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DUAL PIPELINE KLEIN 4B\n"],"metadata":{"id":"Dgu_X5fy02CF"}},{"cell_type":"code","source":["\n","# =============================================================================\n","#@markdown # **CELL 1**: Setup Environment + Load SDNQ Flux2 Klein\n","# =============================================================================\n","\n","from google.colab import drive, userdata\n","from huggingface_hub import login\n","import torch\n","import os\n","import gc\n","\n","drive.mount('/content/drive')\n","\n","# HF Login\n","hf_token = userdata.get('HF_TOKEN')\n","if hf_token:\n"," login(token=hf_token)\n","else:\n"," print(\"⚠️ No HF_TOKEN found in secrets.\")\n","\n","# =============================================\n","# Clean install in correct order\n","# =============================================\n","\n","print(\"🧹 Removing old packages...\")\n","!pip uninstall -y diffusers sdnq > /dev/null 2>&1\n","!rm -rf /usr/local/lib/python3.12/dist-packages/diffusers* /usr/local/lib/python3.12/dist-packages/sdnq*\n","\n","print(\"🔄 Installing sdnq + latest diffusers...\")\n","!pip install -q sdnq\n","!pip install -q git+https://github.com/huggingface/diffusers.git --force-reinstall --no-deps\n","\n","# Now import everything\n","import sdnq\n","from sdnq.common import use_torch_compile as triton_is_available\n","from sdnq.loader import apply_sdnq_options_to_model\n","\n","import torch\n","from diffusers import Flux2KleinPipeline\n","from diffusers.pipelines.flux2.pipeline_flux2_klein import (\n"," compute_empirical_mu,\n"," retrieve_timesteps,\n"," Flux2PipelineOutput,\n",")\n","\n","import shutil\n","from typing import List, Optional\n","import PIL.Image\n","import numpy as np\n","\n","print(\"✅ Environment setup complete!\")\n","print(f\"Diffusers version: {__import__('diffusers').__version__}\")\n","print(f\"SDNQ imported successfully\")\n","print(\"✅ Cell 1 complete!\")"],"metadata":{"id":"ZLPsxaJUwxCO"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":4,"metadata":{"id":"iZIZPI3emVR-","executionInfo":{"status":"ok","timestamp":1777575818520,"user_tz":-120,"elapsed":13,"user":{"displayName":"fukU Google","userId":"02763165356193834046"}}},"outputs":[],"source":["# CELL 2 : Define the dual pipeline class\n","class DualFlux2KleinPipeline:\n"," def __init__(self, pipeline: Flux2KleinPipeline):\n"," self.pipe = pipeline\n"," self.vae = pipeline.vae\n"," self.transformer = pipeline.transformer\n"," self.scheduler = pipeline.scheduler\n"," self.image_processor = pipeline.image_processor\n"," self.default_sample_size = pipeline.default_sample_size\n"," self.vae_scale_factor = pipeline.vae_scale_factor\n","\n"," self.constant_prompt_embeds = None\n"," self.constant_text_ids = None\n"," self.constant_dtype = None\n"," self.constant_device = None\n","\n"," def set_constant_prompt(self, prompt: str, max_sequence_length: int = 512):\n"," print(f\"Encoding constant prompt: '{prompt[:100]}...'\")\n"," with torch.no_grad():\n"," prompt_embeds, text_ids = self.pipe.encode_prompt(\n"," prompt=prompt,\n"," device=\"cuda\",\n"," num_images_per_prompt=1,\n"," max_sequence_length=max_sequence_length,\n"," text_encoder_out_layers=(9, 18, 27),\n"," )\n","\n"," self.constant_prompt_embeds = prompt_embeds\n"," self.constant_text_ids = text_ids\n"," self.constant_dtype = prompt_embeds.dtype\n"," self.constant_device = prompt_embeds.device\n","\n"," # Free text encoder\n"," if hasattr(self.pipe, \"text_encoder\") and self.pipe.text_encoder is not None:\n"," del self.pipe.text_encoder\n"," self.pipe.text_encoder = None\n"," torch.cuda.empty_cache()\n","\n"," def encode_prompt(self, batch_size: int = 2):\n"," if self.constant_prompt_embeds is None:\n"," raise ValueError(\"Call set_constant_prompt first!