diff --git a/.github/workflows/stable-release.yml b/.github/workflows/stable-release.yml index c4302cdd6..a046ff9ea 100644 --- a/.github/workflows/stable-release.yml +++ b/.github/workflows/stable-release.yml @@ -91,6 +91,8 @@ jobs: cd ComfyUI_windows_portable python_embeded/python.exe -s ComfyUI/main.py --quick-test-for-ci --cpu + python_embeded/python.exe -s ./update/update.py ComfyUI/ + ls - name: Upload binaries to release diff --git a/.github/workflows/windows_release_package.yml b/.github/workflows/windows_release_package.yml index 80a45b321..3926a65f3 100644 --- a/.github/workflows/windows_release_package.yml +++ b/.github/workflows/windows_release_package.yml @@ -88,6 +88,8 @@ jobs: cd ComfyUI_windows_portable python_embeded/python.exe -s ComfyUI/main.py --quick-test-for-ci --cpu + python_embeded/python.exe -s ./update/update.py ComfyUI/ + ls - name: Upload binaries to release diff --git a/comfy/ldm/chroma/layers.py b/comfy/ldm/chroma/layers.py new file mode 100644 index 000000000..35da91ee2 --- /dev/null +++ b/comfy/ldm/chroma/layers.py @@ -0,0 +1,183 @@ +import torch +from torch import Tensor, nn + +from comfy.ldm.flux.math import attention +from comfy.ldm.flux.layers import ( + MLPEmbedder, + RMSNorm, + QKNorm, + SelfAttention, + ModulationOut, +) + + + +class ChromaModulationOut(ModulationOut): + @classmethod + def from_offset(cls, tensor: torch.Tensor, offset: int = 0) -> ModulationOut: + return cls( + shift=tensor[:, offset : offset + 1, :], + scale=tensor[:, offset + 1 : offset + 2, :], + gate=tensor[:, offset + 2 : offset + 3, :], + ) + + + + +class Approximator(nn.Module): + def __init__(self, in_dim: int, out_dim: int, hidden_dim: int, n_layers = 5, dtype=None, device=None, operations=None): + super().__init__() + self.in_proj = operations.Linear(in_dim, hidden_dim, bias=True, dtype=dtype, device=device) + self.layers = nn.ModuleList([MLPEmbedder(hidden_dim, hidden_dim, dtype=dtype, device=device, operations=operations) for x in range( n_layers)]) + self.norms = nn.ModuleList([RMSNorm(hidden_dim, dtype=dtype, device=device, operations=operations) for x in range( n_layers)]) + self.out_proj = operations.Linear(hidden_dim, out_dim, dtype=dtype, device=device) + + @property + def device(self): + # Get the device of the module (assumes all parameters are on the same device) + return next(self.parameters()).device + + def forward(self, x: Tensor) -> Tensor: + x = self.in_proj(x) + + for layer, norms in zip(self.layers, self.norms): + x = x + layer(norms(x)) + + x = self.out_proj(x) + + return x + + +class DoubleStreamBlock(nn.Module): + def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False, flipped_img_txt=False, dtype=None, device=None, operations=None): + super().__init__() + + mlp_hidden_dim = int(hidden_size * mlp_ratio) + self.num_heads = num_heads + self.hidden_size = hidden_size + 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_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), + ) + + 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_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), + ) + self.flipped_img_txt = flipped_img_txt + + def forward(self, img: Tensor, txt: Tensor, pe: Tensor, vec: Tensor, attn_mask=None): + (img_mod1, img_mod2), (txt_mod1, txt_mod2) = vec + + # prepare image for attention + img_modulated = self.img_norm1(img) + img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift + img_qkv = self.img_attn.qkv(img_modulated) + img_q, img_k, img_v = img_qkv.view(img_qkv.shape[0], img_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) + img_q, img_k = self.img_attn.norm(img_q, img_k, img_v) + + # prepare txt for attention + txt_modulated = self.txt_norm1(txt) + txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift + txt_qkv = self.txt_attn.qkv(txt_modulated) + txt_q, txt_k, txt_v = txt_qkv.view(txt_qkv.shape[0], txt_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) + txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v) + + # run actual attention + attn = attention(torch.cat((txt_q, img_q), dim=2), + torch.cat((txt_k, img_k), dim=2), + torch.cat((txt_v, img_v), dim=2), + pe=pe, mask=attn_mask) + + txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :] + + # calculate the img bloks + img = img + img_mod1.gate * self.img_attn.proj(img_attn) + img = img + img_mod2.gate * self.img_mlp((1 + img_mod2.scale) * self.img_norm2(img) + img_mod2.shift) + + # calculate the txt bloks + txt += txt_mod1.gate * self.txt_attn.proj(txt_attn) + txt += txt_mod2.gate * self.txt_mlp((1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift) + + if txt.dtype == torch.float16: + txt = torch.nan_to_num(txt, nan=0.0, posinf=65504, neginf=-65504) + + return img, txt + + +class SingleStreamBlock(nn.Module): + """ + A DiT block with parallel linear layers as described in + https://arxiv.org/abs/2302.05442 and adapted modulation interface. + """ + + def __init__( + self, + hidden_size: int, + num_heads: int, + mlp_ratio: float = 4.0, + qk_scale: float = None, + dtype=None, + device=None, + operations=None + ): + super().__init__() + self.hidden_dim = hidden_size + self.num_heads = num_heads + head_dim = hidden_size // num_heads + self.scale = qk_scale or head_dim**-0.5 + + self.mlp_hidden_dim = int(hidden_size * mlp_ratio) + # qkv and mlp_in + self.linear1 = operations.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim, 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.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") + + def forward(self, x: Tensor, pe: Tensor, vec: Tensor, attn_mask=None) -> Tensor: + mod = vec + x_mod = (1 + mod.scale) * self.pre_norm(x) + mod.shift + qkv, mlp = torch.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1) + + q, k, v = qkv.view(qkv.shape[0], qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) + q, k = self.norm(q, k, v) + + # compute attention + attn = attention(q, k, v, pe=pe, mask=attn_mask) + # compute activation in mlp stream, cat again and run second linear layer + output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2)) + x += mod.gate * output + if x.dtype == torch.float16: + x = torch.nan_to_num(x, nan=0.0, posinf=65504, neginf=-65504) + return x + + +class LastLayer(nn.Module): + def __init__(self, hidden_size: int, patch_size: int, out_channels: int, 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, out_channels, bias=True, dtype=dtype, device=device) + + def forward(self, x: Tensor, vec: Tensor) -> Tensor: + shift, scale = vec + shift = shift.squeeze(1) + scale = scale.squeeze(1) + x = (1 + scale[:, None, :]) * self.norm_final(x) + shift[:, None, :] + x = self.linear(x) + return x diff --git a/comfy/ldm/chroma/model.py b/comfy/ldm/chroma/model.py new file mode 100644 index 000000000..636748fc5 --- /dev/null +++ b/comfy/ldm/chroma/model.py @@ -0,0 +1,271 @@ +#Original code can be found on: https://github.com/black-forest-labs/flux + +from dataclasses import dataclass + +import torch +from torch import Tensor, nn +from einops import rearrange, repeat +import comfy.ldm.common_dit + +from comfy.ldm.flux.layers import ( + EmbedND, + timestep_embedding, +) + +from .layers import ( + DoubleStreamBlock, + LastLayer, + SingleStreamBlock, + Approximator, + ChromaModulationOut, +) + + +@dataclass +class ChromaParams: + in_channels: int + out_channels: int + context_in_dim: int + hidden_size: int + mlp_ratio: float + num_heads: int + depth: int + depth_single_blocks: int + axes_dim: list + theta: int + patch_size: int + qkv_bias: bool + in_dim: int + out_dim: int + hidden_dim: int + n_layers: int + + + + +class Chroma(nn.Module): + """ + Transformer model for flow matching on sequences. + """ + + def __init__(self, image_model=None, final_layer=True, dtype=None, device=None, operations=None, **kwargs): + super().