Upload 3 files

README.md

@@ -1,12 +1,13 @@
----
-title: Diffusers Lora Error Test1
-emoji: 🏢
-colorFrom: blue
-colorTo: gray
-sdk: gradio
-sdk_version: 5.9.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Diffusers LoRA test with quantization
+emoji: 🙄
+colorFrom: indigo
+colorTo: purple
+sdk: gradio
+sdk_version: 4.44.0
+app_file: app.py
+pinned: false
+license: mit
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,57 @@
+import spaces
+import gradio as gr
+import torch
+from huggingface_hub import hf_hub_download
+from diffusers import FluxPipeline, FluxTransformer2DModel, GGUFQuantizationConfig, BitsAndBytesConfig
+import os
+import subprocess
+#subprocess.run("pip list", shell=True)
+#subprocess.run("diffusers-cli env", shell=True)
+#from optimum.quanto import freeze, qfloat8, quantize
+
+HF_TOKEN = os.getenv("HF_TOKEN", "")
+device = "cuda" if torch.cuda.is_available() else "cpu"
+flux_repo = "multimodalart/FLUX.1-dev2pro-full"
+ckpt_path = "https://huggingface.co/city96/FLUX.1-dev-gguf/blob/main/flux1-dev-Q2_K.gguf"
+transformer_gguf = FluxTransformer2DModel.from_single_file(ckpt_path, subfolder="transformer", quantization_config=GGUFQuantizationConfig(compute_dtype=torch.bfloat16),
+                                                           torch_dtype=torch.bfloat16, config=flux_repo, token=HF_TOKEN)
+transformer = FluxTransformer2DModel.from_pretrained(flux_repo, subfolder="transformer", torch_dtype=torch.bfloat16, token=HF_TOKEN)
+nf4_quantization_config = BitsAndBytesConfig(load_in_4bit=True)
+transformer_nf4 = FluxTransformer2DModel.from_pretrained(flux_repo, subfolder="transformer", quantization_config=nf4_quantization_config,
+                                                         torch_dtype=torch.bfloat16, token=HF_TOKEN)
+pipe = FluxPipeline.from_pretrained(flux_repo, transformer=transformer, torch_dtype=torch.bfloat16, token=HF_TOKEN)
+hyper_sd_lora = hf_hub_download("ByteDance/Hyper-SD", "Hyper-FLUX.1-dev-8steps-lora.safetensors")
+
+@spaces.GPU(duration=70)
+def infer(prompt: str, mode: str, is_lora: bool, progress=gr.Progress(track_tqdm=True)):
+    global pipe
+    try:
+        pipe.unload_lora_weights()
+        if mode == "Default": pipe.transformer = transformer
+        elif mode == "GGUF": pipe.transformer = transformer_gguf
+        elif mode == "NF4": pipe.transformer = transformer_nf4
+        if is_lora:
+            pipe.load_lora_weights(hyper_sd_lora, adapter_name="hyper-sd")
+            pipe.set_adapters(["hyper-sd"], adapter_weights=[0.125])
+            steps = 8
+        else: steps = 28
+        pipe.to(device)
+        image = pipe(prompt, generator=torch.manual_seed(0), num_inference_steps=steps).images[0]
+        pipe.to("cpu")
+        return image
+    except Exception as e:
+        raise gr.Error(e)
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column():
+            prompt = gr.Textbox(label="Prompt", value="A cat holding a sign that says hello world", lines=1)
+            mode = gr.Radio(label="Mode", choices=["Default", "GGUF", "NF4"], value="Default")
+            is_lora = gr.Checkbox(label="Enable LoRA", value=True)
+            gen_btn = gr.Button("Generate Image")
+        with gr.Column():
+            result = gr.Image(label="Result Image")
+
+    gen_btn.click(infer, [prompt, mode, is_lora], [result])
+
+demo.launch()

requirements.txt

@@ -0,0 +1,10 @@
+huggingface_hub
+torch
+diffusers
+peft
+transformers
+accelerate
+numpy<2
+gguf
+bitsandbytes
+optimum-quanto