update app

app.py

@@ -309,316 +309,5 @@ with gr.Blocks() as demo:
         outputs=[rerun_output, seed]
     )
 
-if __name__ == "__main__":
-    demo.queue(max_size=30).launch(css=css, theme=deep_sky_blue_theme, mcp_server=True, ssr_mode=False, show_error=True)import os
-import gc
-import gradio as gr
-import numpy as np
-import spaces
-import torch
-import random
-import uuid
-import tempfile
-from PIL import Image
-from typing import Iterable
-from gradio.themes import Soft
-from gradio.themes.utils import colors, fonts, sizes
-
-# Rerun imports
-import rerun as rr
-from gradio_rerun import Rerun
-
-colors.deep_sky_blue = colors.Color(
-    name="deep_sky_blue",
-    c50="#E0F7FF",
-    c100="#B3EAFF",
-    c200="#80DFFF",
-    c300="#4DD2FF",
-    c400="#1AC6FF",
-    c500="#00BFFF",
-    c600="#0099CC",
-    c700="#007399",
-    c800="#004C66",
-    c900="#002633",
-    c950="#00131A",
-)
-
-class DeepSkyBlueTheme(Soft):
-    def __init__(
-        self,
-        *,
-        primary_hue: colors.Color | str = colors.gray,
-        secondary_hue: colors.Color | str = colors.deep_sky_blue,
-        neutral_hue: colors.Color | str = colors.slate,
-        text_size: sizes.Size | str = sizes.text_lg,
-        font: fonts.Font | str | Iterable[fonts.Font | str] = (
-            fonts.GoogleFont("Outfit"), "Arial", "sans-serif",
-        ),
-        font_mono: fonts.Font | str | Iterable[fonts.Font | str] = (
-            fonts.GoogleFont("IBM Plex Mono"), "ui-monospace", "monospace",
-        ),
-    ):
-        super().__init__(
-            primary_hue=primary_hue,
-            secondary_hue=secondary_hue,
-            neutral_hue=neutral_hue,
-            text_size=text_size,
-            font=font,
-            font_mono=font_mono,
-        )
-        super().set(
-            background_fill_primary="*primary_50",
-            background_fill_primary_dark="*primary_900",
-            body_background_fill="linear-gradient(135deg, *primary_100, #E0F7FF)",
-            body_background_fill_dark="linear-gradient(135deg, *primary_900, *primary_800)",
-            button_primary_text_color="white",
-            button_primary_text_color_hover="white",
-            button_primary_background_fill="linear-gradient(90deg, *secondary_500, *secondary_600)",
-            button_primary_background_fill_hover="linear-gradient(90deg, *secondary_600, *secondary_700)",
-            button_primary_background_fill_dark="linear-gradient(90deg, *secondary_600, *secondary_700)",
-            button_primary_background_fill_hover_dark="linear-gradient(90deg, *secondary_500, *secondary_600)",
-            button_secondary_text_color="black",
-            button_secondary_text_color_hover="white",
-            button_secondary_background_fill="linear-gradient(90deg, *primary_300, *primary_300)",
-            button_secondary_background_fill_hover="linear-gradient(90deg, *primary_400, *primary_400)",
-            button_secondary_background_fill_dark="linear-gradient(90deg, *primary_500, *primary_600)",
-            button_secondary_background_fill_hover_dark="linear-gradient(90deg, *primary_500, *primary_500)",
-            slider_color="*secondary_500",
-            slider_color_dark="*secondary_600",
-            block_title_text_weight="600",
-            block_border_width="3px",
-            block_shadow="*shadow_drop_lg",
-            button_primary_shadow="*shadow_drop_lg",
-            button_large_padding="11px",
-            color_accent_soft="*primary_100",
-            block_label_background_fill="*primary_200",
-        )
-
-deep_sky_blue_theme = DeepSkyBlueTheme()
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-print("CUDA_VISIBLE_DEVICES=", os.environ.get("CUDA_VISIBLE_DEVICES"))
-print("torch.__version__ =", torch.__version__)
-print("torch.version.cuda =", torch.version.cuda)
-print("Using device:", device)
-
-from diffusers import FlowMatchEulerDiscreteScheduler
-from qwenimage.pipeline_qwenimage_edit_plus import QwenImageEditPlusPipeline
-from qwenimage.transformer_qwenimage import QwenImageTransformer2DModel
-from qwenimage.qwen_fa3_processor import QwenDoubleStreamAttnProcessorFA3
-
-dtype = torch.bfloat16
-
-pipe = QwenImageEditPlusPipeline.from_pretrained(
-    "Qwen/Qwen-Image-Edit-2511",
-    transformer=QwenImageTransformer2DModel.from_pretrained(
-        "linoyts/Qwen-Image-Edit-Rapid-AIO",
-        subfolder='transformer',
-        torch_dtype=dtype,
-        device_map='cuda'
-    ),
-    torch_dtype=dtype
-).to(device)
-
-try:
-    pipe.transformer.set_attn_processor(QwenDoubleStreamAttnProcessorFA3())
-    print("Flash Attention 3 Processor set successfully.")
-except Exception as e:
-    print(f"Warning: Could not set FA3 processor: {e}")
-
-MAX_SEED = np.iinfo(np.int32).max
-TMP_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'tmp_rerun')
-os.makedirs(TMP_DIR, exist_ok=True)
-
-ADAPTER_SPECS = {
-    "Multiple-Angles": {
-        "repo": "dx8152/Qwen-Edit-2509-Multiple-angles",
-        "weights": "镜头转换.safetensors",
-        "adapter_name": "multiple-angles"
-    }
-}
-
-LOADED_ADAPTERS = set()
-
-def update_dimensions_on_upload(image):
-    if image is None:
-        return 1024, 1024
-    
-    original_width, original_height = image.size
-    
-    if original_width > original_height:
-        new_width = 1024
-        aspect_ratio = original_height / original_width
-        new_height = int(new_width * aspect_ratio)
-    else:
-        new_height = 1024
-        aspect_ratio = original_width / original_height
-        new_width = int(new_height * aspect_ratio)
-        
-    new_width = (new_width // 8) * 8
-    new_height = (new_height // 8) * 8
-    
-    return new_width, new_height
-
-@spaces.GPU
-def infer(
-    input_image,
-    prompt,
-    lora_adapter,
-    seed,
-    randomize_seed,
-    guidance_scale,
-    steps,
-    progress=gr.Progress(track_tqdm=True)
-):
-    gc.collect()
-    torch.cuda.empty_cache()
-
-    if input_image is None:
-        raise gr.Error("Please upload an image to edit.")
-
-    spec = ADAPTER_SPECS.get(lora_adapter)
-    if not spec:
-        raise gr.Error(f"Configuration not found for: {lora_adapter}")
-
-    adapter_name = spec["adapter_name"]
-
-    if adapter_name not in LOADED_ADAPTERS:
-        print(f"--- Downloading and Loading Adapter: {lora_adapter} ---")
-        try:
-            pipe.load_lora_weights(
-                spec["repo"], 
-                weight_name=spec["weights"], 
-                adapter_name=adapter_name
-            )
-            LOADED_ADAPTERS.add(adapter_name)
-        except Exception as e:
-            raise gr.Error(f"Failed to load adapter {lora_adapter}: {e}")
-    else:
-        print(f"--- Adapter {lora_adapter} is already loaded. ---")
-
-    pipe.set_adapters([adapter_name], adapter_weights=[1.0])
-
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-
-    generator = torch.Generator(device=device).manual_seed(seed)
-    negative_prompt = "worst quality, low quality, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, jpeg artifacts, signature, watermark, username, blurry"
-
-    original_image = input_image.convert("RGB")
-    width, height = update_dimensions_on_upload(original_image)
-
-    try:
-        progress(0.4, desc="Generating Image...")
-        result_image = pipe(
-            image=original_image,
-            prompt=prompt,
-            negative_prompt=negative_prompt,
-            height=height,
-            width=width,
-            num_inference_steps=steps,
-            generator=generator,
-            true_cfg_scale=guidance_scale,
-        ).images[0]
-        
-        # --- Rerun Visualization Logic ---
-        progress(0.9, desc="Preparing Rerun Visualization...")
