v3p1

app.py

@@ -14,214 +14,69 @@ from datetime import datetime
 from diffusers.models import AutoencoderKL
 from diffusers import StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipeline
 
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
+# ... (keep all the imports and initial setup)
 
-DESCRIPTION = "PonyDiffusion V6 XL"
-if not torch.cuda.is_available():
-    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU. </p>"
-IS_COLAB = utils.is_google_colab() or os.getenv("IS_COLAB") == "1"
-HF_TOKEN = os.getenv("HF_TOKEN")
-CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES") == "1"
-MIN_IMAGE_SIZE = int(os.getenv("MIN_IMAGE_SIZE", "512"))
-MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "2048"))
-USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE") == "1"
-ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD") == "1"
-OUTPUT_DIR = os.getenv("OUTPUT_DIR", "./outputs")
+# ... (keep all the functions like load_pipeline, parse_json_parameters, apply_json_parameters, generate, get_random_prompt)
 
-MODEL = os.getenv(
-    "MODEL",
-    "https://huggingface.co/AstraliteHeart/pony-diffusion-v6/blob/main/v6.safetensors",
-)
-
-torch.backends.cudnn.deterministic = True
-torch.backends.cudnn.benchmark = False
-
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-
-def load_pipeline(model_name):
-    vae = AutoencoderKL.from_pretrained(
-        "madebyollin/sdxl-vae-fp16-fix",
-        torch_dtype=torch.float16,
-    )
-    pipeline = (
-        StableDiffusionXLPipeline.from_single_file
-        if MODEL.endswith(".safetensors")
-        else StableDiffusionXLPipeline.from_pretrained
-    )
-
-    pipe = pipeline(
-        model_name,
-        vae=vae,
-        torch_dtype=torch.float16,
-        custom_pipeline="lpw_stable_diffusion_xl",
-        use_safetensors=True,
-        add_watermarker=False,
-        use_auth_token=HF_TOKEN,
-        variant="fp16",
-    )
-
-    pipe.to(device)
-    return pipe
-
-def parse_json_parameters(json_str):
-    try:
-        params = json.loads(json_str)
-        return params
-    except json.JSONDecodeError:
-        return None
-
-def apply_json_parameters(json_str):
-    params = parse_json_parameters(json_str)
-    if params:
-        return (
-            params.get("prompt", ""),
-            params.get("negative_prompt", ""),
-            params.get("seed", 0),
-            params.get("width", 1024),
-            params.get("height", 1024),
-            params.get("guidance_scale", 7.0),
-            params.get("num_inference_steps", 30),
-            params.get("sampler", "DPM++ 2M SDE Karras"),
-            params.get("aspect_ratio", "1024 x 1024"),
-            params.get("use_upscaler", False),
-            params.get("upscaler_strength", 0.55),
-            params.get("upscale_by", 1.5),
-        )
-    return [gr.update()] * 12
-
-@spaces.GPU
-def generate(
-    prompt: str,
-    negative_prompt: str = "",
-    seed: int = 0,
-    custom_width: int = 1024,
-    custom_height: int = 1024,
-    guidance_scale: float = 7.0,
-    num_inference_steps: int = 30,
-    sampler: str = "DPM++ 2M SDE Karras",
-    aspect_ratio_selector: str = "1024 x 1024",
-    use_upscaler: bool = False,
-    upscaler_strength: float = 0.55,
-    upscale_by: float = 1.5,
-    progress=gr.Progress(track_tqdm=True),
-) -> Image:
-    generator = utils.seed_everything(seed)
-
-    width, height = utils.aspect_ratio_handler(
-        aspect_ratio_selector,
-        custom_width,
-        custom_height,
-    )
-
-    width, height = utils.preprocess_image_dimensions(width, height)
-
-    backup_scheduler = pipe.scheduler
-    pipe.scheduler = utils.get_scheduler(pipe.scheduler.config, sampler)
-
-    if use_upscaler:
-        upscaler_pipe = StableDiffusionXLImg2ImgPipeline(**pipe.components)
-    metadata = {
-        "prompt": prompt,
-        "negative_prompt": negative_prompt,
-        "resolution": f"{width} x {height}",
-        "guidance_scale": guidance_scale,
-        "num_inference_steps": num_inference_steps,
-        "seed": seed,
-        "sampler": sampler,
-    }
+if torch.cuda.is_available():
+    pipe = load_pipeline(MODEL)
+    logger.info("Loaded on Device!")
+else:
+    pipe = None
 
-    if use_upscaler:
-        new_width = int(width * upscale_by)
-        new_height = int(height * upscale_by)
-        metadata["use_upscaler"] = {
-            "upscale_method": "nearest-exact",
-            "upscaler_strength": upscaler_strength,
-            "upscale_by": upscale_by,
-            "new_resolution": f"{new_width} x {new_height}",
+# Define the JavaScript code as a string
+js_code = """
+<script>
+document.addEventListener('DOMContentLoaded', (event) => {
+  const historyDropdown = document.getElementById('history-dropdown');
+  const resultGallery = document.querySelector('.gallery');
+
+  if (historyDropdown && resultGallery) {
+    const observer = new MutationObserver((mutations) => {
+      mutations.forEach((mutation) => {
+        if (mutation.type === 'childList' && mutation.addedNodes.length > 0) {
+          const newImage = mutation.addedNodes[0];
+          if (newImage.tagName === 'IMG') {
+            updateHistory(newImage.src);
+          }
         }
-    else:
-        metadata["use_upscaler"] = None
-    logger.info(json.dumps(metadata, indent=4))
+      });
+    });
 
