Update app_v4.py

app_v4.py

@@ -27,26 +27,37 @@ logger = logging.getLogger(__name__)
 #############################
 
 presets = {
+    "Default": {
+        "scale": 1.25,
+        "steps": 6,
+        "controlnet_conditioning_scale": 0.62,
+        "guidance_scale": 3.3,
+        "guidance_start": 0,
+        "guidance_end": 0.8
+    },
     "Strict Upscale": {
         "scale": 1.0,
         "steps": 8,
         "controlnet_conditioning_scale": 0.75,
         "guidance_scale": 4.0,
+        "guidance_start": 0,
         "guidance_end": 0.9
     },
     "Creative Upscale": {
-        "scale": 2.0,
+        "scale": 1.5,
         "steps": 6,
         "controlnet_conditioning_scale": 0.42,
         "guidance_scale": 3.0,
+        "guidance_start": 0,
         "guidance_end": 0.5
     },
-    "High Detail Upscale": {
+    "Redux": {
         "scale": 1.25,
-        "steps": 10,
-        "controlnet_conditioning_scale": 0.9,
-        "guidance_scale": 10.0,
-        "guidance_end": 0.9
+        "steps": 6,
+        "controlnet_conditioning_scale": 0.3,
+        "guidance_scale": 3.0,
+        "guidance_start": 0.05,
+        "guidance_end": 0.35
     }
 }
 
@@ -65,14 +76,14 @@ except Exception as e:
     print(f"Error setting memory usage: {e}")
 
 text_encoder_2_unquant = T5EncoderModel.from_pretrained(
-    "LPX55/FLUX.1-merged_lightning_v2",
+    "buildborderless/FLUX.1-merged_lightning_v2",
     subfolder="text_encoder_2",
     torch_dtype=torch.bfloat16,
     token=huggingface_token
 )
 
 pipe = FluxControlNetPipeline.from_pretrained(
-    "LPX55/FLUX.1M-8step_upscaler-cnet",
+    "buildborderless/FLUX.1-M_ControlNet_Upscaler",
     torch_dtype=torch.bfloat16,
     text_encoder_2=text_encoder_2_unquant,
     token=huggingface_token
@@ -221,6 +232,7 @@ def process_image(control_image, user_prompt, system_prompt, scale, steps,
             controlnet_conditioning_scale=controlnet_conditioning_scale,
             guidance_scale=guidance_scale,
             seed=seed,
+            guidance_start=guidance_start,
             guidance_end=guidance_end
         )
             
@@ -248,7 +260,7 @@ with gr.Blocks(title="FLUX Turbo Upscaler", fill_height=True) as demo:
         with gr.Column(scale=1):
             prompt = gr.Textbox(lines=4, info="Enter your prompt here or wait for auto-generation...", label="Image Description")
             focus = gr.Textbox(label="Area(s) of Focus", info="e.g. 'face', 'eyes', 'hair', 'clothes', 'background', etc.", value="clothing material, textures, ethnicity")
-            scale = gr.Slider(1, 3, value=1, label="Scale (Upscale Factor)", step=0.1)
+            scale = gr.Slider(1, 3, value=1, label="Scale (Upscale Factor)", step=0.05)
             with gr.Row():
                 generate_button = gr.Button("Generate Image", variant="primary")
                 caption_button = gr.Button("Generate Caption", variant="secondary")
@@ -260,6 +272,8 @@ with gr.Blocks(title="FLUX Turbo Upscaler", fill_height=True) as demo:
             steps = gr.Slider(2, 16, value=8, label="Steps", step=1)
             controlnet_conditioning_scale = gr.Slider(0, 1, value=0.6, label="ControlNet Scale")
             guidance_scale = gr.Slider(1, 30, value=3.5, label="Guidance Scale")
+            guidance_start = gr.Slider(0, 1, value=1.0, label="Guidance End")
+
             guidance_end = gr.Slider(0, 1, value=1.0, label="Guidance End")
             original_dimensions = gr.Markdown(value="Original Image Dimensions: N/A")  # New output for dimensions
 
@@ -306,7 +320,7 @@ with gr.Blocks(title="FLUX Turbo Upscaler", fill_height=True) as demo:
         fn=process_image,
         inputs=[
             control_image, prompt, system_prompt, scale, steps, 
-            controlnet_conditioning_scale, guidance_scale, seed, 
+            controlnet_conditioning_scale, guidance_scale, seed, guidance_start,
             guidance_end, temperature_slider, max_tokens_slider, log_prompt
         ],
         outputs=[log_state, generated_image, prompt]
@@ -340,7 +354,7 @@ with gr.Blocks(title="FLUX Turbo Upscaler", fill_height=True) as demo:
     preset_radio.change(
         fn=update_parameters,
         inputs=[preset_radio],
-        outputs=[scale, steps, controlnet_conditioning_scale, guidance_scale, guidance_end]
+        outputs=[scale, steps, controlnet_conditioning_scale, guidance_scale, guidance_start, guidance_end]
     )
     def hello(profile: gr.OAuthProfile | None) -> str:
         if profile is None: