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| #This independent from streamlit runs full speed ~ 5it/s /w StableDiffusionXLPipeline | |
| from diffusers import StableDiffusionXLPipeline, StableDiffusionPipeline | |
| import torch | |
| import time | |
| import inventory as inv | |
| import utilities as u | |
| start_time = time.time() | |
| card_pre_prompt = " blank magic card,high resolution, detailed high quality intricate border, decorated textbox, high quality magnum opus cgi drawing of" | |
| torch.backends.cuda.matmul.allow_tf32 = True | |
| image_list = [] | |
| item = inv.inventory['Shortsword'] | |
| def generate_image(num_img, prompt, item) : | |
| prompt = card_pre_prompt + prompt | |
| print(prompt) | |
| model_path = ("../models/stable-diffusion/SDXLFaetastic_v20.safetensors") | |
| lora_path = ("../models/stable-diffusion/Loras/blank-card-template.safetensors") | |
| pipe = StableDiffusionXLPipeline.from_single_file(model_path, | |
| custom_pipeline="low_stable_diffusion", | |
| torch_dtype=torch.float16, | |
| variant="fp16" ).to("cuda") | |
| pipe.load_lora_weights(lora_path) | |
| pipe.enable_vae_slicing() | |
| for x in range(num_img): | |
| img_start = time.time() | |
| image = pipe(prompt=prompt,num_inference_steps=50, height = 1024, width = 768).images[0] | |
| image = image.save(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_path = str(os.path.abspath(image)) | |
| # image_list.append(image_path) | |
| del image | |
| del pipe | |
| u.reclaim_mem() | |
| 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} ") | |
| return image_path | |