Update app.py

app.py

@@ -1,18 +1,209 @@
+from diffusers import StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipeline, UNet2DConditionModel
+from diffusers.utils import load_image
+from diffusers import (
+    DDIMScheduler,
+    PNDMScheduler,
+    LMSDiscreteScheduler,
+    EulerDiscreteScheduler,
+    EulerAncestralDiscreteScheduler,
+    DPMSolverMultistepScheduler,
+)
+import torch
+import os
+import random
+import numpy as np
+from PIL import Image
+from typing import Tuple
 import gradio as gr
-from transformers import pipeline
+DESCRIPTION =  """
+    # CosmicMan
+    - CosmicMan: A Text-to-Image Foundation Model for Humans (CVPR 2024 (Highlight))
+    """
 
-pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
+if not torch.cuda.is_available():
+    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"
 
-def predict(input_img):
-    predictions = pipeline(input_img)
-    return input_img, {p["label"]: p["score"] for p in predictions} 
+schedule_map = {
+    "ddim"   : DDIMScheduler,
+    "pndm"   : PNDMScheduler,
+    "lms"    : LMSDiscreteScheduler,
+    "euler"  : EulerDiscreteScheduler,
+    "euler_a": EulerAncestralDiscreteScheduler,
+    "dpm"    : DPMSolverMultistepScheduler,
+}
+
+examples = [
+    "A fit Caucasian elderly woman, her wavy white hair above shoulders, wears a pink floral cotton long-sleeve shirt and a cotton hat against a natural landscape in an upper body shot",
+    "A closeup of a doll with a purple ribbon around her neck, best quality, extremely detailed",
+    "A closeup of a girl with a butterfly painted on her face",
+    "A headshot, an asian elderly male, a blue wall, bald above eyes gray hair",
+    "A closeup portrait shot against a white wall, a fit Caucasian adult female with wavy blonde hair falling above her chest wears a short sleeve silk floral dress and a floral silk normal short sleeve white blouse",
+    "A headshot, an adult caucasian male, fit, a white wall, red crew cut curly hair, short sleeve normal blue t-shirt, best quality, extremely detailed",
+    "A closeup of a man wearing a red shirt with a flower design on it",
+    "There is a man wearing a mask and holding a cell phone",
+    "Two boys playing in the yard",
+]
+
+style_list = [
+    {
+        "name": "(No style)",
+        "prompt": "{prompt}",
+        "negative_prompt": "",
+    },
+    {
+        "name": "Cinematic",
+        "prompt": "cinematic still {prompt} . emotional, harmonious, vignette, highly detailed, high budget, bokeh, cinemascope, moody, epic, gorgeous, film grain, grainy",
+        "negative_prompt": "anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured",
+    },
+    {
+        "name": "Photographic",
+        "prompt": "cinematic photo {prompt} . 35mm photograph, film, bokeh, professional, 4k, highly detailed",
+        "negative_prompt": "drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly",
+    },
+    {
+        "name": "Anime",
+        "prompt": "anime artwork {prompt} . anime style, key visual, vibrant, studio anime,  highly detailed",
+        "negative_prompt": "photo, deformed, black and white, realism, disfigured, low contrast",
+    },
+    {
+        "name": "Fantasy art",
+        "prompt": "ethereal fantasy concept art of  {prompt} . magnificent, celestial, ethereal, painterly, epic, majestic, magical, fantasy art, cover art, dreamy",
+        "negative_prompt": "photographic, realistic, realism, 35mm film, dslr, cropped, frame, text, deformed, glitch, noise, noisy, off-center, deformed, cross-eyed, closed eyes, bad anatomy, ugly, disfigured, sloppy, duplicate, mutated, black and white",
+    },
+    {
+        "name": "Neonpunk",
+        "prompt": "neonpunk style {prompt} . cyberpunk, vaporwave, neon, vibes, vibrant, stunningly beautiful, crisp, detailed, sleek, ultramodern, magenta highlights, dark purple shadows, high contrast, cinematic, ultra detailed, intricate, professional",
+        "negative_prompt": "painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly, disfigured",
+    }
+]
+
+styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list}
+STYLE_NAMES = list(styles.keys())
+DEFAULT_STYLE_NAME = "(No style)"
+CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES", "1") == "1"
+MAX_SEED = np.iinfo(np.int32).max
+NUM_IMAGES_PER_PROMPT = 1 
+ 
+def apply_style(style_name: str, positive: str, negative: str = "") -> Tuple[str, str]:
+    p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME])
+    if not negative:
+        negative = ""
+    return p.replace("{prompt}", positive), n + negative
+
+class NoWatermark:
+    def apply_watermark(self, img):
+        return img
+    
+def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    return seed
