Create app.py

app.py

@@ -0,0 +1,162 @@
+# app.py
+
+import os
+import random
+import uuid
+import base64
+import numpy as np
+from PIL import Image
+import torch
+import streamlit as st
+
+from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
+
+DESCRIPTION = """# DALL•E 3 XL v2 High Fi"""
+
+def create_download_link(filename):
+    with open(filename, "rb") as file:
+        encoded_string = base64.b64encode(file.read()).decode('utf-8')
+        download_link = f'<a href="data:image/png;base64,{encoded_string}" download="{filename}">Download Image</a>'
+        return download_link
+        
+def save_image(img, prompt):
+    unique_name = str(uuid.uuid4()) + ".png"
+    img.save(unique_name)
+
+    # save with prompt to save prompt as image file name
+    filename = f"{prompt}.png"
+    img.save(filename)
+    return filename
+    
+def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    return seed
+
+MAX_SEED = np.iinfo(np.int32).max
+
+if not torch.cuda.is_available():
+    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo may not work on CPU.</p>"
+
+MAX_SEED = np.iinfo(np.int32).max
+
+USE_TORCH_COMPILE = 0
+ENABLE_CPU_OFFLOAD = 0
+
+
+if torch.cuda.is_available():
+    pipe = StableDiffusionXLPipeline.from_pretrained(
+        "fluently/Fluently-XL-v4",
+        torch_dtype=torch.float16,
+        use_safetensors=True,
+    )
+    pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
+    
+    
+    pipe.load_lora_weights("ehristoforu/dalle-3-xl-v2", weight_name="dalle-3-xl-lora-v2.safetensors", adapter_name="dalle")
+    pipe.set_adapters("dalle")
+
+    pipe.to("cuda")
+    
+
+def generate(
+    prompt: str,
+    negative_prompt: str = "",
+    use_negative_prompt: bool = False,
+    seed: int = 0,
+    width: int = 1024,
+    height: int = 1024,
+    guidance_scale: float = 3,
+    randomize_seed: bool = False,
+):
+
+    
+    seed = int(randomize_seed_fn(seed, randomize_seed))
+
+    if not use_negative_prompt:
+        negative_prompt = ""  # type: ignore
+
+    images = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        width=width,
+        height=height,
+        guidance_scale=guidance_scale,
+        num_inference_steps=20,
+        num_images_per_prompt=1,
+        cross_attention_kwargs={"scale": 0.65},
+        output_type="pil",
+    ).images
+    image_paths = [save_image(img, prompt) for img in images]
+
+    download_links = [create_download_link(path) for path in image_paths]
+
+    print(image_paths)
+    return image_paths, seed, download_links
+
+examples = [
+"a modern hospital room with advanced medical equipment and a patient resting comfortably",
+"a team of surgeons performing a delicate operation using state-of-the-art surgical robots",
+"a elderly woman smiling while a nurse checks her vital signs using a holographic display",
+"a child receiving a painless vaccination from a friendly robot nurse in a colorful pediatric clinic",
+"a group of researchers working in a high-tech laboratory, developing new treatments for rare diseases",
+"a telemedicine consultation between a doctor and a patient, using virtual reality technology for a immersive experience"
+]
+
+st.set_page_config(page_title="DALL•E 3 XL v2 High Fi", layout="centered")
+st.markdown(DESCRIPTION)
+
+with st.form(key="generation_form"):
+    prompt = st.text_input("Prompt", max_chars=None, placeholder="Enter your prompt")
+    use_negative_prompt = st.checkbox("Use negative prompt", value=True)
+    if use_negative_prompt:
+        negative_prompt = st.text_area(
+            "Negative prompt",
+            value="""(deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers:1.4), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation, (NSFW:1.25)""",
+            placeholder="Enter a negative prompt",
+            height=100,
+        )
+    else:
+        negative_prompt = ""
+    
+    col1, col2 = st.columns(2)
+    with col1:
+        width = st.slider("Width", min_value=512, max_value=2048, step=8, value=1920)
+    with col2:    
+        height = st.slider("Height", min_value=512, max_value=2048, step=8, value=1080)
+    
+    col3, col4 = st.columns(2)
+    with col3:
+        guidance_scale = st.slider("Guidance Scale", min_value=0.1, max_value=20.0, step=0.1, value=20.0)
+    with col4:
+        randomize_seed = st.checkbox("Randomize seed", value=True)
+        if not randomize_seed:
+            seed = st.slider("Seed", min_value=0, max_value=MAX_SEED, step=1, value=0)
+        else:
+            seed = 0
+    
+    run_button = st.form_submit_button("Run")
+
+if run_button:
+    image_paths, seed, download_links = generate(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        use_negative_prompt=use_negative_prompt,
+        seed=seed,
+        width=width,
+        height=height,
+        guidance_scale=guidance_scale,
+        randomize_seed=randomize_seed,
+    )
+    
+    for image_path in image_paths:
+        st.image(image_path, caption=prompt)
+    
+    for download_link in download_links:
+        st.markdown(download_link, unsafe_allow_html=True)
+    
+    st.text(f"Seed: {seed}")
+    
+st.subheader("Examples")
+for example in examples:
+    st.button(example, key=example, on_click=prompt.setter(example))