import spaces import subprocess #subprocess.run(['sh', './spaces.sh']) @spaces.GPU(required=True) def install_dependencies(): subprocess.run(['sh', './flashattn.sh']) # Install the UltraHDR library print("Installing pillow-ultrahdr...") subprocess.run(['pip', 'install', 'pillow-ultrahdr']) print("✅ pillow-ultrahdr installed.") # Install all dependencies install_dependencies() import os os.environ['PYTORCH_NVML_BASED_CUDA_CHECK'] = '1' os.environ['TORCH_LINALG_PREFER_CUSOLVER'] = '1' os.environ['PYTORCH_ALLOC_CONF'] = 'expandable_segments:True,pinned_use_background_threads:True' os.environ["SAFETENSORS_FAST_GPU"] = "1" os.environ['HF_HUB_ENABLE_HF_TRANSFER'] = '1' import torch torch.backends.cuda.matmul.allow_tf32 = False # torch 2.8 torch.backends.cudnn.allow_tf32 = False # torch 2.8 torch.backends.cuda.matmul.allow_bf16_reduced_precision_reduction = False torch.backends.cuda.matmul.allow_fp16_reduced_precision_reduction = False #torch.backends.fp32_precision = "ieee" torch 2.9 #torch.backends.cuda.matmul.fp32_precision = "ieee" torch 2.9 #torch.backends.cudnn.fp32_precision = "ieee" torch 2.9 #torch.backends.cudnn.conv.fp32_precision = "ieee" torch 2.9 #torch.backends.cudnn.rnn.fp32_precision = "ieee" torch 2.9 torch.backends.cudnn.deterministic = False torch.backends.cudnn.benchmark = False torch.backends.cuda.preferred_blas_library="cublas" torch.backends.cuda.preferred_linalg_library="cusolver" torch.set_float32_matmul_precision("highest") import gradio as gr import numpy as np import random import datetime import threading import io from PIL import Image # For Ultra HDR import pillow_ultrahdr from google.oauth2 import service_account from google.cloud import storage import torch torch.backends.cuda.matmul.allow_tf32 = False torch.backends.cuda.matmul.allow_bf16_reduced_precision_reduction = False torch.backends.cuda.matmul.allow_fp16_reduced_precision_reduction = False torch.backends.cudnn.allow_tf32 = False torch.backends.cudnn.deterministic = False torch.backends.cudnn.benchmark = False torch.backends.cuda.preferred_blas_library="cublas" torch.backends.cuda.preferred_linalg_library="cusolver" torch.set_float32_matmul_precision("highest") from diffusers import StableDiffusion3Pipeline, SD3Transformer2DModel, AutoencoderKL from image_gen_aux import UpscaleWithModel GCS_BUCKET_NAME = os.getenv("GCS_BUCKET_NAME") GCS_SA_KEY = os.getenv("GCS_SA_KEY") # The full JSON key content as a string gcs_client = None if GCS_SA_KEY and GCS_BUCKET_NAME: try: credentials_info = eval(GCS_SA_KEY) # Using eval is safe here if you trust the secret source credentials = service_account.Credentials.from_service_account_info(credentials_info) gcs_client = storage.Client(credentials=credentials) print("✅ GCS Client initialized successfully.") except Exception as e: print(f"❌ Failed to initialize GCS client: ") def upload_to_gcs(image_bytes, filename): if not gcs_client: print("⚠️ GCS client not initialized. Skipping upload.") return try: print(f"--> Starting GCS upload for {filename}...") bucket = gcs_client.bucket(GCS_BUCKET_NAME) blob = bucket.blob(f"stablediff/{filename}") # The image_bytes is already a bytes object, so we can upload it directly blob.upload_from_string(image_bytes, content_type='image/jpeg') print(f"✅ Successfully uploaded {filename} to GCS.") except Exception as e: print(f"❌ An error occurred during GCS upload for {filename}: {e}") device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") from diffusers.models.attention_processor import AttnProcessor2_0 from kernels import get_kernel fa3_kernel = get_kernel("kernels-community/flash-attn3") # Or vllm-flash-attn3 class FlashAttentionProcessor(AttnProcessor2_0): def __call__( self, attn, hidden_states, encoder_hidden_states=None, # This will be present for cross-attention