Yntec John6666 commited on
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Co-authored-by: John Smith <John6666@users.noreply.huggingface.co>

Files changed (3) hide show
  1. README.md +12 -12
  2. app.py +219 -158
  3. externalmod.py +105 -24
README.md CHANGED
@@ -1,13 +1,13 @@
1
- ---
2
- title: Huggingface Diffusion
3
- emoji: 🛕🛕
4
- colorFrom: green
5
- colorTo: blue
6
- sdk: gradio
7
- sdk_version: 4.39.0
8
- app_file: app.py
9
- pinned: true
10
- short_description: Compare 909+ AI Art Models 6 at a time!
11
- ---
12
-
13
  Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
 
1
+ ---
2
+ title: Huggingface Diffusion
3
+ emoji: 🛕🛕
4
+ colorFrom: green
5
+ colorTo: blue
6
+ sdk: gradio
7
+ sdk_version: 4.42.0
8
+ app_file: app.py
9
+ pinned: true
10
+ short_description: Compare 909+ AI Art Models 6 at a time!
11
+ ---
12
+
13
  Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
app.py CHANGED
@@ -1,158 +1,219 @@
1
- import gradio as gr
2
- from random import randint
3
- from all_models import models
4
- from externalmod import gr_Interface_load
5
- import asyncio
6
- from threading import RLock
7
- lock = RLock()
8
-
9
- def load_fn(models):
10
- global models_load
11
- models_load = {}
12
-
13
- for model in models:
14
- if model not in models_load.keys():
15
- try:
16
- m = gr_Interface_load(f'models/{model}')
17
- except Exception as error:
18
- print(error)
19
- m = gr.Interface(lambda: None, ['text'], ['image'])
20
- models_load.update({model: m})
21
-
22
-
23
- load_fn(models)
24
-
25
-
26
- num_models = 6
27
- default_models = models[:num_models]
28
- timeout = 300
29
-
30
- def extend_choices(choices):
31
- return choices[:num_models] + (num_models - len(choices[:num_models])) * ['NA']
32
-
33
-
34
- def update_imgbox(choices):
35
- choices_plus = extend_choices(choices[:num_models])
36
- return [gr.Image(None, label = m, visible = (m != 'NA')) for m in choices_plus]
37
-
38
-
39
- def update_imgbox_gallery(choices):
40
- choices_plus = extend_choices(choices[:num_models])
41
- return [gr.Gallery(None, label = m, visible = (m != 'NA')) for m in choices_plus]
42
-
43
-
44
- async def infer(model_str, prompt, timeout):
45
- from PIL import Image
46
- noise = ""
47
- rand = randint(1, 500)
48
- for i in range(rand):
49
- noise += " "
50
- task = asyncio.create_task(asyncio.to_thread(models_load[model_str], f'{prompt} {noise}'))
51
- await asyncio.sleep(0)
52
- try:
53
- result = await asyncio.wait_for(task, timeout=timeout)
54
- except (Exception, asyncio.TimeoutError) as e:
55
- print(e)
56
- print(f"Task timed out: {model_str}")
57
- if not task.done(): task.cancel()
58
- result = None
59
- if task.done() and result is not None:
60
- with lock:
61
- image = Image.open(result).convert('RGBA')
62
- return image
63
- return None
64
-
65
- def gen_fn(model_str, prompt):
66
- if model_str == 'NA':
67
- return None
68
- try:
69
- loop = asyncio.new_event_loop()
70
- result = loop.run_until_complete(infer(model_str, prompt, timeout))
71
- except (Exception, asyncio.CancelledError) as e:
72
- print(e)
73
- print(f"Task aborted: {model_str}")
74
- result = None
75
- finally:
76
- loop.close()
77
- return result
78
-
79
-
80
- def add_gallery(image, model_str, gallery):
81
- if gallery is None: gallery = []
82
- with lock:
83
- if image is not None: gallery.insert(0, (image, model_str))
84
- return gallery
85
-
86
-
87
- def gen_fn_gallery(model_str, prompt, gallery):
88
- if gallery is None: gallery = []
89
- if model_str == 'NA':
90
- yield gallery
91
- try:
92
- loop = asyncio.new_event_loop()
93
- result = loop.run_until_complete(infer(model_str, prompt, timeout))
