Upload 4 files

app.py

@@ -355,7 +355,7 @@ class GuiSD:
     
         print("Loading model...")
         self.model = Model_Diffusers(
-            base_model_id="cagliostrolab/animagine-xl-3.1",
+            base_model_id="Lykon/dreamshaper-8",
             task_name="txt2img",
             vae_model=None,
             type_model_precision=torch.float16,
@@ -363,16 +363,6 @@ class GuiSD:
             device="cpu",
         )
 
-    def infer_short(self, model, pipe_params, progress=gr.Progress(track_tqdm=True)):
-        progress(0, desc="Start inference...")
-        images, image_list = model(**pipe_params)
-        progress(1, desc="Inference completed.")
-        if not isinstance(images, list): images = [images]
-        img = []
-        for image in images:
-            img.append((image, None))
-        return img
-
     def load_new_model(self, model_name, vae_model, task, progress=gr.Progress(track_tqdm=True)):
 
         yield f"Loading model: {model_name}"
@@ -394,12 +384,13 @@ class GuiSD:
             model_name,
             task_name=task_stablepy[task],
             vae_model=vae_model if vae_model != "None" else None,
-            type_model_precision=torch.float16,
+            type_model_precision=torch.float16 if "flux" not in model_name.lower() else torch.bfloat16,
             retain_task_model_in_cache=False,
         )
         yield f"Model loaded: {model_name}"
 
     @spaces.GPU
+    @torch.inference_mode()
     def generate_pipeline(
         self,
         prompt,
@@ -539,7 +530,7 @@ class GuiSD:
                 vae_model = None
 
             for la in loras_list:
-                if la is not None and la != "None" and la in lora_model_list:
+                if la is not None and la != "None" and la != "" and la in lora_model_list:
                     print(la)
                     lora_type = ("animetarot" in la.lower() or "Hyper-SD15-8steps".lower() in la.lower())
                     if (model_is_xl and lora_type) or (not model_is_xl and not lora_type):
@@ -569,9 +560,9 @@ class GuiSD:
                 params_ip_mode.append(modeip)
                 params_ip_scale.append(scaleip)
 
+        model_precision = torch.float16 if "flux" not in model_name.lower() else torch.bfloat16
+
         # First load
-        model_precision = torch.float16
-        self.model.device = torch.device("cuda:0")
         if not self.model:
             print("Loading model...")
             self.model = Model_Diffusers(
@@ -726,8 +717,10 @@ class GuiSD:
             "ip_adapter_scale": params_ip_scale,
         }
 
-        # Maybe fix lora issue: 'Cannot copy out of meta tensor; no data!''
-        #self.model.pipe.to("cuda:0" if torch.cuda.is_available() else "cpu")
+        self.model.device = torch.device("cuda:0")
+        if hasattr(self.model.pipe, "transformer") and loras_list != ["None"] * 5:
+            self.model.pipe.transformer.to(self.model.device)
+            print("transformer to cuda")
 
         #progress(0, desc="Preparation completed. Starting inference...")
 

env.py

@@ -64,6 +64,12 @@ load_diffusers_format_model = [
     'Eugeoter/artiwaifu-diffusion-2.0',
     'Raelina/Rae-Diffusion-XL-V2',
     'Raelina/Raemu-XL-V4',
+    "camenduru/FLUX.1-dev-diffusers",
+    "black-forest-labs/FLUX.1-schnell",
+    "sayakpaul/FLUX.1-merged",
+    "ostris/OpenFLUX.1",
+    "multimodalart/FLUX.1-dev2pro-full",
+    "Raelina/Raemu-Flux",
 ]
 
 # List all Models for specified user

modutils.py

@@ -227,11 +227,16 @@ def get_model_id_list():
         model_ids.append(model.id) if not model.private else ""
     anime_models = []
     real_models = []
+    anime_models_flux = []
+    real_models_flux = []
     for model in models_ex:
-        if not model.private and not model.gated and "diffusers:FluxPipeline" not in model.tags:
-            anime_models.append(model.id) if "anime" in model.tags else real_models.append(model.id)
+        if not model.private and not model.gated:
+            if "diffusers:FluxPipeline" in model.tags: anime_models_flux.append(model.id) if "anime" in model.tags else real_models_flux.append(model.id)
+            else: anime_models.append(model.id) if "anime" in model.tags else real_models.append(model.id)
     model_ids.extend(anime_models)
     model_ids.extend(real_models)
+    model_ids.extend(anime_models_flux)
+    model_ids.extend(real_models_flux)
     model_id_list = model_ids.copy()
     return model_ids
 
@@ -745,8 +750,7 @@ def move_file_lora(filepaths):
 
 
 def get_civitai_info(path):
-    global civitai_not_exists_list
-    global loras_url_to_path_dict
+    global civitai_not_exists_list, loras_url_to_path_dict
     import requests
     from requests.adapters import HTTPAdapter
     from urllib3.util import Retry
@@ -1242,7 +1246,7 @@ def get_model_pipeline(repo_id: str):
     try:
         if " " in repo_id or not api.repo_exists(repo_id): return default
         model = api.model_info(repo_id=repo_id)
-    except Exception as e:
+    except Exception:
         return default
     if model.private or model.gated: return default
     tags = model.tags

requirements.txt

@@ -1,4 +1,4 @@
-git+https://github.com/R3gm/stablepy.git
+git+https://github.com/R3gm/stablepy.git@flux_beta
 torch==2.2.0
 gdown
 opencv-python