update controlnet_frames, downloads

diffsynth/pipelines/flux_image.py

@@ -258,6 +258,7 @@ def lets_dance_flux(
 ):
     if tiled:
         def flux_forward_fn(hl, hr, wl, wr):
+            tiled_controlnet_frames = [f[:, :, hl: hr, wl: wr] for f in controlnet_frames] if controlnet_frames is not None else None
             return lets_dance_flux(
                 dit=dit,
                 controlnet=controlnet,
@@ -268,7 +269,7 @@ def lets_dance_flux(
                 guidance=guidance,
                 text_ids=text_ids,
                 image_ids=None,
-                controlnet_frames=[f[:, :, hl: hr, wl: wr] for f in controlnet_frames],
+                controlnet_frames=tiled_controlnet_frames,
                 tiled=False,
                 **kwargs
             )

examples/ControlNet/flux_controlnet_quantization.py

@@ -6,10 +6,10 @@ import numpy as np
 
 
 def example_1():
+    download_models(["FLUX.1-dev", "jasperai/Flux.1-dev-Controlnet-Upscaler"])
     model_manager = ModelManager(
             torch_dtype=torch.bfloat16,
-            device="cpu" # To reduce VRAM required, we load models to RAM.
+            device="cpu" 
-            # device="cuda" # To reduce VRAM required, we load models to RAM.
         )
     model_manager.load_models([
         "models/FLUX/FLUX.1-dev/text_encoder/model.safetensors",
@@ -18,11 +18,11 @@ def example_1():
         ])
     model_manager.load_models(
         ["models/FLUX/FLUX.1-dev/flux1-dev.safetensors"],
-        torch_dtype=torch.float8_e4m3fn # Load the DiT model in FP8 format.
+        torch_dtype=torch.float8_e4m3fn 
     )
     model_manager.load_models(
         ["models/ControlNet/jasperai/Flux.1-dev-Controlnet-Upscaler/diffusion_pytorch_model.safetensors"],
-        torch_dtype=torch.float8_e4m3fn # Load the DiT model in FP8 format.
+        torch_dtype=torch.float8_e4m3fn 
     )
     pipe = FluxImagePipeline.from_model_manager(model_manager, controlnet_config_units=[
         ControlNetConfigUnit(
@@ -55,10 +55,10 @@ def example_1():
 
 
 def example_2():
+    download_models(["FLUX.1-dev", "jasperai/Flux.1-dev-Controlnet-Upscaler"])
     model_manager = ModelManager(
             torch_dtype=torch.bfloat16,
-            device="cpu" # To reduce VRAM required, we load models to RAM.
+            device="cpu" 
-            # device="cuda" # To reduce VRAM required, we load models to RAM.
         )
     model_manager.load_models([
         "models/FLUX/FLUX.1-dev/text_encoder/model.safetensors",
@@ -67,11 +67,11 @@ def example_2():
         ])
     model_manager.load_models(
         ["models/FLUX/FLUX.1-dev/flux1-dev.safetensors"],
-        torch_dtype=torch.float8_e4m3fn # Load the DiT model in FP8 format.
+        torch_dtype=torch.float8_e4m3fn 
     )
     model_manager.load_models(
         ["models/ControlNet/jasperai/Flux.1-dev-Controlnet-Upscaler/diffusion_pytorch_model.safetensors"],
-        torch_dtype=torch.float8_e4m3fn # Load the DiT model in FP8 format.
+        torch_dtype=torch.float8_e4m3fn 
     )
     pipe = FluxImagePipeline.from_model_manager(model_manager, controlnet_config_units=[
         ControlNetConfigUnit(
@@ -102,10 +102,10 @@ def example_2():
 
 
 def example_3():
+    download_models(["FLUX.1-dev", "InstantX/FLUX.1-dev-Controlnet-Union-alpha"])
     model_manager = ModelManager(
             torch_dtype=torch.bfloat16,
-            device="cpu" # To reduce VRAM required, we load models to RAM.
+            device="cpu" 
-            # device="cuda" # To reduce VRAM required, we load models to RAM.
         )
     model_manager.load_models([
         "models/FLUX/FLUX.1-dev/text_encoder/model.safetensors",
@@ -114,11 +114,11 @@ def example_3():
         ])
     model_manager.load_models(
         ["models/FLUX/FLUX.1-dev/flux1-dev.safetensors"],
-        torch_dtype=torch.float8_e4m3fn # Load the DiT model in FP8 format.
+        torch_dtype=torch.float8_e4m3fn 
     )
     model_manager.load_models(
         ["models/ControlNet/InstantX/FLUX.1-dev-Controlnet-Union-alpha/diffusion_pytorch_model.safetensors"],
-        torch_dtype=torch.float8_e4m3fn # Load the DiT model in FP8 format.
+        torch_dtype=torch.float8_e4m3fn 
     )
     pipe = FluxImagePipeline.from_model_manager(model_manager, controlnet_config_units=[
         ControlNetConfigUnit(
@@ -153,10 +153,10 @@ def example_3():
 
 
 def example_4():
+    download_models(["FLUX.1-dev", "InstantX/FLUX.1-dev-Controlnet-Union-alpha"])
     model_manager = ModelManager(
             torch_dtype=torch.bfloat16,
-            device="cpu" # To reduce VRAM required, we load models to RAM.
+            device="cpu" 
-            # device="cuda" # To reduce VRAM required, we load models to RAM.
         )
     model_manager.load_models([
         "models/FLUX/FLUX.1-dev/text_encoder/model.safetensors",
@@ -165,11 +165,11 @@ def example_4():
         ])
     model_manager.load_models(
         ["models/FLUX/FLUX.1-dev/flux1-dev.safetensors"],
-        torch_dtype=torch.float8_e4m3fn # Load the DiT model in FP8 format.
+        torch_dtype=torch.float8_e4m3fn 
     )
     model_manager.load_models(
         ["models/ControlNet/InstantX/FLUX.1-dev-Controlnet-Union-alpha/diffusion_pytorch_model.safetensors"],
-        torch_dtype=torch.float8_e4m3fn # Load the DiT model in FP8 format.
+        torch_dtype=torch.float8_e4m3fn 
     )
     pipe = FluxImagePipeline.from_model_manager(model_manager, controlnet_config_units=[
         ControlNetConfigUnit(
@@ -205,10 +205,10 @@ def example_4():
 
 
 def example_5():
+    download_models(["FLUX.1-dev", "alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta"])
     model_manager = ModelManager(
             torch_dtype=torch.bfloat16,
-            device="cpu" # To reduce VRAM required, we load models to RAM.
+            device="cpu" 
-            # device="cuda" # To reduce VRAM required, we load models to RAM.
         )
     model_manager.load_models([
         "models/FLUX/FLUX.1-dev/text_encoder/model.safetensors",
@@ -217,11 +217,11 @@ def example_5():
         ])
     model_manager.load_models(
         ["models/FLUX/FLUX.1-dev/flux1-dev.safetensors"],
-        torch_dtype=torch.float8_e4m3fn # Load the DiT model in FP8 format.
+        torch_dtype=torch.float8_e4m3fn 
     )
     model_manager.load_models(
         ["models/ControlNet/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta/diffusion_pytorch_model.safetensors"],
-        torch_dtype=torch.float8_e4m3fn # Load the DiT model in FP8 format.
+        torch_dtype=torch.float8_e4m3fn 
     )
     pipe = FluxImagePipeline.from_model_manager(model_manager, controlnet_config_units=[
         ControlNetConfigUnit(
@@ -257,10 +257,14 @@ def example_5():
 
 
 def example_6():
+    download_models([
+        "FLUX.1-dev",
+        "jasperai/Flux.1-dev-Controlnet-Surface-Normals",
+        "alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta"
+    ])
     model_manager = ModelManager(
             torch_dtype=torch.bfloat16,
-            device="cpu" # To reduce VRAM required, we load models to RAM.
+            device="cpu" 
-            # device="cuda" # To reduce VRAM required, we load models to RAM.
         )
     model_manager.load_models([
         "models/FLUX/FLUX.1-dev/text_encoder/model.safetensors",
@@ -269,12 +273,12 @@ def example_6():
         ])
     model_manager.load_models(
         ["models/FLUX/FLUX.1-dev/flux1-dev.safetensors"],
-        torch_dtype=torch.float8_e4m3fn # Load the DiT model in FP8 format.
+        torch_dtype=torch.float8_e4m3fn 
     )
     model_manager.load_models(
         ["models/ControlNet/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta/diffusion_pytorch_model.safetensors",
          "models/ControlNet/jasperai/Flux.1-dev-Controlnet-Surface-Normals/diffusion_pytorch_model.safetensors"],
-        torch_dtype=torch.float8_e4m3fn # Load the DiT model in FP8 format.
+        torch_dtype=torch.float8_e4m3fn 
     )
     pipe = FluxImagePipeline.from_model_manager(model_manager, controlnet_config_units=[
         ControlNetConfigUnit(
@@ -314,10 +318,15 @@ def example_6():
 
 
 def example_7():
+    download_models([
+        "FLUX.1-dev",
+        "InstantX/FLUX.1-dev-Controlnet-Union-alpha",
+        "alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta",
+        "jasperai/Flux.1-dev-Controlnet-Upscaler",
+    ])
     model_manager = ModelManager(
             torch_dtype=torch.bfloat16,
-            device="cpu" # To reduce VRAM required, we load models to RAM.
+            device="cpu"
-            # device="cuda" # To reduce VRAM required, we load models to RAM.
         )
     model_manager.load_models([
         "models/FLUX/FLUX.1-dev/text_encoder/model.safetensors",
@@ -326,13 +335,13 @@ def example_7():
         ])
     model_manager.load_models(
         ["models/FLUX/FLUX.1-dev/flux1-dev.safetensors"],
-        torch_dtype=torch.float8_e4m3fn # Load the DiT model in FP8 format.
+        torch_dtype=torch.float8_e4m3fn
     )
     model_manager.load_models(
         ["models/ControlNet/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta/diffusion_pytorch_model.safetensors",
          "models/ControlNet/InstantX/FLUX.1-dev-Controlnet-Union-alpha/diffusion_pytorch_model.safetensors",
          "models/ControlNet/jasperai/Flux.1-dev-Controlnet-Upscaler/diffusion_pytorch_model.safetensors"],
-        torch_dtype=torch.float8_e4m3fn # Load the DiT model in FP8 format.
+        torch_dtype=torch.float8_e4m3fn 
     )
     pipe = FluxImagePipeline.from_model_manager(model_manager, controlnet_config_units=[
         ControlNetConfigUnit(