Spaces:
Runtime error
Runtime error
| import os | |
| import sys | |
| sys.path.append("./") | |
| import torch | |
| from torchvision import transforms | |
| from src.transformer import Transformer2DModel | |
| from src.pipeline import Pipeline | |
| from src.scheduler import Scheduler | |
| from transformers import ( | |
| CLIPTextModelWithProjection, | |
| CLIPTokenizer, | |
| ) | |
| from diffusers import VQModel | |
| device = 'cuda' | |
| dtype = torch.bfloat16 | |
| model_path = "Collov-Labs/Monetico" | |
| model = Transformer2DModel.from_pretrained(model_path,subfolder="transformer",torch_dtype=dtype) | |
| vq_model = VQModel.from_pretrained(model_path, subfolder="vqvae", torch_dtype=dtype) | |
| text_encoder = CLIPTextModelWithProjection.from_pretrained(model_path,subfolder="text_encoder",torch_dtype=dtype) | |
| tokenizer = CLIPTokenizer.from_pretrained(model_path,subfolder="tokenizer",torch_dtype=dtype) | |
| scheduler = Scheduler.from_pretrained(model_path,subfolder="scheduler") | |
| pipe=Pipeline(vq_model, tokenizer=tokenizer,text_encoder=text_encoder,transformer=model,scheduler=scheduler) | |
| pipe = pipe.to(device) | |
| steps = 48 | |
| CFG = 9 | |
| resolution = 512 | |
| negative_prompts = "worst quality, low quality, low res, blurry, distortion, watermark, logo, signature, text, jpeg artifacts, signature, sketch, duplicate, ugly, identifying mark" | |
| prompts = [ | |
| "Two actors are posing for a pictur with one wearing a black and white face paint.", | |
| "A large body of water with a rock in the middle and mountains in the background.", | |
| "A white and blue coffee mug with a picture of a man on it.", | |
| "A statue of a man with a crown on his head.", | |
| "A man in a yellow wet suit is holding a big black dog in the water.", | |
| "A white table with a vase of flowers and a cup of coffee on top of it.", | |
| "A woman stands on a dock in the fog.", | |
| "A woman is standing next to a picture of another woman." | |
| ] | |
| image = pipe(prompt=prompts[0],negative_prompt=negative_prompts,height=resolution,width=resolution,guidance_scale=CFG,num_inference_steps=steps).images[0] | |
| output_dir = "./output" | |
| os.makedirs(output_dir, exist_ok=True) | |
| image.save(output_dir, f"{prompt[:10]}_{resolution}_{steps}_{CFG}.png") | |