__init__() + self.dtype = dtype + params = ChromaParams(**kwargs) + self.params = params + self.patch_size = params.patch_size + self.in_channels = params.in_channels + self.out_channels = params.out_channels + if params.hidden_size % params.num_heads != 0: + raise ValueError( + f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}" + ) + pe_dim = params.hidden_size // params.num_heads + if sum(params.axes_dim) != pe_dim: + raise ValueError(f"Got {params.axes_dim} but expected positional dim {pe_dim}") + self.hidden_size = params.hidden_size + self.num_heads = params.num_heads + self.in_dim = params.in_dim + self.out_dim = params.out_dim + self.hidden_dim = params.hidden_dim + self.n_layers = params.n_layers + 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.txt_in = operations.Linear(params.context_in_dim, self.hidden_size, dtype=dtype, device=device) + # set as nn identity for now, will overwrite it later. + self.distilled_guidance_layer = Approximator( + in_dim=self.in_dim, + hidden_dim=self.hidden_dim, + out_dim=self.out_dim, + n_layers=self.n_layers, + dtype=dtype, device=device, operations=operations + ) + + + self.double_blocks = nn.ModuleList( + [ + DoubleStreamBlock( + self.hidden_size, + self.num_heads, + mlp_ratio=params.mlp_ratio, + qkv_bias=params.qkv_bias, + dtype=dtype, device=device, operations=operations + ) + for _ in range(params.depth) + ] + ) + + self.single_blocks = nn.ModuleList( + [ + SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio, 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.skip_mmdit = [] + self.skip_dit = [] + self.lite = False + + def get_modulations(self, tensor: torch.Tensor, block_type: str, *, idx: int = 0): + # This function slices up the modulations tensor which has the following layout: + # single : num_single_blocks * 3 elements + # double_img : num_double_blocks * 6 elements + # double_txt : num_double_blocks * 6 elements + # final : 2 elements + if block_type == "final": + return (tensor[:, -2:-1, :], tensor[:, -1:, :]) + single_block_count = self.params.depth_single_blocks + double_block_count = self.params.depth + offset = 3 * idx + if block_type == "single": + return ChromaModulationOut.from_offset(tensor, offset) + # Double block modulations are 6 elements so we double 3 * idx. + offset *= 2 + if block_type in {"double_img", "double_txt"}: + # Advance past the single block modulations. + offset += 3 * single_block_count + if block_type == "double_txt": + # Advance past the double block img modulations. + offset += 6 * double_block_count + return ( + ChromaModulationOut.from_offset(tensor, offset), + ChromaModulationOut.from_offset(tensor, offset + 3), + ) + raise ValueError("Bad block_type") + + + def forward_orig( + self, + img: Tensor, + img_ids: Tensor, + txt: Tensor, + txt_ids: Tensor, + timesteps: Tensor, + guidance: Tensor = None, + control = None, + transformer_options={}, + attn_mask: Tensor = None, + ) -> Tensor: + patches_replace = transformer_options.get("patches_replace", {}) + if img.ndim != 3 or txt.ndim != 3: + raise ValueError("Input img and txt tensors must have 3 dimensions.") + + # running on sequences img + img = self.img_in(img) + + # distilled vector guidance + mod_index_length = 344 + distill_timestep = timestep_embedding(timesteps.detach().clone(), 16).to(img.device, img.dtype) + # guidance = guidance * + distil_guidance = timestep_embedding(guidance.detach().clone(), 16).to(img.device, img.dtype) + + # get all modulation index + modulation_index = timestep_embedding(torch.arange(mod_index_length), 