Upload 2 files

app.py

@@ -1,221 +1,161 @@
-import gradio as gr
-import os 
-import sys
-from pathlib import Path
-from all_models import models
-from externalmod import gr_Interface_load
-from prompt_extend import extend_prompt
-from random import randint
-import asyncio
-from threading import RLock
-lock = RLock()
-HF_TOKEN = os.environ.get("HF_TOKEN") if os.environ.get("HF_TOKEN") else None # If private or gated models aren't used, ENV setting is unnecessary.
-
-inference_timeout = 600
-MAX_SEED = 2**32-1
-current_model = models[0]
-text_gen1 = extend_prompt
-#text_gen1=gr.Interface.load("spaces/phenomenon1981/MagicPrompt-Stable-Diffusion") 
-#text_gen1=gr.Interface.load("spaces/Yntec/prompt-extend") 
-#text_gen1=gr.Interface.load("spaces/daspartho/prompt-extend") 
-#text_gen1=gr.Interface.load("spaces/Omnibus/MagicPrompt-Stable-Diffusion_link")
-
-models2 = [gr_Interface_load(f"models/{m}", live=False, preprocess=True, postprocess=False, hf_token=HF_TOKEN) for m in models]
-
-def text_it1(inputs, text_gen1=text_gen1):
-        go_t1 = text_gen1(inputs)
-        return(go_t1)
-
-def set_model(current_model):
-    current_model = models[current_model]
-    return gr.update(label=(f"{current_model}"))
-
-def send_it1(inputs, model_choice, neg_input, height, width, steps, cfg, seed):
-        output1 = gen_fn(model_choice, inputs, neg_input, height, width, steps, cfg, seed)
-        return (output1)
-
-# https://huggingface.co/docs/api-inference/detailed_parameters
-# https://huggingface.co/docs/huggingface_hub/package_reference/inference_client
-async def infer(model_index, prompt, nprompt="", height=None, width=None, steps=None, cfg=None, seed=-1, timeout=inference_timeout):
-    from pathlib import Path
-    kwargs = {}
-    if height is not None and height >= 256: kwargs["height"] = height
-    if width is not None and width >= 256: kwargs["width"] = width
-    if steps is not None and steps >= 1: kwargs["num_inference_steps"] = steps
-    if cfg is not None and cfg > 0: cfg = kwargs["guidance_scale"] = cfg
-    noise = ""
-    if seed >= 0: kwargs["seed"] = seed
-    else:
-        rand = randint(1, 500)
-        for i in range(rand):
-            noise += " "
-    task = asyncio.create_task(asyncio.to_thread(models2[model_index].fn,
-                               prompt=f'{prompt} {noise}', negative_prompt=nprompt, **kwargs, token=HF_TOKEN))
-    await asyncio.sleep(0)
-    try:
-        result = await asyncio.wait_for(task, timeout=timeout)
-    except (Exception, asyncio.TimeoutError) as e:
-        print(e)
-        print(f"Task timed out: {models2[model_index]}")
-        if not task.done(): task.cancel()
-        result = None
-    if task.done() and result is not None:
-        with lock:
-            png_path = "image.png"
-            result.save(png_path)
-            image = str(Path(png_path).resolve())
-        return image
-    return None
-
-def gen_fn(model_index, prompt, nprompt="", height=None, width=None, steps=None, cfg=None, seed=-1):
-    try:
-        loop = asyncio.new_event_loop()
-        result = loop.run_until_complete(infer(model_index, prompt, nprompt,
-                                         height, width, steps, cfg, seed, inference_timeout))
-    except (Exception, asyncio.CancelledError) as e:
-        print(e)
-        print(f"Task aborted: {models2[model_index]}")
-        result = None
-    finally:
-        loop.close()
-    return result
-
-css="""
-.gradio-container {background-image: linear-gradient(#254150, #1e2f40, #182634) !important;
- color: #ffaa66 !important; font-family: 'IBM Plex Sans', sans-serif !important;}
-h1 {font-size: 6em; color: #ffc99f; margin-top: 30px; margin-bottom: 30px;
- text-shadow: 3px 3px 0 rgba(0, 0, 0, 1) !important;}
-h3 {color: #ffc99f; !important;}
-h4 {display: inline-block; color: #ffffff !important;}
-.wrapper img {font-size: 98% !important; white-space: nowrap !important; text-align: center !important;
-display: inline-block !important; color: #ffffff !important;}
-.wrapper {color: #ffffff !important;}
-.text-gray-500 {color: #ffc99f !important;}
