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flux series vram management

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Artiprocher
2025-07-15 20:11:02 +08:00
parent cbd10fb27d
commit af6b1d4246
16 changed files with 629 additions and 27 deletions
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1
diffsynth/models/flux_infiniteyou.py
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@@ -104,6 +104,7 @@ class InfiniteYouImageProjector(nn.Module):
def forward(self, x):
latents = self.latents.repeat(x.size(0), 1, 1)
latents = latents.to(dtype=x.dtype, device=x.device)
x = self.proj_in(x)

3
diffsynth/models/flux_value_control.py
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@@ -40,7 +40,8 @@ class SingleValueEncoder(torch.nn.Module):
emb = self.prefer_proj(value).to(dtype)
emb = self.prefer_value_embedder(emb).squeeze(0)
base_embeddings = emb.expand(self.prefer_len, -1)
learned_embeddings = base_embeddings + self.positional_embedding
positional_embedding = self.positional_embedding.to(dtype=base_embeddings.dtype, device=base_embeddings.device)
learned_embeddings = base_embeddings + positional_embedding
return learned_embeddings
@staticmethod

4
diffsynth/models/step1x_connector.py
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@@ -162,7 +162,7 @@ class TimestepEmbedder(nn.Module):
def forward(self, t):
t_freq = self.timestep_embedding(
t, self.frequency_embedding_size, self.max_period
).type(self.mlp[0].weight.dtype) # type: ignore
).type(t.dtype) # type: ignore
t_emb = self.mlp(t_freq)
return t_emb
@@ -656,7 +656,7 @@ class Qwen2Connector(torch.nn.Module):
mask_float = mask.unsqueeze(-1) # [b, s1, 1]
x_mean = (x * mask_float).sum(
dim=1
) / mask_float.sum(dim=1) * (1 + self.scale_factor)
) / mask_float.sum(dim=1) * (1 + self.scale_factor.to(dtype=x.dtype, device=x.device))
global_out=self.global_proj_out(x_mean)
encoder_hidden_states = self.S(x,t,mask)

