| |
| 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") |
|
|
