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from diffusers import DiffusionPipeline, DDIMScheduler |
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import argparse |
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from diffusers.pipelines.stable_diffusion import safety_checker |
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import torch |
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from datasets import load_dataset |
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import PIL |
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IMAGE_OUTPUT_SIZE = (256, 256) |
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NUM_INFERENCE_STEPS = 100 |
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def resize(image: PIL.Image): |
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return image.resize(IMAGE_OUTPUT_SIZE, resample=PIL.Image.Resampling.LANCZOS) |
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def get_sd_eval(ckpt, guidance_scale=7.5): |
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pipe = DiffusionPipeline.from_pretrained(ckpt, torch_dtype=torch.float16, safety_checker=None) |
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pipe.to("cuda") |
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pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config) |
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def sd_eval(prompt, generator=None): |
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images = pipe(prompt, generator=generator, num_inference_steps=NUM_INFERENCE_STEPS, guidance_scale=guidance_scale).images |
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images = [resize(image) for image in images] |
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return images |
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return sd_eval |
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def get_karlo_eval(ckpt): |
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pipe = DiffusionPipeline.from_pretrained(ckpt, torch_dtype=torch.float16) |
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pipe.to("cuda") |
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def karlo_eval(prompt, generator=None): |
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images = pipe(prompt, prior_num_inference_steps=50, generator=generator, decoder_num_inference_steps=NUM_INFERENCE_STEPS).images |
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return images |
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return karlo_eval |
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def get_if_eval(ckpt): |
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pipe_low = DiffusionPipeline.from_pretrained(ckpt, safety_checker=None, watermarker=None, torch_dtype=torch.float16, variant="fp16") |
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pipe_low.enable_model_cpu_offload() |
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pipe_up = DiffusionPipeline.from_pretrained("DeepFloyd/IF-II-L-v1.0", safety_checker=None, watermarker=None, text_encoder=pipe_low.text_encoder, torch_dtype=torch.float16, variant="fp16") |
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pipe_up.enable_model_cpu_offload() |
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def if_eval(prompt, generator=None): |
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prompt_embeds, negative_prompt_embeds = pipe_low.encode_prompt(prompt) |
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images = pipe_low(prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, num_inference_steps=NUM_INFERENCE_STEPS, generator=generator, output_type="pt").images |
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images = pipe_up(prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, image=images, num_inference_steps=NUM_INFERENCE_STEPS, generator=generator).images |
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return images |
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return if_eval |
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MODELS = { |
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"runwayml/stable-diffusion-v1-5": get_sd_eval, |
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"stabilityai/stable-diffusion-2-1": get_sd_eval, |
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"kakaobrain/karlo-alpha": get_karlo_eval, |
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"DeepFloyd/IF-I-XL-v1.0": get_if_eval, |
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} |
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if __name__ == "__main__": |
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parser = argparse.ArgumentParser(description='Run Parti Prompt Evaluation') |
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parser.add_argument('model_repo_or_id', type=str, help='ID or URL of the model repository.') |
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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")') |
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parser.add_argument('--batch_size', type=int, default=8, help="Batch size for the eval function") |
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parser.add_argument('--upload_to_hub', action='store_true', help='whether to upload the dataset to the Hugging Face dataset hub') |
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parser.add_argument('--seed', type=int, default=0, help='Random seed') |
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args = parser.parse_args() |
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dataset = load_dataset("nateraw/parti-prompts")["train"] |
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eval_fn = MODELS[args.model_repo_or_id](args.model_repo_or_id) |
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def map_fn(batch): |
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generators = [torch.Generator(device="cuda").manual_seed(args.seed) for _ in range(args.batch_size)] |
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batch["images"] = eval_fn(batch["Prompt"], generator=generators) |
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batch["model_name"] = len(batch["images"]) * [args.model_repo_or_id] |
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batch["seed"] = len(batch["images"]) * [args.seed] |
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return batch |
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dataset_images = dataset.map(map_fn, batched=True, batch_size=args.batch_size) |
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if args.upload_to_hub: |
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dataset_images.push_to_hub(args.dataset_repo_or_id) |
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else: |
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dataset_images.save_to_disk(args.dataset_repo_or_id.split("/")[-1]) |
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