import os if 'SPACES_APP' in os.environ: os.system("pip install flash-attn==2.7.3 --no-build-isolation") import sys import torch import diffusers import transformers import argparse import peft import copy import cv2 import gradio as gr import numpy as np from peft import LoraConfig from omegaconf import OmegaConf from safetensors.torch import safe_open from PIL import Image, ImageDraw, ImageFilter from huggingface_hub import hf_hub_download from transformers import pipeline from models import HunyuanVideoTransformer3DModel from pipelines import HunyuanVideoImageToVideoPipeline header = """ # DRA-Ctrl Gradio App
arXiv HuggingFace GitHub Project
""" notice = """ For easier testing, in spatially-aligned image generation tasks, when passing the condition image to `gradio_app`, there's no need to manually input edge maps, depth maps, or other condition images - only the original image is required. The corresponding condition images will be automatically extracted. """ @spaces.GPU def process_image_and_text(condition_image, target_prompt, condition_image_prompt, task): # init models transformer = HunyuanVideoTransformer3DModel.from_pretrained('hunyuanvideo-community/HunyuanVideo-I2V', subfolder="transformer", inference_subject_driven=task in ['subject_driven']) scheduler = diffusers.FlowMatchEulerDiscreteScheduler() vae = diffusers.AutoencoderKLHunyuanVideo.from_pretrained('hunyuanvideo-community/HunyuanVideo-I2V', subfolder="vae") text_encoder = transformers.LlavaForConditionalGeneration.from_pretrained('hunyuanvideo-community/HunyuanVideo-I2V', subfolder="text_encoder") text_encoder_2 = transformers.CLIPTextModel.from_pretrained('hunyuanvideo-community/HunyuanVideo-I2V', subfolder="text_encoder_2") tokenizer = transformers.AutoTokenizer.from_pretrained('hunyuanvideo-community/HunyuanVideo-I2V', subfolder="tokenizer") tokenizer_2 = transformers.CLIPTokenizer.from_pretrained('hunyuanvideo-community/HunyuanVideo-I2V', subfolder="tokenizer_2") image_processor = transformers.CLIPImageProcessor.from_pretrained('hunyuanvideo-community/HunyuanVideo-I2V', subfolder="image_processor") device = "cuda" if torch.cuda.is_available() else "cpu" weight_dtype = torch.bfloat16 transformer.requires_grad_(False) vae.requires_grad_(False).to(device, dtype=weight_dtype) text_encoder.requires_grad_(False).to(device, dtype=weight_dtype) text_encoder_2.requires_grad_(False).to(device, dtype=weight_dtype) transformer.to(device, dtype=weight_dtype) vae.enable_tiling() vae.enable_slicing() # insert LoRA lora_config = LoraConfig( r=16, lora_alpha=16, init_lora_weights="gaussian", target_modules=[ 'attn.to_k', 'attn.to_q', 'attn.to_v', 'attn.to_out.0', 'attn.add_k_proj', 'attn.add_q_proj', 'attn.add_v_proj', 'attn.to_add_out', 'ff.net.0.proj', 'ff.net.2', 'ff_context.net.0.proj', 'ff_context.net.2', 'norm1_context.linear', 'norm1.linear', 'norm.linear', 'proj_mlp', 'proj_out', ] ) transformer.add_adapter(lora_config) # hack LoRA forward def create_hacked_forward(module): lora_forward = module.forward non_lora_forward = module.base_layer.forward img_sequence_length = int((args.img_size / 8 / 2) ** 2) encoder_sequence_length = 144 + 252 # encoder sequence: 144 img 252 txt num_imgs = 4 num_generated_imgs = 3 num_encoder_sequences = 2 if args.task in ['subject_driven', 'style_transfer'] else 1 def hacked_lora_forward(self, x, *args, **kwargs): if x.shape[1] == img_sequence_length * num_imgs and len(x.shape) > 2: return torch.cat(( lora_forward(x[:, :-img_sequence_length*num_generated_imgs], *args, **kwargs), non_lora_forward(x[:, -img_sequence_length*num_generated_imgs:], *args, **kwargs) ), dim=1) elif x.shape[1] == encoder_sequence_length * num_encoder_sequences or x.shape[1] == encoder_sequence_length: return lora_forward(x, *args, **kwargs) elif x.shape[1] == img_sequence_length * num_imgs + encoder_sequence_length * num_encoder_sequences: return torch.cat(( lora_forward(x[:, :(num_imgs - num_generated_imgs)*img_sequence_length], *args, **kwargs), non_lora_forward(x[:, (num_imgs - num_generated_imgs)*img_sequence_length:-num_encoder_sequences*encoder_sequence_length], *args, **kwargs), lora_forward(x[:, -num_encoder_sequences*encoder_sequence_length:], *args, **kwargs) ), dim=1) elif x.shape[1] == 3072: return non_lora_forward(x, *args, **kwargs) else: raise ValueError( f"hacked_lora_forward receives unexpected sequence length: {x.shape[1]}, input shape: {x.shape}!" ) return hacked_lora_forward.