94
- with lock:
95
- if result: gallery.insert(0, result)
96
- except (Exception, asyncio.CancelledError) as e:
97
- print(e)
98
- print(f"Task aborted: {model_str}")
99
- finally:
100
- loop.close()
101
- yield gallery
102
-
103
-
104
- CSS="""
105
- #container { max-width: 1200px; margin: 0 auto; !important; }
106
- .output { width=112px; height=112px; !important; }
107
- .gallery { width=100%; min_height=768px; !important; }
108
- .guide { text-align: center; !important; }
109
- """
110
-
111
- with gr.Blocks(theme='Nymbo/Nymbo_Theme', fill_width=True, css=CSS) as demo:
112
- gr.HTML(
113
- """
114
- <div>
115
- <p> <center>For simultaneous generations without hidden queue check out <a href="https://huggingface.co/spaces/Yntec/ToyWorld">Toy World</a>! For more options like single model x6 check out <a href="https://huggingface.co/spaces/John6666/Diffusion80XX4sg">Diffusion80XX4sg</a> by John6666!</center>
116
- </p></div>
117
- """
118
- )
119
- with gr.Tab('Huggingface Diffusion'):
120
- with gr.Column(scale=2):
121
- txt_input = gr.Textbox(label='Your prompt:', lines=4)
122
- with gr.Row():
123
- gen_button = gr.Button(f'Generate up to {int(num_models)} images from 1 to {int(num_models)*3} minutes total', scale=2)
124
- stop_button = gr.Button('Stop', variant='secondary', interactive=False, scale=1)
125
- gen_button.click(lambda: gr.update(interactive = True), None, stop_button)
126
- gr.Markdown("Scroll down to see more images and select models.", elem_classes="guide")
127
-
128
- with gr.Column(scale=1):
129
- with gr.Group():
130
- with gr.Row():
131
- output = [gr.Image(label=m, show_download_button=True, elem_classes="output", interactive=False, min_width=80, show_share_button=False, visible=True) for m in default_models]
132
- #output = [gr.Image(label=m, show_download_button=True, elem_classes="output", interactive=False, show_share_button=True) for m in default_models]
133
- #output = [gr.Gallery(label=m, show_download_button=True, elem_classes="output", interactive=False, show_share_button=True, container=True, format="png", object_fit="cover") for m in default_models]
134
- current_models = [gr.Textbox(m, visible=False) for m in default_models]
135
-
136
- with gr.Column(scale=2):
137
- gallery = gr.Gallery(label="Output", show_download_button=True, elem_classes="gallery",
138
- interactive=False, show_share_button=True, container=True, format="png",
139
- preview=True, object_fit="cover", columns=2, rows=2)
140
-
141
- for m, o in zip(current_models, output):
142
- #gen_event = gen_button.click(gen_fn, [m, txt_input], o)
143
- #gen_event = gen_button.click(gen_fn_gallery, [m, txt_input, o], o)
144
- gen_event = gr.on(triggers=[gen_button.click, txt_input.submit], fn=gen_fn, inputs=[m, txt_input], outputs=[o])
145
- o.change(add_gallery, [o, m, gallery], [gallery])
146
- stop_button.click(lambda: gr.update(interactive = False), None, stop_button, cancels = [gen_event])
147
-
148
- with gr.Column(scale=4):
149
- with gr.Accordion('Model selection'):
150
- model_choice = gr.CheckboxGroup(models, label = f'Choose up to {int(num_models)} different models from the {len(models)} available!', value=default_models, interactive=True)
151
- model_choice.change(update_imgbox, model_choice, output)
152
- #model_choice.change(update_imgbox_gallery, model_choice, output)
153
- model_choice.change(extend_choices, model_choice, current_models)
154
-
155
- gr.Markdown("Based on the [TestGen](https://huggingface.co/spaces/derwahnsinn/TestGen) Space by derwahnsinn, the [SpacIO](https://huggingface.co/spaces/RdnUser77/SpacIO_v1) Space by RdnUser77 and Omnibus's Maximum Multiplier!")
