ENABLED, ) - x0 = model_wrap.inner_model.model_sampling.noise_scaling( - torch.zeros_like(batch_sigmas), - torch.zeros_like(batch_noise), - batch_latent, - False + ): + noisegen = comfy_extras.nodes_custom_sampler.Noise_RandomNoise( + self.seed + i * 1000 ) + indicies = torch.randperm(dataset_size)[: self.batch_size].tolist() - model_wrap.conds["positive"] = [ - cond[i] for i in indicies - ] - batch_extra_args = make_batch_extra_option_dict(extra_args, indicies, full_size=dataset_size) + if self.real_dataset is None: + batch_latent = torch.stack([latent_image[i] for i in indicies]) + batch_noise = noisegen.generate_noise({"samples": batch_latent}).to( + batch_latent.device + ) + batch_sigmas = [ + model_wrap.inner_model.model_sampling.percent_to_sigma( + torch.rand((1,)).item() + ) + for _ in range(min(self.batch_size, dataset_size)) + ] + batch_sigmas = torch.tensor(batch_sigmas).to(batch_latent.device) - with torch.autocast(xt.device.type, dtype=self.training_dtype): - x0_pred = model_wrap(xt, batch_sigmas, **batch_extra_args) - loss = self.loss_fn(x0_pred, x0) - loss.backward() - if self.loss_callback: - self.loss_callback(loss.item()) - pbar.set_postfix({"loss": f"{loss.item():.4f}"}) + loss = self.fwd_bwd( + model_wrap, + batch_sigmas, + batch_noise, + batch_latent, + cond, + indicies, + extra_args, + dataset_size, + bwd=True, + ) + if self.loss_callback: + self.loss_callback(loss.item()) + pbar.set_postfix({"loss": f"{loss.item():.4f}"}) + else: + total_loss = 0 + for index in indicies: + single_latent = self.real_dataset[index].to(latent_image) + batch_noise = noisegen.generate_noise( + {"samples": single_latent} + ).to(single_latent.device) + batch_sigmas = ( + model_wrap.inner_model.model_sampling.percent_to_sigma( + torch.rand((1,)).item() + ) + ) + batch_sigmas = torch.tensor([batch_sigmas]).to(single_latent.device) + loss = self.fwd_bwd( + model_wrap, + batch_sigmas, + batch_noise, + single_latent, + cond, + [index], + extra_args, + dataset_size, + bwd=False, + ) + total_loss += loss + total_loss = total_loss / self.grad_acc / len(indicies) + total_loss.backward() + if self.loss_callback: + self.loss_callback(total_loss.item()) + pbar.set_postfix({"loss": f"{total_loss.item():.4f}"}) - if (i+1) % self.grad_acc == 0: + if (i + 1) % self.grad_acc == 0: self.optimizer.step() self.optimizer.zero_grad() + ui_pbar.update(1) torch.cuda.empty_cache() return torch.zeros_like(latent_image) @@ -134,233 +227,6 @@ class BiasDiff(torch.nn.Module): return self.passive_memory_usage() -def load_and_process_images(image_files, input_dir, resize_method="None", w=None, h=None): - """Utility function to load and process a list of images. - - Args: - image_files: List of image filenames - input_dir: Base directory containing the images - resize_method: How to handle images of different sizes ("None", "Stretch", "Crop", "Pad") - - Returns: - torch.Tensor: Batch of processed images - """ - if not image_files: - raise ValueError("No valid images found in input") - - output_images = [] - - for file in image_files: - image_path = os.path.join(input_dir, file) - img = node_helpers.pillow(Image.open, image_path) - - if img.mode == "I": - img = img.point(lambda i: i * (1 / 255)) - img = img.convert("RGB") - - if w is None and h is None: - w, h = img.size[0], img.size[1] - - # Resize image to first image - if img.size[0] != w or img.size[1] != h: - if resize_method == "Stretch": - img = img.resize((w, h), Image.Resampling.LANCZOS) - elif resize_method == "Crop": - img = img.crop((0, 0, w, h)) - elif resize_method == "Pad": - img = img.resize((w, h), Image.Resampling.LANCZOS) - elif resize_method == "None": - raise ValueError( - "Your input image size does not match the first image in the dataset. Either select a valid resize method or use the same size for all images." - ) - - img_array = np.array(img).astype(np.float32) / 255.0 - img_tensor = torch.from_numpy(img_array)[None,] - output_images.append(img_tensor) - - return torch.cat(output_images, dim=0) - - -class LoadImageSetNode: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "images": ( - [ - f - for f in os.listdir(folder_paths.get_input_directory()) - if f.endswith((".png", ".jpg", ".jpeg", ".webp", ".bmp", ".gif", ".jpe", ".apng", ".tif", ".tiff")) - ], - {"image_upload": True, "allow_batch": True}, - ) - }, - "optional": { - "resize_method": ( - ["None", "Stretch", "Crop", "Pad"], - {"default": "None"}, - ), - }, - } - - INPUT_IS_LIST = True - RETURN_TYPES = ("IMAGE",) - FUNCTION = "load_images" - CATEGORY = "loaders" - EXPERIMENTAL = True - DESCRIPTION = "Loads a batch of images from a directory for training." - - @classmethod - def VALIDATE_INPUTS(s, images, resize_method): - filenames = images[0] if isinstance(images[0], list) else images - - for image in filenames: - if not folder_paths.exists_annotated_filepath(image): - return "Invalid image file: {}".format(image) - return True - - def load_images(self, input_files, resize_method): - input_dir = folder_paths.get_input_directory() - valid_extensions = [".png", ".jpg", ".jpeg", ".webp", ".bmp", ".gif", ".jpe", ".apng", ".tif", ".tiff"] - image_files = [ - f - for f in input_files - if any(f.lower().endswith(ext) for ext in valid_extensions) - ] - output_tensor = load_and_process_images(image_files, input_dir, resize_method) - return (output_tensor,) - - -class LoadImageSetFromFolderNode: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "folder": (folder_paths.get_input_subfolders(), {"tooltip": "The folder to load images from."