-.gr-box {background-image: linear-gradient(#182634, #1e2f40, #254150) !important;
- border-top-color: #000000 !important; border-right-color: #ffffff !important;
- border-bottom-color: #ffffff !important; border-left-color: #000000 !important;}
-.gr-input {color: #ffc99f; !important; background-color: #254150 !important;}
-:root {--neutral-100: #000000 !important;}
-.gr-button {color: #ffffff !important; text-shadow: 1px 1px 0 rgba(0, 0, 0, 1) !important;
-background-image: linear-gradient(#76635a, #d2a489) !important; border-radius: 24px !important;
-border: solid 1px !important; border-top-color: #ffc99f !important; border-right-color: #000000 !important;
-border-bottom-color: #000000 !important; border-left-color: #ffc99f !important; padding: 6px 30px;}
-.gr-button:active {color: #ffc99f !important; font-size: 98% !important;
-text-shadow: 0px 0px 0 rgba(0, 0, 0, 1) !important; background-image: linear-gradient(#d2a489, #76635a) !important;
-border-top-color: #000000 !important; border-right-color: #ffffff !important;
-border-bottom-color: #ffffff !important; border-left-color: #000000 !important;}
-.gr-button:hover {filter: brightness(130%);}
-"""
-
-with gr.Blocks(theme='John6666/YntecDark', fill_width=True, css=css) as myface:
-    gr.HTML(f"""
-     <div style="text-align: center; max-width: 1200px; margin: 0 auto;">
-              <div>
-                <style>
-                    h1 {{
-                    font-size: 6em;
-                    color: #ffc99f;
-                    margin-top: 30px;
-                    margin-bottom: 30px;
-                    text-shadow: 3px 3px 0 rgba(0, 0, 0, 1) !important;
-                   }}
-                   h3 {{
-                    color: #ffc99f; !important;
-                   }}
-                   h4 {{
-                    display: inline-block;
-                    color: #ffffff !important;
-                   }}
-                   .wrapper img {{
-                    font-size: 98% !important;
-                    white-space: nowrap !important;
-                    text-align: center !important;
-                    display: inline-block !important;
-                    color: #ffffff !important;
-                   }}
-                   .wrapper {{
-                    color: #ffffff !important;
-                   }}
-                   .gradio-container {{
-                   background-image: linear-gradient(#254150, #1e2f40, #182634) !important;
-                   color: #ffaa66 !important;
-                   font-family: 'IBM Plex Sans', sans-serif !important;
-                   }}
-                   .text-gray-500 {{
-                   color: #ffc99f !important;
-                   }}
-                   .gr-box {{
-    background-image: linear-gradient(#182634, #1e2f40, #254150) !important;
-    border-top-color: #000000 !important;
-    border-right-color: #ffffff !important;
-    border-bottom-color: #ffffff !important;
-    border-left-color: #000000 !important;
-                   }}
-                   .gr-input {{
-                   color: #ffc99f; !important;
-                   background-color: #254150 !important;
-                   }}
-                   :root {{
-    --neutral-100: #000000 !important;
-                   }}
-                </style>
-                <body>
-                <div class="center"><h1>Blitz Diffusion</h1>
-                </div>
-                </body>
-              </div>
-              <p style="margin-bottom: 1px; color: #ffaa66;">
-              <h3>{int(len(models))} Stable Diffusion models, but why? For your enjoyment!</h3></p>
-              <br><div class="wrapper">9.3 <img src="https://huggingface.co/Yntec/DucHaitenLofi/resolve/main/NEW.webp" alt="NEW!" style="width:32px;height:16px;">This has become a legacy backup copy of old <u><a href="https://huggingface.co/spaces/Yntec/ToyWorld">ToyWorld</a></u>'s UI! Newer models added dailty over there! 25 new models since last update!</div>
-              <p style="margin-bottom: 1px; font-size: 98%">
-              <br><h4>If a model is already loaded each new image takes less than <b>10</b> seconds to generate!</h4></p>
