Minimal flux with fp8 freeze

.github/workflows/run_tests.yaml

@@ -51,6 +51,7 @@ jobs:
           --test-server
           --do-not-download-clip
           --no-half
+          --precision full
           --disable-opt-split-attention
           --use-cpu all
           --api-server-stop

configs/flux1-inference.yaml

@@ -0,0 +1,4 @@
+model:
+  target: modules.models.flux.FLUX1Inferencer
+  params:
+    state_dict: null

configs/sd_xl_v.yaml

@@ -10,7 +10,7 @@ model:
         num_idx: 1000
 
         weighting_config:
-          target: sgm.modules.diffusionmodules.denoiser_weighting.EpsWeighting
+          target: sgm.modules.diffusionmodules.denoiser_weighting.VWeighting
         scaling_config:
           target: sgm.modules.diffusionmodules.denoiser_scaling.VScaling
         discretization_config:
@@ -21,7 +21,7 @@ model:
       params:
         adm_in_channels: 2816
         num_classes: sequential
-        use_checkpoint: True
+        use_checkpoint: False
         in_channels: 4
         out_channels: 4
         model_channels: 320

extensions-builtin/Lora/network_lora.py

@@ -2,6 +2,7 @@
 
 import lyco_helpers
 import modules.models.sd3.mmdit
+import modules.models.flux.modules.layers
 import network
 from modules import devices
 
@@ -37,7 +38,7 @@ def create_module(self, weights, key, none_ok=False):
         if weight is None and none_ok:
             return None
 
-        is_linear = type(self.sd_module) in [torch.nn.Linear, torch.nn.modules.linear.NonDynamicallyQuantizableLinear, torch.nn.MultiheadAttention, modules.models.sd3.mmdit.QkvLinear]
+        is_linear = type(self.sd_module) in [torch.nn.Linear, torch.nn.modules.linear.NonDynamicallyQuantizableLinear, torch.nn.MultiheadAttention, modules.models.sd3.mmdit.QkvLinear, modules.models.flux.modules.layers.QkvLinear ]
         is_conv = type(self.sd_module) in [torch.nn.Conv2d]
 
         if is_linear:

extensions-builtin/Lora/networks.py

@@ -37,7 +37,7 @@
 
 
 re_digits = re.compile(r"\d+")
-re_x_proj = re.compile(r"(.*)_([qkv]_proj)$")
+re_x_proj = re.compile(r"(.*)_((?:[qkv]|mlp)_proj)$")
 re_compiled = {}
 
 suffix_conversion = {
@@ -183,8 +183,12 @@ def load_network(name, network_on_disk):
     for key_network, weight in sd.items():
 
         if diffusers_weight_map:
-            key_network_without_network_parts, network_name, network_weight = key_network.rsplit(".", 2)
-            network_part = network_name + '.' + network_weight
+            if key_network.startswith("lora_unet"):
+                key_network_without_network_parts, _, network_part = key_network.partition(".")
+                key_network_without_network_parts = key_network_without_network_parts.replace("lora_unet", "diffusion_model")
+            else:
+                key_network_without_network_parts, network_name, network_weight = key_network.rsplit(".", 2)
+                network_part = network_name + '.' + network_weight
         else:
             key_network_without_network_parts, _, network_part = key_network.partition(".")
 
@@ -373,28 +377,34 @@ def allowed_layer_without_weight(layer):
     return False
 
 
-def store_weights_backup(weight):
+def store_weights_backup(weight, dtype):
     if weight is None:
         return None
 
-    return weight.to(devices.cpu, copy=True)
+    if shared.opts.lora_without_backup_weight:
+        return True
+    return weight.to(devices.cpu, dtype=dtype, copy=True)
 
 
 def restore_weights_backup(obj, field, weight):
     if weight is None:
         setattr(obj, field, None)
         return
 
-    getattr(obj, field).copy_(weight)
+    old_weight = getattr(obj, field)
+    old_weight.copy_(weight.to(dtype=old_weight.dtype))
 
