Kohaku-Blueleaf commited on
Commit
84b9ae2
β€’
1 Parent(s): 1b4fd22

use neutral naming

Browse files
Files changed (2) hide show
  1. README.md +1 -1
  2. app.py +11 -11
README.md CHANGED
@@ -1,5 +1,5 @@
1
  ---
2
- title: TITPOP DEMO
3
  emoji: πŸ“‰
4
  colorFrom: yellow
5
  colorTo: red
 
1
  ---
2
+ title: TIPO DEMO
3
  emoji: πŸ“‰
4
  colorFrom: yellow
5
  colorTo: red
app.py CHANGED
@@ -6,7 +6,7 @@ try:
6
  import kgen
7
  except:
8
  GH_TOKEN = os.getenv("GITHUB_TOKEN")
9
- git_url = f"https://{GH_TOKEN}@github.com/KohakuBlueleaf/TITPOP-KGen@titpop"
10
 
11
  ## call pip install
12
  os.system(f"pip install git+{git_url}")
@@ -25,7 +25,7 @@ else:
25
  from spaces import GPU
26
 
27
  import kgen.models as models
28
- import kgen.executor.titpop as titpop
29
  from kgen.formatter import seperate_tags, apply_format
30
  from kgen.generate import generate
31
 
@@ -36,7 +36,7 @@ from meta import DEFAULT_NEGATIVE_PROMPT, DEFAULT_FORMAT
36
  sdxl_pipe = load_model()
37
 
38
  models.load_model(
39
- "Amber-River/titpop",
40
  device="cuda",
41
  subfolder="500M-epoch3",
42
  )
@@ -101,7 +101,7 @@ def generate(
101
  escape_brackets,
102
  ):
103
  default_format = DEFAULT_FORMAT[output_format]
104
- titpop.BAN_TAGS = [t.strip() for t in black_list.split(",") if t.strip()]
105
  generation_setting = {
106
  "seed": seed,
107
  "temperature": temp,
@@ -119,14 +119,14 @@ def generate(
119
  if escape_brackets:
120
  input_prompt = re.sub(r"([()\[\]])", r"\\\1", input_prompt)
121
 
122
- meta, operations, general, nl_prompt = titpop.parse_titpop_request(
123
  seperate_tags(tags.split(",")),
124
  nl_prompt,
125
  tag_length_target=target_length,
126
  generate_extra_nl_prompt="<|generated|>" in default_format or not nl_prompt,
127
  )
128
  t0 = time()
129
- for result, timing in titpop.titpop_runner_generator(
130
  meta, operations, general, nl_prompt, **generation_setting
131
  ):
132
  result = apply_format(result, default_format)
@@ -183,11 +183,11 @@ if __name__ == "__main__":
183
  with gr.Accordion("Introduction and Instructions", open=False):
184
  gr.Markdown(
185
  """
186
- ## TITPOP Demo
187
  **The model for demo is 500M version with 4epoch training (25B token seen)**
188
 
189
  ### What is this
190
- TITPOP is a tool to extend, generate, refine the input prompt for T2I models.
191
  <br>It can work on both Danbooru tags and Natural Language. Which means you can use it on almost all the existed T2I models.
192
  <br>You can take it as "pro max" version of [DTG](https://huggingface.co/KBlueLeaf/DanTagGen-delta-rev2)
193
 
@@ -196,7 +196,7 @@ TITPOP is a tool to extend, generate, refine the input prompt for T2I models.
196
  2. Enter your NL Prompt(optional): put the desired natural language prompt into "Natural Language Prompt" box
197
  3. Enter your black list(optional): put the desired black list into "black list" box
198
  4. Adjust the settings: length, temp, top_p, min_p, top_k, seed ...
199
- 4. Click "TITPOP" button: you will see refined prompt on "result" box
200
  5. If you like the result, click "Generate Image From Result" button
201
  * You will see 2 generated images, left one is based on your prompt, right one is based on refined prompt
202
  * The backend is diffusers, there are no weighting mechanism, so Escape Brackets is default to False
@@ -208,7 +208,7 @@ TITPOP is a tool to extend, generate, refine the input prompt for T2I models.
208
  4. Once the project/research are done, I will open source all these models/codes with Apache2 license.
209
 