-              <p style="margin-bottom: 1px; color: #ffffff;">
-              <br><div class="wrapper">Generate 6 images from 1 prompt at the <u><a href="https://huggingface.co/spaces/Yntec/PrintingPress">PrintingPress</a></u>, and use 6 different models at <u><a href="https://huggingface.co/spaces/Yntec/diffusion80xx">Huggingface Diffusion!</a></u>!
-        </p></p>
-            </div>
-            """, elem_classes="gr-box")
-    with gr.Row():
-        with gr.Column(scale=100):
-            #Model selection dropdown
-            model_name1 = gr.Dropdown(label="Select Model", choices=[m for m in models], type="index",
-                                      value=current_model, interactive=True, elem_classes=["gr-box", "gr-input"])
-    with gr.Row():
-        with gr.Column(scale=100):
-            with gr.Group():
-                magic1 = gr.Textbox(label="Your Prompt", lines=4, elem_classes=["gr-box", "gr-input"]) #Positive
-                with gr.Accordion("Advanced", open=False, visible=True):
-                    neg_input = gr.Textbox(label='Negative prompt', lines=1, elem_classes=["gr-box", "gr-input"])
-                    with gr.Row():
-                        width = gr.Slider(label="Width", info="If 0, the default value is used.", maximum=1216, step=32, value=0, elem_classes=["gr-box", "gr-input"])
-                        height = gr.Slider(label="Height", info="If 0, the default value is used.", maximum=1216, step=32, value=0, elem_classes=["gr-box", "gr-input"])
-                    with gr.Row():
-                        steps = gr.Slider(label="Number of inference steps", info="If 0, the default value is used.", maximum=100, step=1, value=0, elem_classes=["gr-box", "gr-input"])
-                        cfg = gr.Slider(label="Guidance scale", info="If 0, the default value is used.", maximum=30.0, step=0.1, value=0, elem_classes=["gr-box", "gr-input"])
-                        seed = gr.Slider(label="Seed", info="Randomize Seed if -1.", minimum=-1, maximum=MAX_SEED, step=1, value=-1, elem_classes=["gr-box", "gr-input"])
-            run = gr.Button("Generate Image", elem_classes="gr-button")
-
-    with gr.Row():
-        with gr.Column():
-            output1 = gr.Image(label=(f"{current_model}"), show_download_button=True,
-                               interactive=False, show_share_button=False, format=".png", elem_classes="gr-box")
-                
-    with gr.Row():
-        with gr.Column(scale=50):
-            input_text=gr.Textbox(label="Use this box to extend an idea automagically, by typing some words and clicking Extend Idea", lines=2, elem_classes=["gr-box", "gr-input"])
-            see_prompts=gr.Button("Extend Idea -> overwrite the contents of the `Your Prompt´ box above", elem_classes="gr-button")
-            use_short=gr.Button("Copy the contents of this box to the `Your Prompt´ box above", elem_classes="gr-button")
-    def short_prompt(inputs):
-        return (inputs)
-    
-    model_name1.change(set_model, inputs=model_name1, outputs=[output1])
-    gr.on(
-        triggers=[run.click, magic1.submit],
-        fn=send_it1,
-        inputs=[magic1, model_name1, neg_input, height, width, steps, cfg, seed],
-        outputs=[output1],
-    )
-    use_short.click(short_prompt, inputs=[input_text], outputs=magic1)
-    see_prompts.click(text_it1, inputs=[input_text], outputs=magic1)
-    
-myface.queue(default_concurrency_limit=200, max_size=200)
-myface.launch(show_api=False, max_threads=400)
+import gradio as gr
+import os 
+import sys
+from pathlib import Path
+from all_models import models
+from externalmod import gr_Interface_load
+from prompt_extend import extend_prompt
+from random import randint
+import asyncio
+from threading import RLock
+lock = RLock()
+HF_TOKEN = os.environ.get("HF_TOKEN") if os.environ.get("HF_TOKEN") else None # If private or gated models aren't used, ENV setting is unnecessary.
+
+inference_timeout = 300
+MAX_SEED = 2**32-1
+current_model = models[0]
+text_gen1 = extend_prompt
+
+models2 = [gr_Interface_load(f"models/{m}", live=False, preprocess=True, postprocess=False, hf_token=HF_TOKEN) for m in models]
