Add attension couple+reginal LoRA

gen_img_diffusers.py

@@ -92,6 +92,7 @@ from PIL.PngImagePlugin import PngInfo
 
 import library.model_util as model_util
 import library.train_util as train_util
+from networks.lora import LoRANetwork
 import tools.original_control_net as original_control_net
 from tools.original_control_net import ControlNetInfo
 
@@ -634,6 +635,7 @@ class PipelineLike:
         img2img_noise=None,
         clip_prompts=None,
         clip_guide_images=None,
+        networks: Optional[List[LoRANetwork]] = None,
         **kwargs,
     ):
         r"""
@@ -717,6 +719,7 @@ class PipelineLike:
             batch_size = len(prompt)
         else:
             raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+        reginonal_network = " AND " in prompt[0]
 
         vae_batch_size = (
             batch_size
@@ -1010,6 +1013,11 @@ class PipelineLike:
 
             # predict the noise residual
             if self.control_nets:
+                if reginonal_network:
+                    num_sub_and_neg_prompts = len(text_embeddings) // batch_size
+                    text_emb_last = text_embeddings[num_sub_and_neg_prompts - 2::num_sub_and_neg_prompts]   # last subprompt
+                else:
+                    text_emb_last = text_embeddings
                 noise_pred = original_control_net.call_unet_and_control_net(
                     i,
                     num_latent_input,
@@ -1019,7 +1027,7 @@ class PipelineLike:
                     i / len(timesteps),
                     latent_model_input,
                     t,
-                    text_embeddings,
+                    text_emb_last,
                 ).sample
             else:
                 noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
@@ -1890,6 +1898,12 @@ def get_weighted_text_embeddings(
     if isinstance(prompt, str):
         prompt = [prompt]
 
+    # split the prompts with "AND". each prompt must have the same number of splits
+    new_prompts = []
+    for p in prompt:
+        new_prompts.extend(p.split(" AND "))
+    prompt = new_prompts
+
     if not skip_parsing:
         prompt_tokens, prompt_weights = get_prompts_with_weights(pipe, prompt, max_length - 2, layer=layer)
         if uncond_prompt is not None:
@@ -2059,6 +2073,7 @@ class BatchDataExt(NamedTuple):
     negative_scale: float
     strength: float
     network_muls: Tuple[float]
+    num_sub_prompts: int
 
 
 class BatchData(NamedTuple):
@@ -2276,14 +2291,18 @@ def main(args):
                         print(f"metadata for: {network_weight}: {metadata}")
 
                 network, weights_sd = imported_module.create_network_from_weights(
-                    network_mul, network_weight, vae, text_encoder, unet, **net_kwargs
+                    network_mul, network_weight, vae, text_encoder, unet, for_inference=True, **net_kwargs
                 )
             else:
                 raise ValueError("No weight. Weight is required.")
             if network is None:
                 return
 
-            if not args.network_merge:
+            mergiable = hasattr(network, "merge_to")
+            if args.network_merge and not mergiable:
+                print("network is not mergiable. ignore merge option.")
+
+            if not args.network_merge or not mergiable:
                 network.apply_to(text_encoder, unet)
                 info = network.load_state_dict(weights_sd, False)  # network.load_weightsを使うようにするとよい
                 print(f"weights are loaded: {info}")
@@ -2349,12 +2368,12 @@ def main(args):
     if args.diffusers_xformers:
         pipe.enable_xformers_memory_efficient_attention()
 
+    # Extended Textual Inversion および Textual Inversionを処理する
     if args.XTI_embeddings:
         diffusers.models.UNet2DConditionModel.forward = unet_forward_XTI
         diffusers.models.unet_2d_blocks.CrossAttnDownBlock2D.forward = downblock_forward_XTI
         diffusers.models.unet_2d_blocks.CrossAttnUpBlock2D.forward = upblock_forward_XTI
 
-    # Textual Inversionを処理する
     if args.textual_inversion_embeddings:
         token_ids_embeds = []
         for embeds_file in args.textual_inversion_embeddings:
@@ -2558,16 +2577,22 @@ def main(args):
             print(f"resize img2img mask images to {args.W}*{args.H}")
             mask_images = resize_images(mask_images, (args.W, args.H))
 
