delete unused files

_

-# Copyright 2022 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-
-# limitations under the License.
-
-
-import torch
-
-import tqdm
-
-from ..pipeline_utils import DiffusionPipeline
-
-
-class PNDM(DiffusionPipeline):
-    def __init__(self, unet, noise_scheduler):
-        super().__init__()
-        noise_scheduler = noise_scheduler.set_format("pt")
-        self.register_modules(unet=unet, noise_scheduler=noise_scheduler)
-
-    def __call__(self, batch_size=1, generator=None, torch_device=None, num_inference_steps=50):
-        # eta corresponds to η in paper and should be between [0, 1]
-        if torch_device is None:
-            torch_device = "cuda" if torch.cuda.is_available() else "cpu"
-
-        num_trained_timesteps = self.noise_scheduler.timesteps
-        inference_step_times = range(0, num_trained_timesteps, num_trained_timesteps // num_inference_steps)
-
-        self.unet.to(torch_device)
-
-        # Sample gaussian noise to begin loop
-        image = torch.randn(
-            (batch_size, self.unet.in_channels, self.unet.resolution, self.unet.resolution),
-            generator=generator,
-        )
-        image = image.to(torch_device)
-
-        seq = list(inference_step_times)
-        seq_next = [-1] + list(seq[:-1])
-        model = self.unet
-
-        warmup_steps = [len(seq) - (i // 4 + 1) for i in range(3 * 4)]
-
-        ets = []
-        prev_image = image
-        for i, step_idx in enumerate(warmup_steps):
-            i = seq[step_idx]
-            j = seq_next[step_idx]
-
-            t = (torch.ones(image.shape[0]) * i)
-            t_next = (torch.ones(image.shape[0]) * j)
-
-            residual = model(image.to("cuda"), t.to("cuda"))
-            residual = residual.to("cpu")
-
-            image = image.to("cpu")
-            image = self.noise_scheduler.transfer(prev_image.to("cpu"), t_list[0], t_list[1], residual)
-
-            if i % 4 == 0:
-                ets.append(residual)
-                prev_image = image
-
-        for 
-
-        ets = []
-        step_idx = len(seq) - 1
-        while step_idx >= 0:
-            i = seq[step_idx]
-            j = seq_next[step_idx]
-
-            t = (torch.ones(image.shape[0]) * i)
-            t_next = (torch.ones(image.shape[0]) * j)
-
-            residual = model(image.to("cuda"), t.to("cuda"))
-            residual = residual.to("cpu")
-
-            t_list = [t, (t+t_next)/2, t_next]
-
-            ets.append(residual)
-            if len(ets) <= 3:
-                image = image.to("cpu")
-                x_2 = self.noise_scheduler.transfer(image.to("cpu"), t_list[0], t_list[1], residual)
-
-                e_2 = model(x_2.to("cuda"), t_list[1].to("cuda")).to("cpu")
-                x_3 = self.noise_scheduler.transfer(image, t_list[0], t_list[1], e_2)
-                e_3 = model(x_3.to("cuda"), t_list[1].to("cuda")).to("cpu")
-                x_4 = self.noise_scheduler.transfer(image, t_list[0], t_list[2], e_3)
-                e_4 = model(x_4.to("cuda"), t_list[2].to("cuda")).to("cpu")
-                residual = (1 / 6) * (residual + 2 * e_2 + 2 * e_3 + e_4)
-            else:
-                residual = (1 / 24) * (55 * ets[-1] - 59 * ets[-2] + 37 * ets[-3] - 9 * ets[-4])
-
-            img_next = self.noise_scheduler.transfer(image.to("cpu"), t, t_next, residual)
-            image = img_next
-
-            step_idx = step_idx - 1
-
-#            if len(prev_noises) in [1, 2]:
-#                t = (t + t_next) / 2
-#            elif len(prev_noises) == 3:
-#                t = t_next / 2
-
-#            if len(prev_noises) == 0:
-#                ets.append(residual)
-#
-#            if len(ets) > 3:
-#                residual = (1 / 24) * (55 * ets[-1] - 59 * ets[-2] + 37 * ets[-3] - 9 * ets[-4])
-#                step_idx = step_idx - 1
-#            elif len(ets) <= 3 and len(prev_noises) == 3:
-#                residual = (1 / 6) * (prev_noises[-3] + 2 * prev_noises[-2] + 2 * prev_noises[-1] + residual)
-#                prev_noises = []
-#                step_idx = step_idx - 1
-#            elif len(ets) <= 3 and len(prev_noises) < 3:
-#                prev_noises.append(residual)
-#                if len(prev_noises) < 2:
-#                    t_next = (t + t_next) / 2
-#
-#            image = self.noise_scheduler.transfer(image.to("cpu"), t, t_next, residual)
-
-        return image
-
-        # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
-        # Ideally, read DDIM paper in-detail understanding
-
-        # Notation (<variable name> -> <name in paper>
-        # - pred_noise_t -> e_theta(x_t, t)
-        # - pred_original_image -> f_theta(x_t, t) or x_0
-        # - std_dev_t -> sigma_t
-        # - eta -> η
-        # - pred_image_direction -> "direction pointingc to x_t"
-        # - pred_prev_image -> "x_t-1"
-#        for t in tqdm.tqdm(reversed(range(num_inference_steps)), total=num_inference_steps):
-            # 1. predict noise residual
-#            with torch.no_grad():
-#                residual = self.unet(image, inference_step_times[t])
-#
-            # 2. predict previous mean of image x_t-1
-#            pred_prev_image = self.noise_scheduler.step(residual, image, t, num_inference_steps, eta)
-#
-            # 3. optionally sample variance
-#            variance = 0
-#            if eta > 0:
-#                noise = torch.randn(image.shape, generator=generator).to(image.device)
-#                variance = self.noise_scheduler.get_variance(t, num_inference_steps).sqrt() * eta * noise
-#
-            # 4. set current image to prev_image: x_t -> x_t-1
-#            image = pred_prev_image + variance

run_pndm.py

-#!/usr/bin/env python3
-from diffusers import PNDM, UNetModel, PNDMScheduler
-import PIL.Image
-import numpy as np
-import torch
-
-model_id = "fusing/ddim-celeba-hq"
-
-model = UNetModel.from_pretrained(model_id)
-scheduler = PNDMScheduler()
-
-# load model and scheduler
-ddpm = PNDM(unet=model, noise_scheduler=scheduler)
-
-# run pipeline in inference (sample random noise and denoise)
-image = ddpm()
-
-# process image to PIL
-image_processed = image.cpu().permute(0, 2, 3, 1)
-image_processed = (image_processed + 1.0) / 2
-image_processed = torch.clamp(image_processed, 0.0, 1.0)
-image_processed = image_processed * 255
-image_processed = image_processed.numpy().astype(np.uint8)
-image_pil = PIL.Image.fromarray(image_processed[0])
-
-# save image
-image_pil.save("/home/patrick/images/test.png")