Update app.py

app.py

@@ -1,358 +1,195 @@
-import spaces
 import gradio as gr
-import torch
-from PIL import Image
-from diffusers import DiffusionPipeline
-import random
-import uuid
-from typing import Union, List, Optional
 import numpy as np
-import time
-import zipfile
-import os
-import requests
-from urllib.parse import urlparse
-import tempfile
-import shutil
-
-# Description for the app
-DESCRIPTION = """## Qwen Image Hpc/."""
-
-# Helper functions
-def save_image(img):
-    unique_name = str(uuid.uuid4()) + ".png"
-    img.save(unique_name)
-    return unique_name
+import random
+import torch
+import spaces
 
-def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-    return seed
+from PIL import Image
+from diffusers import FlowMatchEulerDiscreteScheduler
+from optimization import optimize_pipeline_
+from qwenimage.pipeline_qwenimage_edit_plus import QwenImageEditPlusPipeline
+from qwenimage.transformer_qwenimage import QwenImageTransformer2DModel
+from qwenimage.qwen_fa3_processor import QwenDoubleStreamAttnProcessorFA3
+from gradio_imageslider import ImageSlider
+from gradio.themes import Soft
+from gradio.themes.utils import colors, fonts, sizes
+
+# --- Custom Theme Definition ---
+colors.orange_red = colors.Color(
+    name="orange_red",
+    c50="#FFF0E5", c100="#FFE0CC", c200="#FFC299", c300="#FFA366",
+    c400="#FF8533", c500="#FF4500", c600="#E63E00", c700="#CC3700",
+    c800="#B33000", c900="#992900", c950="#802200",
+)
+
+class OrangeRedTheme(Soft):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        super().set(
+            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_text_color="white",
+        )
 
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 2048
+orange_red_theme = OrangeRedTheme()
 
-# Load Qwen/Qwen-Image pipeline
-dtype = torch.bfloat16
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 # --- Model Loading ---
-pipe_qwen = DiffusionPipeline.from_pretrained("Qwen/Qwen-Image", torch_dtype=dtype).to(device)
-
-# Aspect ratios
-aspect_ratios = {
-    "1:1": (1328, 1328),
-    "16:9": (1664, 928),
-    "9:16": (928, 1664),
-    "4:3": (1472, 1140),
-    "3:4": (1140, 1472)
-}
+dtype = torch.bfloat16
+device = "cuda" if torch.cuda.is_available() else "cpu"
 
-def load_lora_opt(pipe, lora_input):
-    lora_input = lora_input.strip()
-    if not lora_input:
-        return
+pipe = QwenImageEditPlusPipeline.from_pretrained("Qwen/Qwen-Image-Edit-2509",
+                                                transformer=QwenImageTransformer2DModel.from_pretrained("linoyts/Qwen-Image-Edit-Rapid-AIO",
+                                                                                                         subfolder='transformer',
+                                                                                                         torch_dtype=dtype,
+                                                                                                         device_map='cuda'), torch_dtype=dtype).to(device)
+# Load all LoRA adapters
+pipe.load_lora_weights("dx8152/Qwen-Edit-2509-Multiple-angles", weight_name="镜头转换.safetensors", adapter_name="angles")
+pipe.load_lora_weights("dx8152/Qwen-Image-Edit-2509-Light_restoration", weight_name="移除光影.safetensors", adapter_name="light_restoration")
+pipe.load_lora_weights("autoweeb/Qwen-Image-Edit-2509-Photo-to-Anime", weight_name="Qwen-Image-Edit-2509-Photo-to-Anime_000001000.safetensors", adapter_name="photo_to_anime")
+pipe.load_lora_weights("dx8152/Qwen-Image-Edit-2509-Relight", weight_name="Qwen-Edit-Relight.safetensors", adapter_name="relight")
 
-    # If it's just an ID like "author/model"
-    if "/" in lora_input and not lora_input.startswith("http"):
-        pipe.load_lora_weights(lora_input, adapter_name="default")
-        return
 
-    if lora_input.startswith("http"):
-        url = lora_input
+pipe.transformer.__class__ = QwenImageTransformer2DModel
+pipe.transformer.set_attn_processor(QwenDoubleStreamAttnProcessorFA3())
 
