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sayakpaul
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diffusers-qa-chatbot-artifacts

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paper. Below, we also provide some directions on how to generate the embeddings.
Note that the quality of the outputs generated with this pipeline is dependent on how good the source_embeds and target_embeds are. Please, refer to this discussion for some suggestions on the topic.
Available Pipelines:
Pipeline
Tasks
Demo
StableDiffusionPix2PixZeroPipeline
Text-Based Image Editing
🤗 Space
Usage example
Based on an image generated with the input prompt
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import requests
import torch
from diffusers import DDIMScheduler, StableDiffusionPix2PixZeroPipeline
def download(embedding_url, local_filepath):
r = requests.get(embedding_url)
with open(local_filepath, "wb") as f:
f.write(r.content)
model_ckpt = "CompVis/stable-diffusion-v1-4"
pipeline = StableDiffusionPix2PixZeroPipeline.from_pretrained(
model_ckpt, conditions_input_image=False, torch_dtype=torch.float16
)
pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
pipeline.to("cuda")
prompt = "a high resolution painting of a cat in the style of van gogh"
src_embs_url = "https://github.com/pix2pixzero/pix2pix-zero/raw/main/assets/embeddings_sd_1.4/cat.pt"
target_embs_url = "https://github.com/pix2pixzero/pix2pix-zero/raw/main/assets/embeddings_sd_1.4/dog.pt"
for url in [src_embs_url, target_embs_url]:
download(url, url.split("/")[-1])
src_embeds = torch.load(src_embs_url.split("/")[-1])
target_embeds = torch.load(target_embs_url.split("/")[-1])
images = pipeline(
prompt,
source_embeds=src_embeds,
target_embeds=target_embeds,
num_inference_steps=50,
cross_attention_guidance_amount=0.15,
).images
images[0].save("edited_image_dog.png")
Based on an input image
When the pipeline is conditioned on an input image, we first obtain an inverted
noise from it using a DDIMInverseScheduler with the help of a generated caption. Then
the inverted noise is used to start the generation process.
First, let’s load our pipeline:
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import torch
from transformers import BlipForConditionalGeneration, BlipProcessor
from diffusers import DDIMScheduler, DDIMInverseScheduler, StableDiffusionPix2PixZeroPipeline
captioner_id = "Salesforce/blip-image-captioning-base"
processor = BlipProcessor.from_pretrained(captioner_id)
model = BlipForConditionalGeneration.from_pretrained(captioner_id, torch_dtype=torch.float16, low_cpu_mem_usage=True)
sd_model_ckpt = "CompVis/stable-diffusion-v1-4"
pipeline = StableDiffusionPix2PixZeroPipeline.from_pretrained(
sd_model_ckpt,
caption_generator=model,
caption_processor=processor,
torch_dtype=torch.float16,
safety_checker=None,
)
pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
pipeline.inverse_scheduler = DDIMInverseScheduler.from_config(pipeline.scheduler.config)
pipeline.enable_model_cpu_offload()
Then, we load an input image for conditioning and obtain a suitable caption for it:
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import requests
from PIL import Image
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))
caption = pipeline.generate_caption(raw_image)
Then we employ the generated caption and the input image to get the inverted noise:
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