huggingface / huggingface/diffusers

Wan-AI/Wan2.2-TI2V-5B-Diffusers Image to Video Missing

Offen
#13,258 1 Kommentar 0 Reaktionen 0 zugewiesene Personen Auf GitHub ansehen
bug
Vorherrschende Sprache
Python
Sterne
34.5k
Forks
7.3k
Ø Merge
3 T. 3 Std.
Gemergte PRs (30 T.)
91

Beschreibung

### Describe the bug

I cannot make it that the 5B version takes in an image and generate a video out of it

```
import torch
import numpy as np
from diffusers import WanPipeline, AutoencoderKLWan, WanTransformer3DModel, UniPCMultistepScheduler
from diffusers.utils import export_to_video, load_image

dtype = torch.bfloat16
device = "cuda"

model_id = "Wan-AI/Wan2.2-TI2V-5B-Diffusers"
vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
pipe = WanPipeline.from_pretrained(model_id, vae=vae, torch_dtype=dtype)
pipe.to(device)

height = 704
width = 1280
num_frames = 121
num_inference_steps = 50
guidance_scale = 5.0

prompt = "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage."
negative_prompt = "色调艳丽,过曝,静态,细节模糊不清,字幕,风格,作品,画作,画面,静止,整体发灰,最差质量,低质量,JPEG压缩残留,丑陋的,残缺的,多余的手指,画得不好的手部,画得不好的脸部,畸形的,毁容的,形态畸形的肢体,手指融合,静止不动的画面,杂乱的背景,三条腿,背景人很多,倒着走"

output = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
height=height,
width=width,
num_frames=num_frames,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
).frames[0]
export_to_video(output, "5bit2v_output.mp4", fps=24)

```

It says image is not part of the pipeline

### Reproduction

latest

### Logs

```shell

```

### System Info

latest

### Who can help?

_No response_

Beitragsleitfaden

Beitragsleitfaden öffnen

Rechercherichtung

Start by running the provided WanPipeline example with the Wan-AI/Wan2.2-TI2V-5B-Diffusers model and inspect the WanPipeline entry point and its documented inputs. Done means the 5B pipeline accepts an image alongside the prompt and produces the expected exported video, with coverage for the image-to-video path.

Vom Indexierungsmodell aus dem Issue-Text verfasst.

Bewertung

Tech-Stack
python, pytorch
Bereich
computer-vision, machine-learning
Issue-Typ
Bug
Schwierigkeit
4/5
Geschätzter Aufwand
3-5 Tage
Aktivitätsstatus
Veraltet
Klarheit
Muss geklärt werden
Anfängerfreundlichkeit
30/100

Neue Issues direkt in Ihr Postfach

Eine kurze Übersicht über anfängerfreundliche GitHub-Issues.