| 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( |
| "FireRedTeam/FireRed-Image-Edit-1.0", |
| 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 |
|
|
| 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, |
| 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.") |
|
|
| 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): |
| 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, |
| 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: 2.4em !important;} |
| """ |
|
|
| with gr.Blocks() as demo: |
| with gr.Column(elem_id="col-container"): |
| gr.Markdown("# **FireRed-Image-Edit-1.0-Fast**", elem_id="main-title") |
| gr.Markdown("Perform image edits using [FireRed-Image-Edit-1.0](https://huggingface.co/FireRedTeam/FireRed-Image-Edit-1.0) with 4-step fast inference.") |
|
|
| 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, upscale, change lighting...", |
| ) |
|
|
| run_button = gr.Button("Edit Image", variant="primary") |
|
|
| with gr.Column(): |
| output_image = gr.Image(label="Output Image", interactive=False, format="png", height=395) |
|
|
| 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/1.jpg"], "Convert it to black and white. Apply a vintage Polaroid effect with subtle aging and film grain, and add a watermark that says 'Generated by HF'."], |
| [["examples/2.jpg"], "Transform the image into a dotted cartoon style."], |
| [["examples/3.jpg"], "Convert it to black and white."], |
| ], |
| inputs=[images, prompt], |
| outputs=[output_image, seed], |
| fn=infer_example, |
| cache_examples=False, |
| label="Examples" |
| ) |
|
|
| run_button.click( |
| fn=infer, |
| inputs=[images, prompt, 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) |
