Update app.py

app.py

@@ -1,145 +1,252 @@
-!pip install torch torchvision torchaudio gradio diffusers optimum numpy
-
-
+from __future__ import annotations
+import os
+import random
 import gradio as gr
+import spaces
 import numpy as np
-import random
-#import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
+import uuid
+from optimum.quanto import freeze, qfloat8, quantize
+from diffusers import PixArtAlphaPipeline, LCMScheduler
 import torch
+from typing import Tuple
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo" #Replace to the model you would like to use
-
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
 
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
+DESCRIPTION = """ # Instant Image
+        ### Super fast text to Image Generator.
+        ### <span style='color: red;'>You may change the steps from 4 to 8, if you didn't get satisfied results.
+        ### First Image processing takes time then images generate faster.
+        """
+if not torch.cuda.is_available():
+    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"
 
 MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
+CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES", "1") == "1"
+MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "4192"))
+USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE", "1") == "1"
+ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1"
+
+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": "Realistic",
+        "prompt": "Photorealistic {prompt} . Ulta-realistic, professional, 4k, highly detailed",
+        "negative_prompt": "drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly, disfigured",
+    },
+    {
+        "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": "Digital Art",
+        "prompt": "concept art {prompt} . digital artwork, illustrative, painterly, matte painting, highly detailed",
+        "negative_prompt": "photo, photorealistic, realism, ugly",
+    },
+    {
+        "name": "Pixel art",
+        "prompt": "pixel-art {prompt} . low-res, blocky, pixel art style, 8-bit graphics",
+        "negative_prompt": "sloppy, messy, blurry, noisy, highly detailed, ultra textured, photo, realistic",
+    },
+    {
+        "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": "3D Model",
+        "prompt": "professional 3d model {prompt} . octane render, highly detailed, volumetric, dramatic lighting",
+        "negative_prompt": "ugly, deformed, noisy, low poly, blurry, painting",
+    },
+]
+
+
+styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list}
+STYLE_NAMES = list(styles.keys())
+DEFAULT_STYLE_NAME = "(No style)"
+NUM_IMAGES_PER_PROMPT = 1
 
-#@spaces.GPU #[uncomment to use ZeroGPU]
-def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, progress=gr.Progress(track_tqdm=True)):
+pipe = PixArtAlphaPipeline.from_pretrained(
+        "PixArt-alpha/PixArt-LCM-XL-2-1024-MS",
+        torch_dtype=torch.float16,
+        use_safetensors=True,
+).to("cuda:0")
 
+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
+
+    if USE_TORCH_COMPILE:
+        pipe.transformer = torch.compile(pipe.transformer, mode="reduce-overhead", fullgraph=True)
+        print("Model Compiled!")
+
+def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
-        
+    return seed
+
+@spaces.GPU(duration=30)
+def generate(
+        prompt: str,
+        negative_prompt: str = "",
+        style: str = DEFAULT_STYLE_NAME,
+        use_negative_prompt: bool = False,
+        seed: int = 0,
+        width: int = 1024,
+        height: int = 1024,
+        inference_steps: int = 12,
+        randomize_seed: bool = False,
+        use_resolution_binning: bool = True,
+        progress=gr.Progress(track_tqdm=True),
+):
+    seed = int(randomize_seed_fn(seed, randomize_seed))
     generator = torch.Generator().manual_seed(seed)
     
+    if not use_negative_prompt:
+        negative_prompt = None  # type: ignore
+    prompt, negative_prompt = apply_style(style, prompt, negative_prompt)
+
     image = pipe(
-        prompt = prompt, 
-        negative_prompt = negative_prompt,
-        guidance_scale = guidance_scale, 
-        num_inference_steps = num_inference_steps, 
-        width = width, 
-        height = height,
-        generator = generator
-    ).images[0] 
-    
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        width=width,
+        height=height,
+        guidance_scale=0,
+        num_inference_steps=inference_steps,
+        generator=generator,
+        use_resolution_binning=use_resolution_binning,
+    ).images[0]
     return image, seed
 
