Merge 9e80e7c0da into b08debceca

Signed-off-by: bigcat88 <bigcat88@icloud.com>

app/model_manager.py

@@ -3,8 +3,10 @@ import base64
 import json
 import time
 import logging
+import requests
 import folder_paths
 import glob
+from tqdm.auto import tqdm
 import comfy.utils
 from aiohttp import web
 from PIL import Image
@@ -13,8 +15,9 @@ from folder_paths import map_legacy, filter_files_extensions, filter_files_conte
 
 
 class ModelFileManager:
-    def __init__(self) -> None:
+    def __init__(self, is_download_model_enabled: lambda: bool= lambda: False) -> None:
         self.cache: dict[str, tuple[list[dict], dict[str, float], float]] = {}
+        self.is_download_model_enabled = is_download_model_enabled
 
     def get_cache(self, key: str, default=None) -> tuple[list[dict], dict[str, float], float] | None:
         return self.cache.get(key, default)
@@ -98,6 +101,48 @@ class ModelFileManager:
             except:
                 return web.Response(status=404)
 
+        @routes.post("/download_model")
+        async def post_download_model(request):
+            if not self.is_download_model_enabled():
+                logging.error("Download Model endpoint is disabled")
+                return web.Response(status=403)
+            json_data = await request.json()
+            url = json_data.get("url", None)
+            if url is None:
+                logging.error("URL is not provided")
+                return web.Response(status=400)
+            save_dir = json_data.get("save_dir", None)
+            if save_dir not in folder_paths.folder_names_and_paths:
+                logging.error("Save directory is not valid")
+                return web.Response(status=400)
+from urllib.parse import urlparse, unquote
+
+            default_filename = unquote(urlparse(url).path.split("/")[-1])
+            filename = json_data.get("filename", default_filename)
+            token = json_data.get("token", None)
+
+            save_path = os.path.join(folder_paths.folder_names_and_paths[save_dir][0][0], filename)
+            tmp_path = save_path + ".tmp"
+            headers = {"Authorization": f"Bearer {token}"} if token else {}
+            try:
+                with requests.get(url, headers=headers,stream=True,timeout=10) as r:
+                    r.raise_for_status()
+                    total_size = int(r.headers.get('content-length', 0))
+                    with open(tmp_path, "wb") as f:
+                        with tqdm(total=total_size, unit='iB', unit_scale=True, desc=filename) as pbar:
+                            for chunk in r.iter_content(chunk_size=1024*1024):
+                                if not chunk:
+                                    break
+                                size = f.write(chunk)
+                                pbar.update(size)
+                os.replace(tmp_path, save_path)
+                return web.Response(status=200)
+            except Exception as e:
+                logging.error(f"Failed to download model: {e}")
+                if os.path.exists(tmp_path):
+                    os.remove(tmp_path)
+                return web.Response(status=500)
+
     def get_model_file_list(self, folder_name: str):
         folder_name = map_legacy(folder_name)
         folders = folder_paths.folder_names_and_paths[folder_name]

comfy/text_encoders/llama.py

@@ -937,22 +937,41 @@ class BaseGenerate:
             return torch.argmax(logits, dim=-1, keepdim=True)
 
         # Sampling mode
-        if repetition_penalty != 1.0:
-            for i in range(logits.shape[0]):
-                for token_id in set(token_history):
-                    logits[i, token_id] *= repetition_penalty if logits[i, token_id] < 0 else 1/repetition_penalty
-
-        if presence_penalty is not None and presence_penalty != 0.0:
-            for i in range(logits.shape[0]):
-                for token_id in set(token_history):
-                    logits[i, token_id] -= presence_penalty
+        if len(token_history) > 0 and (repetition_penalty != 1.0 or (presence_penalty is not None and presence_penalty != 0.0)):
+            token_ids = torch.tensor(list(set(token_history)), device=logits.device)
+            token_logits = logits[:, token_ids]
+            if repetition_penalty != 1.0:
+                token_logits = torch.where(token_logits < 0, token_logits * repetition_penalty, token_logits / repetition_penalty)
+            if presence_penalty is not None and presence_penalty != 0.0:
+                token_logits = token_logits - presence_penalty
+            logits[:, token_ids] = token_logits
 