-        # Repo page (no blob/resolve)
-        if "huggingface.co" in url and "/blob/" not in url and "/resolve/" not in url:
-            repo_id = urlparse(url).path.strip("/")
-            pipe.load_lora_weights(repo_id, adapter_name="default")
-            return
+optimize_pipeline_(pipe, image=[Image.new("RGB", (1024, 1024)), Image.new("RGB", (1024, 1024))], prompt="prompt")
 
-        # Blob link → convert to resolve link
-        if "/blob/" in url:
-            url = url.replace("/blob/", "/resolve/")
 
-        # Download direct file
-        tmp_dir = tempfile.mkdtemp()
-        local_path = os.path.join(tmp_dir, os.path.basename(urlparse(url).path))
+MAX_SEED = np.iinfo(np.int32).max
 
-        try:
-            print(f"Downloading LoRA from {url}...")
-            resp = requests.get(url, stream=True)
-            resp.raise_for_status()
-            with open(local_path, "wb") as f:
-                for chunk in resp.iter_content(chunk_size=8192):
-                    f.write(chunk)
-            print(f"Saved LoRA to {local_path}")
-            pipe.load_lora_weights(local_path, adapter_name="default")
-        finally:
-            shutil.rmtree(tmp_dir, ignore_errors=True)
+@spaces.GPU
+def infer(input_image, prompt, lora_adapter, seed=42, randomize_seed=True, guidance_scale=1.0, steps=4, progress=gr.Progress(track_tqdm=True)):
+    """
+    Perform image editing based on the selected LoRA adapter and prompt.
+    """
+    if not input_image:
+        raise gr.Error("Please upload an image for editing.")
+
+    # Set the LoRA adapter based on user selection
+    if lora_adapter == "Multiple Angles":
+        pipe.set_adapters(["angles"], adapter_weights=[1.0])
+    elif lora_adapter == "Light Restoration":
+        pipe.set_adapters(["light_restoration"], adapter_weights=[1.0])
+    elif lora_adapter == "Photo to Anime":
+        pipe.set_adapters(["photo_to_anime"], adapter_weights=[1.0])
+    elif lora_adapter == "Relight":
+        pipe.set_adapters(["relight"], adapter_weights=[1.0])
 
-# Generation function for Qwen/Qwen-Image
-@spaces.GPU(duration=120)
-def generate_qwen(
-    prompt: str,
-    negative_prompt: str = "",
-    seed: int = 0,
-    width: int = 1024,
-    height: int = 1024,
-    guidance_scale: float = 4.0,
-    randomize_seed: bool = False,
-    num_inference_steps: int = 50,
-    num_images: int = 1,
-    zip_images: bool = False,
-    lora_input: str = "",
-    lora_scale: float = 1.0,
-    progress=gr.Progress(track_tqdm=True),
-):
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
-    generator = torch.Generator(device).manual_seed(seed)
-    
-    start_time = time.time()
-
-    current_adapters = pipe_qwen.get_list_adapters()
-    for adapter in current_adapters:
-        pipe_qwen.delete_adapters(adapter)
-    pipe_qwen.disable_lora()
 
-    use_lora = False
-    if lora_input and lora_input.strip() != "":
-        load_lora_opt(pipe_qwen, lora_input)
-        pipe_qwen.set_adapters(["default"], adapter_weights=[lora_scale])
-        use_lora = True
-    
-    images = pipe_qwen(
-        prompt=prompt,
-        negative_prompt=negative_prompt if negative_prompt else "",
-        height=height,
-        width=width,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        num_images_per_prompt=num_images,
-        generator=generator,
-        output_type="pil",
-    ).images
+    generator = torch.Generator(device=device).manual_seed(seed)
     