+
+def text_it1(inputs, text_gen1=text_gen1):
+        go_t1 = text_gen1(inputs)
+        return(go_t1)
+
+def set_model(current_model):
+    current_model = models[current_model]
+    return gr.update(label=(f"{current_model}"))
+
+def send_it1(inputs, model_choice, neg_input, height, width, steps, cfg, seed):
+        output1 = gen_fn(model_choice, inputs, neg_input, height, width, steps, cfg, seed)
+        return (output1)
+
+# https://huggingface.co/docs/api-inference/detailed_parameters
+# https://huggingface.co/docs/huggingface_hub/package_reference/inference_client
+async def infer(model_index, prompt, nprompt="", height=None, width=None, steps=None, cfg=None, seed=-1, timeout=inference_timeout):
+    from pathlib import Path
+    kwargs = {}
+    if height is not None and height >= 256: kwargs["height"] = height
+    if width is not None and width >= 256: kwargs["width"] = width
+    if steps is not None and steps >= 1: kwargs["num_inference_steps"] = steps
+    if cfg is not None and cfg > 0: cfg = kwargs["guidance_scale"] = cfg
+    noise = ""
+    if seed >= 0: kwargs["seed"] = seed
+    else:
+        rand = randint(1, 500)
+        for i in range(rand):
+            noise += " "
+    task = asyncio.create_task(asyncio.to_thread(models2[model_index].fn,
+                               prompt=f'{prompt} {noise}', negative_prompt=nprompt, **kwargs, token=HF_TOKEN))
+    await asyncio.sleep(0)
+    try:
+        result = await asyncio.wait_for(task, timeout=timeout)
+    except asyncio.TimeoutError as e:
+        print(e)
+        print(f"Task timed out: {models2[model_index]}")
+        if not task.done(): task.cancel()
+        result = None
+        raise Exception(f"Task timed out: {models2[model_index]}")
+    except Exception as e:
+        print(e)
+        if not task.done(): task.cancel()
+        result = None
+        raise Exception(e)
+    if task.done() and result is not None and not isinstance(result, tuple):
+        with lock:
+            png_path = "image.png"
+            result.save(png_path)
+            image = str(Path(png_path).resolve())
+        return image
+    return None
+
+def gen_fn(model_index, prompt, nprompt="", height=None, width=None, steps=None, cfg=None, seed=-1):
+    try:
+        loop = asyncio.new_event_loop()
+        result = loop.run_until_complete(infer(model_index, prompt, nprompt,
+                                         height, width, steps, cfg, seed, inference_timeout))
+    except (Exception, asyncio.CancelledError) as e:
+        print(e)
+        print(f"Task aborted: {models2[model_index]}")
+        result = None
+        raise gr.Error(f"Task aborted: {models2[model_index]}, Error: {e}")
+    finally:
+        loop.close()
+    return result
+
+css="""
+.gradio-container {background-image: linear-gradient(#254150, #1e2f40, #182634) !important;
+ color: #ffaa66 !important; font-family: 'IBM Plex Sans', sans-serif !important;}
+h1 {font-size: 6em; color: #ffc99f; margin-top: 30px; margin-bottom: 30px;
+ text-shadow: 3px 3px 0 rgba(0, 0, 0, 1) !important;}
+h3 {color: #ffc99f; !important;}
+h4 {display: inline-block; color: #ffffff !important;}
+.wrapper img {font-size: 98% !important; white-space: nowrap !important; text-align: center !important;
+display: inline-block !important; color: #ffffff !important;}
+.wrapper {color: #ffffff !important;}
+.gr-box {background-image: linear-gradient(#182634, #1e2f40, #254150) !important;
+ border-top-color: #000000 !important; border-right-color: #ffffff !important;
+ border-bottom-color: #ffffff !important; border-left-color: #000000 !important;}
+"""
+
+with gr.Blocks(theme='John6666/YntecDark', fill_width=True, css=css) as myface:
+    gr.HTML(f"""
+        <div style="text-align: center; max-width: 1200px; margin: 0 auto;">
+        <div class="center"><h1>Blitz Diffusion</h1></div>
+        <p style="margin-bottom: 1px; color: #ffaa66;">
+        <h3>{int(len(models))} Stable Diffusion models, but why? For your enjoyment!</h3></p>
