Upload 2 files

app.py

@@ -38,15 +38,16 @@ def change_base_model(repo_id: str, cn_on: bool, progress=gr.Progress(track_tqdm
     global controlnet
     global last_model
     global last_cn_on
+    dtype = torch.bfloat16
     try:
         if (repo_id == last_model and cn_on is last_cn_on) or not is_repo_name(repo_id) or not is_repo_exists(repo_id): return
         if cn_on:
             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=torch.bfloat16)
+            controlnet_union = FluxControlNetModel.from_pretrained(controlnet_model_union_repo, torch_dtype=dtype)
             controlnet = FluxMultiControlNetModel([controlnet_union])
-            pipe = FluxControlNetPipeline.from_pretrained(repo_id, controlnet=controlnet, torch_dtype=torch.bfloat16)
+            pipe = FluxControlNetPipeline.from_pretrained(repo_id, controlnet=controlnet, torch_dtype=dtype)
             #pipe.enable_model_cpu_offload()
             last_model = repo_id
             last_cn_on = cn_on
@@ -56,7 +57,7 @@ def change_base_model(repo_id: str, cn_on: bool, progress=gr.Progress(track_tqdm
             progress(0, desc=f"Loading model: {repo_id}")
             print(f"Loading model: {repo_id}")
             clear_cache()
-            pipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.bfloat16)
+            pipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=dtype)
             #pipe.enable_model_cpu_offload()
             last_model = repo_id
             last_cn_on = cn_on
@@ -113,6 +114,8 @@ def update_selection(evt: gr.SelectData, width, height):
 @spaces.GPU(duration=70)
 def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, cn_on, progress=gr.Progress(track_tqdm=True)):
     pipe.to("cuda")
+    controlnet.to("cuda")
+    controlnet_union.to("cuda")
     generator = torch.Generator(device="cuda").manual_seed(seed)
     
     with calculateDuration("Generating image"):
@@ -145,6 +148,7 @@ def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scal
                     joint_attention_kwargs={"scale": lora_scale},
                 ).images[0]
         except Exception as e:
+            progress(1, desc=f"Inference Error: {e}")
             print(e)
             raise Exception(f"Inference Error: {e}")
     return image

mod.py

@@ -19,6 +19,7 @@ models = [
     "black-forest-labs/FLUX.1-schnell",
     "sayakpaul/FLUX.1-merged",
     "John6666/hyper-flux1-dev-fp8-flux",
+    "John6666/flux1-dev-minus-v1-fp8-flux",
     "John6666/blue-pencil-flux1-v001-fp8-flux",
     "John6666/copycat-flux-test-fp8-v11-fp8-flux",
     "John6666/nepotism-fuxdevschnell-v3aio-fp8-flux",
@@ -26,6 +27,7 @@ models = [
     "John6666/lyh-dalle-anime-v12dalle-fp8-flux",
     "John6666/fluxunchained-artfulnsfw-fut516xfp8e4m3fnv11-fp8-flux",
     "John6666/fastflux-unchained-t5f16-fp8-flux",
+    "John6666/iniverse-mix-xl-sfwnsfw-fluxdfp8v10-fp8-flux",
     "John6666/the-araminta-flux1a1-fp8-flux",
     "John6666/acorn-is-spinning-flux-v11-fp8-flux",
     "John6666/fluxescore-dev-v10fp16-fp8-flux",