Merge branch 'SebastianAigner-main' into development

Add support for full CORS headers for dream server.

ldm/dream/server.py

@@ -16,6 +16,8 @@ class DreamServer(BaseHTTPRequestHandler):
     def do_GET(self):
         if self.path == "/":
             self.send_response(200)
+            self.send_header("Access-Control-Allow-Origin", "*")
+            self.send_header("Access-Control-Allow-Headers", "Origin, X-Requested-With, Content-Type, Accept")
             self.send_header("Content-type", "text/html")
             self.end_headers()
             with open("./static/dream_web/index.html", "rb") as content:
@@ -33,6 +35,8 @@ def do_GET(self):
         elif self.path == "/cancel":
             self.canceled.set()
             self.send_response(200)
+            self.send_header("Access-Control-Allow-Origin", "*")
+            self.send_header("Access-Control-Allow-Headers", "Origin, X-Requested-With, Content-Type, Accept")
             self.send_header("Content-type", "application/json")
             self.end_headers()
             self.wfile.write(bytes('{}', 'utf8'))
@@ -55,6 +59,8 @@ def do_GET(self):
 
     def do_POST(self):
         self.send_response(200)
+        self.send_header("Access-Control-Allow-Origin", "*")
+        self.send_header("Access-Control-Allow-Headers", "Origin, X-Requested-With, Content-Type, Accept")
         self.send_header("Content-type", "application/json")
         self.end_headers()
 
@@ -199,6 +205,11 @@ def image_progress(sample, step):
             print(f"Canceled.")
             return
 
+    def do_OPTIONS(self):
+        self.send_response(200)
+        self.send_header("Access-Control-Allow-Origin", "*")
+        self.send_header("Access-Control-Allow-Headers", "Origin, X-Requested-With, Content-Type, Accept")
+        self.end_headers()
 
 class ThreadingDreamServer(ThreadingHTTPServer):
     def __init__(self, server_address):

ldm/simplet2i.py

@@ -297,7 +297,7 @@ def process_image(image,seed):
                 0.0 <= variation_amount <= 1.0
         ), '-v --variation_amount must be in [0.0, 1.0]'
 
-        if len(with_variations) > 0 or variation_amount > 1.0:
+        if len(with_variations) > 0 or variation_amount > 0.0:
             assert seed is not None,\
                 'seed must be specified when using with_variations'
             if variation_amount == 0.0:
@@ -346,6 +346,7 @@ def process_image(image,seed):
                     callback=step_callback,
                 )
             else:
+                init_latent = None
                 make_image = self._txt2img(
                     prompt,
                     steps=steps,
@@ -361,11 +362,11 @@ def process_image(image,seed):
             if variation_amount > 0 or len(with_variations) > 0:
                 # use fixed initial noise plus random noise per iteration
                 seed_everything(seed)
-                initial_noise = self._get_noise(init_img,width,height)
+                initial_noise = self._get_noise(init_latent,width,height)
                 for v_seed, v_weight in with_variations:
                     seed = v_seed
                     seed_everything(seed)
-                    next_noise = self._get_noise(init_img,width,height)
+                    next_noise = self._get_noise(init_latent,width,height)
                     initial_noise = self.slerp(v_weight, initial_noise, next_noise)
                 if variation_amount > 0:
                     random.seed() # reset RNG to an actually random state, so we can get a random seed for variations
@@ -377,17 +378,16 @@ def process_image(image,seed):
                     x_T = None
                     if variation_amount > 0:
                         seed_everything(seed)
-                        target_noise = self._get_noise(init_img,width,height)
+                        target_noise = self._get_noise(init_latent,width,height)
                         x_T = self.slerp(variation_amount, initial_noise, target_noise)
                     elif initial_noise is not None:
                         # i.e. we specified particular variations
                         x_T = initial_noise
                     else:
                         seed_everything(seed)
                         if self.device.type == 'mps':
-                            x_T = self._get_noise(init_img,width,height)
+                            x_T = self._get_noise(init_latent,width,height)
                         # make_image will do the equivalent of get_noise itself
-                    print(f' DEBUG: seed at make_image() invocation time ={seed}')
                     image = make_image(x_T)
                     results.append([image, seed])
                     if image_callback is not None:
@@ -617,8 +617,8 @@ def load_model(self):
         return self.model
 
     # returns a tensor filled with random numbers from a normal distribution
-    def _get_noise(self,init_img,width,height):
-        if init_img:
+    def _get_noise(self,init_latent,width,height):
+        if init_latent is not None:
             if self.device.type == 'mps':
                 return torch.randn_like(init_latent, device='cpu').to(self.device)
             else: