Added flux demo

examples/apps/flux-demo.py

@@ -0,0 +1,221 @@
+import argparse
+import os
+import re
+import sys
+import time
+
+import gradio as gr
+import modelopt.torch.quantization as mtq
+import torch
+import torch_tensorrt
+from diffusers import FluxPipeline
+
+# Register SDPA as a standalone operator. Converter and lowering pass are defined in register_sdpa.py
+sys.path.append(os.path.join(os.path.dirname(__file__), "../dynamo"))
+from register_sdpa import *
+
+parser = argparse.ArgumentParser(
+    description="Run Flux quantization with different dtypes"
+)
+
+parser.add_argument(
+    "--dtype",
+    choices=["fp8", "int8", "fp16"],
+    default="fp16",
+    help="Select the data type to use (fp8 or int8 or fp16)",
+)
+args = parser.parse_args()
+# Update enabled precisions based on dtype argument
+
+if args.dtype == "fp8":
+    enabled_precisions = {torch.float8_e4m3fn, torch.float16}
+    ptq_config = mtq.FP8_DEFAULT_CFG
+elif args.dtype == "int8":
+    enabled_precisions = {torch.int8, torch.float16}
+    ptq_config = mtq.INT8_DEFAULT_CFG
+    ptq_config["quant_cfg"]["*weight_quantizer"]["axis"] = None
+elif args.dtype == "fp16":
+    enabled_precisions = {torch.float16}
+print(f"\nUsing {args.dtype}")
+
+
+DEVICE = "cuda:0"
+pipe = FluxPipeline.from_pretrained(
+    "black-forest-labs/FLUX.1-dev",
+    torch_dtype=torch.float16,
+)
+
+
+pipe.to(DEVICE).to(torch.float16)
+backbone = pipe.transformer
+backbone.eval()
+
+
+def filter_func(name):
+    pattern = re.compile(
+        r".*(time_emb_proj|time_embedding|conv_in|conv_out|conv_shortcut|add_embedding|pos_embed|time_text_embed|context_embedder|norm_out|x_embedder).*"
+    )
+    return pattern.match(name) is not None
+
+
+def do_calibrate(
+    pipe,
+    prompt: str,
+) -> None:
+    """
+    Run calibration steps on the pipeline using the given prompts.
+    """
+    image = pipe(
+        prompt,
+        output_type="pil",
+        num_inference_steps=20,
+        generator=torch.Generator("cuda").manual_seed(0),
+    ).images[0]
+
+
+def forward_loop(mod):
+    # Switch the pipeline's backbone, run calibration
+    pipe.transformer = mod
+    do_calibrate(
+        pipe=pipe,
+        prompt="test",
+    )
+
+
+if args.dtype != "fp16":
+    backbone = mtq.quantize(backbone, ptq_config, forward_loop)
+    mtq.disable_quantizer(backbone, filter_func)
+
+batch_size = 2
+
+BATCH = torch.export.Dim("batch", min=1, max=8)
+dynamic_shapes = {
+    "hidden_states": {0: BATCH},
+    "encoder_hidden_states": {0: BATCH},
+    "pooled_projections": {0: BATCH},
+    "timestep": {0: BATCH},
+    "txt_ids": {},
+    "img_ids": {},
+    "guidance": {0: BATCH},
+    "joint_attention_kwargs": {},
+    "return_dict": None,
+}
+
+settings = {
+    "strict": False,
+    "allow_complex_guards_as_runtime_asserts": True,
+    "enabled_precisions": enabled_precisions,
+    "truncate_double": True,
+    "min_block_size": 1,
+    "debug": False,
+    "use_python_runtime": True,
+    "immutable_weights": False,
+    "offload_module_to_cpu": True,
+}
+
+trt_gm = torch_tensorrt.MutableTorchTensorRTModule(backbone, **settings)
+trt_gm.set_expected_dynamic_shape_range((), dynamic_shapes)
+pipe.transformer = trt_gm
+
+
+def generate_image(prompt, inference_step, batch_size=2):
+    start_time = time.time()
+    image = pipe(
+        prompt,
+        output_type="pil",
+        num_inference_steps=inference_step,
+        num_images_per_prompt=batch_size,
+    ).images
+    end_time = time.time()
+    return image, end_time - start_time
+
+
+generate_image(["Test"], 2)
+torch.cuda.empty_cache()
+
+
+def model_change(model):
+    if model == "Torch Model":
+        pipe.transformer = backbone
+        backbone.to(DEVICE)
+    else:
+        backbone.to("cpu")
+        pipe.transformer = trt_gm
+        torch.cuda.empty_cache()
+
+
+def load_lora(path):
+    pipe.load_lora_weights(
+        path,
+        adapter_name="lora1",
+    )
+    pipe.set_adapters(["lora1"], adapter_weights=[1])
+    pipe.fuse_lora()
+    pipe.unload_lora_weights()
+    print("LoRA loaded! Begin refitting")
+    generate_image(["Test"], 2)
+    print("Refitting Finished!")
+
+
+# Create Gradio interface
+with gr.Blocks(title="Flux Demo with Torch-TensorRT") as demo:
+    gr.Markdown("# Flux Image Generation Demo Accelerated by Torch-TensorRT")
+
+    with gr.Row():
+        with gr.Column():
+            # Input components
+            prompt_input = gr.Textbox(
+                label="Prompt", placeholder="Enter your prompt here...", lines=3
+            )
+            model_dropdown = gr.Dropdown(
+                choices=["Torch Model", "Torch-TensorRT Accelerated Model"],
+                value="Torch-TensorRT Accelerated Model",
+                label="Model Variant",
+            )
+
+            lora_upload_path = gr.Textbox(
+                label="LoRA Path",
+                placeholder="Enter the LoRA checkpoint path here. It could be a local path or a Hugging Face URL.",
+                value="gokaygokay/Flux-Engrave-LoRA",
+                lines=2,
+            )
+            num_steps = gr.Slider(
+                minimum=20, maximum=100, value=20, step=1, label="Inference Steps"
+            )
+            batch_size = gr.Slider(
+                minimum=1, maximum=8, value=1, step=1, label="Batch Size"
+            )
+
+            generate_btn = gr.Button("Generate Image")
+            load_lora_btn = gr.Button("Load LoRA")
+
+        with gr.Column():
+            # Output component
+            output_image = gr.Gallery(label="Generated Image")
+            time_taken = gr.Textbox(
+                label="Generation Time (seconds)", interactive=False
+            )
+
+    # Connect the button to the generation function
+    model_dropdown.change(model_change, inputs=[model_dropdown])
+    load_lora_btn.click(
+        fn=load_lora,
+        inputs=[
+            lora_upload_path,
+        ],
+    )
+
+    # Update generate button click to include time output
+    generate_btn.click(
+        fn=generate_image,
+        inputs=[
+            prompt_input,
+            num_steps,
+            batch_size,
+        ],
+        outputs=[output_image, time_taken],
+    )
+
+# Launch the interface
+if __name__ == "__main__":
+    demo.launch()

