Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import random
+import torch
+import spaces
+
+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
+
+import math
+from huggingface_hub import hf_hub_download
+from safetensors.torch import load_file
+
+from PIL import Image
+import os
+
+
+# --- Model Loading ---
+dtype = torch.bfloat16
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+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)
+
+
+
+pipe.load_lora_weights("tlennon-ie/QwenEdit2509-FlatLogColor", 
+                       weight_name="QwenEdit2509-FlatLogColor.safetensors",
+                       adapter_name="FlatLogColor")
+pipe.set_adapters(["FlatLogColor"], adapter_weights=[1.])
+pipe.fuse_lora(adapter_names=["FlatLogColor"], lora_scale=1.0)
+pipe.unload_lora_weights()
+
+pipe.transformer.__class__ = QwenImageTransformer2DModel
+pipe.transformer.set_attn_processor(QwenDoubleStreamAttnProcessorFA3())
+
+optimize_pipeline_(pipe, image=[Image.new("RGB", (1024, 1024)), Image.new("RGB", (1024, 1024))], prompt="prompt")
+
+MAX_SEED = np.iinfo(np.int32).max
+
+@spaces.GPU
+def convert_to_anime(
+    image,
+    seed,
+    randomize_seed,
+    true_guidance_scale,
+    num_inference_steps,
+    height,
+    width,
+    progress=gr.Progress(track_tqdm=True)
+):
+    prompt = "flatcolor Desaturate the image and lower the contrast to create a flat, ungraded look similar to a camera's log profile. Preserve details in the highlights and shadows."
+    
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    generator = torch.Generator(device=device).manual_seed(seed)
+
+    pil_images = []
+    if image is not None:
+        if isinstance(image, Image.Image):
+            pil_images.append(image.convert("RGB"))
+        elif hasattr(image, "name"):
+            pil_images.append(Image.open(image.name).convert("RGB"))
+
+    if len(pil_images) == 0:
+        raise gr.Error("Please upload an image first.")
+
+    result = pipe(
+        image=pil_images,
+        prompt=prompt,
+        height=height if height != 0 else None,
+        width=width if width != 0 else None,
+        num_inference_steps=num_inference_steps,
+        generator=generator,
+        true_cfg_scale=true_guidance_scale,
+        num_images_per_prompt=1,
+    ).images[0]
+
+    return result, seed
+
+
+# --- UI ---
+css = '''
+#col-container { 
+    max-width: 900px; 
+    margin: 0 auto; 
+    padding: 2rem;
+    font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Helvetica, Arial, sans-serif;
+}
+.gradio-container.light {
+    background: linear-gradient(to bottom, #f5f5f7, #ffffff);
+}
+.gradio-container.dark {
+    background: linear-gradient(to bottom, #1a1a1a, #0d0d0d);
+}
+#title {
+    text-align: center;
+    font-size: 2.5rem;
+    font-weight: 600;
+    margin-bottom: 0.5rem;
+}
+.light #title {
+    color: #1d1d1f;
+}
+.dark #title {
+    color: #f5f5f7;
+}
+#description {
+    text-align: center;
+    font-size: 1.1rem;
+    margin-bottom: 2rem;
+}
+.light #description {
+    color: #6e6e73;
+}
+.dark #description {
+    color: #a1a1a6;
+}
+.light #description a {
+    color: #0071e3;
+}
+.dark #description a {
+    color: #2997ff;
+}
+.image-container {
+    border-radius: 18px;
+    overflow: hidden;
+}
+.light .image-container {
+    box-shadow: 0 4px 6px rgba(0, 0, 0, 0.07);
+}
+.dark .image-container {
+    box-shadow: 0 4px 6px rgba(255, 255, 255, 0.1);
+}
+#convert-btn {
+    background: linear-gradient(180deg, #0071e3 0%, #0077ed 100%);
+    border: none;
+    border-radius: 12px;
+    color: white;
+    font-size: 1.1rem;
+    font-weight: 500;
+    padding: 0.75rem 2rem;
+    transition: all 0.3s ease;
+}
+#convert-btn:hover {
+    transform: translateY(-2px);
+    box-shadow: 0 8px 16px rgba(0, 113, 227, 0.3);
+}
+'''
+
+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)
+        
+    # Ensure dimensions are multiples of 8
+    new_width = (new_width // 8) * 8
+    new_height = (new_height // 8) * 8
+    
+    return new_width, new_height
+
+
+with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo:
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown("# 🎨 QwenEdit2509-FlatLogColor ", elem_id="title")
+        gr.Markdown(
+            """
+            Turn images into FLAT/LOG style for Pro Color Grading workflows✨ with the <a href='https://huggingface.co/tlennon-ie/QwenEdit2509-FlatLogColor' target='_blank' style='color: #0071e3; text-decoration: none; font-weight: 500;'>tlennon-ie/QwenEdit2509-FlatLogColor</a> model
+            <br>
+            <div style='text-align: center; margin-top: 1rem;'>
+                <a href='https://huggingface.co/spaces/akhaliq/anycoder' target='_blank' style='color: #0071e3; text-decoration: none; font-weight: 500;'>Built with anycoder</a>
+            </div>
+            """,
+            elem_id="description"
+        )
+
+        with gr.Row():
+            with gr.Column(scale=1):
+                image = gr.Image(
+                    label="Upload Photo", 
+                    type="pil",
+                    elem_classes="image-container"
+                )
+                
+                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)
+                    true_guidance_scale = gr.Slider(label="Guidance Scale", minimum=1.0, maximum=10.0, step=0.1, value=1.0)
+                    num_inference_steps = gr.Slider(label="Inference Steps", minimum=1, maximum=40, step=1, value=4)
+                    height = gr.Slider(label="Height", minimum=256, maximum=2048, step=8, value=1024, visible=False)
+                    width = gr.Slider(label="Width", minimum=256, maximum=2048, step=8, value=1024, visible=False)
+                
+                convert_btn = gr.Button("Turn to Flat/LOG Profile", variant="primary", elem_id="convert-btn", size="lg")
+
+            with gr.Column(scale=1):
+                result = gr.Image(
+                    label="Enhanced Result", 
+                    interactive=False,
+                    elem_classes="image-container"
+                )
+    
+    inputs = [
+        image, seed, randomize_seed, true_guidance_scale, 
+        num_inference_steps, height, width
+    ]
+    outputs = [result, seed]
+
+    # Convert button click
+    convert_btn.click(
+        fn=convert_to_anime, 
+        inputs=inputs, 
+        outputs=outputs
+    )
+
+    # Image upload triggers dimension update
+    image.upload(
+        fn=update_dimensions_on_upload,
+        inputs=[image],
+        outputs=[width, height]
+    )
+
+demo.launch()