156
-
157
- demo.queue()
158
- demo.launch()
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import gradio as gr
2
+ from all_models import models
3
+ from externalmod import gr_Interface_load, save_image, randomize_seed
4
+ import asyncio
5
+ import os
6
+ from threading import RLock
7
+ lock = RLock()
8
+ 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.
9
+
10
+
11
+ def load_fn(models):
12
+ global models_load
13
+ models_load = {}
14
+ for model in models:
15
+ if model not in models_load.keys():
16
+ try:
17
+ m = gr_Interface_load(f'models/{model}', hf_token=HF_TOKEN)
18
+ except Exception as error:
19
+ print(error)
20
+ m = gr.Interface(lambda: None, ['text'], ['image'])
21
+ models_load.update({model: m})
22
+
23
+
24
+ load_fn(models)
25
+
26
+
27
+ num_models = 6
28
+ max_images = 6
29
+ inference_timeout = 300
30
+ default_models = models[:num_models]
31
+ MAX_SEED = 2**32-1
32
+
33
+
34
+ def extend_choices(choices):
35
+ return choices[:num_models] + (num_models - len(choices[:num_models])) * ['NA']
36
+
37
+
38
+ def update_imgbox(choices):
39
+ choices_plus = extend_choices(choices[:num_models])
40
+ return [gr.Image(None, label=m, visible=(m!='NA')) for m in choices_plus]
41
+
42
+
43
+ def random_choices():
44
+ import random
45
+ random.seed()
46
+ return random.choices(models, k=num_models)
47
+
48
+
49
+ # https://huggingface.co/docs/api-inference/detailed_parameters
50
+ # https://huggingface.co/docs/huggingface_hub/package_reference/inference_client
51
+ async def infer(model_str, prompt, nprompt="", height=0, width=0, steps=0, cfg=0, seed=-1, timeout=inference_timeout):
52
+ kwargs = {}
53
+ if height > 0: kwargs["height"] = height
54
+ if width > 0: kwargs["width"] = width
55
+ if steps > 0: kwargs["num_inference_steps"] = steps
56
+ if cfg > 0: cfg = kwargs["guidance_scale"] = cfg
57
+ if seed == -1: kwargs["seed"] = randomize_seed()
58
+ else: kwargs["seed"] = seed
59
+ task = asyncio.create_task(asyncio.to_thread(models_load[model_str].fn,
60
+ prompt=prompt, negative_prompt=nprompt, **kwargs, token=HF_TOKEN))
61
+ await asyncio.sleep(0)
62
+ try:
63
+ result = await asyncio.wait_for(task, timeout=timeout)
64
+ except asyncio.TimeoutError as e:
65
+ print(e)
66
+ print(f"Task timed out: {model_str}")
67
+ if not task.done(): task.cancel()
68
+ result = None
69
+ raise Exception(f"Task timed out: {model_str}") from e
70
+ except Exception as e:
71
+ print(e)
72
+ if not task.done(): task.cancel()
73
+ result = None
74
+ raise Exception() from e
75
+ if task.done() and result is not None and not isinstance(result, tuple):
76
+ with lock:
77
+ png_path = "image.png"
78
+ image = save_image(result, png_path, model_str, prompt, nprompt, height, width, steps, cfg, seed)
79
+ return image
80
+ return None
81
+
82
+
83
+ def gen_fn(model_str, prompt, nprompt="", height=0, width=0, steps=0, cfg=0, seed=-1):
84
+ try:
85
+ loop = asyncio.new_event_loop()
86
+ result = loop.run_until_complete(infer(model_str, prompt, nprompt,
87
+ height, width, steps, cfg, seed, inference_timeout))
88
+ except (Exception, asyncio.CancelledError) as e:
89
+ print(e)
90
+ print(f"Task aborted: {model_str}")
91
+ result = None
92
+ raise gr.Error(f"Task aborted: {model_str}, Error: {e}")
93
+ finally:
94
+ loop.close()
95
+ return result
96
+
97
+
98
+ def add_gallery(image, model_str, gallery):
99
+ if gallery is None: gallery = []
100
+ with lock:
101
+ if image is not None: gallery.insert(0, (image, model_str))
102
+ return gallery
103
+
104
+
105
+ CSS="""
106
+ .gradio-container { max-width: 1200px; margin: 0 auto; !important; }
107
+ .output { width=112px; height=112px; max_width=112px; max_height=112px; !important; }
108
+ .gallery { min_width=512px; min_height=512px; max_height=1024px; !important; }
109
+ .guide { text-align: center; !important; }
110
+ """
111
+
112
+
113
+ with gr.Blocks(theme='Nymbo/Nymbo_Theme', fill_width=True, css=CSS) as demo:
114
+ gr.HTML(
115
+ """
116
+ <div>
117
+ <p> <center>For simultaneous generations without hidden queue check out <a href="https://huggingface.co/spaces/Yntec/ToyWorld">Toy World</a>! For more options like single model x6 check out <a href="https://huggingface.co/spaces/John6666/Diffusion80XX4sg">Diffusion80XX4sg</a> by John6666!</center>
118
+ </p></div>
119
+ """
120
+ )
121
+ with gr.Tab('Huggingface Diffusion'):
122
+ with gr.Column(scale=2):
123
+ with gr.Group():
124
+ txt_input = gr.Textbox(label='Your prompt:', lines=4)
125
+ neg_input = gr.Textbox(label='Negative prompt:', lines=1)
126
+ with gr.Accordion("Advanced", open=False, visible=True):
127
+ with gr.Row():
128
+ width = gr.Slider(label="Width", info="If 0, the default value is used.", maximum=1216, step=32, value=0)
129
+ height = gr.Slider(label="Height", info="If 0, the default value is used.", maximum=1216, step=32, value=0)
130
+ with gr.Row():
131
+ steps = gr.Slider(label="Number of inference steps", info="If 0, the default value is used.", maximum=100, step=1, value=0)
132
+ cfg = gr.Slider(label="Guidance scale", info="If 0, the default value is used.", maximum=30.0, step=0.1, value=0)
133
+ seed = gr.Slider(label="Seed", info="Randomize Seed if -1.", minimum=-1, maximum=MAX_SEED, step=1, value=-1)
134
+ seed_rand = gr.Button("Randomize Seed 🎲", size="sm", variant="secondary")
135
+ seed_rand.click(randomize_seed, None, [seed], queue=False)
136
+ with gr.Row():
137
+ gen_button = gr.Button(f'Generate up to {int(num_models)} images in up to 3 minutes total', variant='primary', scale=3)
138
+ random_button = gr.Button(f'Random {int(num_models)} 🎲', variant='secondary', scale=1)
139
+ #stop_button = gr.Button('Stop', variant='stop', interactive=False, scale=1)
140
+ #gen_button.click(lambda: gr.update(interactive=True), None, stop_button)
141
+ gr.Markdown("Scroll down to see more images and select models.", elem_classes="guide")
142
+
143
+ with gr.Column(scale=1):
144
+ with gr.Group():
145
+ with gr.Row():
146
+ output = [gr.Image(label=m, show_download_button=True, elem_classes="output",
147
+ interactive=False, min_width=80, show_share_button=False, format="png",
148
+ visible=True) for m in default_models]
149
+ current_models = [gr.Textbox(m, visible=False) for m in default_models]
150
+
151
+ with gr.Column(scale=2):
152
+ gallery = gr.Gallery(label="Output", show_download_button=True, elem_classes="gallery",
153
+ interactive=False, show_share_button=True, container=True, format="png",
154
+ preview=True, object_fit="cover", columns=2, rows=2)
155
+
156
+ for m, o in zip(current_models, output):
157
+ gen_event = gr.on(triggers=[gen_button.click, txt_input.submit], fn=gen_fn,
158
+ inputs=[m, txt_input, neg_input, height, width, steps, cfg, seed], outputs=[o],
159
+ concurrency_limit=None, queue=False) # Be sure to delete ", queue=False" when activating the stop button
160
+ o.change(add_gallery, [o, m, gallery], [gallery])
161
+ #stop_button.click(lambda: gr.update(interactive=False), None, stop_button, cancels=[gen_event])
162
+
163
+ with gr.Column(scale=4):
164
+ with gr.Accordion('Model selection'):
165
+ model_choice = gr.CheckboxGroup(models, label = f'Choose up to {int(num_models)} different models from the {len(models)} available!', value=default_models, interactive=True)
166
+ model_choice.change(update_imgbox, model_choice, output)
167
+ model_choice.change(extend_choices, model_choice, current_models)
168
+ random_button.click(random_choices, None, model_choice)
169
+
170
+ with gr.Tab('Single model'):
171
+ with gr.Column(scale=2):
172
+ model_choice2 = gr.Dropdown(models, label='Choose model', value=models[0])
173
+ with gr.Group():
174
+ txt_input2 = gr.Textbox(label='Your prompt:', lines=4)
175
+ neg_input2 = gr.Textbox(label='Negative prompt:', lines=1)
176
+ with gr.Accordion("Advanced", open=False, visible=True):
177
+ with gr.Row():
178
+ width2 = gr.Slider(label="Width", info="If 0, the default value is used.", maximum=1216, step=32, value=0)
179
+ height2 = gr.Slider(label="Height", info="If 0, the default value is used.", maximum=1216, step=32, value=0)
180
+ with gr.Row():
181
+ steps2 = gr.Slider(label="Number of inference steps", info="If 0, the default value is used.", maximum=100, step=1, value=0)
182
+ cfg2 = gr.Slider(label="Guidance scale", info="If 0, the default value is used.", maximum=30.0, step=0.1, value=0)
183
+ seed2 = gr.Slider(label="Seed", info="Randomize Seed if -1.", minimum=-1, maximum=MAX_SEED, step=1, value=-1)
184
+ seed_rand2 = gr.Button("Randomize Seed 🎲", size="sm", variant="secondary")
185
+ seed_rand2.click(randomize_seed, None, [seed2], queue=False)
186
+ num_images = gr.Slider(1, max_images, value=max_images, step=1, label='Number of images')
187
+ with gr.Row():
188
+ gen_button2 = gr.Button('Generate', variant='primary', scale=2)
189
+ #stop_button2 = gr.Button('Stop', variant='stop', interactive=False, scale=1)
190
+ #gen_button2.click(lambda: gr.update(interactive=True), None, stop_button2)
191
+
192
+ with gr.Column(scale=1):
193
+ with gr.Group():
194
+ with gr.Row():
195
+ output2 = [gr.Image(label='', show_download_button=True, elem_classes="output",
196
+ interactive=False, min_width=80, visible=True, format="png",
197
+ show_share_button=False, show_label=False) for _ in range(max_images)]
198
+
199
+ with gr.Column(scale=2):
200
+ gallery2 = gr.Gallery(label="Output", show_download_button=True, elem_classes="gallery",
201
+ interactive=False, show_share_button=True, container=True, format="png",
202
+ preview=True, object_fit="cover", columns=2, rows=2)
203
+
204
+ for i, o in enumerate(output2):
205
+ img_i = gr.Number(i, visible=False)
206
+ num_images.change(lambda i, n: gr.update(visible = (i < n)), [img_i, num_images], o, queue=False)
207
+ gen_event2 = gr.on(triggers=[gen_button2.click, txt_input2.submit],
208
+ fn=lambda i, n, m, t1, t2, n1, n2, n3, n4, n5: gen_fn(m, t1, t2, n1, n2, n3, n4, n5) if (i < n) else None,
209
+ inputs=[img_i, num_images, model_choice2, txt_input2, neg_input2,
210
+ height2, width2, steps2, cfg2, seed2], outputs=[o],
211
+ concurrency_limit=None, queue=False) # Be sure to delete ", queue=False" when activating the stop button
212
+ o.change(add_gallery, [o, model_choice2, gallery2], [gallery2])
213
+ #stop_button2.click(lambda: gr.update(interactive=False), None, stop_button2, cancels=[gen_event2])
214
+
215
+ gr.Markdown("Based on the [TestGen](https://huggingface.co/spaces/derwahnsinn/TestGen) Space by derwahnsinn, the [SpacIO](https://huggingface.co/spaces/RdnUser77/SpacIO_v1) Space by RdnUser77 and Omnibus's Maximum Multiplier!")
216
+
217
+ demo.queue(default_concurrency_limit=200, max_size=200)
218
+ demo.launch(show_api=False, max_threads=400)
219
+ # https://github.com/gradio-app/gradio/issues/6339
externalmod.py CHANGED
@@ -9,7 +9,7 @@ import re
9
  import tempfile
10
  import warnings
11
  from pathlib import Path
12
- from typing import TYPE_CHECKING, Callable
13
 
14
  import httpx
15
  import huggingface_hub
@@ -33,11 +33,15 @@ if TYPE_CHECKING:
33
  from gradio.interface import Interface
34
 
35
 
 
 
 
 
36
  @document()
37
  def load(
38
  name: str,
39
  src: str | None = None,
40
- hf_token: str | None = None,
41
  alias: str | None = None,
42
  **kwargs,
43
  ) -> Blocks:
@@ -48,7 +52,7 @@ def load(
48
  Parameters:
49
  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")
50
  src: the source of the model: `models` or `spaces` (or leave empty if source is provided as a prefix in `name`)
51
- 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.
52
  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)
53
  Returns:
54
  a Gradio Blocks object for the given model
@@ -65,7 +69,7 @@ def load(
65
  def load_blocks_from_repo(
66
  name: str,
67
  src: str | None = None,
68
- hf_token: str | None = None,
69
  alias: str | None = None,
70
  **kwargs,
71
  ) -> Blocks:
@@ -89,7 +93,7 @@ def load_blocks_from_repo(
89
  if src.lower() not in factory_methods:
90
  raise ValueError(f"parameter: src must be one of {factory_methods.keys()}")
91
 
92
- if hf_token is not None:
93
  if Context.hf_token is not None and Context.hf_token != hf_token:
94
  warnings.warn(
95
  """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."""
@@ -100,12 +104,16 @@ def load_blocks_from_repo(
100
  return blocks
101
 
102
 
103
- def from_model(model_name: str, hf_token: str | None, alias: str | None, **kwargs):
 
 
104
  model_url = f"https://huggingface.co/{model_name}"
105
  api_url = f"https://api-inference.huggingface.co/models/{model_name}"
106
  print(f"Fetching model from: {model_url}")
107
 
108
- headers = {"Authorization": f"Bearer {hf_token}"} if hf_token is not None else {}
 
 
109
  response = httpx.request("GET", api_url, headers=headers)
110
  if response.status_code != 200:
111
  raise ModelNotFoundError(
@@ -115,7 +123,7 @@ def from_model(model_name: str, hf_token: str | None, alias: str | None, **kwarg
115
 
116
  headers["X-Wait-For-Model"] = "true"
117
  client = huggingface_hub.InferenceClient(
118
- model=model_name, headers=headers, token=hf_token,
119
  )
120
 
121
  # For tasks that are not yet supported by the InferenceClient
@@ -365,10 +373,14 @@ def from_model(model_name: str, hf_token: str | None, alias: str | None, **kwarg
365
  else:
366
  raise ValueError(f"Unsupported pipeline type: {p}")
367
 
368
- def query_huggingface_inference_endpoints(*data):
369
  if preprocess is not None:
370
  data = preprocess(*data)
371
- data = fn(*data) # type: ignore
 
 
 
 
372
  if postprocess is not None:
373
  data = postprocess(data) # type: ignore
374
  return data
@@ -380,7 +392,7 @@ def from_model(model_name: str, hf_token: str | None, alias: str | None, **kwarg
380
  "inputs": inputs,
381
  "outputs": outputs,
382
  "title": model_name,
383
- # "examples": examples,
384
  }
385
 
386
  kwargs = dict(interface_info, **kwargs)
@@ -391,19 +403,12 @@ def from_model(model_name: str, hf_token: str | None, alias: str | None, **kwarg
391
  def from_spaces(
392
  space_name: str, hf_token: str | None, alias: str | None, **kwargs
393
  ) -> Blocks:
394
- client = Client(
395
- space_name,
396
- hf_token=hf_token,
397
- download_files=False,
398
- _skip_components=False,
399
- )
400
-
401
  space_url = f"https://huggingface.co/spaces/{space_name}"
402
 
403
  print(f"Fetching Space from: {space_url}")
404
 
405
  headers = {}
406
- if hf_token is not None:
407
  headers["Authorization"] = f"Bearer {hf_token}"
408
 
409
  iframe_url = (
@@ -440,8 +445,7 @@ def from_spaces(
440
  "Blocks or Interface locally. You may find this Guide helpful: "
441
  "https://gradio.app/using_blocks_like_functions/"
442
  )
443
- if client.app_version < version.Version("4.0.0b14"):
444
- return from_spaces_blocks(space=space_name, hf_token=hf_token)
445
 
446
 
447
  def from_spaces_blocks(space: str, hf_token: str | None) -> Blocks:
@@ -486,7 +490,7 @@ def from_spaces_interface(
486
  config = external_utils.streamline_spaces_interface(config)
487
  api_url = f"{iframe_url}/api/predict/"
488
  headers = {"Content-Type": "application/json"}
489
- if hf_token is not None:
490
  headers["Authorization"] = f"Bearer {hf_token}"
491
 
492
  # The function should call the API with preprocessed data
@@ -526,6 +530,83 @@ def gr_Interface_load(
526
  src: str | None = None,
527
  hf_token: str | None = None,
528
  alias: str | None = None,
529
- **kwargs,
530
  ) -> Blocks:
531
- return load_blocks_from_repo(name, src, hf_token, alias)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
9
  import tempfile
10
  import warnings
11
  from pathlib import Path
12
+ from typing import TYPE_CHECKING, Callable, Literal
13
 
14
  import httpx
15
  import huggingface_hub
 
33
  from gradio.interface import Interface
34
 
35
 
36
+ 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.
37
+ server_timeout = 600
38
+
39
+
40
  @document()
41
  def load(
42
  name: str,
43
  src: str | None = None,
44
+ hf_token: str | Literal[False] | None = None,
45
  alias: str | None = None,
46
  **kwargs,
47
  ) -> Blocks:
 
52
  Parameters:
53
  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")
54
  src: the source of the model: `models` or `spaces` (or leave empty if source is provided as a prefix in `name`)
55
+ 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.
56
  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)
57
  Returns:
58
  a Gradio Blocks object for the given model
 
69
  def load_blocks_from_repo(
70
  name: str,
71
  src: str | None = None,
72
+ hf_token: str | Literal[False] | None = None,
73
  alias: str | None = None,
74
  **kwargs,
75
  ) -> Blocks:
 
93
  if src.lower() not in factory_methods:
94
  raise ValueError(f"parameter: src must be one of {factory_methods.keys()}")
95
 
96
+ if hf_token is not None and hf_token is not False:
97
  if Context.hf_token is not None and Context.hf_token != hf_token:
98
  warnings.warn(
99
  """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."""
 
104
  return blocks
105
 
106
 
107
+ def from_model(
108
+ model_name: str, hf_token: str | Literal[False] | None, alias: str | None, **kwargs
109
+ ):
110
  model_url = f"https://huggingface.co/{model_name}"
111
  api_url = f"https://api-inference.huggingface.co/models/{model_name}"
112
  print(f"Fetching model from: {model_url}")
113
 
114
+ headers = (
115
+ {} if hf_token in [False, None] else {"Authorization": f"Bearer {hf_token}"}
116
+ )
117
  response = httpx.request("GET", api_url, headers=headers)
118
  if response.status_code != 200:
119
  raise ModelNotFoundError(
 
123
 
124
  headers["X-Wait-For-Model"] = "true"
125
  client = huggingface_hub.InferenceClient(
126
+ model=model_name, headers=headers, token=hf_token, timeout=server_timeout,
127
  )
128
 
129
  # For tasks that are not yet supported by the InferenceClient
 
373
  else:
374
  raise ValueError(f"Unsupported pipeline type: {p}")
375
 
376
+ def query_huggingface_inference_endpoints(*data, **kwargs):
377
  if preprocess is not None:
378
  data = preprocess(*data)
379
+ try:
380
+ data = fn(*data, **kwargs) # type: ignore
381
+ except huggingface_hub.utils.HfHubHTTPError as e:
382
+ if "429" in str(e):
383
+ raise TooManyRequestsError() from e
384
  if postprocess is not None:
385
  data = postprocess(data) # type: ignore
386
  return data
 
392
  "inputs": inputs,
393
  "outputs": outputs,
394
  "title": model_name,
395
+ #"examples": examples,
396
  }
397
 
398
  kwargs = dict(interface_info, **kwargs)
 
403
  def from_spaces(
404
  space_name: str, hf_token: str | None, alias: str | None, **kwargs
405
  ) -> Blocks:
 
 
 
 
 
 
 
406
  space_url = f"https://huggingface.co/spaces/{space_name}"
407
 
408
  print(f"Fetching Space from: {space_url}")
409
 
410
  headers = {}
411
+ if hf_token not in [False, None]:
412
  headers["Authorization"] = f"Bearer {hf_token}"
413
 
414
  iframe_url = (
 
445
  "Blocks or Interface locally. You may find this Guide helpful: "
446
  "https://gradio.app/using_blocks_like_functions/"
447
  )
448
+ return from_spaces_blocks(space=space_name, hf_token=hf_token)
 