}) - }, - "optional": { - "resize_method": ( - ["None", "Stretch", "Crop", "Pad"], - {"default": "None"}, - ), - }, - } - - RETURN_TYPES = ("IMAGE",) - FUNCTION = "load_images" - CATEGORY = "loaders" - EXPERIMENTAL = True - DESCRIPTION = "Loads a batch of images from a directory for training." - - def load_images(self, folder, resize_method): - sub_input_dir = os.path.join(folder_paths.get_input_directory(), folder) - valid_extensions = [".png", ".jpg", ".jpeg", ".webp"] - image_files = [ - f - for f in os.listdir(sub_input_dir) - if any(f.lower().endswith(ext) for ext in valid_extensions) - ] - output_tensor = load_and_process_images(image_files, sub_input_dir, resize_method) - return (output_tensor,) - - -class LoadImageTextSetFromFolderNode: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "folder": (folder_paths.get_input_subfolders(), {"tooltip": "The folder to load images from."}), - "clip": (IO.CLIP, {"tooltip": "The CLIP model used for encoding the text."}), - }, - "optional": { - "resize_method": ( - ["None", "Stretch", "Crop", "Pad"], - {"default": "None"}, - ), - "width": ( - IO.INT, - { - "default": -1, - "min": -1, - "max": 10000, - "step": 1, - "tooltip": "The width to resize the images to. -1 means use the original width.", - }, - ), - "height": ( - IO.INT, - { - "default": -1, - "min": -1, - "max": 10000, - "step": 1, - "tooltip": "The height to resize the images to. -1 means use the original height.", - }, - ) - }, - } - - RETURN_TYPES = ("IMAGE", IO.CONDITIONING,) - FUNCTION = "load_images" - CATEGORY = "loaders" - EXPERIMENTAL = True - DESCRIPTION = "Loads a batch of images and caption from a directory for training." - - def load_images(self, folder, clip, resize_method, width=None, height=None): - if clip is None: - raise RuntimeError("ERROR: clip input is invalid: None\n\nIf the clip is from a checkpoint loader node your checkpoint does not contain a valid clip or text encoder model.") - - logging.info(f"Loading images from folder: {folder}") - - sub_input_dir = os.path.join(folder_paths.get_input_directory(), folder) - valid_extensions = [".png", ".jpg", ".jpeg", ".webp"] - - image_files = [] - for item in os.listdir(sub_input_dir): - path = os.path.join(sub_input_dir, item) - if any(item.lower().endswith(ext) for ext in valid_extensions): - image_files.append(path) - elif os.path.isdir(path): - # Support kohya-ss/sd-scripts folder structure - repeat = 1 - if item.split("_")[0].isdigit(): - repeat = int(item.split("_")[0]) - image_files.extend([ - os.path.join(path, f) for f in os.listdir(path) if any(f.lower().endswith(ext) for ext in valid_extensions) - ] * repeat) - - caption_file_path = [ - f.replace(os.path.splitext(f)[1], ".txt") - for f in image_files - ] - captions = [] - for caption_file in caption_file_path: - caption_path = os.path.join(sub_input_dir, caption_file) - if os.path.exists(caption_path): - with open(caption_path, "r", encoding="utf-8") as f: - caption = f.read().strip() - captions.append(caption) - else: - captions.append("") - - width = width if width != -1 else None - height = height if height != -1 else None - output_tensor = load_and_process_images(image_files, sub_input_dir, resize_method, width, height) - - logging.info(f"Loaded {len(output_tensor)} images from {sub_input_dir}.") - - logging.info(f"Encoding captions from {sub_input_dir}.") - conditions = [] - empty_cond = clip.encode_from_tokens_scheduled(clip.tokenize("")) - for text in captions: - if text == "": - conditions.append(empty_cond) - tokens = clip.tokenize(text) - conditions.extend(clip.encode_from_tokens_scheduled(tokens)) - logging.info(f"Encoded {len(conditions)} captions from {sub_input_dir}.") - return (output_tensor, conditions) - - def draw_loss_graph(loss_map, steps): width, height = 500, 300 img = Image.new("RGB", (width, height), "white") @@ -379,10 +245,14 @@ def draw_loss_graph(loss_map, steps): return img -def find_all_highest_child_module_with_forward(model: torch.nn.Module, result = None, name = None): +def find_all_highest_child_module_with_forward( + model: torch.nn.Module, result=None, name=None +): if result is None: result = [] - elif hasattr(model, "forward") and not isinstance(model, (torch.nn.ModuleList, torch.nn.Sequential, torch.nn.ModuleDict)): + elif hasattr(model, "forward") and not isinstance( + model, (torch.nn.ModuleList, torch.nn.Sequential, torch.nn.ModuleDict) + ): result.append(model) logging.debug(f"Found module with forward: {name} ({model.__class__.