Skip to content

add inference file #203

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
kirshdude wants to merge 1 commit into
base: main
Choose a base branch
from
Open

add inference file #203

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
90 changes: 90 additions & 0 deletions inference.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,90 @@
import json
import torch
import numpy as np
from io import BytesIO
from PIL import Image
from pulid import attention_processor as attention
from pulid.pipeline_v1_1 import PuLIDPipeline
from pulid.utils import resize_numpy_image_long

torch.set_grad_enabled(False)


def model_fn(model_dir):
"""Loads the model when the SageMaker container starts."""
base_model = 'RunDiffusion/Juggernaut-XL-v9' # Change as needed
sampler = 'dpmpp_2m' # Default sampler
pipeline = PuLIDPipeline(sdxl_repo=base_model, sampler=sampler)
return pipeline


def input_fn(request_body, request_content_type):
"""Processes input from SageMaker requests."""
if request_content_type == "application/json":
data = json.loads(request_body)

# Load image
id_image_bytes = BytesIO(bytes(data["id_image"]))
id_image = Image.open(id_image_bytes).convert("RGB")
id_image = np.array(id_image)

supp_images = []
for supp_img_bytes in data.get("supp_images", []):
supp_img = Image.open(BytesIO(bytes(supp_img_bytes))).convert("RGB")
supp_images.append(np.array(supp_img))

return {
"id_image": id_image,
"supp_images": supp_images,
"prompt": data["prompt"],
"neg_prompt": data.get("neg_prompt", ""),
"scale": data.get("scale", 7.0),
"seed": data.get("seed", 2691993),
"steps": data.get("steps", 20),
"H": data.get("H", 1024),
"W": data.get("W", 1024),
"id_scale": data.get("id_scale", 1.0),
"num_zero": data.get("num_zero", 0),
"ortho": data.get("ortho", "none")
}
else:
raise ValueError(f"Unsupported content type: {request_content_type}")


def predict_fn(input_data, model):
"""Runs inference."""
id_image = resize_numpy_image_long(input_data["id_image"], 1024)
supp_id_image_list = [
resize_numpy_image_long(supp_img, 1024) for supp_img in input_data["supp_images"]
]
id_image_list = [id_image] + supp_id_image_list

if id_image_list:
uncond_id_embedding, id_embedding = model.get_id_embedding(id_image_list)
else:
uncond_id_embedding = None
id_embedding = None

img = model.inference(
input_data["prompt"],
(1, input_data["H"], input_data["W"]),
input_data["neg_prompt"],
id_embedding,
uncond_id_embedding,
input_data["id_scale"],
input_data["scale"],
input_data["steps"],
input_data["seed"]
)[0]

img_buffer = BytesIO()
Image.fromarray(img).save(img_buffer, format="PNG")
return img_buffer.getvalue()


def output_fn(prediction, response_content_type):
"""Formats the model output."""
if response_content_type == "image/png":
return prediction
else:
raise ValueError(f"Unsupported response content type: {response_content_type}")