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from diffusers import StableDiffusionControlNetPipeline, ControlNetModel | |
from diffusers import UniPCMultistepScheduler | |
import gradio as gr | |
import torch | |
from gradio.components import Markdown | |
# Models | |
controlnet_pose = ControlNetModel.from_pretrained( | |
"lllyasviel/sd-controlnet-openpose", torch_dtype=torch.float16 | |
) | |
controlnet_canny = ControlNetModel.from_pretrained( | |
"lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16 | |
) | |
pipe = StableDiffusionControlNetPipeline.from_pretrained( | |
"dreamlike-art/dreamlike-anime-1.0", | |
controlnet=[controlnet_pose, controlnet_canny], | |
safety_checker=None, torch_dtype=torch.float16 | |
).to('cuda') | |
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config) | |
# This command loads the individual model components on GPU on-demand. So, we don't | |
# need to explicitly call pipe.to("cuda"). | |
#pipe.enable_model_cpu_offload() | |
# xformers | |
pipe.enable_xformers_memory_efficient_attention() | |
# Generator seed, | |
generator = torch.manual_seed(0) | |
negative_prompt = ("worst quality, low quality, lowres, bad anatomy, bad hands, " | |
"missing fingers, extra digit, fewer digits") | |
markdown = """ | |
## Generate controlled outputs with Mult-ControlNet and Stable Diffusion using π€Diffusers | |
This Space uses pose lines and canny edged image as the additional conditioning. Please refer to the "Examples" for what kind of images are appropriate. | |
The Followings are tools available to create such images. In this example, tool 1 is being used. | |
1. [Character bones that look like Openpose for blender Ver4.7 Depth+Canny](https://toyxyz.gumroad.com/l/ciojz) | |
2. [open-pose-editor](https://github.com/ZhUyU1997/open-pose-editor) | |
""" | |
# **This space using these models**: | |
# - ControlNet Model (canny): [lllyasviel/sd-controlnet-canny](https://hf.co/lllyasviel/sd-controlnet-canny) | |
# - ControlNet Model (openpose): [lllyasviel/sd-controlnet-openpose](https://hf.co/lllyasviel/sd-controlnet-openpose) | |
# - SD Base Model: [dreamlike-art/dreamlike-anime-1.0](https://hf.co/dreamlike-art/dreamlike-anime-1.0) | |
def generate_images(pose_image, canny_image, prompt): | |
output = pipe( | |
prompt=prompt, | |
negative_prompt=negative_prompt, | |
image=[pose_image, canny_image], | |
generator=generator, | |
num_images_per_prompt=3, | |
num_inference_steps=20, | |
) | |
all_outputs = [] | |
all_outputs.append(pose_image) | |
all_outputs.append(canny_image) | |
for image in output.images: | |
all_outputs.append(image) | |
return all_outputs | |
gr.Interface( | |
generate_images, | |
inputs=[ | |
gr.Image(type="pil"), | |
gr.Image(type="pil"), | |
gr.Textbox( | |
label="Enter your prompt", | |
max_lines=1, | |
placeholder="Enter your prompt", | |
), | |
], | |
outputs=gr.Gallery().style(grid=[2], height="auto"), | |
description=markdown, | |
examples=[ | |
["p11_clip.png", | |
"c11_clip.png", | |
"masterpiece, a professional portrait of woman wearing white shirts, smile, heart hand sign" | |
], | |
# ["p13_clip.png", | |
# "c13_clip.png", | |
# "masterpiece, a professional portrait of woman wearing white shirts, smile" | |
# ], | |
["p12_clip.png", | |
"c12_clip.png", | |
"masterpiece, a professional portrait of woman wearing white shirts, smile" | |
], | |
], | |
allow_flagging=False, | |
).launch(enable_queue=True) | |