new

run_local.py

@@ -15,9 +15,9 @@ pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
 pipe = pipe.to("cuda")
 
 prompt = "a highly realistic photo of green turtle"
-prompt = "an image of Santa Claus in light blue"
+
 generator = torch.Generator(device="cuda").manual_seed(0)
-image = pipe(prompt, generator=generator, num_inference_steps=25).images[0]
+image = pipe(prompt, generator=generator, num_inference_steps=15).images[0]
 print("Time", time.time() - start_time)
 
 path = "/home/patrick_huggingface_co/images/aa.png"

run_pix2pix0.py

@@ -0,0 +1,62 @@
+#!/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")