449
 
450
 
451
  def from_spaces_blocks(space: str, hf_token: str | None) -> Blocks:
 
490
  config = external_utils.streamline_spaces_interface(config)
491
  api_url = f"{iframe_url}/api/predict/"
492
  headers = {"Content-Type": "application/json"}
493
+ if hf_token not in [False, None]:
494
  headers["Authorization"] = f"Bearer {hf_token}"
495
 
496
  # The function should call the API with preprocessed data
 
530
  src: str | None = None,
531
  hf_token: str | None = None,
532
  alias: str | None = None,
533
+ **kwargs, # ignore
534
  ) -> Blocks:
535
+ try:
536
+ return load_blocks_from_repo(name, src, hf_token, alias)
537
+ except Exception as e:
538
+ print(e)
539
+ return gradio.Interface(lambda: None, ['text'], ['image'])
540
+
541
+
542
+ def list_uniq(l):
543
+ return sorted(set(l), key=l.index)
544
+
545
+
546
+ def get_status(model_name: str):
547
+ from huggingface_hub import AsyncInferenceClient
548
+ client = AsyncInferenceClient(token=HF_TOKEN, timeout=10)
549
+ return client.get_model_status(model_name)
550
+
551
+
552
+ def is_loadable(model_name: str, force_gpu: bool = False):
553
+ try:
554
+ status = get_status(model_name)
555
+ except Exception as e:
556
+ print(e)
557
+ print(f"Couldn't load {model_name}.")
558
+ return False
559
+ gpu_state = isinstance(status.compute_type, dict) and "gpu" in status.compute_type.keys()
560
+ if status is None or status.state not in ["Loadable", "Loaded"] or (force_gpu and not gpu_state):
561
+ print(f"Couldn't load {model_name}. Model state:'{status.state}', GPU:{gpu_state}")
562
+ return status is not None and status.state in ["Loadable", "Loaded"] and (not force_gpu or gpu_state)
563
+
564
+
565
+ def find_model_list(author: str="", tags: list[str]=[], not_tag="", sort: str="last_modified", limit: int=30, force_gpu=False, check_status=False):
566
+ from huggingface_hub import HfApi
567
+ api = HfApi(token=HF_TOKEN)
568
+ default_tags = ["diffusers"]
569
+ if not sort: sort = "last_modified"
570
+ limit = limit * 20 if check_status and force_gpu else limit * 5
571
+ models = []
572
+ try:
573
+ model_infos = api.list_models(author=author, #task="text-to-image",
574
+ tags=list_uniq(default_tags + tags), cardData=True, sort=sort, limit=limit)
575
+ except Exception as e:
576
+ print(f"Error: Failed to list models.")
577
+ print(e)
578
+ return models
579
+ for model in model_infos:
580
+ if not model.private and not model.gated or HF_TOKEN is not None:
581
+ loadable = is_loadable(model.id, force_gpu) if check_status else True
582
+ if not_tag and not_tag in model.tags or not loadable: continue
583
+ models.append(model.id)
584
+ if len(models) == limit: break
585
+ return models
586
+
587
+
588
+ def save_image(image, savefile, modelname, prompt, nprompt, height=0, width=0, steps=0, cfg=0, seed=-1):
589
+ from PIL import Image, PngImagePlugin
590
+ import json
591
+ try:
592
+ metadata = {"prompt": prompt, "negative_prompt": nprompt, "Model": {"Model": modelname.split("/")[-1]}}
593
+ if steps > 0: metadata["num_inference_steps"] = steps
594
+ if cfg > 0: metadata["guidance_scale"] = cfg
595
+ if seed != -1: metadata["seed"] = seed
596
+ if width > 0 and height > 0: metadata["resolution"] = f"{width} x {height}"
597
+ metadata_str = json.dumps(metadata)
598
+ info = PngImagePlugin.PngInfo()
599
+ info.add_text("metadata", metadata_str)
600
+ image.save(savefile, "PNG", pnginfo=info)
601
+ return str(Path(savefile).resolve())
602
+ except Exception as e:
603
+ print(f"Failed to save image file: {e}")
604
+ raise Exception(f"Failed to save image file:") from e
605
+
606
+
607
+ def randomize_seed():
608
+ from random import seed, randint
609
+ MAX_SEED = 2**32-1
610
+ seed()
611
+ rseed = randint(0, MAX_SEED)
612
+ return rseed