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| #Original code can be found on: https://github.com/XLabs-AI/x-flux/blob/main/src/flux/controlnet.py | |
| #modified to support different types of flux controlnets | |
| import torch | |
| import math | |
| from torch import Tensor, nn | |
| from einops import rearrange, repeat | |
| from .layers import (DoubleStreamBlock, EmbedND, LastLayer, | |
| MLPEmbedder, SingleStreamBlock, | |
| timestep_embedding) | |
| from .model import Flux | |
| import comfy.ldm.common_dit | |
| class MistolineCondDownsamplBlock(nn.Module): | |
| def __init__(self, dtype=None, device=None, operations=None): | |
| super().__init__() | |
| self.encoder = nn.Sequential( | |
| operations.Conv2d(3, 16, 3, padding=1, dtype=dtype, device=device), | |
| nn.SiLU(), | |
| operations.Conv2d(16, 16, 1, dtype=dtype, device=device), | |
| nn.SiLU(), | |
| operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device), | |
| nn.SiLU(), | |
| operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device), | |
| nn.SiLU(), | |
| operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device), | |
| nn.SiLU(), | |
| operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device), | |
| nn.SiLU(), | |
| operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device), | |
| nn.SiLU(), | |
| operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device), | |
| nn.SiLU(), | |
| operations.Conv2d(16, 16, 1, dtype=dtype, device=device), | |
| nn.SiLU(), | |
| operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device) | |
| ) | |
| def forward(self, x): | |
| return self.encoder(x) | |
| class MistolineControlnetBlock(nn.Module): | |
| def __init__(self, hidden_size, dtype=None, device=None, operations=None): | |
| super().__init__() | |
| self.linear = operations.Linear(hidden_size, hidden_size, dtype=dtype, device=device) | |
| self.act = nn.SiLU() | |
| def forward(self, x): | |
| return self.act(self.linear(x)) | |
| class ControlNetFlux(Flux): | |
| def __init__(self, latent_input=False, num_union_modes=0, mistoline=False, control_latent_channels=None, image_model=None, dtype=None, device=None, operations=None, **kwargs): | |
| super().__init__(final_layer=False, dtype=dtype, device=device, operations=operations, **kwargs) | |
| self.main_model_double = 19 | |
| self.main_model_single = 38 | |
| self.mistoline = mistoline | |
| # add ControlNet blocks | |
| if self.mistoline: | |
| control_block = lambda : MistolineControlnetBlock(self.hidden_size, dtype=dtype, device=device, operations=operations) | |
| else: | |
| control_block = lambda : operations.Linear(self.hidden_size, self.hidden_size, dtype=dtype, device=device) | |
| self.controlnet_blocks = nn.ModuleList([]) | |
| for _ in range(self.params.depth): | |
| self.controlnet_blocks.append(control_block()) | |
| self.controlnet_single_blocks = nn.ModuleList([]) | |
| for _ in range(self.params.depth_single_blocks): | |
| self.controlnet_single_blocks.append(control_block()) | |
| self.num_union_modes = num_union_modes | |
| self.controlnet_mode_embedder = None | |
| if self.num_union_modes > 0: | |
| self.controlnet_mode_embedder = operations.Embedding(self.num_union_modes, self.hidden_size, dtype=dtype, device=device) | |
| self.gradient_checkpointing = False | |
| self.latent_input = latent_input | |
| if control_latent_channels is None: | |
| control_latent_channels = self.in_channels | |
| else: | |
| control_latent_channels *= 2 * 2 #patch size | |
| self.pos_embed_input = operations.Linear(control_latent_channels, self.hidden_size, bias=True, dtype=dtype, device=device) | |
| if not self.latent_input: | |
| if self.mistoline: | |
| self.input_cond_block = MistolineCondDownsamplBlock(dtype=dtype, device=device, operations=operations) | |
| else: | |
| self.input_hint_block = nn.Sequential( | |
