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commited on
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•
92627a4
1
Parent(s):
8cd501f
PD model and funcitonnal endpoint inference + check progress'
Browse files- feature_extractor/preprocessor_config.json +1 -1
- handler.py +164 -42
- lora/flat2.safetensors +3 -0
- model_index.json +1 -1
- safety_checker/config.json +4 -17
- safety_checker/pytorch_model.bin +2 -2
- text_encoder/config.json +1 -1
- text_encoder/pytorch_model.bin +2 -2
- tokenizer/tokenizer_config.json +1 -2
feature_extractor/preprocessor_config.json
CHANGED
@@ -14,7 +14,7 @@
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0.4578275,
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0.40821073
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],
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-
"image_processor_type": "
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"image_std": [
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0.26862954,
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0.26130258,
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0.4578275,
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0.40821073
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],
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+
"image_processor_type": "CLIPFeatureExtractor",
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"image_std": [
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0.26862954,
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0.26130258,
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handler.py
CHANGED
@@ -7,6 +7,9 @@ from pprint import pprint
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from typing import Any, Dict, List
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import os
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from pathlib import Path
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import torch
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from diffusers import (
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DPMSolverMultistepScheduler,
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DPMSolverSinglestepScheduler,
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EulerAncestralDiscreteScheduler,
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)
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from safetensors.torch import load_file
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from torch import autocast
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-
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# https://huggingface.co/docs/inference-endpoints/guides/custom_handler
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REPO_DIR = Path(__file__).resolve().parent
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"detailed_eye-10": str(REPO_DIR / "lora/detailed_eye-10.safetensors"),
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"add_detail": str(REPO_DIR / "lora/add_detail.safetensors"),
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"MuscleGirl_v1": str(REPO_DIR / "lora/MuscleGirl_v1.safetensors"),
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}
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TEXTUAL_INVERSION = [
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"weight_name": str(REPO_DIR / "embeddings/EasyNegative.safetensors"),
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"token": "easynegative",
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},
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{
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"weight_name": str(REPO_DIR / "embeddings/EasyNegative.safetensors"),
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"token": "EasyNegative",
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},
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{
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"weight_name": str(REPO_DIR / "embeddings/badhandv4.pt"),
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"token": "badhandv4",
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},
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{
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"weight_name": str(REPO_DIR / "embeddings/NegfeetV2.pt"),
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"token": "
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},
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{
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"weight_name": str(REPO_DIR / "embeddings/ng_deepnegative_v1_75t.pt"),
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"token": "ng_deepnegative_v1_75t",
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},
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{
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"weight_name": str(REPO_DIR / "embeddings/ng_deepnegative_v1_75t.pt"),
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"token": "NG_DeepNegative_V1_75T",
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},
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{
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"weight_name": str(REPO_DIR / "embeddings/bad-hands-5.pt"),
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"token": "bad-hands-5",
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]
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def __init__(self, path="."):
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# load the optimized model
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self.pipe = DiffusionPipeline.from_pretrained(
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path,
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)
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self.pipe = self.pipe.to(device)
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# DPM++ 2M SDE Karras
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# increase step to avoid high contrast num_inference_steps=30
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self.pipe.scheduler = DPMSolverMultistepScheduler.from_config(
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self.pipe.scheduler.config,
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use_karras_sigmas=True,
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-
algorithm_type="sde-dpmsolver++",
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)
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# Mode boulardus
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self.pipe.safety_checker = None
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# Load negative embeddings to avoid bad hands, etc
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self.load_embeddings()
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# Load default Lora models
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self.pipe = self.load_selected_loras(
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[
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("polyhedron_new_skin_v1.1", 0.35), # nice Skin
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("detailed_eye-10", 0.3), # nice eyes
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("add_detail", 0.4), # detailed pictures
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("MuscleGirl_v1", 0.3), # shape persons
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],
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)
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# boosts performance by another 20%
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self.pipe.enable_xformers_memory_efficient_attention()
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self.pipe.enable_attention_slicing()
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)
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return self.pipe
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-
def __call__(self, data: Any) ->
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"""
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-
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global device
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# Which Lora do we load ?