examples/dynamo/mutable_torchtrt_module_example.py

@@ -22,6 +22,7 @@
 import torch
 import torch_tensorrt as torch_trt
 import torchvision.models as models
+from diffusers import DiffusionPipeline
 
 np.random.seed(5)
 torch.manual_seed(5)
@@ -31,7 +32,7 @@
 # Initialize the Mutable Torch TensorRT Module with settings.
 # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 settings = {
-    "use_python": False,
+    "use_python_runtime": False,
     "enabled_precisions": {torch.float32},
     "immutable_weights": False,
 }
@@ -40,7 +41,6 @@
 mutable_module = torch_trt.MutableTorchTensorRTModule(model, **settings)
 # You can use the mutable module just like the original pytorch module. The compilation happens while you first call the mutable module.
 mutable_module(*inputs)
-
 # %%
 # Make modifications to the mutable module.
 # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -73,13 +73,12 @@
 # Stable Diffusion with Huggingface
 # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-from diffusers import DiffusionPipeline
 
 with torch.no_grad():
     settings = {
         "use_python_runtime": True,
         "enabled_precisions": {torch.float16},
-        "debug": True,
+        "debug": False,
         "immutable_weights": False,
     }
 
@@ -106,7 +105,7 @@
             "text_embeds": {0: BATCH},
             "time_ids": {0: BATCH},
         },
-        "return_dict": False,
+        "return_dict": None,
     }
     pipe.unet.set_expected_dynamic_shape_range(
         args_dynamic_shapes, kwargs_dynamic_shapes

examples/dynamo/refit_engine_example.py

@@ -101,6 +101,7 @@
 )
 
 # Check the output
+model2.to("cuda")
 expected_outputs, refitted_outputs = exp_program2.module()(*inputs), new_trt_gm(*inputs)
 for expected_output, refitted_output in zip(expected_outputs, refitted_outputs):
     assert torch.allclose(