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diffusers-parti-prompts/if-v-1.0

Images of Parti Prompts for "if-v-1.0" Code that was used to get the results: from diffusers import DiffusionPipeline import torch pipe_low = DiffusionPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0", safety_checker=None, watermarker=None, torch_dtype=torch.float16, variant="fp16") pipe_low.enable_model_cpu_offload() pipe_up = DiffusionPipeline.from_pretrained("DeepFloyd/IF-II-L-v1.0", safety_checker=None, watermarker=None, text_encoder=pipe_low.text_encoder… See the full description on the dataset page: https://huggingface.co/datasets/diffusers-parti-prompts/if-v-1.0.

sourceHugging Faceupdated 3y agoView on Hugging Face

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Images of Parti Prompts for "if-v-1.0"

Code that was used to get the results:

from diffusers import DiffusionPipeline
import torch

pipe_low = DiffusionPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0", safety_checker=None, watermarker=None, torch_dtype=torch.float16, variant="fp16")
pipe_low.enable_model_cpu_offload()

pipe_up = DiffusionPipeline.from_pretrained("DeepFloyd/IF-II-L-v1.0", safety_checker=None, watermarker=None, text_encoder=pipe_low.text_encoder, torch_dtype=torch.float16, variant="fp16")
pipe_up.enable_model_cpu_offload()

prompt = "" # a parti prompt
generator = torch.Generator("cuda").manual_seed(0)

prompt_embeds, negative_prompt_embeds = pipe_low.encode_prompt(prompt)
images = pipe_low(prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, num_inference_steps=100, generator=generator, output_type="pt").images
images = pipe_up(prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, image=images, num_inference_steps=100, generator=generator).images[0]