import os import sys import torch import diffusers import transformers import argparse import peft import copy import cv2 import spaces import gc import gradio as gr import numpy as np from flash_attn import flash_attn_func 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 arXiv HuggingFace 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. """ def init_basemodel(): global transformer, scheduler, vae, text_encoder, text_encoder_2, tokenizer, tokenizer_2, image_processor, pipe, current_task pipe = None current_task = None # init models device = "cuda" if torch.cuda.is_available() else "cpu" weight_dtype = torch.bfloat16 transformer = HunyuanVideoTransformer3DModel.from_pretrained('hunyuanvideo-community/HunyuanVideo-I2V', subfolder="transformer", inference_subject_driven=False, low_cpu_mem_usage=True).requires_grad_(False).to(device, dtype=weight_dtype) torch.cuda.empty_cache() gc.collect() scheduler = diffusers.FlowMatchEulerDiscreteScheduler() vae = diffusers.AutoencoderKLHunyuanVideo.from_pretrained('hunyuanvideo-community/HunyuanVideo-I2V', subfolder="vae", low_cpu_mem_usage=True).requires_grad_(False).to(device, dtype=weight_dtype) torch.cuda.empty_cache() gc.collect() text_encoder = transformers.LlavaForConditionalGeneration.from_pretrained('hunyuanvideo-community/HunyuanVideo-I2V', subfolder="text_encoder", low_cpu_mem_usage=True).requires_grad_(False).to(device, dtype=weight_dtype) torch.cuda.empty_cache() gc.collect() text_encoder_2 = transformers.CLIPTextModel.from_pretrained('hunyuanvideo-community/HunyuanVideo-I2V', subfolder="text_encoder_2", low_cpu_mem_usage=True).requires_grad_(False).to(device, dtype=weight_dtype) torch.cuda.empty_cache() gc.collect() 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") vae.enable_tiling() vae.enable_slicing() 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, ) @spaces.GPU def process_image_and_text(condition_image, target_prompt, condition_image_prompt, task, random_seed, num_steps, inpainting, fill_x1, fill_x2, fill_y1, fill_y2): # set up the model global pipe, current_task, transformer if current_task != task: if current_task is None: # 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) else: def restore_forward(module): def restored_forward(self, x, *args, **kwargs): return module.original_forward(x, *args, **kwargs) return restored_forward.__get__(module, type(module)) for n, m in transformer.named_modules(): if isinstance(m, peft.tuners.lora.layer.Linear): m.forward = restore_forward(m) current_task = task # hack LoRA forward def create_hacked_forward(module): if not hasattr(module, 'original_forward'): module.original_forward = module.forward lora_forward = module.forward non_lora_forward = module.base_layer.forward img_sequence_length = int((512 / 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 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) # 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 task not in ['subject_driven', 'style_transfer']: if 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[edges == 0] = 128 return Image.fromarray(edges).convert("RGB") c_img = get_canny_edge(c_img) elif task == "coloring": c_img = ( c_img.resize((512, 512)) .convert("L") .convert("RGB") ) elif task == "deblurring": blur_radius = 10 c_img = ( c_img.convert("RGB") .filter(ImageFilter.GaussianBlur(blur_radius)) .resize((512, 512)) .convert("RGB") ) elif 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((512, 512)) c_img = get_depth_map(c_img) k = (255 - 128) / 255 b = 128 c_img = c_img.point(lambda x: k * x + b) elif task == "depth_pred": c_img = c_img elif task == "fill": c_img = c_img.resize((512, 512)).convert("RGB") x1, x2 = fill_x1, fill_x2 y1, y2 = fill_y1, fill_y2 mask = Image.new("L", (512, 512), 0) draw = ImageDraw.Draw(mask) draw.rectangle((x1, y1, x2, y2), fill=255) if inpainting: mask = Image.eval(mask, lambda a: 255 - a) c_img = Image.composite( c_img, Image.new("RGB", (512, 512), (255, 255, 255)), mask ) c_img = Image.composite( c_img, Image.new("RGB", (512, 512), (128, 128, 128)), mask ) elif task == "sr": c_img = c_img.resize((int(512 / 4), int(512 / 4))).convert("RGB") c_img = c_img.resize((512, 512)) 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=num_steps, guidance_scale=6.0, num_videos_per_prompt=1, generator=torch.Generator(device=pipe.transformer.device).manual_seed(random_seed), output_type='pt', image_embed_interleave=4, frame_gap=48, mixup=True, mixup_num_imgs=2, enhance_tp=task in ['subject_driven'], ).frames output_images = [] for i in range(10): out = gen_img[:, i:i+1, :, :, :] out = out.squeeze(0).squeeze(0).cpu().to(torch.float32).numpy() out = np.transpose(out, (1, 2, 0)) out = (out * 255).astype(np.uint8) out = Image.fromarray(out) output_images.append(out) return output_images[1:] + [output_images[0]] 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="tp") condition_image_prompt = gr.Textbox(lines=2, label="Condition Image Prompt (Only required by Subject-driven Image Generation and Style Transfer tasks)", elem_id="cp") random_seed = gr.Number(label="Random Seed", precision=0, value=0, elem_id="seed") num_steps = gr.Number(label="Diffusion Inference Steps", precision=0, value=50, elem_id="steps") inpainting = gr.Checkbox(label="Inpainting", value=False, elem_id="inpainting") fill_x1 = gr.Number(label="In/Out-painting Box Left Boundary", precision=0, value=128, elem_id="fill_x1") fill_x2 = gr.Number(label="In/Out-painting Box Right Boundary", precision=0, value=384, elem_id="fill_x2") fill_y1 = gr.Number(label="In/Out-painting Box Top Boundary", precision=0, value=128, elem_id="fill_y1") fill_y2 = gr.Number(label="In/Out-painting Box Bottom Boundary", precision=0, value=384, elem_id="fill_y2") 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") output_images = gr.Gallery( label="Output Images", show_label=True, elem_id="output_gallery", columns=1, rows=10, object_fit="contain", height="auto", ) submit_btn.click( fn=process_image_and_text, inputs=[condition_image, target_prompt, condition_image_prompt, task, random_seed, num_steps, inpainting, fill_x1, fill_x2, fill_y1, fill_y2], outputs=output_images, ) return app if __name__ == "__main__": init_basemodel() create_app().launch(debug=True, ssr_mode=False, max_threads=1)