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images / diffusers_reference /run.py

patrickvonplaten

04c5ec4 about 4 years ago

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1.45 kB

#!/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")

	#!/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")