First model version

push.py

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+from transformers import Trainer
+
+# Assuming your Trainer object is called 'trainer'
+Trainer.push_to_hub(
+    "ranga-godhandaraman/avatar-generator-women",  # Replace with your details
+    overwrite=True  # Set overwrite to True to update existing version (optional)
+)
+

trial1.py

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+import streamlit as st
+from PIL import Image
+import requests
+import torch
+import random
+import numpy as np
+from diffusers import StableDiffusionInstructPix2PixPipeline, EulerAncestralDiscreteScheduler
+
+# Load the model
+model_id = "/home/gopinath28031995/yashwanth/projects/watermark_env/instruction-tuned-sd/woman-avatar-gen"
+pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
+pipe.to("cuda")
+pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
+
+# Function to download image from URL
+def download_image(url):
+    image = Image.open(requests.get(url, stream=True).raw)
+    # Handle image orientation
+    if hasattr(image, '_getexif'):
+        exif = image._getexif()
+        if exif is not None:
+            orientation = exif.get(0x0112)
+            if orientation is not None:
+                if orientation == 3:
+                    image = image.rotate(180, expand=True)
+                elif orientation == 6:
+                    image = image.rotate(270, expand=True)
+                elif orientation == 8:
+                    image = image.rotate(90, expand=True)
+    image = image.convert("RGB")
+    return image
+
+# Streamlit app
+st.title("Instruct Pix2Pix Image Generation")
+
+# Add drag and drop/upload image options
+uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
+image_url = st.text_input("Enter image URL")
+
+# Input prompt from user
+prompt = st.text_input("Enter prompt", "Generate a fantasy version, retain hair and facial features, 8k")
+
+# Input seed, steps, and configuration scales from the user
+seed = st.number_input("Seed", value=42, step=1)
+num_inference_steps = st.number_input("Number of Inference Steps", value=300, step=10, min_value=0)
+text_cfg_scale = st.number_input("Text CFG Scale", value=3.0, step=0.1, min_value=0.0)
+image_cfg_scale = st.number_input("Image CFG Scale", value=7.5, step=0.1, min_value=0.0)
+
+if uploaded_file is not None:
+    # Display the uploaded image
+    image = Image.open(uploaded_file)
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+elif image_url:
+    # Download and display image from URL
+    try:
+        image = download_image(image_url)
+        st.image(image, caption="Image from URL", use_column_width=True)
+    except Exception as e:
+        st.error("Error downloading image from URL. Please make sure the URL is correct.")
+else:
+    # Use default image URL
+    url = "https://raw.githubusercontent.com/timothybrooks/instruct-pix2pix/main/imgs/example.jpg"
+    st.write("Using default image.")
+    image = download_image(url)
+
+# Generate image based on the user input
+if st.button("Generate"):
+    # Generate image
+    generated_images = pipe(prompt, 
+                            image=image, 
+                            num_inference_steps=num_inference_steps, 
+                            image_cfg=image_cfg_scale,
+                            text_cfg_scale=text_cfg_scale,
+                            seed=seed)
+    st.image(generated_images[0], caption="Generated Image", use_column_width=True)