init

README.md

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+---
+title: Kanji Streaming
+emoji: 🉑
+colorFrom: purple
+colorTo: gray
+sdk: gradio
+sdk_version: 4.19.2
+app_file: app.py
+pinned: false
+# suggested_hardware: a10g-small
+models:
+- mistralai/Mixtral-8x7B-Instruct-v0.1
+---
+
+Check out [original repo](https://github.com/AgainstEntropy/kanji) for mroe details!

app.py

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+import argparse
+import os
+from queue import SimpleQueue
+from threading import Thread
+from typing import Iterator
+
+import gradio as gr
+import spaces
+import torch
+from gradio import Chatbot
+from huggingface_hub import InferenceClient
+
+from image_utils import ImageStitcher
+from StreamDiffusionIO import LatentConsistencyModelStreamIO
+
+MAX_MAX_NEW_TOKENS = 2048
+DEFAULT_MAX_NEW_TOKENS = 1024
+MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))
+
+DESCRIPTION = """\
+# Kanji-Streaming Chat
+
+🌍 This Space is adapted from [Llama-2-7b-chat](https://huggingface.co/spaces/huggingface-projects/llama-2-7b-chat) space, demonstrating how to "chat" with LLM with [Kanji-Streaming](https://github.com/AgainstEntropy/kanji).
+
+🔨 The technique behind Kanji-Streaming is [StreamDiffusionIO](https://github.com/AgainstEntropy/StreamDiffusionIO), which is based on [StreamDiffusion](https://github.com/cumulo-autumn/StreamDiffusion), *but especially allows to render text streams into image streams*.
+
+🔎 For more details about Kanji-Streaming, take a look at the [github repository](https://github.com/AgainstEntropy/kanji).
+"""
+
+LICENSE = """
+<p/>
+
+---
+As a derivate work of [Llama-2-7b-chat](https://huggingface.co/meta-llama/Llama-2-7b-chat) by Meta,
+this demo is governed by the original [license](https://huggingface.co/spaces/huggingface-projects/llama-2-7b-chat/blob/main/LICENSE.txt) and [acceptable use policy](https://huggingface.co/spaces/huggingface-projects/llama-2-7b-chat/blob/main/USE_POLICY.md).
+"""
+
+
+parser = argparse.ArgumentParser(description="Gradio launcher for Streaming-Kanji.")
+parser.add_argument(
+    "--sd_model_id_or_path",
+    type=str,
+    default="runwayml/stable-diffusion-v1-5",
+    required=False,
+    help="Path to downloaded sd-1-5 model or model identifier from huggingface.co/models.",
+)
+parser.add_argument(
+    "--lora_path",
+    type=str,
+    default="AgainstEntropy/kanji-lora-sd-v1-5",
+    required=False,
+    help="Path to downloaded LoRA weight or model identifier from huggingface.co/models.",
+)
+parser.add_argument(
+    "--lcm_lora_path",
+    type=str,
+    default="AgainstEntropy/kanji-lcm-lora-sd-v1-5",
+    required=False,
+    help="Path to downloaded LCM-LoRA weight or model identifier from huggingface.co/models.",
+)
+parser.add_argument(
+    "--img_res",
+    type=int,
+    default=64,
+    required=False,
+    help="Image resolution for displaying Kanji characters in ChatBot.",
+)
+parser.add_argument(
+    "--img_per_line",
+    type=int,
+    default=16,
+    required=False,
+    help="Number of Kanji characters to display in a single line.",
+)
+parser.add_argument(
+    "--tmp_dir",
+    type=str,
+    default="./tmp",
+    required=False,
+    help="Path to save temporary images generated by StreamDiffusionIO.",
+)
+
+args = parser.parse_args()
+
+if torch.cuda.is_available():
+    device = "cuda"
+else:
+    device = "cpu"
+    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo works best on GPU.</p>"
+
+DESCRIPTION += "\n<p>This demo will get the best kanji streaming experience in localhost (or SSH forward), instead of shared link generated by Gradio.</p>"
+
+client = InferenceClient(
+    "mistralai/Mixtral-8x7B-Instruct-v0.1"
+)
+
+def format_prompt(message, history):
+    prompt = "<s>"
+    for user_prompt, bot_response in history:
+        prompt += f"[INST] {user_prompt} [/INST]"
+        prompt += f" {bot_response}</s> "
+    prompt += f"[INST] {message} [/INST]"
+    return prompt
+
+lcm_stream = LatentConsistencyModelStreamIO(
+    model_id_or_path=args.sd_model_id_or_path,
+    lcm_lora_path=args.lcm_lora_path,
+    lora_dict={args.lora_path: 1},
+    resolution=128,
+    device=device,
+    use_xformers=True,
