Upload 3 files

model.py

@@ -2,6 +2,7 @@ from multit2i import find_model_list
 
 
 models = [
+    'yodayo-ai/clandestine-xl-1.0',
     'yodayo-ai/kivotos-xl-2.0',
     'yodayo-ai/holodayo-xl-2.1',
     'cagliostrolab/animagine-xl-3.1',

multit2i.py

@@ -124,7 +124,7 @@ def load_from_model(model_name: str, hf_token: str = None):
             f"Could not find model: {model_name}. If it is a private or gated model, please provide your Hugging Face access token (https://huggingface.co/settings/tokens) as the argument for the `hf_token` parameter."
         )
     headers["X-Wait-For-Model"] = "true"
-    client = huggingface_hub.InferenceClient(model=model_name, headers=headers, token=hf_token)
+    client = huggingface_hub.InferenceClient(model=model_name, headers=headers, token=hf_token, timeout=120)
     inputs = gr.components.Textbox(label="Input")
     outputs = gr.components.Image(label="Output")
     fn = client.text_to_image
@@ -325,19 +325,20 @@ async def infer_multi(prompt: str, neg_prompt: str, results: list, image_num: fl
     images = results if results else []
     image_num_offset = len(images)
     prompt, neg_prompt = recom_prompt(prompt, neg_prompt, pos_pre, pos_suf, neg_pre, neg_suf)
-    tasks = [asyncio.to_thread(infer, prompt, neg_prompt, model_name) for i in range(image_num)]
+    tasks = [asyncio.create_task(asyncio.to_thread(infer, prompt, neg_prompt, model_name)) for i in range(image_num)]
+    await asyncio.sleep(0)
     for task in tasks:
         progress(float(len(images) - image_num_offset) / float(image_num), desc="Running inference.")
         try:
-            result = await task
-        except Exception as e:
+            result = await asyncio.wait_for(task, timeout=120)
+        except (Exception, asyncio.TimeoutError) as e:
             print(e)
-            task.cancel()
+            if not task.done(): task.cancel()
             result = None
             image_num_offset += 1
         with lock:
             if result and len(result) == 2 and result[1]: images.append(result)
-        await asyncio.sleep(0.05)
+        await asyncio.sleep(0)
         yield images
 
 
@@ -351,17 +352,18 @@ async def infer_multi_random(prompt: str, neg_prompt: str, results: list, image_
     random.seed()
     model_names = random.choices(list(loaded_models.keys()), k = image_num)
     prompt, neg_prompt = recom_prompt(prompt, neg_prompt, pos_pre, pos_suf, neg_pre, neg_suf)
-    tasks = [asyncio.to_thread(infer, prompt, neg_prompt, model_name) for model_name in model_names]
+    tasks = [asyncio.create_task(asyncio.to_thread(infer, prompt, neg_prompt, model_name)) for model_name in model_names]
+    await asyncio.sleep(0)
     for task in tasks:
         progress(float(len(images) - image_num_offset) / float(image_num), desc="Running inference.")
         try:
-            result = await task
-        except Exception as e:
+            result = await asyncio.wait_for(task, timeout=120)
+        except (Exception, asyncio.TimeoutError) as e:
             print(e)
-            task.cancel()
+            if not task.done(): task.cancel()
             result = None
             image_num_offset += 1
         with lock:
             if result and len(result) == 2 and result[1]: images.append(result)
-        await asyncio.sleep(0.05)
+        await asyncio.sleep(0)
         yield images