FangSen9000

Claude

Add One-to-All-Animation: Batch 1 - Small files and directories

0a2e9f2 10 months ago

8.68 kB

import os
import glob
import torch
import numpy as np
from opensora.sample.pipeline_wanx_vhuman_tokenreplace import WanPipeline
from opensora.model_variants.wanx_diffusers_src import WanTransformer3DModel_Refextractor_2D
from opensora.encoder_variants import get_text_enc
from opensora.vae_variants import get_vae
from datetime import datetime
from einops import rearrange
import imageio
from functools import partial
import traceback
from PIL import Image

from diffusers.utils import export_to_video, load_image
from diffusers.schedulers import FlowMatchEulerDiscreteScheduler

# ===== Model Config =====
model_path = "../pretrained_models/Wan2.1-T2V-1.3B-Diffusers"
vae_path = "../pretrained_models/Wan2.1-T2V-1.3B-Diffusers/vae"
config_path = "configs/wan2.1_t2v_1.3b.json"

model_dtype = torch.bfloat16

# Scheduler
scheduler = FlowMatchEulerDiscreteScheduler(
    shift=7.0,
    num_train_timesteps=1000,
    use_dynamic_shifting=False
)

# VAE and Text Encoder
vae = get_vae('wanx', vae_path, model_dtype)
encoders = get_text_enc('wanx-t2v', model_path, model_dtype)
text_encoder = encoders.text_encoder
tokenizer = encoders.tokenizer

# Model
model = WanTransformer3DModel_Refextractor_2D.from_config(config_path).to(model_dtype)
model.set_up_refextractor("configs/wan2.1_t2v_1.3b_refextractor_2d_withmask2.json", model_dtype)
model.eval()
model.requires_grad_(False)

# Load checkpoint
def load_state_dict(ckpt_dir):
    from safetensors.torch import load_file as safe_load
    shard_files = [f for f in os.listdir(ckpt_dir) if f.endswith('.safetensors')]
    checkpoint = {}
    for shard_file in shard_files:
        state_dict = safe_load(os.path.join(ckpt_dir, shard_file), device='cpu')
        checkpoint.update(state_dict)
    return checkpoint

# change your own path
ckpt_path = "./outputs_wanx1.3b/train1.3b_only_refextractor_2d_2/checkpoint-21366-6788/fp32_model_21366"
checkpoint = load_state_dict(ckpt_path)
model.load_state_dict(checkpoint, strict=True)

# Pipeline
pipe = WanPipeline(
    transformer=model,
    vae=vae.vae, 
    text_encoder=text_encoder,
    tokenizer=tokenizer,
    scheduler=scheduler
)
pipe.to("cuda", dtype=model_dtype)

# ===== Helper Functions =====
def resizecrop(image, th, tw):
    w, h = image.size
    if h / w > th / tw:
        new_w = int(w)
        new_h = int(new_w * th / tw)
    else:
        new_h = int(h)
        new_w = int(new_h * tw / th)
    left = (w - new_w) / 2
    top = (h - new_h) / 2
    right = (w + new_w) / 2
    bottom = (h + new_h) / 2
    image = image.crop((left, top, right, bottom))
    image = image.resize((tw, th))
    return image

def find_cache_dir(cache_dir_root, ref_path, vid_path, alignmode):
    """Find cache directory based on reference, video and alignment mode"""
    refname = os.path.splitext(os.path.basename(ref_path))[0]
    vidname = os.path.splitext(os.path.basename(vid_path))[0]
    job_name = f"ref_{refname}_vid_{vidname}"
    pattern = os.path.join(
        cache_dir_root,
        f"ref_{refname}_driven_{vidname}_align_{alignmode}_*"
    )
    matches = glob.glob(pattern)
    if not matches:
        raise FileNotFoundError(f"❌ Cache directory not found: {pattern}")
    if len(matches) > 1:
        print(f"⚠ Multiple cache directories found, using first: {matches}")
    return matches[0], job_name

# ===== Inference Parameters =====
image_guidance_scale = 2.0
pose_guidance_scale = 0
black_pose_cfg = False
black_image_cfg = True
zero_neg = False
negative_prompt = "" if zero_neg else [
    "Aerial view, aerial view, overexposed, low quality, deformation, a poor composition, bad hands, bad teeth, bad eyes, bad limbs, distortion"
]

controlnet_conditioning_scale = 0  # No pose
num_frames = 81 # training 29 but inference 81
case1 = False

