| | import os |
| | import gc |
| | import gradio as gr |
| | import numpy as np |
| | import spaces |
| | import torch |
| | import random |
| | from PIL import Image |
| | from typing import Iterable |
| | from gradio.themes import Soft |
| | from gradio.themes.utils import colors, fonts, sizes |
| |
|
| | 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, |
| | *, |
| | primary_hue: colors.Color | str = colors.gray, |
| | secondary_hue: colors.Color | str = colors.orange_red, |
| | 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_200, *primary_100)", |
| | 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", |
| | ) |
| |
|
| | orange_red_theme = OrangeRedTheme() |
| |
|
| | 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("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-2509", |
| | transformer=QwenImageTransformer2DModel.from_pretrained( |
| | "prithivMLmods/Qwen-Image-Edit-Rapid-AIO-V19", |
| | |
| | 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 |
| |
|
| | ADAPTER_SPECS = { |
| | "Qwen-Image-Edit-2511-Object-Adder": { |
| | "repo": "prithivMLmods/Qwen-Image-Edit-2511-Object-Adder", |
| | "weights": "Qwen-Image-Edit-2511-Object-Adder.safetensors", |
| | "adapter_name": "object-adder" |
| | }, |
| | "Qwen-Image-Edit-2511-Object-Remover": { |
| | "repo": "prithivMLmods/Qwen-Image-Edit-2511-Object-Remover", |
| | "weights": "Qwen-Image-Edit-2511-Object-Remover.safetensors", |
| | "adapter_name": "object-remover" |
| | }, |
| | "QIE-2511-Object-Remover-v2": { |
| | "repo": "prithivMLmods/QIE-2511-Object-Remover-v2", |
| | "weights": "Qwen-Image-Edit-2511-Object-Remover-v2-9200.safetensors", |
| | "adapter_name": "object-remover" |
| | }, |
| | } |
| |
|
| | 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( |
| | images, |
| | prompt, |
| | lora_adapter, |
| | seed, |
| | randomize_seed, |
| | guidance_scale, |
| | steps, |
| | progress=gr.Progress(track_tqdm=True) |
| | ): |
| | gc.collect() |
| | torch.cuda.empty_cache() |
| |
|
| | if not images: |
| | raise gr.Error("Please upload at least one image to edit.") |
| |
|
| | pil_images = [] |
| | if images is not None: |
| | for item in images: |
| | try: |
| | if isinstance(item, tuple) or isinstance(item, list): |
| | path_or_img = item[0] |
| | else: |
| | path_or_img = item |
| |
|
| | if isinstance(path_or_img, str): |
| | pil_images.append(Image.open(path_or_img).convert("RGB")) |
| | elif isinstance(path_or_img, Image.Image): |
| | pil_images.append(path_or_img.convert("RGB")) |
| | else: |
| | pil_images.append(Image.open(path_or_img.name).convert("RGB")) |
| | except Exception as e: |
| | print(f"Skipping invalid image item: {e}") |
| | continue |
| |
|
| | if not pil_images: |
| | raise gr.Error("Could not process uploaded images.") |
| |
|
| | 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" |
| |
|
| | width, height = update_dimensions_on_upload(pil_images[0]) |
| |
|
| | try: |
| | result_image = pipe( |
| | image=pil_images, |
| | prompt=prompt, |
| | negative_prompt=negative_prompt, |
| | height=height, |
| | width=width, |
| | num_inference_steps=steps, |
| | generator=generator, |
| | true_cfg_scale=guidance_scale, |
| | ).images[0] |
| | |
| | return result_image, seed |
| |
|
| | except Exception as e: |
| | raise e |
| | finally: |
| | gc.collect() |
| | torch.cuda.empty_cache() |
| |
|
| | @spaces.GPU |
| | def infer_example(images, prompt, lora_adapter): |
| | if not images: |
| | return None, 0 |
| | |
| | if isinstance(images, str): |
| | images_list = [images] |
| | else: |
| | images_list = images |
| | |
| | result, seed = infer( |
| | images=images_list, |
| | prompt=prompt, |
| | lora_adapter=lora_adapter, |
| | seed=0, |
| | randomize_seed=True, |
| | guidance_scale=1.0, |
| | steps=4 |
| | ) |
| | return result, seed |
| |
|
| | css=""" |
| | #col-container { |
| | margin: 0 auto; |
| | max-width: 1000px; |
| | } |
| | #main-title h1 {font-size: 2.3em !important;} |
| | """ |
| |
|
| | with gr.Blocks() as demo: |
| | with gr.Column(elem_id="col-container"): |
| | gr.Markdown("# **Qwen-Image-Edit-Object-Manipulator**", 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. Upload one or more images.") |
| |
|
| | with gr.Row(equal_height=True): |
| | with gr.Column(): |
| | images = gr.Gallery( |
| | label="Upload Images", |
| | type="filepath", |
| | columns=2, |
| | rows=1, |
| | height=300, |
| | allow_preview=True |
| | ) |
| | |
| | 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(): |
| | output_image = gr.Image(label="Output Image", interactive=False, format="png", height=363) |
| | |
| | with gr.Row(): |
| | lora_adapter = gr.Dropdown( |
| | label="Choose Manipulator", |
| | choices=list(ADAPTER_SPECS.keys()), |
| | value="Qwen-Image-Edit-2511-Object-Adder" |
| | ) |
| | |
| | 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/D.jpg"], "Add the batman logo to the image while preserving the background lighting and surrounding elements maintaining realism and original details.", "Qwen-Image-Edit-2511-Object-Adder"], |
| | [["examples/A.jpg"], "Add the slim rectangular transparent frame sunglasses to the image while preserving the background lighting and surrounding elements maintaining realism and original details.", "Qwen-Image-Edit-2511-Object-Adder"], |
| | [["examples/B.jpeg"], "Remove the necklace and goggles from the image while preserving the background and remaining elements, maintaining realism and original details.", "Qwen-Image-Edit-2511-Object-Remover"], |
| | [["examples/C.png"], "Add the leather cowboy cap to the image while preserving the background lighting and surrounding elements maintaining realism and original details.", "Qwen-Image-Edit-2511-Object-Adder"], |
| | ], |
| | inputs=[images, prompt, lora_adapter], |
| | outputs=[output_image, 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.") |
| |
|
| | run_button.click( |
| | fn=infer, |
| | inputs=[images, prompt, lora_adapter, seed, randomize_seed, guidance_scale, steps], |
| | outputs=[output_image, seed] |
| | ) |
| |
|
| | if __name__ == "__main__": |
| | demo.queue(max_size=30).launch(css=css, theme=orange_red_theme, mcp_server=True, ssr_mode=False, show_error=True) |
