Upload app.py

app.py

@@ -38,6 +38,8 @@ good_vae = AutoencoderKL.from_pretrained(base_model, subfolder="vae", torch_dtyp
 pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=dtype, vae=taef1).to(device)
 controlnet_union = None
 controlnet = None
+controlnet_union = FluxControlNetModel.from_pretrained(controlnet_model_union_repo, torch_dtype=dtype).to(device)
+controlnet = FluxMultiControlNetModel([controlnet_union]).to(device)
 last_model = models[0]
 last_cn_on = False
 
@@ -60,8 +62,8 @@ def change_base_model(repo_id: str, cn_on: bool, progress=gr.Progress(track_tqdm
             #progress(0, desc=f"Loading model: {repo_id} / Loading ControlNet: {controlnet_model_union_repo}")
             print(f"Loading model: {repo_id} / Loading ControlNet: {controlnet_model_union_repo}")
             #clear_cache()
-            controlnet_union = FluxControlNetModel.from_pretrained(controlnet_model_union_repo, torch_dtype=dtype).to(device)
-            controlnet = FluxMultiControlNetModel([controlnet_union]).to(device)
+            #controlnet_union = FluxControlNetModel.from_pretrained(controlnet_model_union_repo, torch_dtype=dtype).to(device)
+            #controlnet = FluxMultiControlNetModel([controlnet_union]).to(device)
             pipe = FluxControlNetPipeline.from_pretrained(repo_id, controlnet=controlnet, torch_dtype=dtype).to(device)
             #pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe)
             last_model = repo_id
@@ -169,8 +171,8 @@ def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scal
                     yield img
             else:
                 progress(0, desc="Start Inference with ControlNet.")
-                if controlnet is not None: controlnet.to("cuda")
-                if controlnet_union is not None: controlnet_union.to("cuda")
+                #if controlnet is not None: controlnet.to("cuda")
+                #if controlnet_union is not None: controlnet_union.to("cuda")
                 for img in pipe(
                     prompt=prompt_mash,
                     control_image=images,