+
 examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
+    "A Monkey with a happy face in the Sahara desert.",
+    "Eiffel Tower was Made up of ICE.",
+    "photo of 8k ultra realistic harbour, nreal engine 5, port, boats, sunset, beautiful light, full of colour, cinematic lighting, battered, trending on artstation, 4k, hyperrealistic, focused, extreme details",
+    "Color photo of a corgi made of transparent glass, standing on the riverside in Yosemite National Park.",
+    "A close-up photo of a woman. She wore a blue coat with a gray dress underneath and has blue eyes.",
+    "A litter of golden retriever puppies playing in the snow. Their heads pop out of the snow, covered in.",
+    "an astronaut sitting in a diner, eating fries, cinematic, analog film",
 ]
 
-css="""
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
+css = '''
+.gradio-container{max-width: 560px !important}
+h1{text-align:center}
+footer {
+    visibility: hidden
 }
-"""
+'''
 
 with gr.Blocks(css=css) as demo:
-    
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(f"""
-        # Text-to-Image Gradio Template
-        """)
-        
-        with gr.Row():
-            
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-            
-            run_button = gr.Button("Run", scale=0)
-        
-        result = gr.Image(label="Result", show_label=False)
-
-        with gr.Accordion("Advanced Settings", open=False):
-            
-            negative_prompt = gr.Text(
+    gr.Markdown(DESCRIPTION)
+    with gr.Row(equal_height=False):
+        with gr.Group():
+            with gr.Row():
+                prompt = gr.Text(
+                    label="Prompt",
+                    show_label=False,
+                    max_lines=1,
+                    placeholder="Enter your prompt",
+                    container=False,
+                )
+                run_button = gr.Button("Run", scale=0)
+            result = gr.Image(label="Result")
+                    
+    with gr.Accordion("Advanced options", open=False):
+        with gr.Group():
+            with gr.Row():
+                use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=False, visible=True)
+                negative_prompt = gr.Text(
                 label="Negative prompt",
                 max_lines=1,
                 placeholder="Enter a negative prompt",
-                visible=False,
+                visible=True,
+            )
+            style_selection = gr.Radio(
+                show_label=True,
+                container=True,
+                interactive=True,
+                choices=STYLE_NAMES,
+                value=DEFAULT_STYLE_NAME,
+                label="Image Style",
             )
-            
             seed = gr.Slider(
                 label="Seed",
                 minimum=0,
                 maximum=MAX_SEED,
                 step=1,
                 value=0,
-            )
-            
+                )
             randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-            
-            with gr.Row():
-                
+            with gr.Row(visible=True):
                 width = gr.Slider(
                     label="Width",
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=1024, #Replace with defaults that work for your model
+                    value=1024,
                 )
-                
                 height = gr.Slider(
                     label="Height",
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=1024, #Replace with defaults that work for your model
-                )
-            
-            with gr.Row():
-                
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0, #Replace with defaults that work for your model
-                )
-                
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=2, #Replace with defaults that work for your model
+                    value=1024,
                 )
-        
-        gr.Examples(
-            examples = examples,
-            inputs = [prompt]
-        )
+        with gr.Row():
+            inference_steps = gr.Slider(
+                label="Steps",
+                minimum=4,
+                maximum=20,
+                step=1,
+                value=8,
+            )
+
+    gr.Examples(
+        examples=examples,
+        inputs=prompt,
+        outputs=[result, seed],
+        fn=generate,
+        cache_examples=CACHE_EXAMPLES,
+    )
+    use_negative_prompt.change(
+        fn=lambda x: gr.update(visible=x),
+        inputs=use_negative_prompt,
+        outputs=negative_prompt,
+        api_name=False,
+    )
+
     gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn = infer,
-        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-        outputs = [result, seed]
+        triggers=[
+            prompt.submit,
+            negative_prompt.submit,
+            run_button.click,
+        ],
+        fn=generate,
+        inputs=[
+            prompt,
+            negative_prompt,
+            style_selection,
+            use_negative_prompt,
+            seed,
+            width,
+            height,
+            inference_steps,
+            randomize_seed,
+        ],
+        outputs=[result, seed],
+        api_name="run",
     )
 
-demo.queue().launch()
+if __name__ == "__main__":
+    demo.queue(max_size=200).launch()