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18ff08e d4bb530 18ff08e d4bb530 18ff08e d4bb530 18ff08e d4bb530 18ff08e d4bb530 18ff08e d4bb530 18ff08e d4bb530 18ff08e d4bb530 18ff08e d4bb530 18ff08e d4bb530 18ff08e d4bb530 18ff08e d4bb530 18ff08e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 | #!/usr/bin/env python3
from huggingface_hub import HfApi
import torch
import requests
from PIL import Image
from diffusers import DDIMScheduler, StableDiffusionPix2PixZeroPipeline
from diffusers.schedulers.scheduling_ddim_inverse import DDIMInverseScheduler
from transformers import BlipForConditionalGeneration, BlipProcessor
api = HfApi()
img_url = "https://github.com/pix2pixzero/pix2pix-zero/raw/main/assets/test_images/cats/cat_6.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB').resize((512, 512))
processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base", torch_dtype=torch.float16, low_cpu_mem_usage=True)
model_ckpt = "CompVis/stable-diffusion-v1-4"
pipeline = StableDiffusionPix2PixZeroPipeline.from_pretrained(
model_ckpt, caption_generator=model, caption_processor=processor, torch_dtype=torch.float16, safety_checker=None,
)
pipeline.enable_model_cpu_offload()
caption = pipeline.generate_caption(raw_image)
pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
pipeline.inverse_scheduler = DDIMInverseScheduler.from_config(pipeline.scheduler.config)
print(caption)
generator = torch.manual_seed(0)
inv_latents = pipeline.invert(caption, image=raw_image, generator=generator).latents
source_prompts = 4 * ["a cat sitting on the street", "a cat playing in the field", "a face of a cat"]
target_prompts = 4 * ["a dog sitting on the street", "a dog playing in the field", "a face of a dog"]
source_embeds = pipeline.get_embeds(source_prompts, batch_size=2)
target_embeds = pipeline.get_embeds(target_prompts, batch_size=2)
image = pipeline(
caption,
source_embeds=source_embeds,
target_embeds=target_embeds,
num_inference_steps=50,
cross_attention_guidance_amount=0.15,
generator=generator,
latents=inv_latents,
negative_prompt=caption,
).images[0]
path = "/home/patrick_huggingface_co/images/aa.png"
image.save(path)
api.upload_file(
path_or_fileobj=path,
path_in_repo=path.split("/")[-1],
repo_id="patrickvonplaten/images",
repo_type="dataset",
)
print("https://huggingface.co/datasets/patrickvonplaten/images/blob/main/aa.png")
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