+        <br><div class="wrapper">9.3 <img src="https://huggingface.co/Yntec/DucHaitenLofi/resolve/main/NEW.webp" alt="NEW!" style="width:32px;height:16px;">This has become a legacy backup copy of old <u><a href="https://huggingface.co/spaces/Yntec/ToyWorld">ToyWorld</a></u>'s UI! Newer models added dailty over there! 25 new models since last update!</div>
+        <p style="margin-bottom: 1px; font-size: 98%">
+        <br><h4>If a model is already loaded each new image takes less than <b>10</b> seconds to generate!</h4></p>
+        <p style="margin-bottom: 1px; color: #ffffff;">
+        <br><div class="wrapper">Generate 6 images from 1 prompt at the <u><a href="https://huggingface.co/spaces/Yntec/PrintingPress">PrintingPress</a></u>, and use 6 different models at <u><a href="https://huggingface.co/spaces/Yntec/diffusion80xx">Huggingface Diffusion!</a></u>!
+        </p></p></div>
+        """, elem_classes="gr-box")
+    with gr.Row():
+        with gr.Column(scale=100):
+            # Model selection dropdown
+            model_name1 = gr.Dropdown(label="Select Model", choices=[m for m in models], type="index",
+                                      value=current_model, interactive=True, elem_classes=["gr-box", "gr-input"])
+    with gr.Row():
+        with gr.Column(scale=100):
+            with gr.Group():
+                magic1 = gr.Textbox(label="Your Prompt", lines=4, elem_classes=["gr-box", "gr-input"]) #Positive
+                with gr.Accordion("Advanced", open=False, visible=True):
+                    neg_input = gr.Textbox(label='Negative prompt', lines=1, elem_classes=["gr-box", "gr-input"])
+                    with gr.Row():
+                        width = gr.Slider(label="Width", info="If 0, the default value is used.", maximum=1216, step=32, value=0, elem_classes=["gr-box", "gr-input"])
+                        height = gr.Slider(label="Height", info="If 0, the default value is used.", maximum=1216, step=32, value=0, elem_classes=["gr-box", "gr-input"])
+                    with gr.Row():
+                        steps = gr.Slider(label="Number of inference steps", info="If 0, the default value is used.", maximum=100, step=1, value=0, elem_classes=["gr-box", "gr-input"])
+                        cfg = gr.Slider(label="Guidance scale", info="If 0, the default value is used.", maximum=30.0, step=0.1, value=0, elem_classes=["gr-box", "gr-input"])
+                        seed = gr.Slider(label="Seed", info="Randomize Seed if -1.", minimum=-1, maximum=MAX_SEED, step=1, value=-1, elem_classes=["gr-box", "gr-input"])
+            run = gr.Button("Generate Image", variant="primary", elem_classes="gr-button")
+
+    with gr.Row():
+        with gr.Column():
+            output1 = gr.Image(label=(f"{current_model}"), show_download_button=True,
+                               interactive=False, show_share_button=False, format=".png", elem_classes="gr-box")
+                
+    with gr.Row():
+        with gr.Column(scale=50):
+            input_text=gr.Textbox(label="Use this box to extend an idea automagically, by typing some words and clicking Extend Idea", lines=2, elem_classes=["gr-box", "gr-input"])
+            see_prompts=gr.Button("Extend Idea -> overwrite the contents of the `Your Prompt´ box above", variant="primary", elem_classes="gr-button")
+            use_short=gr.Button("Copy the contents of this box to the `Your Prompt´ box above", variant="primary", elem_classes="gr-button")
+    def short_prompt(inputs):
+        return (inputs)
+    
+    model_name1.change(set_model, inputs=model_name1, outputs=[output1])
+    gr.on(
+        triggers=[run.click, magic1.submit],
+        fn=send_it1,
+        inputs=[magic1, model_name1, neg_input, height, width, steps, cfg, seed],
+        outputs=[output1],
+        concurrency_limit=None,
+        queue=False,
+    )
+    use_short.click(short_prompt, inputs=[input_text], outputs=magic1, queue=False)
+    see_prompts.click(text_it1, inputs=[input_text], outputs=magic1, queue=False)
+    
+myface.queue(default_concurrency_limit=200, max_size=200)
+myface.launch(show_api=False, max_threads=400)
+# https://github.com/gradio-app/gradio/issues/6339

