Delete app.py

app.py

@@ -1,82 +0,0 @@
-from diffusers import DDPMPipeline
-image_pipe = DDPMPipeline.from_pretrained("google/ddpm-celebahq-256")
-image_pipe.to("cuda")
-images = image_pipe().images
-image_pipe
-from diffusers import UNet2DModel
-
-repo_id = "google/ddpm-church-256"
-model = UNet2DModel.from_pretrained(repo_id)
-model
-model.config
-model_random = UNet2DModel(**model.config)
-model_random.save_pretrained("my_model")
-model_random = UNet2DModel.from_pretrained("my_model")
-import torch
-
-torch.manual_seed(0)
-
-noisy_sample = torch.randn(
-    1, model.config.in_channels, model.config.sample_size, model.config.sample_size
-)
-noisy_sample.shape
-with torch.no_grad():
-    noisy_residual = model(sample=noisy_sample, timestep=2).sample
-noisy_residual.shape
-from diffusers import DDPMScheduler
-
-scheduler = DDPMScheduler.from_config(repo_id)
-scheduler.config
-scheduler.save_config("my_scheduler")
-new_scheduler = DDPMScheduler.from_config("my_scheduler")
-less_noisy_sample = scheduler.step(
-    model_output=noisy_residual, timestep=2, sample=noisy_sample
-).prev_sample
-less_noisy_sample.shape
-import PIL.Image
-import numpy as np
-
-def display_sample(sample, i):
-    image_processed = sample.cpu().permute(0, 2, 3, 1)
-    image_processed = (image_processed + 1.0) * 127.5
-    image_processed = image_processed.numpy().astype(np.uint8)
-
-    image_pil = PIL.Image.fromarray(image_processed[0])
-    display(f"Image at step {i}")
-    display(image_pil)
-model.to("cuda")
-noisy_sample = noisy_sample.to("cuda")
-import tqdm
-
-sample = noisy_sample
-
-for i, t in enumerate(tqdm.tqdm(scheduler.timesteps)):
-  # 1. predict noise residual
-  with torch.no_grad():
-      residual = model(sample, t).sample
-
-  # 2. compute less noisy image and set x_t -> x_t-1
-  sample = scheduler.step(residual, t, sample).prev_sample
-
-  # 3. optionally look at image
-  if (i + 1) % 50 == 0:
-      display_sample(sample, i + 1)
-from diffusers import DDIMScheduler
-
-scheduler = DDIMScheduler.from_config(repo_id)
-scheduler.set_timesteps(num_inference_steps=50)
-import tqdm
-
-sample = noisy_sample
-
-for i, t in enumerate(tqdm.tqdm(scheduler.timesteps)):
-  # 1. predict noise residual
-  with torch.no_grad():
-      residual = model(sample, t).sample
-
-  # 2. compute previous image and set x_t -> x_t-1
-  sample = scheduler.step(residual, t, sample).prev_sample
-
-  # 3. optionally look at image
-  if (i + 1) % 10 == 0:
-      display_sample(sample, i + 1)