+    regional_network = False
     if networks and mask_images:
-        # mask を領域情報として流用する、現在は1枚だけ対応
+        # mask を領域情報として流用する、現在は一回のコマンド呼び出しで1枚だけ対応
-        # TODO 複数のnetwork classの混在時の考慮
+        regional_network = True
         print("use mask as region")
-        # import cv2
+
-        # for i in range(3):
+        size = None
-        #   cv2.imshow("msk", np.array(mask_images[0])[:,:,i])
+        for i, network in enumerate(networks):
-        #   cv2.waitKey()
+            if i < 3:
-        #   cv2.destroyAllWindows()
+                np_mask = np.array(mask_images[0])
-        networks[0].__class__.set_regions(networks, np.array(mask_images[0]))
+                np_mask = np_mask[:, :, i]
+                size = np_mask.shape
+            else:
+                np_mask = np.full(size, 255, dtype=np.uint8)    
+            mask = torch.from_numpy(np_mask.astype(np.float32) / 255.0)
+            network.set_region(i, i == len(networks) - 1, mask)
         mask_images = None
 
     prev_image = None  # for VGG16 guided
@@ -2623,7 +2648,14 @@ def main(args):
                     height_1st = height_1st - height_1st % 32
 
                     ext_1st = BatchDataExt(
-                        width_1st, height_1st, args.highres_fix_steps, ext.scale, ext.negative_scale, ext.strength, ext.network_muls
+                        width_1st,
+                        height_1st,
+                        args.highres_fix_steps,
+                        ext.scale,
+                        ext.negative_scale,
+                        ext.strength,
+                        ext.network_muls,
+                        ext.num_sub_prompts,
                     )
                     batch_1st.append(BatchData(args.highres_fix_latents_upscaling, base, ext_1st))
                 images_1st = process_batch(batch_1st, True, True)
@@ -2651,7 +2683,7 @@ def main(args):
             (
                 return_latents,
                 (step_first, _, _, _, init_image, mask_image, _, guide_image),
-                (width, height, steps, scale, negative_scale, strength, network_muls),
+                (width, height, steps, scale, negative_scale, strength, network_muls, num_sub_prompts),
             ) = batch[0]
             noise_shape = (LATENT_CHANNELS, height // DOWNSAMPLING_FACTOR, width // DOWNSAMPLING_FACTOR)
 
@@ -2743,8 +2775,11 @@ def main(args):
 
             # generate
             if networks:
+                shared = {}
                 for n, m in zip(networks, network_muls if network_muls else network_default_muls):
                     n.set_multiplier(m)
+                    if regional_network:
+                        n.set_current_generation(batch_size, num_sub_prompts, width, height, shared)
 
             images = pipe(
                 prompts,
@@ -2969,11 +3004,26 @@ def main(args):
                         print("Use previous image as guide image.")
                         guide_image = prev_image
 
+                if regional_network:
+                    num_sub_prompts = len(prompt.split(" AND "))
+                    assert (
+                        len(networks) <= num_sub_prompts
+                    ), "Number of networks must be less than or equal to number of sub prompts."
+                else:
+                    num_sub_prompts = None
+
                 b1 = BatchData(
                     False,
                     BatchDataBase(global_step, prompt, negative_prompt, seed, init_image, mask_image, clip_prompt, guide_image),
                     BatchDataExt(
-                        width, height, steps, scale, negative_scale, strength, tuple(network_muls) if network_muls else None
+                        width,
+                        height,
+                        steps,
+                        scale,
+                        negative_scale,
+                        strength,
+                        tuple(network_muls) if network_muls else None,
+                        num_sub_prompts,
                     ),
                 )
                 if len(batch_data) > 0 and batch_data[-1].ext != b1.ext:  # バッチ分割必要？
@@ -3197,6 +3247,9 @@ def setup_parser() -> argparse.ArgumentParser:
         nargs="*",
         help="ControlNet guidance ratio for steps / ControlNetでガイドするステップ比率",
     )
+    # parser.add_argument(
+    #     "--control_net_image_path", type=str, default=None, nargs="*", help="image for ControlNet guidance / ControlNetでガイドに使う画像"
+    # )
 
     return parser
 

networks/lora.py

@@ -10,7 +10,6 @@ import numpy as np
 import torch
 import re
 
-from library import train_util
 
 RE_UPDOWN = re.compile(r"(up|down)_blocks_(\d+)_(resnets|upsamplers|downsamplers|attentions)_(\d+)_")
 
