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diffusers_reference/run.py

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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")