-    end_time = time.time()
-    duration = end_time - start_time
-    
-    image_paths = [save_image(img) for img in images]
-    zip_path = None
-    if zip_images:
-        zip_name = str(uuid.uuid4()) + ".zip"
-        with zipfile.ZipFile(zip_name, 'w') as zipf:
-            for i, img_path in enumerate(image_paths):
-                zipf.write(img_path, arcname=f"Img_{i}.png")
-        zip_path = zip_name
-
-    # Clean up adapters
-    current_adapters = pipe_qwen.get_list_adapters()
-    for adapter in current_adapters:
-        pipe_qwen.delete_adapters(adapter)
-    pipe_qwen.disable_lora()
-    
-    return image_paths, seed, f"{duration:.2f}", zip_path
+    original_image = input_image.copy().convert("RGB")
 
-# Wrapper function to handle UI logic
-@spaces.GPU(duration=120)
-def generate(
-    prompt: str,
-    negative_prompt: str,
-    use_negative_prompt: bool,
-    seed: int,
-    width: int,
-    height: int,
-    guidance_scale: float,
-    randomize_seed: bool,
-    num_inference_steps: int,
-    num_images: int,
-    zip_images: bool,
-    lora_input: str,
-    lora_scale: float,
-    progress=gr.Progress(track_tqdm=True),
-):
-    final_negative_prompt = negative_prompt if use_negative_prompt else ""
-    return generate_qwen(
+    result = pipe(
+        image=original_image,
         prompt=prompt,
-        negative_prompt=final_negative_prompt,
-        seed=seed,
-        width=width,
-        height=height,
-        guidance_scale=guidance_scale,
-        randomize_seed=randomize_seed,
-        num_inference_steps=num_inference_steps,
-        num_images=num_images,
-        zip_images=zip_images,
-        lora_input=lora_input,
-        lora_scale=lora_scale,
-        progress=progress,
-    )
-
-# Examples
-examples = [
-    "A decadent slice of layered chocolate cake on a ceramic plate with a drizzle of chocolate syrup and powdered sugar dusted on top. photographed from a slightly low angle with high resolution, natural soft lighting, rich contrast, shallow depth of field, and professional color grading to highlight the dessert’s textures --ar 85:128 --v 6.0 --style raw",
-    "A beautifully decorated round chocolate birthday cake with rich chocolate frosting and elegant piping, topped with the name 'Qwen' written in white icing. placed on a wooden cake stand with scattered chocolate shavings around, softly lit with natural light, high resolution, professional food photography, clean background, no branding --ar 85:128 --v 6.0 --style raw",
-    "Realistic still life photography style: A single, fresh apple, resting on a clean, soft-textured surface. The apple is slightly off-center, softly backlit to highlight its natural gloss and subtle color gradients—deep crimson red blending into light golden hues. Fine details such as small blemishes, dew drops, and a few light highlights enhance its lifelike appearance. A shallow depth of field gently blurs the neutral background, drawing full attention to the apple. Hyper-detailed 8K resolution, studio lighting, photorealistic render, emphasizing texture and form.",
-    "一幅精致细腻的工笔画，画面中心是一株蓬勃生长的红色牡丹，花朵繁茂，既有盛开的硕大花瓣，也有含苞待放的花蕾，层次丰富，色彩艳丽而不失典雅。牡丹枝叶舒展，叶片浓绿饱满，脉络清晰可见，与红花相映成趣。一只蓝紫色蝴蝶仿佛被画中花朵吸引，停驻在画面中央的一朵盛开牡丹上，流连忘返，蝶翼轻展，细节逼真，仿佛随时会随风飞舞。整幅画作笔触工整严谨，色彩浓郁鲜明，展现出中国传统工笔画的精妙与神韵，画面充满生机与灵动之感。",
-    "A young girl wearing school uniform stands in a classroom, writing on a chalkboard. The text Introducing Qwen-Image, a foundational image generation model that excels in complex text rendering and precise image editing appears in neat white chalk at the center of the blackboard. Soft natural light filters through windows, casting gentle shadows. The scene is rendered in a realistic photography style with fine details, shallow depth of field, and warm tones. The girl's focused expression and chalk dust in the air add dynamism. Background elements include desks and educational posters, subtly blurred to emphasize the central action. Ultra-detailed 32K resolution, DSLR-quality, soft bokeh effect, documentary-style composition",
-    "手绘风格的水循环示意图，整体画面呈现出一幅生动形象的水循环过程图解。画面中央是一片起伏的山脉和山谷，山谷中流淌着一条清澈的河流，河流最终汇入一片广阔的海洋。山体和陆地上绘制有绿色植被。画面下方为地下水层，用蓝色渐变色块表现，与地表水形成层次分明的空间关系。太阳位于画面右上角，促使地表水蒸发，用上升的曲线箭头表示蒸发过程。云朵漂浮在空中，由白色棉絮状绘制而成，部分云层厚重，表示水汽凝结成雨，用向下箭头连接表示降雨过程。雨水以蓝色线条和点状符号表示，从云中落下，补充河流与地下水。整幅图以卡通手绘风格呈现，线条柔和，色彩明亮，标注清晰。背景为浅黄色纸张质感，带有轻微的手绘纹理。"
-]
-
-css = '''
-.gradio-container {
-    max-width: 590px !important;
-    margin: 0 auto !important;
-}
-h1 {
-    text-align: center;
-}
-footer {
-    visibility: hidden;
+        height=original_image.size[1],
+        width=original_image.size[0],
+        num_inference_steps=steps,
+        generator=generator,
+        true_cfg_scale=guidance_scale,
+        num_images_per_prompt=1,
+    ).images[0]
+
+    return (original_image, result), seed, gr.Button(visible=True)
+
+@spaces.GPU
+def infer_example(input_image, prompt, lora_adapter):
+    """
+    Wrapper function for gr.Examples to call the main infer logic for the slider.
+    """
+    (original_image, generated_image), seed, _ = infer(input_image, prompt, lora_adapter, upscale_image=False)
+    return (original_image, generated_image), seed
+
+# --- UI ---
+css = """
+#col-container {
+    margin: 0 auto;
+    max-width: 960px;
 }
-'''
+#main-title h1 {font-size: 2.1em !important;}
+"""
+
+with gr.Blocks(css=css, theme=orange_red_theme) as demo:
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown("# **Qwen-Image-Edit-2509-LoRAs-Fast**", elem_id="main-title")
+        gr.Markdown("Image manipulation with Qwen Image Edit 2509 and various LoRA adapters.")
 