@@ -61,8 +60,6 @@ class LoRAModule(torch.nn.Module):
 
         self.multiplier = multiplier
         self.org_module = org_module  # remove in applying
-        self.region = None
-        self.region_mask = None
 
     def apply_to(self):
         self.org_forward = self.org_module.forward
@@ -105,39 +102,187 @@ class LoRAModule(torch.nn.Module):
         self.region_mask = None
 
     def forward(self, x):
-        if self.region is None:
         return self.org_forward(x) + self.lora_up(self.lora_down(x)) * self.multiplier * self.scale
 
-        # regional LoRA   FIXME same as additional-network extension
+
-        if x.size()[1] % 77 == 0:
+class LoRAInfModule(LoRAModule):
-            # print(f"LoRA for context: {self.lora_name}")
+    def __init__(self, lora_name, org_module: torch.nn.Module, multiplier=1.0, lora_dim=4, alpha=1):
-            self.region = None
+        super().__init__(lora_name, org_module, multiplier, lora_dim, alpha)
+
+        # check regional or not by lora_name
+        self.text_encoder = False
+        if lora_name.startswith("lora_te_"):
+            self.regional = False
+            self.use_sub_prompt = True
+            self.text_encoder = True
+        elif "attn2_to_k" in lora_name or "attn2_to_v" in lora_name:
+            self.regional = False
+            self.use_sub_prompt = True
+        elif "time_emb" in lora_name:
+            self.regional = False
+            self.use_sub_prompt = False
+        else:
+            self.regional = True
+            self.use_sub_prompt = False
+
+        self.network: LoRANetwork = None
+
+    def set_network(self, network):
+        self.network = network
+
+    def default_forward(self, x):
+        # print("default_forward", self.lora_name, x.size())
         return self.org_forward(x) + self.lora_up(self.lora_down(x)) * self.multiplier * self.scale
 
-        # calculate region mask first time
+    def forward(self, x):
-        if self.region_mask is None:
+        if self.network is None or self.network.sub_prompt_index is None:
+            return self.default_forward(x)
+        if not self.regional and not self.use_sub_prompt:
+            return self.default_forward(x)
+
+        if self.regional:
+            return self.regional_forward(x)
+        else:
+            return self.sub_prompt_forward(x)
+
+    def get_mask_for_x(self, x):
+        # calculate size from shape of x
         if len(x.size()) == 4:
             h, w = x.size()[2:4]
+            area = h * w
         else:
-                seq_len = x.size()[1]
+            area = x.size()[1]
-                ratio = math.sqrt((self.region.size()[0] * self.region.size()[1]) / seq_len)
-                h = int(self.region.size()[0] / ratio + 0.5)
-                w = seq_len // h
 
-            r = self.region.to(x.device)
+        mask = self.network.mask_dic[area]
-            if r.dtype == torch.bfloat16:
+        if mask is None:
-                r = r.to(torch.float)
+            raise ValueError(f"mask is None for resolution {area}")
-            r = r.unsqueeze(0).unsqueeze(1)
+        if len(x.size()) != 4:
-            # print(self.lora_name, self.region.size(), x.size(), r.size(), h, w)
+            mask = torch.reshape(mask, (1, -1, 1))
-            r = torch.nn.functional.interpolate(r, (h, w), mode="bilinear")
+        return mask
-            r = r.to(x.dtype)
 
-            if len(x.size()) == 3:
+    def regional_forward(self, x):
-                r = torch.reshape(r, (1, x.size()[1], -1))
+        if "attn2_to_out" in self.lora_name:
+            return self.to_out_forward(x)
 
-            self.region_mask = r
+        if self.network.mask_dic is None:  # sub_prompt_index >= 3
+            return self.default_forward(x)
 
