Hugging Face Diffusers

import torch from diffusers import DiffusionPipeline  import wandb  # import the autolog function from wandb.integration.diffusers import autolog  # call the autolog before calling the pipeline autolog(init=dict(project="diffusers_logging"))  # Initialize the diffusion pipeline pipeline = DiffusionPipeline.from_pretrained(  "stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16 ).to("cuda")  # Define the prompts, negative prompts, and seed. prompt = ["a photograph of an astronaut riding a horse", "a photograph of a dragon"] negative_prompt = ["ugly, deformed", "ugly, deformed"] generator = torch.Generator(device="cpu").manual_seed(10)  # call the pipeline to generate the images images = pipeline(  prompt,  negative_prompt=negative_prompt,  num_images_per_prompt=2,  generator=generator, )  # Finish the experiment wandb.finish() 

import torch from diffusers import StableDiffusionXLImg2ImgPipeline, StableDiffusionXLPipeline  import wandb from wandb.integration.diffusers import autolog  # initialize the SDXL base pipeline 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()  # initialize the SDXL refiner pipeline 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()  prompt = "a photo of an astronaut riding a horse on mars" negative_prompt = "static, frame, painting, illustration, sd character, low quality, low resolution, greyscale, monochrome, nose, cropped, lowres, jpeg artifacts, deformed iris, deformed pupils, bad eyes, semi-realistic worst quality, bad lips, deformed mouth, deformed face, deformed fingers, deformed toes standing still, posing"  # Make the experiment reproducible by controlling randomness. # The seed would be automatically logged to WandB. seed = 42 generator_base = torch.Generator(device="cuda").manual_seed(seed) generator_refiner = torch.Generator(device="cuda").manual_seed(seed)  # Call WandB Autolog for Diffusers. This would automatically log # the prompts, generated images, pipeline architecture and all # associated experiment configs to Weights & Biases, thus making your # image generation experiments easy to reproduce, share and analyze. autolog(init=dict(project="sdxl"))  # Call the base pipeline to generate the latents image = base_pipeline(  prompt=prompt,  negative_prompt=negative_prompt,  output_type="latent",  generator=generator_base, ).images[0]  # Call the refiner pipeline to generate the refined image image = refiner_pipeline(  prompt=prompt,  negative_prompt=negative_prompt,  image=image[None, :],  generator=generator_refiner, ).images[0]  # Finish the experiment wandb.finish()