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Runtime error
| # Original Stable Diffusion (1.4) | |
| import torch | |
| import models | |
| from models import pipelines | |
| from shared import model_dict | |
| vae, tokenizer, text_encoder, unet, scheduler, dtype = model_dict.vae, model_dict.tokenizer, model_dict.text_encoder, model_dict.unet, model_dict.scheduler, model_dict.dtype | |
| torch.set_grad_enabled(False) | |
| height = 512 # default height of Stable Diffusion | |
| width = 512 # default width of Stable Diffusion | |
| num_inference_steps = 20 # Number of denoising steps | |
| guidance_scale = 7.5 # Scale for classifier-free guidance | |
| batch_size = 1 | |
| # h, w | |
| image_scale = (512, 512) | |
| bg_negative = 'artifacts, blurry, smooth texture, bad quality, distortions, unrealistic, distorted image, bad proportions, duplicate' | |
| def run(prompt, bg_seed=1): | |
| print(f"prompt: {prompt}") | |
| generator = torch.Generator(models.torch_device).manual_seed(bg_seed) | |
| prompts = [prompt] | |
| input_embeddings = models.encode_prompts(prompts=prompts, tokenizer=tokenizer, text_encoder=text_encoder, negative_prompt=bg_negative) | |
| generator = torch.manual_seed(1) # Seed generator to create the inital latent noise | |
| latents = models.get_unscaled_latents(batch_size, unet.config.in_channels, height, width, generator, dtype) | |
| latents = latents * scheduler.init_noise_sigma | |
| pipelines.gligen_enable_fuser(model_dict['unet'], enabled=False) | |
| _, images = pipelines.generate( | |
| model_dict, latents, input_embeddings, num_inference_steps, | |
| guidance_scale=guidance_scale | |
| ) | |
| return images[0] |