Update app.py

app.py

@@ -1,86 +1,235 @@
-import spaces
-import argparse
+from __future__ import annotations
 import os
-import time
-from os import path
-from safetensors.torch import load_file
-from huggingface_hub import hf_hub_download
-
-cache_path = path.join(path.dirname(path.abspath(__file__)), "models")
-os.environ["TRANSFORMERS_CACHE"] = cache_path
-os.environ["HF_HUB_CACHE"] = cache_path
-os.environ["HF_HOME"] = cache_path
-
+import random
+import uuid
 import gradio as gr
+import spaces
+import numpy as np
+import uuid
+from diffusers import PixArtAlphaPipeline, LCMScheduler
 import torch
-from diffusers import StableDiffusionXLPipeline, LCMScheduler
+from typing import Tuple
+from datetime import datetime
+
+
+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
+CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES", "1") == "1"
+MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "3000"))
+USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE", "0") == "1"
+ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1"
+PORT = int(os.getenv("DEMO_PORT", "15432"))
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+
+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
+
+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 torch.cuda.is_available():
+
+    pipe = PixArtAlphaPipeline.from_pretrained(
+        "PixArt-alpha/PixArt-LCM-XL-2-1024-MS",
+        torch_dtype=torch.float16,
+        use_safetensors=True,
+    )
+
+    if os.getenv('CONSISTENCY_DECODER', False):
+        print("Using DALL-E 3 Consistency Decoder")
+        pipe.vae = ConsistencyDecoderVAE.from_pretrained("openai/consistency-decoder", torch_dtype=torch.float16)
+
+    if ENABLE_CPU_OFFLOAD:
+        pipe.enable_model_cpu_offload()
+    else:
+        pipe.to(device)
+        print("Loaded on Device!")
+        
+    # speed-up T5
+    pipe.text_encoder.to_bettertransformer()
+
+    if USE_TORCH_COMPILE:
+        pipe.transformer = torch.compile(pipe.transformer, mode="reduce-overhead", fullgraph=True)
+        print("Model Compiled!")
+
 
-# from scheduling_tcd import TCDScheduler
 
-torch.backends.cuda.matmul.allow_tf32 = True
 
-class timer:
-    def __init__(self, method_name="timed process"):
-        self.method = method_name
 
-    def __enter__(self):
-        self.start = time.time()
-        print(f"{self.method} starts")
 
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        end = time.time()
-        print(f"{self.method} took {str(round(end - self.start, 2))}s")
+def save_image(img):
+    unique_name = str(uuid.uuid4()) + ".png"
+    img.save(unique_name)
+    return unique_name
 
-if not path.exists(cache_path):
-    os.makedirs(cache_path, exist_ok=True)
 
-pipe = StableDiffusionXLPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.bfloat16)
-pipe.to(device="cuda", dtype=torch.bfloat16)
-unet_state = load_file(hf_hub_download("ByteDance/Hyper-SD", "Hyper-SDXL-1step-Unet.safetensors"), device="cuda")
-pipe.unet.load_state_dict(unet_state)
-pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config, timestep_spacing ="trailing")
+
+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 = 4,
+        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)
+
+    images = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        width=width,
+        height=height,
+        guidance_scale=0,
+        num_inference_steps=inference_steps,
+        generator=generator,
+        num_images_per_prompt=NUM_IMAGES_PER_PROMPT,
+        use_resolution_binning=use_resolution_binning,
+        output_type="pil",
+    ).images
+
+    image_paths = [save_image(img) for img in images]
+    print(image_paths)
+    return image_paths, seed
+
+
+examples = [
+    "A Monkey with a happy face in the Sahara desert.",
+    "Eiffel Tower was Made up of ICE.",
+    "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",
+]
 
 with gr.Blocks() as demo:
     gr.Markdown(DESCRIPTION)
-    with gr.Row(equal_height=False):
+    with gr.Accordion("Advanced options", open=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.Gallery(label="Result", columns=NUM_IMAGES_PER_PROMPT,  show_label=False)
-            
-        with gr.Group():
-            with gr.Row(visible=True):
-                seed = gr.Slider(
+                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=True,
+            )
+              
+#                num_imgs = gr.Slider(
+ #                   label="Num Images",
+  #                  minimum=1,
+   #                 maximum=8,
+    #                step=1,
+     #               value=1,
+      #          )
+            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=99999999,
-                step=1,
-                value=0,
-            )
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-            with gr.Row(visible=True):
                 width = gr.Slider(
                     label="Width",
                     minimum=256,
-                    maximum=8192,
+                    maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=2048,
+                    value=1024,
                 )
                 height = gr.Slider(
                     label="Height",
                     minimum=256,
-                    maximum=8192,
+                    maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=2048,
+                    value=1024,
                 )
-        gr.Examples(
+        with gr.Row():
+            inference_steps = gr.Slider(
+                label="Steps",
+                minimum=4,
+                maximum=20,
+                step=1,
+                value=4,
+            )
+
+    gr.Examples(
         examples=examples,
         inputs=prompt,
         outputs=[result, seed],
@@ -88,56 +237,69 @@ with gr.Blocks() as demo:
         cache_examples=CACHE_EXAMPLES,
     )
 
-            def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
-            if randomize_seed:
-            seed = random.randint(0, 99999999)
-            return seed
-            
-            @spaces.GPU(duration=10)
-            def process_image( height, width, prompt, seed, randomize_seed):
-                global pipe
-                with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16), timer("inference"):
-                    return pipe(
-                        prompt=str,,
-                        num_inference_steps=1,
-                        guidance_scale=0.,
-                        height=int(height),
-                        width=int(width),
-                        timesteps=[800],
-                        randomize_seed: bool = False,
-                        use_resolution_binning: bool = True,
-                        progress=gr.Progress(track_tqdm=True),
-                        ).images
-
-                        seed = int(randomize_seed_fn(seed, randomize_seed))
-                        generator = torch.Generator().manual_seed(seed)
-    
-            reactive_controls = [ height, width, prompt, seed, randomize_seed]
 
 
-            btn.click(process_image, inputs=reactive_controls, outputs=[output])
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+    use_negative_prompt.change(
+        fn=lambda x: gr.update(visible=x),
+        inputs=use_negative_prompt,
+        outputs=negative_prompt,
+        api_name=False,
+    )
+
+    gr.on(
+        triggers=[
+            prompt.submit,
+            negative_prompt.submit,
+            run_button.click,
+        ],
+        fn=generate,
+        inputs=[
+            prompt,
+            negative_prompt,
+            style_selection,
+            use_negative_prompt,
+            # num_imgs,
+            seed,
+            width,
+            height,
+            inference_steps,
+            randomize_seed,
+        ],
+        outputs=[result, seed],
+        api_name="run",
+    )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.queue(max_size=20).launch()
+    # demo.queue(max_size=20).launch(server_name="0.0.0.0", server_port=11900, debug=True)
+
+
+
+
+
+
+
+
+
 
 
-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>"
 
 
-CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES", "1") == "1"
-        
-examples = [
-    "A Monkey with a happy face in the Sahara desert.",
-    "Eiffel Tower was Made up of ICE.",
-    "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",
-]