attention_mask=None, temb=None, # This might be present in some attention mechanisms, pass through if not used directly **kwargs, ): # Determine if it's self-attention or cross-attention # For self-attention, encoder_hidden_states is None or identical to hidden_states is_cross_attention = encoder_hidden_states is not None and encoder_hidden_states.shape[1] != hidden_states.shape[1] # SD3.5 uses DiT, where hidden_states are often 3D (B, Seq, Dim) # However, attention can be within a transformer block which might internally reshape. # Ensure your inputs (query, key, value) are properly shaped for the kernel. # The kernel expects (Batch, Heads, Sequence, Dim_Head) query = attn.to_q(hidden_states) if is_cross_attention: key = attn.to_k(encoder_hidden_states) value = attn.to_v(encoder_hidden_states) else: # Self-attention key = attn.to_k(hidden_states) value = attn.to_v(hidden_states) scale = attn.scale query = query * scale b, t, c = query.shape # B=batch_size, T=sequence_length, C=embedding_dim h = attn.heads d = c // h # dim_per_head # Reshape to (Batch, Heads, Sequence, Dim_Head) for Flash Attention kernel q_reshaped = query.reshape(b, t, h, d).permute(0, 2, 1, 3) k_reshaped = key.reshape(b, t, h, d).permute(0, 2, 1, 3) v_reshaped = value.reshape(b, t, h, d).permute(0, 2, 1, 3) out_reshaped = torch.empty_like(q_reshaped) # Call the Flash Attention kernel fa3_kernel.attention(q_reshaped, k_reshaped, v_reshaped, out_reshaped) # Reshape output back to (Batch, Sequence, Heads * Dim_Head) out = out_reshaped.permute(0, 2, 1, 3).reshape(b, t, c) out = attn.to_out(out) return out @spaces.GPU(duration=120) def compile_transformer(): with spaces.aoti_capture(pipe.transformer) as call: pipe("A majestic, ancient Egyptian Sphinx stands sentinel in a large, clear pool under a bright, golden desert sun. Around its weathered stone base, several sleek, playful dolphins gracefully navigate the turquoise waters. The surrounding environment features lush, exotic papyrus plants and distant pyramids under a cloudless sky, conveying a sense of timeless wonder and serene majesty.") exported = torch.export.export( pipe.transformer, args=call.args, kwargs=call.kwargs, ) return spaces.aoti_compile(exported) def load_model(): pipe = StableDiffusion3Pipeline.from_pretrained( "ford442/stable-diffusion-3.5-large-bf16", trust_remote_code=True, transformer=None, # Load transformer separately use_safetensors=True ) ll_transformer=SD3Transformer2DModel.from_pretrained("ford442/stable-diffusion-3.5-large-bf16", subfolder='transformer').to(device, dtype=torch.bfloat16) pipe.transformer=ll_transformer pipe.load_lora_weights("ford442/sdxl-vae-bf16", weight_name="LoRA/UltraReal.safetensors") pipe.to(device=device, dtype=torch.bfloat16) upscaler_2 = UpscaleWithModel.from_pretrained("Kim2091/ClearRealityV1").to(device) return pipe, upscaler_2 def srgb_to_linear(img_tensor): """Converts a batched sRGB tensor [0, 1] to a linear tensor.""" # Using the standard sRGB to linear conversion formula return torch.where( img_tensor <= 0.04045, img_tensor / 12.92, ((img_tensor + 0.055) / 1.055).pow(2.4) ) pipe, upscaler_2 = load_model() fa_processor = FlashAttentionProcessor() for name, module in pipe.transformer.named_modules(): if isinstance(module, AttnProcessor2_0): module.processor = fa_processor compiled_transformer = compile_transformer() spaces.aoti_apply(compiled_transformer, pipe.transformer) MAX_SEED = np.iinfo(np.int32).max MAX_IMAGE_SIZE = 4096 # Consolidated generation function def generate_images(duration, prompt, neg_prompt_1, neg_prompt_2, neg_prompt_3, width, height, guidance, steps, progress=gr.Progress(track_tqdm=True)): @spaces.GPU(duration=duration) def _generate(): seed = random.randint(0, MAX_SEED) generator = torch.Generator(device=device).manual_seed(seed) print('-- generating