Before this post, we already have several rounds of communication with CTM's authors. We shall proceed to elucidate the situation here.We sadly found out our CTM paper (ICLR24) was plagiarized by TCD! It's unbelievableđąâthey not only stole our idea of trajectory consistency but also comitted "verbatim plagiarism," literally copying our proofs word for word! Please help me spread this. pic.twitter.com/aR6pRjhj5X
— Dongjun Kim (@gimdong58085414) March 25, 2024
1. In the [first arXiv version](https://arxiv.org/abs/2402.19159v1), we have provided citations and discussion in A. Related Works: > Kim et al. (2023) proposes a universal framework for CMs and DMs. The core design is similar to ours, with the main differences being that we focus on reducing error in CMs, subtly leverage the semi-linear structure of the PF ODE for parameterization, and avoid the need for adversarial training. 2. In the [first arXiv version](https://arxiv.org/abs/2402.19159v1), we have indicated in D.3 Proof of Theorem 4.2 > In this section, our derivation mainly borrows the proof from (Kim et al., 2023; Chen et al., 2022). and we have never intended to claim credits. As we have mentioned in our email, we would like to extend a formal apology to the CTM authors for the clearly inadequate level of referencing in our paper. We will provide more credits in the revised manuscript. 3. In the updated [second arXiv version](https://arxiv.org/abs/2402.19159v2), we have expanded our discussion to elucidate the relationship with the CTM framework. Additionally, we have removed some proofs that were previously included for completeness. 4. CTM and TCD are different from motivation, method to experiments. TCD is founded on the principles of the Latent Consistency Model (LCM), aimed to design an effective consistency function by utilizing the **exponential integrators**. 5. The experimental results also cannot be obtained from any type of CTM algorithm. 5.1 Here we provide a simple method to check: use our sampler here to sample the checkpoint [CTM released](https://github.com/sony/ctm), or vice versa. 5.2 [CTM](https://github.com/sony/ctm) also provided training script. We welcome anyone to reproduce the experiments on SDXL or LDM based on CTM algorithm. We believe the assertion of plagiarism is not only severe but also detrimental to the academic integrity of the involved parties. We earnestly hope that everyone involved gains a more comprehensive understanding of this matter. ## Introduction TCD, inspired by [Consistency Models](https://arxiv.org/abs/2303.01469), is a novel distillation technology that enables the distillation of knowledge from pre-trained diffusion models into a few-step sampler. In this repository, we release the inference code and our model named TCD-SDXL, which is distilled from [SDXL Base 1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0). We provide the LoRA checkpoint in this [repository]().  âšTCD has following advantages: - `Flexible NFEs`: For TCD, the NFEs can be varied at will (compared with Turbo), without adversely affecting the quality of the results (compared with LCMs), where LCM experiences a notable decline in quality at high NFEs. - `Better than Teacher`: TCD maintains superior generative quality at high NFEs, even exceeding the performance of DPM-Solver++(2S) with origin SDXL. It is worth noting that there is no additional discriminator or LPIPS supervision included during training. - `Freely Change the Detailing`: During inference, the level of detail in the image can be simply modified by adjusing one hyper-parameter gamma. This option does not require the introduction of any additional parameters. - `Versatility`: Integrated with LoRA technology, TCD can be directly applied to various models (including the custom Community Models, styled LoRA, ControlNet, IP-Adapter) that share the same backbone, as demonstrated in the [Usage](#usage-anchor).  - `Avoiding Mode Collapse`: TCD achieves few-step generation without the need for adversarial training, thus circumventing mode collapse caused by the GAN objective. In contrast to the concurrent work [SDXL-Lightning](https://huggingface.co/ByteDance/SDXL-Lightning), which relies on Adversarial Diffusion Distillation, TCD can synthesize results that are more realistic and slightly more diverse, without the presence of "Janus" artifacts.  