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Scripts/run_sdxl_creaprompt.py

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+from daam import trace, set_seed
+from diffusers import StableDiffusionXLPipeline
+from matplotlib import pyplot as plt
+import torch
+import os
+
+# Verify GPU availability
+if not torch.cuda.is_available():
+    raise RuntimeError("CUDA is not available. Please ensure a GPU is available and PyTorch is installed with CUDA support.")
+
+# Create output directory
+output_dir = 'sdxl-creaprompt'
+os.makedirs(output_dir, exist_ok=True)  # Create 'sdxl-creaprompt' folder if it doesn't exist
+
+# Model setup
+model_url = 'https://huggingface.co/ApacheOne/local-checkpoints/blob/main/SDXL(PONY)/creapromptLightning_creapromtHypersdxlV1.safetensors'
+device = 'cuda'  # Explicitly set to GPU
+
+# Load the pipeline from a single .safetensors file
+pipe = StableDiffusionXLPipeline.from_single_file(
+    model_url,
+    torch_dtype=torch.float16,  # Use float16 for faster inference on GPU
+    use_safetensors=True,       # Ensure safetensors format
+    variant='fp16'              # FP16 variant for efficiency
+)
+
+# GPU-specific optimizations
+pipe.enable_model_cpu_offload()  # Offload parts to CPU if VRAM is low
+pipe.enable_vae_slicing()        # Slice VAE operations to reduce memory usage
+pipe = pipe.to(device)
+
+# Prompt and generation settings
+prompt = 'realism eohwx woman, wearing dark black low wasit jeans,white shoes and red crop top, hands by side, ,full body shot,Lake Tahoe,(masterpiece best quality ultra-detailed best shadow amazing realistic picture)'
+gen = set_seed(42)  # Reproducible seed
+
+# Generate image and heatmaps
+with torch.no_grad():
+    with trace(pipe) as tc:
+        out = pipe(
+            prompt,
+            num_inference_steps=6,  # Reduced steps for faster generation (increase to 30-50 for better quality)
+            generator=gen,
+            callback=tc.time_callback,
+            callback_steps=1
+        )
+        # Save the generated image
+        generated_image_path = os.path.join(output_dir, 'generated_image.png')
+        out.images[0].save(generated_image_path)
+
+        # Generate and save heatmaps
+        heat_map = tc.compute_global_heat_map()
+        for word in prompt.split():
+            word_heat_map = heat_map.compute_word_heat_map(word)
+            
+            # Create the heatmap overlay plot
+            fig = plt.figure()
+            word_heat_map.plot_overlay(out.images[0])
+            plt.title(f"Heatmap for '{word}'")
+            
+            # Save the heatmap as a PNG
+            heatmap_path = os.path.join(output_dir, f'heatmap_{word}.png')
+            plt.savefig(heatmap_path, bbox_inches='tight')
+            plt.close(fig)  # Close the figure to free memory
+
+        # Save the experiment
+        exp = tc.to_experiment('sdxl-creaprompt-experiment-gpu')
+        exp.save()  # Saves to 'sdxl-creaprompt-experiment-gpu' folder
+
+print(f"Generation complete! Images saved in '{output_dir}' folder:")
+print(f"- Generated image: {generated_image_path}")
+print(f"- Heatmaps: {output_dir}/heatmap_<word>.png")
+print("Experiment saved in 'sdxl-creaprompt-experiment-gpu'.")

Scripts/run_sdxl_creapromptVAE.py

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+from daam import trace, set_seed
+from diffusers import StableDiffusionXLPipeline, DPMSolverMultistepScheduler, AutoencoderKL
+from matplotlib import pyplot as plt
+import torch
+import os
+
+# Verify GPU availability
+if not torch.cuda.is_available():
+    raise RuntimeError("CUDA is not available. Please ensure a GPU is available and PyTorch is installed with CUDA support.")
+
+# Create output directory
+output_dir = 'sdxl-creaprompt'
+os.makedirs(output_dir, exist_ok=True)  # Create 'sdxl-creaprompt' folder if it doesn't exist
+
+# Model setup
+model_url = 'https://huggingface.co/ApacheOne/local-checkpoints/blob/main/SDXL(PONY)/creapromptLightning_creapromtHypersdxlV1.safetensors'
+vae_url = 'https://huggingface.co/ApacheOne/local-checkpoints/blob/main/SDXL(PONY)/VAES/_bothyper.safetensors'
+device = 'cuda'  # Explicitly set to GPU
+
+# Load the custom VAE
+vae = AutoencoderKL.from_single_file(
+    vae_url,
+    torch_dtype=torch.float16,  # Match the pipeline's dtype
+    use_safetensors=True
+)
+
+# Load the pipeline with the custom VAE
+pipe = StableDiffusionXLPipeline.from_single_file(
+    model_url,
+    torch_dtype=torch.float16,  # Use float16 for faster inference on GPU
+    use_safetensors=True,       # Ensure safetensors format
+    variant='fp16',             # FP16 variant for efficiency
+    vae=vae                     # Pass the custom VAE
+)
+
+# Set the scheduler to DPMSolverMultistepScheduler (dpmpp_sde) with "normal" variant
+pipe.scheduler = DPMSolverMultistepScheduler.from_config(
+    pipe.scheduler.config,
+    use_karras=False  # "normal" variant (linear beta schedule, not Karras)
+)
+
+# GPU-specific optimizations
+pipe.enable_model_cpu_offload()  # Offload parts to CPU if VRAM is low
+pipe.enable_vae_slicing()        # Slice VAE operations to reduce memory usage
+pipe = pipe.to(device)
+
+# Prompt and generation settings
+prompt = '(masterpiece best quality ultra-detailed best shadow amazing realistic picture) realistic woman, full body, white blackground '
+gen = set_seed(42)  # Reproducible seed
+
+# Generate image and heatmaps
+with torch.no_grad():
+    with trace(pipe) as tc:
+        out = pipe(
+            prompt,
+            num_inference_steps=9,  # Reduced steps for faster generation (increase to 30-50 for better quality)
+            generator=gen,
+            callback=tc.time_callback,
+            callback_steps=1,
+            guidance_scale=1.1,  # Set CFG scale to 1.9
+            height=1024,  # Set height to 1024
+            width=1024   # Set width to 1024
+        )
+        # Save the generated image
+        generated_image_path = os.path.join(output_dir, 'generated_image.png')
+        out.images[0].save(generated_image_path)
+
+        # Generate and save heatmaps
+        heat_map = tc.compute_global_heat_map()
+        for word in prompt.split():
+            word_heat_map = heat_map.compute_word_heat_map(word)
+            
+            # Create the heatmap overlay plot
+            fig = plt.figure()
+            word_heat_map.plot_overlay(out.images[0])
+            plt.title(f"Heatmap for '{word}'")
+            
+            # Save the heatmap as a PNG
+            heatmap_path = os.path.join(output_dir, f'heatmap_{word}.png')
+            plt.savefig(heatmap_path, bbox_inches='tight')
+            plt.close(fig)  # Close the figure to free memory
+
+        # Save the experiment
+        exp = tc.to_experiment('sdxl-creaprompt-experiment-gpu')
+        exp.save()  # Saves to 'sdxl-creaprompt-experiment-gpu' folder
+
+print(f"Generation complete! Images saved in '{output_dir}' folder:")
+print(f"- Generated image: {generated_image_path}")
+print(f"- Heatmaps: {output_dir}/heatmap_<word>.png")
+print("Experiment saved in 'sdxl-creaprompt-experiment-gpu'.")

Scripts/run_sdxl_creapromptbest.py

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+from daam import trace, set_seed
+from diffusers import StableDiffusionXLPipeline, DPMSolverMultistepScheduler
+from matplotlib import pyplot as plt
+import torch
+import os
+
+# Verify GPU availability
+if not torch.cuda.is_available():
+    raise RuntimeError("CUDA is not available. Please ensure a GPU is available and PyTorch is installed with CUDA support.")
+
+# Create output directory
+output_dir = 'sdxl-creaprompt'
+os.makedirs(output_dir, exist_ok=True)  # Create 'sdxl-creaprompt' folder if it doesn't exist
+
+# Model setup
+model_url = 'https://huggingface.co/ApacheOne/local-checkpoints/blob/main/SDXL(PONY)/creapromptLightning_creapromtHypersdxlV1.safetensors'
+device = 'cuda'  # Explicitly set to GPU
+
+# Load the pipeline from a single .safetensors file
+pipe = StableDiffusionXLPipeline.from_single_file(
+    model_url,
+    torch_dtype=torch.float16,  # Use float16 for faster inference on GPU
+    use_safetensors=True,       # Ensure safetensors format
+    variant='fp16'              # FP16 variant for efficiency
+)
+
+# Set the scheduler to DPMSolverMultistepScheduler (dpmpp_sde) with "normal" variant
+pipe.scheduler = DPMSolverMultistepScheduler.from_config(
+    pipe.scheduler.config,
+    use_karras=False # "normal" variant (linear beta schedule, not Karras)
+)
+
+# GPU-specific optimizations
+pipe.enable_model_cpu_offload()  # Offload parts to CPU if VRAM is low
+pipe.enable_vae_slicing()        # Slice VAE operations to reduce memory usage
+pipe = pipe.to(device)
+
+# Prompt and generation settings
+prompt = 'realism woman, wearing dark black low waist jeans, white shoes and red crop top, hands by side, full body shot, Lake Tahoe, (masterpiece best quality ultra-detailed best shadow amazing realistic picture)'
+gen = set_seed(42)  # Reproducible seed
+
+# Generate image and heatmaps
+with torch.no_grad():
+    with trace(pipe) as tc:
+        out = pipe(
+            prompt,
+            num_inference_steps=13,  # Reduced steps for faster generation (increase to 30-50 for better quality)
+            generator=gen,
+            callback=tc.time_callback,
+            callback_steps=1,
+            guidance_scale=1.9,  # Set CFG scale to 1.1
+            height=1024,  # Set height to 1024
+            width=1024   # Set width to 1024
+        )
+        # Save the generated image
+        generated_image_path = os.path.join(output_dir, 'generated_image.png')
+        out.images[0].save(generated_image_path)
+
+        # Generate and save heatmaps
+        heat_map = tc.compute_global_heat_map()
+        for word in prompt.split():
+            word_heat_map = heat_map.compute_word_heat_map(word)
+            
+            # Create the heatmap overlay plot
+            fig = plt.figure()
+            word_heat_map.plot_overlay(out.images[0])
+            plt.title(f"Heatmap for '{word}'")
+            
+            # Save the heatmap as a PNG
+            heatmap_path = os.path.join(output_dir, f'heatmap_{word}.png')
+            plt.savefig(heatmap_path, bbox_inches='tight')
+            plt.close(fig)  # Close the figure to free memory
+
+        # Save the experiment
+        exp = tc.to_experiment('sdxl-creaprompt-experiment-gpu')
+        exp.save()  # Saves to 'sdxl-creaprompt-experiment-gpu' folder
+
+print(f"Generation complete! Images saved in '{output_dir}' folder:")
+print(f"- Generated image: {generated_image_path}")
+print(f"- Heatmaps: {output_dir}/heatmap_<word>.png")
+print("Experiment saved in 'sdxl-creaprompt-experiment-gpu'.")

Scripts/run_sdxl_with_daam_gpu.py

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+from daam import trace, set_seed
+from diffusers import DiffusionPipeline
+from matplotlib import pyplot as plt
+import torch
+import os
+
+# Verify GPU availability
+if not torch.cuda.is_available():
+    raise RuntimeError("CUDA is not available. Please ensure a GPU is available and PyTorch is installed with CUDA support.")
+
+# Create output directory
+output_dir = 'sdxl'
+os.makedirs(output_dir, exist_ok=True)  # Create 'sdxl' folder if it doesn't exist
+
+# Model setup
+model_id = 'stabilityai/stable-diffusion-xl-base-1.0'
+device = 'cuda'  # Explicitly set to GPU
+
+# Load the pipeline with float16 for GPU
+pipe = DiffusionPipeline.from_pretrained(
+    model_id,
+    torch_dtype=torch.float16,  # Use float16 for faster inference and lower memory usage on GPU
+    use_safetensors=True,       # Safetensors for faster loading
+    variant='fp16'              # FP16 variant for efficiency
+)
+
+# GPU-specific optimizations
+pipe.enable_model_cpu_offload()  # Offload parts to CPU if VRAM is low
+pipe.enable_vae_slicing()        # Slice VAE operations to reduce memory usage
+pipe = pipe.to(device)
+
+# Prompt and generation settings
+prompt = 'A human holding his hand up'
+gen = set_seed(42)  # Reproducible seed
+
+# Generate image and heatmaps
+with torch.no_grad():
+    with trace(pipe) as tc:
+        out = pipe(
+            prompt,
+            num_inference_steps=15,  # Reduced steps for faster generation (increase to 30-50 for better quality)
+            generator=gen,
+            callback=tc.time_callback,
+            callback_steps=1
+        )
+        # Save the generated image
+        generated_image_path = os.path.join(output_dir, 'generated_image.png')
+        out.images[0].save(generated_image_path)
+
+        # Generate and save heatmaps
+        heat_map = tc.compute_global_heat_map()
+        for word in prompt.split():
+            word_heat_map = heat_map.compute_word_heat_map(word)
+            
+            # Create the heatmap overlay plot
+            fig = plt.figure()
+            word_heat_map.plot_overlay(out.images[0])
+            plt.title(f"Heatmap for '{word}'")
+            
+            # Save the heatmap as a PNG
+            heatmap_path = os.path.join(output_dir, f'heatmap_{word}.png')
+            plt.savefig(heatmap_path, bbox_inches='tight')
+            plt.close(fig)  # Close the figure to free memory
+
+        # Save the experiment
+        exp = tc.to_experiment('sdxl-cat-experiment-gpu')
+        exp.save()  # Saves to 'sdxl-cat-experiment-gpu' folder
+
+print(f"Generation complete! Images saved in '{output_dir}' folder:")
+print(f"- Generated image: {generated_image_path}")
+print(f"- Heatmaps: {output_dir}/heatmap_<word>.png")
+print("Experiment saved in 'sdxl-cat-experiment-gpu'.")