Update image_generator.py

image_generator.py

@@ -1,13 +1,20 @@
-from diffusers import DiffusionPipeline, LCMScheduler, DPMSolverMultistepScheduler
+from diffusers import StableDiffusionXLPipeline, UNet2DConditionModel, LCMScheduler, DPMSolverMultistepScheduler
 import torch
 
 loaded_pipe = None
 loaded_pipe_id = None
 
-def load_model(pipe_id):
+def load_model(pipe_id, unet_model_id):
     global loaded_pipe, loaded_pipe_id
     if loaded_pipe_id != pipe_id:
-        loaded_pipe = DiffusionPipeline.from_pretrained(pipe_id, torch_dtype=torch.float16).to("cuda")
+        unet = UNet2DConditionModel.from_pretrained(
+            unet_model_id,
+            torch_dtype=torch.float16,
+            variant="fp16",
+        )
+        loaded_pipe = StableDiffusionXLPipeline.from_pretrained(
+            pipe_id, unet=unet, torch_dtype=torch.float16, variant="fp16",
+        ).to("cuda")
         loaded_pipe_id = pipe_id
     return loaded_pipe
 
@@ -18,9 +25,9 @@ def set_scheduler(pipe, scheduler_type):
         pipe.scheduler = DPMSolverMultistepScheduler(use_karras_sigmas="yes")
     return pipe
 
-def generate_image(prompt, num_inference_steps, seed, guidance_scale, negative_prompt=None, pipe_id="Linaqruf/animagine-xl", scheduler_type="LCM"):
+def generate_image(prompt, num_inference_steps, seed, guidance_scale, negative_prompt=None, pipe_id="stabilityai/stable-diffusion-xl-base-1.0", unet_model_id="latent-consistency/lcm-sdxl", scheduler_type="LCM"):
     global loaded_pipe
-    pipe = load_model(pipe_id)
+    pipe = load_model(pipe_id, unet_model_id)
     pipe = set_scheduler(pipe, scheduler_type)
     generator = torch.manual_seed(seed)
     image = pipe(prompt=prompt, negative_prompt=negative_prompt, num_inference_steps=num_inference_steps, generator=generator, guidance_scale=guidance_scale).images[0]