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| from diffusers import StableDiffusionPipeline | |
| import torch | |
| from torchmetrics.functional.multimodal import clip_score | |
| from functools import partial | |
| model_ckpt = "sd-pokemon-model" | |
| sd_pipeline = StableDiffusionPipeline.from_pretrained(model_ckpt, torch_dtype=torch.float16).to("cuda") | |
| prompts = [ | |
| "a photo of an astronaut riding a horse on mars", | |
| "A high tech solarpunk utopia in the Amazon rainforest", | |
| "A pikachu fine dining with a view to the Eiffel Tower", | |
| "A mecha robot in a favela in expressionist style", | |
| "an insect robot preparing a delicious meal", | |
| "A small cabin on top of a snowy mountain in the style of Disney, artstation", | |
| ] | |
| images = sd_pipeline(prompts, num_images_per_prompt=1, output_type="numpy").images | |
| clip_score_fn = partial(clip_score, model_name_or_path="openai/clip-vit-base-patch16") | |
| def calculate_clip_score(images, prompts): | |
| images_int = (images * 255).astype("uint8") | |
| clip_score = clip_score_fn(torch.from_numpy(images_int).permute(0, 3, 1, 2), prompts).detach() | |
| return round(float(clip_score), 4) | |
| sd_clip_score = calculate_clip_score(images, prompts) | |
| print(f"CLIP score: {sd_clip_score}") | |