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-#!/usr/bin/env python3
-#!/usr/bin/env python3
-from diffusers import StableDiffusionPipeline, DDIMScheduler
-from time import time
-from PIL import Image
-from einops import rearrange
-import numpy as np
-import torch
-from torch import autocast
-from torchvision.utils import make_grid
-
-torch.manual_seed(42)
-
-prompts = ["a photograph of an astronaut riding a horse"]
-
-pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-3", revision="fp16", torch_dtype=torch.float16, use_auth_token=True)  # make sure you're logged in with `huggingface-cli login`
-#scheduler = DDIMScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", clip_sample=False, set_alpha_to_one=False)
-#pipe.scheduler = scheduler
-pipe.to("cuda")
-
-all_images = []
-num_rows = 1
-num_columns = 4
-for prompt in prompts:
-    with autocast("cuda"):
-        images = pipe(num_columns * [prompt], guidance_scale=7.5, output_type="np")["sample"]  # image here is in [PIL format](https://pillow.readthedocs.io/en/stable/)
-        all_images.append(torch.from_numpy(images))
-
-# additionally, save as grid
-grid = torch.stack(all_images, 0)
-grid = rearrange(grid, 'n b h w c -> (n b) h w c')
-grid = rearrange(grid, 'n h w c -> n c h w')
-grid = make_grid(grid, nrow=num_rows)
-
-# to image
-grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy()
-image = Image.fromarray(grid.astype(np.uint8))
-
-image.save(f"../images/diffusers/batch_{round(time())}.png")
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+++ /dev/null
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-#!/usr/bin/env python3
-from diffusers import StableDiffusionPipeline
-import torch
-
-model_ids = ["nitrosocke/Arcane-Diffusion", "nitrosocke/spider-verse-diffusion", "nitrosocke/mo-di-diffusion", "nitrosocke/archer-diffusion", "nitrosocke/elden-ring-diffusion"]
-prompts = ["arcane style", "spiderverse style", "modern disney style", "archer style", "elden ring style"]
-
-
-for model_id, prompt in zip(model_ids, prompts):
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