Upload 3 files

dc.py

@@ -326,12 +326,12 @@ 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,
             retain_task_model_in_cache=False,
-            #device="cpu",
+            device="cpu",
         )
         self.model.device = torch.device("cpu") #
 
@@ -367,12 +367,13 @@ 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,
@@ -509,7 +510,7 @@ 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):
@@ -539,8 +540,9 @@ 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
-        self.model.device = torch.device("cuda:0")
         model_precision = torch.float16
         if not self.model:
             print("Loading model...")
@@ -695,8 +697,10 @@ 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(1, desc="Inference preparation completed. Starting inference...")
 

env.py

@@ -94,6 +94,12 @@ 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

requirements.txt

@@ -4,7 +4,7 @@ diffusers
 invisible_watermark
 transformers
 xformers
-git+https://github.com/R3gm/stablepy.git
+git+https://github.com/R3gm/stablepy.git@flux_beta
 torch==2.2.0
 gdown
 opencv-python