-        return self.org_forward(x) + self.lora_up(self.lora_down(x)) * self.multiplier * self.scale * self.region_mask
+        # apply mask for LoRA result
+        lx = self.lora_up(self.lora_down(x)) * self.multiplier * self.scale
+        mask = self.get_mask_for_x(lx)
+        # print("regional", self.lora_name, self.network.sub_prompt_index, lx.size(), mask.size())
+        lx = lx * mask
+
+        x = self.org_forward(x)
+        x = x + lx
+
+        if "attn2_to_q" in self.lora_name and self.network.is_last_network:
+            x = self.postp_to_q(x)
+
+        return x
+
+    def postp_to_q(self, x):
+        # repeat x to num_sub_prompts
+        has_real_uncond = x.size()[0] // self.network.batch_size == 3
+        qc = self.network.batch_size  # uncond
+        qc += self.network.batch_size * self.network.num_sub_prompts  # cond
+        if has_real_uncond:
+            qc += self.network.batch_size  # real_uncond
+
+        query = torch.zeros((qc, x.size()[1], x.size()[2]), device=x.device, dtype=x.dtype)
+        query[: self.network.batch_size] = x[: self.network.batch_size]
+
+        for i in range(self.network.batch_size):
+            qi = self.network.batch_size + i * self.network.num_sub_prompts
+            query[qi : qi + self.network.num_sub_prompts] = x[self.network.batch_size + i]
+
+        if has_real_uncond:
+            query[-self.network.batch_size :] = x[-self.network.batch_size :]
+
+        # print("postp_to_q", self.lora_name, x.size(), query.size(), self.network.num_sub_prompts)
+        return query
+
+    def sub_prompt_forward(self, x):
+        if x.size()[0] == self.network.batch_size:  # if uncond in text_encoder, do not apply LoRA
+            return self.org_forward(x)
+
+        emb_idx = self.network.sub_prompt_index
+        if not self.text_encoder:
+            emb_idx += self.network.batch_size
+
+        # apply sub prompt of X
+        lx = x[emb_idx :: self.network.num_sub_prompts]
+        lx = self.lora_up(self.lora_down(lx)) * self.multiplier * self.scale
+
+        # print("sub_prompt_forward", self.lora_name, x.size(), lx.size(), emb_idx)
+
+        x = self.org_forward(x)
+        x[emb_idx :: self.network.num_sub_prompts] += lx
+
+        return x
+
+    def to_out_forward(self, x):
+        # print("to_out_forward", self.lora_name, x.size(), self.network.is_last_network)
+
+        if self.network.is_last_network:
+            masks = [None] * self.network.num_sub_prompts
+            self.network.shared[self.lora_name] = (None, masks)
+        else:
+            lx, masks = self.network.shared[self.lora_name]
+
+        # call own LoRA
+        x1 = x[self.network.batch_size + self.network.sub_prompt_index :: self.network.num_sub_prompts]
+        lx1 = self.lora_up(self.lora_down(x1)) * self.multiplier * self.scale
+
+        if self.network.is_last_network:
+            lx = torch.zeros(
+                (self.network.num_sub_prompts * self.network.batch_size, *lx1.size()[1:]), device=lx1.device, dtype=lx1.dtype
+            )
+            self.network.shared[self.lora_name] = (lx, masks)
+
+        # print("to_out_forward", lx.size(), lx1.size(), self.network.sub_prompt_index, self.network.num_sub_prompts)
+        lx[self.network.sub_prompt_index :: self.network.num_sub_prompts] += lx1
+        masks[self.network.sub_prompt_index] = self.get_mask_for_x(lx1)
+
+        # if not last network, return x and masks
+        x = self.org_forward(x)
+        if not self.network.is_last_network:
+            return x
+
+        lx, masks = self.network.shared.pop(self.lora_name)
+
+        # if last network, combine separated x with mask weighted sum
+        has_real_uncond = x.size()[0] // self.network.batch_size == self.network.num_sub_prompts + 2
+
+        out = torch.zeros((self.network.batch_size * (3 if has_real_uncond else 2), *x.size()[1:]), device=x.device, dtype=x.dtype)
+        out[: self.network.batch_size] = x[: self.network.batch_size]  # uncond
+        if has_real_uncond:
+            out[-self.network.batch_size :] = x[-self.network.batch_size :]  # real_uncond
+
+        # print("to_out_forward", self.lora_name, self.network.sub_prompt_index, self.network.num_sub_prompts)
+        # for i in range(len(masks)):
+        #     if masks[i] is None:
+        #         masks[i] = torch.zeros_like(masks[-1])
+
+        mask = torch.cat(masks)
+        mask_sum = torch.sum(mask, dim=0) + 1e-4
+        for i in range(self.network.batch_size):
+            # 1枚の画像ごとに処理する
+            lx1 = lx[i * self.network.num_sub_prompts : (i + 1) * self.network.num_sub_prompts]
+            lx1 = lx1 * mask
+            lx1 = torch.sum(lx1, dim=0)
+
+            xi = self.network.batch_size + i * self.network.num_sub_prompts
+            x1 = x[xi : xi + self.network.num_sub_prompts]
+            x1 = x1 * mask
+            x1 = torch.sum(x1, dim=0)
+            x1 = x1 / mask_sum
+
+            x1 = x1 + lx1
+            out[self.network.batch_size + i] = x1
+
+        # print("to_out_forward", x.size(), out.size(), has_real_uncond)
+        return out
 
 
 def create_network(multiplier, network_dim, network_alpha, vae, text_encoder, unet, **kwargs):
@@ -421,7 +566,7 @@ def get_block_index(lora_name: str) -> int:
 
