Update app.py

app.py

@@ -2,165 +2,62 @@ import os
 import random
 import uuid
 import json
-import time
-import asyncio
-import re
-from threading import Thread
-
 import gradio as gr
-import spaces
-import torch
 import numpy as np
 from PIL import Image
-import edge_tts
-
-from transformers import (
-    AutoModelForCausalLM,
-    AutoTokenizer,
-    TextIteratorStreamer,
-    Qwen2VLForConditionalGeneration,
-    AutoProcessor,
-)
-from transformers.image_utils import load_image
+import spaces
+import torch
 from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
 
-DESCRIPTION = """
-# SDXL LoRA DLC 🎃
+DESCRIPTIONx = """## STABLE HAMSTER 🐹
+
 """
 
 css = '''
-h1 {
-  text-align: center;
-  display: block;
-}
-
-#duplicate-button {
-  margin: auto;
-  color: #fff;
-  background: #1565c0;
-  border-radius: 100vh;
+.gradio-container{max-width: 560px !important}
+h1{text-align:center}
+footer {
+    visibility: hidden
 }
 '''
 
-MAX_MAX_NEW_TOKENS = 2048
-DEFAULT_MAX_NEW_TOKENS = 1024
-MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))
-
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-
-# -----------------------
-# Progress Bar Helper
-# -----------------------
-def progress_bar_html(label: str) -> str:
-    """
-    Returns an HTML snippet for a thin progress bar with a label.
-    The progress bar is styled as a dark red animated bar.
-    """
-    return f'''
-<div style="display: flex; align-items: center;">
-    <span style="margin-right: 10px; font-size: 14px;">{label}</span>
-    <div style="width: 110px; height: 5px; background-color: #DDA0DD; border-radius: 2px; overflow: hidden;">
-        <div style="width: 100%; height: 100%; background-color: #FF00FF; animation: loading 1.5s linear infinite;"></div>
-    </div>
-</div>
-<style>
-@keyframes loading {{
-    0% {{ transform: translateX(-100%); }}
-    100% {{ transform: translateX(100%); }}
-}}
-</style>
-    '''
+examples = [
+    "3d image, cute girl, in the style of Pixar --ar 1:2 --stylize 750, 4K resolution highlights, Sharp focus, octane render, ray tracing, Ultra-High-Definition, 8k, UHD, HDR, (Masterpiece:1.5), (best quality:1.5)",
+    "Cold coffee in a cup bokeh --ar 85:128 --v 6.0 --style raw5, 4K",
+    "Vector illustration of a horse, vector graphic design with flat colors on an brown background in the style of vector art, using simple shapes and graphics with simple details, professionally designed as a tshirt logo ready for print on a white background. --ar 89:82 --v 6.0 --style raw",
+    "Man in brown leather jacket posing for camera, in the style of sleek and stylized, clockpunk, subtle shades, exacting precision, ferrania p30  --ar 67:101 --v 5",
+    "Commercial photography, giant burger, white lighting, studio light, 8k octane rendering, high resolution photography, insanely detailed, fine details, on white isolated plain, 8k, commercial photography, stock photo, professional color grading, --v 4 --ar 9:16 "
+    
+]
 
-# -----------------------
-# Text Generation Setup
-# -----------------------
-model_id = "prithivMLmods/FastThink-0.5B-Tiny"
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-model = AutoModelForCausalLM.from_pretrained(
-    model_id,
-    device_map="auto",
-    torch_dtype=torch.bfloat16,
-)
-model.eval()
 
-TTS_VOICES = [
-    "en-US-JennyNeural",  # @tts1
-    "en-US-GuyNeural",    # @tts2
-]
+MODEL_ID = os.getenv("MODEL_VAL_PATH")
+MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "4096"))
+USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE", "0") == "1"
+ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1"
+BATCH_SIZE = int(os.getenv("BATCH_SIZE", "1"))  # Allow generating multiple images at once
 