32).to(img.device, img.dtype) + # we need to broadcast the modulation index here so each batch has all of the index + modulation_index = modulation_index.unsqueeze(0).repeat(img.shape[0], 1, 1).to(img.device, img.dtype) + # and we need to broadcast timestep and guidance along too + timestep_guidance = torch.cat([distill_timestep, distil_guidance], dim=1).unsqueeze(1).repeat(1, mod_index_length, 1).to(img.dtype).to(img.device, img.dtype) + # then and only then we could concatenate it together + input_vec = torch.cat([timestep_guidance, modulation_index], dim=-1).to(img.device, img.dtype) + + mod_vectors = self.distilled_guidance_layer(input_vec) + + txt = self.txt_in(txt) + + ids = torch.cat((txt_ids, img_ids), dim=1) + pe = self.pe_embedder(ids) + + blocks_replace = patches_replace.get("dit", {}) + for i, block in enumerate(self.double_blocks): + if i not in self.skip_mmdit: + double_mod = ( + self.get_modulations(mod_vectors, "double_img", idx=i), + self.get_modulations(mod_vectors, "double_txt", idx=i), + ) + if ("double_block", i) in blocks_replace: + def block_wrap(args): + out = {} + out["img"], out["txt"] = block(img=args["img"], + txt=args["txt"], + vec=args["vec"], + pe=args["pe"], + attn_mask=args.get("attn_mask")) + return out + + out = blocks_replace[("double_block", i)]({"img": img, + "txt": txt, + "vec": double_mod, + "pe": pe, + "attn_mask": attn_mask}, + {"original_block": block_wrap}) + txt = out["txt"] + img = out["img"] + else: + img, txt = block(img=img, + txt=txt, + vec=double_mod, + pe=pe, + attn_mask=attn_mask) + + if control is not None: # Controlnet + control_i = control.get("input") + if i < len(control_i): + add = control_i[i] + if add is not None: + img += add + + img = torch.cat((txt, img), 1) + + for i, block in enumerate(self.single_blocks): + if i not in self.skip_dit: + single_mod = self.get_modulations(mod_vectors, "single", idx=i) + if ("single_block", i) in blocks_replace: + def block_wrap(args): + out = {} + out["img"] = block(args["img"], + vec=args["vec"], + pe=args["pe"], + attn_mask=args.get("attn_mask")) + return out + + out = blocks_replace[("single_block", i)]({"img": img, + "vec": single_mod, + "pe": pe, + "attn_mask": attn_mask}, + {"original_block": block_wrap}) + img = out["img"] + else: + img = block(img, vec=single_mod, pe=pe, attn_mask=attn_mask) + + if control is not None: # Controlnet + control_o = control.get("output") + if i < len(control_o): + add = control_o[i] + if add is not None: + img[:, txt.shape[1] :, ...] += add + + img = img[:, txt.shape[1] :, ...] + final_mod = self.get_modulations(mod_vectors, "final") + img = self.final_layer(img, vec=final_mod) # (N, T, patch_size ** 2 * out_channels) + return img + + def forward(self, x, timestep, context, guidance, control=None, transformer_options={}, **kwargs): + bs, c, h, w = x.shape + patch_size = 2 + x = comfy.ldm.common_dit.pad_to_patch_size(x, (patch_size, patch_size)) + + img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size) + + h_len = ((h + (patch_size // 2)) // patch_size) + w_len = ((w + (patch_size // 2)) // patch_size) + img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype) + img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).unsqueeze(1) + img_ids[:, :, 2] = img_ids[:, :, 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0) + img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs) + + txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype) + out = self.forward_orig(img, img_ids, context, txt_ids, timestep, guidance, control, transformer_options, attn_mask=kwargs.get("attention_mask", None)) + return rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=2, pw=2)[:,:,:h,:w] diff --git a/comfy/ldm/lightricks/model.py b/comfy/ldm/lightricks/model.py index 