+    verbose=True,
+)
+
+tmp_dir_template = f"{args.tmp_dir}/%d"
+response_num = 0
+response_cache = ""
+
+stitcher = ImageStitcher(
+    tmp_dir=tmp_dir_template % response_num,
+    img_res=args.img_res,
+    img_per_line=args.img_per_line,
+    verbose=True,
+)
+
+
+@spaces.GPU
+def generate(
+    message: str,
+    chat_history: list[tuple[str, str]],
+    seed: int,
+    system_prompt: str,
+    max_new_tokens: int = 1024,
+    temperature: float = 0.6,
+    top_p: float = 0.9,
+    top_k: int = 50,
+    repetition_penalty: float = 1.2,
+) -> Iterator[str]:
+    
+    if temperature < 1e-2:
+        temperature = 1e-2
+    
+    global response_cache
+
+    generate_kwargs = dict(
+        max_new_tokens=max_new_tokens,
+        do_sample=True,
+        top_p=top_p,
+        top_k=top_k,
+        temperature=temperature,
+        repetition_penalty=repetition_penalty,
+    )
+    formatted_prompt = format_prompt(f"{system_prompt}, {message}", chat_history)
+    stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
+
+    outputs = ""
+    prompt_queue = SimpleQueue()
+
+    lcm_stream.reset(seed)
+    stitcher.reset()
+
+    global response_num
+    response_num += 1
+    stitcher.update_tmp_dir(tmp_dir_template % response_num)
+
+    def append_to_queue():
+        for response in stream:
+            outputs += response.token.text
+            text = text.strip()
+            if text:
+                prompt_queue.put(text)
+        prompt_queue.put(None)
+
+    append_thread = Thread(target=append_to_queue)
+    append_thread.start()
+
+    def show_image(prompt: str = None):
+        image, text = lcm_stream(prompt)
+        img_path = None
+        if image is not None:
+            img_path = stitcher.add(image, text)
+            print(img_path)
+        return img_path
+
+    while True:
+        prompt = prompt_queue.get()
+        if prompt is None:
+            break
+        img_path = show_image(prompt)
+        if img_path is not None:
+            yield (img_path, )
+
+    # Continue to display the remaining images
+    while True:
+        img_path = show_image()
+        if img_path is not None:
+            yield (img_path, )
+        if lcm_stream.stop():
+            break
+
+    response_cache = outputs
+    return outputs
+
+
+chat_interface = gr.ChatInterface(
+    fn=generate,
+    chatbot=Chatbot(height=400),
+    additional_inputs=[
+        gr.Number(
+            label="Seed",
+            info="Random Seed for Kanji Generation (maybe some kind of accent 🤔)",
+            step=1,
+            value=1026,
+        ),
+        gr.Textbox(label="System prompt", lines=4),
+        gr.Slider(
+            label="Max new tokens",
+            minimum=1,
+            maximum=MAX_MAX_NEW_TOKENS,
+            step=1,
+            value=DEFAULT_MAX_NEW_TOKENS,
+        ),
+        gr.Slider(
+            label="Temperature",
+            minimum=0.1,
+            maximum=4.0,
+            step=0.1,
+            value=0.6,
+        ),
+        gr.Slider(
+            label="Top-p (nucleus sampling)",
+            minimum=0.05,
+            maximum=1.0,
+            step=0.05,
+            value=0.9,
+        ),
+        gr.Slider(
+            label="Top-k",
+            minimum=1,
+            maximum=1000,
+            step=1,
+            value=50,
+        ),
+        gr.Slider(
+            label="Repetition penalty",
+            minimum=1.0,
+            maximum=2.0,
+            step=0.05,
+            value=1.2,
+        ),
+    ],
+    stop_btn=None,
+    examples=[
+        ["Hello there! How are you doing?"],
+        ["Can you explain briefly to me what is the Python programming language?"],
+        ["Explain the plot of Cinderella in a sentence."],
+        ["How many hours does it take a man to eat a Helicopter?"],
+        ["Write a 100-word article on 'Benefits of Open-Source in AI research'"],
+    ],
+)
+
+with gr.Blocks(css="style.css") as demo:
+    gr.Markdown(DESCRIPTION)
+    gr.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button")
+    chat_interface.render()
+    gr.Markdown(LICENSE)
+
+if __name__ == "__main__":
+    demo.queue(max_size=20).launch(show_api=False)