Merge branch 'comfyanonymous:master' into master

README.md

@@ -28,7 +28,7 @@
 [github-downloads-latest-shield]: https://img.shields.io/github/downloads/comfyanonymous/ComfyUI/latest/total?style=flat&label=downloads%40latest
 [github-downloads-link]: https://github.com/comfyanonymous/ComfyUI/releases
 
-![ComfyUI Screenshot](comfyui_screenshot.png)
+![ComfyUI Screenshot](https://github.com/user-attachments/assets/7ccaf2c1-9b72-41ae-9a89-5688c94b7abe)
 </div>
 
 This ui will let you design and execute advanced stable diffusion pipelines using a graph/nodes/flowchart based interface. For some workflow examples and see what ComfyUI can do you can check out:

api_server/services/terminal_service.py

@@ -1,5 +1,6 @@
 from app.logger import on_flush
 import os
+import shutil
 
 
 class TerminalService:
@@ -10,15 +11,27 @@ class TerminalService:
         self.subscriptions = set()
         on_flush(self.send_messages)
 
+    def get_terminal_size(self):
+        try:
+            size = os.get_terminal_size()
+            return (size.columns, size.lines)
+        except OSError:
+            try:
+                size = shutil.get_terminal_size()
+                return (size.columns, size.lines)
+            except OSError:
+                return (80, 24)  # fallback to 80x24
+
     def update_size(self):
-        sz = os.get_terminal_size()
+        columns, lines = self.get_terminal_size()
         changed = False
-        if sz.columns != self.cols:
-            self.cols = sz.columns
+        
+        if columns != self.cols:
+            self.cols = columns
             changed = True 
 
-        if sz.lines != self.rows:
-            self.rows = sz.lines
+        if lines != self.rows:
+            self.rows = lines
             changed = True
 
         if changed:

comfy/model_base.py

@@ -153,8 +153,7 @@ class BaseModel(torch.nn.Module):
     def encode_adm(self, **kwargs):
         return None
 
-    def extra_conds(self, **kwargs):
-        out = {}
+    def concat_cond(self, **kwargs):
         if len(self.concat_keys) > 0:
             cond_concat = []
             denoise_mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None))
@@ -193,7 +192,14 @@ class BaseModel(torch.nn.Module):
                     elif ck == "masked_image":
                         cond_concat.append(self.blank_inpaint_image_like(noise))
             data = torch.cat(cond_concat, dim=1)
-            out['c_concat'] = comfy.conds.CONDNoiseShape(data)
+            return data
+        return None
+
+    def extra_conds(self, **kwargs):
+        out = {}
+        concat_cond = self.concat_cond(**kwargs)
+        if concat_cond is not None:
+            out['c_concat'] = comfy.conds.CONDNoiseShape(concat_cond)
 
         adm = self.encode_adm(**kwargs)
         if adm is not None:
@@ -523,9 +529,7 @@ class SD_X4Upscaler(BaseModel):
         return out
 
 class IP2P:
-    def extra_conds(self, **kwargs):
-        out = {}
-
+    def concat_cond(self, **kwargs):
         image = kwargs.get("concat_latent_image", None)
         noise = kwargs.get("noise", None)
         device = kwargs["device"]
@@ -537,18 +541,15 @@ class IP2P:
             image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center")
 
         image = utils.resize_to_batch_size(image, noise.shape[0])
+        return self.process_ip2p_image_in(image)
 
-        out['c_concat'] = comfy.conds.CONDNoiseShape(self.process_ip2p_image_in(image))
-        adm = self.encode_adm(**kwargs)
-        if adm is not None:
-            out['y'] = comfy.conds.CONDRegular(adm)
-        return out
 
 class SD15_instructpix2pix(IP2P, BaseModel):
     def __init__(self, model_config, model_type=ModelType.EPS, device=None):
         super().__init__(model_config, model_type, device=device)
         self.process_ip2p_image_in = lambda image: image
 
+
 class SDXL_instructpix2pix(IP2P, SDXL):
     def __init__(self, model_config, model_type=ModelType.EPS, device=None):
         super().__init__(model_config, model_type, device=device)

nodes.py

@@ -382,6 +382,7 @@ class InpaintModelConditioning:
                              "vae": ("VAE", ),
                              "pixels": ("IMAGE", ),
                              "mask": ("MASK", ),
+                             "add_noise_mask": ("BOOLEAN", {"default": True, "tooltip": "Add a noise mask to the latent so sampling will only happen within the mask. Might improve results or completely break things depending on the model."}),
                              }}
 
     RETURN_TYPES = ("CONDITIONING","CONDITIONING","LATENT")
@@ -390,7 +391,7 @@ class InpaintModelConditioning:
 
     CATEGORY = "conditioning/inpaint"
 
-    def encode(self, positive, negative, pixels, vae, mask):
+    def encode(self, positive, negative, pixels, vae, mask, add_noise_mask):
         x = (pixels.shape[1] // 8) * 8
         y = (pixels.shape[2] // 8) * 8
         mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(pixels.shape[1], pixels.shape[2]), mode="bilinear")
@@ -414,7 +415,8 @@ class InpaintModelConditioning:
         out_latent = {}
 
         out_latent["samples"] = orig_latent
-        out_latent["noise_mask"] = mask
+        if add_noise_mask:
+            out_latent["noise_mask"] = mask
 
         out = []
         for conditioning in [positive, negative]: