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import torch
from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig
from PIL import Image
import os
import json
# for i in range(2):
# pipe.load_lora(pipe.dit, f"models/train/FLUX.1_lora_1126/epoch-{i}.safetensors", alpha=1)
# step = 25
# input_path = "dataset/multi_frame"
# base_path = f"validate_result/multi_frame{step}"
# save_path = f"{base_path}/epoch{i}"
# save_path_GT = f"{base_path}/GT"
# os.makedirs(save_path, exist_ok=True)
# os.makedirs(save_path_GT, exist_ok=True)
# for img in os.listdir(input_path):
# image = Image.open(os.path.join(input_path,img))
# image.save(os.path.join(save_path_GT,img))
# prompt="Convert this image into a line art style: retain the original scenes and characters unchanged, present it as a black-and-white sketch effect, and make it suitable for storyboard design. Requirements: use bold and powerful lines, highlight structures and textures with concise strokes, adopt a style close to comic sketching, roughly outline the scenes and character movements with simple lines, prohibit the depiction of details, and represent the characters' facial features with the simplest lines.",
# # prompt = "Convert this image into a mbti style"
# for fig in os.listdir(input_path):
# if not fig.endswith(".png"):
# continue
# image = pipe(
# prompt = prompt,
# kontext_images=Image.open(os.path.join(input_path,fig)).resize((768, 768)),
# height=768, width=768,
# seed=0,
# num_inference_steps=step
# )
# image.save(os.path.join(save_path,fig))
for i in range(2):
pipe = FluxImagePipeline.from_pretrained(
torch_dtype=torch.bfloat16,
device="cuda",
model_configs=[
ModelConfig(model_id="black-forest-labs/FLUX.1-Kontext-dev", origin_file_pattern="flux1-kontext-dev.safetensors"),
ModelConfig(model_id="black-forest-labs/FLUX.1-Kontext-dev", origin_file_pattern="text_encoder/model.safetensors"),
ModelConfig(model_id="black-forest-labs/FLUX.1-Kontext-dev", origin_file_pattern="text_encoder_2/"),
ModelConfig(model_id="black-forest-labs/FLUX.1-Kontext-dev", origin_file_pattern="ae.safetensors"),
],
)
pipe.load_lora(pipe.dit, f"models/train/FLUX.1_lora_1126/epoch-{i}.safetensors", alpha=1)
step = 25
base_path = "/fi-lib/workspace/sjx/DiffSynth-Studio/validate_result/t2i_1201{step}"
save_path = f"{base_path}/epoch{i}"
os.makedirs(save_path, exist_ok=True)
with open("nano_comprehension_1201.txt", "r") as f:
prompts = f.readlines()
for prompt in prompts:
prompt = prompt.strip()
if prompt == "":
continue
prompt_dict = json.loads(prompt)
fig = f"{prompt_dict["Image_Name"]}.png"
del prompt_dict["Image_Name"]
prompt = json.dumps(prompt_dict, ensure_ascii=False)
image = pipe(
prompt = prompt,
height=768, width=768,
seed=0,
num_inference_steps=step
)
image.save(os.path.join(save_path,fig))
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