Spaces:
Running
on
Zero
Running
on
Zero
Update app.py
Browse files
app.py
CHANGED
@@ -8,33 +8,18 @@ from PIL import Image
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import cv2
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import torch
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from diffusers import StableDiffusion3Pipeline
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from diffusers.models.controlnet_sd3 import ControlNetSD3Model
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from diffusers.utils.torch_utils import randn_tensor
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from diffusers.utils import load_image
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# Clone the specific branch
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subprocess.run(["git", "clone", "-b", "sd3_control", "https://github.com/instantX-research/diffusers_sd3_control.git"])
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# Change directory to the cloned repository and install it
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os.chdir('diffusers_sd3_control')
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subprocess.run(["pip", "install", "-e", "."])
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# Add the path to the examples directory
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sys.path.append(os.path.abspath('./examples/community'))
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# Import the required pipeline
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from pipeline_stable_diffusion_3_controlnet import StableDiffusion3CommonPipeline
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# load pipeline
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pipe =
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)
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pipe.to(
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def resize_image(input_path, output_path, target_height):
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# Open the input image
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@@ -68,21 +53,13 @@ def infer(image_in, prompt, inference_steps, guidance_scale, control_weight, pro
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image_to_canny = np.concatenate([image_to_canny, image_to_canny, image_to_canny], axis=2)
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image_to_canny = Image.fromarray(image_to_canny)
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# controlnet config
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controlnet_conditioning = [
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dict(
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control_index=0,
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control_image=image_to_canny,
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control_weight=control_weight,
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control_pooled_projections='zeros'
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)
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]
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# infer
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image = pipe(
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prompt=prompt,
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negative_prompt=n_prompt,
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num_inference_steps=inference_steps,
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guidance_scale=guidance_scale,
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).images[0]
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import cv2
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import torch
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from diffusers import StableDiffusion3ControlNetPipeline
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from diffusers.models import SD3ControlNetModel, SD3MultiControlNetModel
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from diffusers.utils import load_image
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# load pipeline
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controlnet = SD3ControlNetModel.from_pretrained("InstantX/SD3-Controlnet-Canny")
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pipe = StableDiffusion3ControlNetPipeline.from_pretrained(
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"stabilityai/stable-diffusion-3-medium-diffusers",
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controlnet=controlnet
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)
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pipe.to("cuda", torch.float16)
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def resize_image(input_path, output_path, target_height):
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# Open the input image
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image_to_canny = np.concatenate([image_to_canny, image_to_canny, image_to_canny], axis=2)
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image_to_canny = Image.fromarray(image_to_canny)
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# infer
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image = pipe(
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prompt=prompt,
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negative_prompt=n_prompt,
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control_image=image_to_canny,
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controlnet_conditioning_scale=control_weigth,
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num_inference_steps=inference_steps,
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guidance_scale=guidance_scale,
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).images[0]
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