80
diffsynth/pipelines/flux_image_new.py
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@@ -24,7 +24,6 @@ from ..models.tiler import FastTileWorker
from .wan_video_new import BasePipeline, ModelConfig, PipelineUnitRunner, PipelineUnit
from ..lora.flux_lora import FluxLoRALoader, FluxLoraPatcher
from transformers.models.t5.modeling_t5 import T5LayerNorm, T5DenseActDense, T5DenseGatedActDense
from ..models.flux_dit import RMSNorm
from ..vram_management import gradient_checkpoint_forward, enable_vram_management, AutoWrappedModule, AutoWrappedLinear
@@ -185,22 +184,18 @@ class FluxImagePipeline(BasePipeline):
return loss
def enable_vram_management(self, num_persistent_param_in_dit=None, vram_limit=None, vram_buffer=0.5):
self.vram_management_enabled = True
if num_persistent_param_in_dit is not None:
vram_limit = None
else:
if vram_limit is None:
vram_limit = self.get_vram()
vram_limit = vram_limit - vram_buffer
if self.text_encoder_1 is not None:
dtype = next(iter(self.text_encoder_1.parameters())).dtype
def _enable_vram_management_with_default_config(self, model, vram_limit):
if model is not None:
dtype = next(iter(model.parameters())).dtype
enable_vram_management(
self.text_encoder_1,
model,
module_map = {
torch.nn.Linear: AutoWrappedLinear,
torch.nn.Embedding: AutoWrappedModule,
torch.nn.LayerNorm: AutoWrappedModule,
torch.nn.Conv2d: AutoWrappedModule,
torch.nn.GroupNorm: AutoWrappedModule,
RMSNorm: AutoWrappedModule,
},
module_config = dict(
offload_dtype=dtype,
@@ -212,7 +207,25 @@ class FluxImagePipeline(BasePipeline):
),
vram_limit=vram_limit,
)
def enable_vram_management(self, num_persistent_param_in_dit=None, vram_limit=None, vram_buffer=0.5):
self.vram_management_enabled = True
if num_persistent_param_in_dit is not None:
vram_limit = None
else:
if vram_limit is None:
vram_limit = self.get_vram()
vram_limit = vram_limit - vram_buffer
# Default config
default_vram_management_models = ["text_encoder_1", "vae_decoder", "vae_encoder", "controlnet", "image_proj_model", "ipadapter", "lora_patcher", "value_controller", "step1x_connector"]
for model_name in default_vram_management_models:
self._enable_vram_management_with_default_config(getattr(self, model_name), vram_limit)
# Special config
if self.text_encoder_2 is not None:
from transformers.models.t5.modeling_t5 import T5LayerNorm, T5DenseActDense, T5DenseGatedActDense
dtype = next(iter(self.text_encoder_2.parameters())).dtype
enable_vram_management(
self.text_encoder_2,
@@ -261,14 +274,18 @@ class FluxImagePipeline(BasePipeline):
),
vram_limit=vram_limit,
)
if self.vae_decoder is not None:
dtype = next(iter(self.vae_decoder.parameters())).dtype
if self.ipadapter_image_encoder is not None:
from transformers.models.siglip.modeling_siglip import SiglipVisionEmbeddings, SiglipEncoder, SiglipMultiheadAttentionPoolingHead
dtype = next(iter(self.ipadapter_image_encoder.parameters())).dtype
enable_vram_management(
self.vae_decoder,
self.ipadapter_image_encoder,
module_map = {
SiglipVisionEmbeddings: AutoWrappedModule,
SiglipEncoder: AutoWrappedModule,
SiglipMultiheadAttentionPoolingHead: AutoWrappedModule,
torch.nn.MultiheadAttention: AutoWrappedModule,
torch.nn.Linear: AutoWrappedLinear,
torch.nn.Conv2d: AutoWrappedModule,
torch.nn.GroupNorm: AutoWrappedModule,
torch.nn.LayerNorm: AutoWrappedModule,
},
module_config = dict(
offload_dtype=dtype,
@@ -280,14 +297,25 @@ class FluxImagePipeline(BasePipeline):
),
vram_limit=vram_limit,
)
if self.vae_encoder is not None:
dtype = next(iter(self.vae_encoder.parameters())).dtype
if self.qwenvl is not None:
from transformers.models.qwen2_5_vl.modeling_qwen2_5_vl import (
Qwen2_5_VisionPatchEmbed, Qwen2_5_VLVisionBlock, Qwen2_5_VLPatchMerger,
Qwen2_5_VLDecoderLayer, Qwen2_5_VisionRotaryEmbedding, Qwen2_5_VLRotaryEmbedding, Qwen2RMSNorm
)
dtype = next(iter(self.qwenvl.parameters())).dtype
enable_vram_management(
self.vae_encoder,
self.qwenvl,
module_map = {
Qwen2_5_VisionPatchEmbed: AutoWrappedModule,
Qwen2_5_VLVisionBlock: AutoWrappedModule,
Qwen2_5_VLPatchMerger: AutoWrappedModule,
Qwen2_5_VLDecoderLayer: AutoWrappedModule,
Qwen2_5_VisionRotaryEmbedding: AutoWrappedModule,
Qwen2_5_VLRotaryEmbedding: AutoWrappedModule,
Qwen2RMSNorm: AutoWrappedModule,
torch.nn.Embedding: AutoWrappedModule,
torch.nn.Linear: AutoWrappedLinear,
torch.nn.Conv2d: AutoWrappedModule,
torch.nn.GroupNorm: AutoWrappedModule,
torch.nn.LayerNorm: AutoWrappedModule,
},
module_config = dict(
offload_dtype=dtype,
@@ -774,9 +802,13 @@ class FluxImageUnit_Flex(PipelineUnit):
class FluxImageUnit_InfiniteYou(PipelineUnit):
def __init__(self):
super().__init__(input_params=("infinityou_id_image", "infinityou_guidance"))
super().__init__(
input_params=("infinityou_id_image", "infinityou_guidance"),
onload_model_names=("infinityou_processor",)
)
def process(self, pipe: FluxImagePipeline, infinityou_id_image, infinityou_guidance):
pipe.load_models_to_device("infinityou_processor")
if infinityou_id_image is not None:
return pipe.infinityou_processor.prepare_infinite_you(pipe.image_proj_model, infinityou_id_image, infinityou_guidance, pipe.device)
else:
@@ -816,7 +848,7 @@ class InfinitYou(torch.nn.Module):
self.app_320.prepare(ctx_id=0, det_size=(320, 320))
self.app_160 = FaceAnalysis(name='antelopev2', root=insightface_root_path, providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
self.app_160.prepare(ctx_id=0, det_size=(160, 160))
self.arcface_model = init_recognition_model('arcface', device=self.device)
self.arcface_model = init_recognition_model('arcface', device=self.device).to(torch_dtype)
def _detect_face(self, id_image_cv2):
face_info = self.app_640.get(id_image_cv2)