__name__})") return result @@ -396,12 +266,13 @@ def patch(m): if not hasattr(m, "forward"): return org_forward = m.forward + def fwd(args, kwargs): return org_forward(*args, **kwargs) + def checkpointing_fwd(*args, **kwargs): - return torch.utils.checkpoint.checkpoint( - fwd, args, kwargs, use_reentrant=False - ) + return torch.utils.checkpoint.checkpoint(fwd, args, kwargs, use_reentrant=False) + m.org_forward = org_forward m.forward = checkpointing_fwd @@ -412,130 +283,126 @@ def unpatch(m): del m.org_forward -class TrainLoraNode: +class TrainLoraNode(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return { - "required": { - "model": (IO.MODEL, {"tooltip": "The model to train the LoRA on."}), - "latents": ( - "LATENT", - { - "tooltip": "The Latents to use for training, serve as dataset/input of the model." - }, + def define_schema(cls): + return io.Schema( + node_id="TrainLoraNode", + display_name="Train LoRA", + category="training", + is_experimental=True, + is_input_list=True, # All inputs become lists + inputs=[ + io.Model.Input("model", tooltip="The model to train the LoRA on."), + io.Latent.Input( + "latents", + tooltip="The Latents to use for training, serve as dataset/input of the model.", ), - "positive": ( - IO.CONDITIONING, - {"tooltip": "The positive conditioning to use for training."}, + io.Conditioning.Input( + "positive", tooltip="The positive conditioning to use for training." ), - "batch_size": ( - IO.INT, - { - "default": 1, - "min": 1, - "max": 10000, - "step": 1, - "tooltip": "The batch size to use for training.", - }, + io.Int.Input( + "batch_size", + default=1, + min=1, + max=10000, + tooltip="The batch size to use for training.", ), - "grad_accumulation_steps": ( - IO.INT, - { - "default": 1, - "min": 1, - "max": 1024, - "step": 1, - "tooltip": "The number of gradient accumulation steps to use for training.", - } + io.Int.Input( + "grad_accumulation_steps", + default=1, + min=1, + max=1024, + tooltip="The number of gradient accumulation steps to use for training.", ), - "steps": ( - IO.INT, - { - "default": 16, - "min": 1, - "max": 100000, - "tooltip": "The number of steps to train the LoRA for.", - }, + io.Int.Input( + "steps", + default=16, + min=1, + max=100000, + tooltip="The number of steps to train the LoRA for.", ), - "learning_rate": ( - IO.FLOAT, - { - "default": 0.0005, - "min": 0.0000001, - "max": 1.0, - "step": 0.000001, - "tooltip": "The learning rate to use for training.", - }, + io.Float.Input( + "learning_rate", + default=0.0005, + min=0.0000001, + max=1.0, + step=0.0000001, + tooltip="The learning rate to use for training.", ), - "rank": ( - IO.INT, - { - "default": 8, - "min": 1, - "max": 128, - "tooltip": "The rank of the LoRA layers.", - }, + io.Int.Input( + "rank", + default=8, + min=1, + max=128, + tooltip="The rank of the LoRA layers.", ), - "optimizer": ( - ["AdamW", "Adam", "SGD", "RMSprop"], - { - "default": "AdamW", - "tooltip": "The optimizer to use for training.", - }, + io.Combo.Input( + "optimizer", + options=["AdamW", "Adam", "SGD", "RMSprop"], + default="AdamW", + tooltip="The optimizer to use for training.", ), - "loss_function": ( - ["MSE", "L1", "Huber", "SmoothL1"], - { - "default": "MSE", - "tooltip": "The loss function to use for training.", - }, + io.Combo.Input( + "loss_function", + options=["MSE", "L1", "Huber", "SmoothL1"], + default="MSE", + tooltip="The loss function to use for training.", ), - "seed": ( - IO.INT, - { - "default": 0, - "min": 0, - "max": 0xFFFFFFFFFFFFFFFF, - "tooltip": "The seed to use for training (used in generator for LoRA weight initialization and noise sampling)", - }, + io.Int.Input( + "seed", + default=0, + min=0, + max=0xFFFFFFFFFFFFFFFF, + tooltip="The seed to use for training (used in generator for LoRA weight initialization and noise sampling)", ), - "training_dtype": ( - ["bf16", "fp32"], - {"default": "bf16", "tooltip": "The dtype to use for training."}, + io.Combo.Input( + "training_dtype", + options=["bf16", "fp32"], + default="bf16", + tooltip="The dtype to use for training.", ), - "lora_dtype": ( - ["bf16", "fp32"], - {"default": "bf16", "tooltip": "The dtype to use for lora."}, + io.Combo.Input( + "lora_dtype", + options=["bf16", "fp32"], + default="bf16", + tooltip="The dtype to use for lora.", ), - "algorithm": ( - list(adapter_maps.keys()), - {"default": list(adapter_maps.keys())[0], "tooltip": "The algorithm to use for training."}, + io.Combo.Input( + "algorithm", + options=list(adapter_maps.keys()), + default=list(adapter_maps.keys())[0], + tooltip="The algorithm to use for training.", ), - "gradient_checkpointing": ( - IO.BOOLEAN, - { - "default": True, - "tooltip": "Use gradient checkpointing for training.", - } + io.Boolean.Input( + "gradient_checkpointing", + default=True, + tooltip="Use gradient checkpointing for training.", ), - "existing_lora": ( - folder_paths.get_filename_list("loras") + ["[None]"], - { - "default": "[None]", - "tooltip": "The existing LoRA to append to. Set to None for new LoRA.", - }, + io.Combo.Input( + "existing_lora", + options=folder_paths.get_filename_list("loras") + ["[None]"], + default="[None]", + tooltip="The existing LoRA to append to. Set to None for new