| |
| from diffusers import DiffusionPipeline, DDIMScheduler |
| import argparse |
| import torch |
| from datasets import load_dataset |
| import PIL |
|
|
| IMAGE_OUTPUT_SIZE = (256, 256) |
| NUM_INFERENCE_STEPS = 100 |
|
|
| def resize(image: PIL.Image): |
| return image.resize(IMAGE_OUTPUT_SIZE, resample=PIL.Image.Resampling.LANCZOS) |
|
|
| def get_sd_eval(ckpt, guidance_scale=7.5): |
| pipe = DiffusionPipeline.from_pretrained(ckpt, torch_dtype=torch.float16) |
| pipe.to("cuda") |
| pipe.scheduler = DDIMScheduler.from_config(pipe.config) |
|
|
| def sd_eval(prompt): |
| images = pipe(prompt, num_inference_steps=100, guidance_scale=guidance_scale).images |
| images = [resize(image) for image in images] |
| return images |
|
|
| return sd_eval |
|
|
| def get_karlo_eval(ckpt): |
| pipe = DiffusionPipeline.from_pretrained(ckpt, torch_dtype=torch.float16) |
| pipe.to("cuda") |
|
|
| def karlo_eval(prompt): |
| images = pipe(prompt, prior_num_inference_steps=50, decoder_num_inference_steps=100).images |
| return images |
|
|
| return karlo_eval |
|
|
| def get_if_eval(ckpt): |
| pipe_low = DiffusionPipeline.from_pretrained(ckpt, torch_dtype=torch.float16) |
| pipe_low.enable_model_cpu_offload() |
|
|
| pipe_up = DiffusionPipeline.from_pretrained("DeepFloyd/IF-II-L-v1.0", text_encoder=pipe_low.text_encoder, torch_dtype=torch.float16) |
| pipe_up.enable_model_cpu_offload() |
|
|
| def sd_eval(prompt): |
| images = pipe_low(prompt, num_inference_steps=100, output_type="pt").images |
| images = pipe_up(promtp=prompt, images=images, num_inference_steps=100).images |
| return images |
|
|
| return sd_eval |
|
|
| MODELS = { |
| "runwayml/stable-diffusion-v1-5": get_sd_eval, |
| "stabilityai/stable-diffusion-v2-1": get_sd_eval, |
| "kakaobrain/karlo-alpha": get_karlo_eval, |
| "DeepFloyd/IF-I-XL-v1.0": get_if_eval, |
| } |
|
|
|
|
|
|
|
|
| if __name__ == "__main__": |
| parser = argparse.ArgumentParser(description='Run Parti Prompt Evaluation') |
| parser.add_argument('model_repo_or_id', type=str, help='ID or URL of the model repository.', required=True) |
| parser.add_argument('--dataset_repo_or_id', type=str, default='diffusers/prompt_generations', help='ID or URL of the dataset repository (default: "diffusers/prompt_generations")') |
| parser.add_argument('--batch_size', type=int, default=8, help="Batch size for the eval function") |
| parser.add_argument('--upload_to_hub', action='store_true', help='whether to upload the dataset to the Hugging Face dataset hub') |
|
|
| args = parser.parse_args() |
|
|
| eval_fn = MODELS[args.model_repo_or_id](args.model_repo_or_id) |
|
|
| dataset = load_dataset("nateraw/parti-prompts") |
|
|
| def map_fn(batch): |
| batch["images"] = eval_fn(batch["prompt"]) |
| return batch |
|
|
| dataset_images = dataset.map(map_fn, batched=True, batch_size=8) |
|
|
| if args.upload_to_hub: |
| dataset.push_to_hub(args.dataset_repo_or_id) |
| else: |
| dataset.save_to_disk(args.dataset_repo_or_id.split("/")[-1]) |
|
|