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"width",
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"num_inference_steps",
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"height",
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"seed",
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"guidance_scale",
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]
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missing_fields = [field for field in required_fields if field not in data]
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# Now extract the fields
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prompt = data["prompt"]
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negative_prompt = data["negative_prompt"]
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loras_model = data.
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seed = data
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width = data["width"]
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num_inference_steps = data["num_inference_steps"]
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height = data["height"]
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guidance_scale = data["guidance_scale"]
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# USe this to add automatically some negative prompts
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forced_negative = (
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negative_prompt
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+ """easynegative, badhandv4, bad-artist-anime,
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)
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# Set the generator seed if provided
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@@ -288,15 +405,20 @@ class EndpointHandler:
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negative_prompt=forced_negative,
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generator=generator,
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max_embeddings_multiples=5,
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).images[0]
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#
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-
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#
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-
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except Exception as e:
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# Handle any other exceptions and return an error response
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from typing import Any, Dict, List
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import os
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from pathlib import Path
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from typing import Union
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from concurrent.futures import ThreadPoolExecutor
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import numpy as np
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import torch
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from diffusers import (
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DPMSolverMultistepScheduler,
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DPMSolverSinglestepScheduler,
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EulerAncestralDiscreteScheduler,
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+
utils,
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)
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from safetensors.torch import load_file
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from torch import autocast, tensor
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import torchvision.transforms
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from PIL import Image
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REPO_DIR = Path(__file__).resolve().parent
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"detailed_eye-10": str(REPO_DIR / "lora/detailed_eye-10.safetensors"),
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"add_detail": str(REPO_DIR / "lora/add_detail.safetensors"),
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"MuscleGirl_v1": str(REPO_DIR / "lora/MuscleGirl_v1.safetensors"),
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+
"flat2": str(REPO_DIR / "lora/flat2.safetensors"),
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}
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TEXTUAL_INVERSION = [
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"weight_name": str(REPO_DIR / "embeddings/EasyNegative.safetensors"),
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"token": "easynegative",
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},
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{
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"weight_name": str(REPO_DIR / "embeddings/badhandv4.pt"),
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"token": "badhandv4",
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},
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{
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"weight_name": str(REPO_DIR / "embeddings/NegfeetV2.pt"),
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+
"token": "negfeetv2",
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},
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{
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"weight_name": str(REPO_DIR / "embeddings/ng_deepnegative_v1_75t.pt"),
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"token": "ng_deepnegative_v1_75t",
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},
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{
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"weight_name": str(REPO_DIR / "embeddings/bad-hands-5.pt"),
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"token": "bad-hands-5",
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]
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def __init__(self, path="."):
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+
self.inference_progress = {} # Dictionary to store progress of each request
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+
self.inference_images = {} # Dictionary to store latest image of each request
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self.total_steps = {}
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self.inference_in_progress = False
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+
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self.executor = ThreadPoolExecutor(
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+
max_workers=1
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) # Vous pouvez ajuster max_workers en fonction de vos besoins
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+
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# load the optimized model
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self.pipe = DiffusionPipeline.from_pretrained(
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path,
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)
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self.pipe = self.pipe.to(device)
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+
# https://stablediffusionapi.com/docs/a1111schedulers/
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+
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# DPM++ 2M SDE Karras
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# increase step to avoid high contrast num_inference_steps=30
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+
# self.pipe.scheduler = DPMSolverMultistepScheduler.from_config(
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+
# self.pipe.scheduler.config,
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+
# use_karras_sigmas=True,
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+
# algorithm_type="sde-dpmsolver++",
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# )
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+
# DPM++ 2M Karras
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+
# increase step to avoid high contrast num_inference_steps=30
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self.pipe.scheduler = DPMSolverMultistepScheduler.from_config(
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self.pipe.scheduler.config,
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use_karras_sigmas=True,
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)
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# Mode boulardus
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self.pipe.safety_checker = None
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+
# Disable progress bar
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self.pipe.set_progress_bar_config(disable=True)
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+
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# Load negative embeddings to avoid bad hands, etc
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self.load_embeddings()
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# boosts performance by another 20%
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self.pipe.enable_xformers_memory_efficient_attention()
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self.pipe.enable_attention_slicing()
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)
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return self.pipe
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+
def __call__(self, data: Any) -> Dict:
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+
"""Handle incoming requests."""
