update

README.md

@@ -0,0 +1,21 @@
+# Flux WebUI
+
+Minimal Flux Web UI powered by Gradio & Diffusers.
+
+# Install
+
+## 1. One click install
+
+The easiest way is to use https://pinokio.computer
+
+## 2. Install manually
+
+```
+pip install -r requirements.txt
+```
+
+# Run
+
+```
+python app.py
+```

app.py

@@ -1,20 +1,39 @@
-# schnell
 import gradio as gr
 import numpy as np
 import random
 import torch
-from diffusers import DiffusionPipeline
+from diffusers import FluxPipeline, FlowMatchEulerDiscreteScheduler, FluxTransformer2DModel
 import devicetorch
-
 dtype = torch.bfloat16
 device = devicetorch.get(torch)
-
-pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=dtype).to(device)
-
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 2048
+selected = None
+css="""
+nav {
+  text-align: center;
+}
+#logo{
+  width: 50px;
+  display: inline;
+}
+"""
+def infer(prompt, checkpoint="black-fores-labs/FLUX.1-schnell", seed=42, randomize_seed=False, width=1024, height=1024, num_inference_steps=4, progress=gr.Progress(track_tqdm=True)):
+    global pipe
+    global selected
+    # if the new checkpoint is different from the selected one, re-instantiate the pipe
+    if selected != checkpoint:
+        if checkpoint == "sayakpaul/FLUX.1-merged":
+            transformer = FluxTransformer2DModel.from_pretrained("sayakpaul/FLUX.1-merged", torch_dtype=dtype)
+            pipe = FluxPipeline.from_pretrained("cocktailpeanut/xulf-d", transformer=transformer, torch_dtype=torch.bfloat16)
+        else:
+            pipe = FluxPipeline.from_pretrained(checkpoint, torch_dtype=torch.bfloat16)
 
-def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, num_inference_steps=4, progress=gr.Progress(track_tqdm=True)):
+        #pipe.enable_model_cpu_offload()
+        pipe.to(device)
+        pipe.enable_attention_slicing()
+        selected = checkpoint
+    devicetorch.empty_cache(torch)
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
     generator = torch.Generator().manual_seed(seed)
@@ -27,30 +46,15 @@ def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, num_in
             guidance_scale=0.0
     ).images[0] 
     return image, seed
- 
-examples = [
-    "a tiny astronaut hatching from an egg on the moon",
-    "a cat holding a sign that says hello world",
-    "an anime illustration of a wiener schnitzel",
-]
-
-css="""
-#col-container {
-    margin: 0 auto;
-    max-width: 520px;
-}
-"""
-
+def update_slider(checkpoint, num_inference_steps):
+    if checkpoint == "sayakpaul/FLUX.1-merged":
+        return 8
+    else:
+        return 4
 with gr.Blocks(css=css) as demo:
-    
     with gr.Column(elem_id="col-container"):
-        gr.Markdown(f"""# FLUX.1 [schnell]
-12B param rectified flow transformer distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/) for 4 step generation
-[[blog](https://blackforestlabs.ai/announcing-black-forest-labs/)] [[model](https://huggingface.co/black-forest-labs/FLUX.1-schnell)]
-        """)
-        
+        gr.HTML("<nav><img id='logo' src='file/icon.webp'/></nav>")
         with gr.Row():
-            
             prompt = gr.Text(
                 label="Prompt",
                 show_label=False,
@@ -58,13 +62,16 @@ with gr.Blocks(css=css) as demo:
                 placeholder="Enter your prompt",
                 container=False,
             )
-            
             run_button = gr.Button("Run", scale=0)
-        
         result = gr.Image(label="Result", show_label=False)
-        
-        with gr.Accordion("Advanced Settings", open=False):
-            
+        with gr.Accordion("Advanced Settings"):
+            checkpoint = gr.Dropdown(
+              value= "black-forest-labs/FLUX.1-schnell",
+              choices=[
+                "black-forest-labs/FLUX.1-schnell",
+                "sayakpaul/FLUX.1-merged"
+              ]
+            )
             seed = gr.Slider(
                 label="Seed",
                 minimum=0,
@@ -72,11 +79,8 @@ with gr.Blocks(css=css) as demo:
                 step=1,
                 value=0,
             )
-            
             randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-            
             with gr.Row():
-                
                 width = gr.Slider(
                     label="Width",
                     minimum=256,
@@ -84,18 +88,14 @@ with gr.Blocks(css=css) as demo:
                     step=32,
                     value=1024,
                 )
-                
                 height = gr.Slider(
                     label="Height",
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=1024,
+                    value=576,
                 )
-            
             with gr.Row():
-                
-  
                 num_inference_steps = gr.Slider(
                     label="Number of inference steps",
                     minimum=1,
@@ -103,21 +103,11 @@ with gr.Blocks(css=css) as demo:
                     step=1,
                     value=4,
                 )
-        
-        gr.Examples(
-            examples = examples,
-            fn = infer,
-            inputs = [prompt],
-            outputs = [result, seed],
-            cache_examples="lazy"
-        )
-
+            checkpoint.change(fn=update_slider, inputs=[checkpoint], outputs=[num_inference_steps])
     gr.on(
         triggers=[run_button.click, prompt.submit],
         fn = infer,
-        inputs = [prompt, seed, randomize_seed, width, height, num_inference_steps],
+        inputs = [prompt, checkpoint, seed, randomize_seed, width, height, num_inference_steps],
         outputs = [result, seed]
     )
-
 demo.launch()
-

install.js

@@ -7,7 +7,7 @@ module.exports = {
         uri: "torch.js",
         params: {
           venv: "env",                // Edit this to customize the venv folder path
-          // xformers: true   // uncomment this line if your project requires xformers
+          xformers: true   // uncomment this line if your project requires xformers
         }
       }
     },

pinokio.js

@@ -1,8 +1,8 @@
 const path = require('path')
 module.exports = {
   version: "2.0",
-  title: "flux-all",
-  description: "",
+  title: "flux-webui",
+  description: "Minimal Flux Web UI powered by Gradio & Diffusers",
   icon: "icon.webp",
   menu: async (kernel, info) => {
     let installed = info.exists("env")

requirements.txt

@@ -1,8 +1,8 @@
 gradio
 devicetorch
 accelerate
-git+https://github.com/huggingface/diffusers.git@flux-pipeline
-torch
+git+https://github.com/peanutcocktail/diffusers.git
 transformers==4.42.4
-xformers
 sentencepiece
+protobuf
+einops