Update app.py

app.py

@@ -1,399 +1,234 @@
-import os
-import random
-import uuid
-import json
-import time
-import asyncio
-from threading import Thread
-
 import gradio as gr
 import spaces
-import torch
 import numpy as np
+import random
+from diffusers import DiffusionPipeline
+import torch
 from PIL import Image
-import edge_tts
-import cv2
-
-from transformers import (
-    AutoModelForCausalLM,
-    AutoTokenizer,
-    TextIteratorStreamer,
-    Qwen2VLForConditionalGeneration,
-    AutoProcessor,
-)
-from transformers.image_utils import load_image
-from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
-
-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")
-
-# Load text-only model and tokenizer
-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 = "prithivMLmods/Qwen2-VL-OCR-2B-Instruct" 
-processor = AutoProcessor.from_pretrained(MODEL_ID, trust_remote_code=True)
-model_m = Qwen2VLForConditionalGeneration.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
-
-def clean_chat_history(chat_history):
-    """
-    Filter out any chat entries whose "content" is not a string.
-    This helps prevent errors when concatenating previous messages.
-    """
-    cleaned = []
-    for msg in chat_history:
-        if isinstance(msg, dict) and isinstance(msg.get("content"), str):
-            cleaned.append(msg)
-    return cleaned
-
-# Environment variables and parameters for Stable Diffusion XL
-# Use : SG161222/RealVisXL_V4.0_Lightning or SG161222/RealVisXL_V5.0_Lightning
-MODEL_ID_SD = os.getenv("MODEL_VAL_PATH")  # SDXL Model repository path via env variable
-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"))  # For batched image generation
-
-# Load the SDXL pipeline
-sd_pipe = StableDiffusionXLPipeline.from_pretrained(
-    MODEL_ID_SD,
-    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
-    use_safetensors=True,
-    add_watermarker=False,
-).to(device)
-sd_pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(sd_pipe.scheduler.config)
-
-# Ensure that the text encoder is in half-precision if using CUDA.
-if torch.cuda.is_available():
-    sd_pipe.text_encoder = sd_pipe.text_encoder.half()
-
-# Optional: compile the model for speedup if enabled
-if USE_TORCH_COMPILE:
-    sd_pipe.compile()
-
-# Optional: offload parts of the model to CPU if needed
-if ENABLE_CPU_OFFLOAD:
-    sd_pipe.enable_model_cpu_offload()
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model_repo_id = "stabilityai/stable-diffusion-3.5-large-turbo"
+
+torch_dtype = torch.bfloat16 if torch.cuda.is_available() else torch.float32
+
+pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
+pipe = pipe.to(device)
+
+pipe.load_lora_weights("prithivMLmods/SD3.5-Turbo-Realism-2.0-LoRA", weight_name="SD3.5-Turbo-Realism-2.0-LoRA.safetensors")
+trigger_word = "Turbo Realism"  
+pipe.fuse_lora(lora_scale=1.0)
 
 MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 1024
+
+# Define styles
+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",
+    },
+    {
+        "name": "2560 x 1440",
+        "prompt": "hyper-realistic 4K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic",
+        "negative_prompt": "cartoonish, low resolution, blurry, simplistic, abstract, deformed, ugly",
+    },
+    {
+        "name": "HD+",
+        "prompt": "hyper-realistic 2K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic",
+        "negative_prompt": "cartoonish, low resolution, blurry, simplistic, abstract, deformed, ugly",
+    },
+    {
+        "name": "Style Zero",
+        "prompt": "{prompt}",
+        "negative_prompt": "",
+    },
+]
 