-        
-        run_id = str(uuid.uuid4())
-        rec = rr.new_recording(application_id="Qwen-Image-Edit", recording_id=run_id)
-        
-        # Log images to Rerun
-        # We convert PIL images to numpy arrays for Rerun
-        rec.log("images/original", rr.Image(np.array(original_image)))
-        rec.log("images/edited", rr.Image(np.array(result_image)))
-        
-        # Save RRD
-        rrd_path = os.path.join(TMP_DIR, f"{run_id}.rrd")
-        rec.save(rrd_path)
-        
-        return rrd_path, seed
-
-    except Exception as e:
-        raise e
-    finally:
-        gc.collect()
-        torch.cuda.empty_cache()
-
-@spaces.GPU
-def infer_example(input_image, prompt, lora_adapter):
-    if input_image is None:
-        return None, 0
-    
-    input_pil = input_image.convert("RGB")
-    guidance_scale = 1.0
-    steps = 4
-    # Call main infer but ignore progress for examples if needed
-    result_rrd, seed = infer(input_pil, prompt, lora_adapter, 0, True, guidance_scale, steps)
-    return result_rrd, seed
-
-css="""
-#col-container {
-    margin: 0 auto;
-    max-width: 960px;
-}
-#main-title h1 {font-size: 2.1em !important;}
-"""
-
-with gr.Blocks() as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown("# **Qwen-Image-Edit-2511-LoRAs-Fast**", elem_id="main-title")
-        gr.Markdown("Perform diverse image edits using specialized [LoRA](https://huggingface.co/models?other=base_model:adapter:Qwen/Qwen-Image-Edit-2511) adapters for the [Qwen-Image-Edit](https://huggingface.co/Qwen/Qwen-Image-Edit-2511) model.")
-
-        with gr.Row(equal_height=True):
-            with gr.Column():
-                input_image = gr.Image(label="Upload Image", type="pil", height=290)
-                
-                prompt = gr.Text(
-                    label="Edit Prompt",
-                    show_label=True,
-                    placeholder="e.g., transform into anime..",
-                )
-
-                run_button = gr.Button("Edit Image", variant="primary")
-
-            with gr.Column():
-                # Replaced standard Image with Rerun Viewer
-                rerun_output = Rerun(
-                    label="Rerun Visualization", 
-                    height=353
-                )
-                
-                with gr.Row():
-                    lora_adapter = gr.Dropdown(
-                        label="Choose Editing Style",
-                        choices=list(ADAPTER_SPECS.keys()),
-                        value="Multiple-Angles"
-                    )
-                with gr.Accordion("Advanced Settings", open=False, 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)
-                    guidance_scale = gr.Slider(label="Guidance Scale", minimum=1.0, maximum=10.0, step=0.1, value=1.0)
-                    steps = gr.Slider(label="Inference Steps", minimum=1, maximum=50, step=1, value=4)
-        
-        gr.Examples(
-            examples=[
-                ["examples/A.jpeg", "Rotate the camera 45 degrees to the right.", "Multiple-Angles"],
-            ],
-            inputs=[input_image, prompt, lora_adapter],
-            outputs=[rerun_output, seed],
-            fn=infer_example,
-            cache_examples=False,
-            label="Examples"
-        )
-        
-        gr.Markdown("[*](https://huggingface.co/spaces/prithivMLmods/Qwen-Image-Edit-2511-LoRAs-Fast)This is still an experimental Space for Qwen-Image-Edit-2511; you can use [Qwen-Image-Edit-2509-LoRAs-Fast](https://huggingface.co/spaces/prithivMLmods/Qwen-Image-Edit-2509-LoRAs-Fast) instead. This Space will be updated soon.")
-
-    run_button.click(
-        fn=infer,
-        inputs=[input_image, prompt, lora_adapter, seed, randomize_seed, guidance_scale, steps],
-        outputs=[rerun_output, seed]
-    )
-
 if __name__ == "__main__":
     demo.queue(max_size=30).launch(css=css, theme=deep_sky_blue_theme, mcp_server=True, ssr_mode=False, show_error=True)