test all

_

@@ -1,35 +0,0 @@
-#!/usr/bin/env python3
-import torch
-from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
-import numpy as np
-import gc
-from diffusers.utils import export_to_video
-from PIL import Image
-
-pipe = DiffusionPipeline.from_pretrained("cerspense/zeroscope_v2_576w", torch_dtype=torch.float16)
-pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
-pipe.enable_model_cpu_offload()
-pipe.enable_xformers_memory_efficient_attention()
-pipe.enable_vae_slicing()
-
-prompt = "spiderman running in the desert"
-video_frames = pipe(prompt, num_inference_steps=2, height=320, width=576, num_frames=24, output_type="latent".frames
-# video_path = export_to_video(video_frames, output_video_path="/home/patrick/videos/video_576_spiderman_24.mp4")
-
-pipe.to("cpu")
-del pipe
-gc.collect()
-torch.cuda.empty_cache()
-
-import ipdb; ipdb.set_trace()
-
-pipe = DiffusionPipeline.from_pretrained("cerspense/zeroscope_v2_XL", torch_dtype=torch.float16)
-pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
-pipe.enable_model_cpu_offload()
-pipe.enable_xformers_memory_efficient_attention()
-pipe.enable_vae_slicing()
-
-video = [Image.fromarray(frame).resize((1024, 576)) for frame in video_frames]
-
-video_frames = pipe(prompt, video=video, num_inference_steps=2, strength=0.6).frames
-video_path = export_to_video(video_frames, output_video_path="/home/patrick/videos/video_1024_spiderman_24.mp4")

~

@@ -1,51 +0,0 @@
-#!/usr/bin/env python3
-from diffusers import DiffusionPipeline, EulerDiscreteScheduler, StableDiffusionPipeline, KDPM2DiscreteScheduler, StableDiffusionImg2ImgPipeline, HeunDiscreteScheduler, KDPM2AncestralDiscreteScheduler, DDIMScheduler, StableDiffusionXLImg2ImgPipeline
-import time
-import numpy as np
-import os
-from huggingface_hub import HfApi
-# from compel import Compel
-import torch
-import sys
-from pathlib import Path
-import requests
-from PIL import Image
-from io import BytesIO
-
-path = sys.argv[1]
-
-api = HfApi()
-start_time = time.time()
-pipe = StableDiffusionXLImg2ImgPipeline.from_pretrained(path)
-pipe.to("cuda")
-pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
-# pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
-# pipe = StableDiffusionImg2ImgXLPipeline.from_pretrained(path, torch_dtype=torch.float16, safety_checker=None
-
-# compel = Compel(tokenizer=pipe.tokenizer, text_encoder=pipe.text_encoder)
-
-
-prompt = "An astronaut riding a green horse on Mars"
-
-
-# pipe.unet.to(memory_format=torch.channels_last)
-# pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
-# pipe(prompt=prompt, num_inference_steps=2).images[0]
-# url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
-# 
-# response = requests.get(url)
-init_image = torch.from_numpy(np.load("/home/patrick/images/xl_latents.npy")).to("cuda")
-
-image = pipe(prompt=prompt, image=init_image).images[0]
-
-file_name = f"aaa"
-path = os.path.join(Path.home(), "images", f"{file_name}.png")
-image.save(path)
-
-api.upload_file(
-    path_or_fileobj=path,
-    path_in_repo=path.split("/")[-1],
-    repo_id="patrickvonplaten/images",
-    repo_type="dataset",
-)
-print(f"https://huggingface.co/datasets/patrickvonplaten/images/blob/main/{file_name}.png")