Kohaku-Blueleaf
commited on
Commit
β’
84b9ae2
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Parent(s):
1b4fd22
use neutral naming
Browse files
README.md
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@@ -1,5 +1,5 @@
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---
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title:
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emoji: π
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colorFrom: yellow
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colorTo: red
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---
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title: TIPO DEMO
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emoji: π
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colorFrom: yellow
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colorTo: red
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app.py
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@@ -6,7 +6,7 @@ try:
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import kgen
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except:
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GH_TOKEN = os.getenv("GITHUB_TOKEN")
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git_url = f"https://{GH_TOKEN}@github.com/KohakuBlueleaf/
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## call pip install
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os.system(f"pip install git+{git_url}")
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from spaces import GPU
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import kgen.models as models
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import kgen.executor.
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from kgen.formatter import seperate_tags, apply_format
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from kgen.generate import generate
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sdxl_pipe = load_model()
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models.load_model(
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"Amber-River/
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device="cuda",
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subfolder="500M-epoch3",
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)
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escape_brackets,
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):
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default_format = DEFAULT_FORMAT[output_format]
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generation_setting = {
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"seed": seed,
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"temperature": temp,
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if escape_brackets:
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input_prompt = re.sub(r"([()\[\]])", r"\\\1", input_prompt)
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meta, operations, general, nl_prompt =
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seperate_tags(tags.split(",")),
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nl_prompt,
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tag_length_target=target_length,
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generate_extra_nl_prompt="<|generated|>" in default_format or not nl_prompt,
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)
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t0 = time()
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for result, timing in
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meta, operations, general, nl_prompt, **generation_setting
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):
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result = apply_format(result, default_format)
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with gr.Accordion("Introduction and Instructions", open=False):
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gr.Markdown(
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"""
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##
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**The model for demo is 500M version with 4epoch training (25B token seen)**
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### What is this
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-
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<br>It can work on both Danbooru tags and Natural Language. Which means you can use it on almost all the existed T2I models.
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<br>You can take it as "pro max" version of [DTG](https://huggingface.co/KBlueLeaf/DanTagGen-delta-rev2)
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2. Enter your NL Prompt(optional): put the desired natural language prompt into "Natural Language Prompt" box
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3. Enter your black list(optional): put the desired black list into "black list" box
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4. Adjust the settings: length, temp, top_p, min_p, top_k, seed ...
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4. Click "
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5. If you like the result, click "Generate Image From Result" button
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* You will see 2 generated images, left one is based on your prompt, right one is based on refined prompt
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* The backend is diffusers, there are no weighting mechanism, so Escape Brackets is default to False
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4. Once the project/research are done, I will open source all these models/codes with Apache2 license.
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### Notification
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**
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<br>The generated image is come from [Kohaku-XL-Zeta](https://huggingface.co/KBlueLeaf/Kohaku-XL-Zeta) model**
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"""
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)
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escape_brackets = gr.Checkbox(
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label="Escape Brackets", value=False
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)
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submit = gr.Button("
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with gr.Accordion("Speed statstics", open=False):
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cost_time = gr.Markdown()
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with gr.Column(scale=5):
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import kgen
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except:
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GH_TOKEN = os.getenv("GITHUB_TOKEN")
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git_url = f"https://{GH_TOKEN}@github.com/KohakuBlueleaf/TIPO-KGen@tipo"
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## call pip install
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os.system(f"pip install git+{git_url}")
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from spaces import GPU
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import kgen.models as models
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import kgen.executor.tipo as tipo
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from kgen.formatter import seperate_tags, apply_format
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from kgen.generate import generate
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sdxl_pipe = load_model()
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models.load_model(
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"Amber-River/tipo",
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device="cuda",
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subfolder="500M-epoch3",
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)
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escape_brackets,
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):
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default_format = DEFAULT_FORMAT[output_format]
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tipo.BAN_TAGS = [t.strip() for t in black_list.split(",") if t.strip()]
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generation_setting = {
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"seed": seed,
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"temperature": temp,
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if escape_brackets:
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input_prompt = re.sub(r"([()\[\]])", r"\\\1", input_prompt)
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meta, operations, general, nl_prompt = tipo.parse_tipo_request(
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seperate_tags(tags.split(",")),
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nl_prompt,
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tag_length_target=target_length,
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generate_extra_nl_prompt="<|generated|>" in default_format or not nl_prompt,
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)
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t0 = time()
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for result, timing in tipo.tipo_runner_generator(
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meta, operations, general, nl_prompt, **generation_setting
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):
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result = apply_format(result, default_format)
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with gr.Accordion("Introduction and Instructions", open=False):
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gr.Markdown(
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"""
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## TIPO Demo
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**The model for demo is 500M version with 4epoch training (25B token seen)**
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### What is this
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+
TIPO is a tool to extend, generate, refine the input prompt for T2I models.
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<br>It can work on both Danbooru tags and Natural Language. Which means you can use it on almost all the existed T2I models.
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<br>You can take it as "pro max" version of [DTG](https://huggingface.co/KBlueLeaf/DanTagGen-delta-rev2)
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|
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2. Enter your NL Prompt(optional): put the desired natural language prompt into "Natural Language Prompt" box
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3. Enter your black list(optional): put the desired black list into "black list" box
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4. Adjust the settings: length, temp, top_p, min_p, top_k, seed ...
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4. Click "TIPO" button: you will see refined prompt on "result" box
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5. If you like the result, click "Generate Image From Result" button
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* You will see 2 generated images, left one is based on your prompt, right one is based on refined prompt
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* The backend is diffusers, there are no weighting mechanism, so Escape Brackets is default to False
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4. Once the project/research are done, I will open source all these models/codes with Apache2 license.
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### Notification
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**TIPO is NOT a T2I model. It is Prompt Gen, or, Text-to-Text model.
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<br>The generated image is come from [Kohaku-XL-Zeta](https://huggingface.co/KBlueLeaf/Kohaku-XL-Zeta) model**
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"""
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)
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escape_brackets = gr.Checkbox(
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label="Escape Brackets", value=False
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)
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submit = gr.Button("TIPO!", variant="primary")
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with gr.Accordion("Speed statstics", open=False):
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cost_time = gr.Markdown()
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with gr.Column(scale=5):
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