create app.py

app.py

@@ -0,0 +1,58 @@
+#https://github.com/huggingface/diffusers/tree/main/examples/dreambooth
+#export MODEL_NAME="stabilityai/stable-diffusion-2-1-base"
+#export INSTANCE_DIR="./data_example"
+#export OUTPUT_DIR="./output_example"
+
+#accelerate launch train_lora_dreambooth.py \
+#  --pretrained_model_name_or_path=$MODEL_NAME  \
+#  --instance_data_dir=$INSTANCE_DIR \
+#  --output_dir=$OUTPUT_DIR \
+#  --instance_prompt="style of sks" \
+#  --resolution=512 \
+#  --train_batch_size=1 \
+#  --gradient_accumulation_steps=1 \
+#  --learning_rate=1e-4 \
+#  --lr_scheduler="constant" \
+#  --lr_warmup_steps=0 \
+#  --max_train_steps=30000
+
+from diffusers import StableDiffusionPipeline
+from lora_diffusion import monkeypatch_lora, tune_lora_scale
+import torch
+import os 
+#os.system('python file.py')
+import subprocess
+# If your shell script has shebang,
+# you can omit shell=True argument.
+subprocess.run("./run_lora_db.sh", shell=True)
+
+#####
+model_id = "stabilityai/stable-diffusion-2-1-base"
+pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")
+prompt = "style of sks, baby lion"
+torch.manual_seed(1)
+#image = pipe(prompt, num_inference_steps=50, guidance_scale= 7).images[0] #no need
+#image  # nice. diffusers are cool. #no need
+finetuned_lora_weights = "./lora_weight.pt"
+
+#####
+#my fine tuned weights
+def monkeypatching( alpha): #, prompt, pipe): finetuned_lora_weights
+    monkeypatch_lora(pipe.unet, torch.load(finetuned_lora_weights)) #"./lora_weight.pt")) 
+    tune_lora_scale(pipe.unet, alpha) #1.00)
+    image = pipe(prompt, num_inference_steps=50, guidance_scale=7).images[0]
+    image.save("./illust_lora.jpg") #"./contents/illust_lora.jpg")
+    return image
+ 
+with gr.Blocks() as demo:
+    with gr.Row():
+        in_images =  gr.Image(label="Upload images to fine-tune for LORA")
+        #in_prompt = gr.Textbox(label="Enter a ")
+        in_steps = gr.Number(label="Enter number of steps")
+        in_alpha = gr.Slider(0.1,1.0, step=0.01, label="Set Alpha level - higher value has more chances to overfit")
+        b1 = gr.Button(value="Create LORA model")
+    with gr.Row():
+        out_image = gr.Image(label="Image generated by LORA model")
+    b1.click(fn = monkeypatching, inputs=in_alpha, outputs=out_image)
+
+demo.launch(debug=True, show_error=True)