\")\n","\n"," embeds = self.constant_prompt_embeds.repeat(batch_size, 1, 1)\n"," text_ids = self.constant_text_ids.repeat(batch_size, 1, 1)\n"," return embeds, text_ids\n","\n"," @torch.no_grad()\n"," def __call__(\n"," self,\n"," image1: PIL.Image.Image,\n"," image2: PIL.Image.Image,\n"," num_inference_steps: int = 4,\n"," guidance_scale: float = 3.5,\n"," generator: Optional[torch.Generator] = None,\n"," output_type: str = \"pil\",\n"," ):\n"," batch_size = 2\n"," device = \"cuda\"\n","\n"," # === Image Preprocessing ===\n"," input_images = [image1, image2]\n"," condition_images = []\n"," final_h = final_w = None\n","\n"," for img in input_images:\n"," self.pipe.image_processor.check_image_input(img)\n"," w, h = img.size\n","\n"," if w * h > 1024 * 1024:\n"," img = self.pipe.image_processor._resize_to_target_area(img, 1024 * 1024)\n"," w, h = img.size\n","\n"," multiple_of = self.vae_scale_factor * 2\n"," w = (w // multiple_of) * multiple_of\n"," h = (h // multiple_of) * multiple_of\n","\n"," processed = self.pipe.image_processor.preprocess(\n"," img, height=h, width=w, resize_mode=\"crop\"\n"," )\n"," condition_images.append(processed)\n"," final_h = final_h or h\n"," final_w = final_w or w\n","\n"," final_height = final_h or self.default_sample_size * self.vae_scale_factor\n"," final_width = final_w or self.default_sample_size * self.vae_scale_factor\n","\n"," # === Embeddings ===\n"," prompt_embeds, text_ids = self.encode_prompt(batch_size=batch_size)\n"," if guidance_scale > 1.0:\n"," neg_prompt_embeds, neg_text_ids = self.encode_prompt(batch_size=batch_size)\n"," else:\n"," neg_prompt_embeds = neg_text_ids = None\n","\n"," # === Latents ===\n"," num_channels_latents = self.transformer.config.in_channels // 4\n","\n"," latents, latent_ids = self.pipe.prepare_latents(\n"," batch_size=batch_size,\n"," num_latents_channels=num_channels_latents,\n"," height=final_height,\n"," width=final_width,\n"," dtype=prompt_embeds.dtype,\n"," device=device,\n"," generator=generator,\n"," )\n","\n"," image_latents, image_latent_ids = self.pipe.prepare_image_latents(\n"," images=condition_images,\n"," batch_size=batch_size,\n"," generator=generator,\n"," device=device,\n"," dtype=self.vae.dtype,\n"," )\n","\n"," # === Timesteps ===\n"," sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)\n"," mu = compute_empirical_mu(latents.shape[1], num_inference_steps)\n"," timesteps, _ = retrieve_timesteps(\n"," self.scheduler, num_inference_steps, device, sigmas=sigmas, mu=mu\n"," )\n","\n"," # === Denoising Loop - Key Optimizations ===\n"," self.scheduler.set_begin_index(0)\n","\n"," with self.pipe.progress_bar(total=len(timesteps)) as pb:\n"," for i, t in enumerate(timesteps):\n"," timestep = t.expand(batch_size).to(latents.dtype)\n","\n"," latent_model_input = torch.cat([latents, image_latents], dim=1).to(self.transformer.dtype)\n"," latent_image_ids = torch.cat([latent_ids, image_latent_ids], dim=1)\n","\n"," # Conditional\n"," with self.transformer.cache_context(\"cond\"):\n"," noise_pred = self.transformer(\n"," hidden_states=latent_model_input,\n"," timestep=timestep / 1000,\n"," guidance=None,\n"," encoder_hidden_states=prompt_embeds,\n"," txt_ids=text_ids,\n"," img_ids=latent_image_ids,\n"," joint_attention_kwargs=None,\n"," return_dict=False,\n"," )[0]\n","\n"," noise_pred = noise_pred[:, :latents.shape[1]]\n","\n"," # CFG\n"," if guidance_scale > 1.0:\n"," with self.transformer.cache_context(\"uncond\"):\n"," neg_noise_pred = self.transformer(\n"," hidden_states=latent_model_input,\n"," timestep=timestep / 