├── results ├── edit.png └── input.png ├── README.md ├── main.py └── LICENSE /results/edit.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mihirp1998/Add-it/HEAD/results/edit.png -------------------------------------------------------------------------------- /results/input.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mihirp1998/Add-it/HEAD/results/input.png -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Add-it 2 | Unoffical Implementation of [Add-it: Training-Free Object Insertion in Images With Pretrained Diffusion Models](https://arxiv.org/abs/2411.07232) 3 | 4 | ## Get started: 5 | 6 | To implement this in flux i had to edit the diffusers package - the custom diffusers package can be found [here](https://github.com/mihirp1998/diffusers_addit) 7 | 8 | 9 | 10 | You can simply install the custom diffusers via this: 11 | 12 | ``pip install 'git+https://github.com/mihirp1998/diffusers_addit'`` 13 | 14 | To edit run: 15 | 16 | ``python main.py`` 17 | 18 | vary the gamma hyperparamter [here](https://github.com/mihirp1998/Add-it/blob/46e71ab01c4faf8db75274a46f8d8c9df53fce44/main.py#L20) to vary the input image importance, reducing the gamma gives more importance to the input image while increasing gives more importance to the target prompt. 19 | 20 | ## Results: 21 | 22 | Input Image | Editted image - The horse is wearing a pink dress. 23 | :-------------------------:|:-------------------------: 24 | ![](results/input.png) | ![](results/edit.png) 25 | 26 | -------------------------------------------------------------------------------- /main.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from diffusers import FluxPipeline, FluxInpaintPipeline, FluxImg2ImgPipeline 3 | from diffusers.utils.torch_utils import randn_tensor 4 | from diffusers.utils import load_image 5 | from PIL import Image 6 | import builtins 7 | import numpy as np 8 | import ipdb 9 | st = ipdb.set_trace 10 | builtins.st = st 11 | import gc 12 | import os 13 | import glob 14 | 15 | 16 | height = 512 17 | width = 512 18 | multi_step = 670 19 | single_step = 340 20 | gamma = 1.05 21 | strength = 0.87 22 | add_it = True 23 | guidance_scale = 7.0 24 | num_inference_steps = 34 25 | 26 | 27 | pipe = FluxPipeline.from_pretrained( 28 | "black-forest-labs/FLUX.1-dev", 29 | torch_dtype=torch.bfloat16, 30 | ).to("cuda") 31 | vae_scale_factor = pipe.vae_scale_factor 32 | in_channels = pipe.transformer.config.in_channels 33 | 34 | prompts = ["A horse standing in the garden.", "The horse is wearing a pink dress."] 35 | 36 | initial_prompt = prompts[0] 37 | 38 | 39 | 40 | tmp_height = 2 * (int(height) // (vae_scale_factor * 2)) 41 | tmp_width = 2 * (int(width) // (vae_scale_factor * 2)) 42 | num_channels_latents = in_channels // 4 43 | shape = (1, num_channels_latents, tmp_height, tmp_width) 44 | noise_init = randn_tensor(shape, generator=torch.Generator("cpu").manual_seed(0), device=torch.device("cuda"), dtype=torch.bfloat16) 45 | noise_init_packed = pipe._pack_latents(noise_init, 1, num_channels_latents, tmp_height, tmp_width) 46 | 47 | init_image = pipe( 48 | initial_prompt, 49 | height=height, 50 | width=width, 51 | guidance_scale=guidance_scale, 52 | num_inference_steps=num_inference_steps, 53 | latents=noise_init_packed 54 | ).images[0] 55 | 56 | os.makedirs("results", exist_ok=True) 57 | 58 | init_image.save("results/input.png") 59 | 60 | del pipe 61 | torch.cuda.empty_cache() 62 | gc.collect() 63 | 64 | 65 | img2img_pipe = FluxImg2ImgPipeline.from_pretrained( 66 | "black-forest-labs/FLUX.1-dev", 67 | num_inference_steps=num_inference_steps, 68 | torch_dtype=torch.bfloat16, 69 | # low_cpu_mem_usage=True 70 | ) 71 | img2img_pipe.to("cuda") 72 | 73 | 74 | prompt = initial_prompt 75 | image_to_edit = init_image 76 | previous_prompts = [initial_prompt] 77 | 78 | 79 | 80 | for i, new_prompt in enumerate(prompts[1:]): 81 | prompt = " ".join(previous_prompts) 82 | prompts = [prompt, new_prompt] 83 | print(prompts) 84 | flux_out, target_noise = img2img_pipe(prompts, num_inference_steps=num_inference_steps, height=height, width=width, image=image_to_edit, guidance_scale=guidance_scale, strength=strength, gamma=gamma, add_it=add_it, source_noise=noise_init) 85 | images = flux_out.images 86 | 87 | noise_init = target_noise 88 | images[1].save(f"results/generated_edited_{i+1}.png") 89 | images[0].save(f"results/generated_edited_source_{i+1}.png") 90 | print(f"Generated image {i+1}") 91 | image_to_edit = images[1] 92 | previous_prompts.append(new_prompt) 93 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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