 
-def network_restore_weights_from_backup(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.GroupNorm, torch.nn.LayerNorm, torch.nn.MultiheadAttention]):
+def network_restore_weights_from_backup(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.GroupNorm, torch.nn.LayerNorm, torch.nn.MultiheadAttention], cleanup=False):
     weights_backup = getattr(self, "network_weights_backup", None)
     bias_backup = getattr(self, "network_bias_backup", None)
 
     if weights_backup is None and bias_backup is None:
         return
 
+    if shared.opts.lora_without_backup_weight:
+        return
+
     if weights_backup is not None:
         if isinstance(self, torch.nn.MultiheadAttention):
             restore_weights_backup(self, 'in_proj_weight', weights_backup[0])
@@ -407,55 +417,79 @@ def network_restore_weights_from_backup(self: Union[torch.nn.Conv2d, torch.nn.Li
     else:
         restore_weights_backup(self, 'bias', bias_backup)
 
+    if cleanup:
+        if weights_backup is not None:
+            del self.network_weights_backup
+        if bias_backup is not None:
+            del self.network_bias_backup
 
-def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.GroupNorm, torch.nn.LayerNorm, torch.nn.MultiheadAttention]):
-    """
-    Applies the currently selected set of networks to the weights of torch layer self.
-    If weights already have this particular set of networks applied, does nothing.
-    If not, restores original weights from backup and alters weights according to networks.
-    """
 
+def network_backup_weights(self):
     network_layer_name = getattr(self, 'network_layer_name', None)
-    if network_layer_name is None:
-        return
 
-    current_names = getattr(self, "network_current_names", ())
+    _current_names = getattr(self, "network_current_names", ())
     wanted_names = tuple((x.name, x.te_multiplier, x.unet_multiplier, x.dyn_dim) for x in loaded_networks)
 
+    need_backup = False
+    for net in loaded_networks:
+        if network_layer_name in net.modules:
+            need_backup = True
+            break
+        elif network_layer_name + "_q_proj" in net.modules:
+            need_backup = True
+            break
+
+    if not need_backup:
+        return
+
     weights_backup = getattr(self, "network_weights_backup", None)
     if weights_backup is None and wanted_names != ():
-        if current_names != () and not allowed_layer_without_weight(self):
-            raise RuntimeError(f"{network_layer_name} - no backup weights found and current weights are not unchanged")
-
         if isinstance(self, torch.nn.MultiheadAttention):
-            weights_backup = (store_weights_backup(self.in_proj_weight), store_weights_backup(self.out_proj.weight))
+            weights_backup = (store_weights_backup(self.in_proj_weight, self.org_dtype), store_weights_backup(self.out_proj.weight, self.org_dtype))
         else:
-            weights_backup = store_weights_backup(self.weight)
+            weights_backup = store_weights_backup(self.weight, self.org_dtype)
 
         self.network_weights_backup = weights_backup
 
     bias_backup = getattr(self, "network_bias_backup", None)
     if bias_backup is None and wanted_names != ():
         if isinstance(self, torch.nn.MultiheadAttention) and self.out_proj.bias is not None:
-            bias_backup = store_weights_backup(self.out_proj.bias)
+            bias_backup = store_weights_backup(self.out_proj.bias, self.org_dtype)
         elif getattr(self, 'bias', None) is not None:
-            bias_backup = store_weights_backup(self.bias)
+            bias_backup = store_weights_backup(self.bias, self.org_dtype)
         else:
             bias_backup = None
 
-        # Unlike weight which always has value, some modules don't have bias.
-        # Only report if bias is not None and current bias are not unchanged.
-        if bias_backup is not None and current_names != ():
-            raise RuntimeError("no backup bias found and current bias are not unchanged")
-
         self.network_bias_backup = bias_backup
 
-    if current_names != wanted_names:
+
+def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.GroupNorm, torch.nn.LayerNorm, torch.nn.MultiheadAttention]):
+    """
+    Applies the currently selected set of networks to the weights of torch layer self.
+    If weights already have this particular set of networks applied, does nothing.
+    If not, restores original weights from backup and alters weights according to networks.
+    """
+
+    network_layer_name = getattr(self, 'network_layer_name', None)
+    if network_layer_name is None:
+        return
+
+    current_names = getattr(self, "network_current_names", ())
+    wanted_names = tuple((x.name, x.te_multiplier, x.unet_multiplier, x.dyn_dim) for x in loaded_networks)
+
+    weights_backup = getattr(self, "network_weights_backup", None)
+    if weights_backup is None and wanted_names != ():
+        network_backup_weights(self)
+    elif current_names != () and current_names != wanted_names and not getattr(self, "weights_restored", False):
         network_restore_weights_from_backup(self)
 