| operations.Conv2d(3, 16, 3, padding=1, dtype=dtype, device=device), | |
| nn.SiLU(), | |
| operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device), | |
| nn.SiLU(), | |
| operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device), | |
| nn.SiLU(), | |
| operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device), | |
| nn.SiLU(), | |
| operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device), | |
| nn.SiLU(), | |
| operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device), | |
| nn.SiLU(), | |
| operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device), | |
| nn.SiLU(), | |
| operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device) | |
| ) | |
| def forward_orig( | |
| self, | |
| img: Tensor, | |
| img_ids: Tensor, | |
| controlnet_cond: Tensor, | |
| txt: Tensor, | |
| txt_ids: Tensor, | |
| timesteps: Tensor, | |
| y: Tensor, | |
| guidance: Tensor = None, | |
| control_type: Tensor = None, | |
| ) -> Tensor: | |
| 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) | |
| controlnet_cond = self.pos_embed_input(controlnet_cond) | |
| img = img + controlnet_cond | |
| vec = self.time_in(timestep_embedding(timesteps, 256)) | |
| if self.params.guidance_embed: | |
| vec = vec + self.guidance_in(timestep_embedding(guidance, 256)) | |
| vec = vec + self.vector_in(y) | |
| txt = self.txt_in(txt) | |
| if self.controlnet_mode_embedder is not None and len(control_type) > 0: | |
| control_cond = self.controlnet_mode_embedder(torch.tensor(control_type, device=img.device), out_dtype=img.dtype).unsqueeze(0).repeat((txt.shape[0], 1, 1)) | |
| txt = torch.cat([control_cond, txt], dim=1) | |
| txt_ids = torch.cat([txt_ids[:,:1], txt_ids], dim=1) | |
| ids = torch.cat((txt_ids, img_ids), dim=1) | |
| pe = self.pe_embedder(ids) | |
| controlnet_double = () | |
| for i in range(len(self.double_blocks)): | |
| img, txt = self.double_blocks[i](img=img, txt=txt, vec=vec, pe=pe) | |
| controlnet_double = controlnet_double + (self.controlnet_blocks[i](img),) | |
| img = torch.cat((txt, img), 1) | |
| controlnet_single = () | |
| for i in range(len(self.single_blocks)): | |
| img = self.single_blocks[i](img, vec=vec, pe=pe) | |
| controlnet_single = controlnet_single + (self.controlnet_single_blocks[i](img[:, txt.shape[1] :, ...]),) | |
| repeat = math.ceil(self.main_model_double / len(controlnet_double)) | |
| if self.latent_input: | |
| out_input = () | |
| for x in controlnet_double: | |
| out_input += (x,) * repeat | |
| else: | |
| out_input = (controlnet_double * repeat) | |
| out = {"input": out_input[:self.main_model_double]} | |
| if len(controlnet_single) > 0: | |
| repeat = math.ceil(self.main_model_single / len(controlnet_single)) | |
| out_output = () | |
| if self.latent_input: | |
| for x in controlnet_single: | |
| out_output += (x,) * repeat | |
| else: | |
| out_output = (controlnet_single * repeat) | |
| out["output"] = out_output[:self.main_model_single] | |
| return out | |
| def forward(self, x, timesteps, context, y, guidance=None, hint=None, **kwargs): | |
| patch_size = 2 | |
| if self.latent_input: | |
| hint = comfy.ldm.common_dit.pad_to_patch_size(hint, (patch_size, patch_size)) | |
| elif self.mistoline: | |
| hint = hint * 2.0 - 1.0 | |
| hint = self.input_cond_block(hint) | |
| else: | |
| hint = hint * 2.0 - 1.0 | |
| hint = self.input_hint_block(hint) | |
| hint = rearrange(hint, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size) | |
| bs, c, h, w = x.shape | |
| 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)[:, None] | |
| img_ids[..., 2] = img_ids[..., 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype)[None, :] | |
| 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) | |
| return self.forward_orig(img, img_ids, hint, context, txt_ids, timesteps, y, guidance, control_type=kwargs.get("control_type", [])) | |