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Running
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Zero
File size: 4,764 Bytes
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# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT
# except for the third-party components listed below.
# Hunyuan 3D does not impose any additional limitations beyond what is outlined
# in the repsective licenses of these third-party components.
# Users must comply with all terms and conditions of original licenses of these third-party
# components and must ensure that the usage of the third party components adheres to
# all relevant laws and regulations.
# For avoidance of doubts, Hunyuan 3D means the large language models and
# their software and algorithms, including trained model weights, parameters (including
# optimizer states), machine-learning model code, inference-enabling code, training-enabling code,
# fine-tuning enabling code and other elements of the foregoing made publicly available
# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.
import torch
from diffusers import EulerAncestralDiscreteScheduler
from diffusers import StableDiffusionControlNetPipeline, StableDiffusionXLControlNetImg2ImgPipeline, ControlNetModel, \
AutoencoderKL
class Img2img_Control_Ip_adapter:
def __init__(self, device):
controlnet = ControlNetModel.from_pretrained('lllyasviel/control_v11f1p_sd15_depth', torch_dtype=torch.float16,
variant="fp16", use_safetensors=True)
pipe = StableDiffusionControlNetPipeline.from_pretrained(
'runwayml/stable-diffusion-v1-5', controlnet=controlnet, torch_dtype=torch.float16, use_safetensors=True
)
pipe.load_ip_adapter('h94/IP-Adapter', subfolder="models", weight_name="ip-adapter-plus_sd15.safetensors")
pipe.set_ip_adapter_scale(0.7)
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
# pipe.enable_model_cpu_offload()
self.pipe = pipe.to(device)
def __call__(
self,
prompt,
control_image,
ip_adapter_image,
negative_prompt,
height=512,
width=512,
num_inference_steps=20,
guidance_scale=8.0,
controlnet_conditioning_scale=1.0,
output_type="pil",
**kwargs,
):
results = self.pipe(
prompt=prompt,
negative_prompt=negative_prompt,
image=control_image,
ip_adapter_image=ip_adapter_image,
generator=torch.manual_seed(42),
seed=42,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
controlnet_conditioning_scale=controlnet_conditioning_scale,
strength=1,
# clip_skip=2,
height=height,
width=width,
output_type=output_type,
**kwargs,
).images[0]
return results
################################################################
class HesModel:
def __init__(self, ):
controlnet_depth = ControlNetModel.from_pretrained(
'diffusers/controlnet-depth-sdxl-1.0',
torch_dtype=torch.float16,
variant="fp16",
use_safetensors=True
)
self.pipe = StableDiffusionXLControlNetImg2ImgPipeline.from_pretrained(
'stabilityai/stable-diffusion-xl-base-1.0',
torch_dtype=torch.float16,
variant="fp16",
controlnet=controlnet_depth,
use_safetensors=True,
)
self.pipe.vae = AutoencoderKL.from_pretrained(
'madebyollin/sdxl-vae-fp16-fix',
torch_dtype=torch.float16
)
self.pipe.load_ip_adapter('h94/IP-Adapter', subfolder="sdxl_models", weight_name="ip-adapter_sdxl.safetensors")
self.pipe.set_ip_adapter_scale(0.7)
self.pipe.to("cuda")
def __call__(self,
init_image,
control_image,
ip_adapter_image=None,
prompt='3D image',
negative_prompt='2D image',
seed=42,
strength=0.8,
num_inference_steps=40,
guidance_scale=7.5,
controlnet_conditioning_scale=0.5,
**kwargs
):
image = self.pipe(
prompt=prompt,
image=init_image,
control_image=control_image,
ip_adapter_image=ip_adapter_image,
negative_prompt=negative_prompt,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
strength=strength,
controlnet_conditioning_scale=controlnet_conditioning_scale,
seed=seed,
**kwargs
).images[0]
return image
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