+    if current_names != wanted_names:
+        if hasattr(self, "weights_restored"):
+            self.weights_restored = False
+
         for net in loaded_networks:
             module = net.modules.get(network_layer_name, None)
-            if module is not None and hasattr(self, 'weight') and not isinstance(module, modules.models.sd3.mmdit.QkvLinear):
+            if module is not None and hasattr(self, 'weight') and not all(isinstance(module, linear) for linear in (modules.models.sd3.mmdit.QkvLinear, modules.models.flux.modules.layers.QkvLinear)):
                 try:
                     with torch.no_grad():
                         if getattr(self, 'fp16_weight', None) is None:
@@ -478,6 +512,7 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
                                 self.bias = torch.nn.Parameter(ex_bias).to(self.weight.dtype)
                             else:
                                 self.bias.copy_((bias + ex_bias).to(dtype=self.bias.dtype))
+                        del weight, bias, updown, ex_bias
                 except RuntimeError as e:
                     logging.debug(f"Network {net.name} layer {network_layer_name}: {e}")
                     extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1
@@ -515,7 +550,9 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
 
                 continue
 
-            if isinstance(self, modules.models.sd3.mmdit.QkvLinear) and module_q and module_k and module_v:
+            module_mlp = net.modules.get(network_layer_name + "_mlp_proj", None)
+
+            if any(isinstance(self, linear) for linear in (modules.models.sd3.mmdit.QkvLinear, modules.models.flux.modules.layers.QkvLinear)) and module_q and module_k and module_v and module_mlp is None and self.weight.shape[0] // 3 == module_q.up_model.weight.shape[0]:
                 try:
                     with torch.no_grad():
                         # Send "real" orig_weight into MHA's lora module
@@ -526,6 +563,31 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
                         del qw, kw, vw
                         updown_qkv = torch.vstack([updown_q, updown_k, updown_v])
                         self.weight += updown_qkv
+                        del updown_qkv
+                        del updown_q, updown_k, updown_v
+
+                except RuntimeError as e:
+                    logging.debug(f"Network {net.name} layer {network_layer_name}: {e}")
+                    extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1
+
+                continue
+
+            if any(isinstance(self, linear) for linear in (modules.models.flux.modules.layers.QkvLinear,)) and module_q and module_k and module_v:
+                try:
+                    with torch.no_grad():
+                        qw, kw, vw, mlp = torch.tensor_split(self.weight, (3072, 6144, 9216,), 0)
+                        updown_q, _ = module_q.calc_updown(qw)
+                        updown_k, _ = module_k.calc_updown(kw)
+                        updown_v, _ = module_v.calc_updown(vw)
+                        if module_mlp is not None:
+                            updown_mlp, _ = module_mlp.calc_updown(mlp)
+                        else:
+                            updown_mlp = torch.zeros(3072 * 4, 3072, dtype=updown_q.dtype, device=updown_q.device)
+                        del qw, kw, vw, mlp
+                        updown_qkv_mlp = torch.vstack([updown_q, updown_k, updown_v, updown_mlp])
+                        self.weight += updown_qkv_mlp
+                        del updown_qkv_mlp
+                        del updown_q, updown_k, updown_v, updown_mlp
 
                 except RuntimeError as e:
                     logging.debug(f"Network {net.name} layer {network_layer_name}: {e}")
@@ -539,7 +601,12 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
             logging.debug(f"Network {net.name} layer {network_layer_name}: couldn't find supported operation")
             extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1
 
-        self.network_current_names = wanted_names
+
+        if shared.opts.lora_without_backup_weight:
+            self.network_weights_backup = None
+            self.network_bias_backup = None
+        else:
+            self.network_current_names = wanted_names
 
 
 def network_forward(org_module, input, original_forward):

extensions-builtin/Lora/scripts/lora_script.py

@@ -1,15 +1,17 @@
 import re
+import torch
 
 import gradio as gr
 from fastapi import FastAPI
 
+import gc
 import network
 import networks
 import lora  # noqa:F401
 import lora_patches
 import extra_networks_lora
 import ui_extra_networks_lora
-from modules import script_callbacks, ui_extra_networks, extra_networks, shared
+from modules import script_callbacks, ui_extra_networks, extra_networks, shared, scripts, devices
 
 
 def unload():
@@ -97,6 +99,82 @@ def network_replacement(m):
     d["Prompt"] = re.sub(re_lora, network_replacement, d["Prompt"])
 