148
examples/flux/model_inference_low_vram/EliGen.py Normal file
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@@ -0,0 +1,148 @@
import random
import torch
from PIL import Image, ImageDraw, ImageFont
from diffsynth import download_customized_models
from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig
from modelscope import dataset_snapshot_download
def visualize_masks(image, masks, mask_prompts, output_path, font_size=35, use_random_colors=False):
# Create a blank image for overlays
overlay = Image.new('RGBA', image.size, (0, 0, 0, 0))
colors = [
(165, 238, 173, 80),
(76, 102, 221, 80),
(221, 160, 77, 80),
(204, 93, 71, 80),
(145, 187, 149, 80),
(134, 141, 172, 80),
(157, 137, 109, 80),
(153, 104, 95, 80),
(165, 238, 173, 80),
(76, 102, 221, 80),
(221, 160, 77, 80),
(204, 93, 71, 80),
(145, 187, 149, 80),
(134, 141, 172, 80),
(157, 137, 109, 80),
(153, 104, 95, 80),
]
# Generate random colors for each mask
if use_random_colors:
colors = [(random.randint(0, 255), random.randint(0, 255), random.randint(0, 255), 80) for _ in range(len(masks))]
# Font settings
try:
font = ImageFont.truetype("arial", font_size) # Adjust as needed
except IOError:
font = ImageFont.load_default(font_size)
# Overlay each mask onto the overlay image
for mask, mask_prompt, color in zip(masks, mask_prompts, colors):
# Convert mask to RGBA mode
mask_rgba = mask.convert('RGBA')
mask_data = mask_rgba.getdata()
new_data = [(color if item[:3] == (255, 255, 255) else (0, 0, 0, 0)) for item in mask_data]
mask_rgba.putdata(new_data)
# Draw the mask prompt text on the mask
draw = ImageDraw.Draw(mask_rgba)
mask_bbox = mask.getbbox() # Get the bounding box of the mask
text_position = (mask_bbox[0] + 10, mask_bbox[1] + 10) # Adjust text position based on mask position
draw.text(text_position, mask_prompt, fill=(255, 255, 255, 255), font=font)
# Alpha composite the overlay with this mask
overlay = Image.alpha_composite(overlay, mask_rgba)
# Composite the overlay onto the original image
result = Image.alpha_composite(image.convert('RGBA'), overlay)
# Save or display the resulting image
result.save(output_path)
return result
def example(pipe, seeds, example_id, global_prompt, entity_prompts):
dataset_snapshot_download(dataset_id="DiffSynth-Studio/examples_in_diffsynth", local_dir="./", allow_file_pattern=f"data/examples/eligen/entity_control/example_{example_id}/*.png")
masks = [Image.open(f"./data/examples/eligen/entity_control/example_{example_id}/{i}.png").convert('RGB') for i in range(len(entity_prompts))]
negative_prompt = "worst quality, low quality, monochrome, zombie, interlocked fingers, Aissist, cleavage, nsfw,"
for seed in seeds:
# generate image
image = pipe(
prompt=global_prompt,
cfg_scale=3.0,
negative_prompt=negative_prompt,
num_inference_steps=50,
embedded_guidance=3.5,
seed=seed,
height=1024,
width=1024,
eligen_entity_prompts=entity_prompts,
eligen_entity_masks=masks,
)
image.save(f"eligen_example_{example_id}_{seed}.png")
visualize_masks(image, masks, entity_prompts, f"eligen_example_{example_id}_mask_{seed}.png")
pipe = FluxImagePipeline.from_pretrained(
torch_dtype=torch.bfloat16,
device="cuda",
model_configs=[
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
],
)
pipe.enable_vram_management()
download_from_modelscope = True
if download_from_modelscope:
model_id = "DiffSynth-Studio/Eligen"
downloading_priority = ["ModelScope"]
else:
model_id = "modelscope/EliGen"
downloading_priority = ["HuggingFace"]
EliGen_path = download_customized_models(
model_id=model_id,
origin_file_path="model_bf16.safetensors",
local_dir="models/lora/entity_control",
downloading_priority=downloading_priority)[0]
pipe.load_lora(pipe.dit, EliGen_path, alpha=1)
# example 1
global_prompt = "A breathtaking beauty of Raja Ampat by the late-night moonlight , one beautiful woman from behind wearing a pale blue long dress with soft glow, sitting at the top of a cliff looking towards the beach,pastell light colors, a group of small distant birds flying in far sky, a boat sailing on the sea, best quality, realistic, whimsical, fantastic, splash art, intricate detailed, hyperdetailed, maximalist style, photorealistic, concept art, sharp focus, harmony, serenity, tranquility, soft pastell colors,ambient occlusion, cozy ambient lighting, masterpiece, liiv1, linquivera, metix, mentixis, masterpiece, award winning, view from above\n"
entity_prompts = ["cliff", "sea", "moon", "sailing boat", "a seated beautiful woman", "pale blue long dress with soft glow"]
example(pipe, [0], 1, global_prompt, entity_prompts)
# example 2
global_prompt = "samurai girl wearing a kimono, she's holding a sword glowing with red flame, her long hair is flowing in the wind, she is looking at a small bird perched on the back of her hand. ultra realist style. maximum image detail. maximum realistic render."
entity_prompts = ["flowing hair", "sword glowing with red flame", "A cute bird", "blue belt"]
example(pipe, [0], 2, global_prompt, entity_prompts)
# example 3
global_prompt = "Image of a neverending staircase up to a mysterious palace in the sky, The ancient palace stood majestically atop a mist-shrouded mountain, sunrise, two traditional monk walk in the stair looking at the sunrise, fog,see-through, best quality, whimsical, fantastic, splash art, intricate detailed, hyperdetailed, photorealistic, concept art, harmony, serenity, tranquility, ambient occlusion, halation, cozy ambient lighting, dynamic lighting,masterpiece, liiv1, linquivera, metix, mentixis, masterpiece, award winning,"
entity_prompts = ["ancient palace", "stone staircase with railings", "a traditional monk", "a traditional monk"]
example(pipe, [27], 3, global_prompt, entity_prompts)
# example 4
global_prompt = "A beautiful girl wearing shirt and shorts in the street, holding a sign 'Entity Control'"
entity_prompts = ["A beautiful girl", "sign 'Entity Control'", "shorts", "shirt"]
example(pipe, [21], 4, global_prompt, entity_prompts)
# example 5
global_prompt = "A captivating, dramatic scene in a painting that exudes mystery and foreboding. A white sky, swirling blue clouds, and a crescent yellow moon illuminate a solitary woman standing near the water's edge. Her long dress flows in the wind, silhouetted against the eerie glow. The water mirrors the fiery sky and moonlight, amplifying the uneasy atmosphere."
entity_prompts = ["crescent yellow moon", "a solitary woman", "water", "swirling blue clouds"]
example(pipe, [0], 5, global_prompt, entity_prompts)
# example 6
global_prompt = "Snow White and the 6 Dwarfs."
entity_prompts = ["Dwarf 1", "Dwarf 2", "Dwarf 3", "Snow White", "Dwarf 4", "Dwarf 5", "Dwarf 6"]
example(pipe, [8], 6, global_prompt, entity_prompts)
# example 7, same prompt with different seeds
seeds = range(5, 9)
global_prompt = "A beautiful woman wearing white dress, holding a mirror, with a warm light background;"
entity_prompts = ["A beautiful woman", "mirror", "necklace", "glasses", "earring", "white dress", "jewelry headpiece"]
example(pipe, seeds, 7, global_prompt, entity_prompts)

51
examples/flux/model_inference_low_vram/FLEX.2-preview.py Normal file
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@@ -0,0 +1,51 @@
import torch
from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig
from diffsynth.controlnets.processors import Annotator
import numpy as np
from PIL import Image
pipe = FluxImagePipeline.from_pretrained(
torch_dtype=torch.bfloat16,
device="cuda",
model_configs=[
ModelConfig(model_id="ostris/Flex.2-preview", origin_file_pattern="Flex.2-preview.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
],
)
pipe.enable_vram_management()
image = pipe(
prompt="portrait of a beautiful Asian girl, long hair, red t-shirt, sunshine, beach",
num_inference_steps=50, embedded_guidance=3.5,
seed=0
)
image.save(f"image_1.jpg")
mask = np.zeros((1024, 1024, 3), dtype=np.uint8)
mask[200:400, 400:700] = 255
mask = Image.fromarray(mask)
mask.save(f"image_mask.jpg")
inpaint_image = image
image = pipe(
prompt="portrait of a beautiful Asian girl with sunglasses, long hair, red t-shirt, sunshine, beach",
num_inference_steps=50, embedded_guidance=3.5,
flex_inpaint_image=inpaint_image, flex_inpaint_mask=mask,
seed=4
)
image.save(f"image_2_new.jpg")
control_image = Annotator("canny")(image)
control_image.save("image_control.jpg")
image = pipe(
prompt="portrait of a beautiful Asian girl with sunglasses, long hair, yellow t-shirt, sunshine, beach",
num_inference_steps=50, embedded_guidance=3.5,
flex_control_image=control_image,
seed=4
)
image.save(f"image_3_new.jpg")

