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README.md

@@ -1,5 +1,5 @@
 ---
-title: 866 AI Art Models 6 Outputs (Gradio 4.x, Single Gallery)
+title: 800+ AI Art Models 6 Outputs (Gradio 4.x, Single Gallery) (Huggingface Diffusion)
 emoji: 🛕🛕
 colorFrom: green
 colorTo: blue
@@ -7,7 +7,9 @@ sdk: gradio
 sdk_version: 4.42.0
 app_file: app.py
 pinned: false
-duplicated_from: Yntec/Diffusion80XX
+duplicated_from:
+- Yntec/Diffusion80XX
+- Yntec/HuggingfaceDiffusion
 short_description: Compare up to 6 image models!
 ---
 

app.py

@@ -38,13 +38,13 @@ def extend_choices(choices):
 
 def update_imgbox(choices):
     choices_plus = extend_choices(choices[:num_models])
-    return [gr.Image(None, label = m, visible = (m != 'NA')) for m in choices_plus]
+    return [gr.Image(None, label=m, visible=(m!='NA')) for m in choices_plus]
 
 
 def random_choices():
     import random
     random.seed()
-    return random.choices(models, k = num_models)
+    return random.choices(models, k=num_models)
 
 
 # https://huggingface.co/docs/api-inference/detailed_parameters
@@ -67,12 +67,18 @@ async def infer(model_str, prompt, nprompt="", height=None, width=None, steps=No
     await asyncio.sleep(0)
     try:
         result = await asyncio.wait_for(task, timeout=timeout)
-    except (Exception, asyncio.TimeoutError) as e:
+    except asyncio.TimeoutError as e:
         print(e)
         print(f"Task timed out: {model_str}")
         if not task.done(): task.cancel()
         result = None
-    if task.done() and result is not None:
+        raise Exception(f"Task timed out: {model_str}")
+    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)
@@ -82,8 +88,6 @@ async def infer(model_str, prompt, nprompt="", height=None, width=None, steps=No
 
 
 def gen_fn(model_str, prompt, nprompt="", height=None, width=None, steps=None, cfg=None, seed=-1):
-    if model_str == 'NA':
-        return None
     try:
         loop = asyncio.new_event_loop()
         result = loop.run_until_complete(infer(model_str, prompt, nprompt,
@@ -92,6 +96,7 @@ def gen_fn(model_str, prompt, nprompt="", height=None, width=None, steps=None, c
         print(e)
         print(f"Task aborted: {model_str}")
         result = None
+        raise gr.Error(f"Task aborted: {model_str}, Error: {e}")
     finally:
         loop.close()
     return result
@@ -106,11 +111,12 @@ def add_gallery(image, model_str, gallery):
 
 CSS="""
 .gradio-container { max-width: 1200px; margin: 0 auto; !important; }
-.output { width=112px; height=112px; !important; }
-.gallery { width=100%; min_height=768px; !important; }
+.output { width=112px; height=112px; max_width=112px; max_height=112px; !important; }
+.gallery { min_width=512px; min_height=512px; max_height=1024px; !important; }
 .guide { text-align: center; !important; }
 """
 
+
 with gr.Blocks(theme='Nymbo/Nymbo_Theme', fill_width=True, css=CSS) as demo:
     with gr.Tab('The Dream'):
         with gr.Column(scale=2):
@@ -126,9 +132,9 @@ with gr.Blocks(theme='Nymbo/Nymbo_Theme', fill_width=True, css=CSS) as demo:
                         cfg = gr.Slider(label="Guidance scale", info="If 0, the default value is used.", maximum=30.0, step=0.1, value=0)
                         seed = gr.Slider(label="Seed", info="Randomize Seed if -1.", minimum=-1, maximum=MAX_SEED, step=1, value=-1)
             with gr.Row():
-                gen_button = gr.Button(f'Generate up to {int(num_models)} images in up to 3 minutes total', scale=3)
+                gen_button = gr.Button(f'Generate up to {int(num_models)} images in up to 3 minutes total', variant='primary', scale=3)
                 random_button = gr.Button(f'Random {int(num_models)} 🎲', variant='secondary', scale=1)
-                stop_button = gr.Button('Stop', variant='secondary', interactive=False, scale=1)
+                stop_button = gr.Button('Stop', variant='stop', interactive=False, scale=1)
                 gen_button.click(lambda: gr.update(interactive=True), None, stop_button)
             gr.Markdown("Scroll down to see more images and select models.", elem_classes="guide")
 
@@ -174,8 +180,8 @@ with gr.Blocks(theme='Nymbo/Nymbo_Theme', fill_width=True, css=CSS) as demo:
                         seed2 = gr.Slider(label="Seed", info="Randomize Seed if -1.", minimum=-1, maximum=MAX_SEED, step=1, value=-1)
             num_images = gr.Slider(1, max_images, value=max_images, step=1, label='Number of images')
             with gr.Row():
-                gen_button2 = gr.Button('Generate', scale=2)
-                stop_button2 = gr.Button('Stop', variant='secondary', interactive=False, scale=1)
+                gen_button2 = gr.Button('Generate', variant='primary', scale=2)
+                stop_button2 = gr.Button('Stop', variant='stop', interactive=False, scale=1)
                 gen_button2.click(lambda: gr.update(interactive=True), None, stop_button2)
 
         with gr.Column(scale=1):

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)
 
 
@@ -563,7 +564,7 @@ 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
@@ -576,7 +577,7 @@ def find_model_list(author: str="", tags: list[str]=[], not_tag="", sort: str="l
         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)