externalmod.py

@@ -9,7 +9,7 @@ import re
 import tempfile
 import warnings
 from pathlib import Path
-from typing import TYPE_CHECKING, Callable
+from typing import TYPE_CHECKING, Callable, Literal
 
 import httpx
 import huggingface_hub
@@ -33,6 +33,7 @@ if TYPE_CHECKING:
     from gradio.interface import Interface
 
 
+HF_TOKEN = os.environ.get("HF_TOKEN") if os.environ.get("HF_TOKEN") else None # If private or gated models aren't used, ENV setting is unnecessary.
 server_timeout = 600
 
 
@@ -40,7 +41,7 @@ server_timeout = 600
 def load(
     name: str,
     src: str | None = None,
-    hf_token: str | None = None,
+    hf_token: str | Literal[False] | None = None,
     alias: str | None = None,
     **kwargs,
 ) -> Blocks:
@@ -51,7 +52,7 @@ def load(
     Parameters:
         name: the name of the model (e.g. "gpt2" or "facebook/bart-base") or space (e.g. "flax-community/spanish-gpt2"), can include the `src` as prefix (e.g. "models/facebook/bart-base")
         src: the source of the model: `models` or `spaces` (or leave empty if source is provided as a prefix in `name`)
-        hf_token: optional access token for loading private Hugging Face Hub models or spaces. Find your token here: https://huggingface.co/settings/tokens.  Warning: only provide this if you are loading a trusted private Space as it can be read by the Space you are loading.
+        hf_token: optional access token for loading private Hugging Face Hub models or spaces. Will default to the locally saved token if not provided. Pass `token=False` if you don't want to send your token to the server. Find your token here: https://huggingface.co/settings/tokens.  Warning: only provide a token if you are loading a trusted private Space as it can be read by the Space you are loading.
         alias: optional string used as the name of the loaded model instead of the default name (only applies if loading a Space running Gradio 2.x)
     Returns:
         a Gradio Blocks object for the given model
@@ -68,7 +69,7 @@ def load(
 def load_blocks_from_repo(
     name: str,
     src: str | None = None,
-    hf_token: str | None = None,
+    hf_token: str | Literal[False] | None = None,
     alias: str | None = None,
     **kwargs,
 ) -> Blocks:
@@ -92,7 +93,7 @@ def load_blocks_from_repo(
     if src.lower() not in factory_methods:
         raise ValueError(f"parameter: src must be one of {factory_methods.keys()}")
 
-    if hf_token is not None:
+    if hf_token is not None and hf_token is not False:
         if Context.hf_token is not None and Context.hf_token != hf_token:
             warnings.warn(
                 """You are loading a model/Space with a different access token than the one you used to load a previous model/Space. This is not recommended, as it may cause unexpected behavior."""
@@ -103,12 +104,16 @@ def load_blocks_from_repo(
     return blocks
 
 
-def from_model(model_name: str, hf_token: str | None, alias: str | None, **kwargs):
+def from_model(
+    model_name: str, hf_token: str | Literal[False] | None, alias: str | None, **kwargs
+):
     model_url = f"https://huggingface.co/{model_name}"
     api_url = f"https://api-inference.huggingface.co/models/{model_name}"
     print(f"Fetching model from: {model_url}")
 