55
examples/flux/model_inference_low_vram/FLUX.1-Kontext-dev.py Normal file
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@@ -0,0 +1,55 @@
import torch
from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig
from PIL import Image
pipe = FluxImagePipeline.from_pretrained(
torch_dtype=torch.bfloat16,
device="cuda",
model_configs=[
ModelConfig(model_id="black-forest-labs/FLUX.1-Kontext-dev", origin_file_pattern="flux1-kontext-dev.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
],
)
pipe.enable_vram_management()
image_1 = pipe(
prompt="a beautiful Asian long-haired female college student.",
embedded_guidance=2.5,
seed=1,
)
image_1.save("image_1.jpg")
image_2 = pipe(
prompt="transform the style to anime style.",
kontext_images=image_1,
embedded_guidance=2.5,
seed=2,
)
image_2.save("image_2.jpg")
image_3 = pipe(
prompt="let her smile.",
kontext_images=image_1,
embedded_guidance=2.5,
seed=3,
)
image_3.save("image_3.jpg")
image_4 = pipe(
prompt="let the girl play basketball.",
kontext_images=image_1,
embedded_guidance=2.5,
seed=4,
)
image_4.save("image_4.jpg")
image_5 = pipe(
prompt="move the girl to a park, let her sit on a chair.",
kontext_images=image_1,
embedded_guidance=2.5,
seed=5,
)
image_5.save("image_5.jpg")

38
examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Inpainting-Beta.py Normal file
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@@ -0,0 +1,38 @@
import torch
from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig, ControlNetInput
import numpy as np
from PIL import Image
pipe = FluxImagePipeline.from_pretrained(
torch_dtype=torch.bfloat16,
device="cuda",
model_configs=[
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
ModelConfig(model_id="alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta", origin_file_pattern="diffusion_pytorch_model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
],
)
pipe.enable_vram_management()
image_1 = pipe(
prompt="a cat sitting on a chair",
height=1024, width=1024,
seed=8, rand_device="cuda",
)
image_1.save("image_1.jpg")
mask = np.zeros((1024, 1024, 3), dtype=np.uint8)
mask[100:350, 350: -300] = 255
mask = Image.fromarray(mask)
mask.save("mask.jpg")
image_2 = pipe(
prompt="a cat sitting on a chair, wearing sunglasses",
controlnet_inputs=[ControlNetInput(image=image_1, inpaint_mask=mask, scale=0.9)],
height=1024, width=1024,
seed=9, rand_device="cuda",
)
image_2.save("image_2.jpg")

41
examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Union-alpha.py Normal file
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@@ -0,0 +1,41 @@
import torch
from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig, ControlNetInput
from diffsynth.controlnets.processors import Annotator
from diffsynth import download_models
download_models(["Annotators:Depth"])
pipe = FluxImagePipeline.from_pretrained(
torch_dtype=torch.bfloat16,
device="cuda",
model_configs=[
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
ModelConfig(model_id="InstantX/FLUX.1-dev-Controlnet-Union-alpha", origin_file_pattern="diffusion_pytorch_model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
],
)
pipe.enable_vram_management()
image_1 = pipe(
prompt="a beautiful Asian girl, full body, red dress, summer",
height=1024, width=1024,
seed=6, rand_device="cuda",
)
image_1.save("image_1.jpg")
image_canny = Annotator("canny")(image_1)
image_depth = Annotator("depth")(image_1)
image_2 = pipe(
prompt="a beautiful Asian girl, full body, red dress, winter",
controlnet_inputs=[
ControlNetInput(image=image_canny, scale=0.3, processor_id="canny"),
ControlNetInput(image=image_depth, scale=0.3, processor_id="depth"),
],
height=1024, width=1024,
seed=7, rand_device="cuda",
)
image_2.save("image_2.jpg")

