rickyars
c421531a32
i'm trying to improve the tiled diffusion - these changes in node and node advanced are to ensure that final output is always tile width * grid width by tile height * grid height. i'm also starting work on a new tiled diffusion upscaler.
292 lines
13 KiB
Python
292 lines
13 KiB
Python
import json
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import torch
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import comfy.sample
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from comfy.utils import ProgressBar
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from .utils import gaussian_blend_tiles, parse_tile_prompts
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from .utils import apply_controlnet_to_conditioning
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class TiledImageGeneratorAdvanced:
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"""
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ComfyUI node that generates a tiled image composition with overlapping regions
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using custom samplers and guiders.
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"""
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"json_tile_prompts": ("STRING", {"multiline": True}),
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"grid_width": ("INT", {"default": 4, "min": 1, "max": 16}),
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"grid_height": ("INT", {"default": 6, "min": 1, "max": 16}),
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"tile_width": ("INT", {"default": 1024, "min": 256, "max": 2048}),
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"tile_height": ("INT", {"default": 1024, "min": 256, "max": 2048}),
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"overlap_percent": ("FLOAT", {"default": 0.15, "min": 0.05, "max": 0.5, "step": 0.01}),
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"blend_sigma": ("FLOAT", {"default": 0.4, "min": 0.0, "max": 1.0, "step": 0.01}),
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"noise": ("NOISE",),
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"guider": ("GUIDER",),
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"sampler": ("SAMPLER",),
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"sigmas": ("SIGMAS",),
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"clip": ("CLIP",),
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"vae": ("VAE",),
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"controlnet": ("CONTROL_NET",),
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"controlnet_strength": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01}),
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"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "control_after_generate": True}),
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}
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}
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RETURN_TYPES = ("IMAGE", "IMAGE")
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RETURN_NAMES = ("composite_image", "individual_tiles")
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FUNCTION = "generate_tiled_image"
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CATEGORY = "image/generation"
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def generate_tiled_image(self, json_tile_prompts, grid_width, grid_height,
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tile_width, tile_height, overlap_percent, blend_sigma,
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controlnet, controlnet_strength, seed, noise, guider,
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sampler, sigmas, clip, vae):
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"""Generate a tiled image with uniform grown latents using advanced sampling."""
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# Parse the JSON tile prompts
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tile_prompts = parse_tile_prompts(json_tile_prompts, grid_width, grid_height)
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# Calculate overlap in pixels
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overlap_x = int(tile_width * overlap_percent)
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overlap_y = int(tile_height * overlap_percent)
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# SIMPLE FINAL SIZE CALCULATION - exact grid multiplication
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final_width = tile_width * grid_width
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final_height = tile_height * grid_height
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print(f"Final output size: {final_width} x {final_height}")
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print(f"Tile size: {tile_width} x {tile_height}")
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print(f"Grid: {grid_width} x {grid_height}")
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print(f"Overlap: {overlap_x} x {overlap_y} pixels ({overlap_percent * 100}%)")
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# UNIFORM GENERATION CANVAS SIZE for all tiles
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gen_width = ((tile_width + overlap_x + 7) // 8) * 8
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gen_height = ((tile_height + overlap_y + 7) // 8) * 8
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print(f"Uniform generation canvas: {gen_width} x {gen_height}")
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# Create a blank canvas for the final composite
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final_tensor = torch.ones((1, final_height, final_width, 3), dtype=torch.float32) * 0.5
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# Storage for individual tiles and full grown tiles
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individual_tensors = [] # For blending (cropped)
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full_grown_tensors = [] # For debugging (full canvas)
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positions = []
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# Get device
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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print(f"Using device: {device}")
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# Calculate total number of tiles
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total_tiles = grid_width * grid_height
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pbar = ProgressBar(total_tiles)
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# Determine the guider type for proper handling
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guider_type = guider.__class__.__name__ if hasattr(guider, "__class__") else "Unknown"
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print(f"Using guider of type: {guider_type}")
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# Generate tiles one by one
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for y in range(grid_height):
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for x in range(grid_width):
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idx = y * grid_width + x
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current_prompt = tile_prompts[idx]
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current_seed = seed + idx
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# Calculate position in the final image
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final_pos_x = x * tile_width
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final_pos_y = y * tile_height
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print(f"Generating tile ({x + 1},{y + 1}) with prompt: {current_prompt}")
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# Create UNIFORM working canvas for ALL tiles
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working_tensor = torch.zeros((1, gen_height, gen_width, 3), dtype=torch.float32)
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outpaint_mask = torch.ones((1, gen_height, gen_width, 1), dtype=torch.float32)
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# Check if we have neighbors to copy from
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has_left_neighbor = x > 0
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has_top_neighbor = y > 0
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# COPY OVERLAP REGIONS from final_tensor (only if neighbors exist)
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if has_left_neighbor:
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# Copy left overlap region from previous tile
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source_x = final_pos_x - overlap_x
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if source_x >= 0 and source_x + overlap_x <= final_width:
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source_start_y = final_pos_y
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source_end_y = min(final_pos_y + tile_height, final_height)
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source_height = source_end_y - source_start_y
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# Copy to working tensor left edge (after top overlap region)
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target_start_y = overlap_y
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copy_height = min(source_height, gen_height - target_start_y)
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if copy_height > 0:
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working_tensor[0,
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target_start_y:target_start_y + copy_height,
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:overlap_x,
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:] = final_tensor[0,
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source_start_y:source_start_y + copy_height,
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source_x:source_x + overlap_x,
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:]
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# Mark as "keep" in mask
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outpaint_mask[0,
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target_start_y:target_start_y + copy_height,
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:overlap_x,
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:] = 0
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if has_top_neighbor:
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# Copy top overlap region from previous tile
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source_y = final_pos_y - overlap_y
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if source_y >= 0 and source_y + overlap_y <= final_height:
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source_start_x = final_pos_x