image --') torch.cuda.empty_cache() torch.cuda.reset_peak_memory_stats() # Generate tensor output in sRGB space sd_image_tensor_srgb = pipe( prompt=prompt, prompt_2=prompt, prompt_3=prompt, negative_prompt=neg_prompt_1, negative_prompt_2=neg_prompt_2, negative_prompt_3=neg_prompt_3, guidance_scale=guidance, num_inference_steps=steps, width=width, height=height, generator=generator, max_sequence_length=384, output_type="pt" # Request tensor output ).images # Convert the sRGB tensor [0,1] to a PIL Image for display and upscaling sd_image_pil_srgb = Image.fromarray((sd_image_tensor_srgb.squeeze(0).permute(1, 2, 0).cpu().numpy() * 255).astype(np.uint8)) print('-- got image --') # --- Upscaling --- torch.cuda.empty_cache() torch.cuda.reset_peak_memory_stats() with torch.no_grad(): upscale = upscaler_2(sd_image_pil_srgb, tiling=True, tile_width=256, tile_height=256) upscale2 = upscaler_2(upscale, tiling=True, tile_width=256, tile_height=256) print('-- got upscaled image --') # --- HDR Conversion and Saving --- # Convert the original sRGB tensor to linear space sd_image_tensor_linear = srgb_to_linear(sd_image_tensor_srgb) # Convert the linear tensor to a PIL Image (this will be HDR data) sd_image_pil_linear = Image.fromarray((sd_image_tensor_linear.squeeze(0).permute(1, 2, 0).clamp(0, 1).cpu().numpy() * 255).astype(np.uint8)) # Save to a bytes buffer as JPEG Ultra HDR buffer = io.BytesIO() pillow_ultrahdr.save_ultrahdr( sdr=sd_image_pil_srgb, # The standard dynamic range image hdr=sd_image_pil_linear, # The linear (high dynamic range) image outfile=buffer, quality=90 # Standard JPEG quality setting ) hdr_image_bytes = buffer.getvalue() # For the upscaled image, we will do the same # First convert upscaled PIL image to tensor, normalize to [0,1] upscaled_tensor_srgb = torch.from_numpy(np.array(upscale2)).float().to(device) / 255.0 upscaled_tensor_srgb = upscaled_tensor_srgb.permute(2, 0, 1).unsqueeze(0) # HWC to BCHW upscaled_tensor_linear = srgb_to_linear(upscaled_tensor_srgb) upscaled_pil_linear = Image.fromarray((upscaled_tensor_linear.squeeze(0).permute(1, 2, 0).clamp(0, 1).cpu().numpy() * 255).astype(np.uint8)) upscaled_buffer = io.BytesIO() pillow_ultrahdr.save_ultrahdr(sdr=upscale2, hdr=upscaled_pil_linear, outfile=upscaled_buffer, quality=95) upscaled_hdr_image_bytes = upscaled_buffer.getvalue() # Return the sRGB PIL image for display, and the HDR bytes for upload return sd_image_pil_srgb, hdr_image_bytes, upscaled_hdr_image_bytes, prompt return _generate() # Consolidated upload function def run_inference_and_upload(duration, prompt, neg_prompt_1, neg_prompt_2, neg_prompt_3, width, height, guidance, steps, save_consent, progress=gr.Progress(track_tqdm=True)): # Generate images and get both PIL (for display) and bytes (for upload) sd_image_pil, sd_hdr_bytes, upscaled_hdr_bytes, expanded_prompt = generate_images( duration, prompt, neg_prompt_1, neg_prompt_2, neg_prompt_3, width, height, guidance, steps, progress ) if save_consent: print("✅ User consented to save. Preparing uploads...") timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S") sd_filename = f"sd35ll_{timestamp}.jpg" upscale_filename = f"sd35ll_upscale_{timestamp}.jpg" # Upload using threading sd_thread = threading.Thread(target=upload_to_gcs, args=(sd_hdr_bytes, sd_filename)) upscale_thread = threading.Thread(target=upload_to_gcs, args=(upscaled_hdr_bytes, upscale_filename)) sd_thread.start() upscale_thread.start() else: print("ℹ️ User did not consent to save. Skipping upload.") # Return the standard sRGB PIL image to the Gradio interface for display return sd_image_pil, expanded_prompt css = """ #col-container {margin: 0 auto;max-width: 640px;} body{background-color: blue;} """ with gr.Blocks(theme=gr.themes.Origin(), css=css) as demo: with gr.Column(elem_id="col-container"): gr.Markdown(" # StableDiffusion 3.5 Large with UltraReal lora test") expanded_prompt_output = gr.Textbox(label="Prompt", lines=1) with gr.Row(): prompt = gr.Text( label="Prompt", show_label=False, max_lines=1, placeholder="Enter your prompt", container=False, ) run_button_30 = gr.Button("Run30", scale=0, variant="primary") run_button_60 = gr.Button("Run60", scale=0, variant="primary") run_button_110 = gr.Button("Run110", scale=0, variant="primary") # The result will display the standard PIL image, the HDR is saved/uploaded result = gr.Image(label="Result (SDR Preview)", show_label=False, type="pil") save_consent_checkbox = gr.Checkbox( label="✅ Anonymously upload result to a public gallery (as JPEG Ultra HDR)", value=True, info="Check this box to help us by contributing your image." ) with gr.Accordion("Advanced Settings", open=True): negative_prompt_1 = gr.Text(label="Negative prompt 1", max_lines=1, placeholder="Enter a negative prompt", value="bad anatomy, poorly drawn hands, distorted face, blurry, out of frame, low resolution, grainy, pixelated, disfigured, mutated, extra limbs, bad composition") negative_prompt_2 = gr.Text(label="Negative prompt 2", max_lines=1, placeholder="Enter a second negative prompt", value="unrealistic, cartoon, anime, sketch, painting, drawing, illustration, graphic, digital art, render, 3d, blurry, deformed, disfigured, poorly drawn, bad anatomy, mutated, extra limbs, ugly, out of frame, bad composition, low resolution, grainy, pixelated, noisy, oversaturated, undersaturated, (worst quality, low quality:1.3), (bad hands, missing fingers:1.2)") negative_prompt_3 = gr.Text(label="Negative prompt 3", max_lines=1, placeholder="Enter a third negative prompt", value="(worst quality, low quality:1.3), (bad anatomy, bad hands, missing fingers, extra digit, fewer digits:1.2), (blurry:1.1), cropped, watermark, text, signature, logo, jpeg artifacts, (ugly, deformed, disfigured:1.2), (poorly drawn:1.2), mutated, extra limbs, (bad proportions, gross proportions:1.2), (malformed limbs, missing arms, missing legs, extra arms, extra legs:1.2), (fused fingers, too many fingers, long neck:1.2), (unnatural body, unnatural pose:1.1), out of frame, (bad composition, poorly composed:1.1), (oversaturated, undersaturated:1.1), (grainy, pixelated:1.1), (low resolution, noisy:1.1), (unrealistic, distorted:1.1), (extra fingers, mutated hands, poorly drawn hands, bad hands:1.3), (missing fingers:1.3)") with gr.Row(): width = gr.Slider(label="Width", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1024) height = gr.Slider(label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1024) with gr.Row(): guidance_scale = gr.Slider(label="Guidance scale", minimum=0.0, maximum=30.0, step=0.1, value=4.2) num_inference_steps = gr.Slider(label="Inference steps", minimum=1, maximum=150, step=1, value=60) # Clicks now call the same function with a different duration parameter run_button_30.click( fn=lambda *args: run_inference_and_upload(45, *args), inputs=[ prompt, negative_prompt_1, negative_prompt_2, negative_prompt_3, width, height, guidance_scale, num_inference_steps, save_consent_checkbox ], outputs=[result, expanded_prompt_output], ) run_button_60.click( fn=lambda *args: run_inference_and_upload(70, *args), inputs=[ prompt, negative_prompt_1, negative_prompt_2, negative_prompt_3, width, height, guidance_scale, num_inference_steps, save_consent_checkbox ], outputs=[result, expanded_prompt_output], ) run_button_110.click( fn=lambda *args: run_inference_and_upload(120, *args), inputs=[ prompt, negative_prompt_1, negative_prompt_2, negative_prompt_3, width, height, guidance_scale, num_inference_steps, save_consent_checkbox ], outputs=[result, expanded_prompt_output], ) if __name__ == "__main__": demo.launch()