LoRA.", ), - }, - } + ], + outputs=[ + io.Model.Output( + display_name="model", tooltip="Model with LoRA applied" + ), + io.Custom("LORA_MODEL").Output( + display_name="lora", tooltip="LoRA weights" + ), + io.Custom("LOSS_MAP").Output( + display_name="loss_map", tooltip="Loss history" + ), + io.Int.Output(display_name="steps", tooltip="Total training steps"), + ], + ) - RETURN_TYPES = (IO.MODEL, IO.LORA_MODEL, IO.LOSS_MAP, IO.INT) - RETURN_NAMES = ("model_with_lora", "lora", "loss", "steps") - FUNCTION = "train" - CATEGORY = "training" - EXPERIMENTAL = True - - def train( - self, + @classmethod + def execute( + cls, model, latents, positive, @@ -553,13 +420,74 @@ class TrainLoraNode: gradient_checkpointing, existing_lora, ): + # Extract scalars from lists (due to is_input_list=True) + model = model[0] + batch_size = batch_size[0] + steps = steps[0] + grad_accumulation_steps = grad_accumulation_steps[0] + learning_rate = learning_rate[0] + rank = rank[0] + optimizer = optimizer[0] + loss_function = loss_function[0] + seed = seed[0] + training_dtype = training_dtype[0] + lora_dtype = lora_dtype[0] + algorithm = algorithm[0] + gradient_checkpointing = gradient_checkpointing[0] + existing_lora = existing_lora[0] + + # Handle latents - either single dict or list of dicts + if len(latents) == 1: + latents = latents[0]["samples"] # Single latent dict + else: + latent_list = [] + for latent in latents: + latent = latent["samples"] + bs = latent.shape[0] + if bs != 1: + for sub_latent in latent: + latent_list.append(sub_latent[None]) + else: + latent_list.append(latent) + latents = latent_list + + # Handle conditioning - either single list or list of lists + if len(positive) == 1: + positive = positive[0] # Single conditioning list + else: + # Multiple conditioning lists - flatten + flat_positive = [] + for cond in positive: + if isinstance(cond, list): + flat_positive.extend(cond) + else: + flat_positive.append(cond) + positive = flat_positive + mp = model.clone() dtype = node_helpers.string_to_torch_dtype(training_dtype) lora_dtype = node_helpers.string_to_torch_dtype(lora_dtype) mp.set_model_compute_dtype(dtype) - latents = latents["samples"].to(dtype) - num_images = latents.shape[0] + # latents here can be list of different size latent or one large batch + if isinstance(latents, list): + all_shapes = set() + latents = [t.to(dtype) for t in latents] + for latent in latents: + all_shapes.add(latent.shape) + logging.info(f"Latent shapes: {all_shapes}") + if len(all_shapes) > 1: + multi_res = True + else: + multi_res = False + latents = torch.cat(latents, dim=0) + num_images = len(latents) + elif isinstance(latents, torch.Tensor): + latents = latents.to(dtype) + num_images = latents.shape[0] + else: + logging.error(f"Invalid latents type: {type(latents)}") + logging.info(f"Total Images: {num_images}, Total Captions: {len(positive)}") if len(positive) == 1 and num_images > 1: positive = positive * num_images @@ -591,9 +519,7 @@ class TrainLoraNode: shape = m.weight.shape if len(shape) >= 2: alpha = float(existing_weights.get(f"{key}.alpha", 1.0)) - dora_scale = existing_weights.get( - f"{key}.dora_scale", None - ) + dora_scale = existing_weights.get(f"{key}.dora_scale", None) for adapter_cls in adapters: existing_adapter = adapter_cls.load( n, existing_weights, alpha, dora_scale @@ -605,7 +531,9 @@ class TrainLoraNode: adapter_cls = adapter_maps[algorithm] if existing_adapter is not None: - train_adapter = existing_adapter.to_train().to(lora_dtype) + train_adapter = existing_adapter.to_train().to( + lora_dtype + ) else: # Use LoRA with alpha=1.0 by default train_adapter = adapter_cls.create_train( @@ -629,7 +557,9 @@ class TrainLoraNode: if hasattr(m, "bias") and m.bias is not None: key = "{}.bias".format(n) bias = torch.nn.Parameter( - torch.zeros(m.bias.shape, dtype=lora_dtype, requires_grad=True) + torch.zeros( + m.bias.shape, dtype=lora_dtype, requires_grad=True + ) ) bias_module = BiasDiff(bias) lora_sd["{}.diff_b".format(n)] = bias @@ -657,24 +587,31 @@ class TrainLoraNode: # setup models if gradient_checkpointing: - for m in find_all_highest_child_module_with_forward(mp.model.diffusion_model): + for m in find_all_highest_child_module_with_forward( + mp.model.diffusion_model + ): patch(m) mp.model.requires_grad_(False) - comfy.model_management.load_models_gpu([mp], memory_required=1e20, force_full_load=True) + comfy.model_management.load_models_gpu( + [mp], memory_required=1e20, force_full_load=True + ) # Setup sampler and guider like in test script loss_map = {"loss": []} + def loss_callback(loss): loss_map["loss"].append(loss) + train_sampler = TrainSampler( criterion, optimizer, loss_callback=loss_callback, batch_size=batch_size, grad_acc=grad_accumulation_steps, - total_steps=steps*grad_accumulation_steps, + total_steps=steps * grad_accumulation_steps, seed=seed, - training_dtype=dtype + training_dtype=dtype, + real_dataset=latents if multi_res else None, ) guider = comfy_extras.nodes_custom_sampler.Guider_Basic(mp) guider.set_conds(positive) # Set conditioning from input @@ -684,12 +621,15 @@ class