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from diffusers import StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipeline
from diffusers.utils import load_image
import torch
import time
import utilities as u
import card_generator as card
from PIL import Image
start_time = time.time()
torch.backends.cuda.matmul.allow_tf32 = True
model_path = ("../../models/stable-diffusion/SDXLFaetastic_v24.safetensors")
lora_path = "../../models/stable-diffusion/Loras/blank-card-template-5.safetensors"
detail_lora_path = "../../models/stable-diffusion/Loras/add-detail-xl.safetensors"
mimic_lora_path = "../../models/stable-diffusion/Loras/EnvyMimicXL01.safetensors"
card_pre_prompt = " blank magic card,high resolution, detailed intricate high quality border, textbox, high quality magnum opus drawing of a "
negative_prompts = "text, words, numbers, letters"
image_list = []
def generate_image(num_img, prompt, item, user_input_template, mimic = None) :
prompt = card_pre_prompt + item + ' ' + prompt
print(prompt)
image_path = f"card_templates/{user_input_template}"
init_image = load_image(image_path).convert("RGB")
pipe = StableDiffusionXLImg2ImgPipeline.from_single_file(model_path,
custom_pipeline="low_stable_diffusion",
torch_dtype=torch.float16,
variant="fp16",
local_files_only = True).to("cuda")
# Load LoRAs for controlling image
pipe.load_lora_weights(lora_path, weight_name = "blank-card-template-5.safetensors",adapter_name = 'blank-card-template')
pipe.load_lora_weights(detail_lora_path, weight_name = "add-detail-xl.safetensors", adapter_name = "add-detail-xl")
# If mimic keyword has been detected, load the mimic LoRA and set adapter values
if mimic:
pipe.load_lora_weights(mimic_lora_path, weight_name = "EnvyMimicXL01.safetensors", adapter_name = "EnvyMimicXL")
pipe.set_adapters(['blank-card-template', "add-detail-xl", "EnvyMimicXL"], adapter_weights = [0.9,0.9,1.0])
else :
pipe.set_adapters(['blank-card-template', "add-detail-xl"], adapter_weights = [0.9,0.9])
pipe.enable_vae_slicing()
for x in range(num_img):
img_start = time.time()
image = pipe(prompt=prompt,
strength = .9,
guidance_scale = 5,
image= init_image,
negative_promt = negative_prompts,
num_inference_steps=50,
height = 1024, width = 768).images[0]
image = image.save(str(x) + f"{item}.png")
output_image_path = str(x) + f"{item}.png"
img_time = time.time() - img_start
img_its = 50/img_time
print(f"image gen time = {img_time} and {img_its} it/s")
print(f"Memory after image {x} = {torch.cuda.memory_allocated()}")
image_list.append(output_image_path)
# Delete the image variable to keep VRAM open to load the LLM
del image
print(f"Memory after del {torch.cuda.memory_allocated()}")
print(image_list)
total_time = time.time() - start_time
print(f"Total Time to generate{x} images = {total_time} ")
del pipe
u.reclaim_mem()
return image_list
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