# Cache directory
cache_base_dir = "../input_cache"
# Output directory
output_base_dir = "../output/ref_only_1.3b"
# stage 1 only trained on 512px
max_short = 384 
# ===== Task List =====
# Task list format: (ref_path, vid_path, alignmode, new_h, new_w, ref_cfg, pose_cfg, prompt)
# new_h, new_w, ref_cfg, pose_cfg: "" means use default
task_list = [
    ("../examples/img.png", "../examples/vid.mp4", "ref", "", "", "", "", ""),
    ("../examples/joker2_resize.png", "../examples/douyinvid5_v2.mp4", "pose", "", "", "", "", "a clown in a vibrant red suit dancing joyfully"),
    ("../examples/musk.jpg", "../examples/vid2.mp4", "ref", "", "", "", "", ""),
    ("../examples/maodie.png", "../examples/vid2.mp4", "ref", "", "", "", "", ""),
]

# ===== Main Inference Loop =====
for ref_path, vid_path, alignmode, new_h, new_w, ref_cfg, pose_cfg, prompt in task_list:
    try:
        if not ref_cfg:
            ref_cfg = image_guidance_scale
        else:
            ref_cfg = float(ref_cfg)
        
        if not pose_cfg:
            pose_cfg = pose_guidance_scale
        else:
            pose_cfg = float(pose_cfg)
        
        try:
            cache_dir, job_name = find_cache_dir(cache_base_dir, ref_path, vid_path, alignmode)
        except FileNotFoundError as e:
            print(e)
            continue
        
        print(f"Processing: {job_name}")
        print(f"Using cache: {cache_dir}")
        print(f"Prompt: {prompt}")
        
        # Read from cache
        image_input = Image.open(os.path.join(cache_dir, "image_input.png")).convert("RGB")
        mask_input = Image.open(os.path.join(cache_dir, "mask_input.png")).convert("L")
        
        # Get dimensions
        if new_h and new_w:
            new_h = int(float(new_h))
            new_w = int(float(new_w))
        else:
            new_h, new_w = image_input.height, image_input.width
        
        if min(new_h, new_w) > max_short:
            if new_h < new_w:
                scale = max_short / new_h
                new_h, new_w = max_short, int(new_w * scale)
            else:
                scale = max_short / new_w
                new_w, new_h = max_short, int(new_h * scale)
                

        
        # Ensure dimensions are multiples of 16
        new_h = int(new_h // 16 * 16)
        new_w = int(new_w // 16 * 16)
        
        transform = partial(resizecrop, th=new_h, tw=new_w)
        
        # Apply transform
        image_input = transform(image_input)
        mask_input = transform(mask_input)
        
        # Prepare mask tensor
        mask_np = np.array(mask_input, dtype=np.float32) / 255.0
        mask_tensor = torch.from_numpy(mask_np).unsqueeze(0).unsqueeze(0).unsqueeze(2)  # [1,1,1,H,W]
        
        
        # Prepare output path
        current_date = datetime.now().strftime("%Y-%m-%d")
        output_dir = os.path.join(output_base_dir, f"{current_date}_ref_{ref_cfg}_pose_{pose_cfg}_frame_{num_frames}")
        os.makedirs(output_dir, exist_ok=True)
        output_path = os.path.join(output_dir, f"{job_name}_frames{num_frames}.mp4")
        
        if os.path.exists(output_path):
            print(f"✓ {output_path} exists, skipping!")
            continue
        
        # Save input image for reference
        image_input.save(output_path.replace(".mp4", "_input.png"))
        
        print(f"Generating video...")
        
        # Inference - only using reference image, no control video
        output = pipe(
            image=image_input, 
            image_mask=mask_tensor,
            control_video=None,  # No pose control
            prompt=prompt, 
            negative_prompt=negative_prompt, 
            height=new_h, 
            width=new_w, 
            num_frames=num_frames,
            image_guidance_scale=ref_cfg,
            pose_guidance_scale=pose_cfg,
            num_inference_steps=30,
            generator=torch.Generator(device="cuda").manual_seed(42),
            black_image_cfg=black_image_cfg,
            black_pose_cfg=black_pose_cfg,
            controlnet_conditioning_scale=controlnet_conditioning_scale,
            return_tensor=True,
            case1=case1,
            image_pose=None, 
        ).frames[0]
        
        # Save video
        pred = output.detach().cpu()  # (T,C,H,W)
        
        # Convert to video format
        images = (pred / 2 + 0.5).clamp(0, 1)
        img_tensors = rearrange(images.permute(1, 0, 2, 3) * 255, 't c h w -> t h w c').byte().numpy()
        
        imageio.mimwrite(
            output_path, 
            img_tensors,
            fps=30, 
            quality=8, 
            output_params=["-loglevel", "error"]
        )
        
        print(f"✓ Saved to: {output_path}\n")
        
    except Exception as e:
        print(f"❌ Error processing {ref_path}: {e}")
        traceback.print_exc()
        print()

print("All tasks completed!")