-def save_image(img: Image.Image) -> str:
-    """Save a PIL image with a unique filename and return the path."""
-    unique_name = str(uuid.uuid4()) + ".png"
-    img.save(unique_name)
-    return unique_name
+STYLE_NAMES = [style["name"] for style in style_list]
+DEFAULT_STYLE_NAME = STYLE_NAMES[0]
+
+grid_sizes = {
+    "2x1": (2, 1),
+    "1x2": (1, 2),
+    "2x2": (2, 2),
+    "2x3": (2, 3),
+    "3x2": (3, 2),
+    "1x1": (1, 1)
+}
+
+@spaces.GPU(duration=60)
+def infer(
+    prompt,
+    negative_prompt="",
+    seed=42,
+    randomize_seed=False,
+    width=1024,
+    height=1024,
+    guidance_scale=7.5,
+    num_inference_steps=10,
+    style="Style Zero",
+    grid_size="1x1",
+    progress=gr.Progress(track_tqdm=True),
+):
+    selected_style = next(s for s in style_list if s["name"] == style)
+    styled_prompt = selected_style["prompt"].format(prompt=prompt)
+    styled_negative_prompt = selected_style["negative_prompt"]
 
-def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
-    return seed
-
-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: #FFF0F5; border-radius: 2px; overflow: hidden;">
-        <div style="width: 100%; height: 100%; background-color: #FF69B4; animation: loading 1.5s linear infinite;"></div>
-    </div>
-</div>
-<style>
-@keyframes loading {{
-    0% {{ transform: translateX(-100%); }}
-    100% {{ transform: translateX(100%); }}
-}}
-</style>
-    '''
-
-def downsample_video(video_path):
-    """
-    Downsamples the video to 10 evenly spaced frames.
-    Each frame is returned as a PIL image along with its timestamp.
-    """
-    vidcap = cv2.VideoCapture(video_path)
-    total_frames = int(vidcap.get(cv2.CAP_PROP_FRAME_COUNT))
-    fps = vidcap.get(cv2.CAP_PROP_FPS)
-    frames = []
-    # Sample 10 evenly spaced frames.
-    frame_indices = np.linspace(0, total_frames - 1, 10, dtype=int)
-    for i in frame_indices:
-        vidcap.set(cv2.CAP_PROP_POS_FRAMES, i)
-        success, image = vidcap.read()
-        if success:
-            image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)  # Convert BGR to RGB
-            pil_image = Image.fromarray(image)
-            timestamp = round(i / fps, 2)
-            frames.append((pil_image, timestamp))
-    vidcap.release()
-    return frames
-
-@spaces.GPU(duration=60, enable_queue=True)
-def generate_image_fn(
-    prompt: str,
-    negative_prompt: str = "",
-    use_negative_prompt: bool = False,
-    seed: int = 1,
-    width: int = 1024,
-    height: int = 1024,
-    guidance_scale: float = 3,
-    num_inference_steps: int = 25,
-    randomize_seed: bool = False,
-    use_resolution_binning: bool = True,
-    num_images: int = 1,
-    progress=gr.Progress(track_tqdm=True),
-):
-    """Generate images using the SDXL pipeline."""
-    seed = int(randomize_seed_fn(seed, randomize_seed))
-    generator = torch.Generator(device=device).manual_seed(seed)
+
+    generator = torch.Generator().manual_seed(seed)
+
+    grid_size_x, grid_size_y = grid_sizes.get(grid_size, (1, 1))
+    num_images = grid_size_x * grid_size_y
 