1000,\n"," guidance=None,\n"," encoder_hidden_states=neg_prompt_embeds,\n"," txt_ids=neg_text_ids,\n"," img_ids=latent_image_ids,\n"," joint_attention_kwargs=None,\n"," return_dict=False,\n"," )[0]\n"," neg_noise_pred = neg_noise_pred[:, :latents.shape[1]]\n"," noise_pred = neg_noise_pred + guidance_scale * (noise_pred - neg_noise_pred)\n","\n"," latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]\n","\n"," if i == len(timesteps) - 1 or (i + 1) % self.scheduler.order == 0:\n"," pb.update()\n","\n"," # === Decoding ===\n"," latent_h = 2 * (int(final_height) // (self.vae_scale_factor * 2))\n"," latent_w = 2 * (int(final_width) // (self.vae_scale_factor * 2))\n","\n"," latents = self.pipe._unpack_latents_with_ids(latents, latent_ids, latent_h // 2, latent_w // 2)\n","\n"," bn_mean = self.vae.bn.running_mean.view(1, -1, 1, 1).to(latents.device, latents.dtype)\n"," bn_std = torch.sqrt(self.vae.bn.running_var.view(1, -1, 1, 1) + self.vae.config.batch_norm_eps).to(\n"," latents.device, latents.dtype\n"," )\n"," latents = latents * bn_std + bn_mean\n"," latents = self.pipe._unpatchify_latents(latents)\n","\n"," decoded = self.vae.decode(latents, return_dict=False)[0]\n"," images = self.pipe.image_processor.postprocess(decoded, output_type=output_type)\n","\n"," self.pipe.maybe_free_model_hooks()\n","\n"," return Flux2PipelineOutput(images=images)\n"]},{"cell_type":"code","source":["# =============================================================================\n","# CELL 3: Load Model + Create Dual Pipeline\n","# =============================================================================\n","\n","MODEL_ID = \"codeShare/FLUX.2-klein-AIO-SDNQ-4bit-dynamic\"\n","\n","print(f\"Loading model: {MODEL_ID}\")\n","base_pipe = Flux2KleinPipeline.from_pretrained(\n"," MODEL_ID,\n"," torch_dtype=torch.float16,\n"," low_cpu_mem_usage=True,\n",")\n","\n","base_pipe = base_pipe.to(\"cuda\")\n","\n","# Apply SDNQ optimizations (very important for speed)\n","print(\"🔥 Applying SDNQ optimizations...\")\n","try:\n"," base_pipe.transformer = apply_sdnq_options_to_model(\n"," base_pipe.transformer,\n"," use_quantized_matmul=True\n"," )\n","except Exception as e:\n"," print(f\"SDNQ apply warning: {e}\")\n","\n","# Create the dual wrapper\n","dual_pipe = DualFlux2KleinPipeline(base_pipe)\n","\n","# Set your constant editing prompt\n","dual_pipe.set_constant_prompt(\"remove the white background. the background is dark gray.\")\n","\n","print(\"✅ DualFlux2KleinPipeline ready!\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":264,"referenced_widgets":["8761cc61aa1c4f71a17aa1cfc0eea40b","4387d3c3aa9044ddb8b515f4d11ded5f","5052bbad8ec24f1a96b5f5bc95147a8b","e9ab2fd9f16044d7bcaea3b12a159f64","2029bc102eee4822b88d26f84f91754a","ad2a1949570c4deaaedd329b582f269b","af22bb42b70841759acd03f098c402cc","be5b375901b042dca4db4f30f9619ad4","e82d7e343d314d2bab426ccc5f41653c","890d06b00d1b42fab0d1146d85da50d6","a9fe963598ef4255acce26b7a09dc7c4","ef3bb52f05764dcb96be1099e34a7adc","f1b531a69b1d4a258333327a77c17975","ec11fe3d0afa4e67b69213cc2ae499d8","3b49ae3bb09540d993baf45da309072e","9a2995dbebc8401c86e80eb2c6d8d40f","09ddcdea89ae47af99b9564ffc69fa0f","0ed7330dbe174c5a936c434c7843dde7","303d776c18c847d48d9fac0c277e2a98","a9ef79a8643d484e9c797dfe09706413","3ac0666392954ee38980ecace6f9aed4","22d53ed8d8a945d9a2932aeab9d4f352","ff7bcdea85f94fe5a275560c83a4627f","db875d64e31f4b2ca2f27154162c8770","5443a8a6566c41ac9632e7fdf883932c","62e4c01b25014f2aa39d2234ec10cd3f","472c0cbeadc645a0a58d6d91d18f6fed","317bddc5495b48e78fe5d2a867b2a789","b010ca22784e4be9a9bb4d56f86ad89a","47bd9ff4869d47ff8c543d57c719665d","97c4921746e6403e8ea086e9735c5fe8","306cd23fd39042e5b575936e78eb8e32","9a890e5b97ad4fc98011deb9363581c8","aae1b285a1fc40a786528211ee5786