TrainLoraNode: # Generate dummy sigmas and noise sigmas = torch.tensor(range(num_images)) noise = comfy_extras.nodes_custom_sampler.Noise_RandomNoise(seed) + if multi_res: + # use first latent as dummy latent if multi_res + latents = latents[0].repeat(num_images, 1, 1, 1) guider.sample( noise.generate_noise({"samples": latents}), latents, train_sampler, sigmas, - seed=noise.seed + seed=noise.seed, ) finally: for m in mp.model.modules(): @@ -702,111 +642,118 @@ class TrainLoraNode: for param in lora_sd: lora_sd[param] = lora_sd[param].to(lora_dtype) - return (mp, lora_sd, loss_map, steps + existing_steps) + return io.NodeOutput(mp, lora_sd, loss_map, steps + existing_steps) -class LoraModelLoader: - def __init__(self): - self.loaded_lora = None +class LoraModelLoader(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="LoraModelLoader", + display_name="Load LoRA Model", + category="loaders", + is_experimental=True, + inputs=[ + io.Model.Input( + "model", tooltip="The diffusion model the LoRA will be applied to." + ), + io.Custom("LORA_MODEL").Input( + "lora", tooltip="The LoRA model to apply to the diffusion model." + ), + io.Float.Input( + "strength_model", + default=1.0, + min=-100.0, + max=100.0, + tooltip="How strongly to modify the diffusion model. This value can be negative.", + ), + ], + outputs=[ + io.Model.Output( + display_name="model", tooltip="The modified diffusion model." + ), + ], + ) @classmethod - def INPUT_TYPES(s): - return { - "required": { - "model": ("MODEL", {"tooltip": "The diffusion model the LoRA will be applied to."}), - "lora": (IO.LORA_MODEL, {"tooltip": "The LoRA model to apply to the diffusion model."}), - "strength_model": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01, "tooltip": "How strongly to modify the diffusion model. This value can be negative."}), - } - } - - RETURN_TYPES = ("MODEL",) - OUTPUT_TOOLTIPS = ("The modified diffusion model.",) - FUNCTION = "load_lora_model" - - CATEGORY = "loaders" - DESCRIPTION = "Load Trained LoRA weights from Train LoRA node." - EXPERIMENTAL = True - - def load_lora_model(self, model, lora, strength_model): + def execute(cls, model, lora, strength_model): if strength_model == 0: - return (model, ) + return io.NodeOutput(model) - model_lora, _ = comfy.sd.load_lora_for_models(model, None, lora, strength_model, 0) - return (model_lora, ) + model_lora, _ = comfy.sd.load_lora_for_models( + model, None, lora, strength_model, 0 + ) + return io.NodeOutput(model_lora) -class SaveLoRA: - def __init__(self): - self.output_dir = folder_paths.get_output_directory() +class SaveLoRA(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="SaveLoRA", + display_name="Save LoRA Weights", + category="loaders", + is_experimental=True, + is_output_node=True, + inputs=[ + io.Custom("LORA_MODEL").Input( + "lora", + tooltip="The LoRA model to save. Do not use the model with LoRA layers.", + ), + io.String.Input( + "prefix", + default="loras/ComfyUI_trained_lora", + tooltip="The prefix to use for the saved LoRA file.", + ), + io.Int.Input( + "steps", + optional=True, + tooltip="Optional: The number of steps to LoRA has been trained for, used to name the saved file.", + ), + ], + outputs=[], + ) @classmethod - def INPUT_TYPES(s): - return { - "required": { - "lora": ( - IO.LORA_MODEL, - { - "tooltip": "The LoRA model to save. Do not use the model with LoRA layers." - }, - ), - "prefix": ( - "STRING", - { - "default": "loras/ComfyUI_trained_lora", - "tooltip": "The prefix to use for the saved LoRA file.", - }, - ), - }, - "optional": { - "steps": ( - IO.INT, - { - "forceInput": True, - "tooltip": "Optional: The number of steps to LoRA has been trained for, used to name the saved file.", - }, - ), - }, - } - - RETURN_TYPES = () - FUNCTION = "save" - CATEGORY = "loaders" - EXPERIMENTAL = True - OUTPUT_NODE = True - - def save(self, lora, prefix, steps=None): - full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(prefix, self.output_dir) + def execute(cls, lora, prefix, steps=None): + output_dir = folder_paths.get_output_directory() + full_output_folder, filename, counter, subfolder, filename_prefix = ( + folder_paths.get_save_image_path(prefix, output_dir) + ) if steps is None: output_checkpoint = f"{filename}_{counter:05}_.safetensors" else: output_checkpoint = f"{filename}_{steps}_steps_{counter:05}_.safetensors" output_checkpoint = os.path.join(full_output_folder, output_checkpoint) safetensors.torch.save_file(lora, output_checkpoint) - return {} + return io.NodeOutput() -class LossGraphNode: - def __init__(self): - self.output_dir = folder_paths.get_temp_directory() +class LossGraphNode(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="LossGraphNode", + display_name="Plot Loss Graph", + category="training", + is_experimental=True, + is_output_node=True, + inputs=[ + io.Custom("LOSS_MAP").Input( + "loss", tooltip="Loss map from training node." + ), + io.String.Input( + "filename_prefix", + default="loss_graph", + tooltip="Prefix for the saved loss graph image.", + ), + ], + outputs=[], + hidden=[io.Hidden.prompt, io.Hidden.extra_pnginfo], + ) @classmethod - def INPUT_TYPES(s): - return { - "required": { - "loss": (IO.LOSS_MAP, {"default": {}}), - "filename_prefix": (IO.STRING, {"default": "loss_graph"}), - }, - "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, - } - - RETURN_TYPES = () - FUNCTION = "plot_loss" - OUTPUT_NODE = True - CATEGORY = "training" - EXPERIMENTAL = True - DESCRIPTION = "Plots the loss graph and saves it to the output directory." - - def plot_loss(self, loss, filename_prefix, prompt=None, extra_pnginfo=None): + def execute(cls, loss, filename_prefix, prompt=None, extra_pnginfo=None): loss_values = loss["loss"] width, height = 800, 480 margin = 40 @@ -849,47 +796,27 @@ class LossGraphNode: (margin - 30, height - 10), f"{min_loss:.2f}", font=font, fill="black" ) - metadata = None - if not args.disable_metadata: - metadata = PngInfo() - if prompt is not None: - metadata.add_text("prompt", json.dumps(prompt)) - if extra_pnginfo is not None: - for x in extra_pnginfo: - metadata.add_text(x, json.dumps(extra_pnginfo[x])) + # Convert PIL image to tensor for PreviewImage + img_array = np.array(img).astype(np.float32) / 255.0 + img_tensor = torch.from_numpy(img_array)[None,] # [1, H, W, 3] - date = datetime.datetime.now().strftime("%Y%m%d_%H%M%S") - img.save( - os.path.join(self.output_dir, f"{filename_prefix}_{date}.png"), - pnginfo=metadata, - ) - return { - "ui": { - "images": [ - { - "filename": f"{filename_prefix}_{date}.png", - "subfolder": "", - "type": "temp", - } - ] - } - } + # Return preview UI + return io.NodeOutput(ui=ui.PreviewImage(img_tensor, cls=cls)) -NODE_CLASS_MAPPINGS = { - "TrainLoraNode": TrainLoraNode, - "SaveLoRANode": SaveLoRA, - "LoraModelLoader": LoraModelLoader, - "LoadImageSetFromFolderNode": LoadImageSetFromFolderNode, - "LoadImageTextSetFromFolderNode": LoadImageTextSetFromFolderNode, - "LossGraphNode": LossGraphNode, -} +# ========== Extension Setup ========== -NODE_DISPLAY_NAME_MAPPINGS = { - "TrainLoraNode": "Train LoRA", - "SaveLoRANode": "Save LoRA Weights", - "LoraModelLoader": "Load LoRA Model", - "LoadImageSetFromFolderNode": "Load Image Dataset from Folder", - "LoadImageTextSetFromFolderNode": "Load Image and Text Dataset from Folder", - "LossGraphNode": "Plot Loss Graph", -} + +class TrainingExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + TrainLoraNode, + LoraModelLoader, + SaveLoRA, + LossGraphNode, + ] + + +async def comfy_entrypoint() -> TrainingExtension: + return TrainingExtension() diff --git a/comfyui_version.py b/comfyui_version.py index b4655d553..fa4b4f4b0 100644 --- a/comfyui_version.py +++ b/comfyui_version.py @@ -1,3 +1,3 @@ # This file is automatically generated by the build process when version is # updated in pyproject.toml. -__version__ = "0.3.71" +__version__ = "0.3.75" diff --git a/latent_preview.py b/latent_preview.py index 95d3cb733..ddf6dcf49 100644 --- a/latent_preview.py +++ b/latent_preview.py @@ -37,13 +37,16 @@ class TAESDPreviewerImpl(LatentPreviewer): class Latent2RGBPreviewer(LatentPreviewer): - def __init__(self, latent_rgb_factors, latent_rgb_factors_bias=None): + def __init__(self, latent_rgb_factors, latent_rgb_factors_bias=None, latent_rgb_factors_reshape=None): self.latent_rgb_factors = torch.tensor(latent_rgb_factors, device="cpu").transpose(0, 1) self.latent_rgb_factors_bias = None if latent_rgb_factors_bias is not None: self.latent_rgb_factors_bias = torch.tensor(latent_rgb_factors_bias, device="cpu") + self.latent_rgb_factors_reshape = latent_rgb_factors_reshape def decode_latent_to_preview(self, x0): + if self.latent_rgb_factors_reshape is not None: + x0 = self.latent_rgb_factors_reshape(x0) self.latent_rgb_factors = self.latent_rgb_factors.to(dtype=x0.dtype, device=x0.device) if self.latent_rgb_factors_bias is not None: self.latent_rgb_factors_bias = self.latent_rgb_factors_bias.to(dtype=x0.dtype, device=x0.device) @@ -85,7 +88,7 @@ def get_previewer(device, latent_format): if previewer is None: if latent_format.latent_rgb_factors is not None: - previewer = Latent2RGBPreviewer(latent_format.latent_rgb_factors, latent_format.latent_rgb_factors_bias) + previewer = Latent2RGBPreviewer(latent_format.latent_rgb_factors, latent_format.latent_rgb_factors_bias, latent_format.latent_rgb_factors_reshape) return previewer def prepare_callback(model, steps, x0_output_dict=None): diff --git a/nodes.py b/nodes.py index f023ae3b6..bf73eb90e 100644 --- a/nodes.py +++ b/nodes.py @@ -929,7 +929,7 @@ class CLIPLoader: @classmethod def INPUT_TYPES(s): return {"required": { "clip_name": (folder_paths.get_filename_list("text_encoders"), ), - "type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi", "ltxv", "pixart", "cosmos", "lumina2", "wan", "hidream", "chroma", "ace", "omnigen2", "qwen_image", "hunyuan_image"], ), + "type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi", "ltxv", "pixart", "cosmos", "lumina2", "wan", "hidream", "chroma", "ace", "omnigen2", "qwen_image", "hunyuan_image", "flux2"], ), }, "optional": { "device": (["default", "cpu"], {"advanced": True}), @@ -2278,6 +2278,7 @@ async def