| | --- |
| | license: apache-2.0 |
| | library_name: diffusers |
| | language: |
| | - en |
| | base_model: |
| | - FireRedTeam/FireRed-Image-Edit-1.0 |
| | pipeline_tag: image-to-image |
| | tags: |
| | - art |
| | - 8bit |
| | --- |
| | |
| | # **FireRed-Image-Edit-1.0-8bit** |
| |
|
| | > FireRed-Image-Edit-1.0-8bit is an 8-bit quantized edition of FireRed-Image-Edit-1.0 (FireRedTeam), engineered to deliver the same instruction-driven diffusion transformer image editing capabilities with significantly reduced memory footprint and improved inference efficiency. Built upon the original 1.6B-sample training corpus refined into over 100M high-quality text-to-image and editing pairs through cleaning, stratification, auto-labeling, and dual-stage semantic filtering, this quantized release preserves the model’s multi-stage training pipeline, including large-scale pre-training, supervised fine-tuning, and reinforcement learning with techniques such as Multi-Condition Aware Bucket Sampling for variable resolutions, Stochastic Instruction Alignment, Asymmetric Gradient Optimization for stable DPO, DiffusionNFT with layout-OCR rewards for precise text editing, and differentiable Consistency Loss for strong identity preservation. The 8-bit quantization reduces VRAM requirements and accelerates deployment while maintaining high alignment, semantic consistency, and visual fidelity across diverse editing scenarios such as photo restoration, object insertion and modification, style transfer with text fidelity, multi-image virtual try-on, and layout-aware text editing. Optimized for practical workflows and ComfyUI integration, this version enables broader accessibility on consumer-grade GPUs without substantial quality degradation, making it suitable for research, production, and lightweight deployment environments. |
| |
|
| | --- |
| |
|
| | ## Quick Start with Diffusers 🧨 |
| |
|
| | ### Install the required packages |
| |
|
| | ```py |
| | transformers # - transformers@v4.57.6 |
| | torch # - torch@v2.9.1+cu128 |
| | diffusers # - diffusers@v0.37.0.dev0 |
| | bitsandbytes # - bitsandbytes@v0.49.2 |
| | gradio # - gradio@v6.6.0 |
| | accelerate # - accelerate@v1.12.0 |
| | ``` |
| |
|
| | ### Run FireRed-Image-Edit-1.0-8bit [Demo] |
| |
|
| | ```py |
| | import os |
| | import gc |
| | import gradio as gr |
| | import numpy as np |
| | #import spaces # Uncomment the Spaces-related modules if you are using HF ZeroGPU |
| | |
| | import torch |
| | import random |
| | from PIL import Image |
| | |
| | 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.models import QwenImageTransformer2DModel |
| | from diffusers import QwenImageEditPlusPipeline |
| | from diffusers.utils import load_image |
| | |
| | dtype = torch.bfloat16 |
| | |
| | transformer = QwenImageTransformer2DModel.from_pretrained( |
| | "prithivMLmods/FireRed-Image-Edit-1.0-8bit", |
| | subfolder="transformer", |
| | torch_dtype=dtype |
| | ) |
| | |
| | pipe = QwenImageEditPlusPipeline.from_pretrained( |
| | "prithivMLmods/FireRed-Image-Edit-1.0-8bit", |
| | transformer=transformer, |
| | torch_dtype=dtype |
| | ).to(device) |
| | |
| | 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=20 |
| | ) |
| | 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-1.0-8bit**", elem_id="main-title") |
| | |
| | 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 |
| | ) |
| | |
| | with gr.Row(): |
| | prompt = gr.Text( |
| | label="Edit Prompt", |
| | show_label=True, |
| | placeholder="e.g., transform into anime..", |
| | ) |
| | |
| | with gr.Row(): |
| | run_button = gr.Button("Edit Image", variant="primary") |
| | |
| | with gr.Column(): |
| | output_image = gr.Image(label="Output Image", interactive=False, format="png", height=390) |
| | |
| | with gr.Accordion("Advanced Settings", open=False, 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) |
| | 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=20) |
| | |
| | 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, mcp_server=True, ssr_mode=False, show_error=True) |
| | ``` |
| |
|
| | > [!IMPORTANT] |
| | This repository follows the same release notes, terms and conditions, and license as the original model page, [FireRed-Image-Edit-1.0](https://huggingface.co/FireRedTeam/FireRed-Image-Edit-1.0). |