34
examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Upscaler.py Normal file
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import torch
from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig, ControlNetInput
pipe = FluxImagePipeline.from_pretrained(
torch_dtype=torch.bfloat16,
device="cuda",
model_configs=[
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
ModelConfig(model_id="jasperai/Flux.1-dev-Controlnet-Upscaler", origin_file_pattern="diffusion_pytorch_model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
],
)
pipe.enable_vram_management()
image_1 = pipe(
prompt="a photo of a cat, highly detailed",
height=768, width=768,
seed=0, rand_device="cuda",
)
image_1.save("image_1.jpg")
image_1 = image_1.resize((2048, 2048))
image_2 = pipe(
prompt="a photo of a cat, highly detailed",
controlnet_inputs=[ControlNetInput(image=image_1, scale=0.7)],
input_image=image_1,
denoising_strength=0.99,
height=2048, width=2048, tiled=True,
seed=1, rand_device="cuda",
)
image_2.save("image_2.jpg")

25
examples/flux/model_inference_low_vram/FLUX.1-dev-IP-Adapter.py Normal file
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import torch
from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig
pipe = FluxImagePipeline.from_pretrained(
torch_dtype=torch.bfloat16,
device="cuda",
model_configs=[
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
ModelConfig(model_id="InstantX/FLUX.1-dev-IP-Adapter", origin_file_pattern="ip-adapter.bin", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
ModelConfig(model_id="google/siglip-so400m-patch14-384", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
],
)
pipe.enable_vram_management()
origin_prompt = "a rabbit in a garden, colorful flowers"
image = pipe(prompt=origin_prompt, height=1280, width=960, seed=42)
image.save("style image.jpg")
image = pipe(prompt="A piggy", height=1280, width=960, seed=42,
ipadapter_images=[image], ipadapter_scale=0.7)
image.save("A piggy.jpg")

60
examples/flux/model_inference_low_vram/FLUX.1-dev-InfiniteYou.py Normal file
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import torch
from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig, ControlNetInput
from modelscope import dataset_snapshot_download
from modelscope import snapshot_download
from PIL import Image
import numpy as np
snapshot_download(
"ByteDance/InfiniteYou",
allow_file_pattern="supports/insightface/models/antelopev2/*",
local_dir="models/ByteDance/InfiniteYou",
)
pipe = FluxImagePipeline.from_pretrained(
torch_dtype=torch.bfloat16,
device="cuda",
model_configs=[
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
ModelConfig(model_id="ByteDance/InfiniteYou", origin_file_pattern="infu_flux_v1.0/aes_stage2/image_proj_model.bin", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
ModelConfig(model_id="ByteDance/InfiniteYou", origin_file_pattern="infu_flux_v1.0/aes_stage2/InfuseNetModel/*.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
],
)
pipe.enable_vram_management()
dataset_snapshot_download(
dataset_id="DiffSynth-Studio/examples_in_diffsynth",
local_dir="./",
allow_file_pattern=f"data/examples/infiniteyou/*",
)
height, width = 1024, 1024
controlnet_image = Image.fromarray(np.zeros([height, width, 3]).astype(np.uint8))
controlnet_inputs = [ControlNetInput(image=controlnet_image, scale=1.0, processor_id="None")]
prompt = "A man, portrait, cinematic"
id_image = "data/examples/infiniteyou/man.jpg"
id_image = Image.open(id_image).convert('RGB')
image = pipe(
prompt=prompt, seed=1,
infinityou_id_image=id_image, infinityou_guidance=1.0,
controlnet_inputs=controlnet_inputs,
num_inference_steps=50, embedded_guidance=3.5,
height=height, width=width,
)
image.save("man.jpg")
prompt = "A woman, portrait, cinematic"
id_image = "data/examples/infiniteyou/woman.jpg"
id_image = Image.open(id_image).convert('RGB')
image = pipe(
prompt=prompt, seed=1,
infinityou_id_image=id_image, infinityou_guidance=1.0,
controlnet_inputs=controlnet_inputs,
num_inference_steps=50, embedded_guidance=3.5,
height=height, width=width,
)
image.save("woman.jpg")