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source_end_x = min(final_pos_x + tile_width, final_width)
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source_width = source_end_x - source_start_x
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# Copy to working tensor top edge (after left overlap region)
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target_start_x = overlap_x
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copy_width = min(source_width, gen_width - target_start_x)
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if copy_width > 0:
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working_tensor[0,
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:overlap_y,
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target_start_x:target_start_x + copy_width,
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:] = final_tensor[0,
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source_y:source_y + overlap_y,
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source_start_x:source_start_x + copy_width,
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:]
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# Mark as "keep" in mask
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outpaint_mask[0,
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:overlap_y,
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target_start_x:target_start_x + copy_width,
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:] = 0
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# Handle corner overlap if both neighbors exist
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if has_left_neighbor and has_top_neighbor:
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corner_source_x = final_pos_x - overlap_x
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corner_source_y = final_pos_y - overlap_y
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if (corner_source_x >= 0 and corner_source_y >= 0 and
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corner_source_x + overlap_x <= final_width and
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corner_source_y + overlap_y <= final_height):
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working_tensor[0, :overlap_y, :overlap_x, :] = final_tensor[0,
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corner_source_y:corner_source_y + overlap_y,
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corner_source_x:corner_source_x + overlap_x,
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:]
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outpaint_mask[0, :overlap_y, :overlap_x, :] = 0
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# Get latent shape for the uniform grown canvas
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with torch.no_grad():
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latent_shape = vae.encode(working_tensor).shape
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# Create empty latent tensor with grown shape
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latent_image = torch.zeros(latent_shape, device=device)
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# Create tile-specific conditioning
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if hasattr(clip, 'tokenize') and hasattr(clip, 'encode_from_tokens_scheduled'):
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# Encode the tile-specific prompt
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pos_tokens = clip.tokenize(current_prompt)
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pos_cond = clip.encode_from_tokens_scheduled(pos_tokens)
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# For negative, use empty or a default negative
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neg_tokens = clip.tokenize("")
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neg_cond = clip.encode_from_tokens_scheduled(neg_tokens)
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# Apply controlnet to conditioning if available
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if controlnet is not None and controlnet_strength > 0:
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# Apply controlnet using the utility function
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conditioning = apply_controlnet_to_conditioning(
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positive=pos_cond,
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negative=neg_cond,
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control_net=controlnet,
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image=working_tensor,
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strength=controlnet_strength,
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start_percent=0.0,
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end_percent=1.0,
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vae=vae
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)
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# Set the conditioning with ControlNet applied
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guider.set_conds(conditioning[0], conditioning[1])
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else:
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# Set conditioning without ControlNet
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guider.set_conds(pos_cond, neg_cond)
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else:
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print("Warning: CLIP model doesn't have required tokenize/encode methods")
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# Generate the tile noise
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if noise is not None:
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# Use provided noise
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tile_noise = noise.generate_noise({"samples": latent_image})
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else:
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# Create new noise
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tile_noise = comfy.sample.prepare_noise(latent_image, current_seed)
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# Sample using the guider
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samples = guider.sample(
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tile_noise,
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latent_image,
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sampler,
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sigmas,
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denoise_mask=None,
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disable_pbar=False,
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seed=current_seed
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)
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# Decode the full grown canvas
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full_grown_tensor = vae.decode(samples)
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# EXTRACT the tile_width x tile_height region from consistent position
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# Always extract from overlap_x, overlap_y for uniform behavior
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extract_x = overlap_x
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extract_y = overlap_y
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extracted_tile = full_grown_tensor[0:1,
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extract_y:extract_y + tile_height,
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extract_x:extract_x + tile_width,
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:]
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# Store both versions
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individual_tensors.append(extracted_tile.clone()) # For blending
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full_grown_tensors.append(full_grown_tensor.clone()) # For debugging
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positions.append((final_pos_x, final_pos_y))
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# Place the tile in the final composite at exact grid position
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final_tensor[0,
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final_pos_y:final_pos_y + tile_height,
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final_pos_x:final_pos_x + tile_width,
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:] = extracted_tile[0, :, :, :]
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# Update progress
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pbar.update(1)
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# Apply Gaussian blending for smooth transitions
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if individual_tensors:
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final_tensor = gaussian_blend_tiles(
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individual_tensors, # Use cropped tiles for blending
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positions,
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tile_width,
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tile_height,
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overlap_x,
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overlap_y,
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final_width,
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final_height,
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sigma=blend_sigma
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)
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# Return full grown tiles for debugging
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tile_batch = torch.cat(full_grown_tensors, dim=0)
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else:
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final_tensor = torch.zeros((1, final_height, final_width, 3), dtype=torch.float32)
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tile_batch = torch.zeros((1, gen_height, gen_width, 3), dtype=torch.float32, device=device)
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return final_tensor, tile_batch
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# This part is needed for ComfyUI to recognize the nodes
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NODE_CLASS_MAPPINGS = {
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"TiledImageGeneratorAdvanced": TiledImageGeneratorAdvanced,
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}
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# Add descriptions for the web UI
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NODE_DISPLAY_NAME_MAPPINGS = {
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"TiledImageGeneratorAdvanced": "Tiled Image Generator Advanced",
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} |