Datasets:
sayakpaul
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diffusers-qa-chatbot-artifacts

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--resolution=256 \
--random_flip \
--train_batch_size=4 \
--gradient_accumulation_steps=4 \
--gradient_checkpointing \
--max_train_steps=15000 \
--checkpointing_steps=5000 \
--checkpoints_total_limit=1 \
--learning_rate=5e-05 \
--max_grad_norm=1 \
--lr_warmup_steps=0 \
--conditioning_dropout_prob=0.05 \
--mixed_precision=fp16 \
--seed=42 \
--push_to_hub After training is finished, you can use your new InstructPix2Pix for inference: Copied import PIL
import requests
import torch
from diffusers import StableDiffusionInstructPix2PixPipeline
from diffusers.utils import load_image
pipeline = StableDiffusionInstructPix2PixPipeline.from_pretrained("your_cool_model", torch_dtype=torch.float16).to("cuda")
generator = torch.Generator("cuda").manual_seed(0)
image = load_image("https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/test_pix2pix_4.png")
prompt = "add some ducks to the lake"
num_inference_steps = 20
image_guidance_scale = 1.5
guidance_scale = 10
edited_image = pipeline(
prompt,
image=image,
num_inference_steps=num_inference_steps,
image_guidance_scale=image_guidance_scale,
guidance_scale=guidance_scale,
generator=generator,
).images[0]
edited_image.save("edited_image.png") You should experiment with different num_inference_steps, image_guidance_scale, and guidance_scale values to see how they affect inference speed and quality. The guidance scale parameters are especially impactful because they control how much the original image and edit instruction...
Spectrogram Diffusion Spectrogram Diffusion is by Curtis Hawthorne, Ian Simon, Adam Roberts, Neil Zeghidour, Josh Gardner, Ethan Manilow, and Jesse Engel. An ideal music synthesizer should be both interactive and expressive, generating high-fidelity audio in realtime for arbitrary combinations of instruments and notes....
List of denoised audio samples of a NumPy array of shape (batch_size, num_channels, sample_rate). Output class for audio pipelines.
UniDiffuser The UniDiffuser model was proposed in One Transformer Fits All Distributions in Multi-Modal Diffusion at Scale by Fan Bao, Shen Nie, Kaiwen Xue, Chongxuan Li, Shi Pu, Yaole Wang, Gang Yue, Yue Cao, Hang Su, Jun Zhu. The abstract from the paper is: This paper proposes a unified diffusion framework (dubbed Un...
from diffusers import UniDiffuserPipeline
device = "cuda"
model_id_or_path = "thu-ml/unidiffuser-v1"
pipe = UniDiffuserPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)
pipe.to(device)
# Unconditional image and text generation. The generation task is automatically inferred.
sample = pipe(num_inference_steps=20, guidance_scale=8.0)
image = sample.images[0]
text = sample.text[0]
image.save("unidiffuser_joint_sample_image.png")
print(text) This is also called “joint” generation in the UniDiffuser paper, since we are sampling from the joint image-text distribution. Note that the generation task is inferred from the inputs used when calling the pipeline.
It is also possible to manually specify the unconditional generation task (“mode”) manually with UniDiffuserPipeline.set_joint_mode(): Copied # Equivalent to the above.
pipe.set_joint_mode()
sample = pipe(num_inference_steps=20, guidance_scale=8.0) When the mode is set manually, subsequent calls to the pipeline will use the set mode without attempting to infer the mode.
You can reset the mode with UniDiffuserPipeline.reset_mode(), after which the pipeline will once again infer the mode. You can also generate only an image or only text (which the UniDiffuser paper calls “marginal” generation since we sample from the marginal distribution of images and text, respectively): Copied # Un...
# Image-only generation
pipe.set_image_mode()
sample_image = pipe(num_inference_steps=20).images[0]
# Text-only generation
pipe.set_text_mode()
sample_text = pipe(num_inference_steps=20).text[0] Text-to-Image Generation UniDiffuser is also capable of sampling from conditional distributions; that is, the distribution of images conditioned on a text prompt or the distribution of texts conditioned on an image.
Here is an example of sampling from the conditional image distribution (text-to-image generation or text-conditioned image generation): Copied import torch
from diffusers import UniDiffuserPipeline
device = "cuda"
model_id_or_path = "thu-ml/unidiffuser-v1"
pipe = UniDiffuserPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)
pipe.to(device)
# Text-to-image generation
prompt = "an elephant under the sea"
sample = pipe(prompt=prompt, num_inference_steps=20, guidance_scale=8.0)
t2i_image = sample.images[0]
t2i_image The text2img mode requires that either an input prompt or prompt_embeds be supplied. You can set the text2img mode manually with UniDiffuserPipeline.set_text_to_image_mode(). Image-to-Text Generation Similarly, UniDiffuser can also produce text samples given an image (image-to-text or image-conditioned text ...
from diffusers import UniDiffuserPipeline
from diffusers.utils import load_image
device = "cuda"
model_id_or_path = "thu-ml/unidiffuser-v1"
pipe = UniDiffuserPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)
pipe.to(device)
# Image-to-text generation
image_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unidiffuser/unidiffuser_example_image.jpg"
init_image = load_image(image_url).resize((512, 512))
sample = pipe(image=init_image, num_inference_steps=20, guidance_scale=8.0)
i2t_text = sample.text[0]
print(i2t_text) The img2text mode requires that an input image be supplied. You can set the img2text mode manually with UniDiffuserPipeline.set_image_to_text_mode(). Image Variation The UniDiffuser authors suggest performing image variation through a “round-trip” generation method, where given an input image, we first...
This produces a new image which is semantically similar to the input image: Copied import torch
from diffusers import UniDiffuserPipeline
from diffusers.utils import load_image