Sdxl

In [ ]:

!git clone https://github.com/soumik12345/wandb-addons
!pip install ./wandb-addons[huggingface] > install.log

!git clone https://github.com/soumik12345/wandb-addons !pip install ./wandb-addons[huggingface] > install.log

In [ ]:

from functools import partial

import torch
torch.cuda.empty_cache()

from diffusers import StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipeline
from wandb_addons.diffusers import StableDiffusionXLCallback

from functools import partial import torch torch.cuda.empty_cache() from diffusers import StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipeline from wandb_addons.diffusers import StableDiffusionXLCallback

In [ ]:

base_pipeline = StableDiffusionXLPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0",
    torch_dtype=torch.float16,
    variant="fp16",
    use_safetensors=True,
)

base_pipeline.enable_model_cpu_offload()

base_pipeline = StableDiffusionXLPipeline.from_pretrained( "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True, ) base_pipeline.enable_model_cpu_offload()

In [ ]:

prompt = "seascape by Ray Collins and artgerm, front view of a perfect wave, sunny background, ultra detailed water, 4k resolution"
negative_prompt = "low resolution, low details, blurry, clouds"
num_inference_steps = 50

callback = StableDiffusionXLCallback(
    pipeline=base_pipeline,
    prompt=prompt,
    wandb_project="diffusers-new",
    wandb_entity="geekyrakshit",
    weave_mode=True,
    num_inference_steps=num_inference_steps,
    negative_prompt=negative_prompt,
)

image = base_pipeline(
    prompt=prompt,
    negative_prompt=negative_prompt,
    output_type="latent",
    num_inference_steps=num_inference_steps,
    callback=partial(callback, end_experiment=False)
).images[0]

prompt = "seascape by Ray Collins and artgerm, front view of a perfect wave, sunny background, ultra detailed water, 4k resolution" negative_prompt = "low resolution, low details, blurry, clouds" num_inference_steps = 50 callback = StableDiffusionXLCallback( pipeline=base_pipeline, prompt=prompt, wandb_project="diffusers-new", wandb_entity="geekyrakshit", weave_mode=True, num_inference_steps=num_inference_steps, negative_prompt=negative_prompt, ) image = base_pipeline( prompt=prompt, negative_prompt=negative_prompt, output_type="latent", num_inference_steps=num_inference_steps, callback=partial(callback, end_experiment=False) ).images[0]

In [ ]:

refiner_pipeline = StableDiffusionXLImg2ImgPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-refiner-1.0",
    text_encoder_2=base_pipeline.text_encoder_2,
    vae=base_pipeline.vae,
    torch_dtype=torch.float16,
    use_safetensors=True,
    variant="fp16",
)
refiner_pipeline.enable_model_cpu_offload()

refiner_pipeline = StableDiffusionXLImg2ImgPipeline.from_pretrained( "stabilityai/stable-diffusion-xl-refiner-1.0", text_encoder_2=base_pipeline.text_encoder_2, vae=base_pipeline.vae, torch_dtype=torch.float16, use_safetensors=True, variant="fp16", ) refiner_pipeline.enable_model_cpu_offload()

In [ ]:

num_inference_steps = 50
strength = 0.3

callback.add_refiner_stage(
    refiner_pipeline, num_inference_steps=num_inference_steps, strength=strength
)

image = refiner_pipeline(
    prompt=prompt,
    negative_prompt=negative_prompt,
    image=image[None, :],
    callback=callback
).images[0]

num_inference_steps = 50 strength = 0.3 callback.add_refiner_stage( refiner_pipeline, num_inference_steps=num_inference_steps, strength=strength ) image = refiner_pipeline( prompt=prompt, negative_prompt=negative_prompt, image=image[None, :], callback=callback ).images[0]