Create app.py

app.py

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+import os
+import random
+import uuid
+from typing import Tuple
+import gradio as gr
+import numpy as np
+from PIL import Image
+import spaces
+import torch
+from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
+from huggingface_hub import login
+# Log in to Hugging Face using the provided token
+hf_token = ''
+login(hf_token)
+
+DESCRIPTIONz = """## STABLE IMAGINE 🍺"""
+
+def save_image(img):
+    unique_name = str(uuid.uuid4()) + ".png"
+    img.save(unique_name)
+    return unique_name
+
+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
+DESCRIPTIONz = ""
+
+if not torch.cuda.is_available():
+    DESCRIPTIONz += """
+    <p>⚠️Running on CPU, This may not work on CPU. If it runs for an extended time or if you encounter errors, try running it on a GPU by duplicating the space using @spaces.GPU(). 📍</p>
+    """
+
+USE_TORCH_COMPILE = 0
+ENABLE_CPU_OFFLOAD = 0
+
+if torch.cuda.is_available():
+    pipe = StableDiffusionXLPipeline.from_pretrained(
+        "SG161222/RealVisXL_V4.0_Lightning",
+        torch_dtype=torch.float16,
+        use_safetensors=True,
+    )
+    pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
+    pipe.to("cuda")
+
+    if USE_TORCH_COMPILE:
+        pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
+
+else:
+    # If CUDA is not available, fall back to CPU (not ideal for SDXL)
+    pipe = StableDiffusionXLPipeline.from_pretrained(
+        "SG161222/RealVisXL_V4.0_Lightning",
+        torch_dtype=torch.float32,  # safer for CPU
+        use_safetensors=True,
+    )
+    pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
+    pipe.to("cpu")
+
+    if ENABLE_CPU_OFFLOAD:
+        # Optionally offload to CPU with accelerate or similar, if set up
+        pipe.enable_model_cpu_offload()
+
+
+
+LORA_OPTIONS = {
+        "Realism (face/character)👦🏻": ("prithivMLmods/Canopus-Realism-LoRA", "Canopus-Realism-LoRA.safetensors", "rlms"),
+        "Pixar (art/toons)🙀": ("prithivMLmods/Canopus-Pixar-Art", "Canopus-Pixar-Art.safetensors", "pixar"),
+        "Interior Architecture (house/hotel)🏠": ("prithivMLmods/Canopus-Interior-Architecture-0.1", "Canopus-Interior-Architecture-0.1δ.safetensors", "arch"),
+        "Fashion Product (wearing/usable)👜": ("prithivMLmods/Canopus-Fashion-Product-Dilation", "Canopus-Fashion-Product-Dilation.safetensors", "fashion"),
+        "Minimalistic Image (minimal/detailed)🏞️": ("prithivMLmods/Pegasi-Minimalist-Image-Style", "Pegasi-Minimalist-Image-Style.safetensors", "minimalist"),
+        "Modern Clothing (trend/new)👕": ("prithivMLmods/Canopus-Modern-Clothing-Design", "Canopus-Modern-Clothing-Design.safetensors", "mdrnclth"),
+        "Animaliea (farm/wild)🫎": ("prithivMLmods/Canopus-Animaliea-Artism", "Canopus-Animaliea-Artism.safetensors", "Animaliea"),
+        "Canes Cars (realistic/futurecars)🚘": ("prithivMLmods/Canes-Cars-Model-LoRA", "Canes-Cars-Model-LoRA.safetensors", "car"),
+        "Art Minimalistic (paint/semireal)🎨": ("prithivMLmods/Canopus-Art-Medium-LoRA", "Canopus-Art-Medium-LoRA.safetensors", "mdm"),
+    }
+
+for model_name, weight_name, adapter_name in LORA_OPTIONS.values():
+    pipe.load_lora_weights(model_name, weight_name=weight_name, adapter_name=adapter_name)
+pipe.to("cuda")
+
+
+style_list = [
+    {
+        "name": "3840 x 2160",
+        "prompt": "hyper-realistic 8K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic",
+        "negative_prompt": "cartoonish, low resolution, blurry, simplistic, abstract, deformed, ugly",
+    },
+    # Add more style dicts here if needed
+]
+
+styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list}
+
+def apply_style(style_name: str, positive: str, negative: str = "") -> Tuple[str, str]:
+    if style_name in styles:
+        p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME])
+    else:
+        p, n = styles[DEFAULT_STYLE_NAME]
+
+    if not negative:
+        negative = ""
+    return p.replace("{prompt}", positive), n + negative
+
+
+DEFAULT_STYLE_NAME = "3840 x 2160"
+
+
+@spaces.GPU(duration=60, enable_queue=True)
+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,
+    style_name: str = DEFAULT_STYLE_NAME,
+    lora_model: str = "Realism (face/character)👦🏻",
+    progress=gr.Progress(track_tqdm=True),
+):
+    seed = int(randomize_seed_fn(seed, randomize_seed))
+
+    positive_prompt, effective_negative_prompt = apply_style(style_name, prompt, negative_prompt)
+    
+    if not use_negative_prompt:
+        effective_negative_prompt = ""  # type: ignore
+
+    model_name, weight_name, adapter_name = LORA_OPTIONS[lora_model]
+    pipe.set_adapters(adapter_name)
+
+    images = pipe(
+        prompt=positive_prompt,
+        negative_prompt=effective_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) for img in images]
+    return image_paths, seed
+
+
+with gr.Blocks() as demo:
+    gr.Markdown(DESCRIPTIONz)
+    
+    with gr.Row():
+        input_prompt = gr.Textbox(label="Prompt", placeholder="Enter prompt", lines=2)
+        use_negative_prompt = gr.Checkbox(label="Use negative prompt?", value=False)
+        negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="Enter negative prompt", lines=2)
+    
+    with gr.Row():
+        randomize_seed = gr.Checkbox(label="Randomize Seed", value=False)
+        seed = gr.Number(value=0, label="Seed")
+    
+    with gr.Row():
+        style_dropdown = gr.Dropdown(label="Image Style", choices=list(styles.keys()), value=DEFAULT_STYLE_NAME)
+        lora_dropdown = gr.Dropdown(label="LoRA Model", choices=list(LORA_OPTIONS.keys()), value="Realism (face/character)👦🏻")
+    
+    with gr.Row():
+        width = gr.Slider(512, 2048, value=1024, step=64, label="Width")
+        height = gr.Slider(512, 2048, value=1024, step=64, label="Height")
+    
+    with gr.Row():
+        guidance_scale = gr.Slider(1.0, 15.0, value=3, step=0.5, label="Guidance Scale")
+    
+    output_gallery = gr.Gallery(label="Generated Images", columns=[2], height="auto")
+    output_seed = gr.Number(label="Final Seed", interactive=False)
+
+    
+    generate_button = gr.Button("Generate Images")
+
+    generate_button.click(
+        fn=generate,
+        inputs=[
+            input_prompt,
+            negative_prompt,
+            use_negative_prompt,
+            seed,
+            width,
+            height,
+            guidance_scale,
+            randomize_seed,
+            style_dropdown,
+            lora_dropdown,
+        ],
+        outputs=[output_gallery, output_seed],
+    )
+
+demo.launch()