35
examples/flux/model_inference_low_vram/FLUX.1-dev-LoRAFusion.py Normal file
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import torch
from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig
pipe = FluxImagePipeline.from_pretrained(
torch_dtype=torch.bfloat16,
device="cuda",
model_configs=[
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
ModelConfig(model_id="DiffSynth-Studio/FLUX.1-dev-LoRAFusion", origin_file_pattern="model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn)
],
)
pipe.enable_vram_management()
pipe.enable_lora_patcher()
pipe.load_lora(
pipe.dit,
ModelConfig(model_id="yangyufeng/fgao", origin_file_pattern="30.safetensors"),
hotload=True
)
pipe.load_lora(
pipe.dit,
ModelConfig(model_id="bobooblue/LoRA-bling-mai", origin_file_pattern="10.safetensors"),
hotload=True
)
pipe.load_lora(
pipe.dit,
ModelConfig(model_id="JIETANGAB/E", origin_file_pattern="17.safetensors"),
hotload=True
)
image = pipe(prompt="This is a digital painting in a soft, ethereal style. a beautiful Asian girl Shine like a diamond. Everywhere is shining with bling bling luster.The background is a textured blue with visible brushstrokes, giving the image an impressionistic style reminiscent of Vincent van Gogh's work", seed=0)
image.save("flux.jpg")

21
examples/flux/model_inference_low_vram/FLUX.1-dev-ValueControl.py Normal file
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import torch
from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig
pipe = FluxImagePipeline.from_pretrained(
torch_dtype=torch.bfloat16,
device="cuda",
model_configs=[
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
ModelConfig(model_id="DiffSynth-Studio/FLUX.1-dev-ValueController", origin_file_pattern="single/prefer_embed/value.ckpt", offload_device="cpu", offload_dtype=torch.float8_e4m3fn)
],
)
pipe.load_lora(pipe.dit, ModelConfig(model_id="DiffSynth-Studio/FLUX.1-dev-ValueController", origin_file_pattern="single/dit_lora/dit_value.ckpt"))
pipe.enable_vram_management()
for i in range(10):
image = pipe(prompt="a cat", seed=0, value_controller_inputs=[i/10])
image.save(f"value_control_{i}.jpg")

27
examples/flux/model_inference_low_vram/FLUX.1-dev.py Normal file
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import torch
from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig
pipe = FluxImagePipeline.from_pretrained(
torch_dtype=torch.bfloat16,
device="cuda",
model_configs=[
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
],
)
pipe.enable_vram_management()
prompt = "CG, masterpiece, best quality, solo, long hair, wavy hair, silver hair, blue eyes, blue dress, medium breasts, dress, underwater, air bubble, floating hair, refraction, portrait. The girl's flowing silver hair shimmers with every color of the rainbow and cascades down, merging with the floating flora around her."
negative_prompt = "worst quality, low quality, monochrome, zombie, interlocked fingers, Aissist, cleavage, nsfw,"
image = pipe(prompt=prompt, seed=0)
image.save("flux.jpg")
image = pipe(
prompt=prompt, negative_prompt=negative_prompt,
seed=0, cfg_scale=2, num_inference_steps=50,
)
image.save("flux_cfg.jpg")

33
examples/flux/model_inference_low_vram/Step1X-Edit.py Normal file
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import torch
from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig
from PIL import Image
import numpy as np
pipe = FluxImagePipeline.from_pretrained(
torch_dtype=torch.bfloat16,
device="cuda",
model_configs=[
ModelConfig(model_id="Qwen/Qwen2.5-VL-7B-Instruct", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
ModelConfig(model_id="stepfun-ai/Step1X-Edit", origin_file_pattern="step1x-edit-i1258.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
ModelConfig(model_id="stepfun-ai/Step1X-Edit", origin_file_pattern="vae.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
],
)
pipe.enable_vram_management()
image = Image.fromarray(np.zeros((1248, 832, 3), dtype=np.uint8) + 255)
image = pipe(
prompt="draw red flowers in Chinese ink painting style",
step1x_reference_image=image,
width=832, height=1248, cfg_scale=6,
seed=1, rand_device='cuda'
)
image.save("image_1.jpg")
image = pipe(
prompt="add more flowers in Chinese ink painting style",
step1x_reference_image=image,
width=832, height=1248, cfg_scale=6,
seed=2, rand_device='cuda'
)
image.save("image_2.jpg")