 
 # Create network from weights for inference, weights are not loaded here (because can be merged)
-def create_network_from_weights(multiplier, file, vae, text_encoder, unet, weights_sd=None, **kwargs):
+def create_network_from_weights(multiplier, file, vae, text_encoder, unet, weights_sd=None, for_inference=False, **kwargs):
     if weights_sd is None:
         if os.path.splitext(file)[1] == ".safetensors":
             from safetensors.torch import load_file, safe_open
@@ -450,7 +595,11 @@ def create_network_from_weights(multiplier, file, vae, text_encoder, unet, weigh
         if key not in modules_alpha:
             modules_alpha = modules_dim[key]
 
-    network = LoRANetwork(text_encoder, unet, multiplier=multiplier, modules_dim=modules_dim, modules_alpha=modules_alpha)
+    module_class = LoRAInfModule if for_inference else LoRAModule
+
+    network = LoRANetwork(
+        text_encoder, unet, multiplier=multiplier, modules_dim=modules_dim, modules_alpha=modules_alpha, module_class=module_class
+    )
     return network, weights_sd
 
 
@@ -479,6 +628,7 @@ class LoRANetwork(torch.nn.Module):
         conv_block_alphas=None,
         modules_dim=None,
         modules_alpha=None,
+        module_class=LoRAModule,
         varbose=False,
     ) -> None:
         """
@@ -554,7 +704,7 @@ class LoRANetwork(torch.nn.Module):
                                     skipped.append(lora_name)
                                 continue
 
-                            lora = LoRAModule(lora_name, child_module, self.multiplier, dim, alpha)
+                            lora = module_class(lora_name, child_module, self.multiplier, dim, alpha)
                             loras.append(lora)
             return loras, skipped
 
@@ -750,6 +900,7 @@ class LoRANetwork(torch.nn.Module):
 
         if os.path.splitext(file)[1] == ".safetensors":
             from safetensors.torch import save_file
+            from library import train_util
 
             # Precalculate model hashes to save time on indexing
             if metadata is None:
@@ -762,17 +913,45 @@ class LoRANetwork(torch.nn.Module):
         else:
             torch.save(state_dict, file)
 
-    @staticmethod
+    # mask is a tensor with values from 0 to 1
-    def set_regions(networks, image):
+    def set_region(self, sub_prompt_index, is_last_network, mask):
-        image = image.astype(np.float32) / 255.0
+        if mask.max() == 0:
-        for i, network in enumerate(networks[:3]):
+            mask = torch.ones_like(mask)
-            # NOTE: consider averaging overwrapping area
-            region = image[:, :, i]
-            if region.max() == 0:
-                continue
-            region = torch.tensor(region)
-            network.set_region(region)
 
-    def set_region(self, region):
+        self.mask = mask
-        for lora in self.unet_loras:
+        self.sub_prompt_index = sub_prompt_index
-            lora.set_region(region)
+        self.is_last_network = is_last_network
+
+        for lora in self.text_encoder_loras + self.unet_loras:
+            lora.set_network(self)
+
+    def set_current_generation(self, batch_size, num_sub_prompts, width, height, shared):
+        self.batch_size = batch_size
+        self.num_sub_prompts = num_sub_prompts
+        self.current_size = (height, width)
+        self.shared = shared
+
+        # create masks
+        mask = self.mask
+        mask_dic = {}
+        mask = mask.unsqueeze(0).unsqueeze(1)  # b(1),c(1),h,w
+        ref_weight = self.text_encoder_loras[0].lora_down.weight if self.text_encoder_loras else self.unet_loras[0].lora_down.weight
+        dtype = ref_weight.dtype
+        device = ref_weight.device
+
+        def resize_add(mh, mw):
+            # print(mh, mw, mh * mw)
+            m = torch.nn.functional.interpolate(mask, (mh, mw), mode="bilinear")  # doesn't work in bf16
+            m = m.to(device, dtype=dtype)
+            mask_dic[mh * mw] = m
+
+        h = height // 8
+        w = width // 8
+        for _ in range(4):
+            resize_add(h, w)
+            if h % 2 == 1 or w % 2 == 1:  # add extra shape if h/w is not divisible by 2
+                resize_add(h + h % 2, w + w % 2)
+            h = (h + 1) // 2
+            w = (w + 1) // 2
+
+        self.mask_dic = mask_dic