init_builtin_extra_nodes(): "nodes_images.py", "nodes_video_model.py", "nodes_train.py", + "nodes_dataset.py", "nodes_sag.py", "nodes_perpneg.py", "nodes_stable3d.py", diff --git a/pyproject.toml b/pyproject.toml index 280dbaf53..9009e65fe 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "ComfyUI" -version = "0.3.71" +version = "0.3.75" readme = "README.md" license = { file = "LICENSE" } requires-python = ">=3.9" diff --git a/requirements.txt b/requirements.txt index 8e308cd6c..9291552d3 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ -comfyui-frontend-package==1.30.6 -comfyui-workflow-templates==0.7.9 +comfyui-frontend-package==1.32.9 +comfyui-workflow-templates==0.7.20 comfyui-embedded-docs==0.3.1 torch torchsde @@ -7,7 +7,7 @@ torchvision torchaudio numpy>=1.25.0 einops -transformers>=4.37.2 +transformers>=4.50.3 tokenizers>=0.13.3 sentencepiece safetensors>=0.4.2 diff --git a/server.py b/server.py index 0fd2e49e3..fca5050bd 100644 --- a/server.py +++ b/server.py @@ -174,7 +174,7 @@ def create_block_external_middleware(): else: response = await handler(request) - response.headers['Content-Security-Policy'] = "default-src 'self'; script-src 'self' 'unsafe-inline' blob:; style-src 'self' 'unsafe-inline'; img-src 'self' data: blob:; font-src 'self'; connect-src 'self'; frame-src 'self'; object-src 'self';" + response.headers['Content-Security-Policy'] = "default-src 'self'; script-src 'self' 'unsafe-inline' 'unsafe-eval' blob:; style-src 'self' 'unsafe-inline'; img-src 'self' data: blob:; font-src 'self'; connect-src 'self'; frame-src 'self'; object-src 'self';" return response return block_external_middleware diff --git a/tests-unit/comfy_quant/test_mixed_precision.py b/tests-unit/comfy_quant/test_mixed_precision.py index f8d1fd04e..63361309f 100644 --- a/tests-unit/comfy_quant/test_mixed_precision.py +++ b/tests-unit/comfy_quant/test_mixed_precision.py @@ -37,11 +37,8 @@ class TestMixedPrecisionOps(unittest.TestCase): def test_all_layers_standard(self): """Test that model with no quantization works normally""" - # Configure no quantization - ops.MixedPrecisionOps._layer_quant_config = {} - # Create model - model = SimpleModel(operations=ops.MixedPrecisionOps) + model = SimpleModel(operations=ops.mixed_precision_ops({})) # Initialize weights manually model.layer1.weight = torch.nn.Parameter(torch.randn(20, 10, dtype=torch.bfloat16)) @@ -76,7 +73,6 @@ class TestMixedPrecisionOps(unittest.TestCase): "params": {} } } - ops.MixedPrecisionOps._layer_quant_config = layer_quant_config # Create state dict with mixed precision fp8_weight1 = torch.randn(20, 10, dtype=torch.float32).to(torch.float8_e4m3fn) @@ -99,7 +95,7 @@ class TestMixedPrecisionOps(unittest.TestCase): } # Create model and load state dict (strict=False because custom loading pops keys) - model = SimpleModel(operations=ops.MixedPrecisionOps) + model = SimpleModel(operations=ops.mixed_precision_ops(layer_quant_config)) model.load_state_dict(state_dict, strict=False) # Verify weights are wrapped in QuantizedTensor @@ -132,7 +128,6 @@ class TestMixedPrecisionOps(unittest.TestCase): "params": {} } } - ops.MixedPrecisionOps._layer_quant_config = layer_quant_config # Create and load model fp8_weight = torch.randn(20, 10, dtype=torch.float32).to(torch.float8_e4m3fn) @@ -146,7 +141,7 @@ class TestMixedPrecisionOps(unittest.TestCase): "layer3.bias": torch.randn(40, dtype=torch.bfloat16), } - model = SimpleModel(operations=ops.MixedPrecisionOps) + model = SimpleModel(operations=ops.mixed_precision_ops(layer_quant_config)) model.load_state_dict(state_dict1, strict=False) # Save state dict @@ -170,7 +165,6 @@ class TestMixedPrecisionOps(unittest.TestCase): "params": {} } } - ops.MixedPrecisionOps._layer_quant_config = layer_quant_config # Create and load model fp8_weight = torch.randn(20, 10, dtype=torch.float32).to(torch.float8_e4m3fn) @@ -184,7 +178,7 @@ class TestMixedPrecisionOps(unittest.TestCase): "layer3.bias": torch.randn(40, dtype=torch.bfloat16), } - model = SimpleModel(operations=ops.MixedPrecisionOps) + model = SimpleModel(operations=ops.mixed_precision_ops(layer_quant_config)) model.load_state_dict(state_dict, strict=False) # Add a weight function (simulating LoRA) @@ -210,7 +204,6 @@ class TestMixedPrecisionOps(unittest.TestCase): "params": {} } } - ops.MixedPrecisionOps._layer_quant_config = layer_quant_config # Create state dict state_dict = { @@ -223,7 +216,7 @@ class TestMixedPrecisionOps(unittest.TestCase): } # Load should raise KeyError for unknown format in QUANT_FORMAT_MIXINS - model = SimpleModel(operations=ops.MixedPrecisionOps) + model = SimpleModel(operations=ops.mixed_precision_ops(layer_quant_config)) with self.assertRaises(KeyError): model.load_state_dict(state_dict, strict=False) diff --git a/tests/execution/test_public_api.py b/tests/execution/test_public_api.py new file mode 100644 index 000000000..52bc2fcd8 --- /dev/null +++ b/tests/execution/test_public_api.py @@ -0,0 +1,153 @@ +""" +Tests for public ComfyAPI and ComfyAPISync functions. + +These tests verify that the public API methods work correctly in