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aakashch0179
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Update app.py
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app.py
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# text to Image
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#
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# text generation
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import streamlit as st
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from transformers import AutoTokenizer, AutoModelForCausalLM
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st.title("Text Generation with Bloom")
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tokenizer = AutoTokenizer.from_pretrained("bigscience/bloom")
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model = AutoModelForCausalLM.from_pretrained("bigscience/bloom")
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user_input = st.text_area("Enter your prompt:", height=100)
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if st.button('Generate Text'):
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# text to Image
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import streamlit as st
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import torch
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from diffusers import StableDiffusionXLPipeline, UNet2DConditionModel, EulerDiscreteScheduler
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from huggingface_hub import hf_hub_download
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from safetensors.torch import load_file
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# Model Path/Repo Information
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base = "stabilityai/stable-diffusion-xl-base-1.0"
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repo = "ByteDance/SDXL-Lightning"
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ckpt = "sdxl_lightning_4step_unet.safetensors"
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# Load model (Executed only once for efficiency)
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@st.cache_resource
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def load_sdxl_pipeline():
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unet = UNet2DConditionModel.from_config(base, subfolder="unet").to("cuda", torch.float16)
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unet.load_state_dict(load_file(hf_hub_download(repo, ckpt), device="cuda"))
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pipe = StableDiffusionXLPipeline.from_pretrained(base, unet=unet, torch_dtype=torch.float16, variant="fp16").to("cuda")
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pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
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return pipe
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# Streamlit UI
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st.title("Image Generation")
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prompt = st.text_input("Enter your image prompt:")
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if st.button("Generate Image"):
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if not prompt:
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st.warning("Please enter a prompt.")
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else:
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pipe = load_sdxl_pipeline() # Load the pipeline from cache
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with torch.no_grad():
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image = pipe(prompt).images[0]
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st.image(image)
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# text generation
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# import streamlit as st
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# from transformers import AutoTokenizer, AutoModelForCausalLM
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# st.title("Text Generation with Bloom")
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# tokenizer = AutoTokenizer.from_pretrained("bigscience/bloom")
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# model = AutoModelForCausalLM.from_pretrained("bigscience/bloom")
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# user_input = st.text_area("Enter your prompt:", height=100)
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# if st.button('Generate Text'):
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# inputs = tokenizer(user_input, return_tensors="pt")
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# outputs = model.generate(**inputs, max_length=100) # Adjust max_length as needed
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# generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
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# st.write("Generated Text:")
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# st.write(generated_text)
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