Merge branch 'sd3_5_support' of https://github.com/kohya-ss/sd-scripts …

…into sd3_5_support

library/flux_utils.py

@@ -54,6 +54,10 @@ def analyze_checkpoint_state(ckpt_path: str) -> Tuple[bool, bool, Tuple[int, int
         with safe_open(ckpt_path, framework="pt") as f:
             keys.extend(f.keys())
 
+    # if the key has annoying prefix, remove it
+    if keys[0].startswith("model.diffusion_model."):
+        keys = [key.replace("model.diffusion_model.", "") for key in keys]
+
     is_diffusers = "transformer_blocks.0.attn.add_k_proj.bias" in keys
     is_schnell = not ("guidance_in.in_layer.bias" in keys or "time_text_embed.guidance_embedder.linear_1.bias" in keys)
 
@@ -122,6 +126,13 @@ def load_flow_model(
         sd = convert_diffusers_sd_to_bfl(sd, num_double_blocks, num_single_blocks)
         logger.info("Converted Diffusers to BFL")
 
+    # if the key has annoying prefix, remove it
+    for key in list(sd.keys()):
+        new_key = key.replace("model.diffusion_model.", "")
+        if new_key == key:
+            break  # the model doesn't have annoying prefix
+        sd[new_key] = sd.pop(key)
+
     info = model.load_state_dict(sd, strict=False, assign=True)
     logger.info(f"Loaded Flux: {info}")
     return is_schnell, model

networks/lora_sd3.py

@@ -307,6 +307,7 @@ def create_modules(
             target_replace_modules: List[str],
             filter: Optional[str] = None,
             default_dim: Optional[int] = None,
+            include_conv2d_if_filter: bool = False,
         ) -> List[LoRAModule]:
             prefix = (
                 self.LORA_PREFIX_SD3
@@ -332,8 +333,11 @@ def create_modules(
                             lora_name = prefix + "." + (name + "." if name else "") + child_name
                             lora_name = lora_name.replace(".", "_")
 
-                            if filter is not None and not filter in lora_name:
-                                continue
+                            force_incl_conv2d = False
+                            if filter is not None:
+                                if not filter in lora_name:
+                                    continue
+                                force_incl_conv2d = include_conv2d_if_filter
 
                             dim = None
                             alpha = None
@@ -373,6 +377,10 @@ def create_modules(
                                 elif self.conv_lora_dim is not None:
                                     dim = self.conv_lora_dim
                                     alpha = self.conv_alpha
+                                elif force_incl_conv2d:
+                                    # x_embedder
+                                    dim = default_dim if default_dim is not None else self.lora_dim
+                                    alpha = self.alpha
 
                             if dim is None or dim == 0:
                                 # skipした情報を出力
@@ -428,15 +436,20 @@ def create_modules(
             for filter, in_dim in zip(
                 [
                     "context_embedder",
-                    "t_embedder",
+                    "_t_embedder",  # don't use "t_embedder" because it's used in "context_embedder"
                     "x_embedder",
                     "y_embedder",
                     "final_layer_adaLN_modulation",
                     "final_layer_linear",
                 ],
                 self.emb_dims,
             ):
-                loras, _ = create_modules(True, None, unet, None, filter=filter, default_dim=in_dim)
+                # x_embedder is conv2d, so we need to include it
+                loras, _ = create_modules(
+                    True, None, unet, None, filter=filter, default_dim=in_dim, include_conv2d_if_filter=filter == "x_embedder"
+                )
+                # if len(loras) > 0:
+                #     logger.info(f"create LoRA for {filter}: {len(loras)} modules.")
                 self.unet_loras.extend(loras)
 
         logger.info(f"create LoRA for SD3 MMDiT: {len(self.unet_loras)} modules.")
@@ -540,8 +553,8 @@ def state_dict(self, destination=None, prefix="", keep_vars=False):
             down_weight = torch.cat(down_weights, dim=0)  # (rank, split_dim) * 3 -> (rank*3, sum of split_dim)
 
             # merge up weight (sum of split_dim, rank*3)
-            qkv_dim, rank = up_weights[0].size()
-            split_dim = qkv_dim // 3
+            split_dim, rank = up_weights[0].size()
+            qkv_dim = split_dim * 3
             up_weight = torch.zeros((qkv_dim, down_weight.size(0)), device=down_weight.device, dtype=down_weight.dtype)
             i = 0
             for j in range(3):

sd3_minimal_inference.py

@@ -10,11 +10,13 @@
 
 import torch
 from safetensors.torch import safe_open, load_file
+import torch.amp
 from tqdm import tqdm
 from PIL import Image
 from transformers import CLIPTextModelWithProjection, T5EncoderModel
 
 from library.device_utils import init_ipex, get_preferred_device
+from networks import lora_sd3
 
 init_ipex()
 