-    headers = {"Authorization": f"Bearer {hf_token}"} if hf_token is not None else {}
+    headers = (
+        {} if hf_token in [False, None] else {"Authorization": f"Bearer {hf_token}"}
+    )
     response = httpx.request("GET", api_url, headers=headers)
     if response.status_code != 200:
         raise ModelNotFoundError(
@@ -371,7 +376,11 @@ def from_model(model_name: str, hf_token: str | None, alias: str | None, **kwarg
     def query_huggingface_inference_endpoints(*data, **kwargs):
         if preprocess is not None:
             data = preprocess(*data)
-        data = fn(*data, **kwargs)  # type: ignore
+        try:
+            data = fn(*data, **kwargs)  # type: ignore
+        except huggingface_hub.utils.HfHubHTTPError as e:
+            if "429" in str(e):
+                raise TooManyRequestsError() from e
         if postprocess is not None:
             data = postprocess(data)  # type: ignore
         return data
@@ -383,7 +392,7 @@ def from_model(model_name: str, hf_token: str | None, alias: str | None, **kwarg
         "inputs": inputs,
         "outputs": outputs,
         "title": model_name,
-    #    "examples": examples,
+        #"examples": examples,
     }
 
     kwargs = dict(interface_info, **kwargs)
@@ -394,19 +403,12 @@ def from_model(model_name: str, hf_token: str | None, alias: str | None, **kwarg
 def from_spaces(
     space_name: str, hf_token: str | None, alias: str | None, **kwargs
 ) -> Blocks:
-    client = Client(
-        space_name,
-        hf_token=hf_token,
-        download_files=False,
-        _skip_components=False,
-    )
-
     space_url = f"https://huggingface.co/spaces/{space_name}"
 
     print(f"Fetching Space from: {space_url}")
 
     headers = {}
-    if hf_token is not None:
+    if hf_token not in [False, None]:
         headers["Authorization"] = f"Bearer {hf_token}"
 
     iframe_url = (
@@ -443,8 +445,7 @@ def from_spaces(
                 "Blocks or Interface locally. You may find this Guide helpful: "
                 "https://gradio.app/using_blocks_like_functions/"
             )
-        if client.app_version < version.Version("4.0.0b14"):
-            return from_spaces_blocks(space=space_name, hf_token=hf_token)
+        return from_spaces_blocks(space=space_name, hf_token=hf_token)
 
 
 def from_spaces_blocks(space: str, hf_token: str | None) -> Blocks:
@@ -489,7 +490,7 @@ def from_spaces_interface(
     config = external_utils.streamline_spaces_interface(config)
     api_url = f"{iframe_url}/api/predict/"
     headers = {"Content-Type": "application/json"}
-    if hf_token is not None:
+    if hf_token not in [False, None]:
         headers["Authorization"] = f"Bearer {hf_token}"
 
     # The function should call the API with preprocessed data
@@ -529,7 +530,7 @@ def gr_Interface_load(
     src: str | None = None,
     hf_token: str | None = None,
     alias: str | None = None,
-    **kwargs,
+    **kwargs, # ignore
 ) -> Blocks:
     try:
         return load_blocks_from_repo(name, src, hf_token, alias)
@@ -543,8 +544,8 @@ def list_uniq(l):
 
 
 def get_status(model_name: str):
-    from huggingface_hub import InferenceClient
-    client = InferenceClient(timeout=10)
+    from huggingface_hub import AsyncInferenceClient
+    client = AsyncInferenceClient(token=HF_TOKEN, timeout=10)
     return client.get_model_status(model_name)
 
 
@@ -563,22 +564,22 @@ def is_loadable(model_name: str, force_gpu: bool = False):
 
 def find_model_list(author: str="", tags: list[str]=[], not_tag="", sort: str="last_modified", limit: int=30, force_gpu=False, check_status=False):
     from huggingface_hub import HfApi
-    api = HfApi()
+    api = HfApi(token=HF_TOKEN)
     default_tags = ["diffusers"]
     if not sort: sort = "last_modified"
     limit = limit * 20 if check_status and force_gpu else limit * 5
     models = []
     try:
-        model_infos = api.list_models(author=author, task="text-to-image",
+        model_infos = api.list_models(author=author, #task="text-to-image",
                                        tags=list_uniq(default_tags + tags), cardData=True, sort=sort, limit=limit)
     except Exception as e:
         print(f"Error: Failed to list models.")
         print(e)
         return models
     for model in model_infos:
-        if not model.private and not model.gated:
+        if not model.private and not model.gated or HF_TOKEN is not None:
            loadable = is_loadable(model.id, force_gpu) if check_status else True
            if not_tag and not_tag in model.tags or not loadable: continue
            models.append(model.id)
            if len(models) == limit: break
-    return models
+    return models