 
+class ScriptLora(scripts.Script):
+    name = "Lora"
+
+    def title(self):
+        return self.name
+
+    def show(self, is_img2img):
+        return scripts.AlwaysVisible
+
+    def after_extra_networks_activate(self, p, *args, **kwargs):
+        # check modules and setup org_dtype
+        modules = []
+        if shared.sd_model.is_sdxl:
+            for _i, embedder in enumerate(shared.sd_model.conditioner.embedders):
+                if not hasattr(embedder, 'wrapped'):
+                    continue
+
+                for _name, module in embedder.wrapped.named_modules():
+                    if isinstance(module, (torch.nn.Conv2d, torch.nn.Linear, torch.nn.GroupNorm, torch.nn.LayerNorm, torch.nn.MultiheadAttention)):
+                        if hasattr(module, 'weight'):
+                            modules.append(module)
+                        elif isinstance(module, torch.nn.MultiheadAttention):
+                            modules.append(module)
+
+        else:
+            cond_stage_model = getattr(shared.sd_model.cond_stage_model, 'wrapped', shared.sd_model.cond_stage_model)
+
+            for _name, module in cond_stage_model.named_modules():
+                if isinstance(module, (torch.nn.Conv2d, torch.nn.Linear, torch.nn.GroupNorm, torch.nn.LayerNorm, torch.nn.MultiheadAttention)):
+                    if hasattr(module, 'weight'):
+                        modules.append(module)
+                    elif isinstance(module, torch.nn.MultiheadAttention):
+                        modules.append(module)
+
+        for _name, module in shared.sd_model.model.named_modules():
+            if isinstance(module, (torch.nn.Conv2d, torch.nn.Linear, torch.nn.GroupNorm, torch.nn.LayerNorm, torch.nn.MultiheadAttention)):
+                if hasattr(module, 'weight'):
+                    modules.append(module)
+                elif isinstance(module, torch.nn.MultiheadAttention):
+                    modules.append(module)
+
+        print("Total lora modules after_extra_networks_activate() =", len(modules))
+
+        target_dtype = devices.dtype_inference
+        for module in modules:
+            network_layer_name = getattr(module, 'network_layer_name', None)
+            if network_layer_name is None:
+                continue
+
+            if isinstance(module, torch.nn.MultiheadAttention):
+                org_dtype = torch.float32
+            else:
+                org_dtype = None
+                for _name, param in module.named_parameters():
+                    if param.dtype != target_dtype:
+                        org_dtype = param.dtype
+                        break
+
+            # set org_dtype
+            module.org_dtype = org_dtype
+
+            # backup/restore weights
+            current_names = getattr(module, "network_current_names", ())
+            wanted_names = tuple((x.name, x.te_multiplier, x.unet_multiplier, x.dyn_dim) for x in networks.loaded_networks)
+
+            weights_backup = getattr(module, "network_weights_backup", None)
+
+            if current_names == () and current_names != wanted_names and weights_backup is None:
+                networks.network_backup_weights(module)
+            elif current_names != () and current_names != wanted_names:
+                networks.network_restore_weights_from_backup(module, wanted_names == ())
+                module.weights_restored = True
+        if current_names != wanted_names and wanted_names == ():
+            gc.collect()
+
+
 script_callbacks.on_infotext_pasted(infotext_pasted)
 
 shared.opts.onchange("lora_in_memory_limit", networks.purge_networks_from_memory)

extensions-builtin/canvas-zoom-and-pan/javascript/zoom.js

@@ -816,7 +816,7 @@ onUiLoaded(async() => {
                 // Increase or decrease brush size based on scroll direction
                 adjustBrushSize(elemId, e.deltaY);
             }
-        });
+        }, {passive: false});
 
         // Handle the move event for pan functionality. Updates the panX and panY variables and applies the new transform to the target element.
         function handleMoveKeyDown(e) {

extensions-builtin/hypertile/hypertile.py

@@ -1,7 +1,7 @@
 """
 Hypertile module for splitting attention layers in SD-1.5 U-Net and SD-1.5 VAE
 Warn: The patch works well only if the input image has a width and height that are multiples of 128
-Original author: @tfernd Github: https://github.com/tfernd/HyperTile
+Original author: @tfernd GitHub: https://github.com/tfernd/HyperTile
 """
 
 from __future__ import annotations