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+
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action = data.get("action", None)
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request_id = data.get("request_id")
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+
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# Check if the request_id is valid for all actions
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if not request_id:
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return {"flag": "error", "message": "Missing request_id."}
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+
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if action == "check_progress":
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return self.check_progress(request_id)
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+
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elif action == "inference":
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+
# Check if an inference is already in progress
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+
if self.inference_in_progress:
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+
return {
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+
"flag": "error",
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"message": "Another inference is already in progress. Please wait.",
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+
}
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+
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+
# Set the inference state to in progress
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+
self.clean_request_data(request_id)
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self.inference_in_progress = True
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self.inference_progress[request_id] = 0
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self.inference_images[request_id] = None
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+
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self.executor.submit(self.start_inference, data)
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+
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return {
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"flag": "success",
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"message": "Inference started",
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"request_id": request_id,
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+
}
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+
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+
else:
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+
return {"flag": "error", "message": f"Unsupported action: {action}"}
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+
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+
def clean_request_data(self, request_id: str):
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+
"""Clean up the data related to a specific request ID."""
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+
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# Remove the request ID from the progress dictionary
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+
self.inference_progress.pop(request_id, None)
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+
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# Remove the request ID from the images dictionary
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self.inference_images.pop(request_id, None)
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+
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+
# Remove the request ID from the total_steps dictionary
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+
self.total_steps.pop(request_id, None)
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+
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+
# Set inference to False
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+
self.inference_in_progress = False
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+
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+
def progress_callback(
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+
self,
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+
step: int,
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+
timestep: int,
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+
latents: Any,
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+
request_id: str,
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+
status: str,
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+
):
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+
try:
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+
if status == "progress":
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+
# Latents to numpy
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+
img_data = self.pipe.decode_latents(latents)
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+
img_data = (img_data.squeeze() * 255).astype(np.uint8)
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+
img = Image.fromarray(img_data, "RGB")
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+
# print(img_data)
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else:
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+
# pil object
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# print(latents)
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img = latents
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+
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buffered = BytesIO()
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img.save(buffered, format="PNG")
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+
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+
# print(status)
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+
# Save the image to a file
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+
# img.save("squirel.png", format="PNG")
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+
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# Encode the image into a base64 string representation
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+
img_str = base64.b64encode(buffered.getvalue()).decode()
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+
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+
except Exception as e:
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308 |
+
print(f"Error: {e}")
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+
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310 |
+
# Store progress and image
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311 |
+
progress_percentage = (
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+
step / self.total_steps[request_id]
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+
) * 100 # Assuming self.total_steps is the total number of steps for inference
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+
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+
self.inference_progress[request_id] = progress_percentage
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+
self.inference_images[request_id] = img_str
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+
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318 |
+
def check_progress(self, request_id: str) -> Dict[str, Union[str, float]]:
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319 |
+
progress = self.inference_progress.get(request_id, 0)
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latest_image = self.inference_images.get(request_id, None)
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+
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# print(self.inference_progress)
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+
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324 |
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if progress >= 100:
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status = "complete"
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else:
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status = "in-progress"
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+
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return {
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"flag": "success",
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"status": status,
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332 |
+
"progress": int(progress),
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"image": latest_image,
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+
}
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+
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+
def start_inference(self, data: Dict) -> Dict:
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337 |
+
"""Start a new inference."""
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338 |
+
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global device
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340 |
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# Which Lora do we load ?
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"width",
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"num_inference_steps",
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"height",
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"guidance_scale",
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+
"request_id",
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]
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missing_fields = [field for field in required_fields if field not in data]
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369 |
# Now extract the fields
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prompt = data["prompt"]
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negative_prompt = data["negative_prompt"]
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+
loras_model = data.get("loras_model", None)
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+
seed = data.get("seed", None)
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width = data["width"]
|
375 |
num_inference_steps = data["num_inference_steps"]
|
376 |
height = data["height"]
|
377 |
guidance_scale = data["guidance_scale"]
|
378 |
+
request_id = data["request_id"]
|
379 |
+
|
380 |
+
# Used for progress checker
|
381 |
+
self.total_steps[request_id] = num_inference_steps
|
382 |
|
383 |
# USe this to add automatically some negative prompts
|
384 |
forced_negative = (
|
385 |
negative_prompt
|
386 |
+
+ """, easynegative, badhandv4, bad-artist-anime, negfeetv2, ng_deepnegative_v1_75t, bad-hands-5, """
|
387 |
)
|
388 |
|
389 |
# Set the generator seed if provided
|
|
|
405 |
negative_prompt=forced_negative,
|
406 |
generator=generator,
|
407 |
max_embeddings_multiples=5,
|
408 |
+
callback=lambda step, timestep, latents: self.progress_callback(
|
409 |
+
step, timestep, latents, request_id, "progress"
|
410 |
+
),
|
411 |
+
callback_steps=8, # The frequency at which the callback function is called.