         if temperature != 1.0:
             logits = logits / temperature
 
         if top_k > 0:
-            indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
-            logits[indices_to_remove] = torch.finfo(logits.dtype).min
+            top_k = min(top_k, logits.shape[-1])
+            logits, top_indices = torch.topk(logits, top_k)
+
+            if min_p > 0.0:
+                probs_before_filter = torch.nn.functional.softmax(logits, dim=-1)
+                top_probs, _ = probs_before_filter.max(dim=-1, keepdim=True)
+                min_threshold = min_p * top_probs
+                indices_to_remove = probs_before_filter < min_threshold
+                logits[indices_to_remove] = torch.finfo(logits.dtype).min
+
+            if top_p < 1.0:
+                sorted_logits, sorted_indices = torch.sort(logits, descending=True)
+                cumulative_probs = torch.cumsum(torch.nn.functional.softmax(sorted_logits, dim=-1), dim=-1)
+                sorted_indices_to_remove = cumulative_probs > top_p
+                sorted_indices_to_remove[..., 0] = False
+                indices_to_remove = torch.zeros_like(logits, dtype=torch.bool)
+                indices_to_remove.scatter_(1, sorted_indices, sorted_indices_to_remove)
+                logits[indices_to_remove] = torch.finfo(logits.dtype).min
+
+            probs = torch.nn.functional.softmax(logits, dim=-1)
+            next_token = torch.multinomial(probs, num_samples=1, generator=generator)
+            return top_indices.gather(1, next_token)
 
         if min_p > 0.0:
             probs_before_filter = torch.nn.functional.softmax(logits, dim=-1)

comfy_api_nodes/util/request_logger.py

@@ -9,6 +9,7 @@ from typing import Any
 import folder_paths
 
 logger = logging.getLogger(__name__)
+_SENSITIVE_HEADERS = {"authorization", "x-api-key"}
 
 
 def get_log_directory():
@@ -73,6 +74,10 @@ def _format_data_for_logging(data: Any) -> str:
     return str(data)
 
 
+def _redact_headers(headers: dict) -> dict:
+    return {k: ("***" if k.lower() in _SENSITIVE_HEADERS else v) for k, v in headers.items()}
+
+
 def log_request_response(
     operation_id: str,
     request_method: str,
@@ -101,7 +106,7 @@ def log_request_response(
         log_content.append(f"Method: {request_method}")
         log_content.append(f"URL: {request_url}")
         if request_headers:
-            log_content.append(f"Headers:\n{_format_data_for_logging(request_headers)}")
+            log_content.append(f"Headers:\n{_format_data_for_logging(_redact_headers(request_headers))}")
         if request_params:
             log_content.append(f"Params:\n{_format_data_for_logging(request_params)}")
         if request_data is not None:

comfy_extras/nodes_color.py

@@ -16,23 +16,30 @@ class ColorToRGBInt(io.ComfyNode):
             ],
             outputs=[
                 io.Int.Output(display_name="rgb_int"),
-                io.Color.Output(display_name="hex")
+                io.Color.Output(display_name="hex"),
+                io.Float.Output(display_name="alpha"),
             ],
         )
 
     @classmethod
     def execute(cls, color: str) -> io.NodeOutput:
-        # expect format #RRGGBB
-        if len(color) != 7 or color[0] != "#":
-            raise ValueError("Color must be in format #RRGGBB")
+        # expect format #RRGGBB or #RRGGBBAA
+        if len(color) not in (7, 9) or color[0] != "#":
+            raise ValueError("Color must be in format #RRGGBB or #RRGGBBAA")
         try:
             int(color[1:], 16)
         except ValueError:
-            raise ValueError("Color must be in format #RRGGBB") from None
+            raise ValueError("Color must be in format #RRGGBB or #RRGGBBAA") from None
+
+        alpha = 1.0
+        if len(color) == 9:
+            alpha = int(color[7:9], 16) / 255.0
+            color = color[:7]
+
         r, g, b = hex_to_rgb(color)
 
         rgb_int = r * 256 * 256 + g * 256 + b
-        return io.NodeOutput(rgb_int, color)
+        return io.NodeOutput(rgb_int, color, alpha)
 
 
 class ColorExtension(ComfyExtension):

requirements.txt

@@ -1,6 +1,6 @@
 comfyui-frontend-package==1.45.20
 comfyui-workflow-templates==0.11.2
-comfyui-embedded-docs==0.5.6
+comfyui-embedded-docs==0.5.7
 torch
 torchsde
 torchvision

server.py

@@ -215,7 +215,7 @@ class PromptServer():
         PromptServer.instance = self
 
         self.user_manager = UserManager()
-        self.model_file_manager = ModelFileManager()
+        self.model_file_manager = ModelFileManager(is_download_model_enabled=lambda: self.user_manager.settings.get_settings(None).get("Comfy.ModelDownloadEnabled", False))
         self.custom_node_manager = CustomNodeManager()
         self.subgraph_manager = SubgraphManager()
         self.node_replace_manager = NodeReplaceManager()