+
+print("Loading Model!")
+schedule: str = "euler_a"
+base_model_path: str = "stabilityai/stable-diffusion-xl-base-1.0"
+refiner_model_path: str = "stabilityai/stable-diffusion-xl-refiner-1.0" 
+unet_path: str = "cosmicman/CosmicMan-SDXL"
+SCHEDULER = schedule_map[schedule]
+scheduler = SCHEDULER.from_pretrained(base_model_path, subfolder="scheduler")
+# unet = UNet2DConditionModel.from_pretrained(unet_path)
+
+pipe = StableDiffusionXLPipeline.from_pretrained(
+    base_model_path,
+    # unet=unet,
+    scheduler=scheduler,
+    use_safetensors=True
+).to("cuda")
+pipe.watermark = NoWatermark()
+refiner = StableDiffusionXLImg2ImgPipeline.from_pretrained(
+    base_model_path, # we found use base_model_path instead of refiner_model_path may get a better performance
+    scheduler=scheduler,
+    use_safetensors=True
+).to("cuda")
+refiner.watermark = NoWatermark()
+print("Finish Loading Model!")
+
+def generate_image(prompt, 
+                   n_prompt="", 
+                   style: str = DEFAULT_STYLE_NAME,
+                   steps: int = 50, 
+                   height: int = 1024,
+                   width: int = 1024,
+                   scale: float = 7.5, 
+                   img_num: int = 4, 
+                   seeds: int = 42,
+                   random_seed: bool = False,
+):
+    print("Beign to generate")
+    image_list = []
+    for i in range(img_num):
+        seed = int(randomize_seed_fn(seeds, random_seed))
+        generator = torch.Generator().manual_seed(seed)
+        positive_prompt, negative_prompt = apply_style(style, prompt, n_prompt)
+        image = pipe(positive_prompt, num_inference_steps=steps, 
+                guidance_scale=scale, height=height, 
+                width=width, negative_prompt=negative_prompt, 
+                generator=generator, output_type="latent").images[0]
+        image = refiner(positive_prompt, negative_prompt=negative_prompt, image=image[None, :]).images[0]
+        image_list.append((image,f"Seed {seed}"))
+    return image_list
+
+with gr.Blocks(theme=gr.themes.Soft(),css="style.css") as demo:
+    gr.Markdown(DESCRIPTION)
+    with gr.Group():
+        with gr.Row():
+            with gr.Column():
+                input_prompt = gr.Textbox(label="Input prompt", lines=3, max_lines=5)
+                negative_prompt = gr.Textbox(label="Negative prompt",value="")
+                run_button = gr.Button("Run", scale=0)
+        result = gr.Gallery(label="Result", show_label=False, elem_id="gallery", columns=[2], rows=[2], object_fit="contain", height="auto")
+    with gr.Accordion("Advanced options", open=False):
+        with gr.Row():
+            style_selection = gr.Radio(
+                    show_label=True,
+                    container=True,
+                    interactive=True,
+                    choices=STYLE_NAMES,
+                    value=DEFAULT_STYLE_NAME,
+                    label="Image Style",
+                )
+        with gr.Row():       
+            height = gr.Slider(minimum=512, maximum=1536, value=1024, label="Height", step=64)
+            width = gr.Slider(minimum=512, maximum=1536, value=1024, label="Witdh", step=64) 
+        with gr.Row():
+            steps =  gr.Slider(minimum=1, maximum=50, value=30, label="Number of diffusion steps", step=1)
+            scale = gr.Number(minimum=1, maximum=12, value=7.5, label="Number of scale")        
+        with gr.Row():     
+            seed = gr.Slider(
+                label="Seed",
+                minimum=0,
+                maximum=MAX_SEED,
+                step=1,
+                value=0,
+            )
+            random_seed = gr.Checkbox(label="Randomize seed", value=True)
+            img_num = gr.Slider(minimum=1, maximum=4, value=4, label="Number of images", step=1)   
+
+    gr.Examples(
+        examples=examples,
+        inputs=input_prompt,
+        outputs=result,
+        fn=generate_image,
+        cache_examples=CACHE_EXAMPLES,
+    )
+
+    gr.on(
+        triggers=[
+            input_prompt.submit,
+            negative_prompt.submit,
+            run_button.click,
+        ],
+        fn=generate_image, 
+        inputs = [input_prompt, negative_prompt, style_selection, steps, height, width, scale, img_num, seed, random_seed],
+        outputs= result,
+        api_name="run")
 
-gradio_app = gr.Interface(
-    predict,
-    inputs=gr.Image(label="Select hot dog candidate", sources=['upload', 'webcam'], type="pil"),
-    outputs=[gr.Image(label="Processed Image"), gr.Label(label="Result", num_top_classes=2)],
-    title="Hot Dog? Or Not?",
-)
 
 if __name__ == "__main__":
-    gradio_app.launch(share=True)
+    demo.queue(max_size=20).launch(show_api=False, debug=False)
+