|
412 |
+
# output_type="pt",
|
413 |
).images[0]
|
414 |
|
415 |
+
# print(image)
|
416 |
+
self.progress_callback(
|
417 |
+
num_inference_steps, 0, image, request_id, "complete"
|
418 |
+
)
|
419 |
|
420 |
+
# for debug
|
421 |
+
# image.save("squirelb.png", format="PNG")
|
422 |
|
423 |
except Exception as e:
|
424 |
# Handle any other exceptions and return an error response
|
lora/flat2.safetensors
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:054e950e72181bb45ddbc7106d3625de406477725b5b313a91fe4522f03dbe0a
|
3 |
+
size 6865699
|
model_index.json
CHANGED
@@ -3,7 +3,7 @@
|
|
3 |
"_diffusers_version": "0.20.0",
|
4 |
"feature_extractor": [
|
5 |
"transformers",
|
6 |
-
"
|
7 |
],
|
8 |
"requires_safety_checker": true,
|
9 |
"safety_checker": [
|
|
|
3 |
"_diffusers_version": "0.20.0",
|
4 |
"feature_extractor": [
|
5 |
"transformers",
|
6 |
+
"CLIPFeatureExtractor"
|
7 |
],
|
8 |
"requires_safety_checker": true,
|
9 |
"safety_checker": [
|
safety_checker/config.json
CHANGED
@@ -15,7 +15,7 @@
|
|
15 |
"attention_dropout": 0.0,
|
16 |
"bad_words_ids": null,
|
17 |
"begin_suppress_tokens": null,
|
18 |
-
"bos_token_id":
|
19 |
"chunk_size_feed_forward": 0,
|
20 |
"cross_attention_hidden_size": null,
|
21 |
"decoder_start_token_id": null,
|
@@ -24,7 +24,7 @@
|
|
24 |
"dropout": 0.0,
|
25 |
"early_stopping": false,
|
26 |
"encoder_no_repeat_ngram_size": 0,
|
27 |
-
"eos_token_id":
|
28 |
"exponential_decay_length_penalty": null,
|
29 |
"finetuning_task": null,
|
30 |
"forced_bos_token_id": null,
|
@@ -80,17 +80,11 @@
|
|
80 |
"top_p": 1.0,
|
81 |
"torch_dtype": null,
|
82 |
"torchscript": false,
|
83 |
-
"transformers_version": "4.
|
84 |
"typical_p": 1.0,
|
85 |
"use_bfloat16": false,
|
86 |
"vocab_size": 49408
|
87 |
},
|
88 |
-
"text_config_dict": {
|
89 |
-
"hidden_size": 768,
|
90 |
-
"intermediate_size": 3072,
|
91 |
-
"num_attention_heads": 12,
|
92 |
-
"num_hidden_layers": 12
|
93 |
-
},
|
94 |
"torch_dtype": "float32",
|
95 |
"transformers_version": null,
|
96 |
"vision_config": {
|
@@ -167,15 +161,8 @@
|
|
167 |
"top_p": 1.0,
|
168 |
"torch_dtype": null,
|
169 |
"torchscript": false,
|
170 |
-
"transformers_version": "4.
