test_pipeline.py

from rct_diffusion_pipeline import RCTDiffusionPipeline
from diffusers import UNet2DConditionModel


torch_device = "cuda"

pipeline = RCTDiffusionPipeline()
pipeline.print_class_tokens_to_csv()
output = pipeline([[('aleppo pine tree', 1.0)]], [[('dark green', 1.0)]])

# from PIL import Image
# import torch
# from transformers import CLIPTextModel, CLIPTokenizer
# from diffusers import AutoencoderKL, UNet2DConditionModel, PNDMScheduler

# vae = AutoencoderKL.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="vae", use_safetensors=True)
# tokenizer = CLIPTokenizer.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="tokenizer")
# text_encoder = CLIPTextModel.from_pretrained(
#     "CompVis/stable-diffusion-v1-4", subfolder="text_encoder", use_safetensors=True
# )
# unet = UNet2DConditionModel.from_pretrained(
#     "CompVis/stable-diffusion-v1-4", subfolder="unet", use_safetensors=True
# )

# from diffusers import UniPCMultistepScheduler

# scheduler = UniPCMultistepScheduler.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="scheduler")
# torch_device = "cuda"
# vae.to(torch_device)
# text_encoder.to(torch_device)
# unet.to(torch_device)

# prompt = ["a photograph of an astronaut riding a horse"]
# height = 512  # default height of Stable Diffusion
# width = 512  # default width of Stable Diffusion
# num_inference_steps = 25  # Number of denoising steps
# guidance_scale = 7.5  # Scale for classifier-free guidance
# generator = torch.manual_seed(0)  # Seed generator to create the inital latent noise
# batch_size = len(prompt)

# text_input = tokenizer(
#     prompt, padding="max_length", max_length=tokenizer.model_max_length, truncation=True, return_tensors="pt"
# )

# with torch.no_grad():
#     text_embeddings = text_encoder(text_input.input_ids.to(torch_device))[0]

# text_input = tokenizer(
#     prompt, padding="max_length", max_length=tokenizer.model_max_length, truncation=True, return_tensors="pt"
# )

# with torch.no_grad():
#     text_embeddings = text_encoder(text_input.input_ids.to(torch_device))[0]

# max_length = text_input.input_ids.shape[-1]
# uncond_input = tokenizer([""] * batch_size, padding="max_length", max_length=max_length, return_tensors="pt")
# uncond_embeddings = text_encoder(uncond_input.input_ids.to(torch_device))[0]

# text_embeddings = torch.cat([uncond_embeddings, text_embeddings])

# latents = torch.randn(
#     (batch_size, unet.in_channels, height // 8, width // 8),
#     generator=generator,
# )
# latents = latents.to(torch_device)

# latents = latents * scheduler.init_noise_sigma

# from tqdm.auto import tqdm

# scheduler.set_timesteps(num_inference_steps)

# for t in tqdm(scheduler.timesteps):
#     # expand the latents if we are doing classifier-free guidance to avoid doing two forward passes.
#     latent_model_input = torch.cat([latents] * 2)

#     latent_model_input = scheduler.scale_model_input(latent_model_input, timestep=t)

#     # predict the noise residual
#     with torch.no_grad():
#         noise_pred = unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample

#     # perform guidance
#     noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
#     noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

#     # compute the previous noisy sample x_t -> x_t-1
#     latents = scheduler.step(noise_pred, t, latents).prev_sample
print('test')