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inference.py

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+import os,sys
+import ipdb
+current_dir = os.path.dirname(__file__)
+sys.path.append(os.path.abspath(os.path.join(current_dir, '..')))
+import torch
+from src.condition import Condition
+from PIL import Image
+from src.SubjectGeniusTransformer2DModel import SubjectGeniusTransformer2DModel
+from src.SubjectGeniusPipeline import SubjectGeniusPipeline
+from accelerate.utils import set_seed
+import json
+import argparse
+import cv2
+import numpy as np
+from datetime import datetime
+weight_dtype = torch.bfloat16
+device = torch.device("cuda:0")
+
+
+
+
+def parse_args(input_args=None):
+    parser = argparse.ArgumentParser(description="inference script.")
+    parser.add_argument("--pretrained_model_name_or_path", type=str,default="/data/ydchen/VLP/SubjectGenius/model/FLUX.1-schnell",)
+    parser.add_argument("--transformer",type=str,default="/data/ydchen/VLP/SubjectGenius/model/FLUX.1-schnell/transformer",)
+    parser.add_argument("--condition_types", type=str, nargs='+', default=["fill","subject"],)
+    parser.add_argument("--denoising_lora",type=str,default="/data/ydchen/VLP/SubjectGenius/model/Subject_genuis/Denoising_LoRA/subject_fill_union",)
+    parser.add_argument("--denoising_lora_weight",type=float,default=1.0,)
+    parser.add_argument("--condition_lora_dir",type=str,default="/data/ydchen/VLP/SubjectGenius/model/Subject_genuis/Condition_LoRA",)
+    parser.add_argument("--work_dir",type=str,default="/data/ydchen/VLP/SubjectGenius/output/inference_result",)
+    parser.add_argument("--seed", type=int, default=0)
+    parser.add_argument("--resolution",type=int,default=512,)
+    parser.add_argument("--canny",type=str,default=None)
+    parser.add_argument("--depth",type=str,default=None)
+    parser.add_argument("--fill",type=str,default="/data/ydchen/VLP/SubjectGenius/examples/window/background.jpg")
+    parser.add_argument("--subject",type=str,default="/data/ydchen/VLP/SubjectGenius/examples/window/subject.jpg")
+    parser.add_argument("--json",type=str,default="/data/ydchen/VLP/SubjectGenius/examples/window/1634_rank0_A decorative fabric topper for windows..json")
+    parser.add_argument("--prompt",type=str,default=None)
+    parser.add_argument("--num",type=int,default=1)
+    parser.add_argument("--version",type=str,default="training-free",choices=["training-based","training-free"])
+
+    args = parser.parse_args()
+    args.revision = None
+    args.variant = None
+    args.json = json.load(open(args.json))
+    if args.prompt is None:
+        args.prompt = args.json['description']
+    args.denoising_lora_name = os.path.basename(os.path.normpath(args.denoising_lora))
+    return args
+
+
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    transformer = SubjectGeniusTransformer2DModel.from_pretrained(
+            pretrained_model_name_or_path=args.transformer,
+    ).to(device = device, dtype=weight_dtype)
+
+    for condition_type in args.condition_types:
+        transformer.load_lora_adapter(f"{args.condition_lora_dir}/{condition_type}.safetensors", adapter_name=condition_type)
+
+    pipe = SubjectGeniusPipeline.from_pretrained(
+        args.pretrained_model_name_or_path,
+        torch_dtype = weight_dtype,
+        transformer = None
+    )
+    pipe.transformer = transformer
+
+    if args.version == "training-based":
+        pipe.transformer.load_lora_adapter(args.denoising_lora,adapter_name=args.denoising_lora_name, use_safetensors=True)
+        pipe.transformer.set_adapters([i for i in args.condition_types] + [args.denoising_lora_name],[1.0,1.0,args.denoising_lora_weight])
+    elif args.version == "training-free":
+        pipe.transformer.set_adapters([i for i in args.condition_types])
+
+    pipe = pipe.to(device)
+
+    # load conditions
+    # "no_process = True" means there is no need to run the canny or depth extraction or any other preparation for the input conditional images.
+    # which means the input conditional images can be used directly.
+    conditions = []
+    for condition_type in args.condition_types:
+        if condition_type == "subject":
+            conditions.append(Condition("subject", raw_img=Image.open(args.subject), no_process=True))
+        elif condition_type == "canny":
+            conditions.append(Condition("canny", raw_img=Image.open(args.canny), no_process=True))
+        elif condition_type == "depth":
+            conditions.append(Condition("depth", raw_img=Image.open(args.depth), no_process=True))
+        elif condition_type == "fill":
+            conditions.append(Condition("fill", raw_img=Image.open(args.fill), no_process=True))
+        else:
+            raise ValueError("Only support for subject, canny, depth, fill so far.")
+
+    # load prompt
+    prompt = args.prompt
+
+    if args.seed is not None:
+        set_seed(args.seed)
+
+    output_dir = os.path.join(args.work_dir, f"{datetime.now().strftime('%y_%m_%d-%H:%M')}")
+    os.makedirs(output_dir, exist_ok=True)
+
+    # generate
+    for i in range(args.num):
+        result_img = pipe(
+            prompt=prompt,
+            conditions=conditions,
+            height=512,
+            width=512,
+            num_inference_steps=8,
+            max_sequence_length=512,
+            model_config = {},
+        ).images[0]
+
+        concat_image = Image.new("RGB", (512 + len(args.condition_types) * 512, 512))
+        for j, cond_type in enumerate(args.condition_types):
+            cond_image = conditions[j].condition
+            if cond_type == "fill":
+                cond_image = cv2.rectangle(np.array(cond_image), args.json['bbox'][:2], args.json['bbox'][2:], color=(128, 128, 128),thickness=-1)
+                cond_image = Image.fromarray(cv2.rectangle(cond_image, args.json['bbox'][:2], args.json['bbox'][2:], color=(255, 215, 0), thickness=2))
+            concat_image.paste(cond_image, (j * 512, 0))
+        concat_image.paste(result_img, (j * 512 + 512, 0))
+        concat_image.save(os.path.join(output_dir, f"{i}_result.jpg"))
+        print(f"Done. Output saved at {output_dir}/{i}_result.jpg")