-# Gradio interface
-with gr.Blocks(css=css, theme="bethecloud/storj_theme", delete_cache=(240, 240)) as demo:
-    gr.Markdown(DESCRIPTION)
-    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.Gallery(label="Result", columns=1, show_label=False, preview=True)
-    
-    with gr.Row():
-        aspect_ratio = gr.Dropdown(
-            label="Aspect Ratio",
-            choices=list(aspect_ratios.keys()),
-            value="1:1",
-        )
-        lora = gr.Textbox(label="qwen image lora (optional)", placeholder="enter the path...")
-    with gr.Accordion("Additional Options", open=False):
-        use_negative_prompt = gr.Checkbox(
-            label="Use negative prompt",
-            value=True,
-            visible=True
-        )
-        negative_prompt = gr.Text(
-            label="Negative prompt",
-            max_lines=1,
-            placeholder="Enter a negative prompt",
-            value="text, watermark, copyright, blurry, low resolution",
-            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():
-            width = gr.Slider(
-                label="Width",
-                minimum=512,
-                maximum=2048,
-                step=64,
-                value=1024,
-            )
-            height = gr.Slider(
-                label="Height",
-                minimum=512,
-                maximum=2048,
-                step=64,
-                value=1024,
-            )
-        guidance_scale = gr.Slider(
-            label="Guidance Scale",
-            minimum=0.0,
-            maximum=20.0,
-            step=0.1,
-            value=4.0,
-        )
-        num_inference_steps = gr.Slider(
-            label="Number of inference steps",
-            minimum=1,
-            maximum=100,
-            step=1,
-            value=50,
-        )
-        num_images = gr.Slider(
-            label="Number of images",
-            minimum=1,
-            maximum=5,
-            step=1,
-            value=1,
+            with gr.Column():
+                input_image = gr.Image(label="Upload Image", type="pil", height="300")
+                with gr.Row():
+                    prompt = gr.Text(
+                        label="Edit Prompt",
+                        show_label=False,
+                        max_lines=1,
+                        placeholder="Enter your prompt for editing",
+                        container=False,
+                    )
+                    run_button = gr.Button("Run", variant="primary", scale=0)
+
+                lora_adapter = gr.Dropdown(
+                    label="Choose LoRA Adapter",
+                    choices=["Multiple Angles", "Light Restoration", "Photo to Anime", "Relight"],
+                    value="Multiple Angles"
+                )
+
+                with gr.Accordion("Advanced Settings", open=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,
+                        maximum=10,
+                        step=0.1,
+                        value=1.0,
+                    )
+                    steps = gr.Slider(
+                        label="Steps",
+                        minimum=1,
+                        maximum=30,
+                        value=4,
+                        step=1
+                    )
+
+            with gr.Column():
+                output_slider = ImageSlider(label="Before / After", show_label=False, interactive=False)
+                reuse_button = gr.Button("Reuse this image", visible=False)
+
+        gr.Examples(
+            examples=[
+                ["examples/sea.png", "Rotate the camera 90 degrees to the left.", "Multiple Angles"],
+                ["examples/shadow.jpg", "Remove shadows and relight the image using soft lighting.", "Light Restoration"],
+                ["examples/girl.jpg", "transform into anime", "Photo to Anime"],
+                ["examples/dark.jpg", "Relight the image using soft, diffused lighting that simulates sunlight filtering through curtains.", "Relight"],
+            ],
+            inputs=[input_image, prompt, lora_adapter],
+            outputs=[output_slider, seed],
+            fn=infer,
+            cache_examples="lazy",
+            label="Examples"
         )
-        zip_images = gr.Checkbox(label="Zip generated images", value=False)
-        with gr.Row(): 
-            lora_scale = gr.Slider(
-                label="LoRA Scale",
-                minimum=0,
-                maximum=2,
-                step=0.01,
-                value=1,
-            )
-        
-        gr.Markdown("### Output Information")
-        seed_display = gr.Textbox(label="Seed used", interactive=False)
-        generation_time = gr.Textbox(label="Generation time (seconds)", interactive=False)
-        zip_file = gr.File(label="Download ZIP")
-
-    # Update aspect ratio
-    def set_dimensions(ar):
-        w, h = aspect_ratios[ar]
-        return gr.update(value=w), gr.update(value=h)
-    
-    aspect_ratio.change(
-        fn=set_dimensions,
-        inputs=aspect_ratio,
-        outputs=[width, height]
-    )
-
-    # Negative prompt visibility
-    use_negative_prompt.change(
-        fn=lambda x: gr.update(visible=x),
-        inputs=use_negative_prompt,
-        outputs=negative_prompt
-    )
 