-# -----------------------
-# Multimodal OCR Setup
-# -----------------------
-MODEL_ID = "prithivMLmods/Qwen2-VL-OCR2-2B-Instruct" 
-processor = AutoProcessor.from_pretrained(MODEL_ID, trust_remote_code=True)
-model_m = Qwen2VLForConditionalGeneration.from_pretrained(
+#Load model outside of function
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+pipe = StableDiffusionXLPipeline.from_pretrained(
     MODEL_ID,
-    trust_remote_code=True,
-    torch_dtype=torch.float16
-).to("cuda").eval()
-
-async def text_to_speech(text: str, voice: str, output_file="output.mp3"):
-    """Convert text to speech using Edge TTS and save as MP3"""
-    communicate = edge_tts.Communicate(text, voice)
-    await communicate.save(output_file)
-    return output_file
+    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+    use_safetensors=True,
+    add_watermarker=False,
+).to(device)
+pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
 
-def clean_chat_history(chat_history):
-    """
-    Filter out any chat entries whose "content" is not a string.
-    """
-    cleaned = []
-    for msg in chat_history:
-        if isinstance(msg, dict) and isinstance(msg.get("content"), str):
-            cleaned.append(msg)
-    return cleaned
+# <compile speedup >
+if USE_TORCH_COMPILE:
+    pipe.compile()
 
-# -----------------------
-# Stable Diffusion Image Generation Setup
-# -----------------------
+# Offloading capacity (RAM)
+if ENABLE_CPU_OFFLOAD:
+    pipe.enable_model_cpu_offload()
 
 MAX_SEED = np.iinfo(np.int32).max
-USE_TORCH_COMPILE = False
-ENABLE_CPU_OFFLOAD = False
-
-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)
-    
-    # LoRA options with one example for each.
-    LORA_OPTIONS = {
-        "Realism": ("prithivMLmods/Canopus-Realism-LoRA", "Canopus-Realism-LoRA.safetensors", "rlms"),
-        "Pixar": ("prithivMLmods/Canopus-Pixar-Art", "Canopus-Pixar-Art.safetensors", "pixar"),
-        "Photoshoot": ("prithivMLmods/Canopus-Photo-Shoot-Mini-LoRA", "Canopus-Photo-Shoot-Mini-LoRA.safetensors", "photo"),
-        "Clothing": ("prithivMLmods/Canopus-Clothing-Adp-LoRA", "Canopus-Dress-Clothing-LoRA.safetensors", "clth"),
-        "Interior": ("prithivMLmods/Canopus-Interior-Architecture-0.1", "Canopus-Interior-Architecture-0.1δ.safetensors", "arch"),
-        "Fashion": ("prithivMLmods/Canopus-Fashion-Product-Dilation", "Canopus-Fashion-Product-Dilation.safetensors", "fashion"),
-        "Minimalistic": ("prithivMLmods/Pegasi-Minimalist-Image-Style", "Pegasi-Minimalist-Image-Style.safetensors", "minimalist"),
-        "Modern": ("prithivMLmods/Canopus-Modern-Clothing-Design", "Canopus-Modern-Clothing-Design.safetensors", "mdrnclth"),
-        "Animaliea": ("prithivMLmods/Canopus-Animaliea-Artism", "Canopus-Animaliea-Artism.safetensors", "Animaliea"),
-        "Wallpaper": ("prithivMLmods/Canopus-Liquid-Wallpaper-Art", "Canopus-Liquid-Wallpaper-Minimalize-LoRA.safetensors", "liquid"),
-        "Cars": ("prithivMLmods/Canes-Cars-Model-LoRA", "Canes-Cars-Model-LoRA.safetensors", "car"),
-        "PencilArt": ("prithivMLmods/Canopus-Pencil-Art-LoRA", "Canopus-Pencil-Art-LoRA.safetensors", "Pencil Art"),
-        "ArtMinimalistic": ("prithivMLmods/Canopus-Art-Medium-LoRA", "Canopus-Art-Medium-LoRA.safetensors", "mdm"),
-    }
-
-    # Load all LoRA weights
-    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")
-else:
-    pipe = StableDiffusionXLPipeline.from_pretrained(
-        "SG161222/RealVisXL_V4.0_Lightning",
-        torch_dtype=torch.float32,
-        use_safetensors=True,
-    ).to(device)
 
