Merge branch 'divamgupta:master' into add-notification-sound

Author: AEtheve

backends/model_converter/.gitignore

@@ -3,6 +3,8 @@ optimized_stable_diffusion/
 
 convert_sd_model_bin
 ./convert_sd_model_bin
+convert_sd_model_bin/
+./convert_sd_model_bin/
 
 HF_weights/
 outputs/

backends/model_converter/build.sh

@@ -1 +1 @@
-pyinstaller  convert_model.py  --onefile  --noconfirm --clean
+pyinstaller  convert_model.py    --noconfirm --clean

backends/model_converter/convert_model.py

@@ -33,14 +33,17 @@
 extra_keys = ['temb_coefficients_fp32' , 'causal_mask' , 'aux_output_conv.weight' , 'aux_output_conv.bias', 'alphas_cumprod']
 
 for k in torch_weights['state_dict']:
-    if k not in extra_keys:
-        assert k in SD_SHAPES , k 
+    if k not in SD_SHAPES:
+        continue
     np_arr = torch_weights['state_dict'][k]
     key_bytes = np_arr.tobytes()
     shape = list(np_arr.shape)
     if k not in extra_keys:
-        assert tuple(shape) == SD_SHAPES[k], (k , shape , SD_SHAPES[k] )
+        assert tuple(shape) == SD_SHAPES[k], ( "shape mismatch at" ,  k , shape , SD_SHAPES[k] )
     dtype = str(np_arr.dtype)
+    if dtype == 'int64':
+        np_arr = np_arr.astype('float32')
+        dtype = 'float32'
     assert dtype in ['float16' , 'float32']
     e = s + len(key_bytes)
     out_file.write(key_bytes)

backends/model_converter/fake_torch.py

@@ -1,6 +1,7 @@
 import pickle
 import numpy as np
 import math
+import zipfile
 
 def prod(x):
     return math.prod(x)
@@ -52,13 +53,17 @@ def persistent_load(self, pid):
   return MyPickle(fb0).load(), key_prelookup
 
 def fake_torch_load_zipped(fb0, load_weights=True):
-  import zipfile
+  
   with zipfile.ZipFile(fb0, 'r') as myzip:
-    with myzip.open('archive/data.pkl') as myfile:
+    folder_name = [a for a in myzip.namelist() if a.endswith("/data.pkl")]
+    if len(folder_name)== 0:
+      raise ValueError("Looke like the checkpoints file is in the wrong format")
+    folder_name = folder_name[0].replace("/data.pkl" , "").replace("\\data.pkl" , "")
+    with myzip.open(folder_name+'/data.pkl') as myfile:
       ret = my_unpickle(myfile)
     if load_weights:
       for k,v in ret[1].items():
-        with myzip.open(f'archive/data/{k}') as myfile:
+        with myzip.open(folder_name + f'/data/{k}') as myfile:
           if v[2].dtype == "object":
             print(f"issue assigning object on {k}")
             continue

backends/stable_diffusion_tf/build.sh

@@ -3,4 +3,4 @@ cp stable_diffusion_tf/clip_tokenizer/bpe_simple_vocab_16e6.txt.gz ./dist/diffus
 
 cp -r ../realesrgan_ncnn/models  ./dist/diffusionbee_backend/models 
 cp  ../realesrgan_ncnn/realesrgan_ncnn_macos  ./dist/diffusionbee_backend/realesrgan_ncnn_macos 
-cp  ../model_converter/convert_sd_model_bin  ./dist/diffusionbee_backend/convert_model 
+cp -r ../model_converter/convert_sd_model_bin/m1  ./dist/diffusionbee_backend/convert_model

electron_app/src/background.js

@@ -26,8 +26,8 @@ protocol.registerSchemesAsPrivileged([
 ])
 
 
-// import {menu_template} from "./menu_template"
-// Menu.setApplicationMenu(Menu.buildFromTemplate(menu_template))
+import {menu_template} from "./menu_template"
+Menu.setApplicationMenu(Menu.buildFromTemplate(menu_template))
 
 start_bridge();
 

electron_app/src/components/Outpainting.vue

@@ -20,6 +20,9 @@
                     v-bind:class="{ 'disabled' : !stable_diffusion.is_input_avail}"
                     :rows="2">
         </textarea>
+
+       
+        
 
         <div v-if="stable_diffusion.is_input_avail" style="margin-top: 20px">
 
@@ -43,7 +46,7 @@
             <LoaderModal :loading_percentage="done_percentage" loading_title="Generating" :loading_desc="stable_diffusion.generation_state_msg"></LoaderModal>
         </div>
 
-        <p style="opacity:0.5; zoom:0.8"> Please describe image you want to draw in the box. </p>
+        <p style="opacity:0.5; zoom:0.8; float:left; max-width: calc(100vw - 200px); margin-bottom: 0; "> Please describe image you want to draw in the box. </p>
 
      </div>
 </template>
@@ -117,7 +120,7 @@ export default {
     methods: {
         init_state(){
             let that = this;
-            this.stage_h = this.stage_w / (window.innerWidth  / ( window.innerHeight - 210 ));
+            this.stage_h = this.stage_w / (window.innerWidth  / ( window.innerHeight - 230 ));
 
             this.prompt = ""
 

electron_app/src/components_bare/SDOptionsDropdown.vue

@@ -297,7 +297,7 @@ export default {
         SetGuidenceScale(e) {
             var value = (e.target.value - e.target.min) / (e.target.max - e.target.min) * 100
             e.target.style.background = 'linear-gradient(to right, var(--slider-progress) 0%, var(--slider-progress) ' + value + '%, var(--slider-progress_end) ' + value + '%, var(--slider-progress_end) 100%)'
-            // this.options_model_values.guidence_scale = Number(e.target.value)
+            this.options_model_values.guidence_scale = Number(e.target.value)
         }
     },
 }

electron_app/src/native_functions.js

@@ -463,7 +463,7 @@ function add_custom_pytorch_models(pytorch_model_path, model_name, cb ){
     }
 
     let out_path =  path.join(homedir , ".diffusionbee" , "custom_models" , model_name+".tdict" );
-    let bin_path =  process.env.CONVERT_MODEL_BIN || path.join(path.dirname(__dirname), 'core' , 'convert_model' );
+    let bin_path =  process.env.CONVERT_MODEL_BIN || path.join(path.dirname(__dirname), 'core' , 'convert_model' , 'convert_model' );
 
 
     let proc = require('child_process').spawn( bin_path  , [pytorch_model_path , out_path ]);

electron_app/vue.config.js

@@ -31,7 +31,7 @@ module.exports = {
                     "target": {
                         "target": "dmg",
                         "arch": [
-                            'arm64' , 'x64'
+                            process.env.BUILD_ARCH  //'arm64' , 'x64'
                         ]
                     }
                 },