-    # Run button and prompt submit
     gr.on(
-        triggers=[prompt.submit, run_button.click],
-        fn=generate,
-        inputs=[
-            prompt,
-            negative_prompt,
-            use_negative_prompt,
-            seed,
-            width,
-            height,
-            guidance_scale,
-            randomize_seed,
-            num_inference_steps,
-            num_images,
-            zip_images,
-            lora,
-            lora_scale,
-        ],
-        outputs=[result, seed_display, generation_time, zip_file],
-        api_name="run",
+        triggers=[run_button.click, prompt.submit],
+        fn=infer,
+        inputs=[input_image, prompt, lora_adapter, seed, randomize_seed, guidance_scale, steps],
+        outputs=[output_slider, seed, reuse_button]
     )
 
-    # Examples
-    gr.Examples(
-        examples=examples,
-        inputs=prompt,
-        outputs=[result, seed_display, generation_time, zip_file],
-        fn=generate,
-        cache_examples=False,
+    reuse_button.click(
+        fn=lambda images: images[1] if isinstance(images, (list, tuple)) and len(images) > 1 else images,
+        inputs=[output_slider],
+        outputs=[input_image]
     )
 
-if __name__ == "__main__":
-    demo.queue(max_size=50).launch(share=False, mcp_server=True, ssr_mode=False, debug=True, show_error=True)
+demo.launch(mcp_server=True, ssr_mode=False, show_error=True)