-def save_image(img: Image.Image) -> str:
-    """Save a PIL image with a unique filename and return the path."""
+def save_image(img):
     unique_name = str(uuid.uuid4()) + ".png"
     img.save(unique_name)
     return unique_name
@@ -170,200 +67,156 @@ def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
         seed = random.randint(0, MAX_SEED)
     return seed
 
-@spaces.GPU(duration=180, enable_queue=True)
-def generate_image(
+@spaces.GPU(duration=60, enable_queue=True)
+def generate(
     prompt: str,
     negative_prompt: str = "",
-    seed: int = 0,
+    use_negative_prompt: bool = False,
+    seed: int = 1,
     width: int = 1024,
     height: int = 1024,
-    guidance_scale: float = 3.0,
-    randomize_seed: bool = True,
-    lora_model: str = "Realism",
+    guidance_scale: float = 3,
+    num_inference_steps: int = 25,
+    randomize_seed: bool = False,
+    use_resolution_binning: bool = True, 
+    num_images: int = 4,  # Number of images to generate
     progress=gr.Progress(track_tqdm=True),
 ):
     seed = int(randomize_seed_fn(seed, randomize_seed))
-    effective_negative_prompt = negative_prompt  # Use provided negative prompt if any
-    model_name, weight_name, adapter_name = LORA_OPTIONS[lora_model]
-    pipe.set_adapters(adapter_name)
-    outputs = pipe(
-         prompt=prompt,
-         negative_prompt=effective_negative_prompt,
-         width=width,
-         height=height,
-         guidance_scale=guidance_scale,
-         num_inference_steps=28,
-         num_images_per_prompt=1,
-         cross_attention_kwargs={"scale": 0.65},
-         output_type="pil",
-    )
-    images = outputs.images
+    generator = torch.Generator(device=device).manual_seed(seed)
+
+    #Options 
+    options = {
+        "prompt": [prompt] * num_images,
+        "negative_prompt": [negative_prompt] * num_images if use_negative_prompt else None,
+        "width": width,
+        "height": height,
+        "guidance_scale": guidance_scale,
+        "num_inference_steps": num_inference_steps,
+        "generator": generator,
+        "output_type": "pil",
+    }
+
+    if use_resolution_binning:
+        options["use_resolution_binning"] = True
+
+    #Images potential batches
+    images = []
+    for i in range(0, num_images, BATCH_SIZE):
+        batch_options = options.copy()
+        batch_options["prompt"] = options["prompt"][i:i+BATCH_SIZE]
+        if "negative_prompt" in batch_options:
+            batch_options["negative_prompt"] = options["negative_prompt"][i:i+BATCH_SIZE]
+        images.extend(pipe(**batch_options).images)
+
     image_paths = [save_image(img) for img in images]
     return image_paths, seed
 
