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import streamlit as st
from PIL import Image
from diffusers import StableDiffusionPipeline, ControlNetModel, DDIMScheduler, LMSDiscreteScheduler, UNet2DConditionModel, DiffusionPipeline
from diffusers import DDPMScheduler, DDPMSchedulerV2, PNDMScheduler
from transformers import AutoModelForCausalLM, AutoTokenizer, AutoModelForSeq2SeqLM, LlamaTokenizerFast, LlamaForCausalLM
from accelerate import Accelerator
import torch
from peft import PeftModel, LoraConfig, get_peft_model, prepare_model_for_int8_training, prepare_model_for_int8_bf16_training
# Define a dictionary with all available models, schedulers, features, weights, and adapters
model_dict = {
"Stable Diffusion": {
"Models": [
"stabilityai/stable-diffusion-3-medium"
"CompVis/stable-diffusion-v1-4",
"stabilityai/stable-diffusion-2-1",
"runwayml/stable-diffusion-v1-5",
"runwayml/stable-diffusion-inpainting",
"runwayml/stable-diffusion-video-v1-5",
"stabilityai/stable-diffusion-2-base"
],
"Schedulers": [
"DDIMScheduler",
"LMSDiscreteScheduler"
],
"Features": [
"Unconditional image generation",
"Text-to-image",
"Image-to-image",
"Inpainting",
"Text or image-to-video",
"Depth-to-image"
],
"Adapters": [
"ControlNet",
"T2I-Adapter"
],
"Weights": [
"Stable Diffusion XL",
"SDXL Turbo",
"Kandinsky",
"IP-Adapter",
"ControlNet",
"Latent Consistency Model",
"Textual inversion",
"Shap-E",
"DiffEdit",
"Trajectory Consistency Distillation-LoRA",
"Stable Video Diffusion",
"Marigold Computer Vision"
]
},
"Llama": {
"Models": [
"decapoda-research/llama-7b-hf",
"decapoda-research/llama-13b-hf",
"decapoda-research/llama-30b-hf",
"decapoda-research/llama-65b-hf"
],
"Tokenizers": [
"LlamaTokenizerFast"
],
"Features": [
"AutoPipeline",
"Train a diffusion model",
"Load LoRAs for inference",
"Accelerate inference of text-to-image diffusion models",
"LOAD PIPELINES AND ADAPTERS",
"Load community pipelines and components",
"Load schedulers and models",
"Model files and layouts",
"Load adapters",
"Push files to the Hub",
"GENERATIVE TASKS",
"Unconditional image generation",
"Text-to-image",
"Image-to-image",
"Inpainting",
"Text or image-to-video",
"Depth-to-image",
"INFERENCE TECHNIQUES",
"Overview",
"Distributed inference with multiple GPUs",
"Merge LoRAs",
"Scheduler features",
"Pipeline callbacks",
"Reproducible pipelines",
"Controlling image quality",
"Prompt techniques",
"ADVANCED INFERENCE",
"Outpainting",
"SPECIFIC PIPELINE EXAMPLES",
"Stable Diffusion XL",
"SDXL Turbo",
"Kandinsky",
"IP-Adapter",
"ControlNet",
"T2I-Adapter",
"Latent Consistency Model",
"Textual inversion",
"Shap-E",
"DiffEdit",
"Trajectory Consistency Distillation-LoRA",
"Stable Video Diffusion",
"Marigold Computer Vision"
],
"Weights": [
"LoRA weights"
]
}
}
model_type = st.selectbox("Select a model type:", list(model_dict.keys()))
if model_type == "Stable Diffusion":
model = st.selectbox("Select a Stable Diffusion model:", model_dict[model_type]["Models"])
scheduler = st.selectbox("Select a scheduler:", model_dict[model_type]["Schedulers"])
feature = st.selectbox("Select a feature:", model_dict[model_type]["Features"])
adapter = st.selectbox("Select an adapter:", model_dict[model_type]["Adapters"])
weight = st.selectbox("Select a weight:", model_dict[model_type]["Weights"])
if st.button("Generate Images"):
st.write("Generating images...")
pipe = StableDiffusionPipeline.from_pretrained(model)
pipe.scheduler = eval(scheduler)()
if adapter == "ControlNet":
controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11e_sd15_openpose")
pipe = pipe.to_controlnet(controlnet)
# Define the prompt and number of images to generate
prompt = st.text_input("Enter a prompt:")
num_images = st.slider("Number of images to generate", min_value=1, max_value=10, value=1)
# Generate the images
images = pipe(prompt, num_images=num_images, guidance_scale=7.5).images
# Display the generated images
cols = st.columns(num_images)
for i, image in enumerate(images):
cols[i].image(image, caption=f"Image {i+1}", use_column_width=True)
if model_type == "Llama":
model = st.selectbox("Select a Llama model:", model_dict[model_type]["Models"])
tokenizer = st.selectbox("Select a tokenizer:", model_dict[model_type]["Tokenizers"])
feature = st.selectbox("Select a feature:", model_dict[model_type]["Features"])
weight = st.selectbox("Select a weight:", model_dict[model_type]["Weights"])
if st.button("Generate Text"):
st.write("Generating text...")
tokenizer = AutoTokenizer.from_pretrained(tokenizer)
model = AutoModelForCausalLM.from_pretrained(model)
input_text = st.text_area("Enter a prompt:")
# Tokenize the input text
inputs = tokenizer(input_text, return_tensors="pt")
# Generate the text
output = model.generate(**inputs)
# Decode the generated text
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
st.write("Generated Text:")
st.write(generated_text) |