6e8e06181..056e101a4 100644 --- a/comfy/ldm/lightricks/model.py +++ b/comfy/ldm/lightricks/model.py @@ -1,7 +1,6 @@ import torch from torch import nn import comfy.ldm.modules.attention -from comfy.ldm.genmo.joint_model.layers import RMSNorm import comfy.ldm.common_dit from einops import rearrange import math @@ -262,8 +261,8 @@ class CrossAttention(nn.Module): self.heads = heads self.dim_head = dim_head - self.q_norm = RMSNorm(inner_dim, dtype=dtype, device=device) - self.k_norm = RMSNorm(inner_dim, dtype=dtype, device=device) + self.q_norm = operations.RMSNorm(inner_dim, dtype=dtype, device=device) + self.k_norm = operations.RMSNorm(inner_dim, dtype=dtype, device=device) self.to_q = operations.Linear(query_dim, inner_dim, bias=True, dtype=dtype, device=device) self.to_k = operations.Linear(context_dim, inner_dim, bias=True, dtype=dtype, device=device) diff --git a/comfy/model_base.py b/comfy/model_base.py index d2aa4ce7a..3d33086d8 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -38,6 +38,7 @@ import comfy.ldm.lumina.model import comfy.ldm.wan.model import comfy.ldm.hunyuan3d.model import comfy.ldm.hidream.model +import comfy.ldm.chroma.model import comfy.model_management import comfy.patcher_extension @@ -786,8 +787,8 @@ class PixArt(BaseModel): return out class Flux(BaseModel): - def __init__(self, model_config, model_type=ModelType.FLUX, device=None): - super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.flux.model.Flux) + def __init__(self, model_config, model_type=ModelType.FLUX, device=None, unet_model=comfy.ldm.flux.model.Flux): + super().__init__(model_config, model_type, device=device, unet_model=unet_model) def concat_cond(self, **kwargs): try: @@ -1108,3 +1109,15 @@ class HiDream(BaseModel): if image_cond is not None: out['image_cond'] = comfy.conds.CONDNoiseShape(self.process_latent_in(image_cond)) return out + +class Chroma(Flux): + def __init__(self, model_config, model_type=ModelType.FLOW, device=None): + super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.chroma.model.Chroma) + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + + guidance = kwargs.get("guidance", 0) + if guidance is not None: + out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance])) + return out diff --git a/comfy/model_detection.py b/comfy/model_detection.py index 76de78a8a..9254843ea 100644 --- a/comfy/model_detection.py +++ b/comfy/model_detection.py @@ -164,7 +164,9 @@ def detect_unet_config(state_dict, key_prefix, metadata=None): 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 - dit_config["vec_in_dim"] = 768 + 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 @@ -174,7 +176,16 @@ def detect_unet_config(state_dict, key_prefix, metadata=None): dit_config["axes_dim"] = [16, 56, 56] dit_config["theta"] = 10000 dit_config["qkv_bias"] = True - dit_config["guidance_embed"] = "{}guidance_in.in_layer.weight".format(key_prefix) in state_dict_keys + 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 + dit_config["out_channels"] = 64 + dit_config["in_dim"] = 64 + dit_config["out_dim"] = 3072 + dit_config["hidden_dim"] = 5120 + dit_config["n_layers"] = 5 + else: + dit_config["guidance_embed"] = "{}guidance_in.in_layer.weight".format(key_prefix) in state_dict_keys return dit_config if '{}t5_yproj.weight'.format(key_prefix) in state_dict_keys: #Genmo mochi preview diff --git a/comfy/sd.py b/comfy/sd.py index 748f6c1ec..da9b36d0e 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -714,6 +714,7 @@ class CLIPType(Enum): LUMINA2 = 12 WAN = 13 HIDREAM = 14 + CHROMA = 15 def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION, model_options={}): @@ -818,7 +819,7 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip elif clip_type == CLIPType.LTXV: clip_target.clip = comfy.text_encoders.lt.ltxv_te(**t5xxl_detect(clip_data)) clip_target.tokenizer = comfy.text_encoders.lt.LTXVT5Tokenizer - elif clip_type == CLIPType.PIXART: + elif clip_type == CLIPType.PIXART or clip_type == CLIPType.CHROMA: clip_target.clip = comfy.text_encoders.pixart_t5.pixart_te(**t5xxl_detect(clip_data)) clip_target.tokenizer = comfy.text_encoders.pixart_t5.PixArtTokenizer elif clip_type == CLIPType.WAN: diff --git a/comfy/supported_models.py b/comfy/supported_models.py index 1a4eee7cb..a8c5b8477 100644 --- a/comfy/supported_models.py +++ b/comfy/supported_models.py @@ -1068,7 +1068,34 @@ class HiDream(supported_models_base.BASE): def clip_target(self, state_dict={}): return None # TODO +class Chroma(supported_models_base.BASE): + unet_config = { + "image_model": "chroma", + } -models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, Lumina2, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, Hunyuan3Dv2mini, Hunyuan3Dv2, HiDream] + unet_extra_config = { + } + + sampling_settings = { + "multiplier": 1.0, + } + + latent_format = comfy.latent_formats.Flux + + memory_usage_factor = 3.2 + + supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32] + + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.Chroma(self, device=device) + return out + + def clip_target(self, state_dict={}): + 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.pixart_t5.PixArtTokenizer, comfy.text_encoders.pixart_t5.pixart_te(**t5_detect)) + +models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, Lumina2, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, Hunyuan3Dv2mini, Hunyuan3Dv2, HiDream, Chroma] models += [SVD_img2vid] diff --git a/comfy_api_nodes/apis/client.py b/comfy_api_nodes/apis/client.py index 384e559dc..d3cd9ad2f 100644 --- a/comfy_api_nodes/apis/client.py +++ b/comfy_api_nodes/apis/client.py @@ -297,6 +297,10 @@ class SynchronousOperation(Generic[T, R]): # Convert request model to dict, but use None for EmptyRequest request_dict = None if isinstance(self.request, EmptyRequest) else self.request.model_dump(exclude_none=True) + if request_dict: + for key, value in request_dict.items(): + if isinstance(value, Enum): + request_dict[key] = value.value # Debug log for request logging.debug(f"[DEBUG] API Request: {self.endpoint.method.value} {self.endpoint.path}") diff --git a/comfy_extras/nodes_optimalsteps.py b/comfy_extras/nodes_optimalsteps.py index f6928199b..e7c851ca2 100644 --- a/comfy_extras/nodes_optimalsteps.py +++ b/comfy_extras/nodes_optimalsteps.py @@ -20,13 +20,14 @@ def loglinear_interp(t_steps, num_steps): NOISE_LEVELS = {"FLUX": [0.9968, 0.9886, 0.9819, 0.975, 0.966, 0.9471, 0.9158, 0.8287, 0.5512, 0.2808, 0.001], "Wan":[1.0, 0.997, 0.995, 0.993, 0.991, 0.989, 0.987, 0.985, 0.98, 0.975, 0.973, 0.968, 0.96, 0.946, 0.927, 0.902, 0.864, 0.776, 0.539, 0.208, 0.001], +"Chroma": [0.992, 0.99, 0.988, 0.985, 0.982, 0.978, 0.973, 0.968, 0.961, 0.953, 0.943, 0.931, 0.917, 0.9, 0.881, 0.858, 0.832, 0.802, 0.769, 0.731, 0.69, 0.646, 0.599, 0.55, 0.501, 0.451, 0.402, 0.355, 0.311, 0.27, 0.232, 0.199, 0.169, 0.143, 0.12, 0.101, 0.084, 0.07, 0.058, 0.048, 0.001], } class OptimalStepsScheduler: @classmethod def INPUT_TYPES(s): return {"required": - {"model_type": (["FLUX", "Wan"], ), + {"model_type": (["FLUX", "Wan", "Chroma"], ), "steps": ("INT", {"default": 20, "min": 3, "max": 1000}), "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), } diff --git a/nodes.py b/nodes.py index 73a62d930..f2ced2c35 100644 --- a/nodes.py +++ b/nodes.py @@ -917,7 +917,7 @@ 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"], ), + "type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi", "ltxv", "pixart", "cosmos", "lumina2", "wan", "hidream", "chroma"], ), }, "optional": { "device": (["default", "cpu"], {"advanced": True}),