both sync and async contexts, +ensuring that the sync wrapper generation (via get_type_hints() in async_to_sync.py) correctly +handles string annotations from 'from __future__ import annotations'. +""" + +import pytest +import time +import subprocess +import torch +from pytest import fixture +from comfy_execution.graph_utils import GraphBuilder +from tests.execution.test_execution import ComfyClient + + +@pytest.mark.execution +class TestPublicAPI: + """Test suite for public ComfyAPI and ComfyAPISync methods.""" + + @fixture(scope="class", autouse=True) + def _server(self, args_pytest): + """Start ComfyUI server for testing.""" + pargs = [ + 'python', 'main.py', + '--output-directory', args_pytest["output_dir"], + '--listen', args_pytest["listen"], + '--port', str(args_pytest["port"]), + '--extra-model-paths-config', 'tests/execution/extra_model_paths.yaml', + '--cpu', + ] + p = subprocess.Popen(pargs) + yield + p.kill() + torch.cuda.empty_cache() + + @fixture(scope="class", autouse=True) + def shared_client(self, args_pytest, _server): + """Create shared client with connection retry.""" + client = ComfyClient() + n_tries = 5 + for i in range(n_tries): + time.sleep(4) + try: + client.connect(listen=args_pytest["listen"], port=args_pytest["port"]) + break + except ConnectionRefusedError: + if i == n_tries - 1: + raise + yield client + del client + torch.cuda.empty_cache() + + @fixture + def client(self, shared_client, request): + """Set test name for each test.""" + shared_client.set_test_name(f"public_api[{request.node.name}]") + yield shared_client + + @fixture + def builder(self, request): + """Create GraphBuilder for each test.""" + yield GraphBuilder(prefix=request.node.name) + + def test_sync_progress_update_executes(self, client: ComfyClient, builder: GraphBuilder): + """Test that TestSyncProgressUpdate executes without errors. + + This test validates that api_sync.execution.set_progress() works correctly, + which is the primary code path fixed by adding get_type_hints() to async_to_sync.py. + """ + g = builder + image = g.node("StubImage", content="BLACK", height=256, width=256, batch_size=1) + + # Use TestSyncProgressUpdate with short sleep + progress_node = g.node("TestSyncProgressUpdate", + value=image.out(0), + sleep_seconds=0.5) + output = g.node("SaveImage", images=progress_node.out(0)) + + # Execute workflow + result = client.run(g) + + # Verify execution + assert result.did_run(progress_node), "Progress node should have executed" + assert result.did_run(output), "Output node should have executed" + + # Verify output + images = result.get_images(output) + assert len(images) == 1, "Should have produced 1 image" + + def test_async_progress_update_executes(self, client: ComfyClient, builder: GraphBuilder): + """Test that TestAsyncProgressUpdate executes without errors. + + This test validates that await api.execution.set_progress() works correctly + in async contexts. + """ + g = builder + image = g.node("StubImage", content="WHITE", height=256, width=256, batch_size=1) + + # Use TestAsyncProgressUpdate with short sleep + progress_node = g.node("TestAsyncProgressUpdate", + value=image.out(0), + sleep_seconds=0.5) + output = g.node("SaveImage", images=progress_node.out(0)) + + # Execute workflow + result = client.run(g) + + # Verify execution + assert result.did_run(progress_node), "Async progress node should have executed" + assert result.did_run(output), "Output node should have executed" + + # Verify output + images = result.get_images(output) + assert len(images) == 1, "Should have produced 1 image" + + def test_sync_and_async_progress_together(self, client: ComfyClient, builder: GraphBuilder): + """Test both sync and async progress updates in same workflow. + + This test ensures that both ComfyAPISync and ComfyAPI can coexist and work + correctly in the same workflow execution. + """ + g = builder + image1 = g.node("StubImage", content="BLACK", height=256, width=256, batch_size=1) + image2 = g.node("StubImage", content="WHITE", height=256, width=256, batch_size=1) + + # Use both types of progress nodes + sync_progress = g.node("TestSyncProgressUpdate", + value=image1.out(0), + sleep_seconds=0.3) + async_progress = g.node("TestAsyncProgressUpdate", + value=image2.out(0), + sleep_seconds=0.3) + + # Create outputs + output1 = g.node("SaveImage", images=sync_progress.out(0)) + output2 = g.node("SaveImage", images=async_progress.out(0)) + + # Execute workflow + result = client.run(g) + + # Both should execute successfully + assert result.did_run(sync_progress), "Sync progress node should have executed" + assert result.did_run(async_progress), "Async progress node should have executed" + assert result.did_run(output1), "First output node should have executed" + assert result.did_run(output2), "Second output node should have executed" + + # Verify outputs + images1 = result.get_images(output1) + images2 = result.get_images(output2) + assert len(images1) == 1, "Should have produced 1 image from sync node" + assert len(images2) == 1, "Should have produced 1 image from async node"