From bad7cb29cecac51c5c0f39afec332b007ed73133 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 7 Oct 2022 10:17:52 +0300
Subject: [PATCH] added support for hypernetworks (???)

---
 modules/hypernetwork.py            | 55 ++++++++++++++++++++++++++++++
 modules/sd_hijack_optimizations.py | 17 +++++++--
 modules/shared.py                  |  9 ++++-
 scripts/xy_grid.py                 | 10 ++++++
 4 files changed, 88 insertions(+), 3 deletions(-)
 create mode 100644 modules/hypernetwork.py

diff --git a/modules/hypernetwork.py b/modules/hypernetwork.py
new file mode 100644
index 00000000..9ed1eed9
--- /dev/null
+++ b/modules/hypernetwork.py
@@ -0,0 +1,55 @@
+import glob
+import os
+import torch
+from modules import devices
+
+
+class HypernetworkModule(torch.nn.Module):
+    def __init__(self, dim, state_dict):
+        super().__init__()
+
+        self.linear1 = torch.nn.Linear(dim, dim * 2)
+        self.linear2 = torch.nn.Linear(dim * 2, dim)
+
+        self.load_state_dict(state_dict, strict=True)
+        self.to(devices.device)
+
+    def forward(self, x):
+        return x + (self.linear2(self.linear1(x)))
+
+
+class Hypernetwork:
+    filename = None
+    name = None
+
+    def __init__(self, filename):
+        self.filename = filename
+        self.name = os.path.splitext(os.path.basename(filename))[0]
+        self.layers = {}
+
+        state_dict = torch.load(filename, map_location='cpu')
+        for size, sd in state_dict.items():
+            self.layers[size] = (HypernetworkModule(size, sd[0]), HypernetworkModule(size, sd[1]))
+
+
+def load_hypernetworks(path):
+    res = {}
+
+    for filename in glob.iglob(path + '**/*.pt', recursive=True):
+        hn = Hypernetwork(filename)
+        res[hn.name] = hn
+
+    return res
+
+def apply(self, x, context=None, mask=None, original=None):
+
+
+    if CrossAttention.hypernetwork is not None and context.shape[2] in CrossAttention.hypernetwork:
+        if context.shape[1] == 77 and CrossAttention.noise_cond:
+            context = context + (torch.randn_like(context) * 0.1)
+        h_k, h_v = CrossAttention.hypernetwork[context.shape[2]]
+        k = self.to_k(h_k(context))
+        v = self.to_v(h_v(context))
+    else:
+        k = self.to_k(context)
+        v = self.to_v(context)
diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index ea4cfdfc..d9cca485 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -5,6 +5,8 @@ from torch import einsum
 from ldm.util import default
 from einops import rearrange
 
+from modules import shared
+
 
 # see https://github.com/basujindal/stable-diffusion/pull/117 for discussion
 def split_cross_attention_forward_v1(self, x, context=None, mask=None):
@@ -42,8 +44,19 @@ def split_cross_attention_forward(self, x, context=None, mask=None):
 
     q_in = self.to_q(x)
     context = default(context, x)
-    k_in = self.to_k(context) * self.scale
-    v_in = self.to_v(context)
+
+    hypernetwork = shared.selected_hypernetwork()
+    hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
+
+    if hypernetwork_layers is not None:
+        k_in = self.to_k(hypernetwork_layers[0](context))
+        v_in = self.to_v(hypernetwork_layers[1](context))
+    else:
+        k_in = self.to_k(context)
+        v_in = self.to_v(context)
+
+    k_in *= self.scale
+
     del context, x
 
     q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
diff --git a/modules/shared.py b/modules/shared.py
index 25bb6e6c..879d8424 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -13,7 +13,7 @@ import modules.memmon
 import modules.sd_models
 import modules.styles
 import modules.devices as devices
-from modules import sd_samplers
+from modules import sd_samplers, hypernetwork
 from modules.paths import models_path, script_path, sd_path
 
 sd_model_file = os.path.join(script_path, 'model.ckpt')
@@ -76,6 +76,12 @@ parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram
 
 config_filename = cmd_opts.ui_settings_file
 
+hypernetworks = hypernetwork.load_hypernetworks(os.path.join(models_path, 'hypernetworks'))
+
+
+def selected_hypernetwork():
+    return hypernetworks.get(opts.sd_hypernetwork, None)
+
 
 class State:
     interrupted = False
@@ -206,6 +212,7 @@ options_templates.update(options_section(('system', "System"), {
 
 options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}),
+    "sd_hypernetwork": OptionInfo("None", "Stable Diffusion finetune hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}),
     "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
     "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
     "img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."),
diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py
index 6344e612..c0c364df 100644
--- a/scripts/xy_grid.py
+++ b/scripts/xy_grid.py
@@ -77,6 +77,11 @@ def apply_checkpoint(p, x, xs):
     modules.sd_models.reload_model_weights(shared.sd_model, info)
 
 
+def apply_hypernetwork(p, x, xs):
+    hn = shared.hypernetworks.get(x, None)
+    opts.data["sd_hypernetwork"] = hn.name if hn is not None else 'None'
+
+
 def format_value_add_label(p, opt, x):
     if type(x) == float:
         x = round(x, 8)
@@ -122,6 +127,7 @@ axis_options = [
     AxisOption("Prompt order", str_permutations, apply_order, format_value_join_list),
     AxisOption("Sampler", str, apply_sampler, format_value),
     AxisOption("Checkpoint name", str, apply_checkpoint, format_value),
+    AxisOption("Hypernetwork", str, apply_hypernetwork, format_value),
     AxisOption("Sigma Churn", float, apply_field("s_churn"), format_value_add_label),
     AxisOption("Sigma min", float, apply_field("s_tmin"), format_value_add_label),
     AxisOption("Sigma max", float, apply_field("s_tmax"), format_value_add_label),
@@ -193,6 +199,8 @@ class Script(scripts.Script):
         modules.processing.fix_seed(p)
         p.batch_size = 1
 
+        initial_hn = opts.sd_hypernetwork
+
         def process_axis(opt, vals):
             if opt.label == 'Nothing':
                 return [0]
@@ -300,4 +308,6 @@ class Script(scripts.Script):
         # restore checkpoint in case it was changed by axes
         modules.sd_models.reload_model_weights(shared.sd_model)
 
+        opts.data["sd_hypernetwork"] = initial_hn
+
         return processed