     options = {
-        "prompt": [prompt] * num_images,
-        "negative_prompt": [negative_prompt] * num_images if use_negative_prompt else None,
-        "width": width,
-        "height": height,
+        "prompt": styled_prompt,
+        "negative_prompt": styled_negative_prompt,
         "guidance_scale": guidance_scale,
         "num_inference_steps": num_inference_steps,
+        "width": width,
+        "height": height,
         "generator": generator,
-        "output_type": "pil",
+        "num_images_per_prompt": num_images,
     }
-    if use_resolution_binning:
-        options["use_resolution_binning"] = True
-
-    images = []
-    # Process in batches
-    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 and batch_options["negative_prompt"] is not None:
-            batch_options["negative_prompt"] = options["negative_prompt"][i:i+BATCH_SIZE]
-        # Wrap the pipeline call in autocast if using CUDA
-        if device.type == "cuda":
-            with torch.autocast("cuda", dtype=torch.float16):
-                outputs = sd_pipe(**batch_options)
-        else:
-            outputs = sd_pipe(**batch_options)
-        images.extend(outputs.images)
-    image_paths = [save_image(img) for img in images]
-    return image_paths, seed
-
-@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.
-      - "@image": triggers image generation using the SDXL pipeline.
-      - "@qwen2vl-video": triggers video processing using Qwen2VL.
-    """
-    text = input_dict["text"]
-    files = input_dict.get("files", [])
-    lower_text = text.strip().lower()
-
-    # Branch for image generation.
-    if lower_text.startswith("@image"):
-        # Remove the "@image" tag and use the rest as prompt
-        prompt = text[len("@image"):].strip()
-        yield progress_bar_html("Generating Image")
-        image_paths, used_seed = generate_image_fn(
-            prompt=prompt,
-            negative_prompt="",
-            use_negative_prompt=False,
-            seed=1,
-            width=1024,
-            height=1024,
-            guidance_scale=3,
-            num_inference_steps=25,
-            randomize_seed=True,
-            use_resolution_binning=True,
-            num_images=1,
-        )
-        yield gr.Image(image_paths[0])
-        return
-
-    # New branch for video processing with Qwen2VL.
-    if lower_text.startswith("@video-infer"):
-        prompt = text[len("@video-infer"):].strip()
-        if files:
-            # Assume the first file is a video.
-            video_path = files[0]
-            frames = downsample_video(video_path)
-            messages = [
-                {"role": "system", "content": [{"type": "text", "text": "You are a helpful assistant."}]},
-                {"role": "user", "content": [{"type": "text", "text": prompt}]}
-            ]
-            # Append each frame with its timestamp.
-            for frame in frames:
-                image, timestamp = frame
-                image_path = f"video_frame_{uuid.uuid4().hex}.png"
-                image.save(image_path)
-                messages[1]["content"].append({"type": "text", "text": f"Frame {timestamp}:"})
-                messages[1]["content"].append({"type": "image", "url": image_path})
-        else:
-            messages = [
-                {"role": "system", "content": [{"type": "text", "text": "You are a helpful assistant."}]},
-                {"role": "user", "content": [{"type": "text", "text": prompt}]}
-            ]
-        inputs = processor.apply_chat_template(
-            messages, tokenize=True, add_generation_prompt=True, return_dict=True, return_tensors="pt"
-        ).to("cuda")
-        streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)
-        generation_kwargs = {
-            **inputs,
-            "streamer": streamer,
-            "max_new_tokens": max_new_tokens,
-            "do_sample": True,
-            "temperature": temperature,
-            "top_p": top_p,
-            "top_k": top_k,
-            "repetition_penalty": repetition_penalty,
-        }
-        thread = Thread(target=model_m.generate, kwargs=generation_kwargs)
-        thread.start()
-        buffer = ""
-        yield progress_bar_html("Processing video with Qwen2VL")
-        for new_text in streamer:
-            buffer += new_text
-            buffer = buffer.replace("<|im_end|>", "")
-            time.sleep(0.01)
-            yield buffer
-        return
-
-    # Determine if TTS is requested.
-    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_full = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-        inputs = processor(text=[prompt_full], 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("Thinking...")
-        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 = []
-        yield progress_bar_html("Processing...")
-        for new_text in streamer:
-            outputs.append(new_text)
-            yield "".join(outputs)
-        final_response = "".join(outputs)
-        yield final_response
-        if is_tts and voice:
-            output_file = asyncio.run(text_to_speech(final_response, voice))
-            yield gr.Audio(output_file, autoplay=True)
-
-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=[
-        ["Write the Python Program for Array Rotation"],
-        [{"text": "summarize the letter", "files": ["examples/1.png"]}],
-        [{"text": "@video-infer Describe the Ad", "files": ["examples/coca.mp4"]}],
-        [{"text": "@video-infer Summarize the event in video", "files": ["examples/sky.mp4"]}],
-        [{"text": "@video-infer Describe the video", "files": ["examples/Missing.mp4"]}],
-        ["@image Chocolate dripping from a donut"],
-        ["@tts1 Who is Nikola Tesla, and why did he die?"],
-        [{"text": "Extract JSON from the image", "files": ["examples/document.jpg"]}],
-        ["@tts2 What causes rainbows to form?"],
-    ],
-    cache_examples=False,
-    type="messages",
-    description="# **QwQ Edge `@video-infer 'prompt..', @image, @tts1`**",
-    fill_height=True,
-    textbox=gr.MultimodalTextbox(label="Query Input", file_types=["image", "video"], file_count="multiple", placeholder="‎ @tts1, @tts2-voices, @image for image gen, @video-infer for video, default [text, vision]"),
-    stop_btn="Stop Generation",
-    multimodal=True,
-)
+
+    torch.cuda.empty_cache()  # Clear GPU memory
+    result = pipe(**options)
+
+    grid_img = Image.new('RGB', (width * grid_size_x, height * grid_size_y))
+
+    for i, img in enumerate(result.images[:num_images]):
+        grid_img.paste(img, (i % grid_size_x * width, i // grid_size_x * height))
+
+    return grid_img, seed
+
+examples = [
+    "A tiny astronaut hatching from an egg on the moon, 4k, planet theme",
+    "An anime-style illustration of a delicious, golden-brown wiener schnitzel on a plate, served with fresh lemon slices, parsley --style raw5",
+    "Cold coffee in a cup bokeh --ar 85:128 --v 6.0 --style raw5, 4K, Photo-Realistic",
+    "A cat holding a sign that says hello world --ar 85:128 --v 6.0 --style raw"
+]
+
+css = '''
+.gradio-container{max-width: 585px !important}
+h1{text-align:center}
+footer {
+    visibility: hidden
+}
+'''
+
+with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown("## GRID 6X🪨")
+
+        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, variant="primary")
+
+        result = gr.Image(label="Result", show_label=False)
+
+
+        with gr.Row(visible=True):
+            grid_size_selection = gr.Dropdown(
+                choices=["2x1", "1x2", "2x2", "2x3", "3x2", "1x1"],
+                value="1x1",
+                label="Grid Size"
+            )
+
+        with gr.Accordion("Advanced Settings", open=False):
+            negative_prompt = gr.Text(
+                label="Negative prompt",
+                max_lines=1,
+                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=False,
+            )
+
+            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():
+                width = gr.Slider(
+                    label="Width",
+                    minimum=512,
+                    maximum=MAX_IMAGE_SIZE,
+                    step=32,
+                    value=1024,
+                )
+
+                height = gr.Slider(
+                    label="Height",
+                    minimum=512,
+                    maximum=MAX_IMAGE_SIZE,
+                    step=32,
+                    value=1024,
+                )
+
+            with gr.Row():
+                guidance_scale = gr.Slider(
+                    label="Guidance scale",
+                    minimum=0.0,
+                    maximum=7.5,
+                    step=0.1,
+                    value=0.0,
+                )
+
+                num_inference_steps = gr.Slider(
+                    label="Number of inference steps",
+                    minimum=1,
+                    maximum=50,
+                    step=1,
+                    value=8,
+                )       
+                
+                style_selection = gr.Radio(
+                    show_label=True,
+                    container=True,
+                    interactive=True,
+                    choices=STYLE_NAMES,
+                    value=DEFAULT_STYLE_NAME,
+                    label="Quality Style",
+                )
+
+        gr.Examples(examples=examples, 
+                    inputs=[prompt], 
+                    outputs=[result, seed], 
+                    fn=infer, 
+                    cache_examples=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,
+            style_selection,
+            grid_size_selection,
+        ],
+        outputs=[result, seed],
+    )
 
 if __name__ == "__main__":
-    demo.queue(max_size=20).launch(share=True)
+    demo.launch()