210
  ### Notification
211
- **TITPOP is NOT a T2I model. It is Prompt Gen, or, Text-to-Text model.
212
  <br>The generated image is come from [Kohaku-XL-Zeta](https://huggingface.co/KBlueLeaf/Kohaku-XL-Zeta) model**
213
  """
214
  )
@@ -285,7 +285,7 @@ TITPOP is a tool to extend, generate, refine the input prompt for T2I models.
285
  escape_brackets = gr.Checkbox(
286
  label="Escape Brackets", value=False
287
  )
288
- submit = gr.Button("TITPOP!", variant="primary")
289
  with gr.Accordion("Speed statstics", open=False):
290
  cost_time = gr.Markdown()
291
  with gr.Column(scale=5):
 
6
  import kgen
7
  except:
8
  GH_TOKEN = os.getenv("GITHUB_TOKEN")
9
+ git_url = f"https://{GH_TOKEN}@github.com/KohakuBlueleaf/TIPO-KGen@tipo"
10
 
11
  ## call pip install
12
  os.system(f"pip install git+{git_url}")
 
25
  from spaces import GPU
26
 
27
  import kgen.models as models
28
+ import kgen.executor.tipo as tipo
29
  from kgen.formatter import seperate_tags, apply_format
30
  from kgen.generate import generate
31
 
 
36
  sdxl_pipe = load_model()
37
 
38
  models.load_model(
39
+ "Amber-River/tipo",
40
  device="cuda",
41
  subfolder="500M-epoch3",
42
  )
 
101
  escape_brackets,
102
  ):
103
  default_format = DEFAULT_FORMAT[output_format]
104
+ tipo.BAN_TAGS = [t.strip() for t in black_list.split(",") if t.strip()]
105
  generation_setting = {
106
  "seed": seed,
107
  "temperature": temp,
 
119
  if escape_brackets:
120
  input_prompt = re.sub(r"([()\[\]])", r"\\\1", input_prompt)
121
 
122
+ meta, operations, general, nl_prompt = tipo.parse_tipo_request(
123
  seperate_tags(tags.split(",")),
124
  nl_prompt,
125
  tag_length_target=target_length,
126
  generate_extra_nl_prompt="<|generated|>" in default_format or not nl_prompt,
127
  )
128
  t0 = time()
129
+ for result, timing in tipo.tipo_runner_generator(
130
  meta, operations, general, nl_prompt, **generation_setting
131
  ):
132
  result = apply_format(result, default_format)
 
183
  with gr.Accordion("Introduction and Instructions", open=False):
184
  gr.Markdown(
185
  """
186
+ ## TIPO Demo
187
  **The model for demo is 500M version with 4epoch training (25B token seen)**
188
 
189
  ### What is this
190
+ TIPO is a tool to extend, generate, refine the input prompt for T2I models.
191
  <br>It can work on both Danbooru tags and Natural Language. Which means you can use it on almost all the existed T2I models.
192
  <br>You can take it as "pro max" version of [DTG](https://huggingface.co/KBlueLeaf/DanTagGen-delta-rev2)
193
 
 
196
  2. Enter your NL Prompt(optional): put the desired natural language prompt into "Natural Language Prompt" box
197
  3. Enter your black list(optional): put the desired black list into "black list" box
198
  4. Adjust the settings: length, temp, top_p, min_p, top_k, seed ...
199
+ 4. Click "TIPO" button: you will see refined prompt on "result" box
200
  5. If you like the result, click "Generate Image From Result" button
201
  * You will see 2 generated images, left one is based on your prompt, right one is based on refined prompt
202
  * The backend is diffusers, there are no weighting mechanism, so Escape Brackets is default to False
 
208
  4. Once the project/research are done, I will open source all these models/codes with Apache2 license.
209
 
210
  ### Notification
211
+ **TIPO is NOT a T2I model. It is Prompt Gen, or, Text-to-Text model.
212
  <br>The generated image is come from [Kohaku-XL-Zeta](https://huggingface.co/KBlueLeaf/Kohaku-XL-Zeta) model**
213
  """
214
  )
 
285
  escape_brackets = gr.Checkbox(
286
  label="Escape Brackets", value=False
287
  )
288
+ submit = gr.Button("TIPO!", variant="primary")
289
  with gr.Accordion("Speed statstics", open=False):
290
  cost_time = gr.Markdown()
291
  with gr.Column(scale=5):