Update app.py

app.py

@@ -7,7 +7,7 @@ import numpy
 import uuid
 import json
 import os
-from diffusers import StableDiffusionXLPipeline, ControlNetModel, EulerAncestralDiscreteScheduler
+from diffusers import StableDiffusionXLPipeline, ControlNetModel
 from PIL import Image
 
 # Pre-Initialize
@@ -19,14 +19,15 @@ print(f"[SYSTEM] | Using {DEVICE} type compute device.")
 # Variables
 MAX_SEED = 9007199254740991
 DEFAULT_INPUT = ""
-DEFAULT_NEGATIVE_INPUT = "(deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers:1.4), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation, NSFW"
+DEFAULT_NEGATIVE_INPUT = "deformed, distorted, disfigured, disconnected, disgusting, mutation, mutated, blur, blurry, scribble, abstract, ugly, amputation, limb, limbs, leg, legs, foot, feet, toe, toes, arm, arms, hand, hands, finger, fingers, head, heads, exposed, porn, nude, nudity, naked, nsfw, NSFW"
 DEFAULT_HEIGHT = 1024
 DEFAULT_WIDTH = 1024
 
 REPO = "sd-community/sdxl-flash"
 
-model = StableDiffusionXLPipeline.from_pretrained(REPO, torch_dtype=torch.float16, use_safetensors=True, add_watermarker=False)
-#model.scheduler = EulerAncestralDiscreteScheduler.from_config(model.scheduler.config)
+controlnet = ControlNetModel.from_pretrained("MakiPan/controlnet-encoded-hands-130k", torch_dtype=torch.float32)
+
+model = StableDiffusionXLPipeline.from_pretrained(REPO, controlnet=controlnet, torch_dtype=torch.float16, use_safetensors=True, add_watermarker=False)
 model.load_lora_weights("ehristoforu/dalle-3-xl-v2", adapter_name="base")
 model.set_adapters(["base"], adapter_weights=[0.7])
 model.to(DEVICE)
@@ -68,6 +69,7 @@ def generate(input=DEFAULT_INPUT, negative_input=DEFAULT_NEGATIVE_INPUT, height=
         "num_inference_steps": steps,
         "guidance_scale": guidance,
         "num_images_per_prompt": number,
+        "controlnet_conditioning_scale": 0.5,
         "cross_attention_kwargs": {"scale": 1},
         "generator": torch.Generator().manual_seed(seed),
         "use_resolution_binning": True,