__get__(module, type(module)) for n, m in transformer.named_modules(): if isinstance(m, peft.tuners.lora.layer.Linear): m.forward = create_hacked_forward(m) # load LoRA weights model_root = hf_hub_download( repo_id="Kunbyte/DRA-Ctrl", filename=f"{task}.safetensors", resume_download=True) try: with safe_open(model_root, framework="pt") as f: lora_weights = {} for k in f.keys(): param = f.get_tensor(k) if k.endswith(".weight"): k = k.replace('.weight', '.default.weight') lora_weights[k] = param transformer.load_state_dict(lora_weights, strict=False) except Exception as e: raise ValueError(f'{e}') transformer.requires_grad_(False) pipe = HunyuanVideoImageToVideoPipeline( text_encoder=text_encoder, tokenizer=tokenizer, transformer=transformer, vae=vae, scheduler=copy.deepcopy(scheduler), text_encoder_2=text_encoder_2, tokenizer_2=tokenizer_2, image_processor=image_processor, ) # start generation c_txt = None if condition_image_prompt == "" else condition_image_prompt c_img = condition_image.resize((512, 512)) t_txt = target_prompt if args.task not in ['subject_driven', 'style_transfer']: if args.task == "canny": def get_canny_edge(img): img_np = np.array(img) img_gray = cv2.cvtColor(img_np, cv2.COLOR_BGR2GRAY) edges = cv2.Canny(img_gray, 100, 200) edges_tmp = Image.fromarray(edges).convert("RGB") edges_tmp.save(os.path.join(save_dir, f"edges.png")) edges[edges == 0] = 128 return Image.fromarray(edges).convert("RGB") c_img = get_canny_edge(c_img) elif args.task == "coloring": c_img = ( c_img.resize((args.img_size, args.img_size)) .convert("L") .convert("RGB") ) elif args.task == "deblurring": blur_radius = 10 c_img = ( c_img.convert("RGB") .filter(ImageFilter.GaussianBlur(blur_radius)) .resize((args.img_size, args.img_size)) .convert("RGB") ) elif args.task == "depth": def get_depth_map(img): from transformers import pipeline depth_pipe = pipeline( task="depth-estimation", model="LiheYoung/depth-anything-small-hf", device="cpu", ) return depth_pipe(img)["depth"].convert("RGB").resize((args.img_size, args.img_size)) c_img = get_depth_map(c_img) c_img.save(os.path.join(save_dir, f"depth.png")) k = (255 - 128) / 255 b = 128 c_img = c_img.point(lambda x: k * x + b) elif args.task == "depth_pred": c_img = c_img elif args.task == "fill": c_img = c_img.resize((args.img_size, args.img_size)).convert("RGB") x1, x2 = args.fill_x1, args.fill_x2 y1, y2 = args.fill_y1, args.fill_y2 mask = Image.new("L", (args.img_size, args.img_size), 0) draw = ImageDraw.Draw(mask) draw.rectangle((x1, y1, x2, y2), fill=255) if args.inpainting: mask = Image.eval(mask, lambda a: 255 - a) c_img = Image.composite( c_img, Image.new("RGB", (args.img_size, args.img_size), (255, 255, 255)), mask ) c_img.save(os.path.join(save_dir, f"mask.png")) c_img = Image.composite( c_img, Image.new("RGB", (args.img_size, args.img_size), (128, 128, 128)), mask ) elif args.task == "sr": c_img = c_img.resize((int(args.img_size / 4), int(args.img_size / 4))).convert("RGB") c_img.save(os.path.join(save_dir, f"low_resolution.png")) c_img = c_img.resize((args.img_size, args.img_size)) c_img.save(os.path.join(save_dir, f"low_to_high.png")) gen_img = pipe( image=c_img, prompt=[t_txt.strip()], prompt_condition=[c_txt.strip()] if c_txt is not None else None, prompt_2=[t_txt], height=512, width=512, num_frames=5, num_inference_steps=50, guidance_scale=6.0, num_videos_per_prompt=1, generator=torch.Generator(device=pipe.transformer.device).manual_seed(0), output_type='pt', image_embed_interleave=4, frame_gap=48, mixup=True, mixup_num_imgs=2, ).frames gen_img = gen_img[:, 0:1, :, :, :] gen_img = gen_img.squeeze(0).squeeze(0).cpu().to(torch.float32).numpy() gen_img = np.transpose(gen_img, (1, 2, 0)) gen_img = (gen_img * 255).astype(np.uint8) gen_img = Image.fromarray(gen_img) return gen_img def create_app(): with gr.Blocks() as app: gr.Markdown(header, elem_id="header") with gr.Row(equal_height=False): with gr.Column(variant="panel", elem_classes="inputPanel"): condition_image = gr.Image( type="pil", label="Condition Image", width=300, elem_id="input" ) task = gr.Radio( [ ("Subject-driven Image Generation", "subject_driven"), ("Canny-to-Image", "canny"), ("Colorization", "coloring"), ("Deblurring", "deblurring"), ("Depth-to-Image", "depth"), ("Depth Prediction", "depth_pred"), ("In/Out-Painting", "fill"), ("Super-Resolution", "sr"), ("Style Transfer", "style_transfer") ], label="Task Selection", value="subject_driven", interactive=True, elem_id="task_selection" ) gr.Markdown(notice, elem_id="notice") target_prompt = gr.Textbox(lines=2, label="Target Prompt", elem_id="text") condition_image_prompt = gr.Textbox(lines=2, label="Condition Image Prompt", elem_id="text") submit_btn = gr.Button("Run", elem_id="submit_btn") with gr.Column(variant="panel", elem_classes="outputPanel"): output_image = gr.Image(type="pil", elem_id="output") submit_btn.click( fn=process_image_and_text, inputs=[condition_image, target_prompt, condition_image_prompt, task], outputs=output_image, ) return app if __name__ == "__main__": create_app().launch(debug=True, ssr_mode=False)