	import os
	import glob
	import torch
	import numpy as np
	from opensora.sample.pipeline_wanx_vhuman_tokenreplace import WanPipeline
	from opensora.model_variants.wanx_diffusers_src import WanTransformer3DModel_Refextractor_2D
	from opensora.encoder_variants import get_text_enc
	from opensora.vae_variants import get_vae
	from datetime import datetime
	from einops import rearrange
	import imageio
	from functools import partial
	import traceback
	from PIL import Image

	from diffusers.utils import export_to_video, load_image
	from diffusers.schedulers import FlowMatchEulerDiscreteScheduler

	# ===== Model Config =====
	model_path = "../pretrained_models/Wan2.1-T2V-1.3B-Diffusers"
	vae_path = "../pretrained_models/Wan2.1-T2V-1.3B-Diffusers/vae"
	config_path = "configs/wan2.1_t2v_1.3b.json"

	model_dtype = torch.bfloat16

	# Scheduler
	scheduler = FlowMatchEulerDiscreteScheduler(
	shift=7.0,
	num_train_timesteps=1000,
	use_dynamic_shifting=False
	)

	# VAE and Text Encoder
	vae = get_vae('wanx', vae_path, model_dtype)
	encoders = get_text_enc('wanx-t2v', model_path, model_dtype)
	text_encoder = encoders.text_encoder
	tokenizer = encoders.tokenizer

	# Model
	model = WanTransformer3DModel_Refextractor_2D.from_config(config_path).to(model_dtype)
	model.set_up_refextractor("configs/wan2.1_t2v_1.3b_refextractor_2d_withmask2.json", model_dtype)
	model.eval()
	model.requires_grad_(False)

	# Load checkpoint
	def load_state_dict(ckpt_dir):
	from safetensors.torch import load_file as safe_load
	shard_files = [f for f in os.listdir(ckpt_dir) if f.endswith('.safetensors')]
	checkpoint = {}
	for shard_file in shard_files:
	state_dict = safe_load(os.path.join(ckpt_dir, shard_file), device='cpu')
	checkpoint.update(state_dict)
	return checkpoint

	# change your own path
	ckpt_path = "./outputs_wanx1.3b/train1.3b_only_refextractor_2d_2/checkpoint-21366-6788/fp32_model_21366"
	checkpoint = load_state_dict(ckpt_path)
	model.load_state_dict(checkpoint, strict=True)

	# Pipeline
	pipe = WanPipeline(
	transformer=model,
	vae=vae.vae,
	text_encoder=text_encoder,
	tokenizer=tokenizer,
	scheduler=scheduler
	)
	pipe.to("cuda", dtype=model_dtype)

	# ===== Helper Functions =====
	def resizecrop(image, th, tw):
	w, h = image.size
	if h / w > th / tw:
	new_w = int(w)
	new_h = int(new_w * th / tw)
	else:
	new_h = int(h)
	new_w = int(new_h * tw / th)
	left = (w - new_w) / 2
	top = (h - new_h) / 2
	right = (w + new_w) / 2
	bottom = (h + new_h) / 2
	image = image.crop((left, top, right, bottom))
	image = image.resize((tw, th))
	return image

	def find_cache_dir(cache_dir_root, ref_path, vid_path, alignmode):
	"""Find cache directory based on reference, video and alignment mode"""
	refname = os.path.splitext(os.path.basename(ref_path))[0]
	vidname = os.path.splitext(os.path.basename(vid_path))[0]
	job_name = f"ref_{refname}_vid_{vidname}"
	pattern = os.path.join(
	cache_dir_root,
	f"ref_{refname}_driven_{vidname}_align_{alignmode}_*"
	)
	matches = glob.glob(pattern)
	if not matches:
	raise FileNotFoundError(f"❌ Cache directory not found: {pattern}")
	if len(matches) > 1:
	print(f"⚠ Multiple cache directories found, using first: {matches}")
	return matches[0], job_name

	# ===== Inference Parameters =====
	image_guidance_scale = 2.0
	pose_guidance_scale = 0
	black_pose_cfg = False
	black_image_cfg = True
	zero_neg = False
	negative_prompt = "" if zero_neg else [
	"Aerial view, aerial view, overexposed, low quality, deformation, a poor composition, bad hands, bad teeth, bad eyes, bad limbs, distortion"
	]

	controlnet_conditioning_scale = 0 # No pose
	num_frames = 81 # training 29 but inference 81
	case1 = False

