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mwitiderrick
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Upload 6 files
Browse files- Dockerfile +29 -0
- app.py +142 -0
- chainlit.md +24 -0
- rendering.png +0 -0
- requirements.txt +3 -0
- tools.py +108 -0
Dockerfile
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# Use the official Python 3.9 image
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FROM python:3.9
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# Set the working directory to /code
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WORKDIR /code
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# Copy the current directory contents into the container at /code
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COPY ./requirements.txt /code/requirements.txt
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# Install requirements.txt
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RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
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# Set up a new user named "user" with user ID 1000
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RUN useradd -m -u 1000 user
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# Switch to the "user" user
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USER user
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# Set home to the user's home directory
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ENV HOME=/home/user \
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PATH=/home/user/.local/bin:$PATH
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# Set the working directory to the user's home directory
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WORKDIR $HOME/app
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# Copy the current directory contents into the container at $HOME/app setting the owner to the user
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COPY --chown=user . $HOME/app
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CMD ["chainlit", "run", "app.py", "--port", "7860"]
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app.py
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from langchain.agents import AgentExecutor, AgentType, initialize_agent
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from langchain.agents.structured_chat.prompt import SUFFIX
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from langchain.chat_models import ChatOpenAI
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from langchain.memory import ConversationBufferMemory
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from tools import edit_image_tool, generate_image_tool
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import chainlit as cl
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from chainlit.action import Action
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from chainlit.input_widget import Select, Switch, Slider
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@cl.action_callback("Create variation")
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async def create_variant(action: Action):
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agent_input = f"Create a variation of {action.value}"
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await cl.Message(content=f"Creating a variation of `{action.value}`.").send()
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await main(cl.Message(content=agent_input))
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@cl.author_rename
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def rename(orig_author):
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mapping = {
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"LLMChain": "Assistant",
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}
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return mapping.get(orig_author, orig_author)
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@cl.cache
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def get_memory():
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return ConversationBufferMemory(memory_key="chat_history")
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@cl.on_chat_start
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async def start():
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settings = await cl.ChatSettings(
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[
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Select(
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id="Model",
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label="OpenAI - Model",
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values=["gpt-3.5-turbo", "gpt-3.5-turbo-16k", "gpt-4", "gpt-4-32k"],
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initial_index=1,
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),
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Switch(id="Streaming", label="OpenAI - Stream Tokens", initial=True),
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Slider(
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id="Temperature",
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label="OpenAI - Temperature",
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initial=0,
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min=0,
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max=2,
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step=0.1,
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),
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Slider(
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id="SAI_Steps",
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label="Stability AI - Steps",
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initial=30,
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min=10,
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max=150,
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step=1,
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description="Amount of inference steps performed on image generation.",
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),
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Slider(
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id="SAI_Cfg_Scale",
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label="Stability AI - Cfg_Scale",
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initial=7,
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min=1,
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max=35,
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step=0.1,
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description="Influences how strongly your generation is guided to match your prompt.",
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),
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Slider(
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id="SAI_Width",
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label="Stability AI - Image Width",
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initial=512,
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min=256,
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max=2048,
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step=64,
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tooltip="Measured in pixels",
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),
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Slider(
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id="SAI_Height",
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label="Stability AI - Image Height",
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initial=512,
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min=256,
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max=2048,
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step=64,
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tooltip="Measured in pixels",
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),
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]
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).send()
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await setup_agent(settings)
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@cl.on_settings_update
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async def setup_agent(settings):
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print("Setup agent with following settings: ", settings)
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llm = ChatOpenAI(
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temperature=settings["Temperature"],
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streaming=settings["Streaming"],
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model=settings["Model"],
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)
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memory = get_memory()
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_SUFFIX = "Chat history:\n{chat_history}\n\n" + SUFFIX
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agent = initialize_agent(
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llm=llm,
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tools=[generate_image_tool, edit_image_tool],
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agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,
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memory=memory,
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agent_kwargs={
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"suffix": _SUFFIX,
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"input_variables": ["input", "agent_scratchpad", "chat_history"],
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},
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)
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cl.user_session.set("agent", agent)
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@cl.on_message
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async def main(message: cl.Message):
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agent = cl.user_session.get("agent") # type: AgentExecutor
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cl.user_session.set("generated_image", None)
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# No async implementation in the Stability AI client, fallback to sync
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res = await cl.make_async(agent.run)(
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input=message.content, callbacks=[cl.LangchainCallbackHandler()]
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)
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elements = []
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actions = []
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generated_image_name = cl.user_session.get("generated_image")
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generated_image = cl.user_session.get(generated_image_name)
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if generated_image:
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elements = [
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cl.Image(
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content=generated_image,
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name=generated_image_name,
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display="inline",
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)
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]
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actions = [cl.Action(name="Create variation", value=generated_image_name)]
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await cl.Message(content=res, elements=elements, actions=actions).send()
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chainlit.md
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# 🎨 Chainlit Image Gen demo
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Welcome to our creative image generation demo built with [Chainlit](https://chainlit.io), [LangChain](https://python.langchain.com/en/latest/index.html), and [Stability AI](https://stability.ai/)! 🌟 This app allows you to create and edit unique images simply by chatting with it. Talk about having an artistic conversation! 🎨🗨️
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This demo has also been adapted to use the new [ChatSettings](https://docs.chainlit.io/concepts/chat-settings) feature introduced in chainlit `0.6.2`. You are now able to tweak Stability AI settings to your liking!
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## 🎯 Example
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Try asking:
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```
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Anime style snowy swiss mountains lit by the moon. Clear night sky. HD.
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```
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![Result](https://chainlit-cloud.s3.eu-west-3.amazonaws.com/docs/img-gen-0.jpg)
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You can then ask for modifications:
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```
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change the clear night sky with a starry sky
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```
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![Result](https://chainlit-cloud.s3.eu-west-3.amazonaws.com/docs/img-gen-1.jpg)
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## ⚠️ Disclaimer
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Please note that the primary goal of this demo is to showcase the ease and convenience of building LLM apps using Chainlit and other tools rather than presenting a state-of-the-art image generation application.
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rendering.png
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requirements.txt
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chainlit
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langchain
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stability_sdk==0.8.0
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tools.py
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import io
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import os
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import stability_sdk.interfaces.gooseai.generation.generation_pb2 as generation
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from langchain.tools import StructuredTool, Tool
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from PIL import Image
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from stability_sdk import client
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import chainlit as cl
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os.environ["STABILITY_HOST"] = "grpc.stability.ai:443"
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def get_image_name():
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image_count = cl.user_session.get("image_count")
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if image_count is None:
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image_count = 0
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else:
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image_count += 1
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cl.user_session.set("image_count", image_count)
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return f"image-{image_count}"
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def _generate_image(prompt: str, init_image=None):
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# Set up our connection to the API.
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stability_api = client.StabilityInference(
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key=os.environ["STABILITY_KEY"], # API Key reference.
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verbose=True, # Print debug messages.
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engine="stable-diffusion-xl-beta-v2-2-2", # Set the engine to use for generation.
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# Available engines: stable-diffusion-v1 stable-diffusion-v1-5 stable-diffusion-512-v2-0 stable-diffusion-768-v2-0
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# stable-diffusion-512-v2-1 stable-diffusion-768-v2-1 stable-diffusion-xl-beta-v2-2-2 stable-inpainting-v1-0 stable-inpainting-512-v2-0
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)
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start_schedule = 0.8 if init_image else 1
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cl_chat_settings = cl.user_session.get("chat_settings")
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# Set up our initial generation parameters.
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answers = stability_api.generate(
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prompt=prompt,
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init_image=init_image,
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start_schedule=start_schedule,
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seed=992446758, # If a seed is provided, the resulting generated image will be deterministic.
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# What this means is that as long as all generation parameters remain the same, you can always recall the same image simply by generating it again.
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# Note: This isn't quite the case for CLIP Guided generations, which we tackle in the CLIP Guidance documentation.
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steps=int(cl_chat_settings["SAI_Steps"]), # Amount of inference steps performed on image generation. Defaults to 30.
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cfg_scale=cl_chat_settings["SAI_Cfg_Scale"], # Influences how strongly your generation is guided to match your prompt.
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# Setting this value higher increases the strength in which it tries to match your prompt.
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# Defaults to 7.0 if not specified.
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width=int(cl_chat_settings["SAI_Width"]), # Generation width, defaults to 512 if not included.
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height=int(cl_chat_settings["SAI_Height"]), # Generation height, defaults to 512 if not included.
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samples=1, # Number of images to generate, defaults to 1 if not included.
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sampler=generation.SAMPLER_K_EULER # Choose which sampler we want to denoise our generation with.
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# Defaults to k_dpmpp_2m if not specified. Clip Guidance only supports ancestral samplers.
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# (Available Samplers: ddim, plms, k_euler, k_euler_ancestral, k_heun, k_dpm_2, k_dpm_2_ancestral, k_dpmpp_2s_ancestral, k_lms, k_dpmpp_2m, k_dpmpp_sde)
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)
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# Set up our warning to print to the console if the adult content classifier is tripped.
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# If adult content classifier is not tripped, save generated images.
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for resp in answers:
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for artifact in resp.artifacts:
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if artifact.finish_reason == generation.FILTER:
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raise ValueError(
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"Your request activated the API's safety filters and could not be processed."
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"Please modify the prompt and try again."
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)
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if artifact.type == generation.ARTIFACT_IMAGE:
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name = get_image_name()
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cl.user_session.set(name, artifact.binary)
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cl.user_session.set("generated_image", name)
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return name
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else:
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raise ValueError(
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f"Your request did not generate an image. Please modify the prompt and try again. Finish reason: {artifact.finish_reason}"
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)
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79 |
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def generate_image(prompt: str):
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image_name = _generate_image(prompt)
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return f"Here is {image_name}."
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84 |
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def edit_image(init_image_name: str, prompt: str):
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init_image_bytes = cl.user_session.get(init_image_name)
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if init_image_bytes is None:
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raise ValueError(f"Could not find image `{init_image_name}`.")
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89 |
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init_image = Image.open(io.BytesIO(init_image_bytes))
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image_name = _generate_image(prompt, init_image)
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92 |
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return f"Here is {image_name} based on {init_image_name}."
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94 |
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generate_image_tool = Tool.from_function(
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func=generate_image,
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+
name="GenerateImage",
|
99 |
+
description="Useful to create an image from a text prompt.",
|
100 |
+
return_direct=True,
|
101 |
+
)
|
102 |
+
|
103 |
+
edit_image_tool = StructuredTool.from_function(
|
104 |
+
func=edit_image,
|
105 |
+
name="EditImage",
|
106 |
+
description="Useful to edit an image with a prompt. Works well with commands such as 'replace', 'add', 'change', 'remove'.",
|
107 |
+
return_direct=True,
|
108 |
+
)
|