limits

client.py

 import requests
 import json
+import base64
+from PIL import Image
+import io
 #server hosts on 0.0.0.0
 IP_ADDR = '0.0.0.0'
 PORT = '4315'
 
+masks = [
+        {
+            "seed": 811708258,
+            "mask": "iVBORw0KGgoAAAANSUhEUgAAAEAAAABACAYAAACqaXHeAAAAAXNSR0IArs4c6QAAAKtJREFUeF7t2LENxDAMA0Br/6GVCf4rNQQvAxiBYPEIz+7uK/7GANwAKyADijPwCUEKUIACFKBA8QR+MjgzryEf9QA9oOGe/8k4K2AFrIAqrAoXF0EPIhjEIAYxiEEMFk8AgxjEIAZjGbx4uY4OwfoBXOgdfQMM4GACboAipAgpQrFF6CADvQpTgAIUoAAFLjhJPYMCFKAABSiQmuAX/00BClCAAhS4SNPUMz4jPAhQiCYcNQAAAABJRU5ErkJggg=="
+        }
+    ]
+
 payload = {
-    'prompt': 'Hello World',
+    'prompt': 'test',
     "width": 512,
     "height": 512,
     "scale": 12,
@@ -15,13 +25,12 @@ payload = {
     "n_samples": 1,
     "strength": 0.7,
     "noise": 0.6,
-    "masks": [
-        {
-            "seed": 2023444088,
-            "mask": "iVBORw0KGgoAAAANSUhEUgAAAEAAAABACAYAAACqaXHeAAAAAXNSR0IArs4c6QAAAKtJREFUeF7t2LENxDAMA0Br/6GVCf4rNQQvAxiBYPEIz+7uK/7GANwAKyADijPwCUEKUIACFKBA8QR+MjgzryEf9QA9oOGe/8k4K2AFrIAqrAoXF0EPIhjEIAYxiEEMFk8AgxjEIAZjGbx4uY4OwfoBXOgdfQMM4GACboAipAgpQrFF6CADvQpTgAIUoAAFLjhJPYMCFKAABSiQmuAX/00BClCAAhS4SNPUMz4jPAhQiCYcNQAAAABJRU5ErkJggg=="
-        }
-    ]
+    "masks": None
 }
 
-response = requests.post('http://' + IP_ADDR + ':' + PORT + '/generate-stream', json=payload)
-print(response.text)
\ No newline at end of file
+response = requests.post('http://' + IP_ADDR + ':' + PORT + '/generate', json=payload)
+response = response.json()
+response = response['output'][0]
+response = base64.b64decode(response)
+response = Image.open(io.BytesIO(response))
+response.save("test1.png")
\ No newline at end of file

hydra_node/models.py

@@ -283,7 +283,7 @@ class StableDiffusionModel(nn.Module):
                         # convert RGB or grayscale image into 4-channel
                         mask = mask[0].unsqueeze(0)
                         mask = torch.repeat_interleave(mask, request.latent_channels, dim=0)
-                        mask = (mask > 0.5).float()
+                        mask = (mask < 0.5).float()
 
                         # interpolate start noise
                         noise_x = (noise_x * (1-mask)) + (sample_start_noise_special(mask_seed+seed_offset, request, self.device) * mask)

hydra_node/sanitize.py

@@ -61,6 +61,19 @@ def closest_multiple(num, mult):
     return floor if (num_int - floor) < (ceil - num_int) else ceil
 
 def sanitize_stable_diffusion(request, config):
+    if request.steps > 50:
+        return False, "steps must be smaller than 50"
+
+    pixels = request.height * request.width
+    if (pixels > 1024*512 and pixels <= 1024*1204) and request.n_samples > 1:
+        return False, "n_samples must be 1 or smaller at this resolution"
+
+    if (pixels > 512*512 and pixels <= 1024*512) and request.n_samples > 2:
+        return False, "n_samples must be 2 or smaller this resolution"
+
+    if (pixels > 256*256 and pixels <= 512*512) and request.n_samples > 4:
+        return False, "n_samples must be 4 or smaller at this resolution"
+
     if request.width * request.height == 0:
         return False, "width and height must be non-zero"