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from diffusers import StableDiffusionPipeline, DPMSolverSinglestepScheduler, DPMSolverMultistepScheduler, DEISMultistepScheduler, HeunDiscreteScheduler |
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from diffusers.schedulers.scheduling_unipc_multistep import UniPCMultistepScheduler |
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import time |
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import os |
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from huggingface_hub import HfApi |
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from compel import Compel |
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import torch |
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import sys |
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from pathlib import Path |
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path = sys.argv[1] |
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api = HfApi() |
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start_time = time.time() |
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pipe = StableDiffusionPipeline.from_pretrained(path, torch_dtype=torch.float16) |
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pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config) |
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compel = Compel(tokenizer=pipe.tokenizer, text_encoder=pipe.text_encoder) |
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pipe = pipe.to("cuda") |
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prompt = "a highly realistic photo of green turtle" |
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prompts = ["a cat playing with a ball++ in the forest", "a cat playing with a ball++ in the forest", "a cat playing with a ball-- in the forest"] |
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prompt_embeds = torch.cat([compel.build_conditioning_tensor(prompt) for prompt in prompts]) |
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generator = [torch.Generator(device="cuda").manual_seed(0) for _ in range(prompt_embeds.shape[0])] |
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images = pipe(prompt_embeds=prompt_embeds, generator=generator, num_inference_steps=15).images |
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print("Time", time.time() - start_time) |
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for i, image in enumerate(image): |
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path = os.path.join(Path.home(), "images", f"aa_{i}.png") |
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image.save(path) |
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api.upload_file( |
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path_or_fileobj=path, |
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path_in_repo=path.split("/")[-1], |
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repo_id="patrickvonplaten/images", |
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repo_type="dataset", |
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) |
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print(f"https://huggingface.co/datasets/patrickvonplaten/images/blob/main/aa_{i}.png") |
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