df","4cbe8505eadc48db8b5166bde9d8b7ae","fc16141454df4766b20faa36e019d7ce","1587e4b25bc04c919777bdbb33af8d29","86696059994540ba95c3883b85a6fdee","b74ddbc798494ac18f3ecc8404a316a5","04092baeb5814dd6ac6975f398aef446","f12135ead7024619b9bbbc2163479949","e134f270532d47b8b6ca0333dfe18c2f","5ff144c5fbed46e593810b950890f0a7","950e30ddaed74b008e22e0cbb06fc819","0729bfe471a349dfac60076a500b8b21","b670e338bdbc4874affb8aa0b4e91111","8510d748c75d44be8babfe2f816895f3","0f828f39c0ea4182a3391d0ef719070d","19c336d312d341729a22f0a69a30e81c","374820566dc6426495ca20895925c683","33a574a1503a475d81e63f5fc0eae8b2","eb00907e837247bd97a77d6280844d5a","7df20af42c6c4e089865241d145d83c0","ccb2c5ea1d4b4edca5457fe78c42b47b","44b13ab63d764b59ac94d46926f39404"]},"id":"diD14d3AmbGF","executionInfo":{"status":"ok","timestamp":1777576062847,"user_tz":-120,"elapsed":235780,"user":{"displayName":"fukU Google","userId":"02763165356193834046"}},"outputId":"daf0add1-cb25-410e-b5ab-b481a7f2ed79"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["Loading model: codeShare/FLUX.2-klein-AIO-SDNQ-4bit-dynamic\n"]},{"output_type":"display_data","data":{"text/plain":["model_index.json: 0%| | 0.00/509 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8761cc61aa1c4f71a17aa1cfc0eea40b"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (incomplete total...): 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ef3bb52f05764dcb96be1099e34a7adc"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Fetching 11 files: 0%| | 0/11 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ff7bcdea85f94fe5a275560c83a4627f"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Loading pipeline components...: 0%| | 0/5 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"aae1b285a1fc40a786528211ee5786df"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Loading weights: 0%| | 0/901 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0729bfe471a349dfac60076a500b8b21"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["🔥 Applying SDNQ optimizations...\n","SDNQ apply warning: apply_sdnq_options_to_model() got an unexpected keyword argument 'use_torch_compile'\n","Encoding constant prompt: 'remove the white background. the background is dark gray....'\n","✅ DualFlux2KleinPipeline ready!\n"]}]},{"cell_type":"code","source":["# CELL 4 , test the dual pipe.\n","img1 = Image.new(\"RGB\", (1024, 1024), color=(128, 128, 128))\n","img2 = Image.new(\"RGB\", (1024, 1024), color=(100, 100, 100))\n","\n","result = dual_pipe(\n"," image1=img1,\n"," image2=img2,\n"," num_inference_steps=4,\n"," guidance_scale=3.5,\n",")\n","\n","print(f\"Output shape: {len(result.images)} images\")\n","result.images[0].save(\"edited_1.png\")\n","result.images[1].save(\"edited_2.png\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":66,"referenced_widgets":["d6db15b9bfac4d7d8f4d7429217a58c5","2e191ebcb52843e2bc927b11a0038b80","6d0375f2484648e0a17b0ea89ef0890e","c36c6a5a0cca4786bd3e664f7c8e56a9","f32aadf9638c4ce9b42ab185992991ec","158694ea440b4e3fb8d256c371e30861","af6d083096d743a4ba7c4da62af50b3e","36719f746cd143cab62f27fd61a26cd2","f6b1143aefab4f72ad0b23a4ea31898e","695e81fb79bb457c9adc7b0fc3859e43","a9463594c4194f5a903c92c4228ea3df"]},"id":"9nhPlBxkynDb","executionInfo":{"status":"ok","timestamp":1777576697118,"user_tz":-120,"elapsed":201060,"user":{"displayName":"fukU Google","userId":"02763165356193834046"}},"outputId":"cb77f6f8-b9be-4f7b-e673-db1c42148ef8"},"execution_count":7,"outputs":[{"output_type":"display_data","data":{"text/plain":[" 0%| | 0/4 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d6db15b9bfac4d7d8f4d7429217a58c5"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Output shape: 2 images\n"]}]}]}