-    try:
-        if use_upscaler:
-            latents = pipe(
-                prompt=prompt,
-                negative_prompt=negative_prompt,
-                width=width,
-                height=height,
-                guidance_scale=guidance_scale,
-                num_inference_steps=num_inference_steps,
-                generator=generator,
-                output_type="latent",
-            ).images
-            upscaled_latents = utils.upscale(latents, "nearest-exact", upscale_by)
-            images = upscaler_pipe(
-                prompt=prompt,
-                negative_prompt=negative_prompt,
-                image=upscaled_latents,
-                guidance_scale=guidance_scale,
-                num_inference_steps=num_inference_steps,
-                strength=upscaler_strength,
-                generator=generator,
-                output_type="pil",
-            ).images
-        else:
-            images = pipe(
-                prompt=prompt,
-                negative_prompt=negative_prompt,
-                width=width,
-                height=height,
-                guidance_scale=guidance_scale,
-                num_inference_steps=num_inference_steps,
-                generator=generator,
-                output_type="pil",
-            ).images
+    observer.observe(resultGallery, { childList: true });
 
-        if images and IS_COLAB:
-            for image in images:
-                filepath = utils.save_image(image, metadata, OUTPUT_DIR)
-                logger.info(f"Image saved as {filepath} with metadata")
+    function updateHistory(imageSrc) {
+      const prompt = document.querySelector('#prompt textarea').value;
+      const option = document.createElement('option');
+      option.value = prompt;
+      option.textContent = prompt;
+      option.setAttribute('data-image', imageSrc);
 
-        # Update history after generation
-        history = gr.get_state("history") or []
-        history.insert(0, {"prompt": prompt, "image": images[0], "metadata": metadata})
-        gr.set_state("history", history[:10])  # Keep only the last 10 entries
+      historyDropdown.insertBefore(option, historyDropdown.firstChild);
 
-        return images, metadata, gr.update(choices=[h["prompt"] for h in history])
-    except Exception as e:
-        logger.exception(f"An error occurred: {e}")
-        raise
-    finally:
-        if use_upscaler:
-            del upscaler_pipe
-        pipe.scheduler = backup_scheduler
-        utils.free_memory()
-
-def get_random_prompt():
-    anime_characters = [
-        "Naruto Uzumaki", "Monkey D. Luffy", "Goku", "Eren Yeager", "Light Yagami",
-        "Lelouch Lamperouge", "Edward Elric", "Levi Ackerman", "Spike Spiegel",
-        "Sakura Haruno", "Mikasa Ackerman", "Asuka Langley Soryu", "Rem", "Megumin",
-        "Violet Evergarden"
-    ]
-    styles = ["pixel art", "stylized anime", "digital art", "watercolor", "sketch"]
-    scores = ["score_9", "score_8_up", "score_7_up"]
-    
-    character = random.choice(anime_characters)
-    style = random.choice(styles)
-    score = ", ".join(random.sample(scores, k=3))
-    
-    return f"{score}, {character}, {style}, show accurate"
+      if (historyDropdown.children.length > 10) {
+        historyDropdown.removeChild(historyDropdown.lastChild);
+      }
+    }
 
-if torch.cuda.is_available():
-    pipe = load_pipeline(MODEL)
-    logger.info("Loaded on Device!")
-else:
-    pipe = None
+    historyDropdown.addEventListener('change', (event) => {
+      const selectedOption = event.target.selectedOptions[0];
+      const imageSrc = selectedOption.getAttribute('data-image');
+      if (imageSrc) {
+        const img = document.createElement('img');
+        img.src = imageSrc;
+        resultGallery.innerHTML = '';
+        resultGallery.appendChild(img);
+      }
+    });
+  }
+});
+</script>
+"""
 
 with gr.Blocks(css="style.css") as demo:
+    gr.HTML(js_code)  # Add the JavaScript code to the interface
+    
     title = gr.HTML(
         f"""<h1><span>{DESCRIPTION}</span></h1>""",
         elem_id="title",
@@ -338,7 +193,7 @@ with gr.Blocks(css="style.css") as demo:
         clear_button = gr.Button("Clear All")
         random_prompt_button = gr.Button("Random Prompt")
 
-    history_dropdown = gr.Dropdown(label="Generation History", choices=[], interactive=True)
+    history_dropdown = gr.Dropdown(label="Generation History", choices=[], interactive=True, elem_id="history-dropdown")
     
     with gr.Accordion(label="Generation Parameters", open=False):
         gr_metadata = gr.JSON(label="Metadata", show_label=False)
@@ -451,4 +306,4 @@ with gr.Blocks(css="style.css") as demo:
         outputs=prompt
     )
 
-demo.queue(max_size=20).launch(debug=IS_COLAB, share=IS_COLAB)
+demo.queue(max_size=20).launch(debug=IS_COLAB, share=IS_COLAB)