For more information, please refer to our paper [Trajectory Consistency Distillation](https://arxiv.org/abs/2402.19159). ## Usage To run the model yourself, you can leverage the 𧚠Diffusers library. ```bash pip install diffusers transformers accelerate peft ``` And then we clone the repo. ```bash git clone https://github.com/jabir-zheng/TCD.git cd TCD ``` Here, we demonstrate the applicability of our TCD LoRA to various models, including [SDXL](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0), [SDXL Inpainting](https://huggingface.co/diffusers/stable-diffusion-xl-1.0-inpainting-0.1), a community model named [Animagine XL](https://huggingface.co/cagliostrolab/animagine-xl-3.0), a styled LoRA [Papercut](https://huggingface.co/TheLastBen/Papercut_SDXL), pretrained [Depth Controlnet](https://huggingface.co/diffusers/controlnet-depth-sdxl-1.0), [Canny Controlnet](https://huggingface.co/diffusers/controlnet-canny-sdxl-1.0) and [IP-Adapter](https://github.com/tencent-ailab/IP-Adapter) to accelerate image generation with high quality in few steps. ### Text-to-Image generation ```py import torch from diffusers import StableDiffusionXLPipeline from scheduling_tcd import TCDScheduler device = "cuda" base_model_id = "stabilityai/stable-diffusion-xl-base-1.0" tcd_lora_id = "h1t/TCD-SDXL-LoRA" pipe = StableDiffusionXLPipeline.from_pretrained(base_model_id, torch_dtype=torch.float16, variant="fp16").to(device) pipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config) pipe.load_lora_weights(tcd_lora_id) pipe.fuse_lora() prompt = "Beautiful woman, bubblegum pink, lemon yellow, minty blue, futuristic, high-detail, epic composition, watercolor." image = pipe( prompt=prompt, num_inference_steps=4, guidance_scale=0, # Eta (referred to as `gamma` in the paper) is used to control the stochasticity in every step. # A value of 0.3 often yields good results. # We recommend using a higher eta when increasing the number of inference steps. eta=0.3, generator=torch.Generator(device=device).manual_seed(0), ).images[0] ```  ### Inpainting ```py import torch from diffusers import AutoPipelineForInpainting from diffusers.utils import load_image, make_image_grid from scheduling_tcd import TCDScheduler device = "cuda" base_model_id = "diffusers/stable-diffusion-xl-1.0-inpainting-0.1" tcd_lora_id = "h1t/TCD-SDXL-LoRA" pipe = AutoPipelineForInpainting.from_pretrained(base_model_id, torch_dtype=torch.float16, variant="fp16").to(device) pipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config) pipe.load_lora_weights(tcd_lora_id) pipe.fuse_lora() img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png" mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png" init_image = load_image(img_url).resize((1024, 1024)) mask_image = load_image(mask_url).resize((1024, 1024)) prompt = "a tiger sitting on a park bench" image = pipe( prompt=prompt, image=init_image, mask_image=mask_image, num_inference_steps=8, guidance_scale=0, eta=0.3, # Eta (referred to as `gamma` in the paper) is used to control the stochasticity in every step. A value of 0.3 often yields good results. strength=0.99, # make sure to use `strength` below 1.0 generator=torch.Generator(device=device).manual_seed(0), ).images[0] grid_image = make_image_grid([init_image, mask_image, image], rows=1, cols=3) ```  ### Versatile for Community Models ```py import torch from diffusers import StableDiffusionXLPipeline from scheduling_tcd import TCDScheduler device = "cuda" base_model_id = "cagliostrolab/animagine-xl-3.0" tcd_lora_id = "h1t/TCD-SDXL-LoRA" pipe = StableDiffusionXLPipeline.from_pretrained(base_model_id, torch_dtype=torch.float16, variant="fp16").to(device) pipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config) pipe.load_lora_weights(tcd_lora_id) pipe.fuse_lora() prompt = "A man, clad in a meticulously tailored military uniform, stands with unwavering resolve. The uniform boasts intricate details, and his eyes gleam with determination. Strands of vibrant, windswept hair peek out from beneath the brim of his cap." image = pipe( prompt=prompt, num_inference_steps=8, guidance_scale=0, # Eta (referred to as `gamma` in the paper) is used to control the stochasticity in every step. # A value of 0.3 often yields good results. # We recommend using a higher eta when increasing the number of inference steps. eta=0.3, generator=torch.Generator(device=device).manual_seed(0), ).images[0] ```  ### Combine with styled LoRA ```py import torch from diffusers import StableDiffusionXLPipeline from scheduling_tcd import TCDScheduler device = "cuda" base_model_id = "stabilityai/stable-diffusion-xl-base-1.0" tcd_lora_id = "h1t/TCD-SDXL-LoRA" styled_lora_id = "TheLastBen/Papercut_SDXL" pipe = StableDiffusionXLPipeline.from_pretrained(base_model_id, torch_dtype=torch.float16, variant="fp16").to(device) pipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config) pipe.load_lora_weights(tcd_lora_id, adapter_name="tcd") pipe.load_lora_weights(styled_lora_id, adapter_name="style") pipe.set_adapters(["tcd", "style"], adapter_weights=[1.0, 1.0]) prompt = "papercut of a winter mountain, snow" image = pipe( prompt=prompt, num_inference_steps=4, guidance_scale=0, # Eta (referred to as `gamma` in the paper) is used to control the stochasticity in every step. # A value of 0.3 often yields good results. # We recommend using a higher eta when increasing the number of inference steps. eta=0.3, generator=torch.Generator(device=device).manual_seed(0), ).images[0] ```  ### Compatibility with ControlNet #### Depth ControlNet ```py import torch import numpy as np from PIL import Image from transformers import DPTFeatureExtractor, DPTForDepthEstimation from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline from diffusers.utils import load_image, make_image_grid from scheduling_tcd import TCDScheduler device = "cuda" depth_estimator = DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas").to(device) feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-hybrid-midas") def get_depth_map(image): image = feature_extractor(images=image, return_tensors="pt").pixel_values.to(device) with torch.no_grad(), torch.autocast(device): depth_map = depth_estimator(image).predicted_depth depth_map = torch.nn.functional.interpolate( depth_map.unsqueeze(1), size=(1024, 1024), mode="bicubic", align_corners=False, ) depth_min = torch.amin(depth_map, dim=[1, 2, 3], keepdim=True) depth_max = torch.amax(depth_map, dim=[1, 2, 3], keepdim=True) depth_map = (depth_map - depth_min) / (depth_max - depth_min) image = torch.cat([depth_map] * 3, dim=1) image = image.permute(0, 2, 3, 1).cpu().numpy()[0] image = Image.fromarray((image * 255.0).clip(0, 255).astype(np.uint8)) return image base_model_id = "stabilityai/stable-diffusion-xl-base-1.0" controlnet_id = "diffusers/controlnet-depth-sdxl-1.0" tcd_lora_id = "h1t/TCD-SDXL-LoRA" controlnet = ControlNetModel.from_pretrained( controlnet_id, torch_dtype=torch.float16, variant="fp16", ).to(device) pipe = StableDiffusionXLControlNetPipeline.from_pretrained( base_model_id, controlnet=controlnet, torch_dtype=torch.float16, variant="fp16", ).to(device) pipe.enable_model_cpu_offload() pipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config) pipe.load_lora_weights(tcd_lora_id) pipe.fuse_lora() prompt = "stormtrooper lecture, photorealistic" image = load_image("https://huggingface.co/lllyasviel/sd-controlnet-depth/resolve/main/images/stormtrooper.png") depth_image = get_depth_map(image) controlnet_conditioning_scale = 0.5 # recommended for good generalization image = pipe( prompt, image=depth_image, num_inference_steps=4, guidance_scale=0, eta=0.3, # A parameter (referred to as `gamma` in the paper) is used to control the stochasticity in every step. A value of 0.3 often yields good results. controlnet_conditioning_scale=controlnet_conditioning_scale, generator=torch.Generator(device=device).manual_seed(0), ).images[0] grid_image = make_image_grid([depth_image, image], rows=1, cols=2) ```  #### Canny ControlNet ```py import torch from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline from diffusers.utils import load_image, make_image_grid from scheduling_tcd import TCDScheduler device = "cuda" base_model_id = "stabilityai/stable-diffusion-xl-base-1.0" controlnet_id = "diffusers/controlnet-canny-sdxl-1.0" tcd_lora_id = "h1t/TCD-SDXL-LoRA" controlnet = ControlNetModel.from_pretrained( controlnet_id, torch_dtype=torch.float16, variant="fp16", ).to(device) pipe = StableDiffusionXLControlNetPipeline.from_pretrained( base_model_id, controlnet=controlnet, torch_dtype=torch.float16, variant="fp16", ).to(device) pipe.enable_model_cpu_offload() pipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config) pipe.load_lora_weights(tcd_lora_id) pipe.fuse_lora() prompt = "ultrarealistic shot of a furry blue bird" canny_image = load_image("https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png") controlnet_conditioning_scale = 0.5 # recommended for good generalization image = pipe( prompt, image=canny_image, num_inference_steps=4, guidance_scale=0, eta=0.3, # A parameter (referred to as `gamma` in the paper) is used to control the stochasticity in every step. A value of 0.3 often yields good results. controlnet_conditioning_scale=controlnet_conditioning_scale, generator=torch.Generator(device=device).manual_seed(0), ).images[0] grid_image = make_image_grid([canny_image, image], rows=1, cols=2) ```  ### Compatibility with IP-Adapter â ïž Please refer to the official [repository](https://github.com/tencent-ailab/IP-Adapter/tree/main) for instructions on installing dependencies for IP-Adapter. ```py import torch from diffusers import StableDiffusionXLPipeline from diffusers.utils import load_image, make_image_grid from ip_adapter import IPAdapterXL from scheduling_tcd import TCDScheduler device = "cuda" base_model_path = "stabilityai/stable-diffusion-xl-base-1.0" image_encoder_path = "sdxl_models/image_encoder" ip_ckpt = "sdxl_models/ip-adapter_sdxl.bin" tcd_lora_id = "h1t/TCD-SDXL-LoRA" pipe = StableDiffusionXLPipeline.from_pretrained( base_model_path, torch_dtype=torch.float16, variant="fp16" ) pipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config) pipe.load_lora_weights(tcd_lora_id) pipe.fuse_lora() ip_model = IPAdapterXL(pipe, image_encoder_path, ip_ckpt, device) ref_image = load_image("https://raw.githubusercontent.com/tencent-ailab/IP-Adapter/main/assets/images/woman.png").resize((512, 512)) prompt = "best quality, high quality, wearing sunglasses" image = ip_model.generate( pil_image=ref_image, prompt=prompt, scale=0.5, num_samples=1, num_inference_steps=4, guidance_scale=0, eta=0.3, # A parameter (referred to as `gamma` in the paper) is used to control the stochasticity in every step. A value of 0.3 often yields good results. seed=0, )[0] grid_image = make_image_grid([ref_image, image], rows=1, cols=2) ```  ## Related and Concurrent Works - Luo S, Tan Y, Huang L, et al. Latent consistency models: Synthesizing high-resolution images with few-step inference. arXiv preprint arXiv:2310.04378, 2023. - Luo S, Tan Y, Patil S, et al. LCM-LoRA: A universal stable-diffusion acceleration module. arXiv preprint arXiv:2311.05556, 2023. - Lu C, Zhou Y, Bao F, et al. DPM-Solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps. Advances in Neural Information Processing Systems, 2022, 35: 5775-5787. - Lu C, Zhou Y, Bao F, et al. DPM-solver++: Fast solver for guided sampling of diffusion probabilistic models. arXiv preprint arXiv:2211.01095, 2022. - Zhang Q, Chen Y. Fast sampling of diffusion models with exponential integrator. ICLR 2023, Kigali, Rwanda, May 1-5, 2023. - Kim D, Lai C H, Liao W H, et al. Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion. ICLR 2024. ## Citation ```bibtex @misc{zheng2024trajectory, title={Trajectory Consistency Distillation}, author={Jianbin Zheng and Minghui Hu and Zhongyi Fan and Chaoyue Wang and Changxing Ding and Dacheng Tao and Tat-Jen Cham}, year={2024}, eprint={2402.19159}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` ## Acknowledgments This codebase heavily relies on the đ€[Diffusers](https://github.com/huggingface/diffusers) library and [LCM](https://github.com/luosiallen/latent-consistency-model).We regret to hear about the serious accusations from the CTM team @gimdong58085414. I shall proceed to elucidate the situation and make an archive here. We already have several rounds of communication with CTM's authors. https://t.co/BKn3w1jXuh
— Michael (@Merci0318) March 26, 2024