@@ -104,7 +106,8 @@ def do_sample(
             x_c_nc = torch.cat([x, x], dim=0)
             # print(x_c_nc.shape, timestep.shape, c_crossattn.shape, y.shape)
 
-            model_output = mmdit(x_c_nc, timestep, context=c_crossattn, y=y)
+            with torch.autocast(device_type=device.type, dtype=dtype):
+                model_output = mmdit(x_c_nc, timestep, context=c_crossattn, y=y)
             model_output = model_output.float()
             batched = model_sampling.calculate_denoised(sigma_hat, model_output, x)
 
@@ -153,7 +156,7 @@ def generate_image(
     clip_g.to(device)
     t5xxl.to(device)
 
-    with torch.no_grad():
+    with torch.autocast(device_type=device.type, dtype=mmdit.dtype), torch.no_grad():
         tokens_and_masks = tokenize_strategy.tokenize(prompt)
         lg_out, t5_out, pooled, l_attn_mask, g_attn_mask, t5_attn_mask = encoding_strategy.encode_tokens(
             tokenize_strategy, [clip_l, clip_g, t5xxl], tokens_and_masks, args.apply_lg_attn_mask, args.apply_t5_attn_mask
@@ -233,13 +236,14 @@ def generate_image(
     parser.add_argument("--bf16", action="store_true")
     parser.add_argument("--seed", type=int, default=1)
     parser.add_argument("--steps", type=int, default=50)
-    # parser.add_argument(
-    #     "--lora_weights",
-    #     type=str,
-    #     nargs="*",
-    #     default=[],
-    #     help="LoRA weights, only supports networks.lora, each argument is a `path;multiplier` (semi-colon separated)",
-    # )
+    parser.add_argument(
+        "--lora_weights",
+        type=str,
+        nargs="*",
+        default=[],
+        help="LoRA weights, only supports networks.lora_sd3, each argument is a `path;multiplier` (semi-colon separated)",
+    )
+    parser.add_argument("--merge_lora_weights", action="store_true", help="Merge LoRA weights to model")
     parser.add_argument("--width", type=int, default=target_width)
     parser.add_argument("--height", type=int, default=target_height)
     parser.add_argument("--interactive", action="store_true")
@@ -294,6 +298,30 @@ def generate_image(
     tokenize_strategy = strategy_sd3.Sd3TokenizeStrategy(args.t5xxl_token_length)
     encoding_strategy = strategy_sd3.Sd3TextEncodingStrategy()
 
+    # LoRA
+    lora_models: list[lora_sd3.LoRANetwork] = []
+    for weights_file in args.lora_weights:
+        if ";" in weights_file:
+            weights_file, multiplier = weights_file.split(";")
+            multiplier = float(multiplier)
+        else:
+            multiplier = 1.0
+
+        weights_sd = load_file(weights_file)
+        module = lora_sd3
+        lora_model, _ = module.create_network_from_weights(multiplier, None, vae, [clip_l, clip_g, t5xxl], mmdit, weights_sd, True)
+
+        if args.merge_lora_weights:
+            lora_model.merge_to([clip_l, clip_g, t5xxl], mmdit, weights_sd)
+        else:
+            lora_model.apply_to([clip_l, clip_g, t5xxl], mmdit)
+            info = lora_model.load_state_dict(weights_sd, strict=True)
+            logger.info(f"Loaded LoRA weights from {weights_file}: {info}")
+            lora_model.eval()
+            lora_model.to(device)
+
+        lora_models.append(lora_model)
+
     if not args.interactive:
         generate_image(
             mmdit,
@@ -344,13 +372,13 @@ def generate_image(
                         steps = int(opt[1:].strip())
                     elif opt.startswith("d"):
                         seed = int(opt[1:].strip())
-                    # elif opt.startswith("m"):
-                    #     mutipliers = opt[1:].strip().split(",")
-                    #     if len(mutipliers) != len(lora_models):
-                    #         logger.error(f"Invalid number of multipliers, expected {len(lora_models)}")
-                    #         continue
-                    #     for i, lora_model in enumerate(lora_models):
-                    #         lora_model.set_multiplier(float(mutipliers[i]))
+                    elif opt.startswith("m"):
+                        mutipliers = opt[1:].strip().split(",")
+                        if len(mutipliers) != len(lora_models):
+                            logger.error(f"Invalid number of multipliers, expected {len(lora_models)}")
+                            continue
+                        for i, lora_model in enumerate(lora_models):
+                            lora_model.set_multiplier(float(mutipliers[i]))
                     elif opt.startswith("n"):
                         negative_prompt = opt[1:].strip()
                         if negative_prompt == "-":