	# Cache directory
	cache_base_dir = "../input_cache"
	# Output directory
	output_base_dir = "../output/ref_only_1.3b"
	# stage 1 only trained on 512px
	max_short = 384
	# ===== Task List =====
	# Task list format: (ref_path, vid_path, alignmode, new_h, new_w, ref_cfg, pose_cfg, prompt)
	# new_h, new_w, ref_cfg, pose_cfg: "" means use default
	task_list = [
	("../examples/img.png", "../examples/vid.mp4", "ref", "", "", "", "", ""),
	("../examples/joker2_resize.png", "../examples/douyinvid5_v2.mp4", "pose", "", "", "", "", "a clown in a vibrant red suit dancing joyfully"),
	("../examples/musk.jpg", "../examples/vid2.mp4", "ref", "", "", "", "", ""),
	("../examples/maodie.png", "../examples/vid2.mp4", "ref", "", "", "", "", ""),
	]

	# ===== Main Inference Loop =====
	for ref_path, vid_path, alignmode, new_h, new_w, ref_cfg, pose_cfg, prompt in task_list:
	try:
	if not ref_cfg:
	ref_cfg = image_guidance_scale
	else:
	ref_cfg = float(ref_cfg)

	if not pose_cfg:
	pose_cfg = pose_guidance_scale
	else:
	pose_cfg = float(pose_cfg)

	try:
	cache_dir, job_name = find_cache_dir(cache_base_dir, ref_path, vid_path, alignmode)
	except FileNotFoundError as e:
	print(e)
	continue

	print(f"Processing: {job_name}")
	print(f"Using cache: {cache_dir}")
	print(f"Prompt: {prompt}")

	# Read from cache
	image_input = Image.open(os.path.join(cache_dir, "image_input.png")).convert("RGB")
	mask_input = Image.open(os.path.join(cache_dir, "mask_input.png")).convert("L")

	# Get dimensions
	if new_h and new_w:
	new_h = int(float(new_h))
	new_w = int(float(new_w))
	else:
	new_h, new_w = image_input.height, image_input.width

	if min(new_h, new_w) > max_short:
	if new_h < new_w:
	scale = max_short / new_h
	new_h, new_w = max_short, int(new_w * scale)
	else:
	scale = max_short / new_w
	new_w, new_h = max_short, int(new_h * scale)



	# Ensure dimensions are multiples of 16
	new_h = int(new_h // 16 * 16)
	new_w = int(new_w // 16 * 16)

	transform = partial(resizecrop, th=new_h, tw=new_w)

	# Apply transform
	image_input = transform(image_input)
	mask_input = transform(mask_input)

	# Prepare mask tensor
	mask_np = np.array(mask_input, dtype=np.float32) / 255.0
	mask_tensor = torch.from_numpy(mask_np).unsqueeze(0).unsqueeze(0).unsqueeze(2) # [1,1,1,H,W]


	# Prepare output path
	current_date = datetime.now().strftime("%Y-%m-%d")
	output_dir = os.path.join(output_base_dir, f"{current_date}_ref_{ref_cfg}_pose_{pose_cfg}_frame_{num_frames}")
	os.makedirs(output_dir, exist_ok=True)
	output_path = os.path.join(output_dir, f"{job_name}_frames{num_frames}.mp4")

	if os.path.exists(output_path):
	print(f"✓ {output_path} exists, skipping!")
	continue

	# Save input image for reference
	image_input.save(output_path.replace(".mp4", "_input.png"))

	print(f"Generating video...")

	# Inference - only using reference image, no control video
	output = pipe(
	image=image_input,
	image_mask=mask_tensor,
	control_video=None, # No pose control
	prompt=prompt,
	negative_prompt=negative_prompt,
	height=new_h,
	width=new_w,
	num_frames=num_frames,
	image_guidance_scale=ref_cfg,
	pose_guidance_scale=pose_cfg,
	num_inference_steps=30,
	generator=torch.Generator(device="cuda").manual_seed(42),
	black_image_cfg=black_image_cfg,
	black_pose_cfg=black_pose_cfg,
	controlnet_conditioning_scale=controlnet_conditioning_scale,
	return_tensor=True,
	case1=case1,
	image_pose=None,
	).frames[0]

	# Save video
	pred = output.detach().cpu() # (T,C,H,W)

	# Convert to video format
	images = (pred / 2 + 0.5).clamp(0, 1)
	img_tensors = rearrange(images.permute(1, 0, 2, 3) * 255, 't c h w -> t h w c').byte().numpy()

	imageio.mimwrite(
	output_path,
	img_tensors,
	fps=30,
	quality=8,
	output_params=["-loglevel", "error"]
	)

	print(f"✓ Saved to: {output_path}\n")

	except Exception as e:
	print(f"❌ Error processing {ref_path}: {e}")
	traceback.print_exc()
	print()

	print("All tasks completed!")