|
171 |
"typical_p": 1.0,
|
172 |
"use_bfloat16": false
|
173 |
-
},
|
174 |
-
"vision_config_dict": {
|
175 |
-
"hidden_size": 1024,
|
176 |
-
"intermediate_size": 4096,
|
177 |
-
"num_attention_heads": 16,
|
178 |
-
"num_hidden_layers": 24,
|
179 |
-
"patch_size": 14
|
180 |
}
|
181 |
}
|
|
|
15 |
"attention_dropout": 0.0,
|
16 |
"bad_words_ids": null,
|
17 |
"begin_suppress_tokens": null,
|
18 |
+
"bos_token_id": 49406,
|
19 |
"chunk_size_feed_forward": 0,
|
20 |
"cross_attention_hidden_size": null,
|
21 |
"decoder_start_token_id": null,
|
|
|
24 |
"dropout": 0.0,
|
25 |
"early_stopping": false,
|
26 |
"encoder_no_repeat_ngram_size": 0,
|
27 |
+
"eos_token_id": 49407,
|
28 |
"exponential_decay_length_penalty": null,
|
29 |
"finetuning_task": null,
|
30 |
"forced_bos_token_id": null,
|
|
|
80 |
"top_p": 1.0,
|
81 |
"torch_dtype": null,
|
82 |
"torchscript": false,
|
83 |
+
"transformers_version": "4.31.0",
|
84 |
"typical_p": 1.0,
|
85 |
"use_bfloat16": false,
|
86 |
"vocab_size": 49408
|
87 |
},
|
|
|
|
|
|
|
|
|
|
|
|
|
88 |
"torch_dtype": "float32",
|
89 |
"transformers_version": null,
|
90 |
"vision_config": {
|
|
|
161 |
"top_p": 1.0,
|
162 |
"torch_dtype": null,
|
163 |
"torchscript": false,
|
164 |
+
"transformers_version": "4.31.0",
|
165 |
"typical_p": 1.0,
|
166 |
"use_bfloat16": false
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
167 |
}
|
168 |
}
|
safety_checker/pytorch_model.bin
CHANGED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
-
size
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:753acd54aa6d288d6c0ce9d51468eb28f495fcbaacf0edf755fa5fc7ce678cd9
|
3 |
+
size 1216062333
|
text_encoder/config.json
CHANGED
@@ -19,6 +19,6 @@
|
|
19 |
"pad_token_id": 1,
|
20 |
"projection_dim": 768,
|
21 |
"torch_dtype": "float32",
|
22 |
-
"transformers_version": "4.
|
23 |
"vocab_size": 49408
|
24 |
}
|
|
|
19 |
"pad_token_id": 1,
|
20 |
"projection_dim": 768,
|
21 |
"torch_dtype": "float32",
|
22 |
+
"transformers_version": "4.31.0",
|
23 |
"vocab_size": 49408
|
24 |
}
|
text_encoder/pytorch_model.bin
CHANGED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
-
size
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:38a67003cd791d4fc008ae1fd24615b8b168f83cc8e853b746a7ec7bb3d64f42
|
3 |
+
size 492306077
|
tokenizer/tokenizer_config.json
CHANGED
@@ -8,6 +8,7 @@
|
|
8 |
"rstrip": false,
|
9 |
"single_word": false
|
10 |
},
|
|
|
11 |
"do_lower_case": true,
|
12 |
"eos_token": {
|
13 |
"__type": "AddedToken",
|
@@ -19,9 +20,7 @@
|
|
19 |
},
|
20 |
"errors": "replace",
|
21 |
"model_max_length": 77,
|
22 |
-
"name_or_path": "openai/clip-vit-large-patch14",
|
23 |
"pad_token": "<|endoftext|>",
|
24 |
-
"special_tokens_map_file": "./special_tokens_map.json",
|
25 |
"tokenizer_class": "CLIPTokenizer",
|
26 |
"unk_token": {
|
27 |
"__type": "AddedToken",
|
|
|
8 |
"rstrip": false,
|
9 |
"single_word": false
|
10 |
},
|
11 |
+
"clean_up_tokenization_spaces": true,
|
12 |
"do_lower_case": true,
|
13 |
"eos_token": {
|
14 |
"__type": "AddedToken",
|
|
|
20 |
},
|
21 |
"errors": "replace",
|
22 |
"model_max_length": 77,
|
|
|
23 |
"pad_token": "<|endoftext|>",
|
|
|
24 |
"tokenizer_class": "CLIPTokenizer",
|
25 |
"unk_token": {
|
26 |
"__type": "AddedToken",
|