Code cleaning and removed redundant code-2

Signed-off-by: Amit Raj <amitraj@qti.qualcommm.com>

Author: Amit Raj

examples/diffusers/flux/flux_1_schnell.py

@@ -4,20 +4,48 @@
 # SPDX-License-Identifier: BSD-3-Clause
 #
 # -----------------------------------------------------------------------------
+
+"""
+FLUX.1-schnell Image Generation Example
+
+This example demonstrates how to use the QEFFFluxPipeline to generate images
+using the FLUX.1-schnell model from Black Forest Labs. FLUX.1-schnell is a
+fast, distilled version of the FLUX.1 text-to-image model optimized for
+speed with minimal quality loss.
+
+Key Features:
+- Fast inference with only 4 steps
+- High-quality image generation from text prompts
+- Optimized for Qualcomm Cloud AI 100 using ONNX runtime
+- Deterministic output using fixed random seed
+
+Output:
+- Generates an image based on the text prompt
+- Saves the image as 'cat_with_sign.png' in the current directory
+"""
+
 import torch
 
 from QEfficient import QEFFFluxPipeline
 
-pipeline = QEFFFluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", use_onnx_function=True)
+# Initialize the FLUX.1-schnell pipeline from pretrained weights
+# use_onnx_function=True enables ONNX-based optimizations for faster compilation
+pipeline = QEFFFluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", use_onnx_function=False)
 
+# Generate an image from a text prompt
 output = pipeline(
     prompt="A cat holding a sign that says hello world",
     guidance_scale=0.0,
     num_inference_steps=4,
     max_sequence_length=256,
     generator=torch.manual_seed(42),
 )
+
+# Extract the generated image from the output
 image = output.images[0]
+
+# Save the generated image to disk
 image.save("cat_with_sign.png")
 
+# Print the output object (contains perf info)
 print(output)

examples/diffusers/flux/flux_1_shnell_custom.py

@@ -37,17 +37,17 @@
 # Option 1: Basic initialization with custom image dimensions
 pipeline = QEFFFluxPipeline.from_pretrained(
     "black-forest-labs/FLUX.1-schnell",
-    height=256,
-    width=256,
+    height=512,
+    width=512,
 )
 
 # Option 2: Advanced initialization with custom modules
 # Uncomment and modify to use your own custom components:
 #
 # pipeline = QEFFFluxPipeline.from_pretrained(
 #     "black-forest-labs/FLUX.1-schnell",
-#     height=256,
-#     width=256,
+#     height=512,
+#     width=512,
 #     text_encoder=custom_text_encoder,      # Your custom CLIP text encoder
 #     transformer=custom_transformer,         # Your custom transformer model
 #     tokenizer=custom_tokenizer,             # Your custom tokenizer

pyproject.toml

@@ -22,7 +22,7 @@ dependencies = [
     "transformers==4.55.0",
     "huggingface-hub==0.34.0",
     "hf_transfer==0.1.9",
-    "peft==0.17.0",
+    "peft==0.13.2",
     "datasets==2.20.0",
     "fsspec==2023.6.0",
     "multidict==6.0.4",
@@ -71,4 +71,4 @@ target-version = "py310"
 [tool.pytest.ini_options]
 addopts = "-W ignore -s -v"
 junit_logging = "all"
-doctest_optionflags = "NUMBER NORMALIZE_WHITESPACE ELLIPSIS"
+doctest_optionflags = "NUMBER NORMALIZE_WHITESPACE ELLIPSIS"