-# -----------------------
-# Main Chat/Generation Function
-# -----------------------
-@spaces.GPU
-def generate(
-    input_dict: dict,
-    chat_history: list[dict],
-    max_new_tokens: int = 1024,
-    temperature: float = 0.6,
-    top_p: float = 0.9,
-    top_k: int = 50,
-    repetition_penalty: float = 1.2,
-):
-    """
-    Generates chatbot responses with support for multimodal input, TTS, and image generation.
-    Special commands:
-      - "@tts1" or "@tts2": triggers text-to-speech.
-      - "@<lora_command>": triggers image generation using the new LoRA pipeline.
-         Available commands (case-insensitive): @realism, @pixar, @photoshoot, @clothing, @interior, @fashion, 
-         @minimalistic, @modern, @animaliea, @wallpaper, @cars, @pencilart, @artminimalistic.
-    """
-    text = input_dict["text"]
-    files = input_dict.get("files", [])
-    
-    # Check for image generation command based on LoRA tags.
-    lora_mapping = { key.lower(): key for key in LORA_OPTIONS }
-    for key_lower, key in lora_mapping.items():
-        command_tag = "@" + key_lower
-        if text.strip().lower().startswith(command_tag):
-            prompt_text = text.strip()[len(command_tag):].strip()
-            yield progress_bar_html(f"Processing Image Generation ({key} style)")
-            image_paths, used_seed = generate_image(
-                prompt=prompt_text,
-                negative_prompt="",
-                seed=1,
-                width=1024,
-                height=1024,
-                guidance_scale=3,
-                randomize_seed=True,
-                lora_model=key,
+with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
+    gr.Markdown(DESCRIPTIONx)  
+    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=2, show_label=False) 
+    with gr.Accordion("Advanced options", open=False, visible=True):
+        num_images = gr.Slider(
+            label="Number of Images",
+            minimum=1,
+            maximum=4,
+            step=1,
+            value=4,
+        )
+        with gr.Row():
+            use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=True)
+            negative_prompt = gr.Text(
+                label="Negative prompt",
+                max_lines=5,
+                lines=4,
+                placeholder="Enter a 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",
+                visible=True,
+            )
+        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(visible=True):
+            width = gr.Slider(
+                label="Width",
+                minimum=512,
+                maximum=MAX_IMAGE_SIZE,
+                step=64,
+                value=1024,
+            )
+            height = gr.Slider(
+                label="Height",
+                minimum=512,
+                maximum=MAX_IMAGE_SIZE,
+                step=64,
+                value=1024,
+            )
+        with gr.Row():
+            guidance_scale = gr.Slider(
+                label="Guidance Scale",
+                minimum=0.1,
+                maximum=6,
+                step=0.1,
+                value=3.0,
+            )
+            num_inference_steps = gr.Slider(
+                label="Number of inference steps",
+                minimum=1,
+                maximum=25,
+                step=1,
+                value=23,
             )
-            yield progress_bar_html("Finalizing Image Generation")
-            yield gr.Image(image_paths[0])
-            return 
-    
-    # Check for TTS command (@tts1 or @tts2)
-    tts_prefix = "@tts"
-    is_tts = any(text.strip().lower().startswith(f"{tts_prefix}{i}") for i in range(1, 3))
-    voice_index = next((i for i in range(1, 3) if text.strip().lower().startswith(f"{tts_prefix}{i}")), None)
-    
-    if is_tts and voice_index:
-        voice = TTS_VOICES[voice_index - 1]
-        text = text.replace(f"{tts_prefix}{voice_index}", "").strip()
-        conversation = [{"role": "user", "content": text}]
-    else:
-        voice = None
-        text = text.replace(tts_prefix, "").strip()
-        conversation = clean_chat_history(chat_history)
-        conversation.append({"role": "user", "content": text})
-    
-    if files:
-        if len(files) > 1:
-            images = [load_image(image) for image in files]
-        elif len(files) == 1:
-            images = [load_image(files[0])]
-        else:
-            images = []
-        messages = [{
-            "role": "user",
-            "content": [
-                *[{"type": "image", "image": image} for image in images],
-                {"type": "text", "text": text},
-            ]
-        }]
-        prompt = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-        inputs = processor(text=[prompt], images=images, return_tensors="pt", padding=True).to("cuda")
-        streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)
-        generation_kwargs = {**inputs, "streamer": streamer, "max_new_tokens": max_new_tokens}
-        thread = Thread(target=model_m.generate, kwargs=generation_kwargs)
-        thread.start()
-
-        buffer = ""
-        yield progress_bar_html("Processing with Qwen2VL Ocr")
-        for new_text in streamer:
-            buffer += new_text
-            buffer = buffer.replace("<|im_end|>", "")
-            time.sleep(0.01)
-            yield buffer
-    else:
-        input_ids = tokenizer.apply_chat_template(conversation, add_generation_prompt=True, return_tensors="pt")
-        if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH:
-            input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:]
-            gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.")
-        input_ids = input_ids.to(model.device)
-        streamer = TextIteratorStreamer(tokenizer, timeout=20.0, skip_prompt=True, skip_special_tokens=True)
-        generation_kwargs = {
-            "input_ids": input_ids,
-            "streamer": streamer,
-            "max_new_tokens": max_new_tokens,
-            "do_sample": True,
-            "top_p": top_p,
-            "top_k": top_k,
-            "temperature": temperature,
-            "num_beams": 1,
-            "repetition_penalty": repetition_penalty,
-        }
-        t = Thread(target=model.generate, kwargs=generation_kwargs)
-        t.start()
-
-        outputs = []
-        for new_text in streamer:
-            outputs.append(new_text)
-            yield "".join(outputs)
 
-        final_response = "".join(outputs)
-        yield final_response
+    gr.Examples(
+        examples=examples,
+        inputs=prompt,
+        cache_examples=False
+    )
 
-        if is_tts and voice:
-            output_file = asyncio.run(text_to_speech(final_response, voice))
-            yield gr.Audio(output_file, autoplay=True)
+    use_negative_prompt.change(
+        fn=lambda x: gr.update(visible=x),
+        inputs=use_negative_prompt,
+        outputs=negative_prompt,
+        api_name=False,
+    )
 
-# -----------------------
-# Gradio Chat Interface
-# -----------------------
-demo = gr.ChatInterface(
-    fn=generate,
-    additional_inputs=[
-        gr.Slider(label="Max new tokens", minimum=1, maximum=MAX_MAX_NEW_TOKENS, step=1, value=DEFAULT_MAX_NEW_TOKENS),
-        gr.Slider(label="Temperature", minimum=0.1, maximum=4.0, step=0.1, value=0.6),
-        gr.Slider(label="Top-p (nucleus sampling)", minimum=0.05, maximum=1.0, step=0.05, value=0.9),
-        gr.Slider(label="Top-k", minimum=1, maximum=1000, step=1, value=50),
-        gr.Slider(label="Repetition penalty", minimum=1.0, maximum=2.0, step=0.05, value=1.2),
-    ],
-    examples=[
-        ['@realism Chocolate dripping from a donut against a yellow background, in the style of brocore, hyper-realistic'],
-        ["@pixar A young man with light brown wavy hair and light brown eyes sitting in an armchair and looking directly at the camera, pixar style, disney pixar, office background, ultra detailed, 1 man"],
-        ["@realism A futuristic cityscape with neon lights"],
-        ["@photoshoot A portrait of a person with dramatic lighting"],
-        [{"text": "summarize the letter", "files": ["examples/1.png"]}],
-        ["Python Program for Array Rotation"],
-        ["@tts1 Who is Nikola Tesla, and why did he die?"],
-        ["@clothing Fashionable streetwear in an urban environment"],
-        ["@interior A modern living room interior with minimalist design"],
-        ["@fashion A runway model in haute couture"],
-        ["@minimalistic A simple and elegant design of a serene landscape"],
-        ["@modern A contemporary art piece with abstract geometric shapes"],
-        ["@animaliea A cute animal portrait with vibrant colors"],
-        ["@wallpaper A scenic mountain range perfect for a desktop wallpaper"],
-        ["@cars A sleek sports car cruising on a city street"],
-        ["@pencilart A detailed pencil sketch of a historic building"],
-        ["@artminimalistic An artistic minimalist composition with subtle tones"],
-        ["@tts2 What causes rainbows to form?"],
-    ],
-    cache_examples=False,
-    type="messages",
-    description=DESCRIPTION,
-    css=css,
-    fill_height=True,
-    textbox=gr.MultimodalTextbox(label="Query Input", file_types=["image"], file_count="multiple", placeholder="default [text, vision] , scroll down examples to explore more art styles"),
-    stop_btn="Stop Generation",
-    theme=gr.themes.Soft(),
-    multimodal=True,
-)
+    gr.on(
+        triggers=[
+            prompt.submit,
+            negative_prompt.submit,
+            run_button.click,
+        ],
+        fn=generate,
+        inputs=[
+            prompt,
+            negative_prompt,
+            use_negative_prompt,
+            seed,
+            width,
+            height,
+            guidance_scale,
+            num_inference_steps,
+            randomize_seed,
+            num_images
+        ],
+        outputs=[result, seed],
+        api_name="run",
+    )   
 
 if __name__ == "__main__":
-    demo.queue(max_size=30).launch(ssr_mode=False, share=True)
+    demo.queue(max_size=40).launch(ssr_mode=False)